diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 882cece..9bb2e75 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,6 +1,6 @@ - [ ] All declared geometries are `geometry(Geometry, 4326)` for general geoms, or `geometry(Point, 4326)` -- [ ] Include python is activated for new functions. Include this before importing modules: `plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')` +- [ ] Existing functions in crankshaft python library called from the extension are kept at least from version N to version N+1 (to avoid breakage during upgrades). - [ ] Docs for public-facing functions are written - [ ] New functions follow the naming conventions: `CDB_NameOfFunction`. Where internal functions begin with an underscore `_`. - [ ] If appropriate, new functions accepts an arbitrary query as an input (see [Crankshaft Issue #6](https://github.com/CartoDB/crankshaft/issues/6) for more information) diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..a165028 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,57 @@ +language: c + +env: + global: + - PAGER=cat + +before_install: + - ./check-up-to-date-with-master.sh + - sudo apt-get -y install python-pip + + - sudo apt-get -y install python-software-properties + - sudo add-apt-repository -y ppa:cartodb/sci + - sudo add-apt-repository -y ppa:cartodb/postgresql-9.5 + - sudo add-apt-repository -y ppa:cartodb/gis + - sudo add-apt-repository -y ppa:cartodb/gis-testing + - sudo apt-get update + + - sudo apt-get -y install python-joblib=0.8.3-1-cdb1 + - sudo apt-get -y install python-numpy=1:1.6.1-6ubuntu1 + + # Install pysal + - sudo pip install -I pysal==1.11.2 + + - sudo apt-get -y install python-scipy=0.14.0-2-cdb6 + - sudo apt-get -y --no-install-recommends install python-sklearn-lib=0.14.1-3-cdb2 + - sudo apt-get -y --no-install-recommends install python-sklearn=0.14.1-3-cdb2 + - sudo apt-get -y --no-install-recommends install python-scikits-learn=0.14.1-3-cdb2 + + # Force instalation of libgeos-3.5.0 (presumably needed because of existing version of postgis) + - sudo apt-get -y install libgeos-3.5.0=3.5.0-1cdb2 + + # Install postgres db and build deps + - sudo /etc/init.d/postgresql stop # stop travis default instance + - sudo apt-get -y remove --purge postgresql-9.1 + - sudo apt-get -y remove --purge postgresql-9.2 + - sudo apt-get -y remove --purge postgresql-9.3 + - sudo apt-get -y remove --purge postgresql-9.4 + - sudo apt-get -y remove --purge postgis + - sudo apt-get -y autoremove + + - sudo apt-get -y install postgresql-9.5=9.5.2-2ubuntu1 + - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-2ubuntu1 + - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-2ubuntu1 + - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2 + - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2 + + # configure it to accept local connections from postgres + - echo -e "# TYPE DATABASE USER ADDRESS METHOD \nlocal all postgres trust\nlocal all all trust\nhost all all 127.0.0.1/32 trust" \ + | sudo tee /etc/postgresql/9.5/main/pg_hba.conf + - sudo /etc/init.d/postgresql restart 9.5 + +install: + - sudo make install + +script: + - make test || { cat src/pg/test/regression.diffs; false; } + - ./check-compatibility.sh diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f642d45..ed694bd 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,10 +1,7 @@ # Development process -Please read the Working Process/Quickstart Guide in [README.md](https://github.com/CartoDB/crankshaft/blob/master/README.md) first. - For any modification of crankshaft, such as adding new features, -refactoring or bug-fixing, topic branch must be created out of the `develop` -branch and be used for the development process. +refactoring or bugfixing, a topic branch must be created out of the `develop`. Modifications are done inside `src/pg/sql` and `src/py/crankshaft`. @@ -14,81 +11,47 @@ Take into account: (inside `src/pg/test`, `src/py/crankshaft/test`) as well as to detect any bugs that are being fixed. * Add or modify the corresponding documentation files in the `doc` folder. - Since we expect to have highly technical functions here, an extense - background explanation would be of great help to users of this extension. -* Convention: snake case(i.e. `snake_case` and not `CamelCase`) - shall be used for all function names. - Prefix function names intended for public use with `cdb_` - and private functions (to be used only internally inside - the extension) with `_cdb_`. +* Naming conventions for function names: + - use `CamelCase` + - prefix "public" functions with `CDB_`. E.g: `CDB_SpatialMarkovTrend` + - prefix "private" functions with an underscore. E.g: `_CDB_MyObscureInternalImplementationDetail` Once the code is ready to be tested, update the local development installation with `sudo make install`. This will update the 'dev' version of the extension in `src/pg/` and make it available to PostgreSQL. -It will also install the python package (crankshaft) in a virtual -environment `env/dev`. - -The version number of the Python package, defined in -`src/pg/crankshaft/setup.py` will be overridden when -the package is released and always match the extension version number, -but for development it shall be kept as '0.0.0'. Run the tests with `make test`. -To use the python extension for custom tests, activate the virtual -environment with: - -``` -source envs/dev/bin/activate -``` - Update extension in a working database with: -* `ALTER EXTENSION crankshaft UPDATE TO 'current';` - `ALTER EXTENSION crankshaft UPDATE TO 'dev';` - -Note: we keep the current development version install as 'dev' always; -we update through the 'current' alias to allow changing the extension -contents but not the version identifier. This will fail if the -changes involve incompatible function changes such as a different -return type; in that case the offending function (or the whole extension) -should be dropped manually before the update. +```sql +ALTER EXTENSION crankshaft UPDATE TO 'current'; +ALTER EXTENSION crankshaft UPDATE TO 'dev'; +``` If the extension has not previously been installed in a database, it can be installed directly with: - -* `CREATE EXTENSION IF NOT EXISTS plpythonu;` - `CREATE EXTENSION IF NOT EXISTS postgis;` - `CREATE EXTENSION IF NOT EXISTS cartodb;` - `CREATE EXTENSION crankshaft WITH VERSION 'dev';` - -Note: the development extension uses the development python virtual -environment automatically. - -Before proceeding to the release process peer code reviewing of the code is -a must. +```sql +CREATE EXTENSION IF NOT EXISTS plpythonu; +CREATE EXTENSION IF NOT EXISTS postgis; +CREATE EXTENSION crankshaft WITH VERSION 'dev'; +``` Once the feature or bugfix is completed and all the tests are passing -a Pull-Request shall be created on the topic branch, reviewed by a peer -and then merged back into the `develop` branch when all CI tests pass. +a pull request shall be created, reviewed by a peer +and then merged back into the `develop` branch once all the CI tests pass. -When the changes in the `develop` branch are to be released in a new -version of the extension, a PR must be created on the `develop` branch. -The release manage will take hold of the PR at this moment to proceed -to the release process for a new revision of the extension. +## Relevant development targets in the Makefile -## Relevant development tasks available in the Makefile +```shell +# Show a short description of the available targets +make help -``` -* `make help` show a short description of the available targets - -* `sudo make install` will generate the extension scripts for the development - version ('dev'/'current') and install the python package into the - development virtual environment `envs/dev`. - Intended for use by developers. - -* `make test` will run the tests for the installed development extension. - Intended for use by developers. +# Generate the extension scripts and install the python package. +sudo make install + +# Run the tests against the installed extension. +make test ``` diff --git a/Makefile b/Makefile index 6c3e219..50f690c 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,6 @@ PYP_DIR = src/py # Generate and install developmet versions of the extension # and python package. # The extension is named 'dev' with a 'current' alias for easily upgrading. -# The Python package is installed in a virtual environment envs/dev/ # Requires sudo. install: ## Generate and install development version of the extension; requires sudo. $(MAKE) -C $(PYP_DIR) install @@ -29,7 +28,6 @@ release: ## Generate a new release of the extension. Only for telease manager $(MAKE) -C $(PYP_DIR) release # Install the current release. -# The Python package is installed in a virtual environment envs/X.Y.Z/ # Requires sudo. # Use the RELEASE_VERSION environment variable to deploy a specific version: # sudo make deploy RELEASE_VERSION=1.0.0 @@ -52,11 +50,7 @@ clean-release: ## clean up current release rm -rf release/python/$(RELEASE_VERSION) rm -f release/$(RELEASE_VERSION)--*.sql -# Cleanup all virtual environments -clean-environments: ## clean up all virtual environments - rm -rf envs/* - -clean-all: clean-dev clean-release clean-environments +clean-all: clean-dev clean-release help: @IFS=$$'\n' ; \ diff --git a/Makefile.global b/Makefile.global index 77f6c69..da85992 100644 --- a/Makefile.global +++ b/Makefile.global @@ -1,6 +1,6 @@ SELF_DIR := $(dir $(lastword $(MAKEFILE_LIST))) -EXTENSION = crankshaft -PACKAGE = crankshaft -EXTVERSION = $(shell grep default_version $(SELF_DIR)/src/pg/$(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/") -RELEASE_VERSION ?= $(EXTVERSION) -SED = sed +EXTENSION = crankshaft +PACKAGE = crankshaft +EXTVERSION = $(shell grep default_version $(SELF_DIR)/src/pg/$(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/") +RELEASE_VERSION ?= $(EXTVERSION) +SED = sed diff --git a/NEWS.md b/NEWS.md index 0b8c2da..e78a7e1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,24 @@ +0.2.0 (2016-08-11) +------------------ +* Adds Gravity Model + +0.1.0 (2016-06-29) +------------------ +* Adds Spatial Markov function +* Adds Spacial interpolation function +* Adds `CDB_pyAgg (columns Numeric[])` helper function +* Adds Segmentation Functions + +0.0.4 (2016-06-20) +------------------ +* Remove cartodb extension dependency from tests +* Declare all correct dependencies with correct versions in setup.py + +0.0.3 (2016-06-16) +------------------ +* Adds new functions: kmeans, weighted centroids. +* Replaces moran functions with new areas of interest naming. + 0.0.2 (2016-03-16) ------------------ * New versioning approach using per-version Python virtual environments diff --git a/README.md b/README.md index 68a64fb..9dad032 100644 --- a/README.md +++ b/README.md @@ -1,71 +1,66 @@ -# crankshaft +# Crankshaft [![Build Status](https://travis-ci.org/CartoDB/crankshaft.svg?branch=develop)](https://travis-ci.org/CartoDB/crankshaft) CartoDB Spatial Analysis extension for PostgreSQL. ## Code organization -* *doc* documentation -* *src* source code -* - *src/pg* contains the PostgreSQL extension source code -* - *src/py* Python module source code -* *release* reseleased versions -* *env* base directory for Python virtual environments +* `doc/` documentation +* `src/` source code + - `pg/` contains the PostgreSQL extension source code + - `py/` Python module source code +* `release` reseleased versions ## Requirements -* pip, virtualenv, PostgreSQL -* python-scipy system package (see [src/py/README.md](https://github.com/CartoDB/crankshaft/blob/master/src/py/README.md)) +* PostgreSQL +* plpythonu and postgis extensions +* python-scipy system package (see [src/py/README.md](https://github.com/CartoDB/crankshaft/blob/develop/src/py/README.md)) -# Working Process -- Quickstart Guide +# Development Process -We distinguish two roles regarding the development cycle of crankshaft: +We distinguish two roles: * *developers* will implement new functionality and bugfixes into - the codebase and will request for new releases of the extension. -* A *release manager* will attend these requests and will handle - the release process. The release process is sequential: - no concurrent releases will ever be in the works. + the codebase. +* A *release manager* will handle the release process. -We use the default `develop` branch as the basis for development. -The `master` branch is used to merge and tag releases to be -deployed in production. +We use the branch `develop` as the main integration branch for development. The `master` is reserved to handle releases. -Developers shall create a new topic branch from `develop` for any new feature -or bugfix and commit their changes to it and eventually merge back into -the `develop` branch. When a new release is required a Pull Request -will be open against the `develop` branch. +The process is as follows: + +1. Create a new **topic branch** from `develop` for any new feature +or bugfix and commit their changes to it: +```shell +git fetch && git checkout -b my-cool-feature origin/develop +``` +1. Code, commit, push, repeat. +1. Write some **tests** for your feature or bugfix. +1. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/develop/NEWS.md) doc. +1. Create a pull request and mention relevant people for a **peer review**. +1. Address the comments and improvements you get from the peer review. +1. Mention `@CartoDB/dataservices` in the PR to get it merged into `develop`. + +In order for a pull request to be accepted, the following criteria should be met: +* The peer review should pass and no major issue should be left unaddressed. +* CI tests must pass (travis will take care of that). -The `develop` pull requests will be handled by the release manage, -who will merge into master where new releases are prepared and tagged. -The `master` branch is the sole responsibility of the release masters -and developers must not commit or merge into it. ## Development Guidelines For a detailed description of the development process please see -the [CONTRIBUTING.md](https://github.com/CartoDB/crankshaft/blob/master/CONTRIBUTING.md) guide. +the [CONTRIBUTING.md](https://github.com/CartoDB/crankshaft/blob/develop/CONTRIBUTING.md) guide. -Any modification to the source code (`src/pg/sql` for the SQL extension, -`src/py/crankshaft` for the Python package) shall always be done -in a topic branch created from the `develop` branch. -Tests, documentation and peer code reviewing are required for all -modifications. +## Testing -The tests (both for SQL and Python) are executed by running, -from the top directory: +The tests (both for SQL and Python) are executed by running, from the top directory: -``` +```shell sudo make install make test ``` -To request a new release, which will be handled by them -release manager, a Pull Request must be created in the `develop` -branch. - ## Release -The release and deployment process is described in the -[RELEASE.md](https://github.com/CartoDB/crankshaft/blob/master/RELEASE.md) guide and it is the responsibility of the designated -release manager. +The release process is described in the +[RELEASE.md](https://github.com/CartoDB/crankshaft/blob/develop/RELEASE.md) guide and is the responsibility of the designated *release manager*. diff --git a/RELEASE.md b/RELEASE.md index 0db48a2..005557a 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,93 +1,44 @@ # Release & Deployment Process -Please read the Working Process/Quickstart Guide in README.md -and the Development guidelines in CONTRIBUTING.md. - The release process of a new version of the extension shall be performed by the designated *Release Manager*. -Note that we expect to gradually automate more of this process. - -Having checked PR to be released it shall be -merged back into the `master` branch to prepare the new release. - -The version number in `pg/cranckshaft.control` must first be updated. -To do so [Semantic Versioning 2.0](http://semver.org/) is in order. - -Thew `NEWS.md` will be updated. - -We now will explain the process for the case of backwards-compatible -releases (updating the minor or patch version numbers). - -TODO: document the complex case of major releases. - -The next command must be executed to produce the main installation -script for the new release, `release/cranckshaft--X.Y.Z.sql` and -also to copy the python package to `release/python/X.Y.Z/crankshaft`. - -``` +## Release steps +1. Make sure `develop` branch passes all the tests. +1. Merge `develop` into `master` +1. Update the version number in `src/pg/crankshaft.control`. +1. Generate the next release files with this command: +```shell make release ``` +1. Generate an upgrade path from the previous to the next release by copying the generated release file. E.g: +```shell +cp release/cranckshaft--X.Y.Z.sql release/cranckshaft--A.B.C--X.Y.Z.sql +``` +NOTE: you can rely on this thanks to the compatibility checks. TODO: automate this step [#94](https://github.com/CartoDB/crankshaft/issues/94) +1. Commit and push the generated files. +1. Tag the release: +``` +git tag -a X.Y.Z -m "Release X.Y.Z" +git push origin X.Y.Z +``` +1. Deploy and test in staging -Then, the release manager shall produce upgrade and downgrade scripts -to migrate to/from the previous release. In the case of minor/patch -releases this simply consist in extracting the functions that have changed -and placing them in the proper `release/cranckshaft--X.Y.Z--A.B.C.sql` -file. + +## Some remarks +* Version numbers shall follow [Semantic Versioning 2.0](http://semver.org/). +* CI tests will take care of **forward compatibility** of the extension at postgres level. +* **Major version changes** (breaking forward compatibility) are a major event and are out of the scope of this doc. They **shall be avoided as much as we can**. +* We will go forward, never backwards. **Generating upgrade paths automatically is easy** and we'll rely on the CI checks for that. + +## Deploy commands The new release can be deployed for staging/smoke tests with this command: - -``` +```shell sudo make deploy ``` -This will copy the current 'X.Y.Z' released version of the extension to -PostgreSQL. The corresponding Python extension will be installed in a -virtual environment in `envs/X.Y.Z`. - -It can be activated with: - -``` -source envs/X.Y.Z/bin/activate -``` - -But note that this is needed only for using the package directly; -the 'X.Y.Z' version of the extension will automatically use the -python package from this virtual environment. - -The `sudo make deploy` operation can be also used for installing -the new version after it has been released. - -To install a specific version 'X.Y.Z' different from the current one -(which must be present in `releases/`) you can: - -``` +To install a specific version 'X.Y.Z' different from the default one: +```shell sudo make deploy RELEASE_VERSION=X.Y.Z ``` - -TODO: testing procedure for the new release. - -TODO: procedure for staging deployment. - -TODO: procedure for merging to master, tagging and deploying -in production. - -## Relevant release & deployment tasks available in the Makefile - -``` -* `make help` show a short description of the available targets - -* `make release` will generate a new release (version number defined in - `src/pg/crankshaft.control`) into `release/`. - Intended for use by the release manager. - -* `sudo make deploy` will install the current release X.Y.Z from the - `release/` files into PostgreSQL and a Python virtual environment - `envs/X.Y.Z`. - Intended for use by the release manager and deployment jobs. - -* `sudo make deploy RELEASE_VERSION=X.Y.Z` will install specified version - previously generated in `release/` - into PostgreSQL and a Python virtual environment `envs/X.Y.Z`. - Intended for use by the release manager and deployment jobs. -``` diff --git a/check-compatibility.sh b/check-compatibility.sh new file mode 100755 index 0000000..966850c --- /dev/null +++ b/check-compatibility.sh @@ -0,0 +1,108 @@ +#!/bin/bash + +export PGUSER=postgres + +DBNAME=crankshaft_compatcheck + +function die { + echo $1 + exit -1 +} + +# Create fresh DB +psql -c "CREATE DATABASE $DBNAME;" || die "Could not create DB" + +# Hook for cleanup +function cleanup { + psql -c "DROP DATABASE IF EXISTS crankshaft_compatcheck;" +} +trap cleanup EXIT + +# Deploy previous release +(cd src/py && sudo make deploy RUN_OPTIONS="--no-deps") || die "Could not deploy python extension" +(cd src/pg && sudo make deploy) || die " Could not deploy last release" +psql -c "SELECT * FROM pg_available_extension_versions WHERE name LIKE 'crankshaft';" + +# Install in the fresh DB +psql $DBNAME <<'EOF' +-- Install dependencies +CREATE EXTENSION plpythonu; +CREATE EXTENSION postgis VERSION '2.2.2'; + +-- Create role publicuser if it does not exist +DO +$$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_user + WHERE usename = 'publicuser') THEN + + CREATE ROLE publicuser LOGIN; + END IF; +END +$$ LANGUAGE plpgsql; + +-- Install the default version +CREATE EXTENSION crankshaft; +\dx +EOF + +# Save public function signatures +psql $DBNAME <<'EOF' +CREATE TABLE release_function_signatures AS + SELECT + p.proname as name, + pg_catalog.pg_get_function_result(p.oid) as result_type, + pg_catalog.pg_get_function_arguments(p.oid) as arguments, + CASE + WHEN p.proisagg THEN 'agg' + WHEN p.proiswindow THEN 'window' + WHEN p.prorettype = 'pg_catalog.trigger'::pg_catalog.regtype THEN 'trigger' + ELSE 'normal' + END as type + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE + n.nspname = 'cdb_crankshaft' + AND p.proname LIKE 'cdb_%' + ORDER BY 1, 2, 4; +EOF + +# Deploy current dev branch +make clean-dev || die "Could not clean dev files" +sudo make install || die "Could not deploy current dev branch" + +# Check it can be upgraded +psql $DBNAME -c "ALTER EXTENSION crankshaft update to 'dev';" || die "Cannot upgrade to dev version" + +# Check against saved public function signatures +psql $DBNAME <<'EOF' +CREATE TABLE dev_function_signatures AS + SELECT + p.proname as name, + pg_catalog.pg_get_function_result(p.oid) as result_type, + pg_catalog.pg_get_function_arguments(p.oid) as arguments, + CASE + WHEN p.proisagg THEN 'agg' + WHEN p.proiswindow THEN 'window' + WHEN p.prorettype = 'pg_catalog.trigger'::pg_catalog.regtype THEN 'trigger' + ELSE 'normal' + END as type + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE + n.nspname = 'cdb_crankshaft' + AND p.proname LIKE 'cdb_%' + ORDER BY 1, 2, 4; +EOF + +echo "Functions in development not in latest release (ok):" +psql $DBNAME -c "SELECT * FROM dev_function_signatures EXCEPT SELECT * FROM release_function_signatures;" + +echo "Functions in latest release not in development (compat issue):" +psql $DBNAME -c "SELECT * FROM release_function_signatures EXCEPT SELECT * FROM dev_function_signatures;" + +# Fail if there's a signature mismatch / missing functions +psql $DBNAME -c "SELECT * FROM release_function_signatures EXCEPT SELECT * FROM dev_function_signatures;" | fgrep '(0 rows)' \ + || die "Function signatures changed" diff --git a/check-up-to-date-with-master.sh b/check-up-to-date-with-master.sh new file mode 100755 index 0000000..2d8fcfe --- /dev/null +++ b/check-up-to-date-with-master.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +CURRENT_BRANCH=$(git rev-parse --abbrev-ref HEAD) + +if [[ "$CURRENT_BRANCH" == "master" || "$CURRENT_BRANCH" == "HEAD" ]] +then + echo "master branch or detached HEAD" + exit 0 +fi + +# Add remote-master +git remote add -t master remote-master https://github.com/CartoDB/crankshaft.git + +# Fetch master reference +git fetch --depth=1 remote-master master + +# Compare HEAD with master +# NOTE: travis by default uses --depth=50 so we are actually checking that the tip +# of the branch is no more than 50 commits away from master as well. +git rev-list HEAD | grep $(git rev-parse remote-master/master) || + { echo "Your branch is not up to date with latest release"; + echo "Please update it by running the following:"; + echo " git fetch && git merge origin/develop"; + false; } diff --git a/doc/04_markov.md b/doc/04_markov.md new file mode 100644 index 0000000..a45df59 --- /dev/null +++ b/doc/04_markov.md @@ -0,0 +1,47 @@ +## Spatial Markov + +### CDB_SpatialMarkovTrend(subquery text, column_names text array) + +This function takes time series data associated with geometries and outputs likelihoods that the next value of a geometry will move up, down, or stay static as compared to the most recent measurement. For more information, read about [Spatial Dynamics in PySAL](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/dynamics.html). + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM real_estate_history`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments | +| column_names | TEXT Array | Names of column that form the history of measurements for the geometries (e.g., `Array['y2011', 'y2012', 'y2013', 'y2014', 'y2015', 'y2016']`). | +| num_classes (optional) | INT | Number of quantile classes to separate data into. | +| weight type (optional) | TEXT | Type of weight to use when finding neighbors. Currently available options are 'knn' (default) and 'queen'. Read more about weight types in [PySAL's weights documentation](https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/weights.html). | +| num_ngbrs (optional) | INT | Number of neighbors if using k-nearest neighbors weight type. Defaults to 5. | +| permutations (optional) | INT | Number of permutations to check against a random arrangement of the values in `column_name`. This influences the accuracy of the output field `significance`. Defaults to 99. | +| geom_col (optional) | TEXT | The column name for the geometries. Defaults to `'the_geom'` | +| id_col (optional) | TEXT | The column name for the unique ID of each geometry/value pair. Defaults to `'cartodb_id'`. | + +#### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| trend | NUMERIC | The probability that the measure at this location will move up (a positive number) or down (a negative number) | +| trend_up | NUMERIC | The probability that a measure will move up in subsequent steps of time | +| trend_down | NUMERIC | The probability that a measure will move down in subsequent steps of time | +| volatility | NUMERIC | A measure of the variance of the probabilities returned from the Spatial Markov predictions | +| rowid | NUMERIC | id of the row that corresponds to the `id_col` (by default `cartodb_id` of the input rows) | + + +#### Example Usage + +```sql +SELECT + c.cartodb_id, + c.the_geom, + m.trend, + m.trend_up, + m.trend_down, + m.volatility +FROM CDB_SpatialMarkovTrend('SELECT * FROM nyc_real_estate' + Array['m03y2009','m03y2010','m03y2011','m03y2012','m03y2013','m03y2014','m03y2015','m03y2016']) As m +JOIN nyc_real_estate As c +ON c.cartodb_id = m.rowid; +``` diff --git a/doc/04_pyAgg.md b/doc/04_pyAgg.md new file mode 100644 index 0000000..95aded9 --- /dev/null +++ b/doc/04_pyAgg.md @@ -0,0 +1,23 @@ +## PyAgg Helper Function + +### CDB_pyAgg (columns Numeric[]) + +Currently it's not possible to pass a multidiemensional array between plpsql and plpythonu. This function aims to +help fix that by aggergating the columns provided in the argument across rows in to a rows * columns + 1 length 1D array. The first element of the array is the array\_length of the columns argument so that python can reconstruct +the 2D array. + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| columns | NUMERIC[] | The columns to aggregate across rows| + +#### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| result | NUMERIC[] | An columns * rows + 1 array where the first entry is the no of columns| + + diff --git a/doc/07_gravity.md b/doc/07_gravity.md new file mode 100644 index 0000000..e4e439e --- /dev/null +++ b/doc/07_gravity.md @@ -0,0 +1,78 @@ +## Gravity Model + +Gravity Models are derived from Newton's Law of Gravity and are used to predict the interaction between a group of populated areas (sources) and a specific target among a group of potential targets, in terms of an attraction factor (weight) + +**CDB_Gravity** is based on the model defined in *Huff's Law of Shopper attraction (1963)* + +### CDB_Gravity(t_id bigint[], t_geom geometry[], t_weight numeric[], s_id bigint[], s_geom geometry[], s_pop numeric[], target bigint, radius integer, minval numeric DEFAULT -10e307) + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| t_id | bigint[] | Array of targets ID | +| t_geom | geometry[] | Array of targets' geometries | +| t_weight | numeric[] | Array of targets's weights | +| s_id | bigint[] | Array of sources ID | +| s_geom | geometry[] | Array of sources' geometries | +| s_pop | numeric[] | Array of sources's population | +| target | bigint | ID of the target under study | +| radius | integer | Radius in meters around the target under study that will be taken into account| +| minval (optional) | numeric | Lowest accepted value of weight, defaults to numeric min_value | + +### CDB_Gravity( target_query text, weight_column text, source_query text, pop_column text, target bigint, radius integer, minval numeric DEFAULT -10e307) + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| target_query | text | Query that defines targets | +| weight_column | text | Column name of weights | +| source_query | text | Query that defines sources | +| pop_column | text | Column name of population | +| target | bigint | cartodb_id of the target under study | +| radius | integer | Radius in meters around the target under study that will be taken into account| +| minval (optional) | numeric | Lowest accepted value of weight, defaults to numeric min_value | + + +### Returns + +| Column Name | Type | Description | +|-------------|------|-------------| +| the_geom | geometry | Geometries of the sources within the radius | +| source_id | bigint | ID of the source | +| target_id | bigint | Target ID from input | +| dist | numeric | Distance in meters source to target (if not points, distance between centroids) | +| h | numeric | Probability of patronage | +| hpop | numeric | Patronaging population | + + +#### Example Usage + +```sql +with t as ( +SELECT + array_agg(cartodb_id::bigint) as id, + array_agg(the_geom) as g, + array_agg(coalesce(gla,0)::numeric) as w +FROM + abel.centros_comerciales_de_madrid +WHERE not no_cc +), +s as ( +SELECT + array_agg(cartodb_id::bigint) as id, + array_agg(center) as g, + array_agg(coalesce(t1_1, 0)::numeric) as p +FROM + sscc_madrid +) +select + g.the_geom, + trunc(g.h,2) as h, + round(g.hpop) as hpop, + trunc(g.dist/1000,2) as dist_km +FROM t, s, CDB_Gravity1(t.id, t.g, t.w, s.id, s.g, s.p, newmall_ID, 100000, 5000) g +``` + + diff --git a/doc/11_kmeans.md b/doc/11_kmeans.md new file mode 100644 index 0000000..6153010 --- /dev/null +++ b/doc/11_kmeans.md @@ -0,0 +1,62 @@ +## K-Means Functions + +### CDB_KMeans(subquery text, no_clusters INTEGER) + +This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and +the number of the cluster each point in the input was assigend to. + + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column name `the_geom` and id column name `cartodb_id` unless otherwise specified in the input arguments | +| no\_clusters | INTEGER | The number of clusters to try and find | + +#### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| cartodb\_id | INTEGER | The CartoDB id of the row in the input table.| +| cluster\_no | INTEGER | The cluster that this point belongs to. | + + +#### Example Usage + +```sql +SELECT + customers.*, + km.cluster_no + FROM cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3 + WHERE customers.cartodb_id = km.cartodb_id +``` + +### CDB_WeightedMean(subquery text, weight_column text, category_column text) + +Function that computes the weighted centroid of a number of clusters by some weight column. + +### Arguments + +| Name | Type | Description | +|------|------|-------------| +| subquery | TEXT | SQL query that exposes the data to be analyzed (e.g., `SELECT * FROM interesting_table`). This query must have the geometry column and the columns specified as the weight and category columns| +| weight\_column | TEXT | The name of the column to use as a weight | +| category\_column | TEXT | The name of the column to use as a category | + +### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| the\_geom | GEOMETRY | A point for the weighted cluster center | +| class | INTEGER | The cluster class | + +### Example Usage + +```sql +SELECT ST_TRANSFORM(the_geom, 3857) as the_geom_webmercator, class +FROM cdb_weighted_mean('SELECT *, customer_value FROM customers','customer_value','cluster_no') +``` diff --git a/doc/12_segmentation.md b/doc/12_segmentation.md new file mode 100644 index 0000000..b6b0c95 --- /dev/null +++ b/doc/12_segmentation.md @@ -0,0 +1,83 @@ + +## Segmentation Functions + +### CDB_CreateAndPredictSegment(query TEXT, variable_name TEXT, target_query TEXT) + +This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data. + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| query | TEXT | The input query to train the algorithm, which should have both the variable of interest and the features that will be used to predict it | +| variable\_name| TEXT | Specify the variable in the query to predict, all other columns are assumed to be features | +| target\_table | TEXT | The query which returns the `cartodb_id` and features for the rows your would like to predict the target variable for | +| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used. Values should be between 1 and x. | +| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth. Values should be between 1 and n. | +| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster. Values should be within the range 0 to 1. | +| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster. Values should be between 0 and 1 (??) | +| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf. Values should range from x to y | + +#### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query | +| prediction | NUMERIC | The predicted value of the variable of interest | +| accuracy | NUMERIC | The mean squared accuracy of the model. | + +#### Example Usage + +```sql +SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment( +'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric FROM late_night_agg', +'agg', +'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny'); +``` + +### CDB_CreateAndPredictSegment(target numeric[], train_features numeric[], prediction_features numeric[], prediction_ids numeric[]) + +This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data. + + +#### Arguments + +| Name | Type | Description | +|------|------|-------------| +| target | numeric[] | An array of target values of the variable you want to predict| +| train\_features| numeric[] | 1D array of length n features \* n\_rows + 1 with the first entry in the array being the number of features in each row. These are the features the model will be trained on. CDB\_Crankshaft.CDB_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this. | +| prediction\_features | numeric[] | 1D array of length nfeatures\* n\_rows\_ + 1 with the first entry in the array being the number of features in each row. These are the features that will be used to predict the target variable CDB\_Crankshaft.CDB\_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this. | +| prediction\_ids | numeric[] | 1D array of length n\_rows with the ids that can use used to re-join the data with inputs | +| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used | +| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth | +| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster| +| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster | +| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf | + + +#### Returns + +A table with the following columns. + +| Column Name | Type | Description | +|-------------|------|-------------| +| cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query | +| prediction | NUMERIC | The predicted value of the variable of interest | +| accuracy | NUMERIC | The mean squared accuracy of the model. | + +#### Example Usage + +```sql +WITH training As ( + SELECT array_agg(agg) As target, + cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features + FROM late_night_agg), +target AS ( + SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features, + array_agg(cartodb_id) As cartodb_ids FROM late_night_agg) + +SELECT cdb_crankshaft.CDB_CreateAndPredictSegment(training.target, training.features, target.features, target.cartodb_ids) +FROM training, target; +``` diff --git a/release/crankshaft--0.0.2--0.0.3.sql b/release/crankshaft--0.0.2--0.0.3.sql new file mode 100644 index 0000000..8a865d5 --- /dev/null +++ b/release/crankshaft--0.0.2--0.0.3.sql @@ -0,0 +1,413 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit + +-- [MANUALLY] DROP FUNCTIONS REMOVED SINCE 0.0.2 version + +DROP FUNCTION IF EXISTS cdb_moran_local(TEXT, TEXT, float, INT, INT, TEXT, TEXT, TEXT); +DROP FUNCTION IF EXISTS cdb_moran_local_rate(TEXT, TEXT, TEXT, FLOAT, INT, INT, TEXT, TEXT, TEXT); +DROP FUNCTION IF EXISTS _cdb_crankshaft_virtualenvs_path(); +DROP FUNCTION IF EXISTS _cdb_crankshaft_activate_py(); + +-- [END MANUALLY] DROP FUNCTIONS REMOVED SINCE 0.0.2 version + +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() + RETURNS text AS $$ + SELECT '0.0.3'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() + RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION + _cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) + RETURNS + TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS + TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS + TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS + TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS + TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) + RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) + RETURNS Numeric[] AS + $$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) + RETURNS GEOMETRY AS + $$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)( +SFUNC = CDB_WeightedMeanS, +FINALFUNC = CDB_WeightedMeanF, +STYPE = Numeric[], +INITCOND = "{0.0,0.0,0.0}" +); +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS + $$ + DECLARE + result numeric; + qualified_name text; + BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; + END; + $$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) + RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.0.3--0.0.2.sql b/release/crankshaft--0.0.3--0.0.2.sql new file mode 100644 index 0000000..a2ccd2f --- /dev/null +++ b/release/crankshaft--0.0.3--0.0.2.sql @@ -0,0 +1,209 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit + +-- [MANUALLY] DROP FUNCTIONS INTRODUCED IN 0.0.3 version + +DROP FUNCTION IF EXISTS CDB_AreasOfInterestGlobal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS _CDB_AreasOfInterestLocal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_AreasOfInterestLocal(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialHotspots(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialColdspots(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialOutliers(TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_AreasOfInterestGlobalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_AreasOfInterestLocalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS _CDB_AreasOfInterestLocalRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialHotspotsRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialColdspotsRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_GetSpatialOutliersRate(TEXT,TEXT,TEXT,TEXT,INT,INT,TEXT,TEXT); +DROP FUNCTION IF EXISTS CDB_KMeans(text,integer,integer); +DROP AGGREGATE IF EXISTS CDB_WeightedMean(geometry(Point, 4326), NUMERIC); +DROP FUNCTION IF EXISTS CDB_WeightedMeanS(Numeric[], GEOMETRY(Point, 4326), NUMERIC); +DROP FUNCTION IF EXISTS CDB_WeightedMeanF(Numeric[]); + + +-- [END MANUALLY] DROP FUNCTIONS INTRODUCED IN 0.0.3 version + +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.2'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path() +RETURNS text +AS $$ + BEGIN + -- RETURN '/opt/virtualenvs/crankshaft'; + RETURN '/home/ubuntu/crankshaft/envs'; + END; +$$ language plpgsql IMMUTABLE STRICT; + +-- Use the crankshaft python module +CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py() +RETURNS VOID +AS $$ + import os + # plpy.notice('%',str(os.environ)) + # activate virtualenv + crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version'] + base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path'] + default_venv_path = os.path.join(base_path, crankshaft_version) + venv_path = os.environ.get('CRANKSHAFT_VENV', default_venv_path) + activate_path = venv_path + '/bin/activate_this.py' + exec(open(activate_path).read(), dict(__file__=activate_path)) +$$ LANGUAGE plpythonu; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +-- Moran's I +CREATE OR REPLACE FUNCTION + cdb_moran_local ( + t TEXT, + attr TEXT, + significance float DEFAULT 0.05, + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_column TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id', + w_type TEXT DEFAULT 'knn') +RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +AS $$ + plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate +CREATE OR REPLACE FUNCTION + cdb_moran_local_rate(t TEXT, + numerator TEXT, + denominator TEXT, + significance FLOAT DEFAULT 0.05, + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_column TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id', + w_type TEXT DEFAULT 'knn') +RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric) +AS $$ + plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type) +$$ LANGUAGE plpythonu; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.0.3--0.0.4.sql b/release/crankshaft--0.0.3--0.0.4.sql new file mode 100644 index 0000000..69038a3 --- /dev/null +++ b/release/crankshaft--0.0.3--0.0.4.sql @@ -0,0 +1,8 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.4'::text; +$$ language 'sql' STABLE STRICT; diff --git a/release/crankshaft--0.0.3.sql b/release/crankshaft--0.0.3.sql new file mode 100644 index 0000000..caacd75 --- /dev/null +++ b/release/crankshaft--0.0.3.sql @@ -0,0 +1,403 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.3'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" +); +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.0.4--0.0.3.sql b/release/crankshaft--0.0.4--0.0.3.sql new file mode 100644 index 0000000..bd8ed82 --- /dev/null +++ b/release/crankshaft--0.0.4--0.0.3.sql @@ -0,0 +1,8 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.3'::text; +$$ language 'sql' STABLE STRICT; diff --git a/release/crankshaft--0.0.4--0.1.0.sql b/release/crankshaft--0.0.4--0.1.0.sql new file mode 100644 index 0000000..5a67163 --- /dev/null +++ b/release/crankshaft--0.0.4--0.1.0.sql @@ -0,0 +1,258 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES + +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit + +-------------------------------------------------------------------------------- + +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.1.0'::text; +$$ language 'sql' STABLE STRICT; + +-------------------------------------------------------------------------------- + +-- PyAgg stuff +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql; + + +CREATE AGGREGATE CDB_PyAgg(NUMERIC[])( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + INITCOND = "{}" +); + +-------------------------------------------------------------------------------- + +-- Segmentation stuff +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import create_and_predict_segment_agg + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=float) + return a.reshape(len(a)/dimension, dimension) + + return create_and_predict_segment_agg(np.array(target, dtype=float), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ LANGUAGE plpythonu; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import create_and_predict_segment + model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} + return create_and_predict_segment(query,variable_name,target_table, model_params) +$$ LANGUAGE plpythonu; + +-------------------------------------------------------------------------------- + +-- Spatial interpolation + +-- 0: nearest neighbor +-- 1: barymetric +-- 2: IDW + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN query text, + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + output numeric; +BEGIN + EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs; + SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a; + + RETURN output; +END; +$$ +language plpgsql IMMUTABLE; + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN geomin geometry[], + IN colin numeric[], + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + gs2 geometry[]; + vs2 numeric[]; + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + output := -999.999; + -- nearest + IF method = 0 THEN + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v) + SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1; + RETURN output; + + -- barymetric + ELSIF method = 1 THEN + WITH a as (SELECT unnest(geomin) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom as v FROM b), + d as (SELECT v FROM c WHERE ST_Within(point, v)) + SELECT v INTO g FROM d; + IF g is null THEN + -- out of the realm of the input data + RETURN -888.888; + END IF; + -- vertex of the selected cell + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg); + RETURN output; + + -- IDW + -- p1: limit the number of neighbors, 0->no limit + -- p2: order of distance decay, 0-> order 1 + ELSIF method = 2 THEN + + IF p2 = 0 THEN + p2 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g) + SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b; + IF p1::integer>0 THEN + gs2:=gs; + vs2:=vs; + FOR i IN 1..p1 + LOOP + gs2 := gs2 || gs[i]; + vs2 := vs2 || vs[i]; + END LOOP; + ELSE + gs2:=gs; + vs2:=vs; + END IF; + + WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v), + b as ( + SELECT + (1/ST_distance(point, a.g)^p2::integer) as k, + (a.v/ST_distance(point, a.g)^p2::integer) as f + FROM a + ) + SELECT sum(b.f)/sum(b.k) INTO output FROM b; + RETURN output; + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE; + + +-------------------------------------------------------------------------------- + +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import spatial_markov_trend + + ## TODO: use named parameters or a dictionary + return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; diff --git a/release/crankshaft--0.0.4.sql b/release/crankshaft--0.0.4.sql new file mode 100644 index 0000000..c855958 --- /dev/null +++ b/release/crankshaft--0.0.4.sql @@ -0,0 +1,403 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.4'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" +); +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.1.0--0.0.4.sql b/release/crankshaft--0.1.0--0.0.4.sql new file mode 100644 index 0000000..983dbce --- /dev/null +++ b/release/crankshaft--0.1.0--0.0.4.sql @@ -0,0 +1,81 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES + +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit + +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.0.4'::text; +$$ language 'sql' STABLE STRICT; + +-------------------------------------------------------------------------------- + +-- Spatial Markov + +DROP FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT); + + +-------------------------------------------------------------------------------- + +-- Spatial interpolation + +DROP FUNCTION CDB_SpatialInterpolation( + IN geomin geometry[], + IN colin numeric[], + IN point geometry, + IN method integer, + IN p1 numeric, + IN p2 numeric + ); + +DROP FUNCTION CDB_SpatialInterpolation( + IN query text, + IN point geometry, + IN method integer, + IN p1 numeric, + IN p2 numeric + ); + +-------------------------------------------------------------------------------- + +-- Segmentation stuff + +DROP FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER, + max_depth INTEGER, + subsample DOUBLE PRECISION, + learning_rate DOUBLE PRECISION, + min_samples_leaf INTEGER); + +DROP FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER, + max_depth INTEGER, + subsample DOUBLE PRECISION, + learning_rate DOUBLE PRECISION, + min_samples_leaf INTEGER); + +-------------------------------------------------------------------------------- + +-- PyAgg stuff + +DROP AGGREGATE CDB_PyAgg(NUMERIC[]); +DROP FUNCTION CDB_PyAggS(Numeric[], Numeric[]); \ No newline at end of file diff --git a/release/crankshaft--0.1.0--0.2.0.sql b/release/crankshaft--0.1.0--0.2.0.sql new file mode 100644 index 0000000..1cb3087 --- /dev/null +++ b/release/crankshaft--0.1.0--0.2.0.sql @@ -0,0 +1,827 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.2.0'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_pyagg' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) ( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + INITCOND = "{}" + ); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import create_and_predict_segment_agg + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=float) + return a.reshape(len(a)/dimension, dimension) + + return create_and_predict_segment_agg(np.array(target, dtype=float), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ LANGUAGE plpythonu; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import create_and_predict_segment + model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} + return create_and_predict_segment(query,variable_name,target_table, model_params) +$$ LANGUAGE plpythonu; +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN target_query text, + IN weight_column text, + IN source_query text, + IN pop_column text, + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_id bigint[]; + t_geom geometry[]; + t_weight numeric[]; + s_id bigint[]; + s_geom geometry[]; + s_pop numeric[]; +BEGIN + EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight; + EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop; + RETURN QUERY + SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g; +END; +$$ language plpgsql; + +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN t_id bigint[], + IN t_geom geometry[], + IN t_weight numeric[], + IN s_id bigint[], + IN s_geom geometry[], + IN s_pop numeric[], + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_type text; + s_type text; + t_center geometry[]; + s_center geometry[]; +BEGIN + t_type := GeometryType(t_geom[1]); + s_type := GeometryType(s_geom[1]); + IF t_type = 'POINT' THEN + t_center := t_geom; + ELSE + WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp; + END IF; + IF s_type = 'POINT' THEN + s_center := s_geom; + ELSE + WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp; + END IF; + RETURN QUERY + with target0 as( + SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td + ), + source0 as( + SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp + ), + prev0 as( + SELECT + source0.sg, + source0.sd as sourc_id, + coalesce(source0.sp,0) as sp, + target.td as targ_id, + coalesce(target.tw,0) as tw, + GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance + FROM source0 + CROSS JOIN LATERAL + ( + SELECT + * + FROM target0 + WHERE tw > minval + AND ST_DWithin(geography(source0.sc), geography(tc), radius) + ) AS target + ), + deno as( + SELECT + sourc_id, + sum(tw/distance) as h_deno + FROM + prev0 + GROUP BY sourc_id + ) + SELECT + p.sg as the_geom, + p.sourc_id as source_id, + p.targ_id as target_id, + case when p.distance > 1 then p.distance else 0.0 end as dist, + 100*(p.tw/p.distance)/d.h_deno as h, + p.sp*(p.tw/p.distance)/d.h_deno as hpop + FROM + prev0 p, + deno d + WHERE + p.targ_id = target AND + p.sourc_id = d.sourc_id; +END; +$$ language plpgsql; +-- 0: nearest neighbor +-- 1: barymetric +-- 2: IDW + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN query text, + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + output numeric; +BEGIN + EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs; + SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a; + + RETURN output; +END; +$$ +language plpgsql IMMUTABLE; + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN geomin geometry[], + IN colin numeric[], + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + gs2 geometry[]; + vs2 numeric[]; + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + output := -999.999; + -- nearest + IF method = 0 THEN + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v) + SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1; + RETURN output; + + -- barymetric + ELSIF method = 1 THEN + WITH a as (SELECT unnest(geomin) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom as v FROM b), + d as (SELECT v FROM c WHERE ST_Within(point, v)) + SELECT v INTO g FROM d; + IF g is null THEN + -- out of the realm of the input data + RETURN -888.888; + END IF; + -- vertex of the selected cell + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg); + RETURN output; + + -- IDW + -- p1: limit the number of neighbors, 0->no limit + -- p2: order of distance decay, 0-> order 1 + ELSIF method = 2 THEN + + IF p2 = 0 THEN + p2 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g) + SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b; + IF p1::integer>0 THEN + gs2:=gs; + vs2:=vs; + FOR i IN 1..p1 + LOOP + gs2 := gs2 || gs[i]; + vs2 := vs2 || vs[i]; + END LOOP; + ELSE + gs2:=gs; + vs2:=vs; + END IF; + + WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v), + b as ( + SELECT + (1/ST_distance(point, a.g)^p2::integer) as k, + (a.v/ST_distance(point, a.g)^p2::integer) as f + FROM a + ) + SELECT sum(b.f)/sum(b.k) INTO output FROM b; + RETURN output; + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_weightedmean' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) ( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" + ); + END IF; +END +$$ LANGUAGE plpgsql; +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import spatial_markov_trend + + ## TODO: use named parameters or a dictionary + return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.1.0.sql b/release/crankshaft--0.1.0.sql new file mode 100644 index 0000000..d5a5b66 --- /dev/null +++ b/release/crankshaft--0.1.0.sql @@ -0,0 +1,686 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.1.0'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql; + + +CREATE AGGREGATE CDB_PyAgg(NUMERIC[])( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + INITCOND = "{}" +); + + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import create_and_predict_segment_agg + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=float) + return a.reshape(len(a)/dimension, dimension) + + return create_and_predict_segment_agg(np.array(target, dtype=float), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ LANGUAGE plpythonu; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import create_and_predict_segment + model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} + return create_and_predict_segment(query,variable_name,target_table, model_params) +$$ LANGUAGE plpythonu; +-- 0: nearest neighbor +-- 1: barymetric +-- 2: IDW + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN query text, + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + output numeric; +BEGIN + EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs; + SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a; + + RETURN output; +END; +$$ +language plpgsql IMMUTABLE; + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN geomin geometry[], + IN colin numeric[], + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + gs2 geometry[]; + vs2 numeric[]; + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + output := -999.999; + -- nearest + IF method = 0 THEN + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v) + SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1; + RETURN output; + + -- barymetric + ELSIF method = 1 THEN + WITH a as (SELECT unnest(geomin) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom as v FROM b), + d as (SELECT v FROM c WHERE ST_Within(point, v)) + SELECT v INTO g FROM d; + IF g is null THEN + -- out of the realm of the input data + RETURN -888.888; + END IF; + -- vertex of the selected cell + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg); + RETURN output; + + -- IDW + -- p1: limit the number of neighbors, 0->no limit + -- p2: order of distance decay, 0-> order 1 + ELSIF method = 2 THEN + + IF p2 = 0 THEN + p2 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g) + SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b; + IF p1::integer>0 THEN + gs2:=gs; + vs2:=vs; + FOR i IN 1..p1 + LOOP + gs2 := gs2 || gs[i]; + vs2 := vs2 || vs[i]; + END LOOP; + ELSE + gs2:=gs; + vs2:=vs; + END IF; + + WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v), + b as ( + SELECT + (1/ST_distance(point, a.g)^p2::integer) as k, + (a.v/ST_distance(point, a.g)^p2::integer) as f + FROM a + ) + SELECT sum(b.f)/sum(b.k) INTO output FROM b; + RETURN output; + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" +); +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import spatial_markov_trend + + ## TODO: use named parameters or a dictionary + return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft--0.2.0.sql b/release/crankshaft--0.2.0.sql new file mode 100644 index 0000000..1cb3087 --- /dev/null +++ b/release/crankshaft--0.2.0.sql @@ -0,0 +1,827 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.2.0'::text; +$$ language 'sql' STABLE STRICT; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu; +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_pyagg' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) ( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + INITCOND = "{}" + ); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import create_and_predict_segment_agg + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=float) + return a.reshape(len(a)/dimension, dimension) + + return create_and_predict_segment_agg(np.array(target, dtype=float), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ LANGUAGE plpythonu; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import create_and_predict_segment + model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} + return create_and_predict_segment(query,variable_name,target_table, model_params) +$$ LANGUAGE plpythonu; +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN target_query text, + IN weight_column text, + IN source_query text, + IN pop_column text, + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_id bigint[]; + t_geom geometry[]; + t_weight numeric[]; + s_id bigint[]; + s_geom geometry[]; + s_pop numeric[]; +BEGIN + EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight; + EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop; + RETURN QUERY + SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g; +END; +$$ language plpgsql; + +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN t_id bigint[], + IN t_geom geometry[], + IN t_weight numeric[], + IN s_id bigint[], + IN s_geom geometry[], + IN s_pop numeric[], + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_type text; + s_type text; + t_center geometry[]; + s_center geometry[]; +BEGIN + t_type := GeometryType(t_geom[1]); + s_type := GeometryType(s_geom[1]); + IF t_type = 'POINT' THEN + t_center := t_geom; + ELSE + WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp; + END IF; + IF s_type = 'POINT' THEN + s_center := s_geom; + ELSE + WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp; + END IF; + RETURN QUERY + with target0 as( + SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td + ), + source0 as( + SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp + ), + prev0 as( + SELECT + source0.sg, + source0.sd as sourc_id, + coalesce(source0.sp,0) as sp, + target.td as targ_id, + coalesce(target.tw,0) as tw, + GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance + FROM source0 + CROSS JOIN LATERAL + ( + SELECT + * + FROM target0 + WHERE tw > minval + AND ST_DWithin(geography(source0.sc), geography(tc), radius) + ) AS target + ), + deno as( + SELECT + sourc_id, + sum(tw/distance) as h_deno + FROM + prev0 + GROUP BY sourc_id + ) + SELECT + p.sg as the_geom, + p.sourc_id as source_id, + p.targ_id as target_id, + case when p.distance > 1 then p.distance else 0.0 end as dist, + 100*(p.tw/p.distance)/d.h_deno as h, + p.sp*(p.tw/p.distance)/d.h_deno as hpop + FROM + prev0 p, + deno d + WHERE + p.targ_id = target AND + p.sourc_id = d.sourc_id; +END; +$$ language plpgsql; +-- 0: nearest neighbor +-- 1: barymetric +-- 2: IDW + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN query text, + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + output numeric; +BEGIN + EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs; + SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a; + + RETURN output; +END; +$$ +language plpgsql IMMUTABLE; + +CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation( + IN geomin geometry[], + IN colin numeric[], + IN point geometry, + IN method integer DEFAULT 1, + IN p1 numeric DEFAULT 0, + IN p2 numeric DEFAULT 0 + ) +RETURNS numeric AS +$$ +DECLARE + gs geometry[]; + vs numeric[]; + gs2 geometry[]; + vs2 numeric[]; + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + output := -999.999; + -- nearest + IF method = 0 THEN + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v) + SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1; + RETURN output; + + -- barymetric + ELSIF method = 1 THEN + WITH a as (SELECT unnest(geomin) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom as v FROM b), + d as (SELECT v FROM c WHERE ST_Within(point, v)) + SELECT v INTO g FROM d; + IF g is null THEN + -- out of the realm of the input data + RETURN -888.888; + END IF; + -- vertex of the selected cell + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg); + RETURN output; + + -- IDW + -- p1: limit the number of neighbors, 0->no limit + -- p2: order of distance decay, 0-> order 1 + ELSIF method = 2 THEN + + IF p2 = 0 THEN + p2 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g) + SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b; + IF p1::integer>0 THEN + gs2:=gs; + vs2:=vs; + FOR i IN 1..p1 + LOOP + gs2 := gs2 || gs[i]; + vs2 := vs2 || vs[i]; + END LOOP; + ELSE + gs2:=gs; + vs2:=vs; + END IF; + + WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v), + b as ( + SELECT + (1/ST_distance(point, a.g)^p2::integer) as k, + (a.v/ST_distance(point, a.g)^p2::integer) as f + FROM a + ) + SELECT sum(b.f)/sum(b.k) INTO output FROM b; + RETURN output; + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE; +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import moran_local + # TODO: use named parameters or a dictionary + return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + + +-- Moran's I Local Rate (internal function) +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + from crankshaft.clustering import moran_local_rate + # TODO: use named parameters or a dictionary + return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- Moran's I Local Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL; +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_weightedmean' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) ( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" + ); + END IF; +END +$$ LANGUAGE plpgsql; +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import spatial_markov_trend + + ## TODO: use named parameters or a dictionary + return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; diff --git a/release/crankshaft.control b/release/crankshaft.control index 49c0d22..6f48fdd 100644 --- a/release/crankshaft.control +++ b/release/crankshaft.control @@ -1,5 +1,5 @@ comment = 'CartoDB Spatial Analysis extension' -default_version = '0.0.2' -requires = 'plpythonu, postgis, cartodb' +default_version = '0.2.0' +requires = 'plpythonu, postgis' superuser = true schema = cdb_crankshaft diff --git a/release/python/0.0.3/crankshaft/crankshaft/__init__.py b/release/python/0.0.3/crankshaft/crankshaft/__init__.py new file mode 100644 index 0000000..d07e330 --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/__init__.py @@ -0,0 +1,2 @@ +import random_seeds +import clustering diff --git a/release/python/0.0.3/crankshaft/crankshaft/clustering/__init__.py b/release/python/0.0.3/crankshaft/crankshaft/clustering/__init__.py new file mode 100644 index 0000000..338e8ea --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/clustering/__init__.py @@ -0,0 +1,2 @@ +from moran import * +from kmeans import * diff --git a/release/python/0.0.3/crankshaft/crankshaft/clustering/kmeans.py b/release/python/0.0.3/crankshaft/crankshaft/clustering/kmeans.py new file mode 100644 index 0000000..4134062 --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/clustering/kmeans.py @@ -0,0 +1,18 @@ +from sklearn.cluster import KMeans +import plpy + +def kmeans(query, no_clusters, no_init=20): + data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids, + array_agg(ST_X(the_geom) order by cartodb_id) xs, + array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a + where the_geom is not null + '''.format(query=query)) + + xs = data[0]['xs'] + ys = data[0]['ys'] + ids = data[0]['ids'] + + km = KMeans(n_clusters= no_clusters, n_init=no_init) + labels = km.fit_predict(zip(xs,ys)) + return zip(ids,labels) + diff --git a/release/python/0.0.3/crankshaft/crankshaft/clustering/moran.py b/release/python/0.0.3/crankshaft/crankshaft/clustering/moran.py new file mode 100644 index 0000000..39b3ff6 --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/clustering/moran.py @@ -0,0 +1,260 @@ +""" +Moran's I geostatistics (global clustering & outliers presence) +""" + +# TODO: Fill in local neighbors which have null/NoneType values with the +# average of the their neighborhood + +import pysal as ps +import plpy + +# crankshaft module +import crankshaft.pysal_utils as pu + +# High level interface --------------------------------------- + +def moran(subquery, attr_name, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (global) + Implementation building neighbors with a PostGIS database and Moran's I + core clusters with PySAL. + Andy Eschbacher + """ + qvals = {"id_col": id_col, + "attr1": attr_name, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + attr_vals = pu.get_attributes(result) + + ## calculate weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global + moran_global = ps.esda.moran.Moran(attr_vals, weight, + permutations=permutations) + + return zip([moran_global.I], [moran_global.EI]) + +def moran_local(subquery, attr, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I implementation for PL/Python + Andy Eschbacher + """ + + # geometries with attributes that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = {"id_col": id_col, + "attr1": attr, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(5) + + attr_vals = pu.get_attributes(result) + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local(attr_vals, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Rate (global) + Andy Eschbacher + """ + qvals = {"id_col": id_col, + "attr1": numerator, + "attr2": denominator, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global rate + lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight, + permutations=permutations) + + return zip([lisa_rate.I], [lisa_rate.EI]) + +def moran_local_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Local Rate + Andy Eschbacher + """ + # geometries with values that are null are ignored + # resulting in a collection of not as near neighbors + + query = pu.construct_neighbor_query(w_type, + {"id_col": id_col, + "numerator": numerator, + "denominator": denominator, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs}) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(5) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, + permutations=permutations) + + # find units of significance + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_local_bv(subquery, attr1, attr2, + permutations, geom_col, id_col, w_type, num_ngbrs): + """ + Moran's I (local) Bivariate (untested) + """ + plpy.notice('** Constructing query') + + qvals = {"num_ngbrs": num_ngbrs, + "attr1": attr1, + "attr2": attr2, + "subquery": subquery, + "geom_col": geom_col, + "id_col": id_col} + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(4) + except plpy.SPIError: + plpy.error("Error: areas of interest query failed, " \ + "check input parameters") + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(4) + + ## collect attributes + attr1_vals = pu.get_attributes(result, 1) + attr2_vals = pu.get_attributes(result, 2) + + # create weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight, + permutations=permutations) + + plpy.notice("len of Is: %d" % len(lisa.Is)) + + # find clustering of significance + lisa_sig = quad_position(lisa.q) + + plpy.notice('** Finished calculations') + + return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order) + +# Low level functions ---------------------------------------- + +def map_quads(coord): + """ + Map a quadrant number to Moran's I designation + HH=1, LH=2, LL=3, HL=4 + Input: + @param coord (int): quadrant of a specific measurement + Output: + classification (one of 'HH', 'LH', 'LL', or 'HL') + """ + if coord == 1: + return 'HH' + elif coord == 2: + return 'LH' + elif coord == 3: + return 'LL' + elif coord == 4: + return 'HL' + else: + return None + +def quad_position(quads): + """ + Produce Moran's I classification based of n + Input: + @param quads ndarray: an array of quads classified by + 1-4 (PySAL default) + Output: + @param list: an array of quads classied by 'HH', 'LL', etc. + """ + return [map_quads(q) for q in quads] diff --git a/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/__init__.py b/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/__init__.py new file mode 100644 index 0000000..835880d --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/__init__.py @@ -0,0 +1 @@ +from pysal_utils import * diff --git a/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/pysal_utils.py new file mode 100644 index 0000000..02b5e35 --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -0,0 +1,152 @@ +""" + Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects +""" + +import numpy as np +import pysal as ps + +def construct_neighbor_query(w_type, query_vals): + """Return query (a string) used for finding neighbors + @param w_type text: type of neighbors to calculate ('knn' or 'queen') + @param query_vals dict: values used to construct the query + """ + + if w_type.lower() == 'knn': + return knn(query_vals) + else: + return queen(query_vals) + +## Build weight object +def get_weight(query_res, w_type='knn', num_ngbrs=5): + """ + Construct PySAL weight from return value of query + @param query_res: query results with attributes and neighbors + """ + if w_type.lower() == 'knn': + row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs + weights = {x['id']: row_normed_weights for x in query_res} + else: + weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) + if len(x['neighbors']) > 0 + else [] for x in query_res} + + neighbors = {x['id']: x['neighbors'] for x in query_res} + + return ps.W(neighbors, weights) + +def query_attr_select(params): + """ + Create portion of SELECT statement for attributes inolved in query. + @param params: dict of information used in query (column names, + table name, etc.) + """ + + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')] + + template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, " + + attr_string = "" + + for idx, val in enumerate(sorted(attrs)): + attr_string += template % {"col": val, "alias_num": idx + 1} + + return attr_string + +def query_attr_where(params): + """ + Create portion of WHERE clauses for weeding out NULL-valued geometries + """ + attrs = sorted([k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]) + + attr_string = [] + + for attr in attrs: + attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr) + + if len(attrs) == 2: + attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1]) + + out = " AND ".join(attr_string) + + return out + +def knn(params): + """SQL query for k-nearest neighbors. + @param vars: dict of values to fill template + """ + + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE " \ + "i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "%(attr_where_j)s " \ + "ORDER BY " \ + "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \ + "LIMIT {num_ngbrs})" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## SQL query for finding queens neighbors (all contiguous polygons) +def queen(params): + """SQL query for queen neighbors. + @param params dict: information to fill query + """ + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \ + "%(attr_where_j)s)" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## to add more weight methods open a ticket or pull request + +def get_attributes(query_res, attr_num=1): + """ + @param query_res: query results with attributes and neighbors + @param attr_num: attribute number (1, 2, ...) + """ + return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float) + +def empty_zipped_array(num_nones): + """ + prepare return values for cases of empty weights objects (no neighbors) + Input: + @param num_nones int: number of columns (e.g., 4) + Output: + [(None, None, None, None)] + """ + + return [tuple([None] * num_nones)] diff --git a/release/python/0.0.3/crankshaft/crankshaft/random_seeds.py b/release/python/0.0.3/crankshaft/crankshaft/random_seeds.py new file mode 100644 index 0000000..b7c8eed --- /dev/null +++ b/release/python/0.0.3/crankshaft/crankshaft/random_seeds.py @@ -0,0 +1,10 @@ +import random +import numpy + +def set_random_seeds(value): + """ + Set the seeds of the RNGs (Random Number Generators) + used internally. + """ + random.seed(value) + numpy.random.seed(value) diff --git a/release/python/0.0.3/crankshaft/setup.py b/release/python/0.0.3/crankshaft/setup.py new file mode 100644 index 0000000..33a3b62 --- /dev/null +++ b/release/python/0.0.3/crankshaft/setup.py @@ -0,0 +1,48 @@ + +""" +CartoDB Spatial Analysis Python Library +See: +https://github.com/CartoDB/crankshaft +""" + +from setuptools import setup, find_packages + +setup( + name='crankshaft', + + version='0.0.3', + + description='CartoDB Spatial Analysis Python Library', + + url='https://github.com/CartoDB/crankshaft', + + author='Data Services Team - CartoDB', + author_email='dataservices@cartodb.com', + + license='MIT', + + classifiers=[ + 'Development Status :: 3 - Alpha', + 'Intended Audience :: Mapping comunity', + 'Topic :: Maps :: Mapping Tools', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 2.7', + ], + + keywords='maps mapping tools spatial analysis geostatistics', + + packages=find_packages(exclude=['contrib', 'docs', 'tests']), + + extras_require={ + 'dev': ['unittest'], + 'test': ['unittest', 'nose', 'mock'], + }, + + # The choice of component versions is dictated by what's + # provisioned in the production servers. + install_requires=['pysal==1.9.1', 'scikit-learn==0.17.1'], + + requires=['pysal', 'numpy', 'sklearn'], + + test_suite='test' +) diff --git a/release/python/0.0.3/crankshaft/test/fixtures/kmeans.json b/release/python/0.0.3/crankshaft/test/fixtures/kmeans.json new file mode 100644 index 0000000..8f31c79 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/fixtures/kmeans.json @@ -0,0 +1 @@ +[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}] \ No newline at end of file diff --git a/release/python/0.0.3/crankshaft/test/fixtures/moran.json b/release/python/0.0.3/crankshaft/test/fixtures/moran.json new file mode 100644 index 0000000..2f75cf1 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/fixtures/moran.json @@ -0,0 +1,52 @@ +[[0.9319096128346788, "HH"], +[-1.135787401862846, "HL"], +[0.11732030672508517, "LL"], +[0.6152779669180425, "LL"], +[-0.14657336660125297, "LH"], +[0.6967858120189607, "LL"], +[0.07949310115714454, "HH"], +[0.4703198759258987, "HH"], +[0.4421125200498064, "HH"], +[0.5724288737143592, "LL"], +[0.8970743435692062, "LL"], +[0.18327334401918674, "LL"], +[-0.01466729201304962, "HL"], +[0.3481559372544409, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329988, "HH"], +[0.4373841193538136, "HH"], +[0.15971286468915544, "LL"], +[1.0543588860308968, "HH"], +[1.7372866900020818, "HH"], +[1.091998586053999, "LL"], +[0.1171572584252222, "HH"], +[0.08438455015300014, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329985, "HH"], +[1.1627044812890683, "HH"], +[0.06547094736902978, "LL"], +[0.795275137550483, "HH"], +[0.18562939195219, "LL"], +[0.3010757406693439, "LL"], +[2.8205795942839376, "HH"], +[0.11259190602909264, "LL"], +[-0.07116352791516614, "HL"], +[-0.09945240794119009, "LH"], +[0.18562939195219, "LL"], +[0.1832733440191868, "LL"], +[-0.39054253768447705, "HL"], +[-0.1672071289487642, "HL"], +[0.3337669247916343, "HH"], +[0.2584386102554792, "HH"], +[-0.19733845476322634, "HL"], +[-0.9379282899805409, "LH"], +[-0.028770969951095866, "LH"], +[0.051367269430983485, "LL"], +[-0.2172548045913472, "LH"], +[0.05136726943098351, "LL"], +[0.04191046803899837, "LL"], +[0.7482357030403517, "HH"], +[-0.014585767863118111, "LH"], +[0.5410013139159929, "HH"], +[1.0223932668429925, "LL"], +[1.4179402898927476, "LL"]] \ No newline at end of file diff --git a/release/python/0.0.3/crankshaft/test/fixtures/neighbors.json b/release/python/0.0.3/crankshaft/test/fixtures/neighbors.json new file mode 100644 index 0000000..055b359 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/fixtures/neighbors.json @@ -0,0 +1,54 @@ +[ + {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5}, + {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7}, + {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2}, + {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1}, + {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3}, + {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05}, + {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4}, + {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7}, + {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5}, + {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04}, + {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08}, + {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2}, + {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4}, + {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2}, + {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3}, + {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4}, + {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6}, + {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3}, + {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7}, + {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8}, + {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1}, + {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4}, + {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1}, + {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3}, + {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4}, + {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6}, + {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3}, + {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8}, + {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3}, + {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1}, + {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9}, + {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3}, + {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4}, + {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3}, + {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3}, + {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2}, + {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5}, + {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4}, + {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6}, + {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5}, + {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4}, + {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2}, + {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3}, + {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2}, + {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3}, + {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2}, + {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3}, + {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5}, + {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2}, + {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6}, + {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01}, + {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01} + ] diff --git a/release/python/0.0.3/crankshaft/test/helper.py b/release/python/0.0.3/crankshaft/test/helper.py new file mode 100644 index 0000000..7d28b94 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/helper.py @@ -0,0 +1,13 @@ +import unittest + +from mock_plpy import MockPlPy +plpy = MockPlPy() + +import sys +sys.modules['plpy'] = plpy + +import os + +def fixture_file(name): + dir = os.path.dirname(os.path.realpath(__file__)) + return os.path.join(dir, 'fixtures', name) diff --git a/release/python/0.0.3/crankshaft/test/mock_plpy.py b/release/python/0.0.3/crankshaft/test/mock_plpy.py new file mode 100644 index 0000000..63c88f6 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/mock_plpy.py @@ -0,0 +1,34 @@ +import re + +class MockPlPy: + def __init__(self): + self._reset() + + def _reset(self): + self.infos = [] + self.notices = [] + self.debugs = [] + self.logs = [] + self.warnings = [] + self.errors = [] + self.fatals = [] + self.executes = [] + self.results = [] + self.prepares = [] + self.results = [] + + def _define_result(self, query, result): + pattern = re.compile(query, re.IGNORECASE | re.MULTILINE) + self.results.append([pattern, result]) + + def notice(self, msg): + self.notices.append(msg) + + def info(self, msg): + self.infos.append(msg) + + def execute(self, query): # TODO: additional arguments + for result in self.results: + if result[0].match(query): + return result[1] + return [] diff --git a/release/python/0.0.3/crankshaft/test/test_cluster_kmeans.py b/release/python/0.0.3/crankshaft/test/test_cluster_kmeans.py new file mode 100644 index 0000000..aba8e07 --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/test_cluster_kmeans.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file +import numpy as np +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class KMeansTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read()) + self.params = {"subquery": "select * from table", + "no_clusters": "10" + } + + def test_kmeans(self): + data = self.cluster_data + plpy._define_result('select' ,data) + clusters = cc.kmeans('subquery', 2) + labels = [a[1] for a in clusters] + c1 = [a for a in clusters if a[1]==0] + c2 = [a for a in clusters if a[1]==1] + + self.assertEqual(len(np.unique(labels)),2) + self.assertEqual(len(c1),20) + self.assertEqual(len(c2),20) + diff --git a/release/python/0.0.3/crankshaft/test/test_clustering_moran.py b/release/python/0.0.3/crankshaft/test/test_clustering_moran.py new file mode 100644 index 0000000..393e93b --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/test_clustering_moran.py @@ -0,0 +1,83 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class MoranTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read()) + self.moran_data = json.loads(open(fixture_file('moran.json')).read()) + + def test_map_quads(self): + """Test map_quads""" + self.assertEqual(cc.map_quads(1), 'HH') + self.assertEqual(cc.map_quads(2), 'LH') + self.assertEqual(cc.map_quads(3), 'LL') + self.assertEqual(cc.map_quads(4), 'HL') + self.assertEqual(cc.map_quads(33), None) + self.assertEqual(cc.map_quads('andy'), None) + + def test_quad_position(self): + """Test lisa_sig_vals""" + + quads = np.array([1, 2, 3, 4], np.int) + + ans = np.array(['HH', 'LH', 'LL', 'HL']) + test_ans = cc.quad_position(quads) + + self.assertTrue((test_ans == ans).all()) + + def test_moran_local(self): + """Test Moran's I local""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + self.assertEqual(res_quad, exp_quad) + + def test_moran_local_rate(self): + """Test Moran's I rate""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None? ', result == None + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + + def test_moran(self): + """Test Moran's I global""" + data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1235) + result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None?', result == None + result_moran = result[0][0] + expected_moran = np.array([row[0] for row in self.moran_data]).mean() + self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2) diff --git a/release/python/0.0.3/crankshaft/test/test_pysal_utils.py b/release/python/0.0.3/crankshaft/test/test_pysal_utils.py new file mode 100644 index 0000000..4ea0d9b --- /dev/null +++ b/release/python/0.0.3/crankshaft/test/test_pysal_utils.py @@ -0,0 +1,107 @@ +import unittest + +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds + + +class PysalUtilsTest(unittest.TestCase): + """Testing class for utility functions related to PySAL integrations""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + def test_query_attr_select(self): + """Test query_attr_select""" + + ans = "i.\"{attr1}\"::numeric As attr1, " \ + "i.\"{attr2}\"::numeric As attr2, " + + self.assertEqual(pu.query_attr_select(self.params), ans) + + def test_query_attr_where(self): + """Test pu.query_attr_where""" + + ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \ + "idx_replace.\"{attr2}\" IS NOT NULL AND " \ + "idx_replace.\"{attr2}\" <> 0" + + self.assertEqual(pu.query_attr_where(self.params), ans) + + def test_knn(self): + """Test knn neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0 " \ + "ORDER BY " \ + "j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.knn(self.params), ans) + + def test_queen(self): + """Test queen neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "ST_Touches(i.\"the_geom\", " \ + "j.\"the_geom\") AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0)" \ + ") As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.queen(self.params), ans) + + def test_construct_neighbor_query(self): + """Test construct_neighbor_query""" + + # Compare to raw knn query + self.assertEqual(pu.construct_neighbor_query('knn', self.params), + pu.knn(self.params)) + + def test_get_attributes(self): + """Test get_attributes""" + + ## need to add tests + + self.assertEqual(True, True) + + def test_get_weight(self): + """Test get_weight""" + + self.assertEqual(True, True) + + def test_empty_zipped_array(self): + """Test empty_zipped_array""" + ans2 = [(None, None)] + ans4 = [(None, None, None, None)] + self.assertEqual(pu.empty_zipped_array(2), ans2) + self.assertEqual(pu.empty_zipped_array(4), ans4) diff --git a/release/python/0.0.4/crankshaft/crankshaft/__init__.py b/release/python/0.0.4/crankshaft/crankshaft/__init__.py new file mode 100644 index 0000000..d07e330 --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/__init__.py @@ -0,0 +1,2 @@ +import random_seeds +import clustering diff --git a/release/python/0.0.4/crankshaft/crankshaft/clustering/__init__.py b/release/python/0.0.4/crankshaft/crankshaft/clustering/__init__.py new file mode 100644 index 0000000..338e8ea --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/clustering/__init__.py @@ -0,0 +1,2 @@ +from moran import * +from kmeans import * diff --git a/release/python/0.0.4/crankshaft/crankshaft/clustering/kmeans.py b/release/python/0.0.4/crankshaft/crankshaft/clustering/kmeans.py new file mode 100644 index 0000000..4134062 --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/clustering/kmeans.py @@ -0,0 +1,18 @@ +from sklearn.cluster import KMeans +import plpy + +def kmeans(query, no_clusters, no_init=20): + data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids, + array_agg(ST_X(the_geom) order by cartodb_id) xs, + array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a + where the_geom is not null + '''.format(query=query)) + + xs = data[0]['xs'] + ys = data[0]['ys'] + ids = data[0]['ids'] + + km = KMeans(n_clusters= no_clusters, n_init=no_init) + labels = km.fit_predict(zip(xs,ys)) + return zip(ids,labels) + diff --git a/release/python/0.0.4/crankshaft/crankshaft/clustering/moran.py b/release/python/0.0.4/crankshaft/crankshaft/clustering/moran.py new file mode 100644 index 0000000..39b3ff6 --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/clustering/moran.py @@ -0,0 +1,260 @@ +""" +Moran's I geostatistics (global clustering & outliers presence) +""" + +# TODO: Fill in local neighbors which have null/NoneType values with the +# average of the their neighborhood + +import pysal as ps +import plpy + +# crankshaft module +import crankshaft.pysal_utils as pu + +# High level interface --------------------------------------- + +def moran(subquery, attr_name, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (global) + Implementation building neighbors with a PostGIS database and Moran's I + core clusters with PySAL. + Andy Eschbacher + """ + qvals = {"id_col": id_col, + "attr1": attr_name, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + attr_vals = pu.get_attributes(result) + + ## calculate weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global + moran_global = ps.esda.moran.Moran(attr_vals, weight, + permutations=permutations) + + return zip([moran_global.I], [moran_global.EI]) + +def moran_local(subquery, attr, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I implementation for PL/Python + Andy Eschbacher + """ + + # geometries with attributes that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = {"id_col": id_col, + "attr1": attr, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(5) + + attr_vals = pu.get_attributes(result) + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local(attr_vals, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Rate (global) + Andy Eschbacher + """ + qvals = {"id_col": id_col, + "attr1": numerator, + "attr2": denominator, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global rate + lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight, + permutations=permutations) + + return zip([lisa_rate.I], [lisa_rate.EI]) + +def moran_local_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Local Rate + Andy Eschbacher + """ + # geometries with values that are null are ignored + # resulting in a collection of not as near neighbors + + query = pu.construct_neighbor_query(w_type, + {"id_col": id_col, + "numerator": numerator, + "denominator": denominator, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs}) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(5) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, + permutations=permutations) + + # find units of significance + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_local_bv(subquery, attr1, attr2, + permutations, geom_col, id_col, w_type, num_ngbrs): + """ + Moran's I (local) Bivariate (untested) + """ + plpy.notice('** Constructing query') + + qvals = {"num_ngbrs": num_ngbrs, + "attr1": attr1, + "attr2": attr2, + "subquery": subquery, + "geom_col": geom_col, + "id_col": id_col} + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(4) + except plpy.SPIError: + plpy.error("Error: areas of interest query failed, " \ + "check input parameters") + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(4) + + ## collect attributes + attr1_vals = pu.get_attributes(result, 1) + attr2_vals = pu.get_attributes(result, 2) + + # create weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight, + permutations=permutations) + + plpy.notice("len of Is: %d" % len(lisa.Is)) + + # find clustering of significance + lisa_sig = quad_position(lisa.q) + + plpy.notice('** Finished calculations') + + return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order) + +# Low level functions ---------------------------------------- + +def map_quads(coord): + """ + Map a quadrant number to Moran's I designation + HH=1, LH=2, LL=3, HL=4 + Input: + @param coord (int): quadrant of a specific measurement + Output: + classification (one of 'HH', 'LH', 'LL', or 'HL') + """ + if coord == 1: + return 'HH' + elif coord == 2: + return 'LH' + elif coord == 3: + return 'LL' + elif coord == 4: + return 'HL' + else: + return None + +def quad_position(quads): + """ + Produce Moran's I classification based of n + Input: + @param quads ndarray: an array of quads classified by + 1-4 (PySAL default) + Output: + @param list: an array of quads classied by 'HH', 'LL', etc. + """ + return [map_quads(q) for q in quads] diff --git a/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/__init__.py b/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/__init__.py new file mode 100644 index 0000000..835880d --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/__init__.py @@ -0,0 +1 @@ +from pysal_utils import * diff --git a/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py new file mode 100644 index 0000000..02b5e35 --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -0,0 +1,152 @@ +""" + Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects +""" + +import numpy as np +import pysal as ps + +def construct_neighbor_query(w_type, query_vals): + """Return query (a string) used for finding neighbors + @param w_type text: type of neighbors to calculate ('knn' or 'queen') + @param query_vals dict: values used to construct the query + """ + + if w_type.lower() == 'knn': + return knn(query_vals) + else: + return queen(query_vals) + +## Build weight object +def get_weight(query_res, w_type='knn', num_ngbrs=5): + """ + Construct PySAL weight from return value of query + @param query_res: query results with attributes and neighbors + """ + if w_type.lower() == 'knn': + row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs + weights = {x['id']: row_normed_weights for x in query_res} + else: + weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) + if len(x['neighbors']) > 0 + else [] for x in query_res} + + neighbors = {x['id']: x['neighbors'] for x in query_res} + + return ps.W(neighbors, weights) + +def query_attr_select(params): + """ + Create portion of SELECT statement for attributes inolved in query. + @param params: dict of information used in query (column names, + table name, etc.) + """ + + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')] + + template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, " + + attr_string = "" + + for idx, val in enumerate(sorted(attrs)): + attr_string += template % {"col": val, "alias_num": idx + 1} + + return attr_string + +def query_attr_where(params): + """ + Create portion of WHERE clauses for weeding out NULL-valued geometries + """ + attrs = sorted([k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]) + + attr_string = [] + + for attr in attrs: + attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr) + + if len(attrs) == 2: + attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1]) + + out = " AND ".join(attr_string) + + return out + +def knn(params): + """SQL query for k-nearest neighbors. + @param vars: dict of values to fill template + """ + + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE " \ + "i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "%(attr_where_j)s " \ + "ORDER BY " \ + "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \ + "LIMIT {num_ngbrs})" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## SQL query for finding queens neighbors (all contiguous polygons) +def queen(params): + """SQL query for queen neighbors. + @param params dict: information to fill query + """ + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \ + "%(attr_where_j)s)" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## to add more weight methods open a ticket or pull request + +def get_attributes(query_res, attr_num=1): + """ + @param query_res: query results with attributes and neighbors + @param attr_num: attribute number (1, 2, ...) + """ + return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float) + +def empty_zipped_array(num_nones): + """ + prepare return values for cases of empty weights objects (no neighbors) + Input: + @param num_nones int: number of columns (e.g., 4) + Output: + [(None, None, None, None)] + """ + + return [tuple([None] * num_nones)] diff --git a/release/python/0.0.4/crankshaft/crankshaft/random_seeds.py b/release/python/0.0.4/crankshaft/crankshaft/random_seeds.py new file mode 100644 index 0000000..b7c8eed --- /dev/null +++ b/release/python/0.0.4/crankshaft/crankshaft/random_seeds.py @@ -0,0 +1,10 @@ +import random +import numpy + +def set_random_seeds(value): + """ + Set the seeds of the RNGs (Random Number Generators) + used internally. + """ + random.seed(value) + numpy.random.seed(value) diff --git a/release/python/0.0.4/crankshaft/setup.py b/release/python/0.0.4/crankshaft/setup.py new file mode 100644 index 0000000..32d1ead --- /dev/null +++ b/release/python/0.0.4/crankshaft/setup.py @@ -0,0 +1,48 @@ + +""" +CartoDB Spatial Analysis Python Library +See: +https://github.com/CartoDB/crankshaft +""" + +from setuptools import setup, find_packages + +setup( + name='crankshaft', + + version='0.0.4', + + description='CartoDB Spatial Analysis Python Library', + + url='https://github.com/CartoDB/crankshaft', + + author='Data Services Team - CartoDB', + author_email='dataservices@cartodb.com', + + license='MIT', + + classifiers=[ + 'Development Status :: 3 - Alpha', + 'Intended Audience :: Mapping comunity', + 'Topic :: Maps :: Mapping Tools', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 2.7', + ], + + keywords='maps mapping tools spatial analysis geostatistics', + + packages=find_packages(exclude=['contrib', 'docs', 'tests']), + + extras_require={ + 'dev': ['unittest'], + 'test': ['unittest', 'nose', 'mock'], + }, + + # The choice of component versions is dictated by what's + # provisioned in the production servers. + install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'], + + requires=['pysal', 'numpy', 'sklearn'], + + test_suite='test' +) diff --git a/release/python/0.0.4/crankshaft/test/fixtures/kmeans.json b/release/python/0.0.4/crankshaft/test/fixtures/kmeans.json new file mode 100644 index 0000000..8f31c79 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/fixtures/kmeans.json @@ -0,0 +1 @@ +[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}] \ No newline at end of file diff --git a/release/python/0.0.4/crankshaft/test/fixtures/moran.json b/release/python/0.0.4/crankshaft/test/fixtures/moran.json new file mode 100644 index 0000000..2f75cf1 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/fixtures/moran.json @@ -0,0 +1,52 @@ +[[0.9319096128346788, "HH"], +[-1.135787401862846, "HL"], +[0.11732030672508517, "LL"], +[0.6152779669180425, "LL"], +[-0.14657336660125297, "LH"], +[0.6967858120189607, "LL"], +[0.07949310115714454, "HH"], +[0.4703198759258987, "HH"], +[0.4421125200498064, "HH"], +[0.5724288737143592, "LL"], +[0.8970743435692062, "LL"], +[0.18327334401918674, "LL"], +[-0.01466729201304962, "HL"], +[0.3481559372544409, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329988, "HH"], +[0.4373841193538136, "HH"], +[0.15971286468915544, "LL"], +[1.0543588860308968, "HH"], +[1.7372866900020818, "HH"], +[1.091998586053999, "LL"], +[0.1171572584252222, "HH"], +[0.08438455015300014, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329985, "HH"], +[1.1627044812890683, "HH"], +[0.06547094736902978, "LL"], +[0.795275137550483, "HH"], +[0.18562939195219, "LL"], +[0.3010757406693439, "LL"], +[2.8205795942839376, "HH"], +[0.11259190602909264, "LL"], +[-0.07116352791516614, "HL"], +[-0.09945240794119009, "LH"], +[0.18562939195219, "LL"], +[0.1832733440191868, "LL"], +[-0.39054253768447705, "HL"], +[-0.1672071289487642, "HL"], +[0.3337669247916343, "HH"], +[0.2584386102554792, "HH"], +[-0.19733845476322634, "HL"], +[-0.9379282899805409, "LH"], +[-0.028770969951095866, "LH"], +[0.051367269430983485, "LL"], +[-0.2172548045913472, "LH"], +[0.05136726943098351, "LL"], +[0.04191046803899837, "LL"], +[0.7482357030403517, "HH"], +[-0.014585767863118111, "LH"], +[0.5410013139159929, "HH"], +[1.0223932668429925, "LL"], +[1.4179402898927476, "LL"]] \ No newline at end of file diff --git a/release/python/0.0.4/crankshaft/test/fixtures/neighbors.json b/release/python/0.0.4/crankshaft/test/fixtures/neighbors.json new file mode 100644 index 0000000..055b359 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/fixtures/neighbors.json @@ -0,0 +1,54 @@ +[ + {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5}, + {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7}, + {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2}, + {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1}, + {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3}, + {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05}, + {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4}, + {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7}, + {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5}, + {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04}, + {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08}, + {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2}, + {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4}, + {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2}, + {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3}, + {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4}, + {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6}, + {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3}, + {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7}, + {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8}, + {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1}, + {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4}, + {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1}, + {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3}, + {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4}, + {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6}, + {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3}, + {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8}, + {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3}, + {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1}, + {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9}, + {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3}, + {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4}, + {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3}, + {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3}, + {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2}, + {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5}, + {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4}, + {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6}, + {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5}, + {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4}, + {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2}, + {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3}, + {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2}, + {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3}, + {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2}, + {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3}, + {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5}, + {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2}, + {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6}, + {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01}, + {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01} + ] diff --git a/release/python/0.0.4/crankshaft/test/helper.py b/release/python/0.0.4/crankshaft/test/helper.py new file mode 100644 index 0000000..7d28b94 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/helper.py @@ -0,0 +1,13 @@ +import unittest + +from mock_plpy import MockPlPy +plpy = MockPlPy() + +import sys +sys.modules['plpy'] = plpy + +import os + +def fixture_file(name): + dir = os.path.dirname(os.path.realpath(__file__)) + return os.path.join(dir, 'fixtures', name) diff --git a/release/python/0.0.4/crankshaft/test/mock_plpy.py b/release/python/0.0.4/crankshaft/test/mock_plpy.py new file mode 100644 index 0000000..63c88f6 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/mock_plpy.py @@ -0,0 +1,34 @@ +import re + +class MockPlPy: + def __init__(self): + self._reset() + + def _reset(self): + self.infos = [] + self.notices = [] + self.debugs = [] + self.logs = [] + self.warnings = [] + self.errors = [] + self.fatals = [] + self.executes = [] + self.results = [] + self.prepares = [] + self.results = [] + + def _define_result(self, query, result): + pattern = re.compile(query, re.IGNORECASE | re.MULTILINE) + self.results.append([pattern, result]) + + def notice(self, msg): + self.notices.append(msg) + + def info(self, msg): + self.infos.append(msg) + + def execute(self, query): # TODO: additional arguments + for result in self.results: + if result[0].match(query): + return result[1] + return [] diff --git a/release/python/0.0.4/crankshaft/test/test_cluster_kmeans.py b/release/python/0.0.4/crankshaft/test/test_cluster_kmeans.py new file mode 100644 index 0000000..aba8e07 --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/test_cluster_kmeans.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file +import numpy as np +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class KMeansTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read()) + self.params = {"subquery": "select * from table", + "no_clusters": "10" + } + + def test_kmeans(self): + data = self.cluster_data + plpy._define_result('select' ,data) + clusters = cc.kmeans('subquery', 2) + labels = [a[1] for a in clusters] + c1 = [a for a in clusters if a[1]==0] + c2 = [a for a in clusters if a[1]==1] + + self.assertEqual(len(np.unique(labels)),2) + self.assertEqual(len(c1),20) + self.assertEqual(len(c2),20) + diff --git a/release/python/0.0.4/crankshaft/test/test_clustering_moran.py b/release/python/0.0.4/crankshaft/test/test_clustering_moran.py new file mode 100644 index 0000000..393e93b --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/test_clustering_moran.py @@ -0,0 +1,83 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class MoranTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read()) + self.moran_data = json.loads(open(fixture_file('moran.json')).read()) + + def test_map_quads(self): + """Test map_quads""" + self.assertEqual(cc.map_quads(1), 'HH') + self.assertEqual(cc.map_quads(2), 'LH') + self.assertEqual(cc.map_quads(3), 'LL') + self.assertEqual(cc.map_quads(4), 'HL') + self.assertEqual(cc.map_quads(33), None) + self.assertEqual(cc.map_quads('andy'), None) + + def test_quad_position(self): + """Test lisa_sig_vals""" + + quads = np.array([1, 2, 3, 4], np.int) + + ans = np.array(['HH', 'LH', 'LL', 'HL']) + test_ans = cc.quad_position(quads) + + self.assertTrue((test_ans == ans).all()) + + def test_moran_local(self): + """Test Moran's I local""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + self.assertEqual(res_quad, exp_quad) + + def test_moran_local_rate(self): + """Test Moran's I rate""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None? ', result == None + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + + def test_moran(self): + """Test Moran's I global""" + data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1235) + result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None?', result == None + result_moran = result[0][0] + expected_moran = np.array([row[0] for row in self.moran_data]).mean() + self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2) diff --git a/release/python/0.0.4/crankshaft/test/test_pysal_utils.py b/release/python/0.0.4/crankshaft/test/test_pysal_utils.py new file mode 100644 index 0000000..4ea0d9b --- /dev/null +++ b/release/python/0.0.4/crankshaft/test/test_pysal_utils.py @@ -0,0 +1,107 @@ +import unittest + +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds + + +class PysalUtilsTest(unittest.TestCase): + """Testing class for utility functions related to PySAL integrations""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + def test_query_attr_select(self): + """Test query_attr_select""" + + ans = "i.\"{attr1}\"::numeric As attr1, " \ + "i.\"{attr2}\"::numeric As attr2, " + + self.assertEqual(pu.query_attr_select(self.params), ans) + + def test_query_attr_where(self): + """Test pu.query_attr_where""" + + ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \ + "idx_replace.\"{attr2}\" IS NOT NULL AND " \ + "idx_replace.\"{attr2}\" <> 0" + + self.assertEqual(pu.query_attr_where(self.params), ans) + + def test_knn(self): + """Test knn neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0 " \ + "ORDER BY " \ + "j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.knn(self.params), ans) + + def test_queen(self): + """Test queen neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "ST_Touches(i.\"the_geom\", " \ + "j.\"the_geom\") AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0)" \ + ") As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.queen(self.params), ans) + + def test_construct_neighbor_query(self): + """Test construct_neighbor_query""" + + # Compare to raw knn query + self.assertEqual(pu.construct_neighbor_query('knn', self.params), + pu.knn(self.params)) + + def test_get_attributes(self): + """Test get_attributes""" + + ## need to add tests + + self.assertEqual(True, True) + + def test_get_weight(self): + """Test get_weight""" + + self.assertEqual(True, True) + + def test_empty_zipped_array(self): + """Test empty_zipped_array""" + ans2 = [(None, None)] + ans4 = [(None, None, None, None)] + self.assertEqual(pu.empty_zipped_array(2), ans2) + self.assertEqual(pu.empty_zipped_array(4), ans4) diff --git a/release/python/0.1.0/crankshaft/crankshaft/__init__.py b/release/python/0.1.0/crankshaft/crankshaft/__init__.py new file mode 100644 index 0000000..4e06bc5 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/__init__.py @@ -0,0 +1,5 @@ +"""Import all modules""" +import crankshaft.random_seeds +import crankshaft.clustering +import crankshaft.space_time_dynamics +import crankshaft.segmentation diff --git a/release/python/0.1.0/crankshaft/crankshaft/clustering/__init__.py b/release/python/0.1.0/crankshaft/crankshaft/clustering/__init__.py new file mode 100644 index 0000000..ed34fe0 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/clustering/__init__.py @@ -0,0 +1,3 @@ +"""Import all functions from for clustering""" +from moran import * +from kmeans import * diff --git a/release/python/0.1.0/crankshaft/crankshaft/clustering/kmeans.py b/release/python/0.1.0/crankshaft/crankshaft/clustering/kmeans.py new file mode 100644 index 0000000..4134062 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/clustering/kmeans.py @@ -0,0 +1,18 @@ +from sklearn.cluster import KMeans +import plpy + +def kmeans(query, no_clusters, no_init=20): + data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids, + array_agg(ST_X(the_geom) order by cartodb_id) xs, + array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a + where the_geom is not null + '''.format(query=query)) + + xs = data[0]['xs'] + ys = data[0]['ys'] + ids = data[0]['ids'] + + km = KMeans(n_clusters= no_clusters, n_init=no_init) + labels = km.fit_predict(zip(xs,ys)) + return zip(ids,labels) + diff --git a/release/python/0.1.0/crankshaft/crankshaft/clustering/moran.py b/release/python/0.1.0/crankshaft/crankshaft/clustering/moran.py new file mode 100644 index 0000000..3282f5f --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/clustering/moran.py @@ -0,0 +1,262 @@ +""" +Moran's I geostatistics (global clustering & outliers presence) +""" + +# TODO: Fill in local neighbors which have null/NoneType values with the +# average of the their neighborhood + +import pysal as ps +import plpy +from collections import OrderedDict + +# crankshaft module +import crankshaft.pysal_utils as pu + +# High level interface --------------------------------------- + +def moran(subquery, attr_name, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (global) + Implementation building neighbors with a PostGIS database and Moran's I + core clusters with PySAL. + Andy Eschbacher + """ + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr_name), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + attr_vals = pu.get_attributes(result) + + ## calculate weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global + moran_global = ps.esda.moran.Moran(attr_vals, weight, + permutations=permutations) + + return zip([moran_global.I], [moran_global.EI]) + +def moran_local(subquery, attr, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I implementation for PL/Python + Andy Eschbacher + """ + + # geometries with attributes that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(5) + + attr_vals = pu.get_attributes(result) + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local(attr_vals, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Rate (global) + Andy Eschbacher + """ + qvals = OrderedDict([("id_col", id_col), + ("attr1", numerator), + ("attr2", denominator) + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global rate + lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight, + permutations=permutations) + + return zip([lisa_rate.I], [lisa_rate.EI]) + +def moran_local_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Local Rate + Andy Eschbacher + """ + # geometries with values that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = OrderedDict([("id_col", id_col), + ("numerator", numerator), + ("denominator", denominator), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(5) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_local_bv(subquery, attr1, attr2, + permutations, geom_col, id_col, w_type, num_ngbrs): + """ + Moran's I (local) Bivariate (untested) + """ + plpy.notice('** Constructing query') + + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr1), + ("attr2", attr2), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(4) + except plpy.SPIError: + plpy.error("Error: areas of interest query failed, " \ + "check input parameters") + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(4) + + ## collect attributes + attr1_vals = pu.get_attributes(result, 1) + attr2_vals = pu.get_attributes(result, 2) + + # create weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight, + permutations=permutations) + + plpy.notice("len of Is: %d" % len(lisa.Is)) + + # find clustering of significance + lisa_sig = quad_position(lisa.q) + + plpy.notice('** Finished calculations') + + return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order) + +# Low level functions ---------------------------------------- + +def map_quads(coord): + """ + Map a quadrant number to Moran's I designation + HH=1, LH=2, LL=3, HL=4 + Input: + @param coord (int): quadrant of a specific measurement + Output: + classification (one of 'HH', 'LH', 'LL', or 'HL') + """ + if coord == 1: + return 'HH' + elif coord == 2: + return 'LH' + elif coord == 3: + return 'LL' + elif coord == 4: + return 'HL' + else: + return None + +def quad_position(quads): + """ + Produce Moran's I classification based of n + Input: + @param quads ndarray: an array of quads classified by + 1-4 (PySAL default) + Output: + @param list: an array of quads classied by 'HH', 'LL', etc. + """ + return [map_quads(q) for q in quads] diff --git a/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/__init__.py b/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/__init__.py new file mode 100644 index 0000000..fdf073b --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions for pysal_utils""" +from crankshaft.pysal_utils.pysal_utils import * diff --git a/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py new file mode 100644 index 0000000..4622925 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -0,0 +1,188 @@ +""" + Utilities module for generic PySAL functionality, mainly centered on + translating queries into numpy arrays or PySAL weights objects +""" + +import numpy as np +import pysal as ps + +def construct_neighbor_query(w_type, query_vals): + """Return query (a string) used for finding neighbors + @param w_type text: type of neighbors to calculate ('knn' or 'queen') + @param query_vals dict: values used to construct the query + """ + + if w_type.lower() == 'knn': + return knn(query_vals) + else: + return queen(query_vals) + +## Build weight object +def get_weight(query_res, w_type='knn', num_ngbrs=5): + """ + Construct PySAL weight from return value of query + @param query_res dict-like: query results with attributes and neighbors + """ + # if w_type.lower() == 'knn': + # row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs + # weights = {x['id']: row_normed_weights for x in query_res} + # else: + # weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) + # if len(x['neighbors']) > 0 + # else [] for x in query_res} + + neighbors = {x['id']: x['neighbors'] for x in query_res} + print 'len of neighbors: %d' % len(neighbors) + + built_weight = ps.W(neighbors) + built_weight.transform = 'r' + + return built_weight + +def query_attr_select(params): + """ + Create portion of SELECT statement for attributes inolved in query. + @param params: dict of information used in query (column names, + table name, etc.) + """ + + attr_string = "" + template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, " + + if 'time_cols' in params: + ## if markov analysis + attrs = params['time_cols'] + + for idx, val in enumerate(attrs): + attr_string += template % {"col": val, "alias_num": idx + 1} + else: + ## if moran's analysis + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')] + + for idx, val in enumerate(sorted(attrs)): + attr_string += template % {"col": params[val], "alias_num": idx + 1} + + return attr_string + +def query_attr_where(params): + """ + Construct where conditions when building neighbors query + Create portion of WHERE clauses for weeding out NULL-valued geometries + Input: dict of params: + {'subquery': ..., + 'numerator': 'data1', + 'denominator': 'data2', + '': ...} + Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL' + Input: + {'subquery': ..., + 'time_cols': ['time1', 'time2', 'time3'], + 'etc': ...} + Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT + NULL AND idx_replace."time3" IS NOT NULL' + """ + attr_string = [] + template = "idx_replace.\"%s\" IS NOT NULL" + + if 'time_cols' in params: + ## markov where clauses + attrs = params['time_cols'] + # add values to template + for attr in attrs: + attr_string.append(template % attr) + else: + ## moran where clauses + + # get keys + attrs = sorted([k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]) + # add values to template + for attr in attrs: + attr_string.append(template % params[attr]) + + if len(attrs) == 2: + attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]]) + + out = " AND ".join(attr_string) + + return out + +def knn(params): + """SQL query for k-nearest neighbors. + @param vars: dict of values to fill template + """ + + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE " \ + "i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "%(attr_where_j)s " \ + "ORDER BY " \ + "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \ + "LIMIT {num_ngbrs})" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## SQL query for finding queens neighbors (all contiguous polygons) +def queen(params): + """SQL query for queen neighbors. + @param params dict: information to fill query + """ + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \ + "%(attr_where_j)s)" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## to add more weight methods open a ticket or pull request + +def get_attributes(query_res, attr_num=1): + """ + @param query_res: query results with attributes and neighbors + @param attr_num: attribute number (1, 2, ...) + """ + return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float) + +def empty_zipped_array(num_nones): + """ + prepare return values for cases of empty weights objects (no neighbors) + Input: + @param num_nones int: number of columns (e.g., 4) + Output: + [(None, None, None, None)] + """ + + return [tuple([None] * num_nones)] diff --git a/release/python/0.1.0/crankshaft/crankshaft/random_seeds.py b/release/python/0.1.0/crankshaft/crankshaft/random_seeds.py new file mode 100644 index 0000000..31958cb --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/random_seeds.py @@ -0,0 +1,11 @@ +"""Random seed generator used for non-deterministic functions in crankshaft""" +import random +import numpy + +def set_random_seeds(value): + """ + Set the seeds of the RNGs (Random Number Generators) + used internally. + """ + random.seed(value) + numpy.random.seed(value) diff --git a/release/python/0.1.0/crankshaft/crankshaft/segmentation/__init__.py b/release/python/0.1.0/crankshaft/crankshaft/segmentation/__init__.py new file mode 100644 index 0000000..b825e85 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/segmentation/__init__.py @@ -0,0 +1 @@ +from segmentation import * diff --git a/release/python/0.1.0/crankshaft/crankshaft/segmentation/segmentation.py b/release/python/0.1.0/crankshaft/crankshaft/segmentation/segmentation.py new file mode 100644 index 0000000..ed61139 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/segmentation/segmentation.py @@ -0,0 +1,176 @@ +""" +Segmentation creation and prediction +""" + +import sklearn +import numpy as np +import plpy +from sklearn.ensemble import GradientBoostingRegressor +from sklearn import metrics +from sklearn.cross_validation import train_test_split + +# Lower level functions +#---------------------- + +def replace_nan_with_mean(array): + """ + Input: + @param array: an array of floats which may have null-valued entries + Output: + array with nans filled in with the mean of the dataset + """ + # returns an array of rows and column indices + indices = np.where(np.isnan(array)) + + # iterate through entries which have nan values + for row, col in zip(*indices): + array[row, col] = np.mean(array[~np.isnan(array[:, col]), col]) + + return array + +def get_data(variable, feature_columns, query): + """ + Fetch data from the database, clean, and package into + numpy arrays + Input: + @param variable: name of the target variable + @param feature_columns: list of column names + @param query: subquery that data is pulled from for the packaging + Output: + prepared data, packaged into NumPy arrays + """ + + columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns]) + + try: + data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format( + variable=variable, + columns=columns, + query=query)) + except Exception, e: + plpy.error('Failed to access data to build segmentation model: %s' % e) + + # extract target data from plpy object + target = np.array(data[0]['target']) + + # put n feature data arrays into an n x m array of arrays + features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns]) + + return replace_nan_with_mean(target), replace_nan_with_mean(features) + +# High level interface +# -------------------- + +def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters): + """ + Version of create_and_predict_segment that works on arrays that come stright form the SQL calling + the function. + + Input: + @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict + @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model + @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from + @param model_parameters: A dictionary containing parameters for the model. + """ + + clean_target = replace_nan_with_mean(target) + clean_features = replace_nan_with_mean(features) + target_features = replace_nan_with_mean(target_features) + + model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2) + prediction = model.predict(target_features) + accuracy_array = [accuracy]*prediction.shape[0] + return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array)) + + + +def create_and_predict_segment(query, variable, target_query, model_params): + """ + generate a segment with machine learning + Stuart Lynn + """ + + ## fetch column names + try: + columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys() + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + ## extract column names to be used in building the segmentation model + feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator']) + ## get data from database + target, features = get_data(variable, feature_columns, query) + + model, accuracy = train_model(target, features, model_params, 0.2) + cartodb_ids, result = predict_segment(model, feature_columns, target_query) + accuracy_array = [accuracy]*result.shape[0] + return zip(cartodb_ids, result, accuracy_array) + + +def train_model(target, features, model_params, test_split): + """ + Train the Gradient Boosting model on the provided data and calculate the accuracy of the model + Input: + @param target: 1D Array of the variable that the model is to be trianed to predict + @param features: 2D Array NSamples * NFeatures to use in trining the model + @param model_params: A dictionary of model parameters, the full specification can be found on the + scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) + @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray + """ + features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split) + model = GradientBoostingRegressor(**model_params) + model.fit(features_train, target_train) + accuracy = calculate_model_accuracy(model, features, target) + return model, accuracy + +def calculate_model_accuracy(model, features, target): + """ + Calculate the mean squared error of the model prediction + Input: + @param model: model trained from input features + @param features: features to make a prediction from + @param target: target to compare prediction to + Output: + mean squared error of the model prection compared to the target + """ + prediction = model.predict(features) + return metrics.mean_squared_error(prediction, target) + +def predict_segment(model, features, target_query): + """ + Use the provided model to predict the values for the new feature set + Input: + @param model: The pretrained model + @features: A list of features to use in the model prediction (list of column names) + @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it. + """ + + batch_size = 1000 + joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features]) + + try: + cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format( + joined_features=joined_features, + target_query=target_query)) + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + results = [] + + while True: + rows = cursor.fetch(batch_size) + if not rows: + break + batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows]) + + #Need to fix this. Should be global mean. This will cause weird effects + batch = replace_nan_with_mean(batch) + prediction = model.predict(batch) + results.append(prediction) + + try: + cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids'] + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + return cartodb_ids, np.concatenate(results) diff --git a/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/__init__.py b/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/__init__.py new file mode 100644 index 0000000..a439286 --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions from clustering libraries.""" +from markov import * diff --git a/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/markov.py b/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/markov.py new file mode 100644 index 0000000..bbf524d --- /dev/null +++ b/release/python/0.1.0/crankshaft/crankshaft/space_time_dynamics/markov.py @@ -0,0 +1,189 @@ +""" +Spatial dynamics measurements using Spatial Markov +""" + + +import numpy as np +import pysal as ps +import plpy +import crankshaft.pysal_utils as pu + +def spatial_markov_trend(subquery, time_cols, num_classes=7, + w_type='knn', num_ngbrs=5, permutations=0, + geom_col='the_geom', id_col='cartodb_id'): + """ + Predict the trends of a unit based on: + 1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile) + 2. average class of its neighbors + + Inputs: + @param subquery string: e.g., SELECT the_geom, cartodb_id, + interesting_time_column FROM table_name + @param time_cols list of strings: list of strings of column names + @param num_classes (optional): number of classes to break distribution + of values into. Currently uses quantile bins. + @param w_type string (optional): weight type ('knn' or 'queen') + @param num_ngbrs int (optional): number of neighbors (if knn type) + @param permutations int (optional): number of permutations for test + stats + @param geom_col string (optional): name of column which contains the + geometries + @param id_col string (optional): name of column which has the ids of + the table + + Outputs: + @param trend_up float: probablity that a geom will move to a higher + class + @param trend_down float: probablity that a geom will move to a lower + class + @param trend float: (trend_up - trend_down) / trend_static + @param volatility float: a measure of the volatility based on + probability stddev(prob array) + """ + + if len(time_cols) < 2: + plpy.error('More than one time column needs to be passed') + + qvals = {"id_col": id_col, + "time_cols": time_cols, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + try: + query_result = plpy.execute( + pu.construct_neighbor_query(w_type, qvals) + ) + if len(query_result) == 0: + return zip([None], [None], [None], [None], [None]) + except plpy.SPIError, err: + plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals))) + plpy.error('Query failed, check the input parameters') + return zip([None], [None], [None], [None], [None]) + + ## build weight + weights = pu.get_weight(query_result, w_type) + weights.transform = 'r' + + ## prep time data + t_data = get_time_data(query_result, time_cols) + + plpy.debug('shape of t_data %d, %d' % t_data.shape) + plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape) + plpy.debug('first num elements: %f' % t_data[0, 0]) + + sp_markov_result = ps.Spatial_Markov(t_data, + weights, + k=num_classes, + fixed=False, + permutations=permutations) + + ## get lag classes + lag_classes = ps.Quantiles( + ps.lag_spatial(weights, t_data[:, -1]), + k=num_classes).yb + + ## look up probablity distribution for each unit according to class and lag class + prob_dist = get_prob_dist(sp_markov_result.P, + lag_classes, + sp_markov_result.classes[:, -1]) + + ## find the ups and down and overall distribution of each cell + trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, + sp_markov_result.classes[:, -1]) + + ## output the results + return zip(trend, trend_up, trend_down, volatility, weights.id_order) + +def get_time_data(markov_data, time_cols): + """ + Extract the time columns and bin appropriately + """ + num_attrs = len(time_cols) + return np.array([[x['attr' + str(i)] for x in markov_data] + for i in range(1, num_attrs+1)], dtype=float).transpose() + +## not currently used +def rebin_data(time_data, num_time_per_bin): + """ + Convert an n x l matrix into an (n/m) x l matrix where the values are + reduced (averaged) for the intervening states: + 1 2 3 4 1.5 3.5 + 5 6 7 8 -> 5.5 7.5 + 9 8 7 6 8.5 6.5 + 5 4 3 2 4.5 2.5 + + if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix. + + This process effectively resamples the data at a longer time span n + units longer than the input data. + For cases when there is a remainder (remainder(5/3) = 2), the remaining + two columns are binned together as the last time period, while the + first three are binned together for the first period. + + Input: + @param time_data n x l ndarray: measurements of an attribute at + different time intervals + @param num_time_per_bin int: number of columns to average into a new + column + Output: + ceil(n / m) x l ndarray of resampled time series + """ + + if time_data.shape[1] % num_time_per_bin == 0: + ## if fit is perfect, then use it + n_max = time_data.shape[1] / num_time_per_bin + else: + ## fit remainders into an additional column + n_max = time_data.shape[1] / num_time_per_bin + 1 + + return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1) + for i in range(n_max)]).T + +def get_prob_dist(transition_matrix, lag_indices, unit_indices): + """ + Given an array of transition matrices, look up the probability + associated with the arrangements passed + + Input: + @param transition_matrix ndarray[k,k,k]: + @param lag_indices ndarray: + @param unit_indices ndarray: + + Output: + Array of probability distributions + """ + + return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] + for i in range(len(lag_indices))]) + +def get_prob_stats(prob_dist, unit_indices): + """ + get the statistics of the probability distributions + + Outputs: + @param trend_up ndarray(float): sum of probabilities for upward + movement (relative to the unit index of that prob) + @param trend_down ndarray(float): sum of probabilities for downward + movement (relative to the unit index of that prob) + @param trend ndarray(float): difference of upward and downward + movements + """ + + num_elements = len(unit_indices) + trend_up = np.empty(num_elements, dtype=float) + trend_down = np.empty(num_elements, dtype=float) + trend = np.empty(num_elements, dtype=float) + + for i in range(num_elements): + trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum() + trend_down[i] = prob_dist[i, :unit_indices[i]].sum() + if prob_dist[i, unit_indices[i]] > 0.0: + trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]] + else: + trend[i] = None + + ## calculate volatility of distribution + volatility = prob_dist.std(axis=1) + + return trend_up, trend_down, trend, volatility diff --git a/release/python/0.1.0/crankshaft/setup.py b/release/python/0.1.0/crankshaft/setup.py new file mode 100644 index 0000000..273cce1 --- /dev/null +++ b/release/python/0.1.0/crankshaft/setup.py @@ -0,0 +1,49 @@ + +""" +CartoDB Spatial Analysis Python Library +See: +https://github.com/CartoDB/crankshaft +""" + +from setuptools import setup, find_packages + +setup( + name='crankshaft', + + version='0.1.0', + + description='CartoDB Spatial Analysis Python Library', + + url='https://github.com/CartoDB/crankshaft', + + author='Data Services Team - CartoDB', + author_email='dataservices@cartodb.com', + + license='MIT', + + classifiers=[ + 'Development Status :: 3 - Alpha', + 'Intended Audience :: Mapping comunity', + 'Topic :: Maps :: Mapping Tools', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 2.7', + ], + + keywords='maps mapping tools spatial analysis geostatistics', + + packages=find_packages(exclude=['contrib', 'docs', 'tests']), + + extras_require={ + 'dev': ['unittest'], + 'test': ['unittest', 'nose', 'mock'], + }, + + # The choice of component versions is dictated by what's + # provisioned in the production servers. + # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation. + install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'], + + requires=['pysal', 'numpy', 'sklearn'], + + test_suite='test' +) diff --git a/release/python/0.1.0/crankshaft/test/fixtures/kmeans.json b/release/python/0.1.0/crankshaft/test/fixtures/kmeans.json new file mode 100644 index 0000000..8f31c79 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/fixtures/kmeans.json @@ -0,0 +1 @@ +[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}] \ No newline at end of file diff --git a/release/python/0.1.0/crankshaft/test/fixtures/markov.json b/release/python/0.1.0/crankshaft/test/fixtures/markov.json new file mode 100644 index 0000000..d60e4e0 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/fixtures/markov.json @@ -0,0 +1 @@ +[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]] diff --git a/release/python/0.1.0/crankshaft/test/fixtures/moran.json b/release/python/0.1.0/crankshaft/test/fixtures/moran.json new file mode 100644 index 0000000..2f75cf1 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/fixtures/moran.json @@ -0,0 +1,52 @@ +[[0.9319096128346788, "HH"], +[-1.135787401862846, "HL"], +[0.11732030672508517, "LL"], +[0.6152779669180425, "LL"], +[-0.14657336660125297, "LH"], +[0.6967858120189607, "LL"], +[0.07949310115714454, "HH"], +[0.4703198759258987, "HH"], +[0.4421125200498064, "HH"], +[0.5724288737143592, "LL"], +[0.8970743435692062, "LL"], +[0.18327334401918674, "LL"], +[-0.01466729201304962, "HL"], +[0.3481559372544409, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329988, "HH"], +[0.4373841193538136, "HH"], +[0.15971286468915544, "LL"], +[1.0543588860308968, "HH"], +[1.7372866900020818, "HH"], +[1.091998586053999, "LL"], +[0.1171572584252222, "HH"], +[0.08438455015300014, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329985, "HH"], +[1.1627044812890683, "HH"], +[0.06547094736902978, "LL"], +[0.795275137550483, "HH"], +[0.18562939195219, "LL"], +[0.3010757406693439, "LL"], +[2.8205795942839376, "HH"], +[0.11259190602909264, "LL"], +[-0.07116352791516614, "HL"], +[-0.09945240794119009, "LH"], +[0.18562939195219, "LL"], +[0.1832733440191868, "LL"], +[-0.39054253768447705, "HL"], +[-0.1672071289487642, "HL"], +[0.3337669247916343, "HH"], +[0.2584386102554792, "HH"], +[-0.19733845476322634, "HL"], +[-0.9379282899805409, "LH"], +[-0.028770969951095866, "LH"], +[0.051367269430983485, "LL"], +[-0.2172548045913472, "LH"], +[0.05136726943098351, "LL"], +[0.04191046803899837, "LL"], +[0.7482357030403517, "HH"], +[-0.014585767863118111, "LH"], +[0.5410013139159929, "HH"], +[1.0223932668429925, "LL"], +[1.4179402898927476, "LL"]] \ No newline at end of file diff --git a/release/python/0.1.0/crankshaft/test/fixtures/neighbors.json b/release/python/0.1.0/crankshaft/test/fixtures/neighbors.json new file mode 100644 index 0000000..055b359 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/fixtures/neighbors.json @@ -0,0 +1,54 @@ +[ + {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5}, + {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7}, + {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2}, + {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1}, + {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3}, + {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05}, + {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4}, + {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7}, + {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5}, + {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04}, + {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08}, + {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2}, + {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4}, + {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2}, + {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3}, + {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4}, + {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6}, + {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3}, + {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7}, + {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8}, + {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1}, + {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4}, + {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1}, + {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3}, + {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4}, + {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6}, + {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3}, + {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8}, + {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3}, + {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1}, + {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9}, + {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3}, + {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4}, + {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3}, + {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3}, + {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2}, + {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5}, + {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4}, + {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6}, + {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5}, + {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4}, + {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2}, + {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3}, + {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2}, + {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3}, + {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2}, + {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3}, + {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5}, + {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2}, + {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6}, + {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01}, + {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01} + ] diff --git a/release/python/0.1.0/crankshaft/test/fixtures/neighbors_markov.json b/release/python/0.1.0/crankshaft/test/fixtures/neighbors_markov.json new file mode 100644 index 0000000..45a20e7 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/fixtures/neighbors_markov.json @@ -0,0 +1 @@ +[{"neighbors": [10, 7, 21, 23, 1], "y1995": 0.87654416055651474, "y1997": 0.85637566664752718, "y1996": 0.8631470006766887, "y1999": 0.84461540228037335, "y1998": 0.84811668329242784, "y2006": 0.86302631339545688, "y2007": 0.86148266513456728, "y2004": 0.86416611731111015, "y2005": 0.87119374831581786, "y2002": 0.85012592862683589, "y2003": 0.8550965633336135, "y2000": 0.83271652434603094, "y2001": 0.83786313566577242, "id": 0, "y2008": 0.86252252380501315, "y2009": 0.86746356478544273}, {"neighbors": [5, 7, 22, 29, 3], "y1995": 0.91889509774542122, "y1997": 0.92333257900976462, "y1996": 0.91757931190043385, "y1999": 0.92552387732371888, "y1998": 0.92517289327379471, "y2006": 0.91706053906277052, "y2007": 0.90139504820726424, "y2004": 0.89815175749309051, "y2005": 0.91832090781161113, "y2002": 0.89431990798552208, "y2003": 0.88924793576523797, "y2000": 0.90746978227271013, "y2001": 0.89830489127332913, "id": 1, "y2008": 0.87897455159080617, "y2009": 0.86216858051752643}, {"neighbors": [11, 8, 13, 18, 17], "y1995": 0.82591007476914713, "y1997": 0.81989792988843901, "y1996": 0.82548595539161707, "y1999": 0.81731522200916285, "y1998": 0.81503235035017918, "y2006": 0.81814804358939286, "y2007": 0.83675961003285626, "y2004": 0.82668195534569056, "y2005": 0.82373723764184559, "y2002": 0.80849979516360859, "y2003": 0.82258550658074148, "y2000": 0.78964559168205917, "y2001": 0.8058444152731008, "id": 2, "y2008": 0.8357419865626442, "y2009": 0.84647177436289112}, {"neighbors": [4, 14, 9, 5, 12], "y1995": 1.0908817638059434, "y1997": 1.0845641754849344, "y1996": 1.0853768890893893, "y1999": 1.098988414417104, "y1998": 1.0841540389418189, "y2006": 1.1316479722785828, "y2007": 1.1295850763954971, "y2004": 1.1139980568106316, "y2005": 1.1216802898290368, "y2002": 1.1116069731657288, "y2003": 1.1088862051501811, "y2000": 1.1450694824791507, "y2001": 1.1215113292620285, "id": 3, "y2008": 1.1137181812756343, "y2009": 1.0993677488645406}, {"neighbors": [14, 3, 9, 31, 12], "y1995": 1.1073144618319228, "y1997": 1.1328363804627946, "y1996": 1.1137394350312471, "y1999": 1.1591002514611153, "y1998": 1.144725587086376, "y2006": 1.1173646811350333, "y2007": 1.1086324218539598, "y2004": 1.1102496406140896, "y2005": 1.11943471361418, "y2002": 1.1475230282561595, "y2003": 1.1184328424005199, "y2000": 1.1689820101690329, "y2001": 1.1721248787169682, "id": 4, "y2008": 1.0964251552643696, "y2009": 1.0776233718455337}, {"neighbors": [29, 1, 22, 7, 4], "y1995": 1.422697571371182, "y1997": 1.4427350196405593, "y1996": 1.4211843379728528, "y1999": 1.4440068434166562, "y1998": 1.4357757095632602, "y2006": 1.4405276647793266, "y2007": 1.4524121586440921, "y2004": 1.4059372049179741, "y2005": 1.4078864636665769, "y2002": 1.4197822680667809, "y2003": 1.3909220829548647, "y2000": 1.4418473669388905, "y2001": 1.4478283203013527, "id": 5, "y2008": 1.4330609762040207, "y2009": 1.4174430982377491}, {"neighbors": [12, 47, 9, 25, 20], "y1995": 1.1307388498039153, "y1997": 1.1107470843142355, "y1996": 1.1311051255854685, "y1999": 1.130881491772973, "y1998": 1.1336463608751246, "y2006": 1.1088003408832796, "y2007": 1.0840170924825394, "y2004": 1.1244623853593112, "y2005": 1.1167100811401538, "y2002": 1.1306293052597198, "y2003": 1.1194498381213465, "y2000": 1.1088813841947593, "y2001": 1.1185662918783175, "id": 6, "y2008": 1.0695920556329086, "y2009": 1.0787522517402164}, {"neighbors": [21, 1, 22, 10, 0], "y1995": 1.0470612357366649, "y1997": 1.0425337165747406, "y1996": 1.0451683097376836, "y1999": 1.0207254480945218, "y1998": 1.0323998680588111, "y2006": 1.0405109962442973, "y2007": 1.0174964540280445, "y2004": 1.0140090547678748, "y2005": 1.0317674181861733, "y2002": 0.99669586934394627, "y2003": 0.99327675611171373, "y2000": 0.99854316295509526, "y2001": 0.98802579761429143, "id": 7, "y2008": 0.9936394033949828, "y2009": 0.98279746069218921}, {"neighbors": [11, 13, 17, 18, 15], "y1995": 0.98996985668705595, "y1997": 0.99491000469481983, "y1996": 1.0014356415938011, "y1999": 1.0045584503565237, "y1998": 1.0018840754492748, "y2006": 0.92232873520447411, "y2007": 0.91284090705064902, "y2004": 0.93694786512729977, "y2005": 0.94308212820743131, "y2002": 0.96834820215592055, "y2003": 0.95335147249088092, "y2000": 0.99127006477048718, "y2001": 0.97925917470464008, "id": 8, "y2008": 0.89689832627117483, "y2009": 0.88928857608264111}, {"neighbors": [12, 6, 4, 3, 14], "y1995": 0.87418390853652306, "y1997": 0.84425695187978567, "y1996": 0.86416601430334228, "y1999": 0.83903043942542854, "y1998": 0.8404493987171674, "y2006": 0.87204140839730271, "y2007": 0.86633032299764789, "y2004": 0.86981997840756087, "y2005": 0.86837929279319737, "y2002": 0.86107306112852877, "y2003": 0.85007719735663123, "y2000": 0.85787080050645603, "y2001": 0.86036185149249467, "id": 9, "y2008": 0.84946077011565357, "y2009": 0.83287145944123797}, {"neighbors": [0, 7, 21, 23, 22], "y1995": 1.1419611801631209, "y1997": 1.1489271154554144, "y1996": 1.146602624490825, "y1999": 1.1443662376135306, "y1998": 1.1490959392942743, "y2006": 1.1049125811637337, "y2007": 1.1105984164317646, "y2004": 1.1119989015058092, "y2005": 1.1025779214946556, "y2002": 1.1259666377127024, "y2003": 1.1221399558345004, "y2000": 1.144501826035474, "y2001": 1.1234975172649961, "id": 10, "y2008": 1.1050979494645479, "y2009": 1.1002009697391872}, {"neighbors": [8, 13, 18, 17, 2], "y1995": 0.97282462974938089, "y1997": 0.96252588061647382, "y1996": 0.96700147279313231, "y1999": 0.96057686787383312, "y1998": 0.96538780087103548, "y2006": 0.91010201260822066, "y2007": 0.89280392121658247, "y2004": 0.94103988614185807, "y2005": 0.9212251863828258, "y2002": 0.94804194711420009, "y2003": 0.9543028555845573, "y2000": 0.95831051250950716, "y2001": 0.94480908623936988, "id": 11, "y2008": 0.89298242828382146, "y2009": 0.89165384824292859}, {"neighbors": [33, 9, 6, 25, 31], "y1995": 0.94325467991401402, "y1997": 0.96455242154753429, "y1996": 0.96436902092427723, "y1999": 0.94117647058823528, "y1998": 0.95243008993884537, "y2006": 0.9346681464882507, "y2007": 0.94281559150403071, "y2004": 0.96918424441756057, "y2005": 0.94781280876672958, "y2002": 0.95388717527096822, "y2003": 0.94597005193649519, "y2000": 0.94809269652332606, "y2001": 0.93539181553564288, "id": 12, "y2008": 0.965203150896216, "y2009": 0.967154410723015}, {"neighbors": [18, 17, 11, 8, 19], "y1995": 0.97478408425654373, "y1997": 0.98712808751954773, "y1996": 0.98169225257738801, "y1999": 0.985598971191053, "y1998": 0.98474769442356791, "y2006": 0.98416665248276058, "y2007": 0.98423613480079708, "y2004": 0.97399471186978948, "y2005": 0.96910087128357136, "y2002": 0.9820996926750224, "y2003": 0.98776529543110569, "y2000": 0.98687072733199255, "y2001": 0.99237486444837619, "id": 13, "y2008": 0.99823861244053191, "y2009": 0.99545704236827348}, {"neighbors": [4, 31, 3, 29, 12], "y1995": 0.85570268988941878, "y1997": 0.85986131704895119, "y1996": 0.85575915188345031, "y1999": 0.85380119644969055, "y1998": 0.85693406055397725, "y2006": 0.82803647591954255, "y2007": 0.81987360180979219, "y2004": 0.83998883284341452, "y2005": 0.83478547261894065, "y2002": 0.85472102128186755, "y2003": 0.84564834502399988, "y2000": 0.86191535266765262, "y2001": 0.84981450830432048, "id": 14, "y2008": 0.82265395167873867, "y2009": 0.83994039782937002}, {"neighbors": [19, 8, 17, 16, 13], "y1995": 0.87022046646521634, "y1997": 0.85961813213722393, "y1996": 0.85996258309339635, "y1999": 0.8394713575455558, "y1998": 0.85689572413110093, "y2006": 0.94202108334913126, "y2007": 0.94222309998743192, "y2004": 0.86763340229291142, "y2005": 0.89179316746010362, "y2002": 0.86776297543511893, "y2003": 0.86720209304280604, "y2000": 0.82785596604704892, "y2001": 0.86008789452656809, "id": 15, "y2008": 0.93902708112840494, "y2009": 0.94479183757120588}, {"neighbors": [28, 26, 15, 19, 32], "y1995": 0.90134907329491731, "y1997": 0.90403990934606904, "y1996": 0.904077381347274, "y1999": 0.90399237579083946, "y1998": 0.90201769385650832, "y2006": 0.91108803862404764, "y2007": 0.90543476309316473, "y2004": 0.94338264626469681, "y2005": 0.91981795862151561, "y2002": 0.93695966482853577, "y2003": 0.94242697007039, "y2000": 0.90906631602055099, "y2001": 0.92693339421265908, "id": 16, "y2008": 0.91737137682250491, "y2009": 0.94793657442067902}, {"neighbors": [13, 18, 11, 19, 8], "y1995": 1.1977611005602815, "y1997": 1.1843915817489725, "y1996": 1.1822256425225894, "y1999": 1.1928672308275252, "y1998": 1.1826786457339149, "y2006": 1.2392938410349985, "y2007": 1.2341867605077472, "y2004": 1.2385704217423759, "y2005": 1.2441989281116201, "y2002": 1.2262477774195681, "y2003": 1.2239707531714479, "y2000": 1.2017286912636342, "y2001": 1.2132869128474402, "id": 17, "y2008": 1.2362673914436095, "y2009": 1.2675439750795283}, {"neighbors": [13, 17, 11, 8, 19], "y1995": 1.2491967813733067, "y1997": 1.2699116090397236, "y1996": 1.2575477330927329, "y1999": 1.3062566740535762, "y1998": 1.2802065055312271, "y2006": 1.3210776560048689, "y2007": 1.329362443219563, "y2004": 1.3054484140490119, "y2005": 1.3030330249408666, "y2002": 1.3257518058685978, "y2003": 1.3079549159235695, "y2000": 1.3479002255103918, "y2001": 1.3439986302151703, "id": 18, "y2008": 1.3300124123891741, "y2009": 1.3328846185074705}, {"neighbors": [26, 17, 28, 15, 16], "y1995": 1.0676800411188558, "y1997": 1.0363730321443168, "y1996": 1.0379927554499979, "y1999": 1.0329609259280523, "y1998": 1.027684488045026, "y2006": 0.94241549375546196, "y2007": 0.92754546923532677, "y2004": 0.99614160423102482, "y2005": 0.97356208269708677, "y2002": 1.0274762326434594, "y2003": 1.0316273366809443, "y2000": 1.0505901631347052, "y2001": 1.0340505678899605, "id": 19, "y2008": 0.92549226593721745, "y2009": 0.92138101880290568}, {"neighbors": [30, 25, 24, 37, 47], "y1995": 1.0947561397632881, "y1997": 1.1165429913770684, "y1996": 1.1152679554712275, "y1999": 1.1314326394231322, "y1998": 1.1310394841195361, "y2006": 1.1090538904302065, "y2007": 1.1057776900012568, "y2004": 1.1402994437897009, "y2005": 1.1197940058085571, "y2002": 1.133670175399079, "y2003": 1.139822558851451, "y2000": 1.1388962186541665, "y2001": 1.1244221220249986, "id": 20, "y2008": 1.1116682481010467, "y2009": 1.0998515545336902}, {"neighbors": [23, 22, 7, 10, 34], "y1995": 0.76530058421804126, "y1997": 0.76542450966153397, "y1996": 0.76612841163904621, "y1999": 0.76014283909933289, "y1998": 0.7672268310234307, "y2006": 0.76842416021983684, "y2007": 0.77487117798086069, "y2004": 0.76533287692895391, "y2005": 0.78205934309410463, "y2002": 0.76156903267949927, "y2003": 0.76651951668098528, "y2000": 0.74480073263159763, "y2001": 0.76098396210261965, "id": 21, "y2008": 0.77768682781054099, "y2009": 0.78801192267396702}, {"neighbors": [21, 34, 5, 7, 29], "y1995": 0.98391336093764348, "y1997": 0.98295341320156315, "y1996": 0.98075815675295552, "y1999": 0.96913802803963667, "y1998": 0.97386015032669815, "y2006": 0.93965462091114671, "y2007": 0.93069644684632924, "y2004": 0.9635616201227476, "y2005": 0.94745351657235244, "y2002": 0.97209860866113018, "y2003": 0.97441312580606143, "y2000": 0.97370819354423843, "y2001": 0.96419154157867693, "id": 22, "y2008": 0.94020973488297466, "y2009": 0.94358232339833159}, {"neighbors": [21, 10, 22, 34, 7], "y1995": 0.83561828119099946, "y1997": 0.81738501913392403, "y1996": 0.82298088022609361, "y1999": 0.80904800725677739, "y1998": 0.81748588141426259, "y2006": 0.87170334233473346, "y2007": 0.8786379876833581, "y2004": 0.85954307066870839, "y2005": 0.86790023653402792, "y2002": 0.83451612857812574, "y2003": 0.85175031934895873, "y2000": 0.80071489233375537, "y2001": 0.83358255807316928, "id": 23, "y2008": 0.87497981001981484, "y2009": 0.87888675419592222}, {"neighbors": [27, 20, 30, 32, 47], "y1995": 0.98845573274970278, "y1997": 0.99665282989553183, "y1996": 1.0209242772035507, "y1999": 0.99386618594343845, "y1998": 0.99141823200404444, "y2006": 0.97906748937234156, "y2007": 0.9932312332800689, "y2004": 1.0111665058188304, "y2005": 0.9998802359352077, "y2002": 0.99669586934394627, "y2003": 1.0255909749831356, "y2000": 0.98733194819247994, "y2001": 0.99644997431653437, "id": 24, "y2008": 1.0020493856497013, "y2009": 0.99602148231561483}, {"neighbors": [20, 33, 6, 30, 12], "y1995": 1.1493091345649815, "y1997": 1.143009615936718, "y1996": 1.1524194939429724, "y1999": 1.1398468268822266, "y1998": 1.1426554202510555, "y2006": 1.0889107875354573, "y2007": 1.0860369499254896, "y2004": 1.0856975145267398, "y2005": 1.1244348633192611, "y2002": 1.0423089214343333, "y2003": 1.0557727834721793, "y2000": 1.0831239730629278, "y2001": 1.0519262599166714, "id": 25, "y2008": 1.0599731384290745, "y2009": 1.0216094265950888}, {"neighbors": [28, 19, 16, 32, 17], "y1995": 1.1136826889802023, "y1997": 1.1189343096757198, "y1996": 1.1057147027213501, "y1999": 1.1432271991365353, "y1998": 1.1377866945457653, "y2006": 1.1268023587150906, "y2007": 1.1235793669317915, "y2004": 1.1482023546040769, "y2005": 1.1238659840114973, "y2002": 1.1600919581655105, "y2003": 1.1446778932605579, "y2000": 1.1825702862895446, "y2001": 1.1622624279436105, "id": 26, "y2008": 1.115925801617498, "y2009": 1.1257082797404696}, {"neighbors": [32, 24, 36, 16, 28], "y1995": 1.303794309231981, "y1997": 1.3120636604057812, "y1996": 1.3075218596998686, "y1999": 1.3062566740535762, "y1998": 1.3153226688859194, "y2006": 1.2865667454509278, "y2007": 1.2973409698906584, "y2004": 1.2683078569016086, "y2005": 1.2617743046198988, "y2002": 1.2920319347677043, "y2003": 1.2718351646774422, "y2000": 1.3121023910310281, "y2001": 1.2998915587009874, "id": 27, "y2008": 1.2939020510829768, "y2009": 1.2934544564717687}, {"neighbors": [26, 16, 19, 32, 27], "y1995": 0.83953719020532513, "y1997": 0.82006005316292385, "y1996": 0.82701447583159737, "y1999": 0.80294863992835086, "y1998": 0.8118887636743225, "y2006": 0.8389109342655191, "y2007": 0.84349246817602375, "y2004": 0.83108634437662732, "y2005": 0.84373783646216949, "y2002": 0.82596790474192727, "y2003": 0.82435704751379402, "y2000": 0.78772975118465016, "y2001": 0.82848010958278628, "id": 28, "y2008": 0.85637272428125033, "y2009": 0.86539395164519117}, {"neighbors": [5, 39, 22, 14, 31], "y1995": 1.2345008725695852, "y1997": 1.2353793515744536, "y1996": 1.2426021999018138, "y1999": 1.2452262575926329, "y1998": 1.2358129278404693, "y2006": 1.2365329681906834, "y2007": 1.2796200872578414, "y2004": 1.1967443443492951, "y2005": 1.2153657295128597, "y2002": 1.1937780418204111, "y2003": 1.1835533748469893, "y2000": 1.2256766974812463, "y2001": 1.2112664802237314, "id": 29, "y2008": 1.2796839248335934, "y2009": 1.2590773758694083}, {"neighbors": [37, 20, 24, 25, 27], "y1995": 0.97696620404861145, "y1997": 0.98035944080980575, "y1996": 0.9740071914763756, "y1999": 0.95543282313901556, "y1998": 0.97581530789338955, "y2006": 0.92100464312607799, "y2007": 0.9147530387633086, "y2004": 0.9298883479571457, "y2005": 0.93442917452618346, "y2002": 0.93679072759857129, "y2003": 0.92540049332494034, "y2000": 0.96480308308405971, "y2001": 0.9468637634838194, "id": 30, "y2008": 0.90249622070947177, "y2009": 0.90213630440783921}, {"neighbors": [35, 14, 33, 12, 4], "y1995": 0.84986885942491119, "y1997": 0.84295996568390696, "y1996": 0.89868510090623221, "y1999": 0.85659367787716301, "y1998": 0.87280533962476625, "y2006": 0.92562487931452408, "y2007": 0.96635366357254426, "y2004": 0.92698332540482575, "y2005": 0.94745351657235244, "y2002": 0.90448992922937876, "y2003": 0.95495898185605821, "y2000": 0.88937573313051443, "y2001": 0.89440100450887505, "id": 31, "y2008": 1.025203118044723, "y2009": 1.0394296020754366}, {"neighbors": [36, 27, 28, 16, 26], "y1995": 1.0192280751235561, "y1997": 1.0097442843101825, "y1996": 1.0025820319237864, "y1999": 0.99765073314119712, "y1998": 1.0030341681355639, "y2006": 0.94779637858468868, "y2007": 0.93759089358493275, "y2004": 0.97583768316642261, "y2005": 0.96101679691008712, "y2002": 0.99747298060178258, "y2003": 0.99550758543481688, "y2000": 1.0075901875261932, "y2001": 0.99192968437874551, "id": 32, "y2008": 0.93353431146829191, "y2009": 0.94121705123804411}, {"neighbors": [44, 25, 12, 35, 31], "y1995": 0.86367410708901315, "y1997": 0.85544345781923936, "y1996": 0.85558931627900803, "y1999": 0.84336613427334628, "y1998": 0.85103025143102673, "y2006": 0.89455097373003656, "y2007": 0.88283929116469462, "y2004": 0.85951183386707053, "y2005": 0.87194227372077004, "y2002": 0.84667960913556228, "y2003": 0.84374557883664714, "y2000": 0.83434853662160158, "y2001": 0.85813595114434105, "id": 33, "y2008": 0.90349490610221961, "y2009": 0.9060067497610369}, {"neighbors": [22, 39, 21, 29, 23], "y1995": 1.0094753356447226, "y1997": 1.0069881886439402, "y1996": 1.0041105523637666, "y1999": 0.99291086334982948, "y1998": 0.99513686502304577, "y2006": 0.96382634438484593, "y2007": 0.95011400973122428, "y2004": 0.975119236728752, "y2005": 0.96134614808826613, "y2002": 0.99291167539274383, "y2003": 0.98983209318633369, "y2000": 1.0058162611397035, "y2001": 0.98850522230466298, "id": 34, "y2008": 0.94346860300667812, "y2009": 0.9463776450423077}, {"neighbors": [31, 38, 44, 33, 14], "y1995": 1.0571257066143651, "y1997": 1.0575301194645879, "y1996": 1.0545941857842291, "y1999": 1.0510385688532684, "y1998": 1.0488078570498685, "y2006": 1.0247627521629479, "y2007": 1.0234752320591773, "y2004": 1.0329697933620496, "y2005": 1.0219168238570018, "y2002": 1.0420048344203974, "y2003": 1.0402553971511816, "y2000": 1.0480002306104303, "y2001": 1.030249414987729, "id": 35, "y2008": 1.0251768368501768, "y2009": 1.0435957064486703}, {"neighbors": [32, 43, 27, 28, 42], "y1995": 1.070841888164505, "y1997": 1.0793762307014196, "y1996": 1.0666949726007404, "y1999": 1.0794043012481198, "y1998": 1.0738798776109699, "y2006": 1.087727556316465, "y2007": 1.0885954360198933, "y2004": 1.1032213602455734, "y2005": 1.0916793915985508, "y2002": 1.0938347765734742, "y2003": 1.1052447043433509, "y2000": 1.0531800956589803, "y2001": 1.0745277096056161, "id": 36, "y2008": 1.0917733838297285, "y2009": 1.1096083021948762}, {"neighbors": [30, 40, 20, 42, 41], "y1995": 0.8671922185905101, "y1997": 0.86675155621455668, "y1996": 0.86628895935887062, "y1999": 0.86511809486628932, "y1998": 0.86425631732335095, "y2006": 0.84488343470424199, "y2007": 0.83374328958471722, "y2004": 0.84517414191529749, "y2005": 0.84843857600526962, "y2002": 0.85411284725399572, "y2003": 0.84886336375435456, "y2000": 0.86287327291635718, "y2001": 0.8516979624450659, "id": 37, "y2008": 0.82812044014430564, "y2009": 0.82878598934619596}, {"neighbors": [35, 31, 45, 39, 44], "y1995": 0.8838921149583755, "y1997": 0.90282398478743275, "y1996": 0.92288667453925455, "y1999": 0.92023285988219217, "y1998": 0.91229185518735723, "y2006": 0.93869676706720051, "y2007": 0.96947770975097391, "y2004": 0.99223700402629367, "y2005": 0.97984969609868555, "y2002": 0.93682451504456421, "y2003": 0.98655146182882891, "y2000": 0.92652175166361039, "y2001": 0.94278865361566122, "id": 38, "y2008": 1.0036262573224608, "y2009": 0.98102350657197357}, {"neighbors": [29, 34, 38, 22, 35], "y1995": 0.970820642185237, "y1997": 0.94534081352108112, "y1996": 0.95320232993219844, "y1999": 0.93967000034446724, "y1998": 0.94215592860799646, "y2006": 0.91035556215514757, "y2007": 0.90430364292511256, "y2004": 0.92879505989982103, "y2005": 0.9211054223180335, "y2002": 0.93412151936513388, "y2003": 0.93501274320242933, "y2000": 0.93092108910210503, "y2001": 0.92662519262599163, "id": 39, "y2008": 0.89994694483851023, "y2009": 0.9007386435858511}, {"neighbors": [41, 37, 42, 30, 45], "y1995": 0.95861858457245008, "y1997": 0.98254810501535106, "y1996": 0.95774543235102894, "y1999": 0.98684823919808018, "y1998": 0.98919471947721893, "y2006": 0.97163003599581876, "y2007": 0.97007020126757271, "y2004": 0.9493488753775261, "y2005": 0.97152609359561659, "y2002": 0.95601578436851964, "y2003": 0.94905384541254967, "y2000": 0.98882204635713133, "y2001": 0.97662233890759653, "id": 40, "y2008": 0.97158948117089283, "y2009": 0.95884908006927827}, {"neighbors": [40, 45, 44, 37, 42], "y1995": 0.83980438854721107, "y1997": 0.85746999875029983, "y1996": 0.84726737166133714, "y1999": 0.85567509846023126, "y1998": 0.85467221160427542, "y2006": 0.8333891885768886, "y2007": 0.83511679264592342, "y2004": 0.81743586206088703, "y2005": 0.83550405700769481, "y2002": 0.84502402428191115, "y2003": 0.82645665158259707, "y2000": 0.84818516243622177, "y2001": 0.85265681182580899, "id": 41, "y2008": 0.82136617314598481, "y2009": 0.80921873783836296}, {"neighbors": [43, 40, 46, 37, 36], "y1995": 0.95118156405662746, "y1997": 0.94688098462868708, "y1996": 0.9466212002600608, "y1999": 0.95124410099780687, "y1998": 0.95085829660091703, "y2006": 0.96895367966714574, "y2007": 0.9700163384024274, "y2004": 0.97583768316642261, "y2005": 0.95571723704302525, "y2002": 0.96804411514198463, "y2003": 0.97136213864358201, "y2000": 0.95440787445922959, "y2001": 0.96364362764682376, "id": 42, "y2008": 0.97082732652905901, "y2009": 0.9878236640328002}, {"neighbors": [36, 42, 32, 27, 46], "y1995": 1.0891004415267045, "y1997": 1.0849289528525252, "y1996": 1.0824896838138709, "y1999": 1.0945424900391545, "y1998": 1.0865692335830259, "y2006": 1.1450297539219478, "y2007": 1.1447474729339102, "y2004": 1.1334273474293739, "y2005": 1.1468606844516303, "y2002": 1.1229257675733433, "y2003": 1.1302103089739621, "y2000": 1.1055818811158884, "y2001": 1.1214085953998059, "id": 43, "y2008": 1.1408403740471014, "y2009": 1.1614292649793569}, {"neighbors": [33, 41, 45, 35, 40], "y1995": 1.0633603345917013, "y1997": 1.0869149629649646, "y1996": 1.0736582323828732, "y1999": 1.1166986255755473, "y1998": 1.0976484597942771, "y2006": 1.0839806574563229, "y2007": 1.0983176831786272, "y2004": 1.0927882684985315, "y2005": 1.0700320368873319, "y2002": 1.0881584856466706, "y2003": 1.0804431312806149, "y2000": 1.1185670222649935, "y2001": 1.0976428286056732, "id": 44, "y2008": 1.0929823187788443, "y2009": 1.0917612486217978}, {"neighbors": [41, 44, 40, 35, 33], "y1995": 0.79772064970019041, "y1997": 0.7858115114280021, "y1996": 0.78829195801876151, "y1999": 0.77035744221561353, "y1998": 0.77615921755360906, "y2006": 0.79949806580432425, "y2007": 0.80172181625581262, "y2004": 0.79603865293896003, "y2005": 0.78966436120841943, "y2002": 0.81437881076636964, "y2003": 0.80788827809912023, "y2000": 0.77751193519846906, "y2001": 0.79902973574567659, "id": 45, "y2008": 0.82168154748053679, "y2009": 0.85587910681858015}, {"neighbors": [42, 43, 40, 36, 37], "y1995": 1.0052446952315301, "y1997": 1.0047589936197736, "y1996": 1.0000769567582628, "y1999": 1.0063956091903872, "y1998": 1.0061394183885444, "y2006": 0.97292595590233411, "y2007": 0.96519561197191939, "y2004": 0.99030032232474696, "y2005": 0.97682565346267858, "y2002": 1.0081498135355325, "y2003": 1.0057431552702318, "y2000": 1.0016297948675874, "y2001": 0.99860738542320637, "id": 46, "y2008": 0.9617340332161447, "y2009": 0.95890283625473927}, {"neighbors": [20, 6, 24, 25, 30], "y1995": 0.95808418788867844, "y1997": 0.9654440995572009, "y1996": 0.93825679674127938, "y1999": 0.96987289157318213, "y1998": 0.95561201303757848, "y2006": 1.1704973973021624, "y2007": 1.1702515395802287, "y2004": 1.0533361880299275, "y2005": 1.0983262971945267, "y2002": 1.0078119390756035, "y2003": 1.0348423554112989, "y2000": 0.96608031008233231, "y2001": 0.99727184521431422, "id": 47, "y2008": 1.1873055260044207, "y2009": 1.1424264534188653}] diff --git a/release/python/0.1.0/crankshaft/test/helper.py b/release/python/0.1.0/crankshaft/test/helper.py new file mode 100644 index 0000000..7d28b94 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/helper.py @@ -0,0 +1,13 @@ +import unittest + +from mock_plpy import MockPlPy +plpy = MockPlPy() + +import sys +sys.modules['plpy'] = plpy + +import os + +def fixture_file(name): + dir = os.path.dirname(os.path.realpath(__file__)) + return os.path.join(dir, 'fixtures', name) diff --git a/release/python/0.1.0/crankshaft/test/mock_plpy.py b/release/python/0.1.0/crankshaft/test/mock_plpy.py new file mode 100644 index 0000000..a982ebe --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/mock_plpy.py @@ -0,0 +1,52 @@ +import re + +class MockCursor: + def __init__(self, data): + self.cursor_pos = 0 + self.data = data + + def fetch(self, batch_size): + batch = self.data[self.cursor_pos : self.cursor_pos + batch_size] + self.cursor_pos += batch_size + return batch + + +class MockPlPy: + def __init__(self): + self._reset() + + def _reset(self): + self.infos = [] + self.notices = [] + self.debugs = [] + self.logs = [] + self.warnings = [] + self.errors = [] + self.fatals = [] + self.executes = [] + self.results = [] + self.prepares = [] + self.results = [] + + def _define_result(self, query, result): + pattern = re.compile(query, re.IGNORECASE | re.MULTILINE) + self.results.append([pattern, result]) + + def notice(self, msg): + self.notices.append(msg) + + def debug(self, msg): + self.notices.append(msg) + + def info(self, msg): + self.infos.append(msg) + + def cursor(self, query): + data = self.execute(query) + return MockCursor(data) + + def execute(self, query): # TODO: additional arguments + for result in self.results: + if result[0].match(query): + return result[1] + return [] diff --git a/release/python/0.1.0/crankshaft/test/test_cluster_kmeans.py b/release/python/0.1.0/crankshaft/test/test_cluster_kmeans.py new file mode 100644 index 0000000..aba8e07 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/test_cluster_kmeans.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file +import numpy as np +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class KMeansTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read()) + self.params = {"subquery": "select * from table", + "no_clusters": "10" + } + + def test_kmeans(self): + data = self.cluster_data + plpy._define_result('select' ,data) + clusters = cc.kmeans('subquery', 2) + labels = [a[1] for a in clusters] + c1 = [a for a in clusters if a[1]==0] + c2 = [a for a in clusters if a[1]==1] + + self.assertEqual(len(np.unique(labels)),2) + self.assertEqual(len(c1),20) + self.assertEqual(len(c2),20) + diff --git a/release/python/0.1.0/crankshaft/test/test_clustering_moran.py b/release/python/0.1.0/crankshaft/test/test_clustering_moran.py new file mode 100644 index 0000000..2b683cf --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/test_clustering_moran.py @@ -0,0 +1,88 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class MoranTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.params_markov = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read()) + self.moran_data = json.loads(open(fixture_file('moran.json')).read()) + + def test_map_quads(self): + """Test map_quads""" + self.assertEqual(cc.map_quads(1), 'HH') + self.assertEqual(cc.map_quads(2), 'LH') + self.assertEqual(cc.map_quads(3), 'LL') + self.assertEqual(cc.map_quads(4), 'HL') + self.assertEqual(cc.map_quads(33), None) + self.assertEqual(cc.map_quads('andy'), None) + + def test_quad_position(self): + """Test lisa_sig_vals""" + + quads = np.array([1, 2, 3, 4], np.int) + + ans = np.array(['HH', 'LH', 'LL', 'HL']) + test_ans = cc.quad_position(quads) + + self.assertTrue((test_ans == ans).all()) + + def test_moran_local(self): + """Test Moran's I local""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + self.assertEqual(res_quad, exp_quad) + + def test_moran_local_rate(self): + """Test Moran's I rate""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None? ', result == None + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + + def test_moran(self): + """Test Moran's I global""" + data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1235) + result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None?', result == None + result_moran = result[0][0] + expected_moran = np.array([row[0] for row in self.moran_data]).mean() + self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2) diff --git a/release/python/0.1.0/crankshaft/test/test_pysal_utils.py b/release/python/0.1.0/crankshaft/test/test_pysal_utils.py new file mode 100644 index 0000000..171fdbc --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/test_pysal_utils.py @@ -0,0 +1,142 @@ +import unittest + +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds + + +class PysalUtilsTest(unittest.TestCase): + """Testing class for utility functions related to PySAL integrations""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + self.params_array = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + def test_query_attr_select(self): + """Test query_attr_select""" + + ans = "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " + + ans_array = "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " + + self.assertEqual(pu.query_attr_select(self.params), ans) + self.assertEqual(pu.query_attr_select(self.params_array), ans_array) + + def test_query_attr_where(self): + """Test pu.query_attr_where""" + + ans = "idx_replace.\"andy\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" <> 0" + + ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \ + "idx_replace.\"_2014_jan\" IS NOT NULL AND " \ + "idx_replace.\"_2014_feb\" IS NOT NULL" + + self.assertEqual(pu.query_attr_where(self.params), ans) + self.assertEqual(pu.query_attr_where(self.params_array), ans_array) + + def test_knn(self): + """Test knn neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0 " \ + "ORDER BY " \ + "j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + ans_array = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"_2013_dec\" IS NOT NULL AND " \ + "j.\"_2014_jan\" IS NOT NULL AND " \ + "j.\"_2014_feb\" IS NOT NULL " \ + "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"_2013_dec\" IS NOT NULL AND " \ + "i.\"_2014_jan\" IS NOT NULL AND " \ + "i.\"_2014_feb\" IS NOT NULL "\ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.knn(self.params), ans) + self.assertEqual(pu.knn(self.params_array), ans_array) + + def test_queen(self): + """Test queen neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "ST_Touches(i.\"the_geom\", " \ + "j.\"the_geom\") AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0)" \ + ") As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.queen(self.params), ans) + + def test_construct_neighbor_query(self): + """Test construct_neighbor_query""" + + # Compare to raw knn query + self.assertEqual(pu.construct_neighbor_query('knn', self.params), + pu.knn(self.params)) + + def test_get_attributes(self): + """Test get_attributes""" + + ## need to add tests + + self.assertEqual(True, True) + + def test_get_weight(self): + """Test get_weight""" + + self.assertEqual(True, True) + + def test_empty_zipped_array(self): + """Test empty_zipped_array""" + ans2 = [(None, None)] + ans4 = [(None, None, None, None)] + self.assertEqual(pu.empty_zipped_array(2), ans2) + self.assertEqual(pu.empty_zipped_array(4), ans4) diff --git a/release/python/0.1.0/crankshaft/test/test_segmentation.py b/release/python/0.1.0/crankshaft/test/test_segmentation.py new file mode 100644 index 0000000..d02e8b1 --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/test_segmentation.py @@ -0,0 +1,64 @@ +import unittest +import numpy as np +from helper import plpy, fixture_file +import crankshaft.segmentation as segmentation +import json + +class SegmentationTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + + def generate_random_data(self,n_samples,random_state, row_type=False): + x1 = random_state.uniform(size=n_samples) + x2 = random_state.uniform(size=n_samples) + x3 = random_state.randint(0, 4, size=n_samples) + + y = x1+x2*x2+x3 + cartodb_id = range(len(x1)) + + if row_type: + return [ {'features': vals} for vals in zip(x1,x2,x3)], y + else: + return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))] + + def test_replace_nan_with_mean(self): + test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan]) + + def test_create_and_predict_segment(self): + n_samples = 1000 + + random_state_train = np.random.RandomState(13) + random_state_test = np.random.RandomState(134) + training_data = self.generate_random_data(n_samples, random_state_train) + test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True) + + + ids = [{'cartodb_ids': range(len(test_data))}] + rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}] + + plpy._define_result('select \* from \(select \* from training\) a limit 1',rows) + plpy._define_result('.*from \(select \* from training\) as a' ,training_data) + plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids) + plpy._define_result('.*select \* from test.*' ,test_data) + + model_parameters = {'n_estimators': 1200, + 'max_depth': 3, + 'subsample' : 0.5, + 'learning_rate': 0.01, + 'min_samples_leaf': 1} + + result = segmentation.create_and_predict_segment( + 'select * from training', + 'target', + 'select * from test', + model_parameters) + + prediction = [r[1] for r in result] + + accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y)))) + + self.assertEqual(len(result),len(test_data)) + self.assertTrue( result[0][2] < 0.01) + self.assertTrue( accuracy < 0.5*np.mean(test_y) ) diff --git a/release/python/0.1.0/crankshaft/test/test_space_time_dynamics.py b/release/python/0.1.0/crankshaft/test/test_space_time_dynamics.py new file mode 100644 index 0000000..54ffc9d --- /dev/null +++ b/release/python/0.1.0/crankshaft/test/test_space_time_dynamics.py @@ -0,0 +1,324 @@ +import unittest +import numpy as np + +import unittest + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.space_time_dynamics as std +from crankshaft import random_seeds +import json + +class SpaceTimeTests(unittest.TestCase): + """Testing class for Markov Functions.""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read()) + self.markov_data = json.loads(open(fixture_file('markov.json')).read()) + + self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T + + self.transition_matrix = np.array([ + [[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ], + [ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ], + [ 0. , 0.14 , 0.74 , 0.12 , 0. ], + [ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857], + [ 0. , 0. , 0. , 0.16666667, 0.83333333]], + [[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ], + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ], + [ 0. , 0. , 0.06372549, 0.90196078, 0.03431373], + [ 0. , 0. , 0. , 0.19444444, 0.80555556]], + [[ 0.84693878, 0.15306122, 0. , 0. , 0. ], + [ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ], + [ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0. , 0. , 0. , 0.10204082, 0.89795918]], + [[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ], + [ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725], + [ 0. , 0. , 0. , 0.09661836, 0.90338164]], + [[ 0.33333333, 0.66666667, 0. , 0. , 0. ], + [ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ], + [ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ], + [ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808], + [ 0. , 0. , 0. , 0.02352941, 0.97647059]]] + ) + + def test_spatial_markov(self): + """Test Spatial Markov.""" + data = [ { 'id': d['id'], + 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'], + 'neighbors': d['neighbors'] } for d in self.neighbors_data] + print(str(data[0])) + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + + result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id') + + self.assertTrue(result != None) + result = [(row[0], row[1], row[2], row[3], row[4]) for row in result] + print result[0] + expected = self.markov_data + for ([res_trend, res_up, res_down, res_vol, res_id], + [exp_trend, exp_up, exp_down, exp_vol, exp_id] + ) in zip(result, expected): + self.assertAlmostEqual(res_trend, exp_trend) + + def test_get_time_data(self): + """Test get_time_data""" + data = [ { 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'] } for d in self.neighbors_data] + + result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009']) + + ## expected was prepared from PySAL example: + ### f = ps.open(ps.examples.get_path("usjoin.csv")) + ### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose() + ### rpci = pci / (pci.mean(axis = 0)) + + expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652 + , 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631 + , 0.86148267, 0.86252252, 0.86746356], + [ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978 + , 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054 + , 0.90139505, 0.87897455, 0.86216858], + [ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559 + , 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804 + , 0.83675961, 0.83574199, 0.84647177], + [ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948 + , 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797 + , 1.12958508, 1.11371818, 1.09936775], + [ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201 + , 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468 + , 1.10863242, 1.09642516, 1.07762337], + [ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737 + , 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766 + , 1.45241216, 1.43306098, 1.4174431 ], + [ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138 + , 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034 + , 1.08401709, 1.06959206, 1.07875225], + [ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316 + , 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511 + , 1.01749645, 0.9936394, 0.98279746], + [ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006 + , 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874 + , 0.91284091, 0.89689833, 0.88928858], + [ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708 + , 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141 + , 0.86633032, 0.84946077, 0.83287146], + [ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183 + , 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258 + , 1.11059842, 1.10509795, 1.10020097], + [ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051 + , 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201 + , 0.89280392, 0.89298243, 0.89165385], + [ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927 + , 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815 + , 0.94281559, 0.96520315, 0.96715441], + [ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073 + , 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665 + , 0.98423613, 0.99823861, 0.99545704], + [ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535 + , 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648 + , 0.8198736, 0.82265395, 0.8399404 ], + [ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597 + , 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108 + , 0.9422231, 0.93902708, 0.94479184], + [ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632 + , 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804 + , 0.90543476, 0.91737138, 0.94793657], + [ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869 + , 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384 + , 1.23418676, 1.23626739, 1.26754398], + [ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023 + , 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766 + , 1.32936244, 1.33001241, 1.33288462], + [ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016 + , 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549 + , 0.92754547, 0.92549227, 0.92138102], + [ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622 + , 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389 + , 1.10577769, 1.11166825, 1.09985155], + [ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073 + , 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416 + , 0.77487118, 0.77768683, 0.78801192], + [ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819 + , 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462 + , 0.93069645, 0.94020973, 0.94358232], + [ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489 + , 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334 + , 0.87863799, 0.87497981, 0.87888675], + [ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195 + , 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749 + , 0.99323123, 1.00204939, 0.99602148], + [ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397 + , 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079 + , 1.08603695, 1.05997314, 1.02160943], + [ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029 + , 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236 + , 1.12357937, 1.1159258, 1.12570828], + [ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239 + , 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675 + , 1.29734097, 1.29390205, 1.29345446], + [ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975 + , 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093 + , 0.84349247, 0.85637272, 0.86539395], + [ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767 + , 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297 + , 1.27962009, 1.27968392, 1.25907738], + [ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308 + , 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464 + , 0.91475304, 0.90249622, 0.9021363 ], + [ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573 + , 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488 + , 0.96635366, 1.02520312, 1.0394296 ], + [ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019 + , 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638 + , 0.93759089, 0.93353431, 0.94121705], + [ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854 + , 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097 + , 0.88283929, 0.90349491, 0.90600675], + [ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626 + , 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634 + , 0.95011401, 0.9434686, 0.94637765], + [ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023 + , 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275 + , 1.02347523, 1.02517684, 1.04359571], + [ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801 + , 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756 + , 1.08859544, 1.09177338, 1.1096083 ], + [ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327 + , 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343 + , 0.83374329, 0.82812044, 0.82878599], + [ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175 + , 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677 + , 0.96947771, 1.00362626, 0.98102351], + [ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109 + , 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556 + , 0.90430364, 0.89994694, 0.90073864], + [ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205 + , 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004 + , 0.9700702, 0.97158948, 0.95884908], + [ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516 + , 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919 + , 0.83511679, 0.82136617, 0.80921874], + [ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787 + , 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368 + , 0.97001634, 0.97082733, 0.98782366], + [ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188 + , 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975 + , 1.14474747, 1.14084037, 1.16142926], + [ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702 + , 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066 + , 1.09831768, 1.09298232, 1.09176125], + [ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194 + , 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807 + , 0.80172182, 0.82168155, 0.85587911], + [ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979 + , 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596 + , 0.96519561, 0.96173403, 0.95890284], + [ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031 + , 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974 + , 1.17025154, 1.18730553, 1.14242645]]) + + self.assertTrue(np.allclose(result, expected)) + self.assertTrue(type(result) == type(expected)) + self.assertTrue(result.shape == expected.shape) + + def test_rebin_data(self): + """Test rebin_data""" + ## sample in double the time (even case since 10 % 2 = 0): + ## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2 + ## = 0.5, 2.5, 4.5, 6.5, 8.5 + ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float) + for i in range(0, 10, 2)]).T + + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even)) + + ## sample in triple the time (uneven since 10 % 3 = 1): + ## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1 + ## = 1, 4, 7, 9 + ans_odd = np.array([i * np.ones(10, dtype=float) + for i in (1, 4, 7, 9)]).T + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd)) + + def test_get_prob_dist(self): + """Test get_prob_dist""" + lag_indices = np.array([1, 2, 3, 4]) + unit_indices = np.array([1, 3, 2, 4]) + answer = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices) + + self.assertTrue(np.array_equal(result, answer)) + + def test_get_prob_stats(self): + """Test get_prob_stats""" + + probs = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + unit_indices = np.array([1, 3, 2, 4]) + answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.]) + answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941]) + answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059]) + answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223]) + + result = std.get_prob_stats(probs, unit_indices) + result_up = result[0] + result_down = result[1] + result_trend = result[2] + result_volatility = result[3] + + self.assertTrue(np.allclose(result_up, answer_up)) + self.assertTrue(np.allclose(result_down, answer_down)) + self.assertTrue(np.allclose(result_trend, answer_trend)) + self.assertTrue(np.allclose(result_volatility, answer_volatility)) diff --git a/release/python/0.2.0/crankshaft/crankshaft/__init__.py b/release/python/0.2.0/crankshaft/crankshaft/__init__.py new file mode 100644 index 0000000..4e06bc5 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/__init__.py @@ -0,0 +1,5 @@ +"""Import all modules""" +import crankshaft.random_seeds +import crankshaft.clustering +import crankshaft.space_time_dynamics +import crankshaft.segmentation diff --git a/release/python/0.2.0/crankshaft/crankshaft/clustering/__init__.py b/release/python/0.2.0/crankshaft/crankshaft/clustering/__init__.py new file mode 100644 index 0000000..ed34fe0 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/clustering/__init__.py @@ -0,0 +1,3 @@ +"""Import all functions from for clustering""" +from moran import * +from kmeans import * diff --git a/release/python/0.2.0/crankshaft/crankshaft/clustering/kmeans.py b/release/python/0.2.0/crankshaft/crankshaft/clustering/kmeans.py new file mode 100644 index 0000000..4134062 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/clustering/kmeans.py @@ -0,0 +1,18 @@ +from sklearn.cluster import KMeans +import plpy + +def kmeans(query, no_clusters, no_init=20): + data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids, + array_agg(ST_X(the_geom) order by cartodb_id) xs, + array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a + where the_geom is not null + '''.format(query=query)) + + xs = data[0]['xs'] + ys = data[0]['ys'] + ids = data[0]['ids'] + + km = KMeans(n_clusters= no_clusters, n_init=no_init) + labels = km.fit_predict(zip(xs,ys)) + return zip(ids,labels) + diff --git a/release/python/0.2.0/crankshaft/crankshaft/clustering/moran.py b/release/python/0.2.0/crankshaft/crankshaft/clustering/moran.py new file mode 100644 index 0000000..3282f5f --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/clustering/moran.py @@ -0,0 +1,262 @@ +""" +Moran's I geostatistics (global clustering & outliers presence) +""" + +# TODO: Fill in local neighbors which have null/NoneType values with the +# average of the their neighborhood + +import pysal as ps +import plpy +from collections import OrderedDict + +# crankshaft module +import crankshaft.pysal_utils as pu + +# High level interface --------------------------------------- + +def moran(subquery, attr_name, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (global) + Implementation building neighbors with a PostGIS database and Moran's I + core clusters with PySAL. + Andy Eschbacher + """ + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr_name), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + attr_vals = pu.get_attributes(result) + + ## calculate weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global + moran_global = ps.esda.moran.Moran(attr_vals, weight, + permutations=permutations) + + return zip([moran_global.I], [moran_global.EI]) + +def moran_local(subquery, attr, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I implementation for PL/Python + Andy Eschbacher + """ + + # geometries with attributes that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(5) + + attr_vals = pu.get_attributes(result) + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local(attr_vals, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Rate (global) + Andy Eschbacher + """ + qvals = OrderedDict([("id_col", id_col), + ("attr1", numerator), + ("attr2", denominator) + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + plpy.notice('** Query: %s' % query) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(2) + plpy.notice('** Query returned with %d rows' % len(result)) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(2) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + ## calculate moran global rate + lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight, + permutations=permutations) + + return zip([lisa_rate.I], [lisa_rate.EI]) + +def moran_local_rate(subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Local Rate + Andy Eschbacher + """ + # geometries with values that are null are ignored + # resulting in a collection of not as near neighbors + + qvals = OrderedDict([("id_col", id_col), + ("numerator", numerator), + ("denominator", denominator), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(5) + except plpy.SPIError: + plpy.error('Error: areas of interest query failed, check input parameters') + plpy.notice('** Query failed: "%s"' % query) + plpy.notice('** Error: %s' % plpy.SPIError) + return pu.empty_zipped_array(5) + + ## collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) + +def moran_local_bv(subquery, attr1, attr2, + permutations, geom_col, id_col, w_type, num_ngbrs): + """ + Moran's I (local) Bivariate (untested) + """ + plpy.notice('** Constructing query') + + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr1), + ("attr2", attr2), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) + + try: + result = plpy.execute(query) + # if there are no neighbors, exit + if len(result) == 0: + return pu.empty_zipped_array(4) + except plpy.SPIError: + plpy.error("Error: areas of interest query failed, " \ + "check input parameters") + plpy.notice('** Query failed: "%s"' % query) + return pu.empty_zipped_array(4) + + ## collect attributes + attr1_vals = pu.get_attributes(result, 1) + attr2_vals = pu.get_attributes(result, 2) + + # create weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight, + permutations=permutations) + + plpy.notice("len of Is: %d" % len(lisa.Is)) + + # find clustering of significance + lisa_sig = quad_position(lisa.q) + + plpy.notice('** Finished calculations') + + return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order) + +# Low level functions ---------------------------------------- + +def map_quads(coord): + """ + Map a quadrant number to Moran's I designation + HH=1, LH=2, LL=3, HL=4 + Input: + @param coord (int): quadrant of a specific measurement + Output: + classification (one of 'HH', 'LH', 'LL', or 'HL') + """ + if coord == 1: + return 'HH' + elif coord == 2: + return 'LH' + elif coord == 3: + return 'LL' + elif coord == 4: + return 'HL' + else: + return None + +def quad_position(quads): + """ + Produce Moran's I classification based of n + Input: + @param quads ndarray: an array of quads classified by + 1-4 (PySAL default) + Output: + @param list: an array of quads classied by 'HH', 'LL', etc. + """ + return [map_quads(q) for q in quads] diff --git a/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/__init__.py b/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/__init__.py new file mode 100644 index 0000000..fdf073b --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions for pysal_utils""" +from crankshaft.pysal_utils.pysal_utils import * diff --git a/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py new file mode 100644 index 0000000..4622925 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -0,0 +1,188 @@ +""" + Utilities module for generic PySAL functionality, mainly centered on + translating queries into numpy arrays or PySAL weights objects +""" + +import numpy as np +import pysal as ps + +def construct_neighbor_query(w_type, query_vals): + """Return query (a string) used for finding neighbors + @param w_type text: type of neighbors to calculate ('knn' or 'queen') + @param query_vals dict: values used to construct the query + """ + + if w_type.lower() == 'knn': + return knn(query_vals) + else: + return queen(query_vals) + +## Build weight object +def get_weight(query_res, w_type='knn', num_ngbrs=5): + """ + Construct PySAL weight from return value of query + @param query_res dict-like: query results with attributes and neighbors + """ + # if w_type.lower() == 'knn': + # row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs + # weights = {x['id']: row_normed_weights for x in query_res} + # else: + # weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) + # if len(x['neighbors']) > 0 + # else [] for x in query_res} + + neighbors = {x['id']: x['neighbors'] for x in query_res} + print 'len of neighbors: %d' % len(neighbors) + + built_weight = ps.W(neighbors) + built_weight.transform = 'r' + + return built_weight + +def query_attr_select(params): + """ + Create portion of SELECT statement for attributes inolved in query. + @param params: dict of information used in query (column names, + table name, etc.) + """ + + attr_string = "" + template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, " + + if 'time_cols' in params: + ## if markov analysis + attrs = params['time_cols'] + + for idx, val in enumerate(attrs): + attr_string += template % {"col": val, "alias_num": idx + 1} + else: + ## if moran's analysis + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')] + + for idx, val in enumerate(sorted(attrs)): + attr_string += template % {"col": params[val], "alias_num": idx + 1} + + return attr_string + +def query_attr_where(params): + """ + Construct where conditions when building neighbors query + Create portion of WHERE clauses for weeding out NULL-valued geometries + Input: dict of params: + {'subquery': ..., + 'numerator': 'data1', + 'denominator': 'data2', + '': ...} + Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL' + Input: + {'subquery': ..., + 'time_cols': ['time1', 'time2', 'time3'], + 'etc': ...} + Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT + NULL AND idx_replace."time3" IS NOT NULL' + """ + attr_string = [] + template = "idx_replace.\"%s\" IS NOT NULL" + + if 'time_cols' in params: + ## markov where clauses + attrs = params['time_cols'] + # add values to template + for attr in attrs: + attr_string.append(template % attr) + else: + ## moran where clauses + + # get keys + attrs = sorted([k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]) + # add values to template + for attr in attrs: + attr_string.append(template % params[attr]) + + if len(attrs) == 2: + attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]]) + + out = " AND ".join(attr_string) + + return out + +def knn(params): + """SQL query for k-nearest neighbors. + @param vars: dict of values to fill template + """ + + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE " \ + "i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "%(attr_where_j)s " \ + "ORDER BY " \ + "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \ + "LIMIT {num_ngbrs})" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## SQL query for finding queens neighbors (all contiguous polygons) +def queen(params): + """SQL query for queen neighbors. + @param params dict: information to fill query + """ + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = "SELECT " \ + "i.\"{id_col}\" As id, " \ + "%(attr_select)s" \ + "(SELECT ARRAY(SELECT j.\"{id_col}\" " \ + "FROM ({subquery}) As j " \ + "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \ + "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \ + "%(attr_where_j)s)" \ + ") As neighbors " \ + "FROM ({subquery}) As i " \ + "WHERE " \ + "%(attr_where_i)s " \ + "ORDER BY i.\"{id_col}\" ASC;" % replacements + + return query.format(**params) + +## to add more weight methods open a ticket or pull request + +def get_attributes(query_res, attr_num=1): + """ + @param query_res: query results with attributes and neighbors + @param attr_num: attribute number (1, 2, ...) + """ + return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float) + +def empty_zipped_array(num_nones): + """ + prepare return values for cases of empty weights objects (no neighbors) + Input: + @param num_nones int: number of columns (e.g., 4) + Output: + [(None, None, None, None)] + """ + + return [tuple([None] * num_nones)] diff --git a/release/python/0.2.0/crankshaft/crankshaft/random_seeds.py b/release/python/0.2.0/crankshaft/crankshaft/random_seeds.py new file mode 100644 index 0000000..31958cb --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/random_seeds.py @@ -0,0 +1,11 @@ +"""Random seed generator used for non-deterministic functions in crankshaft""" +import random +import numpy + +def set_random_seeds(value): + """ + Set the seeds of the RNGs (Random Number Generators) + used internally. + """ + random.seed(value) + numpy.random.seed(value) diff --git a/release/python/0.2.0/crankshaft/crankshaft/segmentation/__init__.py b/release/python/0.2.0/crankshaft/crankshaft/segmentation/__init__.py new file mode 100644 index 0000000..b825e85 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/segmentation/__init__.py @@ -0,0 +1 @@ +from segmentation import * diff --git a/release/python/0.2.0/crankshaft/crankshaft/segmentation/segmentation.py b/release/python/0.2.0/crankshaft/crankshaft/segmentation/segmentation.py new file mode 100644 index 0000000..ed61139 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/segmentation/segmentation.py @@ -0,0 +1,176 @@ +""" +Segmentation creation and prediction +""" + +import sklearn +import numpy as np +import plpy +from sklearn.ensemble import GradientBoostingRegressor +from sklearn import metrics +from sklearn.cross_validation import train_test_split + +# Lower level functions +#---------------------- + +def replace_nan_with_mean(array): + """ + Input: + @param array: an array of floats which may have null-valued entries + Output: + array with nans filled in with the mean of the dataset + """ + # returns an array of rows and column indices + indices = np.where(np.isnan(array)) + + # iterate through entries which have nan values + for row, col in zip(*indices): + array[row, col] = np.mean(array[~np.isnan(array[:, col]), col]) + + return array + +def get_data(variable, feature_columns, query): + """ + Fetch data from the database, clean, and package into + numpy arrays + Input: + @param variable: name of the target variable + @param feature_columns: list of column names + @param query: subquery that data is pulled from for the packaging + Output: + prepared data, packaged into NumPy arrays + """ + + columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns]) + + try: + data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format( + variable=variable, + columns=columns, + query=query)) + except Exception, e: + plpy.error('Failed to access data to build segmentation model: %s' % e) + + # extract target data from plpy object + target = np.array(data[0]['target']) + + # put n feature data arrays into an n x m array of arrays + features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns]) + + return replace_nan_with_mean(target), replace_nan_with_mean(features) + +# High level interface +# -------------------- + +def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters): + """ + Version of create_and_predict_segment that works on arrays that come stright form the SQL calling + the function. + + Input: + @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict + @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model + @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from + @param model_parameters: A dictionary containing parameters for the model. + """ + + clean_target = replace_nan_with_mean(target) + clean_features = replace_nan_with_mean(features) + target_features = replace_nan_with_mean(target_features) + + model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2) + prediction = model.predict(target_features) + accuracy_array = [accuracy]*prediction.shape[0] + return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array)) + + + +def create_and_predict_segment(query, variable, target_query, model_params): + """ + generate a segment with machine learning + Stuart Lynn + """ + + ## fetch column names + try: + columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys() + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + ## extract column names to be used in building the segmentation model + feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator']) + ## get data from database + target, features = get_data(variable, feature_columns, query) + + model, accuracy = train_model(target, features, model_params, 0.2) + cartodb_ids, result = predict_segment(model, feature_columns, target_query) + accuracy_array = [accuracy]*result.shape[0] + return zip(cartodb_ids, result, accuracy_array) + + +def train_model(target, features, model_params, test_split): + """ + Train the Gradient Boosting model on the provided data and calculate the accuracy of the model + Input: + @param target: 1D Array of the variable that the model is to be trianed to predict + @param features: 2D Array NSamples * NFeatures to use in trining the model + @param model_params: A dictionary of model parameters, the full specification can be found on the + scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) + @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray + """ + features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split) + model = GradientBoostingRegressor(**model_params) + model.fit(features_train, target_train) + accuracy = calculate_model_accuracy(model, features, target) + return model, accuracy + +def calculate_model_accuracy(model, features, target): + """ + Calculate the mean squared error of the model prediction + Input: + @param model: model trained from input features + @param features: features to make a prediction from + @param target: target to compare prediction to + Output: + mean squared error of the model prection compared to the target + """ + prediction = model.predict(features) + return metrics.mean_squared_error(prediction, target) + +def predict_segment(model, features, target_query): + """ + Use the provided model to predict the values for the new feature set + Input: + @param model: The pretrained model + @features: A list of features to use in the model prediction (list of column names) + @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it. + """ + + batch_size = 1000 + joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features]) + + try: + cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format( + joined_features=joined_features, + target_query=target_query)) + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + results = [] + + while True: + rows = cursor.fetch(batch_size) + if not rows: + break + batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows]) + + #Need to fix this. Should be global mean. This will cause weird effects + batch = replace_nan_with_mean(batch) + prediction = model.predict(batch) + results.append(prediction) + + try: + cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids'] + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + return cartodb_ids, np.concatenate(results) diff --git a/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/__init__.py b/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/__init__.py new file mode 100644 index 0000000..a439286 --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions from clustering libraries.""" +from markov import * diff --git a/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/markov.py b/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/markov.py new file mode 100644 index 0000000..bbf524d --- /dev/null +++ b/release/python/0.2.0/crankshaft/crankshaft/space_time_dynamics/markov.py @@ -0,0 +1,189 @@ +""" +Spatial dynamics measurements using Spatial Markov +""" + + +import numpy as np +import pysal as ps +import plpy +import crankshaft.pysal_utils as pu + +def spatial_markov_trend(subquery, time_cols, num_classes=7, + w_type='knn', num_ngbrs=5, permutations=0, + geom_col='the_geom', id_col='cartodb_id'): + """ + Predict the trends of a unit based on: + 1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile) + 2. average class of its neighbors + + Inputs: + @param subquery string: e.g., SELECT the_geom, cartodb_id, + interesting_time_column FROM table_name + @param time_cols list of strings: list of strings of column names + @param num_classes (optional): number of classes to break distribution + of values into. Currently uses quantile bins. + @param w_type string (optional): weight type ('knn' or 'queen') + @param num_ngbrs int (optional): number of neighbors (if knn type) + @param permutations int (optional): number of permutations for test + stats + @param geom_col string (optional): name of column which contains the + geometries + @param id_col string (optional): name of column which has the ids of + the table + + Outputs: + @param trend_up float: probablity that a geom will move to a higher + class + @param trend_down float: probablity that a geom will move to a lower + class + @param trend float: (trend_up - trend_down) / trend_static + @param volatility float: a measure of the volatility based on + probability stddev(prob array) + """ + + if len(time_cols) < 2: + plpy.error('More than one time column needs to be passed') + + qvals = {"id_col": id_col, + "time_cols": time_cols, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + try: + query_result = plpy.execute( + pu.construct_neighbor_query(w_type, qvals) + ) + if len(query_result) == 0: + return zip([None], [None], [None], [None], [None]) + except plpy.SPIError, err: + plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals))) + plpy.error('Query failed, check the input parameters') + return zip([None], [None], [None], [None], [None]) + + ## build weight + weights = pu.get_weight(query_result, w_type) + weights.transform = 'r' + + ## prep time data + t_data = get_time_data(query_result, time_cols) + + plpy.debug('shape of t_data %d, %d' % t_data.shape) + plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape) + plpy.debug('first num elements: %f' % t_data[0, 0]) + + sp_markov_result = ps.Spatial_Markov(t_data, + weights, + k=num_classes, + fixed=False, + permutations=permutations) + + ## get lag classes + lag_classes = ps.Quantiles( + ps.lag_spatial(weights, t_data[:, -1]), + k=num_classes).yb + + ## look up probablity distribution for each unit according to class and lag class + prob_dist = get_prob_dist(sp_markov_result.P, + lag_classes, + sp_markov_result.classes[:, -1]) + + ## find the ups and down and overall distribution of each cell + trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, + sp_markov_result.classes[:, -1]) + + ## output the results + return zip(trend, trend_up, trend_down, volatility, weights.id_order) + +def get_time_data(markov_data, time_cols): + """ + Extract the time columns and bin appropriately + """ + num_attrs = len(time_cols) + return np.array([[x['attr' + str(i)] for x in markov_data] + for i in range(1, num_attrs+1)], dtype=float).transpose() + +## not currently used +def rebin_data(time_data, num_time_per_bin): + """ + Convert an n x l matrix into an (n/m) x l matrix where the values are + reduced (averaged) for the intervening states: + 1 2 3 4 1.5 3.5 + 5 6 7 8 -> 5.5 7.5 + 9 8 7 6 8.5 6.5 + 5 4 3 2 4.5 2.5 + + if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix. + + This process effectively resamples the data at a longer time span n + units longer than the input data. + For cases when there is a remainder (remainder(5/3) = 2), the remaining + two columns are binned together as the last time period, while the + first three are binned together for the first period. + + Input: + @param time_data n x l ndarray: measurements of an attribute at + different time intervals + @param num_time_per_bin int: number of columns to average into a new + column + Output: + ceil(n / m) x l ndarray of resampled time series + """ + + if time_data.shape[1] % num_time_per_bin == 0: + ## if fit is perfect, then use it + n_max = time_data.shape[1] / num_time_per_bin + else: + ## fit remainders into an additional column + n_max = time_data.shape[1] / num_time_per_bin + 1 + + return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1) + for i in range(n_max)]).T + +def get_prob_dist(transition_matrix, lag_indices, unit_indices): + """ + Given an array of transition matrices, look up the probability + associated with the arrangements passed + + Input: + @param transition_matrix ndarray[k,k,k]: + @param lag_indices ndarray: + @param unit_indices ndarray: + + Output: + Array of probability distributions + """ + + return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] + for i in range(len(lag_indices))]) + +def get_prob_stats(prob_dist, unit_indices): + """ + get the statistics of the probability distributions + + Outputs: + @param trend_up ndarray(float): sum of probabilities for upward + movement (relative to the unit index of that prob) + @param trend_down ndarray(float): sum of probabilities for downward + movement (relative to the unit index of that prob) + @param trend ndarray(float): difference of upward and downward + movements + """ + + num_elements = len(unit_indices) + trend_up = np.empty(num_elements, dtype=float) + trend_down = np.empty(num_elements, dtype=float) + trend = np.empty(num_elements, dtype=float) + + for i in range(num_elements): + trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum() + trend_down[i] = prob_dist[i, :unit_indices[i]].sum() + if prob_dist[i, unit_indices[i]] > 0.0: + trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]] + else: + trend[i] = None + + ## calculate volatility of distribution + volatility = prob_dist.std(axis=1) + + return trend_up, trend_down, trend, volatility diff --git a/release/python/0.2.0/crankshaft/setup.py b/release/python/0.2.0/crankshaft/setup.py new file mode 100644 index 0000000..0b1f2d6 --- /dev/null +++ b/release/python/0.2.0/crankshaft/setup.py @@ -0,0 +1,49 @@ + +""" +CartoDB Spatial Analysis Python Library +See: +https://github.com/CartoDB/crankshaft +""" + +from setuptools import setup, find_packages + +setup( + name='crankshaft', + + version='0.2.0', + + description='CartoDB Spatial Analysis Python Library', + + url='https://github.com/CartoDB/crankshaft', + + author='Data Services Team - CartoDB', + author_email='dataservices@cartodb.com', + + license='MIT', + + classifiers=[ + 'Development Status :: 3 - Alpha', + 'Intended Audience :: Mapping comunity', + 'Topic :: Maps :: Mapping Tools', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 2.7', + ], + + keywords='maps mapping tools spatial analysis geostatistics', + + packages=find_packages(exclude=['contrib', 'docs', 'tests']), + + extras_require={ + 'dev': ['unittest'], + 'test': ['unittest', 'nose', 'mock'], + }, + + # The choice of component versions is dictated by what's + # provisioned in the production servers. + # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation. + install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'], + + requires=['pysal', 'numpy', 'sklearn'], + + test_suite='test' +) diff --git a/release/python/0.2.0/crankshaft/test/fixtures/kmeans.json b/release/python/0.2.0/crankshaft/test/fixtures/kmeans.json new file mode 100644 index 0000000..8f31c79 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/fixtures/kmeans.json @@ -0,0 +1 @@ +[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}] \ No newline at end of file diff --git a/release/python/0.2.0/crankshaft/test/fixtures/markov.json b/release/python/0.2.0/crankshaft/test/fixtures/markov.json new file mode 100644 index 0000000..d60e4e0 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/fixtures/markov.json @@ -0,0 +1 @@ +[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]] diff --git a/release/python/0.2.0/crankshaft/test/fixtures/moran.json b/release/python/0.2.0/crankshaft/test/fixtures/moran.json new file mode 100644 index 0000000..2f75cf1 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/fixtures/moran.json @@ -0,0 +1,52 @@ +[[0.9319096128346788, "HH"], +[-1.135787401862846, "HL"], +[0.11732030672508517, "LL"], +[0.6152779669180425, "LL"], +[-0.14657336660125297, "LH"], +[0.6967858120189607, "LL"], +[0.07949310115714454, "HH"], +[0.4703198759258987, "HH"], +[0.4421125200498064, "HH"], +[0.5724288737143592, "LL"], +[0.8970743435692062, "LL"], +[0.18327334401918674, "LL"], +[-0.01466729201304962, "HL"], +[0.3481559372544409, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329988, "HH"], +[0.4373841193538136, "HH"], +[0.15971286468915544, "LL"], +[1.0543588860308968, "HH"], +[1.7372866900020818, "HH"], +[1.091998586053999, "LL"], +[0.1171572584252222, "HH"], +[0.08438455015300014, "LL"], +[0.06547094736902978, "LL"], +[0.15482141569329985, "HH"], +[1.1627044812890683, "HH"], +[0.06547094736902978, "LL"], +[0.795275137550483, "HH"], +[0.18562939195219, "LL"], +[0.3010757406693439, "LL"], +[2.8205795942839376, "HH"], +[0.11259190602909264, "LL"], +[-0.07116352791516614, "HL"], +[-0.09945240794119009, "LH"], +[0.18562939195219, "LL"], +[0.1832733440191868, "LL"], +[-0.39054253768447705, "HL"], +[-0.1672071289487642, "HL"], +[0.3337669247916343, "HH"], +[0.2584386102554792, "HH"], +[-0.19733845476322634, "HL"], +[-0.9379282899805409, "LH"], +[-0.028770969951095866, "LH"], +[0.051367269430983485, "LL"], +[-0.2172548045913472, "LH"], +[0.05136726943098351, "LL"], +[0.04191046803899837, "LL"], +[0.7482357030403517, "HH"], +[-0.014585767863118111, "LH"], +[0.5410013139159929, "HH"], +[1.0223932668429925, "LL"], +[1.4179402898927476, "LL"]] \ No newline at end of file diff --git a/release/python/0.2.0/crankshaft/test/fixtures/neighbors.json b/release/python/0.2.0/crankshaft/test/fixtures/neighbors.json new file mode 100644 index 0000000..055b359 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/fixtures/neighbors.json @@ -0,0 +1,54 @@ +[ + {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5}, + {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7}, + {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2}, + {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1}, + {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3}, + {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05}, + {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4}, + {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7}, + {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5}, + {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04}, + {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08}, + {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2}, + {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4}, + {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2}, + {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3}, + {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4}, + {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6}, + {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3}, + {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7}, + {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8}, + {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1}, + {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4}, + {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1}, + {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3}, + {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4}, + {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6}, + {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3}, + {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8}, + {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3}, + {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1}, + {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9}, + {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3}, + {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4}, + {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3}, + {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3}, + {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2}, + {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5}, + {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4}, + {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6}, + {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5}, + {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4}, + {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2}, + {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3}, + {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2}, + {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3}, + {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2}, + {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3}, + {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5}, + {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2}, + {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6}, + {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01}, + {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01} + ] diff --git a/release/python/0.2.0/crankshaft/test/fixtures/neighbors_markov.json b/release/python/0.2.0/crankshaft/test/fixtures/neighbors_markov.json new file mode 100644 index 0000000..45a20e7 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/fixtures/neighbors_markov.json @@ -0,0 +1 @@ +[{"neighbors": [10, 7, 21, 23, 1], "y1995": 0.87654416055651474, "y1997": 0.85637566664752718, "y1996": 0.8631470006766887, "y1999": 0.84461540228037335, "y1998": 0.84811668329242784, "y2006": 0.86302631339545688, "y2007": 0.86148266513456728, "y2004": 0.86416611731111015, "y2005": 0.87119374831581786, "y2002": 0.85012592862683589, "y2003": 0.8550965633336135, "y2000": 0.83271652434603094, "y2001": 0.83786313566577242, "id": 0, "y2008": 0.86252252380501315, "y2009": 0.86746356478544273}, {"neighbors": [5, 7, 22, 29, 3], "y1995": 0.91889509774542122, "y1997": 0.92333257900976462, "y1996": 0.91757931190043385, "y1999": 0.92552387732371888, "y1998": 0.92517289327379471, "y2006": 0.91706053906277052, "y2007": 0.90139504820726424, "y2004": 0.89815175749309051, "y2005": 0.91832090781161113, "y2002": 0.89431990798552208, "y2003": 0.88924793576523797, "y2000": 0.90746978227271013, "y2001": 0.89830489127332913, "id": 1, "y2008": 0.87897455159080617, "y2009": 0.86216858051752643}, {"neighbors": [11, 8, 13, 18, 17], "y1995": 0.82591007476914713, "y1997": 0.81989792988843901, "y1996": 0.82548595539161707, "y1999": 0.81731522200916285, "y1998": 0.81503235035017918, "y2006": 0.81814804358939286, "y2007": 0.83675961003285626, "y2004": 0.82668195534569056, "y2005": 0.82373723764184559, "y2002": 0.80849979516360859, "y2003": 0.82258550658074148, "y2000": 0.78964559168205917, "y2001": 0.8058444152731008, "id": 2, "y2008": 0.8357419865626442, "y2009": 0.84647177436289112}, {"neighbors": [4, 14, 9, 5, 12], "y1995": 1.0908817638059434, "y1997": 1.0845641754849344, "y1996": 1.0853768890893893, "y1999": 1.098988414417104, "y1998": 1.0841540389418189, "y2006": 1.1316479722785828, "y2007": 1.1295850763954971, "y2004": 1.1139980568106316, "y2005": 1.1216802898290368, "y2002": 1.1116069731657288, "y2003": 1.1088862051501811, "y2000": 1.1450694824791507, "y2001": 1.1215113292620285, "id": 3, "y2008": 1.1137181812756343, "y2009": 1.0993677488645406}, {"neighbors": [14, 3, 9, 31, 12], "y1995": 1.1073144618319228, "y1997": 1.1328363804627946, "y1996": 1.1137394350312471, "y1999": 1.1591002514611153, "y1998": 1.144725587086376, "y2006": 1.1173646811350333, "y2007": 1.1086324218539598, "y2004": 1.1102496406140896, "y2005": 1.11943471361418, "y2002": 1.1475230282561595, "y2003": 1.1184328424005199, "y2000": 1.1689820101690329, "y2001": 1.1721248787169682, "id": 4, "y2008": 1.0964251552643696, "y2009": 1.0776233718455337}, {"neighbors": [29, 1, 22, 7, 4], "y1995": 1.422697571371182, "y1997": 1.4427350196405593, "y1996": 1.4211843379728528, "y1999": 1.4440068434166562, "y1998": 1.4357757095632602, "y2006": 1.4405276647793266, "y2007": 1.4524121586440921, "y2004": 1.4059372049179741, "y2005": 1.4078864636665769, "y2002": 1.4197822680667809, "y2003": 1.3909220829548647, "y2000": 1.4418473669388905, "y2001": 1.4478283203013527, "id": 5, "y2008": 1.4330609762040207, "y2009": 1.4174430982377491}, {"neighbors": [12, 47, 9, 25, 20], "y1995": 1.1307388498039153, "y1997": 1.1107470843142355, "y1996": 1.1311051255854685, "y1999": 1.130881491772973, "y1998": 1.1336463608751246, "y2006": 1.1088003408832796, "y2007": 1.0840170924825394, "y2004": 1.1244623853593112, "y2005": 1.1167100811401538, "y2002": 1.1306293052597198, "y2003": 1.1194498381213465, "y2000": 1.1088813841947593, "y2001": 1.1185662918783175, "id": 6, "y2008": 1.0695920556329086, "y2009": 1.0787522517402164}, {"neighbors": [21, 1, 22, 10, 0], "y1995": 1.0470612357366649, "y1997": 1.0425337165747406, "y1996": 1.0451683097376836, "y1999": 1.0207254480945218, "y1998": 1.0323998680588111, "y2006": 1.0405109962442973, "y2007": 1.0174964540280445, "y2004": 1.0140090547678748, "y2005": 1.0317674181861733, "y2002": 0.99669586934394627, "y2003": 0.99327675611171373, "y2000": 0.99854316295509526, "y2001": 0.98802579761429143, "id": 7, "y2008": 0.9936394033949828, "y2009": 0.98279746069218921}, {"neighbors": [11, 13, 17, 18, 15], "y1995": 0.98996985668705595, "y1997": 0.99491000469481983, "y1996": 1.0014356415938011, "y1999": 1.0045584503565237, "y1998": 1.0018840754492748, "y2006": 0.92232873520447411, "y2007": 0.91284090705064902, "y2004": 0.93694786512729977, "y2005": 0.94308212820743131, "y2002": 0.96834820215592055, "y2003": 0.95335147249088092, "y2000": 0.99127006477048718, "y2001": 0.97925917470464008, "id": 8, "y2008": 0.89689832627117483, "y2009": 0.88928857608264111}, {"neighbors": [12, 6, 4, 3, 14], "y1995": 0.87418390853652306, "y1997": 0.84425695187978567, "y1996": 0.86416601430334228, "y1999": 0.83903043942542854, "y1998": 0.8404493987171674, "y2006": 0.87204140839730271, "y2007": 0.86633032299764789, "y2004": 0.86981997840756087, "y2005": 0.86837929279319737, "y2002": 0.86107306112852877, "y2003": 0.85007719735663123, "y2000": 0.85787080050645603, "y2001": 0.86036185149249467, "id": 9, "y2008": 0.84946077011565357, "y2009": 0.83287145944123797}, {"neighbors": [0, 7, 21, 23, 22], "y1995": 1.1419611801631209, "y1997": 1.1489271154554144, "y1996": 1.146602624490825, "y1999": 1.1443662376135306, "y1998": 1.1490959392942743, "y2006": 1.1049125811637337, "y2007": 1.1105984164317646, "y2004": 1.1119989015058092, "y2005": 1.1025779214946556, "y2002": 1.1259666377127024, "y2003": 1.1221399558345004, "y2000": 1.144501826035474, "y2001": 1.1234975172649961, "id": 10, "y2008": 1.1050979494645479, "y2009": 1.1002009697391872}, {"neighbors": [8, 13, 18, 17, 2], "y1995": 0.97282462974938089, "y1997": 0.96252588061647382, "y1996": 0.96700147279313231, "y1999": 0.96057686787383312, "y1998": 0.96538780087103548, "y2006": 0.91010201260822066, "y2007": 0.89280392121658247, "y2004": 0.94103988614185807, "y2005": 0.9212251863828258, "y2002": 0.94804194711420009, "y2003": 0.9543028555845573, "y2000": 0.95831051250950716, "y2001": 0.94480908623936988, "id": 11, "y2008": 0.89298242828382146, "y2009": 0.89165384824292859}, {"neighbors": [33, 9, 6, 25, 31], "y1995": 0.94325467991401402, "y1997": 0.96455242154753429, "y1996": 0.96436902092427723, "y1999": 0.94117647058823528, "y1998": 0.95243008993884537, "y2006": 0.9346681464882507, "y2007": 0.94281559150403071, "y2004": 0.96918424441756057, "y2005": 0.94781280876672958, "y2002": 0.95388717527096822, "y2003": 0.94597005193649519, "y2000": 0.94809269652332606, "y2001": 0.93539181553564288, "id": 12, "y2008": 0.965203150896216, "y2009": 0.967154410723015}, {"neighbors": [18, 17, 11, 8, 19], "y1995": 0.97478408425654373, "y1997": 0.98712808751954773, "y1996": 0.98169225257738801, "y1999": 0.985598971191053, "y1998": 0.98474769442356791, "y2006": 0.98416665248276058, "y2007": 0.98423613480079708, "y2004": 0.97399471186978948, "y2005": 0.96910087128357136, "y2002": 0.9820996926750224, "y2003": 0.98776529543110569, "y2000": 0.98687072733199255, "y2001": 0.99237486444837619, "id": 13, "y2008": 0.99823861244053191, "y2009": 0.99545704236827348}, {"neighbors": [4, 31, 3, 29, 12], "y1995": 0.85570268988941878, "y1997": 0.85986131704895119, "y1996": 0.85575915188345031, "y1999": 0.85380119644969055, "y1998": 0.85693406055397725, "y2006": 0.82803647591954255, "y2007": 0.81987360180979219, "y2004": 0.83998883284341452, "y2005": 0.83478547261894065, "y2002": 0.85472102128186755, "y2003": 0.84564834502399988, "y2000": 0.86191535266765262, "y2001": 0.84981450830432048, "id": 14, "y2008": 0.82265395167873867, "y2009": 0.83994039782937002}, {"neighbors": [19, 8, 17, 16, 13], "y1995": 0.87022046646521634, "y1997": 0.85961813213722393, "y1996": 0.85996258309339635, "y1999": 0.8394713575455558, "y1998": 0.85689572413110093, "y2006": 0.94202108334913126, "y2007": 0.94222309998743192, "y2004": 0.86763340229291142, "y2005": 0.89179316746010362, "y2002": 0.86776297543511893, "y2003": 0.86720209304280604, "y2000": 0.82785596604704892, "y2001": 0.86008789452656809, "id": 15, "y2008": 0.93902708112840494, "y2009": 0.94479183757120588}, {"neighbors": [28, 26, 15, 19, 32], "y1995": 0.90134907329491731, "y1997": 0.90403990934606904, "y1996": 0.904077381347274, "y1999": 0.90399237579083946, "y1998": 0.90201769385650832, "y2006": 0.91108803862404764, "y2007": 0.90543476309316473, "y2004": 0.94338264626469681, "y2005": 0.91981795862151561, "y2002": 0.93695966482853577, "y2003": 0.94242697007039, "y2000": 0.90906631602055099, "y2001": 0.92693339421265908, "id": 16, "y2008": 0.91737137682250491, "y2009": 0.94793657442067902}, {"neighbors": [13, 18, 11, 19, 8], "y1995": 1.1977611005602815, "y1997": 1.1843915817489725, "y1996": 1.1822256425225894, "y1999": 1.1928672308275252, "y1998": 1.1826786457339149, "y2006": 1.2392938410349985, "y2007": 1.2341867605077472, "y2004": 1.2385704217423759, "y2005": 1.2441989281116201, "y2002": 1.2262477774195681, "y2003": 1.2239707531714479, "y2000": 1.2017286912636342, "y2001": 1.2132869128474402, "id": 17, "y2008": 1.2362673914436095, "y2009": 1.2675439750795283}, {"neighbors": [13, 17, 11, 8, 19], "y1995": 1.2491967813733067, "y1997": 1.2699116090397236, "y1996": 1.2575477330927329, "y1999": 1.3062566740535762, "y1998": 1.2802065055312271, "y2006": 1.3210776560048689, "y2007": 1.329362443219563, "y2004": 1.3054484140490119, "y2005": 1.3030330249408666, "y2002": 1.3257518058685978, "y2003": 1.3079549159235695, "y2000": 1.3479002255103918, "y2001": 1.3439986302151703, "id": 18, "y2008": 1.3300124123891741, "y2009": 1.3328846185074705}, {"neighbors": [26, 17, 28, 15, 16], "y1995": 1.0676800411188558, "y1997": 1.0363730321443168, "y1996": 1.0379927554499979, "y1999": 1.0329609259280523, "y1998": 1.027684488045026, "y2006": 0.94241549375546196, "y2007": 0.92754546923532677, "y2004": 0.99614160423102482, "y2005": 0.97356208269708677, "y2002": 1.0274762326434594, "y2003": 1.0316273366809443, "y2000": 1.0505901631347052, "y2001": 1.0340505678899605, "id": 19, "y2008": 0.92549226593721745, "y2009": 0.92138101880290568}, {"neighbors": [30, 25, 24, 37, 47], "y1995": 1.0947561397632881, "y1997": 1.1165429913770684, "y1996": 1.1152679554712275, "y1999": 1.1314326394231322, "y1998": 1.1310394841195361, "y2006": 1.1090538904302065, "y2007": 1.1057776900012568, "y2004": 1.1402994437897009, "y2005": 1.1197940058085571, "y2002": 1.133670175399079, "y2003": 1.139822558851451, "y2000": 1.1388962186541665, "y2001": 1.1244221220249986, "id": 20, "y2008": 1.1116682481010467, "y2009": 1.0998515545336902}, {"neighbors": [23, 22, 7, 10, 34], "y1995": 0.76530058421804126, "y1997": 0.76542450966153397, "y1996": 0.76612841163904621, "y1999": 0.76014283909933289, "y1998": 0.7672268310234307, "y2006": 0.76842416021983684, "y2007": 0.77487117798086069, "y2004": 0.76533287692895391, "y2005": 0.78205934309410463, "y2002": 0.76156903267949927, "y2003": 0.76651951668098528, "y2000": 0.74480073263159763, "y2001": 0.76098396210261965, "id": 21, "y2008": 0.77768682781054099, "y2009": 0.78801192267396702}, {"neighbors": [21, 34, 5, 7, 29], "y1995": 0.98391336093764348, "y1997": 0.98295341320156315, "y1996": 0.98075815675295552, "y1999": 0.96913802803963667, "y1998": 0.97386015032669815, "y2006": 0.93965462091114671, "y2007": 0.93069644684632924, "y2004": 0.9635616201227476, "y2005": 0.94745351657235244, "y2002": 0.97209860866113018, "y2003": 0.97441312580606143, "y2000": 0.97370819354423843, "y2001": 0.96419154157867693, "id": 22, "y2008": 0.94020973488297466, "y2009": 0.94358232339833159}, {"neighbors": [21, 10, 22, 34, 7], "y1995": 0.83561828119099946, "y1997": 0.81738501913392403, "y1996": 0.82298088022609361, "y1999": 0.80904800725677739, "y1998": 0.81748588141426259, "y2006": 0.87170334233473346, "y2007": 0.8786379876833581, "y2004": 0.85954307066870839, "y2005": 0.86790023653402792, "y2002": 0.83451612857812574, "y2003": 0.85175031934895873, "y2000": 0.80071489233375537, "y2001": 0.83358255807316928, "id": 23, "y2008": 0.87497981001981484, "y2009": 0.87888675419592222}, {"neighbors": [27, 20, 30, 32, 47], "y1995": 0.98845573274970278, "y1997": 0.99665282989553183, "y1996": 1.0209242772035507, "y1999": 0.99386618594343845, "y1998": 0.99141823200404444, "y2006": 0.97906748937234156, "y2007": 0.9932312332800689, "y2004": 1.0111665058188304, "y2005": 0.9998802359352077, "y2002": 0.99669586934394627, "y2003": 1.0255909749831356, "y2000": 0.98733194819247994, "y2001": 0.99644997431653437, "id": 24, "y2008": 1.0020493856497013, "y2009": 0.99602148231561483}, {"neighbors": [20, 33, 6, 30, 12], "y1995": 1.1493091345649815, "y1997": 1.143009615936718, "y1996": 1.1524194939429724, "y1999": 1.1398468268822266, "y1998": 1.1426554202510555, "y2006": 1.0889107875354573, "y2007": 1.0860369499254896, "y2004": 1.0856975145267398, "y2005": 1.1244348633192611, "y2002": 1.0423089214343333, "y2003": 1.0557727834721793, "y2000": 1.0831239730629278, "y2001": 1.0519262599166714, "id": 25, "y2008": 1.0599731384290745, "y2009": 1.0216094265950888}, {"neighbors": [28, 19, 16, 32, 17], "y1995": 1.1136826889802023, "y1997": 1.1189343096757198, "y1996": 1.1057147027213501, "y1999": 1.1432271991365353, "y1998": 1.1377866945457653, "y2006": 1.1268023587150906, "y2007": 1.1235793669317915, "y2004": 1.1482023546040769, "y2005": 1.1238659840114973, "y2002": 1.1600919581655105, "y2003": 1.1446778932605579, "y2000": 1.1825702862895446, "y2001": 1.1622624279436105, "id": 26, "y2008": 1.115925801617498, "y2009": 1.1257082797404696}, {"neighbors": [32, 24, 36, 16, 28], "y1995": 1.303794309231981, "y1997": 1.3120636604057812, "y1996": 1.3075218596998686, "y1999": 1.3062566740535762, "y1998": 1.3153226688859194, "y2006": 1.2865667454509278, "y2007": 1.2973409698906584, "y2004": 1.2683078569016086, "y2005": 1.2617743046198988, "y2002": 1.2920319347677043, "y2003": 1.2718351646774422, "y2000": 1.3121023910310281, "y2001": 1.2998915587009874, "id": 27, "y2008": 1.2939020510829768, "y2009": 1.2934544564717687}, {"neighbors": [26, 16, 19, 32, 27], "y1995": 0.83953719020532513, "y1997": 0.82006005316292385, "y1996": 0.82701447583159737, "y1999": 0.80294863992835086, "y1998": 0.8118887636743225, "y2006": 0.8389109342655191, "y2007": 0.84349246817602375, "y2004": 0.83108634437662732, "y2005": 0.84373783646216949, "y2002": 0.82596790474192727, "y2003": 0.82435704751379402, "y2000": 0.78772975118465016, "y2001": 0.82848010958278628, "id": 28, "y2008": 0.85637272428125033, "y2009": 0.86539395164519117}, {"neighbors": [5, 39, 22, 14, 31], "y1995": 1.2345008725695852, "y1997": 1.2353793515744536, "y1996": 1.2426021999018138, "y1999": 1.2452262575926329, "y1998": 1.2358129278404693, "y2006": 1.2365329681906834, "y2007": 1.2796200872578414, "y2004": 1.1967443443492951, "y2005": 1.2153657295128597, "y2002": 1.1937780418204111, "y2003": 1.1835533748469893, "y2000": 1.2256766974812463, "y2001": 1.2112664802237314, "id": 29, "y2008": 1.2796839248335934, "y2009": 1.2590773758694083}, {"neighbors": [37, 20, 24, 25, 27], "y1995": 0.97696620404861145, "y1997": 0.98035944080980575, "y1996": 0.9740071914763756, "y1999": 0.95543282313901556, "y1998": 0.97581530789338955, "y2006": 0.92100464312607799, "y2007": 0.9147530387633086, "y2004": 0.9298883479571457, "y2005": 0.93442917452618346, "y2002": 0.93679072759857129, "y2003": 0.92540049332494034, "y2000": 0.96480308308405971, "y2001": 0.9468637634838194, "id": 30, "y2008": 0.90249622070947177, "y2009": 0.90213630440783921}, {"neighbors": [35, 14, 33, 12, 4], "y1995": 0.84986885942491119, "y1997": 0.84295996568390696, "y1996": 0.89868510090623221, "y1999": 0.85659367787716301, "y1998": 0.87280533962476625, "y2006": 0.92562487931452408, "y2007": 0.96635366357254426, "y2004": 0.92698332540482575, "y2005": 0.94745351657235244, "y2002": 0.90448992922937876, "y2003": 0.95495898185605821, "y2000": 0.88937573313051443, "y2001": 0.89440100450887505, "id": 31, "y2008": 1.025203118044723, "y2009": 1.0394296020754366}, {"neighbors": [36, 27, 28, 16, 26], "y1995": 1.0192280751235561, "y1997": 1.0097442843101825, "y1996": 1.0025820319237864, "y1999": 0.99765073314119712, "y1998": 1.0030341681355639, "y2006": 0.94779637858468868, "y2007": 0.93759089358493275, "y2004": 0.97583768316642261, "y2005": 0.96101679691008712, "y2002": 0.99747298060178258, "y2003": 0.99550758543481688, "y2000": 1.0075901875261932, "y2001": 0.99192968437874551, "id": 32, "y2008": 0.93353431146829191, "y2009": 0.94121705123804411}, {"neighbors": [44, 25, 12, 35, 31], "y1995": 0.86367410708901315, "y1997": 0.85544345781923936, "y1996": 0.85558931627900803, "y1999": 0.84336613427334628, "y1998": 0.85103025143102673, "y2006": 0.89455097373003656, "y2007": 0.88283929116469462, "y2004": 0.85951183386707053, "y2005": 0.87194227372077004, "y2002": 0.84667960913556228, "y2003": 0.84374557883664714, "y2000": 0.83434853662160158, "y2001": 0.85813595114434105, "id": 33, "y2008": 0.90349490610221961, "y2009": 0.9060067497610369}, {"neighbors": [22, 39, 21, 29, 23], "y1995": 1.0094753356447226, "y1997": 1.0069881886439402, "y1996": 1.0041105523637666, "y1999": 0.99291086334982948, "y1998": 0.99513686502304577, "y2006": 0.96382634438484593, "y2007": 0.95011400973122428, "y2004": 0.975119236728752, "y2005": 0.96134614808826613, "y2002": 0.99291167539274383, "y2003": 0.98983209318633369, "y2000": 1.0058162611397035, "y2001": 0.98850522230466298, "id": 34, "y2008": 0.94346860300667812, "y2009": 0.9463776450423077}, {"neighbors": [31, 38, 44, 33, 14], "y1995": 1.0571257066143651, "y1997": 1.0575301194645879, "y1996": 1.0545941857842291, "y1999": 1.0510385688532684, "y1998": 1.0488078570498685, "y2006": 1.0247627521629479, "y2007": 1.0234752320591773, "y2004": 1.0329697933620496, "y2005": 1.0219168238570018, "y2002": 1.0420048344203974, "y2003": 1.0402553971511816, "y2000": 1.0480002306104303, "y2001": 1.030249414987729, "id": 35, "y2008": 1.0251768368501768, "y2009": 1.0435957064486703}, {"neighbors": [32, 43, 27, 28, 42], "y1995": 1.070841888164505, "y1997": 1.0793762307014196, "y1996": 1.0666949726007404, "y1999": 1.0794043012481198, "y1998": 1.0738798776109699, "y2006": 1.087727556316465, "y2007": 1.0885954360198933, "y2004": 1.1032213602455734, "y2005": 1.0916793915985508, "y2002": 1.0938347765734742, "y2003": 1.1052447043433509, "y2000": 1.0531800956589803, "y2001": 1.0745277096056161, "id": 36, "y2008": 1.0917733838297285, "y2009": 1.1096083021948762}, {"neighbors": [30, 40, 20, 42, 41], "y1995": 0.8671922185905101, "y1997": 0.86675155621455668, "y1996": 0.86628895935887062, "y1999": 0.86511809486628932, "y1998": 0.86425631732335095, "y2006": 0.84488343470424199, "y2007": 0.83374328958471722, "y2004": 0.84517414191529749, "y2005": 0.84843857600526962, "y2002": 0.85411284725399572, "y2003": 0.84886336375435456, "y2000": 0.86287327291635718, "y2001": 0.8516979624450659, "id": 37, "y2008": 0.82812044014430564, "y2009": 0.82878598934619596}, {"neighbors": [35, 31, 45, 39, 44], "y1995": 0.8838921149583755, "y1997": 0.90282398478743275, "y1996": 0.92288667453925455, "y1999": 0.92023285988219217, "y1998": 0.91229185518735723, "y2006": 0.93869676706720051, "y2007": 0.96947770975097391, "y2004": 0.99223700402629367, "y2005": 0.97984969609868555, "y2002": 0.93682451504456421, "y2003": 0.98655146182882891, "y2000": 0.92652175166361039, "y2001": 0.94278865361566122, "id": 38, "y2008": 1.0036262573224608, "y2009": 0.98102350657197357}, {"neighbors": [29, 34, 38, 22, 35], "y1995": 0.970820642185237, "y1997": 0.94534081352108112, "y1996": 0.95320232993219844, "y1999": 0.93967000034446724, "y1998": 0.94215592860799646, "y2006": 0.91035556215514757, "y2007": 0.90430364292511256, "y2004": 0.92879505989982103, "y2005": 0.9211054223180335, "y2002": 0.93412151936513388, "y2003": 0.93501274320242933, "y2000": 0.93092108910210503, "y2001": 0.92662519262599163, "id": 39, "y2008": 0.89994694483851023, "y2009": 0.9007386435858511}, {"neighbors": [41, 37, 42, 30, 45], "y1995": 0.95861858457245008, "y1997": 0.98254810501535106, "y1996": 0.95774543235102894, "y1999": 0.98684823919808018, "y1998": 0.98919471947721893, "y2006": 0.97163003599581876, "y2007": 0.97007020126757271, "y2004": 0.9493488753775261, "y2005": 0.97152609359561659, "y2002": 0.95601578436851964, "y2003": 0.94905384541254967, "y2000": 0.98882204635713133, "y2001": 0.97662233890759653, "id": 40, "y2008": 0.97158948117089283, "y2009": 0.95884908006927827}, {"neighbors": [40, 45, 44, 37, 42], "y1995": 0.83980438854721107, "y1997": 0.85746999875029983, "y1996": 0.84726737166133714, "y1999": 0.85567509846023126, "y1998": 0.85467221160427542, "y2006": 0.8333891885768886, "y2007": 0.83511679264592342, "y2004": 0.81743586206088703, "y2005": 0.83550405700769481, "y2002": 0.84502402428191115, "y2003": 0.82645665158259707, "y2000": 0.84818516243622177, "y2001": 0.85265681182580899, "id": 41, "y2008": 0.82136617314598481, "y2009": 0.80921873783836296}, {"neighbors": [43, 40, 46, 37, 36], "y1995": 0.95118156405662746, "y1997": 0.94688098462868708, "y1996": 0.9466212002600608, "y1999": 0.95124410099780687, "y1998": 0.95085829660091703, "y2006": 0.96895367966714574, "y2007": 0.9700163384024274, "y2004": 0.97583768316642261, "y2005": 0.95571723704302525, "y2002": 0.96804411514198463, "y2003": 0.97136213864358201, "y2000": 0.95440787445922959, "y2001": 0.96364362764682376, "id": 42, "y2008": 0.97082732652905901, "y2009": 0.9878236640328002}, {"neighbors": [36, 42, 32, 27, 46], "y1995": 1.0891004415267045, "y1997": 1.0849289528525252, "y1996": 1.0824896838138709, "y1999": 1.0945424900391545, "y1998": 1.0865692335830259, "y2006": 1.1450297539219478, "y2007": 1.1447474729339102, "y2004": 1.1334273474293739, "y2005": 1.1468606844516303, "y2002": 1.1229257675733433, "y2003": 1.1302103089739621, "y2000": 1.1055818811158884, "y2001": 1.1214085953998059, "id": 43, "y2008": 1.1408403740471014, "y2009": 1.1614292649793569}, {"neighbors": [33, 41, 45, 35, 40], "y1995": 1.0633603345917013, "y1997": 1.0869149629649646, "y1996": 1.0736582323828732, "y1999": 1.1166986255755473, "y1998": 1.0976484597942771, "y2006": 1.0839806574563229, "y2007": 1.0983176831786272, "y2004": 1.0927882684985315, "y2005": 1.0700320368873319, "y2002": 1.0881584856466706, "y2003": 1.0804431312806149, "y2000": 1.1185670222649935, "y2001": 1.0976428286056732, "id": 44, "y2008": 1.0929823187788443, "y2009": 1.0917612486217978}, {"neighbors": [41, 44, 40, 35, 33], "y1995": 0.79772064970019041, "y1997": 0.7858115114280021, "y1996": 0.78829195801876151, "y1999": 0.77035744221561353, "y1998": 0.77615921755360906, "y2006": 0.79949806580432425, "y2007": 0.80172181625581262, "y2004": 0.79603865293896003, "y2005": 0.78966436120841943, "y2002": 0.81437881076636964, "y2003": 0.80788827809912023, "y2000": 0.77751193519846906, "y2001": 0.79902973574567659, "id": 45, "y2008": 0.82168154748053679, "y2009": 0.85587910681858015}, {"neighbors": [42, 43, 40, 36, 37], "y1995": 1.0052446952315301, "y1997": 1.0047589936197736, "y1996": 1.0000769567582628, "y1999": 1.0063956091903872, "y1998": 1.0061394183885444, "y2006": 0.97292595590233411, "y2007": 0.96519561197191939, "y2004": 0.99030032232474696, "y2005": 0.97682565346267858, "y2002": 1.0081498135355325, "y2003": 1.0057431552702318, "y2000": 1.0016297948675874, "y2001": 0.99860738542320637, "id": 46, "y2008": 0.9617340332161447, "y2009": 0.95890283625473927}, {"neighbors": [20, 6, 24, 25, 30], "y1995": 0.95808418788867844, "y1997": 0.9654440995572009, "y1996": 0.93825679674127938, "y1999": 0.96987289157318213, "y1998": 0.95561201303757848, "y2006": 1.1704973973021624, "y2007": 1.1702515395802287, "y2004": 1.0533361880299275, "y2005": 1.0983262971945267, "y2002": 1.0078119390756035, "y2003": 1.0348423554112989, "y2000": 0.96608031008233231, "y2001": 0.99727184521431422, "id": 47, "y2008": 1.1873055260044207, "y2009": 1.1424264534188653}] diff --git a/release/python/0.2.0/crankshaft/test/helper.py b/release/python/0.2.0/crankshaft/test/helper.py new file mode 100644 index 0000000..7d28b94 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/helper.py @@ -0,0 +1,13 @@ +import unittest + +from mock_plpy import MockPlPy +plpy = MockPlPy() + +import sys +sys.modules['plpy'] = plpy + +import os + +def fixture_file(name): + dir = os.path.dirname(os.path.realpath(__file__)) + return os.path.join(dir, 'fixtures', name) diff --git a/release/python/0.2.0/crankshaft/test/mock_plpy.py b/release/python/0.2.0/crankshaft/test/mock_plpy.py new file mode 100644 index 0000000..a982ebe --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/mock_plpy.py @@ -0,0 +1,52 @@ +import re + +class MockCursor: + def __init__(self, data): + self.cursor_pos = 0 + self.data = data + + def fetch(self, batch_size): + batch = self.data[self.cursor_pos : self.cursor_pos + batch_size] + self.cursor_pos += batch_size + return batch + + +class MockPlPy: + def __init__(self): + self._reset() + + def _reset(self): + self.infos = [] + self.notices = [] + self.debugs = [] + self.logs = [] + self.warnings = [] + self.errors = [] + self.fatals = [] + self.executes = [] + self.results = [] + self.prepares = [] + self.results = [] + + def _define_result(self, query, result): + pattern = re.compile(query, re.IGNORECASE | re.MULTILINE) + self.results.append([pattern, result]) + + def notice(self, msg): + self.notices.append(msg) + + def debug(self, msg): + self.notices.append(msg) + + def info(self, msg): + self.infos.append(msg) + + def cursor(self, query): + data = self.execute(query) + return MockCursor(data) + + def execute(self, query): # TODO: additional arguments + for result in self.results: + if result[0].match(query): + return result[1] + return [] diff --git a/release/python/0.2.0/crankshaft/test/test_cluster_kmeans.py b/release/python/0.2.0/crankshaft/test/test_cluster_kmeans.py new file mode 100644 index 0000000..aba8e07 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/test_cluster_kmeans.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file +import numpy as np +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class KMeansTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read()) + self.params = {"subquery": "select * from table", + "no_clusters": "10" + } + + def test_kmeans(self): + data = self.cluster_data + plpy._define_result('select' ,data) + clusters = cc.kmeans('subquery', 2) + labels = [a[1] for a in clusters] + c1 = [a for a in clusters if a[1]==0] + c2 = [a for a in clusters if a[1]==1] + + self.assertEqual(len(np.unique(labels)),2) + self.assertEqual(len(c1),20) + self.assertEqual(len(c2),20) + diff --git a/release/python/0.2.0/crankshaft/test/test_clustering_moran.py b/release/python/0.2.0/crankshaft/test/test_clustering_moran.py new file mode 100644 index 0000000..2b683cf --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/test_clustering_moran.py @@ -0,0 +1,88 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class MoranTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.params_markov = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read()) + self.moran_data = json.loads(open(fixture_file('moran.json')).read()) + + def test_map_quads(self): + """Test map_quads""" + self.assertEqual(cc.map_quads(1), 'HH') + self.assertEqual(cc.map_quads(2), 'LH') + self.assertEqual(cc.map_quads(3), 'LL') + self.assertEqual(cc.map_quads(4), 'HL') + self.assertEqual(cc.map_quads(33), None) + self.assertEqual(cc.map_quads('andy'), None) + + def test_quad_position(self): + """Test lisa_sig_vals""" + + quads = np.array([1, 2, 3, 4], np.int) + + ans = np.array(['HH', 'LH', 'LL', 'HL']) + test_ans = cc.quad_position(quads) + + self.assertTrue((test_ans == ans).all()) + + def test_moran_local(self): + """Test Moran's I local""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + self.assertEqual(res_quad, exp_quad) + + def test_moran_local_rate(self): + """Test Moran's I rate""" + data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None? ', result == None + result = [(row[0], row[1]) for row in result] + expected = self.moran_data + for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected): + self.assertAlmostEqual(res_val, exp_val) + + def test_moran(self): + """Test Moran's I global""" + data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data] + plpy._define_result('select', data) + random_seeds.set_random_seeds(1235) + result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id') + print 'result == None?', result == None + result_moran = result[0][0] + expected_moran = np.array([row[0] for row in self.moran_data]).mean() + self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2) diff --git a/release/python/0.2.0/crankshaft/test/test_pysal_utils.py b/release/python/0.2.0/crankshaft/test/test_pysal_utils.py new file mode 100644 index 0000000..171fdbc --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/test_pysal_utils.py @@ -0,0 +1,142 @@ +import unittest + +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds + + +class PysalUtilsTest(unittest.TestCase): + """Testing class for utility functions related to PySAL integrations""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + self.params_array = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + def test_query_attr_select(self): + """Test query_attr_select""" + + ans = "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " + + ans_array = "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " + + self.assertEqual(pu.query_attr_select(self.params), ans) + self.assertEqual(pu.query_attr_select(self.params_array), ans_array) + + def test_query_attr_where(self): + """Test pu.query_attr_where""" + + ans = "idx_replace.\"andy\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" <> 0" + + ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \ + "idx_replace.\"_2014_jan\" IS NOT NULL AND " \ + "idx_replace.\"_2014_feb\" IS NOT NULL" + + self.assertEqual(pu.query_attr_where(self.params), ans) + self.assertEqual(pu.query_attr_where(self.params_array), ans_array) + + def test_knn(self): + """Test knn neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0 " \ + "ORDER BY " \ + "j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + ans_array = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"_2013_dec\" IS NOT NULL AND " \ + "j.\"_2014_jan\" IS NOT NULL AND " \ + "j.\"_2014_feb\" IS NOT NULL " \ + "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"_2013_dec\" IS NOT NULL AND " \ + "i.\"_2014_jan\" IS NOT NULL AND " \ + "i.\"_2014_feb\" IS NOT NULL "\ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.knn(self.params), ans) + self.assertEqual(pu.knn(self.params_array), ans_array) + + def test_queen(self): + """Test queen neighbors constructor""" + + ans = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE " \ + "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "ST_Touches(i.\"the_geom\", " \ + "j.\"the_geom\") AND " \ + "j.\"andy\" IS NOT NULL AND " \ + "j.\"jay_z\" IS NOT NULL AND " \ + "j.\"jay_z\" <> 0)" \ + ") As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"andy\" IS NOT NULL AND " \ + "i.\"jay_z\" IS NOT NULL AND " \ + "i.\"jay_z\" <> 0 " \ + "ORDER BY i.\"cartodb_id\" ASC;" + + self.assertEqual(pu.queen(self.params), ans) + + def test_construct_neighbor_query(self): + """Test construct_neighbor_query""" + + # Compare to raw knn query + self.assertEqual(pu.construct_neighbor_query('knn', self.params), + pu.knn(self.params)) + + def test_get_attributes(self): + """Test get_attributes""" + + ## need to add tests + + self.assertEqual(True, True) + + def test_get_weight(self): + """Test get_weight""" + + self.assertEqual(True, True) + + def test_empty_zipped_array(self): + """Test empty_zipped_array""" + ans2 = [(None, None)] + ans4 = [(None, None, None, None)] + self.assertEqual(pu.empty_zipped_array(2), ans2) + self.assertEqual(pu.empty_zipped_array(4), ans4) diff --git a/release/python/0.2.0/crankshaft/test/test_segmentation.py b/release/python/0.2.0/crankshaft/test/test_segmentation.py new file mode 100644 index 0000000..d02e8b1 --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/test_segmentation.py @@ -0,0 +1,64 @@ +import unittest +import numpy as np +from helper import plpy, fixture_file +import crankshaft.segmentation as segmentation +import json + +class SegmentationTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + + def generate_random_data(self,n_samples,random_state, row_type=False): + x1 = random_state.uniform(size=n_samples) + x2 = random_state.uniform(size=n_samples) + x3 = random_state.randint(0, 4, size=n_samples) + + y = x1+x2*x2+x3 + cartodb_id = range(len(x1)) + + if row_type: + return [ {'features': vals} for vals in zip(x1,x2,x3)], y + else: + return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))] + + def test_replace_nan_with_mean(self): + test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan]) + + def test_create_and_predict_segment(self): + n_samples = 1000 + + random_state_train = np.random.RandomState(13) + random_state_test = np.random.RandomState(134) + training_data = self.generate_random_data(n_samples, random_state_train) + test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True) + + + ids = [{'cartodb_ids': range(len(test_data))}] + rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}] + + plpy._define_result('select \* from \(select \* from training\) a limit 1',rows) + plpy._define_result('.*from \(select \* from training\) as a' ,training_data) + plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids) + plpy._define_result('.*select \* from test.*' ,test_data) + + model_parameters = {'n_estimators': 1200, + 'max_depth': 3, + 'subsample' : 0.5, + 'learning_rate': 0.01, + 'min_samples_leaf': 1} + + result = segmentation.create_and_predict_segment( + 'select * from training', + 'target', + 'select * from test', + model_parameters) + + prediction = [r[1] for r in result] + + accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y)))) + + self.assertEqual(len(result),len(test_data)) + self.assertTrue( result[0][2] < 0.01) + self.assertTrue( accuracy < 0.5*np.mean(test_y) ) diff --git a/release/python/0.2.0/crankshaft/test/test_space_time_dynamics.py b/release/python/0.2.0/crankshaft/test/test_space_time_dynamics.py new file mode 100644 index 0000000..54ffc9d --- /dev/null +++ b/release/python/0.2.0/crankshaft/test/test_space_time_dynamics.py @@ -0,0 +1,324 @@ +import unittest +import numpy as np + +import unittest + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.space_time_dynamics as std +from crankshaft import random_seeds +import json + +class SpaceTimeTests(unittest.TestCase): + """Testing class for Markov Functions.""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read()) + self.markov_data = json.loads(open(fixture_file('markov.json')).read()) + + self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T + + self.transition_matrix = np.array([ + [[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ], + [ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ], + [ 0. , 0.14 , 0.74 , 0.12 , 0. ], + [ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857], + [ 0. , 0. , 0. , 0.16666667, 0.83333333]], + [[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ], + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ], + [ 0. , 0. , 0.06372549, 0.90196078, 0.03431373], + [ 0. , 0. , 0. , 0.19444444, 0.80555556]], + [[ 0.84693878, 0.15306122, 0. , 0. , 0. ], + [ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ], + [ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0. , 0. , 0. , 0.10204082, 0.89795918]], + [[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ], + [ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725], + [ 0. , 0. , 0. , 0.09661836, 0.90338164]], + [[ 0.33333333, 0.66666667, 0. , 0. , 0. ], + [ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ], + [ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ], + [ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808], + [ 0. , 0. , 0. , 0.02352941, 0.97647059]]] + ) + + def test_spatial_markov(self): + """Test Spatial Markov.""" + data = [ { 'id': d['id'], + 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'], + 'neighbors': d['neighbors'] } for d in self.neighbors_data] + print(str(data[0])) + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + + result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id') + + self.assertTrue(result != None) + result = [(row[0], row[1], row[2], row[3], row[4]) for row in result] + print result[0] + expected = self.markov_data + for ([res_trend, res_up, res_down, res_vol, res_id], + [exp_trend, exp_up, exp_down, exp_vol, exp_id] + ) in zip(result, expected): + self.assertAlmostEqual(res_trend, exp_trend) + + def test_get_time_data(self): + """Test get_time_data""" + data = [ { 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'] } for d in self.neighbors_data] + + result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009']) + + ## expected was prepared from PySAL example: + ### f = ps.open(ps.examples.get_path("usjoin.csv")) + ### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose() + ### rpci = pci / (pci.mean(axis = 0)) + + expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652 + , 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631 + , 0.86148267, 0.86252252, 0.86746356], + [ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978 + , 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054 + , 0.90139505, 0.87897455, 0.86216858], + [ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559 + , 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804 + , 0.83675961, 0.83574199, 0.84647177], + [ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948 + , 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797 + , 1.12958508, 1.11371818, 1.09936775], + [ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201 + , 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468 + , 1.10863242, 1.09642516, 1.07762337], + [ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737 + , 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766 + , 1.45241216, 1.43306098, 1.4174431 ], + [ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138 + , 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034 + , 1.08401709, 1.06959206, 1.07875225], + [ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316 + , 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511 + , 1.01749645, 0.9936394, 0.98279746], + [ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006 + , 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874 + , 0.91284091, 0.89689833, 0.88928858], + [ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708 + , 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141 + , 0.86633032, 0.84946077, 0.83287146], + [ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183 + , 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258 + , 1.11059842, 1.10509795, 1.10020097], + [ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051 + , 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201 + , 0.89280392, 0.89298243, 0.89165385], + [ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927 + , 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815 + , 0.94281559, 0.96520315, 0.96715441], + [ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073 + , 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665 + , 0.98423613, 0.99823861, 0.99545704], + [ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535 + , 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648 + , 0.8198736, 0.82265395, 0.8399404 ], + [ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597 + , 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108 + , 0.9422231, 0.93902708, 0.94479184], + [ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632 + , 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804 + , 0.90543476, 0.91737138, 0.94793657], + [ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869 + , 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384 + , 1.23418676, 1.23626739, 1.26754398], + [ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023 + , 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766 + , 1.32936244, 1.33001241, 1.33288462], + [ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016 + , 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549 + , 0.92754547, 0.92549227, 0.92138102], + [ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622 + , 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389 + , 1.10577769, 1.11166825, 1.09985155], + [ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073 + , 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416 + , 0.77487118, 0.77768683, 0.78801192], + [ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819 + , 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462 + , 0.93069645, 0.94020973, 0.94358232], + [ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489 + , 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334 + , 0.87863799, 0.87497981, 0.87888675], + [ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195 + , 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749 + , 0.99323123, 1.00204939, 0.99602148], + [ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397 + , 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079 + , 1.08603695, 1.05997314, 1.02160943], + [ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029 + , 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236 + , 1.12357937, 1.1159258, 1.12570828], + [ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239 + , 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675 + , 1.29734097, 1.29390205, 1.29345446], + [ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975 + , 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093 + , 0.84349247, 0.85637272, 0.86539395], + [ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767 + , 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297 + , 1.27962009, 1.27968392, 1.25907738], + [ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308 + , 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464 + , 0.91475304, 0.90249622, 0.9021363 ], + [ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573 + , 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488 + , 0.96635366, 1.02520312, 1.0394296 ], + [ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019 + , 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638 + , 0.93759089, 0.93353431, 0.94121705], + [ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854 + , 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097 + , 0.88283929, 0.90349491, 0.90600675], + [ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626 + , 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634 + , 0.95011401, 0.9434686, 0.94637765], + [ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023 + , 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275 + , 1.02347523, 1.02517684, 1.04359571], + [ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801 + , 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756 + , 1.08859544, 1.09177338, 1.1096083 ], + [ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327 + , 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343 + , 0.83374329, 0.82812044, 0.82878599], + [ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175 + , 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677 + , 0.96947771, 1.00362626, 0.98102351], + [ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109 + , 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556 + , 0.90430364, 0.89994694, 0.90073864], + [ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205 + , 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004 + , 0.9700702, 0.97158948, 0.95884908], + [ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516 + , 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919 + , 0.83511679, 0.82136617, 0.80921874], + [ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787 + , 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368 + , 0.97001634, 0.97082733, 0.98782366], + [ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188 + , 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975 + , 1.14474747, 1.14084037, 1.16142926], + [ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702 + , 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066 + , 1.09831768, 1.09298232, 1.09176125], + [ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194 + , 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807 + , 0.80172182, 0.82168155, 0.85587911], + [ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979 + , 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596 + , 0.96519561, 0.96173403, 0.95890284], + [ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031 + , 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974 + , 1.17025154, 1.18730553, 1.14242645]]) + + self.assertTrue(np.allclose(result, expected)) + self.assertTrue(type(result) == type(expected)) + self.assertTrue(result.shape == expected.shape) + + def test_rebin_data(self): + """Test rebin_data""" + ## sample in double the time (even case since 10 % 2 = 0): + ## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2 + ## = 0.5, 2.5, 4.5, 6.5, 8.5 + ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float) + for i in range(0, 10, 2)]).T + + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even)) + + ## sample in triple the time (uneven since 10 % 3 = 1): + ## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1 + ## = 1, 4, 7, 9 + ans_odd = np.array([i * np.ones(10, dtype=float) + for i in (1, 4, 7, 9)]).T + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd)) + + def test_get_prob_dist(self): + """Test get_prob_dist""" + lag_indices = np.array([1, 2, 3, 4]) + unit_indices = np.array([1, 3, 2, 4]) + answer = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices) + + self.assertTrue(np.array_equal(result, answer)) + + def test_get_prob_stats(self): + """Test get_prob_stats""" + + probs = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + unit_indices = np.array([1, 3, 2, 4]) + answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.]) + answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941]) + answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059]) + answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223]) + + result = std.get_prob_stats(probs, unit_indices) + result_up = result[0] + result_down = result[1] + result_trend = result[2] + result_volatility = result[3] + + self.assertTrue(np.allclose(result_up, answer_up)) + self.assertTrue(np.allclose(result_down, answer_down)) + self.assertTrue(np.allclose(result_trend, answer_trend)) + self.assertTrue(np.allclose(result_volatility, answer_volatility)) diff --git a/src/pg/.gitignore b/src/pg/.gitignore index b58a014..56825ae 100644 --- a/src/pg/.gitignore +++ b/src/pg/.gitignore @@ -4,3 +4,4 @@ results/ crankshaft--dev.sql crankshaft--dev--current.sql crankshaft--current--dev.sql +crankshaft--*--dev.sql diff --git a/src/pg/Makefile b/src/pg/Makefile index 8a745c4..6775aad 100644 --- a/src/pg/Makefile +++ b/src/pg/Makefile @@ -7,22 +7,19 @@ include ../../Makefile.global # requires sudo. In additionof the current development version # named 'dev', an alias 'current' is generating for ease of # update (upgrade to 'current', then to 'dev'). -# the python module is installed in a virtualenv in envs/dev/ # * test runs the tests for the currently generated Development # extension. -DATA = $(EXTENSION)--dev.sql \ - $(EXTENSION)--current--dev.sql \ - $(EXTENSION)--dev--current.sql +DATA = \ + $(EXTENSION)--dev.sql \ + $(EXTENSION)--current--dev.sql \ + $(EXTENSION)--dev--current.sql \ + $(EXTENSION)--$(RELEASE_VERSION)--dev.sql SOURCES_DATA_DIR = sql SOURCES_DATA = $(wildcard $(SOURCES_DATA_DIR)/*.sql) -VIRTUALENV_PATH = $(realpath ../../envs) -ESC_VIRVIRTUALENV_PATH = $(subst /,\/,$(VIRTUALENV_PATH)) - -REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g' \ - -e 's/@@VIRTUALENV_PATH@@/$(ESC_VIRVIRTUALENV_PATH)/g' +REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g' $(DATA): $(SOURCES_DATA) $(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > $@ @@ -54,7 +51,6 @@ release: ../../release/$(EXTENSION).control $(SOURCES_DATA) $(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > ../../release/$(EXTENSION)--$(EXTVERSION).sql # Install the current relese into the PostgreSQL extensions directory -# and the Python package in a virtual environment envs/X.Y.Z deploy: $(INSTALL_DATA) ../../release/$(EXTENSION).control '$(DESTDIR)$(datadir)/extension/' $(INSTALL_DATA) ../../release/*.sql '$(DESTDIR)$(datadir)/extension/' diff --git a/src/pg/crankshaft.control b/src/pg/crankshaft.control index 49c0d22..6f48fdd 100644 --- a/src/pg/crankshaft.control +++ b/src/pg/crankshaft.control @@ -1,5 +1,5 @@ comment = 'CartoDB Spatial Analysis extension' -default_version = '0.0.2' -requires = 'plpythonu, postgis, cartodb' +default_version = '0.2.0' +requires = 'plpythonu, postgis' superuser = true schema = cdb_crankshaft diff --git a/src/pg/sql/02_py.sql b/src/pg/sql/02_py.sql deleted file mode 100644 index 7da5f47..0000000 --- a/src/pg/sql/02_py.sql +++ /dev/null @@ -1,23 +0,0 @@ -CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path() -RETURNS text -AS $$ - BEGIN - -- RETURN '/opt/virtualenvs/crankshaft'; - RETURN '@@VIRTUALENV_PATH@@'; - END; -$$ language plpgsql IMMUTABLE STRICT; - --- Use the crankshaft python module -CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py() -RETURNS VOID -AS $$ - import os - # plpy.notice('%',str(os.environ)) - # activate virtualenv - crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version'] - base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path'] - default_venv_path = os.path.join(base_path, crankshaft_version) - venv_path = os.environ.get('CRANKSHAFT_VENV', default_venv_path) - activate_path = venv_path + '/bin/activate_this.py' - exec(open(activate_path).read(), dict(__file__=activate_path)) -$$ LANGUAGE plpythonu; diff --git a/src/pg/sql/03_random_seeds.sql b/src/pg/sql/03_random_seeds.sql index 9a0cca6..2b62be3 100644 --- a/src/pg/sql/03_random_seeds.sql +++ b/src/pg/sql/03_random_seeds.sql @@ -4,7 +4,6 @@ CREATE OR REPLACE FUNCTION _cdb_random_seeds (seed_value INTEGER) RETURNS VOID AS $$ - plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') from crankshaft import random_seeds random_seeds.set_random_seeds(seed_value) $$ LANGUAGE plpythonu; diff --git a/src/pg/sql/04_py_agg.sql b/src/pg/sql/04_py_agg.sql new file mode 100644 index 0000000..a3e881b --- /dev/null +++ b/src/pg/sql/04_py_agg.sql @@ -0,0 +1,31 @@ +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_pyagg' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) ( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + INITCOND = "{}" + ); + END IF; +END +$$ LANGUAGE plpgsql; diff --git a/src/pg/sql/05_segmentation.sql b/src/pg/sql/05_segmentation.sql new file mode 100644 index 0000000..7dac003 --- /dev/null +++ b/src/pg/sql/05_segmentation.sql @@ -0,0 +1,53 @@ + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import create_and_predict_segment_agg + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=float) + return a.reshape(len(a)/dimension, dimension) + + return create_and_predict_segment_agg(np.array(target, dtype=float), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ LANGUAGE plpythonu; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment ( + query TEXT, + variable_name TEXT, + target_table TEXT, + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import create_and_predict_segment + model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} + return create_and_predict_segment(query,variable_name,target_table, model_params) +$$ LANGUAGE plpythonu; diff --git a/src/pg/sql/07_gravity.sql b/src/pg/sql/07_gravity.sql new file mode 100644 index 0000000..47e5b8e --- /dev/null +++ b/src/pg/sql/07_gravity.sql @@ -0,0 +1,115 @@ +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN target_query text, + IN weight_column text, + IN source_query text, + IN pop_column text, + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_id bigint[]; + t_geom geometry[]; + t_weight numeric[]; + s_id bigint[]; + s_geom geometry[]; + s_pop numeric[]; +BEGIN + EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight; + EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop; + RETURN QUERY + SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g; +END; +$$ language plpgsql; + +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN t_id bigint[], + IN t_geom geometry[], + IN t_weight numeric[], + IN s_id bigint[], + IN s_geom geometry[], + IN s_pop numeric[], + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_type text; + s_type text; + t_center geometry[]; + s_center geometry[]; +BEGIN + t_type := GeometryType(t_geom[1]); + s_type := GeometryType(s_geom[1]); + IF t_type = 'POINT' THEN + t_center := t_geom; + ELSE + WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp; + END IF; + IF s_type = 'POINT' THEN + s_center := s_geom; + ELSE + WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp; + END IF; + RETURN QUERY + with target0 as( + SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td + ), + source0 as( + SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp + ), + prev0 as( + SELECT + source0.sg, + source0.sd as sourc_id, + coalesce(source0.sp,0) as sp, + target.td as targ_id, + coalesce(target.tw,0) as tw, + GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance + FROM source0 + CROSS JOIN LATERAL + ( + SELECT + * + FROM target0 + WHERE tw > minval + AND ST_DWithin(geography(source0.sc), geography(tc), radius) + ) AS target + ), + deno as( + SELECT + sourc_id, + sum(tw/distance) as h_deno + FROM + prev0 + GROUP BY sourc_id + ) + SELECT + p.sg as the_geom, + p.sourc_id as source_id, + p.targ_id as target_id, + case when p.distance > 1 then p.distance else 0.0 end as dist, + 100*(p.tw/p.distance)/d.h_deno as h, + p.sp*(p.tw/p.distance)/d.h_deno as hpop + FROM + prev0 p, + deno d + WHERE + p.targ_id = target AND + p.sourc_id = d.sourc_id; +END; +$$ language plpgsql; diff --git a/src/pg/sql/08_interpolation.sql b/src/pg/sql/08_interpolation.sql index 04f1584..0937f09 100644 --- a/src/pg/sql/08_interpolation.sql +++ b/src/pg/sql/08_interpolation.sql @@ -14,9 +14,12 @@ $$ DECLARE gs geometry[]; vs numeric[]; + output numeric; BEGIN EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs; - RETURN QUERY SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) FROM a; + SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a; + + RETURN output; END; $$ language plpgsql IMMUTABLE; @@ -71,11 +74,11 @@ BEGIN SELECT array_agg(v) INTO vertex FROM a; -- retrieve the value of each vertex - WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); - WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); - WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c) + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; diff --git a/src/pg/sql/10_moran.sql b/src/pg/sql/10_moran.sql index a336867..3be31a2 100644 --- a/src/pg/sql/10_moran.sql +++ b/src/pg/sql/10_moran.sql @@ -10,7 +10,6 @@ CREATE OR REPLACE FUNCTION id_col TEXT DEFAULT 'cartodb_id') RETURNS TABLE (moran NUMERIC, significance NUMERIC) AS $$ - plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') from crankshaft.clustering import moran_local # TODO: use named parameters or a dictionary return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) @@ -28,7 +27,6 @@ CREATE OR REPLACE FUNCTION id_col TEXT) RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) AS $$ - plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') from crankshaft.clustering import moran_local # TODO: use named parameters or a dictionary return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) @@ -122,7 +120,6 @@ CREATE OR REPLACE FUNCTION id_col TEXT DEFAULT 'cartodb_id') RETURNS TABLE (moran FLOAT, significance FLOAT) AS $$ - plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') from crankshaft.clustering import moran_local # TODO: use named parameters or a dictionary return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) @@ -143,7 +140,6 @@ CREATE OR REPLACE FUNCTION RETURNS TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) AS $$ - plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') from crankshaft.clustering import moran_local_rate # TODO: use named parameters or a dictionary return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) diff --git a/src/pg/sql/11_kmeans.sql b/src/pg/sql/11_kmeans.sql new file mode 100644 index 0000000..f20942f --- /dev/null +++ b/src/pg/sql/11_kmeans.sql @@ -0,0 +1,63 @@ +CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20) +RETURNS table (cartodb_id integer, cluster_no integer) as $$ + + from crankshaft.clustering import kmeans + return kmeans(query,no_clusters,no_init) + +$$ language plpythonu; + + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC) +RETURNS Numeric[] AS +$$ +DECLARE + newX NUMERIC; + newY NUMERIC; + newW NUMERIC; +BEGIN + IF weight IS NULL OR the_geom IS NULL THEN + newX = state[1]; + newY = state[2]; + newW = state[3]; + ELSE + newX = state[1] + ST_X(the_geom)*weight; + newY = state[2] + ST_Y(the_geom)*weight; + newW = state[3] + weight; + END IF; + RETURN Array[newX,newY,newW]; + +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[]) +RETURNS GEOMETRY AS +$$ +BEGIN + IF state[3] = 0 THEN + RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326); + ELSE + RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326); + END IF; +END +$$ LANGUAGE plpgsql; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_weightedmean' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) ( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + INITCOND = "{0.0,0.0,0.0}" + ); + END IF; +END +$$ LANGUAGE plpgsql; diff --git a/src/pg/sql/11_markov.sql b/src/pg/sql/11_markov.sql new file mode 100644 index 0000000..1124abd --- /dev/null +++ b/src/pg/sql/11_markov.sql @@ -0,0 +1,81 @@ +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import spatial_markov_trend + + ## TODO: use named parameters or a dictionary + return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; diff --git a/src/pg/test/expected/01_install_test.out b/src/pg/test/expected/01_install_test.out index e40d267..c8a763e 100644 --- a/src/pg/test/expected/01_install_test.out +++ b/src/pg/test/expected/01_install_test.out @@ -1,6 +1,18 @@ -- Install dependencies CREATE EXTENSION plpythonu; -CREATE EXTENSION postgis; -CREATE EXTENSION cartodb; +CREATE EXTENSION postgis VERSION '2.2.2'; +-- Create role publicuser if it does not exist +DO +$$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_user + WHERE usename = 'publicuser') THEN + + CREATE ROLE publicuser LOGIN; + END IF; +END +$$ LANGUAGE plpgsql; -- Install the extension CREATE EXTENSION crankshaft VERSION 'dev'; diff --git a/src/pg/test/expected/05_kmeans_test.out b/src/pg/test/expected/05_kmeans_test.out new file mode 100644 index 0000000..8c6ffa1 --- /dev/null +++ b/src/pg/test/expected/05_kmeans_test.out @@ -0,0 +1,10 @@ +\pset format unaligned +\set ECHO all +SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2); +clusters +2 +(1 row) +SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p; +clusters +52 +(1 row) diff --git a/src/pg/test/expected/05_markov_test.out b/src/pg/test/expected/05_markov_test.out new file mode 100644 index 0000000..2f8d510 --- /dev/null +++ b/src/pg/test/expected/05_markov_test.out @@ -0,0 +1,10 @@ +SET client_min_messages TO WARNING; +\set ECHO none +_cdb_random_seeds + +(1 row) +cartodb_id|trend_test|trend_up_test|trend_down_test|volatility_test +1|t|t|t|t +2|t|t|t|t +3|t|t|t|t +(3 rows) diff --git a/src/pg/test/expected/06_segmentation_test.out b/src/pg/test/expected/06_segmentation_test.out new file mode 100644 index 0000000..a4c17a9 --- /dev/null +++ b/src/pg/test/expected/06_segmentation_test.out @@ -0,0 +1,27 @@ +\pset format unaligned +\set ECHO none +_cdb_random_seeds + +(1 row) +within_tolerance +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +t +(20 rows) diff --git a/src/pg/test/expected/07_gravity_test.out b/src/pg/test/expected/07_gravity_test.out new file mode 100644 index 0000000..064f3c5 --- /dev/null +++ b/src/pg/test/expected/07_gravity_test.out @@ -0,0 +1,14 @@ +SET client_min_messages TO WARNING; +\set ECHO none + the_geom | h | hpop | dist +--------------------------------------------+-------------------------+--------------------------+---------------- + 01010000001361C3D32B650140DD24068195B34440 | 1.51078258369747945249 | 12.08626066957983561994 | 4964.714459152 + 01010000002497FF907EFB0040713D0AD7A3B04440 | 98.29730954183620807430 | 688.08116679285345652007 | 99.955141922 + 0101000000A167B3EA733501401D5A643BDFAF4440 | 63.70532894711274639196 | 382.23197368267647835174 | 2488.330566505 + 010100000062A1D634EF380140BE9F1A2FDDB44440 | 35.35415870080995954879 | 176.77079350404979774397 | 4359.370460594 + 010100000052B81E85EB510140355EBA490CB24440 | 33.12290506987740864904 | 132.49162027950963459615 | 3703.664449828 + 0101000000C286A757CA320140736891ED7CAF4440 | 65.45251754279248087849 | 196.35755262837744263547 | 2512.092358644 + 01010000007DD0B359F5390140C976BE9F1AAF4440 | 62.83927792471345639225 | 125.67855584942691278449 | 2926.25725244 + 0101000000D237691A140D01407E6FD39FFDB44440 | 53.54905726651871279586 | 53.54905726651871279586 | 3744.515577777 +(8 rows) + diff --git a/src/pg/test/expected/08_interpolation_test.out b/src/pg/test/expected/08_interpolation_test.out index c13a547..29a9fed 100644 --- a/src/pg/test/expected/08_interpolation_test.out +++ b/src/pg/test/expected/08_interpolation_test.out @@ -1,7 +1,12 @@ SET client_min_messages TO WARNING; \set ECHO none +<<<<<<< HEAD nn | nni | idw -----+--------------------------+----------------- 200 | 780.79470198683925288365 | 341.46260750526 +======= +cdb_spatialinterpolation +t +>>>>>>> ebbf68af5700f33737ac18516e2bdc22ea330b24 (1 row) diff --git a/src/pg/test/fixtures/markov_usjoin_example.sql b/src/pg/test/fixtures/markov_usjoin_example.sql new file mode 100644 index 0000000..350bb5b --- /dev/null +++ b/src/pg/test/fixtures/markov_usjoin_example.sql @@ -0,0 +1,180 @@ +-- +-- PostgreSQL database dump +-- + +SET statement_timeout = 0; +SET lock_timeout = 0; +SET client_encoding = 'UTF8'; +SET standard_conforming_strings = on; +SET check_function_bodies = false; +SET client_min_messages = warning; + +SET search_path = public, pg_catalog; + +SET default_tablespace = ''; + +SET default_with_oids = false; + +-- +-- Name: markov_usjoin_example; Type: TABLE; Schema: public; Owner: postgres; Tablespace: +-- + +CREATE TABLE markov_usjoin_example ( + cartodb_id integer NOT NULL, + the_geom geometry(Geometry,4326), + name text, + state_fips text, + y1929 numeric, + y1930 numeric, + y1931 numeric, + y1932 numeric, + y1933 numeric, + y1934 numeric, + y1935 numeric, + y1936 numeric, + y1937 numeric, + y1938 numeric, + y1939 numeric, + y1940 numeric, + y1941 numeric, + y1942 numeric, + y1943 numeric, + y1944 numeric, + y1945 numeric, + y1946 numeric, + y1947 numeric, + y1948 numeric, + y1949 numeric, + y1950 numeric, + y1951 numeric, + y1952 numeric, + y1953 numeric, + y1954 numeric, + y1955 numeric, + y1956 numeric, + y1957 numeric, + y1958 numeric, + y1959 numeric, + y1960 numeric, + y1961 numeric, + y1962 numeric, + y1963 numeric, + y1964 numeric, + y1965 numeric, + y1966 numeric, + y1967 numeric, + y1968 numeric, + y1969 numeric, + y1970 numeric, + y1971 numeric, + y1972 numeric, + y1973 numeric, + y1974 numeric, + y1975 numeric, + y1976 numeric, + y1977 numeric, + y1978 numeric, + y1979 numeric, + y1980 numeric, + y1981 numeric, + y1982 numeric, + y1983 numeric, + y1984 numeric, + y1985 numeric, + y1986 numeric, + y1987 numeric, + y1988 numeric, + y1989 numeric, + y1990 numeric, + y1991 numeric, + y1992 numeric, + y1993 numeric, + y1994 numeric, + y1995 numeric, + y1996 numeric, + y1997 numeric, + y1998 numeric, + y1999 numeric, + y2000 numeric, + y2001 numeric, + y2002 numeric, + y2003 numeric, + y2004 numeric, + y2005 numeric, + y2006 numeric, + y2007 numeric, + y2008 numeric, + y2009 numeric +); + + +ALTER TABLE public.markov_usjoin_example OWNER TO postgres; + +-- +-- Data for Name: markov_usjoin_example; Type: TABLE DATA; Schema: public; Owner: postgres +-- + +COPY markov_usjoin_example (cartodb_id, the_geom, name, state_fips, y1929, y1930, y1931, y1932, y1933, y1934, y1935, y1936, y1937, y1938, y1939, y1940, y1941, y1942, y1943, y1944, y1945, y1946, y1947, y1948, y1949, y1950, y1951, y1952, y1953, y1954, y1955, y1956, y1957, y1958, y1959, y1960, y1961, y1962, y1963, y1964, y1965, y1966, y1967, y1968, y1969, y1970, y1971, y1972, y1973, y1974, y1975, y1976, y1977, y1978, y1979, y1980, y1981, y1982, y1983, y1984, y1985, y1986, y1987, y1988, y1989, y1990, y1991, y1992, y1993, y1994, y1995, y1996, y1997, y1998, y1999, y2000, y2001, y2002, y2003, y2004, y2005, y2006, y2007, y2008, y2009) FROM stdin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tah 49 0.47829861111111111111 0.48115942028985507246 0.41884222474460839955 0.45118343195266272189 0.47603833865814696486 0.45588235294117647059 0.53878116343490304709 0.53341013824884792627 0.46786090621707060063 0.55849056603773584906 0.50945494994438264739 0.46738072054527750730 0.51030927835051546392 0.56482670089858793325 0.70684243565599497803 0.65603502188868042527 0.68187347931873479319 0.62265224815025611838 0.66666666666666666667 0.69218061674008810573 0.69916897506925207756 0.64963855421686746988 0.69832654907281772953 0.66583229036295369212 0.65549652635880670208 0.65134575569358178054 0.65927977839335180055 0.63950527464532557294 0.66263345195729537367 0.69132186012449652142 0.69562146892655367232 0.69548872180451127820 0.68737672583826429980 0.70037688442211055276 0.70184615384615384615 0.70155836518670979124 0.69606475020932179738 0.67243159525038719670 0.64862932061978545888 0.65271213144271888364 0.64018980812873942645 0.66620825147347740668 0.69018867924528301887 0.69843777426715815341 0.69315814773273513860 0.69616908850726552180 0.71142422986600359166 0.72783766645529486367 0.72635445362718089991 0.71993865030674846626 0.70968918056694922979 0.68044054988343114398 0.67003245582401730977 0.65805542508219821512 0.65402873599594911070 0.63896473265073947668 0.63134893140755742685 0.61123864657151412317 0.57720940854076273122 0.54800683134085891615 0.54180718688219560414 0.56089168162776780371 0.58299519785578676991 0.57111925964728479134 0.57533274778731166813 0.58666317306432595310 0.59029016809090055404 0.59616993307839388145 0.59433644229688729071 0.59526861048809142369 0.59256997455470737913 0.58826279527559055118 0.58892121384138697699 0.59517860117560267485 0.59417897070616538516 0.58141704992334866360 0.59344562119858789503 0.57853049889503842919 0.57498609308362692379 0.57315507629107981221 0.57090033373786407767 +37 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 Rhode Island 44 0.75868055555555555556 0.76135265700483091787 0.80703745743473325766 0.85059171597633136095 0.89297124600638977636 0.88235294117647058824 0.89335180055401662050 0.81912442396313364055 0.77028451001053740780 0.84528301886792452830 0.80088987764182424917 0.73028237585199610516 0.80240549828178694158 0.73748395378690629012 0.75392341494036409291 0.79674796747967479675 0.77737226277372262774 0.77916903813318155948 0.82429378531073446328 0.78909691629955947137 0.76343490304709141274 0.74843373493975903614 0.77657168701944821348 0.73633708802670004172 0.75807110747854515734 0.76480331262939958592 0.77483181638306292046 0.72680974899963623136 0.72028469750889679715 0.75686561699011351153 0.77083333333333333333 0.76349965823650034176 0.76561472715318869165 0.77167085427135678392 0.78523076923076923077 0.79123787121434872096 0.80100474462740720067 0.80356220960247805885 0.79523241954707985697 0.80913796984019806437 0.79740045388900350732 0.80825147347740667976 0.81037735849056603774 0.81183078813410566965 0.79666720076910751482 0.79333626889769558198 0.80729382511396601741 0.81306277742549143944 0.80394857667584940312 0.78660531697341513292 0.78342904019688269073 0.78318192780770158373 0.78002163721601153985 0.77870227470978997517 0.78732831191847585290 0.78026166097838452787 0.78265735756542130789 0.77787204311807565625 0.76049326330212377255 0.76106968800766443121 0.76198782804202038997 0.75531119090365050868 0.75803149313181699736 0.74234328618823118561 0.74782109316938044727 0.74551290449364601074 0.75268413309543932138 0.75056763862332695985 0.74814585908529048208 0.74794403503150699562 0.74750636132315521628 0.73043799212598425197 0.74216514108659145202 0.77042431165369696104 0.79461295344120005661 0.78468750694305583328 0.77540300284972991366 0.75508966812039191911 0.74950862228815130725 0.76184712441314553991 0.78282387742718446602 +14 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 Kansas 20 0.46180555555555555556 0.45120772946859903382 0.45516458569807037457 0.39349112426035502959 0.39936102236421725240 0.42205882352941176471 0.50138504155124653740 0.44585253456221198157 0.45100105374077976818 0.48176100628930817610 0.42491657397107897664 0.41382667964946445959 0.47422680412371134021 0.54621309370988446727 0.65599497802887633396 0.73108192620387742339 0.70863746958637469586 0.64655663062037564030 0.74011299435028248588 0.74449339207048458150 0.71966759002770083102 0.70506024096385542169 0.72455902306648575305 0.76220275344180225282 0.71066612178177360033 0.74161490683229813665 0.69370795409576573011 0.66242269916333212077 0.68007117437722419929 0.76821677041376785060 0.74364406779661016949 0.73991797676008202324 0.73208415516107823800 0.72393216080402010050 0.73353846153846153846 0.73978241693619523670 0.75048841752721183366 0.74703149199793495096 0.72276519666269368296 0.73553905019131217646 0.73199917474726635032 0.74970530451866404715 0.78207547169811320755 0.80972441635948744953 0.84505688190995032847 0.83913107294877440188 0.85453791960215499378 0.85237793278376664553 0.83872819100091827365 0.82638036809815950920 0.84222039923434509161 0.80697805289814293754 0.81125135232600072124 0.80446889887942025096 0.78315083233116020001 0.77372013651877133106 0.76373714224804135799 0.74378680507036630402 0.71171500342543959808 0.68025992418877827300 0.66259642594100089158 0.68005685218432076601 0.70103860328332650858 0.69687445433909551248 0.69285859063576785352 0.69933184855233853007 0.68516605628071493411 0.69075645315487571702 0.68420609057197437914 0.68586457332051692833 0.68254452926208651399 0.68444881889763779528 0.68542302325031339436 0.69172556578853430428 0.71015142223689796689 0.69277255659979115288 0.68833737399515120582 0.68319871707116734790 0.67765622102725755609 0.69657790492957746479 0.70229065533980582524 +1 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 Alabama 01 0.28038194444444444444 0.25797101449275362319 0.25425652667423382520 0.23964497041420118343 0.26517571884984025559 0.31029411764705882353 0.30055401662049861496 0.28917050691244239631 0.28134878819810326660 0.30691823899371069182 0.28031145717463848721 0.27361246348588120740 0.32216494845360824742 0.33247753530166880616 0.41305712492153170119 0.46153846153846153846 0.47688564476885644769 0.42914058053500284576 0.45480225988700564972 0.48513215859030837004 0.46149584487534626039 0.43807228915662650602 0.47263681592039800995 0.46141009595327492699 0.47445852063751532489 0.47163561076604554865 0.50375939849624060150 0.49327028010185522008 0.50569395017793594306 0.53753203954595386305 0.53884180790960451977 0.53246753246753246753 0.52169625246548323471 0.52355527638190954774 0.54092307692307692308 0.55571890620405763011 0.56656433156572704438 0.55988642230252968508 0.54684147794994040524 0.56628404231375196939 0.56694862801733030741 0.58526522593320235756 0.60849056603773584906 0.62208179743724767421 0.63451369972760775517 0.63863202700719213269 0.65824008841000138141 0.67507926442612555485 0.66769972451790633609 0.66462167689161554192 0.65618448637316561845 0.63445614599244312244 0.62834475297511720159 0.61631886197409917466 0.61921640610165200329 0.61433447098976109215 0.61733198315834354847 0.60894300828425990618 0.58972368120575473852 0.57658183029949598034 0.57754777687327983874 0.59216038300418910832 0.61556788147265755872 0.60981316570630347477 0.60776298898723059253 0.61771256386741779117 0.61611418912573950606 0.60734345124282982792 0.59357793010450612428 0.59070276620741215422 0.58491094147582697201 0.57753444881889763780 0.57870337519808888574 0.59877204255015349468 0.61476956460210387282 0.61465484680841609456 0.61879545744545106546 0.59910429663817886688 0.59313925458928240312 0.60187426643192488263 0.61199180825242718447 +29 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 New Mexico 35 0.35590277777777777778 0.32270531400966183575 0.32803632236095346198 0.30769230769230769231 0.33706070287539936102 0.36323529411764705882 0.40443213296398891967 0.39516129032258064516 0.38145416227608008430 0.42515723270440251572 0.39710789766407119021 0.36806231742940603700 0.40721649484536082474 0.40821566110397946085 0.48524795982423101067 0.55096935584740462789 0.57299270072992700730 0.52874217416050085373 0.57231638418079096045 0.61894273127753303965 0.63434903047091412742 0.58024096385542168675 0.60877431026684758028 0.59365874009178139341 0.59011033919084593380 0.60538302277432712215 0.61060546102097348635 0.59148781375045471080 0.61672597864768683274 0.66825338703771512267 0.66913841807909604520 0.64388243335611756664 0.64168310322156476003 0.63002512562814070352 0.63200000000000000000 0.62746251102616877389 0.62796539212950041864 0.61590087764584408880 0.59356376638855780691 0.60972316002700877785 0.60264080874767897669 0.62809430255402750491 0.64735849056603773585 0.66017202036159382131 0.66287453933664476847 0.67048290033758990166 0.69691946401436662522 0.70095117311350665821 0.69869146005509641873 0.70010224948875255624 0.69446723179290857716 0.67545622638475761717 0.67320591417237648756 0.66389317587063007448 0.65618077093486929553 0.63794084186575654152 0.63950327772744230667 0.61014073260804471504 0.57940168988353505367 0.55492148123463989670 0.54599372020002325852 0.55954518252543387193 0.58608494955887279902 0.57359874279727606076 0.58192014053104519965 0.58777675881042840299 0.59010235702882899803 0.58191921606118546845 0.56840656253511630520 0.56547046886681619139 0.55605597964376590331 0.54633366141732283465 0.57222261642896000378 0.58175674067728040742 0.59266946554082739752 0.59112621920060432358 0.59929394751392964995 0.58236363991942581112 0.58075282773966252550 0.59758289319248826291 0.61053170509708737864 +5 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 Colorado 08 0.55034722222222222222 0.55845410628019323671 0.53461975028376844495 0.52366863905325443787 0.56389776357827476038 0.54117647058823529412 0.61495844875346260388 0.62442396313364055300 0.56059009483667017914 0.63647798742138364780 0.57397107897664071190 0.53067185978578383642 0.55756013745704467354 0.57445442875481386393 0.65222849968612680477 0.66604127579737335835 0.72323600973236009732 0.68924302788844621514 0.76779661016949152542 0.80066079295154185022 0.79224376731301939058 0.73301204819277108434 0.81230212573496155586 0.78431372549019607843 0.74090723334695545566 0.73416149068322981366 0.73961218836565096953 0.71298654056020371044 0.74875444839857651246 0.78982057854265836690 0.79837570621468926554 0.79972658920027341080 0.79454306377383300460 0.77606783919597989950 0.78123076923076923077 0.77565421934725080859 0.78146804353893385431 0.76974703149199793495 0.74898688915375446961 0.76097231600270087779 0.76047039405818031772 0.79666011787819253438 0.83264150943396226415 0.84096893101632438125 0.85082518827111039897 0.86070747101130192279 0.87318690426854537920 0.87444514901712111604 0.86857208448117539027 0.87310838445807770961 0.87466958344727007565 0.86896052737358308546 0.87565813198701767039 0.86861705696839562504 0.85891512121020317742 0.83907849829351535836 0.82161701220487128924 0.78710450144725022457 0.74980589175610870062 0.71999833381930270338 0.71899833313951234640 0.73694643925792938360 0.76264750772437925772 0.74897852278679937140 0.76096209715559759476 0.76961876064456963186 0.77832034306820671738 0.78366993307839388145 0.78520058433531857512 0.79728719427533910072 0.80269720101781170483 0.81075295275590551181 0.80957449324723858180 0.80823873777397015778 0.80409453276097929148 0.78968650714301584128 0.79511717919271830207 0.77566346586353235680 0.76330428333024290747 0.76509316314553990610 0.76025864684466019417 +7 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 Delaware 10 0.89583333333333333333 0.82801932367149758454 0.87968217934165720772 0.87278106508875739645 0.90095846645367412141 0.94852941176470588235 0.97091412742382271468 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 0.82862644415917843389 0.91902071563088512241 0.94058786741713570982 0.92822384428223844282 0.89072282299373932840 0.94011299435028248588 0.92786343612334801762 1.00000000000000000000 1.00000000000000000000 0.97467209407507914971 0.93617021276595744681 0.95668165100122599101 0.95486542443064182195 0.99208547685001978631 1.00000000000000000000 0.94128113879003558719 0.97070670084218235079 0.96186440677966101695 0.95864661654135338346 0.92537804076265614727 0.92116834170854271357 0.93815384615384615385 0.94384004704498676860 0.96790399106893664527 0.93185338151781104801 0.90226460071513706794 0.91807337384650011254 0.91211058386630905715 0.90530451866404715128 0.92301886792452830189 0.93084079340003510620 0.94071462906585483096 0.93160135035960663438 0.92954828014919187733 0.93202282815472415980 0.90828741965105601469 0.88527607361963190184 0.87038556193601312551 0.86847817348661467964 0.85632888568337540570 0.85398912970542843723 0.85632002025444648395 0.84277588168373151308 0.85572669615733091723 0.83745882822636989720 0.81904544416533455127 0.80443204065480901404 0.81133465131604450130 0.80924596050269299820 0.83170159699214533001 0.80649554740701938188 0.80477670427673805824 0.80341936329097340495 0.79478511284314646133 0.79588910133843212237 0.76988987526688391954 0.78957065043255366870 0.78315521628498727735 0.76906988188976377953 0.77258213297381678848 0.79633992527545750934 0.80482569932543987924 0.79979559643626830190 0.79318191484836884862 0.76971818590733968279 0.74635638791025403301 0.74636883802816901408 0.76105506674757281553 +19 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 Massachusetts 25 0.78645833333333333333 0.80772946859903381643 0.86152099886492622020 0.90680473372781065089 0.89297124600638977636 0.89558823529411764706 0.89058171745152354571 0.82258064516129032258 0.77133825079030558483 0.84528301886792452830 0.80533926585094549499 0.75851996105160662123 0.77405498281786941581 0.68870346598202824134 0.79221594475831763967 0.81238273921200750469 0.81995133819951338200 0.79453614114968696642 0.81073446327683615819 0.83259911894273127753 0.81994459833795013850 0.79807228915662650602 0.82180009045680687472 0.79057154776804338757 0.79566816510012259910 0.79917184265010351967 0.81954887218045112782 0.79810840305565660240 0.81708185053380782918 0.84987184181618454778 0.85805084745762711864 0.85816814764183185236 0.85634451019066403682 0.85866834170854271357 0.86123076923076923077 0.86209938253454866216 0.86547585821936924365 0.85983479607640681466 0.85411203814064362336 0.87846049966239027684 0.86795956261605116567 0.88133595284872298625 0.89584905660377358491 0.89626119010005265929 0.88944079474443198205 0.88419198590929106121 0.88948749827324216052 0.88700063411540900444 0.87649219467401285583 0.86707566462167689162 0.86336705860906024975 0.85802717260229922019 0.85322755138838802741 0.85908877407233442931 0.87720741819102474840 0.88447098976109215017 0.89111549325800778127 0.89599760455135243038 0.89079698561315368806 0.88865747490315324697 0.87056634492382835213 0.86860412926391382406 0.88407847224807355843 0.86872708224201152436 0.86696844807783257888 0.87911044150399580768 0.87804801702820296115 0.88485898661567877629 0.88021125969210023598 0.89164797607604400299 0.90460559796437659033 0.93484251968503937008 0.92828591026277821141 0.93377120963327859880 0.94035095995094108213 0.92852540602990513009 0.92552422270426608821 0.91707899008468112569 0.91527906545521972928 0.92809198943661971831 0.94034435679611650485 +41 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 Texas 48 0.41579861111111111111 0.39806763285024154589 0.39500567536889897843 0.39349112426035502959 0.41054313099041533546 0.43235294117647058824 0.45152354570637119114 0.42857142857142857143 0.44046364594309799789 0.50817610062893081761 0.46384872080088987764 0.42648490749756572541 0.45360824742268041237 0.46148908857509627728 0.59259259259259259259 0.65353345841150719199 0.64355231143552311436 0.59590210586226522482 0.64802259887005649718 0.66850220264317180617 0.72022160664819944598 0.65686746987951807229 0.67299864314789687924 0.65248226950354609929 0.65304454434000817327 0.67494824016563146998 0.67154728927582113178 0.64896325936704256093 0.66049822064056939502 0.68509703405346027096 0.68785310734463276836 0.66814764183185235817 0.66436554898093359632 0.65295226130653266332 0.66307692307692307692 0.67156718612172890326 0.67903991068936645269 0.67888487351574599897 0.67699642431466030989 0.69885212694125590817 0.69589436765009284093 0.71630648330058939096 0.72849056603773584906 0.73582587326663156047 0.75036051914757250441 0.76236606487597240569 0.79265091863517060367 0.80684844641724793912 0.80107897153351698806 0.80899795501022494888 0.81387293774496399599 0.80046627542406945896 0.82156509195816804904 0.80259008253371804335 0.77872017216279511361 0.76200227531285551763 0.75659542717049512338 0.70690687693382573111 0.66161224023749714547 0.63831382513433581872 0.63274799395278520758 0.65297725912627169360 0.67565052302423407661 0.66862231534834992142 0.66971826227957570434 0.67437442683086597668 0.67380348702538579522 0.67390654875717017208 0.68103157658163838634 0.68896187119512976610 0.68340966921119592875 0.68580216535433070866 0.67454291728754227867 0.67335379929083077509 0.68231992075097881976 0.67524272923193139150 0.69005997192803368636 0.67449592239845109812 0.66790283701094010755 0.67798195422535211268 0.67646389563106796117 +48 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 Wyoming 56 0.58593750000000000000 0.56521739130434782609 0.54029511918274687855 0.55325443786982248521 0.59265175718849840256 0.60441176470588235294 0.68698060941828254848 0.63479262672811059908 0.63962065331928345627 0.70566037735849056604 0.65294771968854282536 0.58617332035053554041 0.66924398625429553265 0.60590500641848523748 0.72190834902699309479 0.76547842401500938086 0.76520681265206812652 0.77518497438816163916 0.85084745762711864407 0.88656387665198237885 0.91246537396121883657 0.82843373493975903614 0.88421528720036182723 0.79766374634960367126 0.78953821005312627707 0.76935817805383022774 0.75702413929560743965 0.73153874136049472535 0.75871886120996441281 0.79531307213474917613 0.80437853107344632768 0.79015721120984278879 0.78468113083497698882 0.78580402010050251256 0.78000000000000000000 0.76095266098206409879 0.76555958693831984371 0.75012906556530717605 0.74398092967818831943 0.74611748818365968940 0.73942644935011347225 0.76994106090373280943 0.80547169811320754717 0.82657539055643321046 0.86684826149655503926 0.90591516218993101424 0.92568034258875535295 0.91464806594800253646 0.93572084481175390266 0.95950920245398773006 0.96363139185124418923 0.94485087225661226787 0.92888568337540569780 0.88914983560356975106 0.80530413317298563200 0.76751990898748577929 0.75904706070457815914 0.69887214292843597165 0.64827586206896551724 0.62348481692839588453 0.63507384579602279335 0.67309994015559545183 0.70234151062800134013 0.68273092369477911647 0.68532531585703668671 0.68639460238438359754 0.67342786490124268319 0.66019359464627151052 0.66917631194516237780 0.66557193207305350849 0.67165394402035623410 0.67002952755905511811 0.68880531706047919771 0.70983555841127055520 0.74399735836596065852 0.74920571441267302095 0.78012419718429671218 0.81254766980228032777 0.80572964954570739848 0.82851012323943661972 0.80597694174757281553 +28 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 New Jersey 34 0.79687500000000000000 0.81835748792270531401 0.83541430192962542565 0.86834319526627218935 0.83546325878594249201 0.84264705882352941176 0.86565096952908587258 0.81682027649769585253 0.78714436248682824025 0.87672955974842767296 0.83314794215795328142 0.79844206426484907498 0.82216494845360824742 0.74903722721437740693 0.89704959196484620213 0.97185741088180112570 0.96228710462287104623 0.86852589641434262948 0.88757062146892655367 0.90748898678414096916 0.89972299168975069252 0.86843373493975903614 0.90456806874717322479 0.88193575302461410096 0.91213731099305271761 0.91884057971014492754 0.91017016224772457459 0.89050563841396871590 0.90782918149466192171 0.92456975466861955328 0.93926553672316384181 0.94463431305536568694 0.93030900723208415516 0.93907035175879396985 0.94276923076923076923 0.94795648338723904734 0.95283282165782863522 0.94269488900361383583 0.92777115613825983313 0.95363493135268962413 0.93356715494120074273 0.94990176817288801572 0.96735849056603773585 0.96910654730559943830 0.96827431501361961224 0.96521356230735358873 0.97209559331399364553 0.97691819911223842739 0.97130394857667584940 0.96196319018404907975 0.95424300428402151126 0.94686068011898062545 0.94172376487558600793 0.93934107226732872576 0.95170580416482055826 0.94135381114903299204 0.94078772051377711453 0.93377582593073160994 0.92395524092258506508 0.92231432498854500771 0.91464123735318060240 0.92631657690005984440 0.93634366973160108700 0.92882835690588440719 0.91551246537396121884 0.91330407441372985720 0.91642407737815757348 0.92002270554493307839 0.90942802562085627599 0.91610594894798675638 0.90460559796437659033 0.91001476377952755906 0.89782161356701908749 0.91002117988624735251 0.91438275390348601349 0.90210846719544979893 0.89621879120411722173 0.89312185868225998866 0.89323196736510291118 0.90289392605633802817 0.91252654733009708738 +6 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 Connecticut 09 0.88888888888888888889 0.88985507246376811594 0.90919409761634506243 0.91715976331360946746 0.93130990415335463259 0.96029411764705882353 0.97783933518005540166 0.92857142857142857143 0.90621707060063224447 0.96729559748427672956 0.92992213570634037820 0.89386562804284323272 0.98367697594501718213 0.91014120667522464698 1.00000000000000000000 1.00000000000000000000 0.95194647201946472019 0.89926010244735344337 0.96158192090395480226 0.94713656387665198238 0.92132963988919667590 0.91132530120481927711 0.97602894617819990954 0.95869837296620775970 0.97793216183081324070 0.97308488612836438923 0.98021369212504946577 0.97635503819570753001 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 0.99717336683417085427 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 0.99386503067484662577 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 +26 0106000020E61000000100000001030000000100000054000000000000A0B1C95DC0000000A0B83443400000006057D45DC0000000207B434340FFFFFF9FC7E45DC000000000FC59434000000060DAF85DC0000000600F764340000000A0A1FF5DC0000000E0417F4340FFFFFFDF9FFF5DC00000008023884340FFFFFFDF95FF5DC0000000C0988D434000000000A6FF5DC0000000C053944340FFFFFF3FA2FF5DC0000000C0E3A74340000000C0ADFF5DC0000000A0C7B84340FFFFFF3FB0FF5DC00000000040DC4340000000A0B2FF5DC0000000E0BD9644400000008083FF5DC0000000E0A1FE44400000002071D65DC00000004096FE4440FFFFFF7FD5D35DC0000000809FFE4440FFFFFFFFCB8B5DC00000004095FF4440FFFFFF5F25415DC00000000059FF4440FFFFFF5F723F5DC00000000059FF44400100006095FC5CC00000004052FF4440FFFFFFDF88C15CC0FFFFFF7F90FF4440FFFFFFFF30915CC0000000407DFF44400000004072825CC0000000C06BFF4440FFFFFF3F63825CC0FFFFFF9FB67F4440000000A062825CC0000000E0380E4440FFFFFF1F7F825CC0000000E054F443400000008083825CC000000080F5C44340000000E0C7825CC0000000E0EA56434000000060D5825CC0000000201B49434000000040F9825CC0FFFFFFDF9F11434000000000F9825CC0000000E09BCC4240000000C0C2825CC0000000608F7F424000000020BF825CC000000080C26B4240FFFFFF9F57825CC000000020A71B424000000000D6825CC0000000C0D4184240FFFFFFBFD8865CC0FFFFFF5F800F4240000000E034885CC0000000E057054240000000A02E8D5CC0000000E03502424000000020E48E5CC00000002059024240FFFFFF5FA2935CC0000000C0F70742400000004065935CC0000000E0260B4240000000C02D945CC000000020440E4240FFFFFFBFFA955CC00000008099114240000000E051985CC00000004054134240FFFFFF5F5C9C5CC0000000407F0F4240000000C0CF9D5CC000000020F70F4240000000A0E7A15CC0000000A0DA134240FFFFFF9F47A65CC0000000A0B5114240FFFFFF1FBBA75CC0000000A02C1242400000008090AD5CC000000040770D4240000000A08CAE5CC000000080010B4240FFFFFFFF99AE5CC0000000E08507424000000000E1AD5CC000000020B5044240000000C010AF5CC0000000E06BFE4140000000A0B3AC5CC000000040B0F44140000000604AAA5CC0000000E0B3F04140FFFFFF7F58AA5CC0000000207CEF41400000008019AC5CC00000008079EC4140FFFFFFBFA4AB5CC000000040E2E14140FFFFFF5F08AC5CC000000020C6DD4140000000A083AA5CC0000000E0B7D84140FFFFFF3FBBAA5CC00000004005D4414001000000CFA95CC0000000A0C3D2414000000060E6A85CC00000000041CE4140FFFFFFBFBFA95CC000000020DCCA41400000000089A95CC000000080F8C54140FFFFFF5FF8AA5CC00000008004C241400000000041A95CC0000000C0B2B94140000000A0AEA55CC000000080DFAD4140000000E092A55CC0000000C002A7414000000020C3A35CC0000000002F9C414000000000DBA35CC00000000051964140000000C092A45CC0000000C0ED914140FFFFFF7F3CA55CC0000000C0F7904140FFFFFF7F0AA85CC0000000E023914140000000A0A5A85CC000000020308F4140FFFFFFBF11A65CC000000040BC894140000000C082A85CC0000000C05B85414000000080B2A75CC0FFFFFF5FDC7F4140000000E005E85CC000000040DAE5414000000080A2F85CC00000004029004240000000A0354A5DC000000000D67A4240000000E09D755DC0000000008BBA4240FFFFFF5FA79A5DC0000000E080F14240000000A0B1C95DC0000000A0B8344340 Nevada 32 0.75347222222222222222 0.80483091787439613527 0.74006810442678774120 0.81360946745562130178 0.79073482428115015974 0.80294117647058823529 0.91135734072022160665 0.97119815668202764977 0.80295047418335089568 0.98113207547169811321 0.95773081201334816463 0.87147030185004868549 0.84364261168384879725 1.00000000000000000000 0.94852479598242310107 0.92745465916197623515 0.97992700729927007299 1.00000000000000000000 1.00000000000000000000 0.96806167400881057269 0.99445983379501385042 0.95951807228915662651 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 0.90505638413968715897 0.91174377224199288256 0.94983522519223727572 0.97069209039548022599 0.98769651401230348599 0.97205785667324128863 1.00000000000000000000 0.97661538461538461538 0.94354601587768303440 0.92073681272676528049 0.89597315436241610738 0.87246722288438617402 0.92595093405356740941 0.93253558902413864246 0.97170923379174852652 0.98622641509433962264 0.97542566262945409865 0.97965069700368530684 0.95259063554968442683 0.96822765575355712115 0.97894736842105263158 0.98140495867768595041 1.00000000000000000000 0.98122322486555464406 0.94702146474797009406 0.92174540209159754778 0.87136818090317385761 0.85226913095765554782 0.82110352673492605233 0.81714011618611096307 0.79983032238746381874 0.77122630737611326787 0.75727912692131461657 0.75853781447455130442 0.77326451226810293238 0.79227934333469828388 0.79252662825213899075 0.79268292682926829268 0.80685837809511332373 0.80783798165711960434 0.81088671128107074570 0.79225193842004719631 0.79584534871301933141 0.78936386768447837150 0.75120570866141732283 0.72655455427044159039 0.73413293353323338331 0.75904523798292372282 0.77222333311115554667 0.79866870826421674961 0.75591105548276064381 0.74774707954756165400 0.73965669014084507042 0.72074104975728155340 +2 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 Arizona 04 0.52083333333333333333 0.50241545893719806763 0.48694665153234960272 0.47485207100591715976 0.49201277955271565495 0.53235294117647058824 0.57617728531855955679 0.53225806451612903226 0.53108535300316122234 0.60125786163522012579 0.54505005561735261402 0.49172346640701071081 0.54810996563573883162 0.58857509627727856226 0.63025737602008788449 0.65666041275797373358 0.68369829683698296837 0.63574274331246442800 0.67005649717514124294 0.72907488986784140969 0.72299168975069252078 0.65879518072289156627 0.73405698778833107191 0.71589486858573216521 0.70126685737637923989 0.70227743271221532091 0.69331222793826671943 0.67297198981447799200 0.67366548042704626335 0.69022336140607835958 0.69703389830508474576 0.70369104579630895420 0.69132149901380670611 0.68059045226130653266 0.67815384615384615385 0.67921199647162599236 0.67317890036282444879 0.66778523489932885906 0.65649582836710369487 0.69592617600720234076 0.71982669692593356715 0.75500982318271119843 0.78207547169811320755 0.78760751272599613832 0.78977727928216631950 0.78218112432115074123 0.76364138693189667081 0.77032339885859226379 0.76239669421487603306 0.77566462167689161554 0.78424938474159146842 0.77096229600450196961 0.76869816083663901911 0.73441588941823793867 0.73764162288752452687 0.73293515358361774744 0.73591643127431647391 0.72177862062082044116 0.69102534825302580498 0.65793310284500354063 0.64224522231267201613 0.64373877917414721724 0.65379890555783047314 0.63317618299284092893 0.63360583744341598541 0.64764836892440717935 0.64588224246408113438 0.64564412045889101338 0.63998763906056860321 0.64437146213820356723 0.64094147582697201018 0.62937992125984251969 0.62044986872915631874 0.62989933604626258299 0.63932260955705457805 0.63882778999755604435 0.65226915061035260091 0.63661431951968396143 0.62061932134248099388 0.61335460680751173709 0.60825621966019417476 +27 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 New Hampshire 33 0.59548611111111111111 0.62512077294685990338 0.63337116912599318956 0.63165680473372781065 0.66453674121405750799 0.70000000000000000000 0.68975069252077562327 0.61866359447004608295 0.59536354056902002107 0.67044025157232704403 0.62402669632925472747 0.56377799415774099318 0.60824742268041237113 0.54621309370988446727 0.61268047708725674827 0.65916197623514696685 0.67579075425790754258 0.65452475811041548093 0.68757062146892655367 0.71090308370044052863 0.70581717451523545706 0.64963855421686746988 0.68204432383536861149 0.65790571547768043388 0.67552104617899468737 0.70517598343685300207 0.72378314206569054214 0.69116042197162604583 0.71423487544483985765 0.73379714390333211278 0.75000000000000000000 0.75085440874914559125 0.74983563445101906640 0.75125628140703517588 0.74676923076923076923 0.75036753895912966774 0.75579123639408317053 0.76484254001032524522 0.75375446960667461263 0.77447670492910195814 0.77243655869610068083 0.76542239685658153242 0.77396226415094339623 0.77637352992803229770 0.78192597340169844576 0.77484221341552913548 0.77248238707003729797 0.79365884590995561192 0.79109274563820018365 0.79611451942740286299 0.80038282745419742959 0.79708979821529061822 0.79906238730616660656 0.79889955042608870697 0.82543198936641559592 0.82673492605233219568 0.84309545381868571124 0.84709052799680606847 0.83905001141813199361 0.82305160994709876286 0.79989921308679303795 0.77472321962896469180 0.79388005807244164836 0.77366858739305046272 0.76079318964934801703 0.78016507271059871610 0.78279650671424546906 0.77802342256214149140 0.77556467018766153500 0.79245434155719320731 0.79170483460559796438 0.82017716535433070866 0.80276260081837318763 0.81709145427286356822 0.82296334732770413699 0.81668110822279988447 0.79826464208242950108 0.78221500792052099427 0.77359540144631930280 0.77870085093896713615 0.79418234223300970874 +32 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 North Dakota 38 0.33159722222222222222 0.30048309178743961353 0.21225879682179341657 0.26035502958579881657 0.23322683706070287540 0.26470588235294117647 0.37673130193905817175 0.26958525345622119816 0.34351949420442571128 0.35471698113207547170 0.35483870967741935484 0.34566699123661148978 0.45446735395189003436 0.42682926829268292683 0.60640301318267419962 0.64477798624140087555 0.63625304136253041363 0.61923733636881047240 0.84406779661016949153 0.81662995594713656388 0.67091412742382271468 0.65542168674698795181 0.65309814563545906829 0.54985398414685022945 0.54597466285247241520 0.56480331262939958592 0.58963197467352592006 0.56602400873044743543 0.56868327402135231317 0.67630904430611497620 0.59286723163841807910 0.62235133287764866712 0.54339250493096646943 0.72864321608040201005 0.65907692307692307692 0.62099382534548662158 0.68741278258442645828 0.64713474445018069179 0.61787842669845053635 0.61197389151474229124 0.62966783577470600371 0.63143418467583497053 0.69226415094339622642 0.76829910479199578726 0.98894407947444319821 0.89828269484808454425 0.87498273242160519409 0.78427393785668991756 0.73771808999081726354 0.83190184049079754601 0.76547260960714611248 0.65077578583487418603 0.74590695997115037865 0.73743541568811648661 0.72067852395721248180 0.70005688282138794084 0.68277993924212545968 0.65505539474997504741 0.61954784197305320850 0.53088682467613612696 0.55653758188936698066 0.59395571514063435069 0.60566578565312883892 0.61784529422035969967 0.60232416728599418958 0.62337875016376260972 0.59736438476226249726 0.63234942638623326960 0.58427913248679626924 0.60789810958026273630 0.59320610687022900763 0.61683070866141732283 0.61775349464273043355 0.63706242117036719735 0.68656540402849191000 0.65933479970672532160 0.67296159244608906469 0.64255959947587663544 0.66534396439829408492 0.71539392605633802817 0.73331310679611650485 +30 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 New York 36 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 1.00000000000000000000 0.93087557603686635945 0.88303477344573234984 0.99245283018867924528 0.91768631813125695217 0.84615384615384615385 0.85567010309278350515 0.75032092426187419769 0.86880100439422473321 0.96185115697310819262 1.00000000000000000000 0.96585088218554354013 0.97457627118644067797 0.97522026431718061674 0.95789473684210526316 0.89542168674698795181 0.90547263681592039801 0.85815602836879432624 0.87617490805067429506 0.90062111801242236025 0.90819153146022952117 0.87886504183339396144 0.89750889679715302491 0.93482240937385573050 0.95162429378531073446 0.95283663704716336295 0.94214332675871137410 0.93592964824120603015 0.94461538461538461538 0.95677741840635107321 0.95506558749651130338 0.94398554465668559628 0.93516090584028605483 0.96668917398154400180 0.94965958324736950691 0.96011787819253438114 0.97716981132075471698 0.97209057398630858346 0.95817977888158948886 0.95288419198590929106 0.96076806188700096698 0.94825618262523779328 0.93583562901744719927 0.91809815950920245399 0.90484003281378178835 0.89195272931907709623 0.89174179588892895781 0.89539018989465208347 0.89803152098234065447 0.89527872582480091013 0.89186164259446783563 0.88965964667132448348 0.86919387988125142727 0.85824967717748989878 0.85149435980928014885 0.87204518252543387193 0.89126307560585191527 0.88000698445957744020 0.86443483548408891291 0.86332372592689637102 0.86771840861426738035 0.87434273422562141491 0.85627598606585009552 0.86072839901740895012 0.86234096692111959288 0.85007381889763779528 0.83660919132429811490 0.84081768639489778920 0.85091277890466531440 0.85120753627052367304 0.86325549742673642125 0.85838890735923962998 0.88103096606712404969 0.89297241784037558685 0.88827366504854368932 +36 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 Pennsylvania 42 0.67013888888888888889 0.68792270531400966184 0.68104426787741203178 0.66420118343195266272 0.66613418530351437700 0.70882352941176470588 0.71606648199445983380 0.69239631336405529954 0.67017913593256059009 0.70817610062893081761 0.66852057842046718576 0.63291139240506329114 0.66666666666666666667 0.61039794608472400513 0.71939736346516007533 0.78173858661663539712 0.77858880778588807786 0.73363688104723961298 0.77062146892655367232 0.79074889867841409692 0.77839335180055401662 0.74795180722891566265 0.77476255088195386703 0.74634960367125573634 0.77400899060073559461 0.75652173913043478261 0.75781559161060546102 0.75045471080392870135 0.77366548042704626335 0.79238374221896741120 0.79096045197740112994 0.78639781271360218729 0.76725838264299802761 0.76601758793969849246 0.77261538461538461538 0.78271096736254042929 0.78984091543399385989 0.78471863706763035622 0.77377830750893921335 0.78933153274814314652 0.78708479471838250464 0.80098231827111984283 0.81018867924528301887 0.82201158504476040021 0.82807242429097900977 0.83722295611331278438 0.85232766956761983699 0.86239695624603677869 0.86007805325987144169 0.84918200408997955010 0.84085315832649712879 0.81606238443604791382 0.80663541291020555355 0.79762463933436220895 0.78834103424267358694 0.76860068259385665529 0.76986622608324894740 0.75786006587483780816 0.73724594656314226992 0.72158120548173449411 0.72585959607706322441 0.74143476959904248953 0.76331757435878345680 0.75257551946918107211 0.75032092426187419769 0.74885366173195336041 0.74304316524243277929 0.74205305927342256214 0.73300370828182941904 0.73048168322118978960 0.72786259541984732824 0.72684547244094488189 0.71158258236949785946 0.73391875490826015564 0.74788905137034765791 0.73471972272212224222 0.72585172897792522649 0.71138012633719906909 0.70467272390135360653 0.71537558685446009390 0.73625227548543689320 +43 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 Vermont 50 0.55034722222222222222 0.55652173913043478261 0.53802497162315550511 0.53994082840236686391 0.53993610223642172524 0.56323529411764705882 0.57340720221606648199 0.54262672811059907834 0.51106427818756585880 0.57484276729559748428 0.54616240266963292547 0.50243427458617332035 0.55240549828178694158 0.49743260590500641849 0.58505963590709353421 0.59599749843652282677 0.62956204379562043796 0.62037564029595902106 0.63672316384180790960 0.65638766519823788546 0.62326869806094182825 0.56337349397590361446 0.60334690185436454093 0.57571964956195244055 0.58520637515324887617 0.60289855072463768116 0.60308666402849228334 0.60203710440160058203 0.61209964412811387900 0.63419992676675210546 0.64689265536723163842 0.65721120984278879016 0.65548980933596318212 0.65075376884422110553 0.65476923076923076923 0.66627462511026168774 0.68545911247557912364 0.70443985544656685596 0.68772348033373063170 0.70402880936304298897 0.69898906540127914174 0.71394891944990176817 0.72754716981132075472 0.73301737756714060032 0.72872937029322224003 0.71466314398943196830 0.71722613620665837823 0.72961318960050729233 0.70925160697887970615 0.71942740286298568507 0.71579618995533679701 0.69957392073317790819 0.70082942661377569419 0.69026370529423605985 0.69422115323754668017 0.68532423208191126280 0.69253317699728188456 0.69078750374288851183 0.68472253939255537794 0.67467821885283458991 0.67903244563321316432 0.67530670257330939557 0.67818188586531660648 0.67375589313776846517 0.66836700222957908249 0.67316258351893095768 0.66857607913106081948 0.66607911089866156788 0.65630969771884481402 0.66226102744846737157 0.65875318066157760814 0.66193405511811023622 0.66557865607038955510 0.68182575378977178078 0.69835841313269493844 0.69408340554111399942 0.67883118540257751691 0.67263802241214088749 0.66786575190061190432 0.67745011737089201878 0.69690533980582524272 +25 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 Nebraska 31 0.51736111111111111111 0.50338164251207729469 0.46878547105561861521 0.45414201183431952663 0.43929712460063897764 0.38088235294117647059 0.56648199445983379501 0.45622119815668202765 0.43730242360379346681 0.50943396226415094340 0.44493882091212458287 0.43037974683544303797 0.47336769759450171821 0.52759948652118100128 0.63967357187696170747 0.68230143839899937461 0.72141119221411192214 0.67501422879908935686 0.71977401129943502825 0.85792951541850220264 0.74570637119113573407 0.75180722891566265060 0.74219810040705563094 0.73466833541927409262 0.68492031058438904781 0.72587991718426501035 0.65294815987336762960 0.60931247726445980356 0.68896797153024911032 0.74148663493225924570 0.71398305084745762712 0.72624743677375256323 0.70151216305062458909 0.71984924623115577889 0.72030769230769230769 0.70332255219053219641 0.74127825844264582752 0.74599896747547754259 0.71275327771156138260 0.71280666216520369120 0.73695069114916443161 0.74577603143418467583 0.77754716981132075472 0.79462875197472353870 0.84425572824867809646 0.80214296198444150888 0.85205138831330294240 0.81838934686112872543 0.80268595041322314050 0.83026584867075664622 0.80065627563576702215 0.74539753999517646113 0.77064551027767760548 0.75340535462658525129 0.73428698018861953288 0.73765642775881683732 0.73245216649789479294 0.70940213594171074958 0.68668645809545558347 0.66580580663973007873 0.65426212350273287592 0.67654099341711549970 0.69858169229051111194 0.68755020080321285141 0.68127153570704682116 0.69330538451460762479 0.69477572229004288353 0.71836161567877629063 0.69080795595010675357 0.69051052013243618498 0.68826972010178117048 0.68476870078740157480 0.68823765935807374820 0.70200613978724923253 0.73734610123119015048 0.71921171321291297296 0.71019948109395602059 0.67965892867619736765 0.68384943445206749490 0.69923708920187793427 0.70268886529126213592 +17 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 Maine 23 0.52170138888888888889 0.55652173913043478261 0.55732122587968217934 0.55769230769230769231 0.59265175718849840256 0.61176470588235294118 0.59556786703601108033 0.58294930875576036866 0.53740779768177028451 0.59245283018867924528 0.55061179087875417130 0.51217137293086660175 0.54209621993127147766 0.55006418485237483954 0.69177652228499686127 0.68918073796122576610 0.65632603406326034063 0.64541832669322709163 0.66045197740112994350 0.68281938325991189427 0.65540166204986149584 0.57590361445783132530 0.59656264133876074175 0.59991656236962870254 0.59215365753984470781 0.59958592132505175983 0.62564305500593589236 0.60712986540560203710 0.61209964412811387900 0.65800073233247894544 0.65324858757062146893 0.65584415584415584416 0.62557527942143326759 0.62280150753768844221 0.63046153846153846154 0.65039694207586004116 0.66508512419759977672 0.65539494062983995870 0.63361144219308700834 0.64641008327706504614 0.64782339591499896843 0.67249508840864440079 0.67811320754716981132 0.67825171142706687730 0.69203653260695401378 0.69925143108762659621 0.69332780770824699544 0.72390615091946734306 0.70500459136822773186 0.69028629856850715746 0.68334700574241181296 0.67593858027172602299 0.66577713667508113956 0.66248406361135341877 0.66782707766314323691 0.66353811149032992036 0.66796354527527580877 0.67187344046312007186 0.66659054578670929436 0.65868288415878702045 0.65457223708183122068 0.65376271693596648713 0.65748427204705356810 0.64082416623013794308 0.63543003851091142490 0.63968950609196908162 0.63354931605471562275 0.63614364244741873805 0.62661535003933026183 0.62824415251521948094 0.62603053435114503817 0.63048720472440944882 0.64022327869627947681 0.65993193879250850765 0.67755554507288079626 0.67099913350663200693 0.65333248266768746544 0.63246826902391801772 0.62340070461709623586 0.64014818075117370892 0.66876516990291262136 +13 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 Iowa 19 0.50434027777777777778 0.49275362318840579710 0.45402951191827468785 0.43934911242603550296 0.40415335463258785942 0.39558823529411764706 0.58864265927977839335 0.45276497695852534562 0.55110642781875658588 0.57610062893081761006 0.52836484983314794216 0.48782862706913339825 0.52319587628865979381 0.53594351732991014121 0.64281230382925298180 0.62726704190118824265 0.66423357664233576642 0.70859419464997154240 0.68757062146892655367 0.90418502202643171806 0.75124653739612188366 0.73831325301204819277 0.74084124830393487110 0.71964956195244055069 0.67715570085819370658 0.74037267080745341615 0.66086268302334784329 0.63950527464532557294 0.69003558718861209964 0.72867081655071402417 0.71539548022598870056 0.70437457279562542720 0.71663379355687047995 0.71733668341708542714 0.74830769230769230769 0.74948544545721846516 0.79067820262349986045 0.79168817759421786267 0.73730631704410011919 0.74544226873733963538 0.75345574582215803590 0.75874263261296660118 0.75566037735849056604 0.78515007898894154818 0.86492549270950168242 0.82137090855717011595 0.85536676336510567758 0.83449587824984147115 0.83597337006427915519 0.86278118609406952965 0.83073557560842220399 0.77747407347857544819 0.79105661738189686260 0.75333825404281017245 0.72694474333818596114 0.72798634812286689420 0.71390502584874487022 0.69967062581095917756 0.68047499429093400320 0.63793893447744407881 0.64201263712834825755 0.65005984440454817475 0.66481777910136619142 0.66139339968569931902 0.63945003715965137491 0.67136119481200052404 0.66300435095627132438 0.67856716061185468451 0.66855826497359253849 0.66335576204208052974 0.65178117048346055980 0.65755413385826771654 0.64606542255020222806 0.67185454891601818139 0.68010283503938865041 0.68935101868515185852 0.67321679213984943218 0.64883734574540903135 0.64913777118486927499 0.67352552816901408451 0.68232327063106796117 +39 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 South Dakota 46 0.36979166666666666667 0.35362318840579710145 0.27355278093076049943 0.27958579881656804734 0.20607028753993610224 0.27058823529411764706 0.42797783933518005540 0.28110599078341013825 0.34035827186512118019 0.40251572327044025157 0.38375973303670745273 0.35150925024342745862 0.40807560137457044674 0.48587933247753530167 0.53107344632768361582 0.61225766103814884303 0.66058394160583941606 0.64428002276607854297 0.71751412429378531073 0.84030837004405286344 0.61828254847645429363 0.61831325301204819277 0.67706919945725915875 0.55360867751355861494 0.58684102983244789538 0.60331262939958592133 0.53106450336367233874 0.51618770461986176792 0.59395017793594306050 0.63712925668253387038 0.55225988700564971751 0.63909774436090225564 0.61242603550295857988 0.65703517587939698492 0.62153846153846153846 0.58806233460746839165 0.63578007256488975719 0.64532782653588022716 0.61430274135876042908 0.62412784154850326356 0.61790798432019806066 0.63968565815324165029 0.66754716981132075472 0.71353343865192206424 0.82727127062970677776 0.76001761338617349185 0.78284293410692084542 0.70906785034876347495 0.72899449035812672176 0.75122699386503067485 0.74359675508157870750 0.65455422461612669829 0.68164442841687702849 0.66530228812990673019 0.64529400594974365466 0.66092150170648464164 0.63646538400042637105 0.62311607944904681106 0.60365380223795387075 0.57512392218936143625 0.57243090281815715006 0.60734590065828845003 0.63138889922942337044 0.62741400384145276759 0.62715357070468211607 0.64217869775972749902 0.62127899333270729646 0.64937858508604206501 0.62577255871446229913 0.63539997863932500267 0.63727735368956743003 0.64259350393700787402 0.65117434187185127368 0.65983674829252040646 0.70927873956318694278 0.70574773934102068475 0.69597209816681553315 0.65163397414585492735 0.66749490079732987206 0.70033744131455399061 0.69210785800970873786 +35 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 Oregon 41 0.57986111111111111111 0.58647342995169082126 0.57321225879682179342 0.56065088757396449704 0.57188498402555910543 0.64558823529411764706 0.63434903047091412742 0.63133640552995391705 0.58587987355110642782 0.66792452830188679245 0.63515016685205784205 0.59298928919182083739 0.70274914089347079038 0.71758664955070603338 0.86691776522284996861 0.86866791744840525328 0.82542579075425790754 0.78486055776892430279 0.84971751412429378531 0.90638766519823788546 0.88864265927977839335 0.79855421686746987952 0.82767978290366350068 0.79766374634960367126 0.78299959133633020025 0.77308488612836438923 0.78274633953304313415 0.75300109130592942888 0.73131672597864768683 0.77114610032954961553 0.79237288135593220339 0.78024606971975393028 0.77218934911242603550 0.77167085427135678392 0.78184615384615384615 0.78947368421052631579 0.79598102149037119732 0.78291171915332989158 0.76305125148986889154 0.77605221697051541751 0.75861357540746853724 0.77406679764243614931 0.79471698113207547170 0.81183078813410566965 0.82278481012658227848 0.84045207691178629091 0.85384721646636275729 0.87672796448953709575 0.86730945821854912764 0.86666666666666666667 0.85817154315923799107 0.81968003858831095747 0.78341146772448611612 0.74669529624907736697 0.74890815874422431799 0.73185437997724687144 0.72200607578745403187 0.70675716139335263000 0.68102306462662708381 0.66905485899945849127 0.66759700740396170097 0.68271244763614602035 0.70007072925585377657 0.68301030207787672429 0.68927775150327680562 0.70159177256648761955 0.70955019250633862334 0.70653083173996175908 0.69797168221148443645 0.69310050197586243725 0.68760814249363867684 0.69758858267716535433 0.68275030156815440290 0.69934080578758239928 0.71163734138402754847 0.69357239663178475416 0.68282931393815660755 0.66907867717520974713 0.65416280363434081216 0.65835900821596244131 0.66766535194174757282 +4 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 California 06 0.86024305555555555556 0.85700483091787439614 0.85017026106696935301 0.85798816568047337278 0.87220447284345047923 0.88676470588235294118 0.91412742382271468144 0.88824884792626728111 0.83772391991570073762 0.96981132075471698113 0.86874304783092324805 0.82181110029211295034 0.87027491408934707904 0.82541720154043645700 0.97237915881983678594 0.98999374609130706692 0.96289537712895377129 0.95105293113261240751 0.95649717514124293785 0.97081497797356828194 0.96620498614958448753 0.90457831325301204819 0.94075079149706015378 0.92073425114726741761 0.91908459337964854924 0.92215320910973084886 0.94143252869014641868 0.90760276464168788650 0.91779359430604982206 0.95056755767118271695 0.96751412429378531073 0.96479835953520164046 0.94674556213017751479 0.94346733668341708543 0.95446153846153846154 0.96265804175242575713 0.95367010884733463578 0.94553433144037170883 0.92443384982121573302 0.94688273688948908395 0.93666185269238704353 0.94597249508840864440 0.94981132075471698113 0.95681937862032648763 0.95289216471719275757 0.96183766329076765008 0.97955518718054979970 0.99112238427393785669 0.98370064279155188246 0.98343558282208588957 0.98860632576793364324 0.96703915105715893561 0.95239812477461233321 0.92424344091793598604 0.91720994999683524274 0.90597269624573378840 0.90118850930021851516 0.87972851581994210999 0.85065083352363553323 0.82113550214520764777 0.80493855874714114044 0.81870885697187312986 0.81986375311767114619 0.79350445259298061812 0.77450847915681372880 0.76879994759596488930 0.76676996275080602247 0.76371295411089866157 0.75174176873806045623 0.75509986115561251736 0.76106870229007633588 0.79416830708661417323 0.77461623974076964923 0.78294186240213226720 0.79723100146233312892 0.79235264058299451221 0.79671217727872059887 0.78557878473784053351 0.77773039124791396254 0.77716035798122065728 0.77559921116504854369 +12 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 Indiana 18 0.52690972222222222222 0.49661835748792270531 0.49716231555051078320 0.45857988165680473373 0.46964856230031948882 0.52794117647058823529 0.58310249307479224377 0.55414746543778801843 0.57639620653319283456 0.59371069182389937107 0.57619577308120133482 0.53651411879259980526 0.62199312714776632302 0.58600770218228498074 0.71437539234149403641 0.74921826141338336460 0.76094890510948905109 0.68525896414342629482 0.74180790960451977401 0.80176211453744493392 0.75401662049861495845 0.73445783132530120482 0.77385798281320669380 0.74176053400083437630 0.79403351042092357989 0.74741200828157349896 0.75464978235061337554 0.73044743543106584212 0.72669039145907473310 0.73782497253753203955 0.75247175141242937853 0.75495557074504442925 0.73833004602235371466 0.75345477386934673367 0.76492307692307692308 0.76947956483387239047 0.79709740440971253140 0.78497676819824470831 0.75160905840286054827 0.76547377897816790457 0.76624716319372807922 0.74852652259332023576 0.77452830188679245283 0.78199052132701421801 0.81717673449767665438 0.79847350653163070600 0.80535985633374775521 0.82637920101458465441 0.82633149678604224059 0.81860940695296523517 0.80339075745146294777 0.75962697966074443283 0.74684457266498377209 0.71784204522579346440 0.70909551237420089879 0.70790671217292377702 0.70047433779246389170 0.68973949495957680407 0.66974195021694450788 0.65047694422460115800 0.65007559018490522154 0.65922351885098743268 0.67211406023154524811 0.67291775798847564170 0.67941355313830146612 0.69281409668544477925 0.68378877515885685667 0.68041945506692160612 0.66715361276547926733 0.67238064722845241910 0.66521628498727735369 0.66464074803149606299 0.65256983372359800374 0.66773755979153280503 0.68609368366432378886 0.66933280010664533760 0.65433201480158223810 0.63178377955527741380 0.61470424624513257927 0.62312940140845070423 0.62905794902912621359 +18 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 Maryland 24 0.66666666666666666667 0.68792270531400966184 0.72417707150964812713 0.75739644970414201183 0.74440894568690095847 0.76911764705882352941 0.75900277008310249307 0.71198156682027649770 0.70073761854583772392 0.79622641509433962264 0.73748609566184649611 0.69133398247322297955 0.74742268041237113402 0.71694480102695763800 0.80790960451977401130 0.82801751094434021263 0.79805352798053527981 0.74843483210017074559 0.76553672316384180791 0.82599118942731277533 0.82880886426592797784 0.79132530120481927711 0.82089552238805970149 0.81101376720901126408 0.82427462198610543523 0.80248447204968944099 0.81005144440047487139 0.79447071662422699163 0.80676156583629893238 0.82680336872940314903 0.82062146892655367232 0.82262474367737525632 0.82412886259040105194 0.82851758793969849246 0.83753846153846153846 0.84710379300205821817 0.85263745464694390176 0.84770263293753226639 0.83814064362336114422 0.86225523295070898042 0.86837218898287600578 0.89842829076620825147 0.92339622641509433962 0.92873442162541688608 0.93286332318538695722 0.93673858799354175840 0.95013123359580052493 0.95573874445149017121 0.94019742883379247016 0.92658486707566462168 0.91468416735028712059 0.90280569177586622719 0.89455463397042913812 0.88881433268469435684 0.90056332679283498956 0.89266211604095563140 0.90395992112135585994 0.89659646671324483481 0.87764329755651975337 0.85916607656100304078 0.85285110671783540722 0.86112357869539198085 0.87745225775229870082 0.85063733193644141785 0.84805080737787987298 0.85307218655836499410 0.84189438757942842833 0.83185946462715105163 0.82093493650972019328 0.82372102958453487130 0.82608142493638676845 0.83346456692913385827 0.83800468317604484496 0.86368720401703909950 0.87997075333742157649 0.88095714190495234286 0.88373527285100591213 0.86030547787143332095 0.84974967550528462822 0.86267605633802816901 0.89424681432038834951 +33 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 Ohio 39 0.66927083333333333333 0.63864734299516908213 0.63904653802497162316 0.59171597633136094675 0.61501597444089456869 0.66911764705882352941 0.71468144044321329640 0.68317972350230414747 0.68282402528977871444 0.70566037735849056604 0.68409343715239154616 0.64070107108081791626 0.70532646048109965636 0.65532734274711168164 0.78656622724419334589 0.82051282051282051282 0.81508515815085158151 0.74558907228229937393 0.79152542372881355932 0.84746696035242290749 0.80664819944598337950 0.77493975903614457831 0.82994120307553143374 0.79933249895702962036 0.82713526767470371884 0.81366459627329192547 0.82588049070043529877 0.79374317933794106948 0.79822064056939501779 0.79714390333211277920 0.81709039548022598870 0.81715652768284347232 0.79059829059829059829 0.78988693467336683417 0.80123076923076923077 0.80976183475448397530 0.82277421155456321518 0.82111512648425400103 0.78879618593563766389 0.81386450596443844249 0.81163606354446049103 0.80569744597249508841 0.81660377358490566038 0.82341583289450588029 0.83608396090370132992 0.84147952443857331572 0.84086199751346871115 0.85643627140139505390 0.86214416896235078053 0.85132924335378323108 0.84322304256676693100 0.81220355334030066726 0.79206635412910205554 0.77065020465678051399 0.77011203240711437433 0.76501706484641638225 0.76187176890689122209 0.74523405529493961473 0.71591687599908654944 0.69725496730120381555 0.69097181842849943792 0.70287253141831238779 0.71537058407474965566 0.70689715383272219312 0.70937774474697655564 0.72261889165465740862 0.71640529627195041788 0.70545530592734225621 0.69988200921451848522 0.69860087578767489053 0.69089058524173027990 0.69881889763779527559 0.68511554199484377587 0.70255348516218081435 0.71571772253408179631 0.69408340554111399942 0.68259538088554293735 0.65795083409930964348 0.64554051548303356202 0.65142678990610328638 0.66402457524271844660 +3 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 Arkansas 05 0.26909722222222222222 0.22028985507246376812 0.24404086265607264472 0.23224852071005917160 0.25079872204472843450 0.27500000000000000000 0.28670360110803324100 0.28456221198156682028 0.26975763962065331928 0.29056603773584905660 0.27697441601779755284 0.25316455696202531646 0.29381443298969072165 0.30872913992297817715 0.35091023226616446955 0.42964352720450281426 0.45012165450121654501 0.43027888446215139442 0.41864406779661016949 0.48788546255506607930 0.45041551246537396122 0.40819277108433734940 0.43283582089552238806 0.42845223195661243221 0.43563547200653861872 0.44472049689440993789 0.46537396121883656510 0.44743543106584212441 0.44270462633451957295 0.48114243866715488832 0.49858757062146892655 0.48017771701982228298 0.49671268902038132807 0.49434673366834170854 0.50953846153846153846 0.52249338429873566598 0.52637454646943901758 0.54052658750645327827 0.52896305125148986889 0.54220121539500337610 0.54033422735712812049 0.55972495088408644401 0.58415094339622641509 0.59943830086010180797 0.63851946803396891524 0.64230148246000293556 0.64304461942257217848 0.65377298668357641091 0.65082644628099173554 0.66554192229038854806 0.64606690365509069365 0.60985609775705442560 0.61767039307609087631 0.60068442595450580420 0.59978479650610798152 0.60068259385665529010 0.60033043756328945265 0.58558738397045613335 0.55647408084037451473 0.54217519890032073978 0.53544985851067953638 0.54267654099341711550 0.56788147265755872390 0.57359874279727606076 0.57411661374231470847 0.58135726450936722128 0.58052399286317964128 0.58084369024856596558 0.56829419035846724351 0.56765993805404250774 0.56600508905852417303 0.54766240157480314961 0.55658837720854324842 0.56945336855381832893 0.59139582055757347045 0.58799351240862938523 0.58508783122793585981 0.56795024739405080868 0.57611718894863712220 0.58318661971830985915 0.59718219053398058252 +31 0106000020E610000006000000010300000001000000020100000000006044FF54C0000000C0997E414000000040910755C0000000207E7E414000000000BA1455C0000000E0977E414000000060A01255C000000020F39A4140000000E0750E55C0000000407CA14140FFFFFF9F800B55C0FFFFFFBFD89E414000000060810655C0000000406E9F4140000000E0BC0255C0000000C0E3A24140000000A0F90155C0FFFFFF5F71A5414000000080DE0155C000000020A3A9414000000060670055C0000000E0B9AF414000000040CF0055C0000000002DB44140000000E019FD54C0000000804CBA4140FFFFFF9F3DFA54C000000040FDBC41400000000066F854C0FFFFFFDF5AC141400000000022F554C0000000A070C2414000000000A8F154C0000000C0BBC641400000006010EB54C00000000051C841400000008049E754C0000000C02EC9414000000000E8E354C00000000010C74140000000005FE054C0000000609FC74140000000A054DD54C0000000A073CC414000000020C6D854C00000002007D0414000000020F4D554C0000000009ED341400000004019D354C00000008002D44140FFFFFF7F95D054C00000000073D841400000002036D054C0000000C0B0D94140000000409BCF54C000000080EEDB4140000000C0DCCB54C0000000204CDD4140000000C031C954C0000000A09FE14140FFFFFF5F90C754C000000080C4E1414000000060D6C354C0000000802BE44140000000202BBF54C00000002012E34140000000C09DBD54C0000000605BE541400000006009BA54C0000000C0A4EF41400000002080BA54C000000060C4F64140000000A057B954C0000000E063F9414000000020CDB654C00000004045F9414000000040C1B354C000000020DFF5414000000040B1B154C00000000071FA4140000000A082B154C00000008066FE4140000000C0D8B054C0FFFFFF1FF1FF4140FFFFFF7F35A954C0000000E09E064240000000E031A854C0000000A0F406424000000080AEA654C0000000A08005424000000020E7A554C0000000A0DF024240FFFFFF7FC5A654C0FFFFFF3F74004240000000005BA654C0000000604DFB41400000002077A354C0000000C061FA4140FFFFFF9F71A054C0FFFFFF3F7CFC4140FFFFFF9F699E54C00000006020FF4140000000A0239A54C0FFFFFFFFA309424000000040ED9754C000000020A20C4240FFFFFF9FF69354C0000000A0A20F4240000000A0C99054C000000080680F4240000000804B8D54C000000060D112424000000000DC8954C0FFFFFF1FDF114240000000A08E8754C000000020520C4240FFFFFFFFF98454C000000060D10C4240000000204F8154C0000000609A10424000000020B87B54C000000080B4214240FFFFFF5F507A54C00000006037254240FFFFFF1F287554C000000000D62A424000000060C06E54C0000000E0272A4240000000C0676D54C0FFFFFF7FB72A424000000060626F54C0FFFFFF5F512E4240000000C06D6F54C00000000026324240000000A0B06C54C000000040633C4240000000A0FA6C54C0FFFFFFBF7942424000000040E16A54C0000000A0794B424000000060195654C0FFFFFF9F5349424000000000D23954C0000000E05848424000000060A43554C0000000C01E484240000000801B2754C0FFFFFF7F55474240FFFFFF1FDC1B54C0000000A08846424000000000140354C00000008008464240000000208A0154C000000060C3454240000000A0EAED53C00000004021464240FFFFFFBFA8E053C0FFFFFF9F1946424000000060E4CD53C0000000405F464240000000A03CC953C000000040E545424000000020FDB253C00000008092454240FFFFFF5F31AF53C0000000C0E5454240000000205D9D53C0FFFFFF3F4F454240000000608F9453C000000000D4454240FFFFFF7F4E8353C000000080B7464240000000E0867953C0000000E0C646424000000040E47053C000000020D7464240000000C07B5453C0000000C0E6464240000000C0594B53C0000000603447424000000000253B53C000000040EE46424000000000FC3A53C0000000A0EE464240000000C0112453C0FFFFFF7F1247424000000000D81F53C0000000E024474240000000E0221553C0000000E02C47424000000040270853C0000000205147424000000000F10253C0000000404A474240FFFFFF1F200253C000000000D741424000000000D40553C0000000E0744042400000000078FE52C0000000E0D5374240000000A010FE52C0000000A022354240000000601A0053C0000000009F35424000000060E1FC52C0000000C0C72E42400000006066FB52C0000000E02C364240000000802CFB52C0000000E0EB2C42400000002034F352C0000000C0700E42400000006020F352C0000000205209424000000000BBF652C0FFFFFF7F860D4240000000207BFA52C0000000A0551F4240000000E047FD52C0000000C035214240000000A048FC52C0FFFFFFDFAC25424000000000930053C000000080E82842400000008042FD52C000000080CD18424000000040CEFE52C0000000A0B915424000000000B90B53C0000000C05928424000000080020E53C0FFFFFF3FF7254240000000A0360753C0000000605316424000000080150953C0FFFFFF7FE7124240000000E0090F53C000000000E9144240000000A0211353C0000000E06B1B4240000000E0A11153C0000000A0200E424000000000C11E53C0000000A0360A424000000040E81A53C0000000608007424000000000792153C0000000E0EA00424000000020042653C0FFFFFFFF4B01424000000080892953C00000002069084240FFFFFF7F862853C000000040C0044240000000802B2C53C0000000A059064240000000207F2E53C0000000C01214424000000040142C53C0000000C0B2254240FFFFFF7FB43153C0FFFFFFBFDD2D424000000020243B53C0FFFFFF9F3B32424000000040722F53C0FFFFFF5F57284240000000A0472D53C0000000A01022424000000020AF2F53C000000000F11D4240000000E0AE3053C00000000082124240000000A0662C53C0000000A019FF4140000000A06E2F53C000000060E3F74140000000409F2C53C00000008084F84140000000C0341A53C0000000A01DFD414000000060C61753C00000000057F7414000000060AE0D53C0000000400AFD4140000000A0BC0553C0FFFFFFBF40FB4140000000C0AA0153C000000080D0F54140000000C0DB0453C0000000E0DEE04140000000A0C10253C00000006088D7414000000020B50A53C00000002032D9414000000020B40653C000000020F2D44140000000805B0153C0000000E0A4D541400000000039FF52C00000004044F24140000000005BF452C0000000E035F6414000000000EFEF52C00000008046EF4140FFFFFFFFADEE52C00000006024D54140FFFFFFFFDBF152C0FFFFFF5F12CA4140000000400EF952C000000060CDD0414000000000640353C0000000C011B54140000000A09F0B53C000000060B7AB414000000060EC1F53C0000000A046B5414000000000292253C000000020A0B9414000000000571D53C00000000091C04140000000E0931C53C0FFFFFFBF82C6414000000060362153C000000040F0C94140000000C0851F53C00000004059C5414000000040D82853C0000000609AC24140000000C03D2853C0000000000DB84140FFFFFFDF242D53C000000040BAB4414000000020A44653C0000000806CC6414000000080EC3E53C000000040DFB7414000000040792C53C0000000C0DAAC414000000040542753C000000000EFA2414000000060D62553C000000040F2A74140000000609F2253C000000040DFA64140000000C0742053C0FFFFFF9FD79F414000000040F72853C0000000201596414000000000BC2653C000000060BC91414000000040F32753C0000000A03C88414000000080642B53C00000004015834140000000A05D3653C000000060B97D414000000060413C53C0000000C082834140FFFFFF1F5E3C53C0000000E0F6884140FFFFFF7F104353C0000000C0CB8B414000000000B34653C000000020438B4140000000E0134753C0000000E073884140FFFFFF7F134053C000000000C586414000000060703A53C000000040DE77414000000040692953C0000000C00274414000000040E22A53C0000000E0287C4140FFFFFFDFE72153C0000000E0C07C4140000000607B1C53C0000000E021824140000000C0231B53C0000000A01E794140000000004C1753C0000000E0B778414000000000151553C000000020EC7C414000000040241453C0000000C072794140000000E0121E53C0000000E07C644140FFFFFF1F052853C0FFFFFFDF205C4140FFFFFFDF3B4353C0000000207659414000000080884953C0000000A0D561414000000000484853C000000040AB574140000000C0054A53C0000000C08F54414000000000796253C0000000407F3A4140000000808D6553C0FFFFFF3FE1354140000000C0056753C000000080AF37414000000040916653C000000000D1344140000000A0087053C0000000C00B27414000000020527753C0000000A0AB184140FFFFFFDF3D7953C0000000E0DA08414000000000507B53C0FFFFFFBFF0074140000000A07C7D53C0FFFFFF1F3918414000000080587D53C0000000E00CFF4040000000403A8253C00000008007F54040000000201AA553C0FFFFFF1FE5F0404000000040F7A953C0FFFFFF1F6EF94040000000A0C4C453C000000040FC264140000000009EDC53C0000000603D4F4140000000A02EDD53C0000000E02951414000000060B8EA53C0000000207C664140000000C0E6EB53C0000000A01267414000000080E0FA53C0FFFFFFDF6967414000000060D11454C0000000204A68414000000060F02354C00000006059684140FFFFFF3F343354C0000000607668414000000020483254C0000000E06678414000000040C53554C0000000204280414000000000433954C000000060A587414000000000613B54C0000000C0F58C4140000000808D4254C000000000C384414000000000324454C0000000808288414000000000D24154C0000000007E8D414000000080234354C0000000C0E5904140000000C0274354C0000000606593414000000020A95454C000000000F6944140000000E02D5754C000000060D8944140000000A0FB7054C0000000405B974140000000C0B77754C00000006070974140FFFFFF1F2C7E54C0000000A019984140FFFFFF7F738D54C000000060B898414000000020D09154C0FFFFFFFFF5984140FFFFFF7F879454C0000000809397414000000080749654C0FFFFFF7FA9984140000000400C9754C0000000E06A974140000000A0C49754C0000000806397414000000060EF9854C0FFFFFF9FA79A4140000000E0069C54C000000020B4954140FFFFFF1FDF9D54C00000004035964140000000A093A154C0000000A0C8934140000000607BA454C0000000802293414000000040E3A954C0000000604A8F414000000040E8AB54C0000000208C8F4140000000C008AC54C0000000C0848C414000000080A1AC54C000000080AD8B4140000000C05DB154C0FFFFFF1FEF8A414000000000D0B854C000000060168741400000004077C054C0000000201983414000000060CDC654C0000000200980414000000020D5E054C0000000A0FA7E4140000000A029E354C000000020A97E41400000002008FC54C000000060A47E41400000006044FF54C0000000C0997E41400103000000010000000700000000000020BD0153C0000000804247424000000020EAFF52C00000006040474240FFFFFFDF5CFA52C0000000A0714542400000000030FB52C000000040AB3C4240000000C08BFE52C0000000A02F3D4240000000807BFE52C0000000604B42424000000020BD0153C000000080424742400103000000010000000800000000000020BAF952C00000008031474240000000E033F852C0000000E026474240000000607BF152C000000040581D4240000000C0E0E252C000000000E9E44140000000A0F9EC52C0000000E061064240000000606AEF52C00000000071064240000000801FF252C000000020D31C424000000020BAF952C00000008031474240010300000001000000090000000000002070DF52C0000000E0D2D54140000000E026E252C0000000E069E24140000000203FDD52C0000000E006CF4140000000E0AEE152C0000000602C9D414000000060F4EF52C0000000404C984140000000A042EC52C000000020149E41400000000060E152C00000008003A44140000000406DDE52C00000006041C841400000002070DF52C0000000E0D2D5414001030000000100000008000000000000A0160153C0FFFFFF3FE3884140000000C071FE52C000000020DF8E4140000000E0A6F652C0000000806995414000000080E0F052C0000000C09C98414000000040F0F352C0000000A0FE94414000000040DAF952C000000080F5904140000000202F0053C0FFFFFF3FE3884140000000A0160153C0FFFFFF3FE38841400103000000010000000900000000000060D52253C0000000003E4B4140FFFFFF7F872353C0000000202A4E4140000000806A2253C0FFFFFFFF944E4140000000C0011F53C0000000004C594140000000C0AA1B53C0000000205E614140000000C0FA1753C0000000200C684140000000C0691253C0000000E04170414000000060081E53C000000080BD58414000000060D52253C0000000003E4B4140 North Carolina 37 0.28819444444444444444 0.28212560386473429952 0.28149829738933030647 0.27662721893491124260 0.33226837060702875399 0.37205882352941176471 0.37534626038781163435 0.34216589861751152074 0.34141201264488935722 0.37106918238993710692 0.35038932146829810901 0.31548198636806231743 0.36254295532646048110 0.36713735558408215661 0.43502824858757062147 0.47904940587867417136 0.50060827250608272506 0.49288560045532157086 0.50847457627118644068 0.55231277533039647577 0.53684210526315789474 0.51903614457831325301 0.53912256897331524197 0.51314142678347934919 0.52063751532488761749 0.53540372670807453416 0.54135338345864661654 0.52237177155329210622 0.50676156583629893238 0.55108019040644452581 0.55826271186440677966 0.55673274094326725906 0.55522682445759368836 0.56438442211055276382 0.57753846153846153846 0.59070861511320199941 0.59810214903711973207 0.60996386164171399071 0.60190703218116805721 0.61985145172180958812 0.62946152259129358366 0.64538310412573673870 0.66226415094339622642 0.68439529576970335264 0.69940714629065854831 0.69616908850726552180 0.69609062025141594143 0.70818008877615726062 0.69536271808999081726 0.69325153374233128834 0.68006562756357670221 0.66299541763807380014 0.66238730616660656329 0.65020465678051399047 0.66333312234951579214 0.67053469852104664391 0.67414592549165911635 0.67092524204012376485 0.65425896323361498059 0.64402049402257674845 0.64112106058844051634 0.64957360861759425494 0.66556229758403752373 0.66771433560328269600 0.67704884805080737788 0.68554303681383466527 0.68669984662096597490 0.68534894837476099426 0.67951455219687605349 0.67964327672754459041 0.66165394402035623410 0.66914370078740157480 0.65398897797961162752 0.65981295066752338117 0.66531440162271805274 0.66140105312270879158 0.66371060354727574327 0.63935227739424637710 0.62981642870387539403 0.62976819248826291080 0.63645327669902912621 +11 0106000020E6100000010000000103000000010000004601000000000080940456C0FFFFFF1F68C14240FFFFFFDF9F0556C0FFFFFF7FF6BC424000000000F31356C000000060AFB84240000000C0FC1656C00000004064B44240000000E0DE1A56C000000020CCB54240000000E0ED1D56C0000000004CB3424000000080B92056C000000040FFA5424000000060172056C000000000FFA04240FFFFFF3FD81C56C000000060539A4240000000800A1B56C0000000A01594424000000080D41C56C000000040A18C4240FFFFFFDF831E56C0000000003C894240FFFFFF9F671F56C000000020BA884240FFFFFFFF1A2156C0000000604A884240FFFFFF1FCB2356C00000000052894240000000604F2756C000000040F58D4240000000400E2C56C00000002055914240FFFFFF9F4D2F56C00000004012924240000000C0C62F56C0000000407893424000000020403756C000000080E199424000000020AE3B56C0000000C0F49B424000000020903F56C0000000202A9C424000000080294456C000000040CA974240FFFFFFFF794756C0000000805A8E4240000000C05D4956C0FFFFFF7FED8B4240000000E0D94A56C0000000E03888424000000040284B56C0000000804A834240000000A09D4956C0000000E0CC7F4240FFFFFF9F4F4856C0000000807A7E424000000080624C56C0000000804E7E4240FFFFFF7F714D56C000000060B583424000000020364F56C0000000805785424000000040E65056C0000000E0268B4240000000E0305256C0000000E0AD8B424000000020695356C0000000E0ED8A424000000020D25356C0000000E0CB87424000000060E95056C0000000C08C834240000000A0C45056C0000000A01C814240000000E0185256C000000000E67F424000000020E75356C0000000403D81424000000040825856C0000000A04C864240000000C0515856C0000000C0AE8C4240000000801F5B56C0000000E08F914240FFFFFF7F315C56C0000000202995424000000040F75D56C0FFFFFFBFB49C4240FFFFFF9FC75D56C0000000407AA0424000000080555F56C0000000A0C4A0424000000080E36056C0FFFFFF1F61A3424000000080E36056C00000000009A7424000000080096056C0FFFFFF1F2BAA4240000000E0FF5D56C0000000C071AB424000000020E35B56C00000002088AD4240000000605D5B56C0000000409CB4424000000020085D56C00000000002BA424000000080AA5F56C0000000E0F0BE424000000020996156C0000000E035C9424000000000DB6056C0FFFFFFBFD6CE4240000000403A6156C0000000803FD3424000000020DB6056C00000000005D74240000000A0606156C000000020EFD84240FFFFFF3F366556C0000000A061DA424000000040A76A56C0000000006BDF424000000040416B56C00000002059E44240000000403A6C56C00000008003E74240000000E09E6E56C0000000A0A5EB424000000080827656C000000020D9F34240FFFFFF5F1B7756C000000000E7F34240FFFFFFDF797756C00000000029F24240FFFFFF9FA27956C0000000A01DF0424000000020067C56C0000000C063F0424000000080A67E56C0000000A0B8F44240000000A0537D56C00000006058FB424000000020B18056C0000000A012FC4240000000E0AE8256C0FFFFFF5F21FF424000000040A38756C00000008021044340000000209F8856C0000000E0E706434000000020488D56C000000040610B434000000080429056C000000040A30F434000000060899256C0000000405A154340000000C08C9556C0000000C02718434000000060589756C000000080FD1D434000000060A39756C0000000606A294340FFFFFFBFF49656C0FFFFFF1FC32E434000000020BC9556C00000004007324340FFFFFF5F519356C0000000A0B3364340000000A0029156C0000000606442434000000000B89056C0000000C031444340000000A06B8F56C0000000000A484340000000E0C18B56C0000000601D4E4340000000C0BF8B56C0000000A052544340FFFFFF7FF18C56C000000080A5594340000000A0948C56C0000000E0AA5C434000000020758A56C0000000E0F4624340000000C0A68856C0000000C08A64434000000060CA8756C00000002077664340000000603D8756C0000000C04C6A434000000000808856C0FFFFFF1F306D4340000000809C8F56C0000000A00E75434000000000DA9156C0000000205D764340000000A0769456C00000006063764340000000C06F9A56C0000000A02D7B4340000000E0119E56C000000060C67A434000000080F2A156C000000040207243400000004080A456C0000000A0876F43400000004024A856C0000000E0BD704340FFFFFFDFCEAA56C000000060B67743400000004030AD56C000000060D6844340FFFFFF1F49AD56C00000006072874340000000802FAC56C000000060FE8B434000000000DFAD56C00000008075924340FFFFFFDFF6AD56C00000006012994340000000A0DEAE56C0FFFFFF7FC49C4340000000C03CAF56C000000040B89F4340000000C0E0B156C0000000A0FDA54340000000806EB656C0000000A0DBAC434000000040AABC56C00000006046B343400000006053C256C0FFFFFF7FE2B84340000000E01EC456C000000080ABBC4340000000C0FDC556C0000000E0B3C3434000000000FFC956C000000060BBC643400000000002CD56C000000080CDCC4340000000A054D456C000000020CCD74340000000607ED756C000000000C1DC434000000020E6D756C00000006071E14340000000006ED856C000000000E2E64340FFFFFF7FBFDC56C00000006078EE434000000000DDDC56C0000000A04FF1434000000080C7DB56C0000000206FF34340000000808BDB56C0000000C0FEF54340000000A09FDC56C0000000A018F94340000000C02FDF56C000000080BC004440000000A041E056C0000000008A084440FFFFFF3F08E156C0000000C038114440000000406BE056C0000000A0A819444000000080EEDF56C0000000202D2044400000000026DF56C0000000C0A1274440000000C0B5DC56C0000000809A2F4440000000E0CDDA56C0FFFFFF1F85314440FFFFFF3FB0D856C0000000E03832444000000040DBD756C0000000209533444000000060AAD856C0FFFFFF7F3F394440000000A0FCD756C0000000C0774044400000006074D856C0000000C0A5434440000000806CDA56C0FFFFFF9F24464440000000C04FDA56C00000002057494440000000000AD856C0000000803D4D4440000000A0C5D056C0000000A0DC514440FFFFFF1FC1CD56C0000000A068524440FFFFFF5F66CA56C000000000025444400000002044C856C0000000A050574440000000E0ADC756C0000000A04A5A4440000000A0EFC556C0000000607A614440000000A0B0C556C0000000A0B76A4440FFFFFF3F26C356C0000000409670444000000000EEBE56C00000004043764440000000407CBD56C000000020AA7944400000000019BD56C0000000A001894440FFFFFF5F4CBD56C0000000A05B8D4440FFFFFFBF61BF56C0000000C07A924440000000202BC156C0000000C039954440000000C09AC356C0000000A08F964440000000607FC656C000000080A29D4440000000E08CC656C0000000E047A24440000000A0B0C456C0000000E0DDAA44400000000092C356C00000006060B3444000000060C2C156C00000008035B64440000000600BC056C0000000C02DB7444000000020C7BC56C000000000EBB544400000006006B656C000000060E9B84440000000E0E9B156C0000000C093B944400000006053AD56C0000000A09BB94440000000A029AA56C0000000402DBB4440000000E071A656C0000000203AC14440000000209DA256C00000000053C34440000000A01E9D56C0000000A086C34440FFFFFF7FD69B56C00000000094C54440FFFFFF7F129B56C0000000609CC84440000000A04B9656C0000000E01DCB4440FFFFFFDFB79556C00000008028CD4440FFFFFF1FD59556C00000006015D34440FFFFFF9FDD9456C0000000A082DC444000000040839356C0000000E0D3E0444000000000579056C00000000010E44440000000A0888C56C0000000802FE74440000000A0E38956C0000000A023F7444000000080218956C000000080F2FD4440FFFFFF5FA28956C0000000604704454000000020C28A56C000000040D007454000000060AA8A56C000000080470D454000000000458B56C0FFFFFF9F6C0F4540000000C0428C56C000000040B40F4540000000A0C78E56C0000000C071144540000000E0B59456C0000000C041194540000000E0889756C000000020E81A4540000000200F9A56C0000000000F1F454000000040C09A56C0FFFFFF3FC8214540000000205F9B56C0000000E0992B454000000020439C56C0000000E0162E4540000000406D9F56C0000000A0C3314540000000C011A456C0000000C0FE354540000000E0C5A656C0000000A0F33A4540000000807EA956C0000000E0E13C4540FFFFFF9FB6A956C000000040523F454000000060DAA856C0000000C032414540000000E0E09A56C00000000012414540000000C01B7B56C0000000A086404540000000606A7556C0FFFFFF5F71404540000000C0A15956C0000000C0AD3F4540FFFFFF1F015756C000000060BB3F4540000000E0193C56C0000000A0D43E4540FFFFFFFFF43056C000000000D63E4540000000A0372D56C000000000AD3E4540000000C0101356C0FFFFFFDFF83E454000000000760C56C0000000A0AB3E45400000002007F355C0000000E09B3E45400000008090F555C00000002038284540000000C0A7F055C0000000C00614454000000040EAEA55C000000040A80745400000004035E755C00000006075EC4440FFFFFF3FE9E155C0FFFFFF9F9EDC4440000000E016E255C0000000A01FBC4440000000A013E255C00000002091A64440000000E007E255C0000000A03D964440000000A00CE255C000000060458144400000004017E255C0FFFFFF9F695F44400000000061E255C0FFFFFF5F4F3F44400000008048E255C000000000DB3D44400000000043E255C0000000E045154440000000204AE255C00000002093F14340000000E046E255C0000000E0FECD4340000000E077E255C0000000001DBD4340000000E092E255C000000000DEAC4340FFFFFF1F40E655C0000000604CAB4340000000E003E855C0FFFFFFFF58A743400000006014E755C0000000C019A643400000004069E755C00000008005A4434000000060D7E655C0000000800BA143400000008069E555C000000020D79F434000000080ABE555C000000000AF9A43400000008007E655C0000000405C994340FFFFFF3FE8E655C0000000C018994340000000803BE955C0000000A091954340FFFFFF9FE6EA55C000000060C69243400000008034EA55C0FFFFFF3FB99043400000008062EA55C000000020868E4340000000406DE855C0000000004E8D43400000002060E855C000000080638B4340000000202BE755C000000060D48A4340000000E075E555C0000000E0FD874340000000603BE555C0FFFFFF7F747F434000000000E1E555C0000000203E7F4340000000E010E355C0000000E0107D43400000006024E255C0FFFFFF9F5A7B434000000080EEE155C00000002049774340FFFFFF3F82E255C000000080D2734340000000A0C7E355C000000000566F4340000000803BE355C000000060CF6D43400000004081E055C0000000E0D4654340000000C037E155C0000000E06A634340FFFFFF1F83E055C000000040866243400000004088E055C0000000004A5E4340FFFFFF1FCFE255C0FFFFFFFFCD574340000000A0A9E555C0000000A0095643400000002003E855C0FFFFFF9F47524340FFFFFFBF3BE855C0000000C0BB4F434000000040ABE755C0000000E0B24C434000000080FFE855C000000040ED4B4340FFFFFF5FC8E955C0FFFFFF9F744943400000008011EB55C00000000012464340FFFFFF5FB0E955C0000000A0F741434000000080D3E955C0000000800E404340000000A083EB55C0FFFFFF3F834043400000002057EC55C0000000E0A23D4340000000E063F055C0FFFFFFFFA93B4340000000E08DF055C000000020823A4340000000004BEF55C0FFFFFF7F0539434000000040E6EF55C0000000E07F354340FFFFFF5F2DF255C000000060663043400000008068F555C0000000801F2D4340000000C067F655C0000000E09E244340000000803BF755C0FFFFFFBF8624434000000040F0F755C0FFFFFF7F8C284340000000608AF855C00000000064284340000000A0DCF855C0000000007C264340000000C080FA55C0FFFFFF5FF92343400000004079FA55C000000040B32643400000004042FB55C0000000C002274340FFFFFF9FB8FE55C0000000E0DB1E4340000000C01AFF55C0000000600E1E43400000006096FE55C0FFFFFFFFB0194340000000A0AAFB55C0000000A0E7154340FFFFFFBFA5FB55C0000000E02914434000000020D6FC55C00000006086114340000000E04DFE55C000000080DD104340000000E02F0156C000000000390D434000000000CA0056C000000000D20B434000000060C0FD55C000000040620C4340000000406BFE55C0000000206209434000000000390256C000000040EC06434000000000C20256C000000080C605434000000080A70256C000000020E704434000000080630156C0000000C04A044340000000A0DE0156C0000000E00D014340000000A0630156C0FFFFFF9FCEFC424000000080B80256C0000000E066FA4240FFFFFF5FAC0256C0000000A09DF74240FFFFFFBF220456C00000002003F74240000000600D0556C000000000D5F8424000000040600556C0000000803AF64240000000C0F20156C0000000A073F54240000000A0B30156C0000000E0EFF34240FFFFFF1FDF0256C000000040B0F24240FFFFFFBF670656C0FFFFFF5FFDF34240000000407E0656C00000006099F24240000000E0D80456C00000006014EF4240FFFFFFFF300256C0000000E0FFEB4240FFFFFF5FB20256C000000040ECE9424000000080B60556C00000006066EA424000000080810556C000000080A7E84240FFFFFFDF460256C0000000A020E7424000000060A30456C0FFFFFF9F21DE4240000000808D0856C000000000B2D9424000000020330A56C00000006091D44240000000A0160A56C00000000072D0424000000040960856C000000080B2CA424000000080940456C0FFFFFF1F68C14240 Illinois 17 0.82291666666666666667 0.77971014492753623188 0.76163450624290578888 0.71893491124260355030 0.69808306709265175719 0.74264705882352941176 0.79362880886426592798 0.74884792626728110599 0.77028451001053740780 0.81509433962264150943 0.78309232480533926585 0.73125608568646543330 0.76632302405498281787 0.66495507060333761232 0.78970495919648462021 0.86679174484052532833 0.89111922141119221411 0.87307911212293682413 0.92598870056497175141 1.00000000000000000000 0.93351800554016620499 0.88240963855421686747 0.91632745364088647671 0.87234042553191489362 0.90273804658765835717 0.90144927536231884058 0.89829837752275425406 0.89196071298654056020 0.89750889679715302491 0.91944342731600146466 0.93290960451977401130 0.92276144907723855092 0.91683103221564760026 0.91143216080402010050 0.91723076923076923077 0.92061158482799176713 0.93776165224672062517 0.92901393908105317501 0.90226460071513706794 0.91042088678820616700 0.89828760057767691355 0.89980353634577603143 0.91962264150943396226 0.92434614709496226084 0.94584201249799711585 0.94877440187876119184 0.96505042132891283326 0.96677235256816740647 0.96246556473829201102 0.94856850715746421268 0.93364324127244553824 0.89050566765817187877 0.88351965380454381536 0.85694155539153190633 0.84112918539148047345 0.83515358361774744027 0.82652027927303736076 0.81265595368799281365 0.78963233614980589176 0.76898404631982338485 0.76109625150211264876 0.77633153800119688809 0.79365670252764024867 0.79497118910424305919 0.79001418823052496453 0.80047163631599633172 0.80267317745015181394 0.80679373804971319312 0.79635352286773794808 0.80033109046245861369 0.79249363867684478372 0.79377460629921259843 0.77598807918824948556 0.79305585302586801837 0.80675975281852917590 0.79093070274833922104 0.78314406022712772745 0.76701934171669958735 0.76465788985722232524 0.77114509976525821596 0.77618704490291262136 +40 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 Tennessee 47 0.32812500000000000000 0.31400966183574879227 0.31441543700340522134 0.29289940828402366864 0.32587859424920127796 0.36029411764705882353 0.36565096952908587258 0.35023041474654377880 0.35194942044257112750 0.37735849056603773585 0.34593993325917686318 0.33106134371957156767 0.37457044673539518900 0.36007702182284980745 0.45699937225360954175 0.54033771106941838649 0.55474452554744525547 0.49630051223676721685 0.50395480225988700565 0.53138766519823788546 0.52686980609418282548 0.49542168674698795181 0.50746268656716417910 0.49144764288694201085 0.52145484266448712709 0.52753623188405797101 0.52512861100118717847 0.51727901054929065115 0.52526690391459074733 0.55364335408275357012 0.56426553672316384181 0.55297334244702665755 0.55555555555555555556 0.55621859296482412060 0.57076923076923076923 0.57777124375183769480 0.59391571308958972928 0.60196179659266907589 0.58951132300357568534 0.61692550078775602071 0.61213121518465029915 0.62652259332023575639 0.65113207547169811321 0.66842197647884851676 0.68867168722961063932 0.68927051225598121239 0.69305152645393010084 0.70691185795814838301 0.70110192837465564738 0.70501022494887525562 0.69437608239905204630 0.66878366428169466999 0.66325279480706815723 0.65054015969938938469 0.65042091271599468321 0.65147895335608646189 0.65272078025902041251 0.64851781614931629903 0.63525919159625485271 0.62106885491731578290 0.61569174710237624530 0.62915170556552962298 0.65156535010981647619 0.65793609219486642221 0.66688061617458279846 0.67784619415695008516 0.68238019219332018656 0.67070984703632887189 0.65524216204067872795 0.65619993591797500801 0.65073791348600508906 0.64564468503937007874 0.64001040705787743324 0.65793293829275838271 0.67222510495778102741 0.66062343086938167922 0.65424694823699544894 0.63195979113292785481 0.62262191730020396811 0.62798928990610328638 0.63546723300970873786 +20 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 Michigan 26 0.68576388888888888889 0.63478260869565217391 0.61293984108967082860 0.58284023668639053254 0.55431309904153354633 0.66617647058823529412 0.73407202216066481994 0.71313364055299539171 0.72181243414120126449 0.71949685534591194969 0.69521690767519466073 0.66212268743914313535 0.71219931271477663230 0.67394094993581514763 0.84996861268047708726 0.86991869918699186992 0.80596107055961070560 0.75640295959021058623 0.82824858757062146893 0.86068281938325991189 0.84598337950138504155 0.82795180722891566265 0.85753052917232021710 0.82811848143512724239 0.89987740089906007356 0.85962732919254658385 0.88563514048278591215 0.82757366315023644962 0.82170818505338078292 0.82240937385573050165 0.83262711864406779661 0.83321941216678058783 0.79783037475345167653 0.81407035175879396985 0.83938461538461538462 0.86709791237871214349 0.89729277142059726486 0.89055240061951471347 0.84719904648390941597 0.87913571910871033086 0.85516814524448112234 0.82396856581532416503 0.84924528301886792453 0.87168685272950675794 0.88960102547668642846 0.86980772053427271393 0.86738499792789059262 0.89841471147748890298 0.91333792470156106520 0.90327198364008179959 0.88424026980220581533 0.83358790899589999196 0.80238009376126938334 0.76910689122995370060 0.77492246344705361099 0.77224118316268486917 0.78526888024303149816 0.77717337059586785108 0.73669787622744918931 0.71637439080268255092 0.70845447145016862426 0.71147516457211250748 0.71913040241223988386 0.70815435655666142832 0.72258631173569353422 0.74878815668806498100 0.75046170219425924187 0.73037165391969407266 0.71833913922912686819 0.71576951831677881021 0.71534351145038167939 0.72864173228346456693 0.71420799924312306346 0.72368577615953927798 0.74168592858153686495 0.70852496167433180031 0.69150610352600910212 0.65421547728472806211 0.63862414240682366030 0.64581499413145539906 0.65003033980582524272 +24 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 Montana 30 0.51388888888888888889 0.48405797101449275362 0.43359818388195232690 0.50147928994082840237 0.47603833865814696486 0.53529411764705882353 0.65927977839335180055 0.54723502304147465438 0.53951527924130663857 0.65031446540880503145 0.59288097886540600667 0.55404089581304771178 0.61254295532646048110 0.57830551989730423620 0.72190834902699309479 0.73983739837398373984 0.73357664233576642336 0.74445076835515082527 0.83841807909604519774 0.90418502202643171806 0.78227146814404432133 0.79710843373493975904 0.81682496607869742198 0.76053400083437630371 0.73968124233755619125 0.73333333333333333333 0.74950534230312623664 0.70170971262277191706 0.70569395017793594306 0.75723178322958623215 0.71433615819209039548 0.70915926179084073821 0.66568047337278106509 0.74214824120603015075 0.71692307692307692308 0.69567774184063510732 0.71113591962042980742 0.70469798657718120805 0.66865315852205005959 0.66509115462525320729 0.67753249432638745616 0.71218074656188605108 0.71490566037735849057 0.76443742320519571704 0.80307643005928537093 0.78966681344488477910 0.80038679375604365244 0.78630310716550412175 0.76170798898071625344 0.78946830265848670757 0.75644881961534955793 0.73502693142535573599 0.73883880274071402813 0.71609743004764141448 0.69909487942274827521 0.66581342434584755404 0.63422693599104620796 0.62206807066573510330 0.59356017355560630281 0.55658766193193651852 0.56684885839438694422 0.58064033512866546978 0.61456278152105126010 0.59766020604155753449 0.61049929058847375177 0.59377047032621511856 0.58734779478511284315 0.57908102294455066922 0.56655242162040678728 0.56936879205382890099 0.56027989821882951654 0.55533956692913385827 0.57574682466472716952 0.58777753980152780752 0.61236379074484645502 0.61136661556577573374 0.61645612691931436349 0.60512780396221618133 0.60495086222881513073 0.61056704812206572770 0.62005081917475728155 +9 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 Georgia 13 0.30121527777777777778 0.29661835748792270531 0.29057888762769580023 0.29585798816568047337 0.32587859424920127796 0.35882352941176470588 0.37119113573407202216 0.34792626728110599078 0.32982086406743940991 0.36477987421383647799 0.34371523915461624027 0.32814021421616358325 0.35996563573883161512 0.36392811296534017972 0.45511613308223477715 0.51969981238273921201 0.53467153284671532847 0.48093340922026180990 0.50112994350282485876 0.54405286343612334802 0.53684210526315789474 0.51325301204819277108 0.54454997738579828132 0.53400083437630371297 0.54188802615447486718 0.53913043478260869565 0.56311832212109220419 0.54528919607129865406 0.54199288256227758007 0.57964115708531673380 0.58615819209039548023 0.58031442241968557758 0.57330703484549638396 0.57914572864321608040 0.60338461538461538462 0.61217289032637459571 0.63019815796818308680 0.63551884357253484770 0.63289630512514898689 0.65068647310375872158 0.65236228595007220961 0.66679764243614931238 0.69169811320754716981 0.70879410215903106898 0.72055760294824547348 0.71436958755320710407 0.71170051112032048626 0.72213062777425491439 0.71407254361799816345 0.71462167689161554192 0.70385561936013125513 0.68124447302837848702 0.68049044356292823657 0.67462926927464268939 0.68668903095132603329 0.69311717861205915813 0.70047433779246389170 0.69817347040622816648 0.67686686458095455583 0.66130711875702919982 0.65135480869868589371 0.66345002992220227409 0.68082492647879983621 0.67515278505325650428 0.67998783865955003040 0.69337088955849600419 0.69583998497511503428 0.70464866156787762906 0.68959995505112934038 0.69779985047527501869 0.69567430025445292621 0.68750000000000000000 0.67636415241609309586 0.68203993241474500845 0.68540968913628001321 0.66642227110133528850 0.66985666283867126026 0.64027144896642090235 0.62849990728722417949 0.62586194248826291080 0.62738925970873786408 +21 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 Minnesota 27 0.51996527777777777778 0.53333333333333333333 0.51872871736662883087 0.53698224852071005917 0.49201277955271565495 0.52647058823529411765 0.62465373961218836565 0.54377880184331797235 0.56902002107481559536 0.62138364779874213836 0.57508342602892102336 0.51022395326192794547 0.52835051546391752577 0.51155327342747111682 0.59447583176396735719 0.62914321450906816760 0.67518248175182481752 0.67729083665338645418 0.71751412429378531073 0.79900881057268722467 0.73573407202216066482 0.69253012048192771084 0.71551334237901402081 0.68126825198164372132 0.69922353902738046588 0.71387163561076604555 0.70834982192322912545 0.66860676609676245908 0.68683274021352313167 0.73819113877700476016 0.72916666666666666667 0.73650034176349965824 0.73504273504273504274 0.73209798994974874372 0.75692307692307692308 0.74683916495148485739 0.77616522467206251744 0.77361899845121321631 0.75852205005959475566 0.77942831420211568760 0.77965752011553538271 0.79626719056974459725 0.80660377358490566038 0.81235738107776022468 0.87021310687389841372 0.85689123734037868780 0.85868213841690841276 0.85339251743817374762 0.86765381083562901745 0.86615541922290388548 0.85762464679609880594 0.82964868558565801109 0.81644428416877028489 0.80467020063074548749 0.79713905943414140135 0.81086461888509670080 0.80440228108511432074 0.79254416608443956483 0.77182005023978077187 0.73278627067105427584 0.73520176764740086057 0.74846648713345302214 0.76272195957264639095 0.75774401955648681683 0.74549016958313627458 0.76860343246429975108 0.76949322315084358469 0.78474545889101338432 0.77390718058208787504 0.78775499305778062587 0.78353689567430025445 0.78988681102362204724 0.77662669410345561626 0.79848171152518978606 0.81947261663286004057 0.81106001022017818658 0.79537237888647866956 0.76989419748499012379 0.76133877248284813647 0.77572990023474178404 0.77594053398058252427 +23 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 Missouri 29 0.53906250000000000000 0.54202898550724637681 0.55732122587968217934 0.53994082840236686391 0.53354632587859424920 0.53970588235294117647 0.58171745152354570637 0.53686635944700460829 0.53530031612223393045 0.59748427672955974843 0.56062291434927697442 0.50535540408958130477 0.54982817869415807560 0.51732991014120667522 0.60451977401129943503 0.66791744840525328330 0.68734793187347931873 0.67785998861696072851 0.69152542372881355932 0.75770925110132158590 0.73518005540166204986 0.68771084337349397590 0.70284938941655359566 0.69211514392991239049 0.70984879444217409072 0.71469979296066252588 0.71943015433320142461 0.69297926518734085122 0.69359430604982206406 0.74844379348224093739 0.75070621468926553672 0.73684210526315789474 0.72813938198553583169 0.73178391959798994975 0.74676923076923076923 0.74478094678035871802 0.76416410828914317611 0.74728962312854930305 0.73110846245530393325 0.75849651136619401305 0.73468124613162781102 0.75500982318271119843 0.77490566037735849057 0.77988414955239599789 0.79105912514020189072 0.77528254806986643182 0.79196021549937836718 0.79974635383639822448 0.80245638200183654729 0.79621676891615541922 0.79418466867195333151 0.75488383310555510893 0.75420122610890732059 0.74045494195799503456 0.74156592189379074625 0.73720136518771331058 0.73911421414485956404 0.72387463818744385667 0.69650605160995661110 0.67005456741783646436 0.66220878396712796062 0.66393626570915619390 0.69091315191899638909 0.68245154531168150864 0.68559556786703601108 0.69654788418708240535 0.69158293423482643128 0.69009918738049713193 0.68131250702326104057 0.67828153369646480829 0.67114503816793893130 0.67531988188976377953 0.66595709453865985477 0.68468146878941481640 0.70055191282607670173 0.68535181852518385212 0.67296159244608906469 0.65229890677253437115 0.64079362136102354905 0.65608494718309859155 0.66569326456310679612 +8 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 Florida 12 0.44965277777777777778 0.45410628019323671498 0.45175936435868331442 0.47189349112426035503 0.46006389776357827476 0.51176470588235294118 0.52077562326869806094 0.51843317972350230415 0.51317175974710221286 0.57861635220125786164 0.55061179087875417130 0.50827653359298928919 0.52319587628865979381 0.50513478818998716303 0.63339610797237915882 0.69668542839274546592 0.71411192214111922141 0.66533864541832669323 0.65932203389830508475 0.66134361233480176211 0.66925207756232686981 0.62843373493975903614 0.62777023971053821800 0.61493533583646224447 0.64037597057621577442 0.64679089026915113872 0.66204986149584487535 0.65150963986904328847 0.65338078291814946619 0.69095569388502380081 0.70974576271186440678 0.69138755980861244019 0.67028270874424720579 0.66363065326633165829 0.67692307692307692308 0.69038518082916789180 0.69718113312866313145 0.69308208569953536396 0.69344457687723480334 0.73126266036461850101 0.75489993810604497627 0.78703339882121807466 0.80867924528301886792 0.82552220466912410040 0.83880788335202691876 0.82430647291941875826 0.81433899709904682967 0.80862396956246036779 0.80463728191000918274 0.80991820040899795501 0.80931546805213745329 0.80786236835758501487 0.80742877749729534800 0.79104878212440448232 0.79986075068042281157 0.78293515358361774744 0.78372328518893567127 0.76968759357221279569 0.74971454670015985385 0.73282792518848669138 0.73826413924099701516 0.74263165769000598444 0.75155418233257640621 0.72152959664745940283 0.73143706506317140734 0.73169134023319795624 0.73596894857107083607 0.73542065009560229446 0.72260928194179121250 0.71905372209761828474 0.70687022900763358779 0.69254429133858267717 0.68241916790841789068 0.70200613978724923253 0.71411387329591018444 0.72123353107156346508 0.73284845391518863511 0.72231240099348757163 0.70055627665492304840 0.69336854460093896714 0.69335937500000000000 +38 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 South Carolina 45 0.23524305555555555556 0.23478260869565217391 0.23269012485811577753 0.23520710059171597633 0.27955271565495207668 0.31029411764705882353 0.31717451523545706371 0.29723502304147465438 0.28767123287671232877 0.31446540880503144654 0.30700778642936596218 0.30185004868549172347 0.33848797250859106529 0.34980744544287548139 0.40677966101694915254 0.45778611632270168856 0.45802919708029197080 0.44393853158793397837 0.44802259887005649718 0.50165198237885462555 0.48365650969529085873 0.44578313253012048193 0.50429669832654907282 0.49895702962035878181 0.50388230486309767062 0.48281573498964803313 0.48634744756628413138 0.45834849036013095671 0.45765124555160142349 0.48187477114610032955 0.49152542372881355932 0.49111414900888585099 0.49243918474687705457 0.50314070351758793970 0.51353846153846153846 0.52543369597177300794 0.54646943901758303098 0.56169334021683014972 0.55780691299165673421 0.57843799234751294171 0.58324736950691149164 0.60196463654223968566 0.61773584905660377358 0.63243812532912058978 0.64556962025316455696 0.65448407456333480112 0.65202376018787125294 0.66670894102726696259 0.65243342516069788797 0.64764826175869120654 0.64205633032540333607 0.62657769917195916070 0.62394518571943743238 0.60866939542374018654 0.61871004493955313627 0.62059158134243458476 0.62175558279592815648 0.61173769837309112686 0.59630052523407170587 0.58503769733827633607 0.57502810404310578749 0.60031418312387791741 0.61084018910769459852 0.59944124323380478435 0.60147962975474630093 0.61201362504912878292 0.60954080195323504554 0.60955425430210325048 0.60076974941004607259 0.60194382142475702232 0.59910941475826972010 0.59844980314960629921 0.58825894652191395255 0.60158016229980247971 0.61028822114250672202 0.60114643737919082850 0.60263281017396112458 0.58650969041519175484 0.57404042277025774152 0.57786825117370892019 0.58470494538834951456 +34 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 Oklahoma 40 0.39496527777777777778 0.35555555555555555556 0.34165720771850170261 0.31952662721893491124 0.35463258785942492013 0.37058823529411764706 0.41274238227146814404 0.36981566820276497696 0.39620653319283456270 0.43522012578616352201 0.38820912124582869855 0.36416747809152872444 0.37113402061855670103 0.40179717586649550706 0.49089767733835530446 0.59224515322076297686 0.59002433090024330900 0.54183266932270916335 0.58079096045197740113 0.62940528634361233480 0.64598337950138504155 0.55132530120481927711 0.58344640434192672999 0.58448060075093867334 0.60277891295463833265 0.60372670807453416149 0.59992085476850019786 0.57948344852673699527 0.58967971530249110320 0.65690223361406078360 0.65572033898305084746 0.65481886534518113465 0.64003944773175542406 0.62625628140703517588 0.63230769230769230769 0.64569244339900029403 0.65894501814122243930 0.64971605575632421270 0.64410011918951132300 0.66351564258383974792 0.65978956055291933155 0.68310412573673870334 0.70018867924528301887 0.70563454449710373881 0.72488383271911552636 0.73183619550858652576 0.75631993369249896395 0.75764109067850348763 0.75596877869605142332 0.75531697341513292434 0.77340260687266429678 0.77015837285955462658 0.79358095924990984493 0.79292759847010668993 0.74213557820115197164 0.72167235494880546075 0.70697649629590150829 0.66314003393552250724 0.61493491664763644668 0.59386845503394843171 0.59173547311702911191 0.60644823459006582885 0.62245467743736738265 0.61204819277108433735 0.61093845010472265387 0.61345473601467312983 0.60706795630262622468 0.60202557361376673040 0.59293179008877401955 0.59273203033215849621 0.58404580152671755725 0.57866633858267716535 0.59270559852408997375 0.59634468480045691440 0.60660880230199537714 0.61134439778710924482 0.61932712347411849772 0.62098840279271703205 0.60784350083441498238 0.63046508215962441315 0.63918385922330097087 +10 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 Idaho 16 0.44010416666666666667 0.48599033816425120773 0.42451759364358683314 0.40532544378698224852 0.36261980830670926518 0.59264705882352941176 0.55263157894736842105 0.54723502304147465438 0.44573234984193888303 0.53584905660377358491 0.48609566184649610679 0.45082765335929892892 0.51202749140893470790 0.58664955070603337612 0.64344005021971123666 0.68480300187617260788 0.69038929440389294404 0.68468981217985202049 0.72203389830508474576 0.74063876651982378855 0.70747922437673130194 0.64048192771084337349 0.67706919945725915875 0.68585732165206508135 0.63342868818961994279 0.64554865424430641822 0.63157894736842105263 0.62859221535103674063 0.63451957295373665480 0.66532405712193335774 0.66772598870056497175 0.64866712235133287765 0.64924391847468770546 0.65703517587939698492 0.66707692307692307692 0.66157012643340194061 0.71281049399944180854 0.66881775942178626742 0.65673420738974970203 0.65496286293045239703 0.67443779657520115535 0.69901768172888015717 0.70962264150943396226 0.72845357205546779006 0.75452651818618811088 0.78996036988110964333 0.76958143389970990468 0.77564996829422954978 0.74724517906336088154 0.74836400817995910020 0.71953331510345456203 0.70222686711150414020 0.67832672196177425171 0.64557471650003355029 0.65288942338122665992 0.62963594994311717861 0.62074295155358951127 0.59726519612735801976 0.57597625028545329984 0.56433540217436580997 0.57382641392409970152 0.59343207660083782166 0.60287384134311134274 0.60192072638379605378 0.61678264982095804338 0.61725402856019913533 0.61445519141077409459 0.60806046845124282983 0.58517810989998876278 0.58536259745807967532 0.58104325699745547074 0.59498031496062992126 0.59424300480143806618 0.60648247304918969087 0.61116090381621774612 0.61867626474705058988 0.61679639317766152014 0.60536248606575010267 0.59647691451882069349 0.59275968309859154930 0.58758722694174757282 +45 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 Washington 53 0.64322916666666666667 0.63574879227053140097 0.60612939841089670829 0.59467455621301775148 0.60063897763578274760 0.65147058823529411765 0.67867036011080332410 0.65552995391705069124 0.63119072708113804004 0.73207547169811320755 0.68298109010011123471 0.64070107108081791626 0.74226804123711340206 0.76765083440308087291 0.92215944758317639674 0.95497185741088180113 0.86313868613138686131 0.79738190096755833808 0.84971751412429378531 0.89427312775330396476 0.88365650969529085873 0.82939759036144578313 0.84758028041610131162 0.82311222361284939508 0.84429914180629342051 0.86004140786749482402 0.83735654926790660863 0.79010549290651145871 0.80498220640569395018 0.83522519223727572318 0.84039548022598870056 0.83253588516746411483 0.83333333333333333333 0.84170854271356783920 0.84153846153846153846 0.84034107615407233167 0.85905665643315657270 0.87377387712958182757 0.85005959475566150179 0.86653162277740265586 0.84526511244068495977 0.82612966601178781925 0.82660377358490566038 0.83043707214323328067 0.85114564973561929178 0.86878027300748568912 0.90247271722613620666 0.91071655041217501585 0.89898989898989898990 0.90869120654396728016 0.90830370978032996081 0.87732132808103545301 0.85849260728452939055 0.83412735690800509964 0.83068548642319134122 0.79755403868031854380 0.78548206576773437084 0.77462820640782513225 0.74446220598310116465 0.71937351605781647020 0.72372756522076210412 0.74902752842609216038 0.77805904031567583665 0.76539200279378383098 0.75717856901560705358 0.75720555482772173457 0.74742542335743575297 0.75546725621414913958 0.75337116529947185077 0.76449855815443768023 0.77333333333333333333 0.77578740157480314961 0.75813051396674471960 0.76642631065419671117 0.77678192367564507760 0.77726676886844853252 0.76002722130066777253 0.75248860813955762423 0.75620248470239198962 0.76269072769953051643 0.77023285800970873786 +15 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 Kentucky 21 0.34114583333333333333 0.31400966183574879227 0.33030646992054483541 0.31213017751479289941 0.32747603833865814696 0.34264705882352941176 0.36703601108033240997 0.33870967741935483871 0.35932560590094836670 0.37358490566037735849 0.33926585094549499444 0.31158714703018500487 0.33934707903780068729 0.34531450577663671374 0.44005021971123666039 0.47967479674796747967 0.48844282238442822384 0.47011952191235059761 0.48870056497175141243 0.54790748898678414097 0.51966759002770083102 0.47710843373493975904 0.52148349163274536409 0.51606174384647476012 0.53330608908868001635 0.53374741200828157350 0.53264740799366838148 0.52273554019643506730 0.53060498220640569395 0.56499450750640790919 0.56638418079096045198 0.55809979494190020506 0.56903353057199211045 0.57223618090452261307 0.58646153846153846154 0.57982946192296383417 0.59559028746860173039 0.60118740320082601962 0.59618593563766388558 0.61174881836596893991 0.61295646791829997937 0.62554027504911591356 0.63981132075471698113 0.65209759522555731087 0.66415638519468033969 0.67620725084397475415 0.68144771377262052770 0.69270767279644895371 0.69960973370064279155 0.69366053169734151329 0.69638136906389572509 0.66170914060615805129 0.65705012621709340065 0.64342749781923102731 0.62402683714159123995 0.62923777019340159272 0.61599957362895059425 0.59861263599161592973 0.58378625256907969856 0.56525180155787895197 0.56603481024925378920 0.57914422501496110114 0.60458623385325540707 0.60485419940632093592 0.60181744476724545639 0.60638019127472815407 0.60146492628415813691 0.60214507648183556405 0.59599393190246095067 0.59684396026914450497 0.59127226463104325700 0.59778543307086614173 0.58695806428723479742 0.60200851954974893506 0.60797679135808292844 0.59745828612055366704 0.59293522181106716005 0.57481469892241800794 0.56449100686074541072 0.57405369718309859155 0.59257433252427184466 +47 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 Wisconsin 55 0.58420138888888888889 0.56811594202898550725 0.53234960272417707151 0.53550295857988165680 0.53194888178913738019 0.55882352941176470588 0.63850415512465373961 0.59677419354838709677 0.58061116965226554268 0.63773584905660377358 0.57063403781979977753 0.53261927945472249270 0.57731958762886597938 0.55584082156611039795 0.66101694915254237288 0.69355847404627892433 0.71897810218978102190 0.68924302788844621514 0.73220338983050847458 0.78964757709251101322 0.76842105263157894737 0.72578313253012048193 0.78516508367254635911 0.75052148518982060909 0.75153248876174908051 0.73457556935817805383 0.74198654531064503364 0.72389959985449254274 0.73309608540925266904 0.75979494690589527646 0.78566384180790960452 0.77170198222829801777 0.75739644970414201183 0.75753768844221105528 0.75630769230769230769 0.76918553366656865628 0.77839799051074518560 0.78084667010841507486 0.75804529201430274136 0.77447670492910195814 0.77388075097998762121 0.78251473477406679764 0.79962264150943396226 0.80656485869756011936 0.82198365646531004647 0.82518714222809335095 0.83727034120734908136 0.84590995561192136969 0.85135445362718089991 0.84785276073619631902 0.84595752438246285662 0.81686630758099525685 0.79379733141002524342 0.77782996712071395021 0.76245331983036901070 0.74982935153583617747 0.73788839737781804615 0.72512226769138636590 0.70143868463119433661 0.67484483692256425209 0.67058185060278326937 0.67923399162178336326 0.69653426646316494807 0.69397590361445783133 0.69721640429700695899 0.71069697366697235687 0.70657651735687231978 0.70369263862332695985 0.69642656478255983818 0.70076364413115454448 0.69694656488549618321 0.69468503937007874016 0.68972776082688805317 0.71007353466124080817 0.72307656021510448606 0.70437023706369837144 0.69382416741099910680 0.67539553712866446326 0.66454663452623771556 0.67110475352112676056 0.67650182038834951456 +22 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 Mississippi 28 0.24826388888888888889 0.19516908212560386473 0.19863791146424517594 0.18786982248520710059 0.20926517571884984026 0.25588235294117647059 0.24515235457063711911 0.26382488479262672811 0.23603793466807165437 0.25283018867924528302 0.22803114571746384872 0.20934761441090555015 0.26374570446735395189 0.28177150192554557125 0.33458882611424984306 0.39337085678549093183 0.38260340632603406326 0.34775184974388161639 0.37853107344632768362 0.44162995594713656388 0.39058171745152354571 0.37108433734939759036 0.38489371325192220715 0.37797246558197747184 0.38414384961176951369 0.38426501035196687371 0.41353383458646616541 0.38232084394325209167 0.37829181494661921708 0.42328817283046503112 0.43961864406779661017 0.42276144907723855092 0.43458251150558842867 0.42776381909547738693 0.46123076923076923077 0.45780652749191414290 0.47111359196204298074 0.47470314919979349510 0.47699642431466030989 0.49448570785505289219 0.49680214565710748917 0.51886051080550098232 0.54094339622641509434 0.56310338774793751097 0.57891363563531485339 0.57771906649053280493 0.58088133720127089377 0.60329740012682308180 0.60365013774104683196 0.59366053169734151329 0.59693738036642056330 0.56885601736473993086 0.56985214569058781103 0.55700194591692947729 0.54528767643521741882 0.53828213879408418658 0.52880669402547567020 0.51367401936320990119 0.49842429778488239324 0.48714958137209980422 0.48610303523665542505 0.49236983842010771993 0.51394110858802069761 0.51374192421861358477 0.52253226133369366935 0.54202148565439538844 0.53792218361661501862 0.53907743785850860421 0.53053713900438251489 0.53436398590195450176 0.52641221374045801527 0.51656003937007874016 0.52560372761891246245 0.53639846743295019157 0.55108731543940751922 0.54435779510764513764 0.55548466675173323125 0.53343242133260321123 0.53350639718153161506 0.54267532276995305164 0.55593901699029126214 +46 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 West Virginia 54 0.39930555555555555556 0.39420289855072463768 0.40408626560726447219 0.38017751479289940828 0.41373801916932907348 0.46029411764705882353 0.46675900277008310249 0.44930875576036866359 0.44046364594309799789 0.46540880503144654088 0.43159065628476084538 0.39629990262901655307 0.42783505154639175258 0.39345314505776636714 0.46390458254865034526 0.51282051282051282051 0.54014598540145985401 0.52646556630620375640 0.58305084745762711864 0.61123348017621145374 0.56675900277008310249 0.50891566265060240964 0.53459972862957937585 0.52023362536503963287 0.52145484266448712709 0.50724637681159420290 0.52156707558369608231 0.53728628592215351037 0.56725978647686832740 0.56792383742218967411 0.56391242937853107345 0.55536568694463431306 0.54930966469428007890 0.55339195979899497487 0.57015384615384615385 0.58041752425757130256 0.59335752162991906224 0.58621579762519359835 0.57616209773539928486 0.57911321179383299572 0.57726428718795131009 0.61237721021611001965 0.63735849056603773585 0.64630507284535720555 0.64508892805640121775 0.65419051812710993689 0.68711147948611686697 0.69486366518706404566 0.69478879706152433425 0.68537832310838445808 0.67742229514173730745 0.65696599405096872739 0.63945185719437432384 0.63336241025296920083 0.60927906829546173808 0.59254835039817974972 0.58284922453765389330 0.57211298532787703364 0.54578670929435944280 0.52934560753113675178 0.52444082645268829709 0.54529473369239976062 0.56654133938875032573 0.56287759734590536057 0.56496182690358759543 0.57031966461417529150 0.56070992581463048174 0.55467256214149139579 0.54466794021800202270 0.54058528249492683969 0.53348600508905852417 0.53924704724409448819 0.55188154875943139620 0.57359415530330073059 0.58082928440020755696 0.56619787153680375036 0.56088639360299434307 0.55500361801576281462 0.55199332468014092342 0.57337514671361502347 0.60381902305825242718 +16 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 Louisiana 22 0.35937500000000000000 0.34299516908212560386 0.36095346197502837684 0.35650887573964497041 0.36261980830670926518 0.38970588235294117647 0.40166204986149584488 0.38018433179723502304 0.37197049525816649104 0.43773584905660377358 0.40044493882091212458 0.35443037974683544304 0.38573883161512027491 0.37997432605905006418 0.49340866290018832392 0.54784240150093808630 0.54075425790754257908 0.47410358565737051793 0.50000000000000000000 0.56112334801762114537 0.59501385041551246537 0.53831325301204819277 0.54726368159203980100 0.53316645807259073842 0.54883530854107069881 0.55445134575569358178 0.55282944202611792639 0.54638050200072753729 0.57508896797153024911 0.59868180153789820579 0.59498587570621468927 0.57758031442241968558 0.57232084155161078238 0.56721105527638190955 0.58738461538461538462 0.58894442810937959424 0.59698576611777839799 0.60170366546205472380 0.60238379022646007151 0.61872608597794283142 0.59851454507943057561 0.61021611001964636542 0.62792452830188679245 0.63068281551693873969 0.63996154462425893286 0.66196976368706883898 0.68462494819726481558 0.70475586556753329106 0.70420110192837465565 0.71073619631901840491 0.71215021420107556285 0.71010531393198810194 0.72390912369275153264 0.70844796349728242636 0.68770175327552376733 0.66143344709897610922 0.64600543623087992325 0.60025950693682004192 0.56021922813427723224 0.54621568709126504769 0.54258247082994146606 0.56938210652304009575 0.59844395637121691546 0.59256155055002619172 0.59850685764475373286 0.61505960958993842526 0.61166932732337934704 0.60510277246653919694 0.59582537363748735813 0.59681725942539784257 0.58134860050890585242 0.57416338582677165354 0.59405378556730291634 0.61119440279860069965 0.62347280532100570782 0.61712102024039636517 0.63342690655437880141 0.65394168149727182055 0.64872983497125903950 0.65525968309859154930 0.66654657160194174757 +44 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 Virginia 51 0.37673611111111111111 0.37101449275362318841 0.41997729852440408627 0.42011834319526627219 0.45527156549520766773 0.47058823529411764706 0.48476454293628808864 0.44930875576036866359 0.44573234984193888303 0.49056603773584905660 0.47385984427141268076 0.45472249269717624148 0.50000000000000000000 0.50385109114249037227 0.52919020715630885122 0.56285178236397748593 0.57785888077858880779 0.57029026750142287991 0.57344632768361581921 0.63215859030837004405 0.62714681440443213296 0.60578313253012048193 0.64224332881049298960 0.62995410930329578640 0.62648140580302411116 0.64347826086956521739 0.64780371982588049070 0.62277191706074936340 0.61850533807829181495 0.65324057121933357744 0.66560734463276836158 0.66165413533834586466 0.65910585141354372124 0.66614321608040201005 0.68246153846153846154 0.70655689503087327257 0.71532235556795981021 0.70882808466701084151 0.70727056019070321812 0.73193787981093855503 0.73406230658139055086 0.74557956777996070727 0.77207547169811320755 0.78743198174477795331 0.79666720076910751482 0.80493174812857771907 0.81972648155822627435 0.82866201648700063412 0.82552800734618916437 0.82208588957055214724 0.81988879773949503236 0.81807219229841627140 0.81435268662098809953 0.81023954908407703147 0.81878599911386796633 0.81331058020477815700 0.81468848265202792730 0.81031041022058089630 0.79159625485270609728 0.77294122547590286167 0.76675582432065744079 0.76817773788150807899 0.78516919182518706027 0.76706827309236947791 0.76930612796432673468 0.77652954277479365911 0.76551788900366231571 0.76168140535372848948 0.75199460613552084504 0.75678201431165224821 0.75798982188295165394 0.76678149606299212598 0.77454528252796896805 0.79091406677613574165 0.81256191329779706590 0.80617209891355062321 0.81459742248309302029 0.79486828466939158665 0.78816984980530317078 0.79608641431924882629 0.81938334344660194175 +\. + + +-- +-- Name: markov_usjoin_example_pkey; Type: CONSTRAINT; Schema: public; Owner: postgres; Tablespace: +-- + +ALTER TABLE ONLY markov_usjoin_example + ADD CONSTRAINT markov_usjoin_example_pkey PRIMARY KEY (cartodb_id); + + +-- +-- PostgreSQL database dump complete +-- + diff --git a/src/pg/test/fixtures/ml_values.sql b/src/pg/test/fixtures/ml_values.sql new file mode 100644 index 0000000..c87a10f --- /dev/null +++ b/src/pg/test/fixtures/ml_values.sql @@ -0,0 +1,2005 @@ +SET client_min_messages TO WARNING; +\set ECHO none +CREATE TABLE ml_values (cartodb_id integer, target float, the_geom geometry, x1 float , x2 float, x3 float, class text); +INSERT INTO ml_values(cartodb_id, target,x1,x2,x3, class) VALUES +(0,1.24382137034,0.811403626309,0.657584780869,0,'train'), +(1,1.72727475342,0.447764244847,0.528687533966,1,'train'), +(2,3.32104694099,0.62774565606,0.832647155118,2,'train'), +(3,3.95282364134,0.881898806954,0.266317168772,3,'train'), +(4,3.80247130968,0.665074747038,0.370670423211,3,'train'), +(5,2.10381192188,0.883366590314,0.469516061027,1,'train'), +(6,1.17893557213,0.711670077404,0.683568207806,0,'train'), +(7,1.80674380603,0.66527165164,0.376127843149,1,'train'), +(8,0.276910799403,0.12432503742,0.390622275329,0,'train'), +(9,1.04011429426,0.0385633572229,0.0393819379254,1,'train'), +(10,1.32965631694,0.208292255072,0.348373451724,1,'train'), +(11,3.91621773493,0.266859692111,0.805827551541,3,'train'), +(12,0.819422907018,0.141031077681,0.823645451233,0,'train'), +(13,0.86861841075,0.865895751347,0.0521791088729,0,'train'), +(14,3.52651073623,0.299745485848,0.476198750922,3,'train'), +(15,3.60697425252,0.806256087392,0.89482856745,2,'train'), +(16,3.25898808929,0.250608720014,0.0915388948687,3,'train'), +(17,1.66522265308,0.429070089585,0.485955310186,1,'train'), +(18,3.162287269,0.131313802907,0.175992801258,3,'train'), +(19,3.52084351408,0.890659436085,0.793841343087,2,'train'), +(20,2.58431797652,0.825925910476,0.87085708704,1,'train'), +(21,2.36135058874,0.836884033221,0.724200632088,1,'train'), +(22,2.95373698573,0.715546042878,0.488048094811,2,'train'), +(23,4.06612839317,0.350181776217,0.846136287455,3,'train'), +(24,1.71396870328,0.203801924342,0.71425960192,1,'train'), +(25,1.02568147198,0.37388512127,0.807339055608,0,'train'), +(26,2.84381682437,0.830147511036,0.11691583866,2,'train'), +(27,3.65269921585,0.507457283779,0.381106195272,3,'train'), +(28,1.36070847289,0.13136938957,0.478893603336,1,'train'), +(29,0.166643149194,0.158235885087,0.0916911342867,0,'train'), +(30,4.02134208186,0.995768081662,0.159918729994,3,'train'), +(31,1.06182066152,0.9315232293,0.360967356166,0,'train'), +(32,1.88309332845,0.0711466915165,0.901080815981,1,'train'), +(33,1.3106116557,0.145148601676,0.406771500996,1,'train'), +(34,4.08110160149,0.809824588582,0.520842598979,3,'train'), +(35,1.95817443835,0.720172089254,0.487854844289,1,'train'), +(36,3.09445483037,0.462289717081,0.795088116679,2,'train'), +(37,3.9825595986,0.896251729074,0.293782010221,3,'train'), +(38,3.30192844335,0.116150213431,0.431019987837,3,'train'), +(39,1.494298122,0.899654633056,0.771131304607,0,'train'), +(40,2.39157161067,0.0900380728132,0.549120695164,2,'train'), +(41,1.63988698756,0.615331744134,0.156701127709,1,'train'), +(42,2.81254514925,0.792540827545,0.141436634949,2,'train'), +(43,1.76453635553,0.171175181353,0.770299405539,1,'train'), +(44,4.19365739446,0.956470538768,0.487018331989,3,'train'), +(45,4.17724977996,0.689416001736,0.698450984842,3,'train'), +(46,3.44571626758,0.121638801413,0.569278021857,3,'train'), +(47,0.807182622364,0.358783853615,0.669625842354,0,'train'), +(48,3.7363232527,0.190185523211,0.739011318916,3,'train'), +(49,1.86578481285,0.985653593244,0.938153089644,0,'train'), +(50,3.3144357104,0.630893004142,0.826766415775,2,'train'), +(51,1.02755553675,0.847642795972,0.424161220266,0,'train'), +(52,4.52608792887,0.723755345588,0.895730195587,3,'train'), +(53,3.55148802171,0.331736804231,0.468776297904,3,'train'), +(54,4.74137857538,0.911580430367,0.910932568864,3,'train'), +(55,2.13162450741,0.982444321588,0.386238508981,1,'train'), +(56,1.31154833663,0.21649611054,0.308305410407,1,'train'), +(57,1.38663340377,0.371776142608,0.121890365347,1,'train'), +(58,1.99685029785,0.18457064921,0.901265581635,1,'train'), +(59,3.70115259653,0.483173481131,0.466882335707,3,'train'), +(60,4.20176093595,0.465662529454,0.857961774494,3,'train'), +(61,1.7184797125,0.346403723018,0.609980318927,1,'train'), +(62,3.14156465929,0.126578834574,0.122416603103,3,'train'), +(63,1.18710602542,0.125258709523,0.248691205919,1,'train'), +(64,3.13150427478,0.124611730533,0.0830213481477,3,'train'), +(65,2.17689203809,0.70068505942,0.690077516419,1,'train'), +(66,1.18342205287,0.773038174481,0.640612112268,0,'train'), +(67,1.9975132827,0.994622569346,0.0537653546163,1,'train'), +(68,0.432033448975,0.421818049735,0.101071258228,0,'train'), +(69,0.661083575367,0.280197452927,0.617159721984,0,'train'), +(70,3.22520898428,0.328672092283,0.946856320674,2,'train'), +(71,3.45938438304,0.0542900404295,0.636470221309,3,'train'), +(72,2.34118444802,0.835758112414,0.710933425577,1,'train'), +(73,1.39379113863,0.0184347707593,0.612663339748,1,'train'), +(74,2.73864731229,0.707545994952,0.176355655807,2,'train'), +(75,2.45820282065,0.552259848697,0.951810365543,1,'train'), +(76,2.58033950412,0.0331182580413,0.739744040921,2,'train'), +(77,0.310831960497,0.25330953236,0.239838337504,0,'train'), +(78,2.8502591452,0.840003315424,0.101271070764,2,'train'), +(79,4.05058044205,0.467543162702,0.763568778404,3,'train'), +(80,0.976286667289,0.776077091797,0.447447846673,0,'train'), +(81,3.79106063676,0.710208952573,0.284344305711,3,'train'), +(82,1.0419324905,0.447613188073,0.770921074055,0,'train'), +(83,3.30507844095,0.669803310816,0.797041485828,2,'train'), +(84,0.421245261502,0.0797069093013,0.584412826862,0,'train'), +(85,2.36278003018,0.67380980013,0.830042306182,1,'train'), +(86,2.5676448622,0.728200751562,0.916211826292,1,'train'), +(87,1.54096382777,0.958926642927,0.762913615585,0,'train'), +(88,3.87941054679,0.71383264077,0.406912651582,3,'train'), +(89,1.3714934845,0.739373292283,0.795059867062,0,'train'), +(90,2.11791863693,0.997242892611,0.347384145181,1,'train'), +(91,1.83165883949,0.774846387789,0.238353627404,1,'train'), +(92,3.93892077639,0.110313686527,0.910278578164,3,'train'), +(93,0.46853056251,0.460889170503,0.087415055953,0,'train'), +(94,2.16415058019,0.790359655066,0.611384433172,1,'train'), +(95,3.19504017985,0.0194446630378,0.419041187492,3,'train'), +(96,1.05622112802,0.729589180698,0.571517232742,0,'train'), +(97,1.45680483768,0.41739274432,0.198524792802,1,'train'), +(98,4.03277024539,0.342001341974,0.831125082894,3,'train'), +(99,3.19597374514,0.136152364733,0.244584096794,3,'train'), +(100,0.674198979717,0.141247074788,0.730035550456,0,'train'), +(101,1.6030553627,0.449039378135,0.392448703099,1,'train'), +(102,3.14484686869,0.422000710067,0.850203598337,2,'train'), +(103,2.93201375767,0.621183774587,0.557521284873,2,'train'), +(104,1.92255465696,0.0239666590258,0.947938815503,1,'train'), +(105,0.750374417884,0.498514838544,0.501856134106,0,'train'), +(106,2.66183539,0.334440387094,0.572184413373,2,'train'), +(107,1.50762714512,0.449439358706,0.241221446846,1,'train'), +(108,4.02466160506,0.989637668904,0.187146830464,3,'train'), +(109,3.9611061532,0.999626040389,0.980550923111,2,'train'), +(110,1.24886880389,0.760865649238,0.698572225794,0,'train'), +(111,3.69094389192,0.158127308309,0.729942863252,3,'train'), +(112,4.47144774067,0.566340168609,0.951371416464,3,'train'), +(113,3.84989330608,0.488585062607,0.601089214236,3,'train'), +(114,3.90544176126,0.333414678464,0.756324720474,3,'train'), +(115,3.60658729206,0.542168232709,0.253809100212,3,'train'), +(116,2.80910993644,0.98391434046,0.90840277189,1,'train'), +(117,0.529897517275,0.37845063724,0.389161765896,0,'train'), +(118,3.33158531793,0.172039843893,0.399431438462,3,'train'), +(119,4.08360113059,0.632897538088,0.67134461531,3,'train'), +(120,0.36058924894,0.130904507554,0.479254359799,0,'train'), +(121,0.948394575974,0.874257647434,0.272280973519,0,'train'), +(122,3.02851043918,0.5276474372,0.70771675265,2,'train'), +(123,0.649506349365,0.144414854231,0.710697893013,0,'train'), +(124,3.86111624,0.0753604101866,0.886428694152,3,'train'), +(125,2.24716868088,0.973162166433,0.5234563157,1,'train'), +(126,1.95686980382,0.956760054448,0.0104761333011,1,'train'), +(127,3.05292710244,0.0385433904369,0.119932114149,3,'train'), +(128,2.62313724007,0.952837884549,0.818718117256,1,'train'), +(129,3.73871514167,0.710940230426,0.16665806684,3,'train'), +(130,1.63871472777,0.33635764867,0.549870056556,1,'train'), +(131,0.325099905771,0.184044265052,0.375573748708,0,'train'), +(132,2.11620242739,0.112128848688,0.0638245932143,2,'train'), +(133,0.1525548278,0.151989744456,0.0237714817413,0,'train'), +(134,3.14701565035,0.998058126006,0.385950157845,2,'train'), +(135,3.64472496462,0.644207576462,0.0227461680587,3,'train'), +(136,1.4811667713,0.432870913159,0.2197631865,1,'train'), +(137,0.486812051883,0.44065685385,0.214837608517,0,'train'), +(138,2.52640998137,0.523932372796,0.0497755821311,2,'train'), +(139,3.58537193033,0.799598306898,0.886438730782,2,'train'), +(140,3.41697465653,0.366453771497,0.224768514327,3,'train'), +(141,1.68831002325,0.66346637256,0.157618687637,1,'train'), +(142,2.50832573117,0.440842333094,0.25977566876,2,'train'), +(143,3.34293652186,0.644421725216,0.835771976464,2,'train'), +(144,1.52687970225,0.455824019792,0.266562717682,1,'train'), +(145,3.38355056147,0.936893026941,0.668324423112,2,'train'), +(146,2.9186681931,0.683796057327,0.484636085919,2,'train'), +(147,3.43119285958,0.35825012194,0.270079132188,3,'train'), +(148,2.44982622143,0.531373877867,0.958359193394,1,'train'), +(149,1.56467859084,0.562383273711,0.0479094680276,1,'train'), +(150,1.4147566619,0.346784386037,0.260714932186,1,'train'), +(151,2.64807483346,0.101312694159,0.739433661189,2,'train'), +(152,0.355070783716,0.330671039063,0.156204176171,0,'train'), +(153,1.63481376891,0.545070698823,0.299571477422,1,'train'), +(154,1.7223421552,0.699505451379,0.151118178335,1,'train'), +(155,4.54679308537,0.635100879441,0.954825746369,3,'train'), +(156,1.97215514937,0.867886595262,0.322906416958,1,'train'), +(157,1.21902077771,0.788118404039,0.656431545303,0,'train'), +(158,3.44049935676,0.749514849173,0.831254778985,2,'train'), +(159,1.00582794774,0.975372732143,0.17451422749,0,'train'), +(160,4.30589162814,0.722820935848,0.763590657549,3,'train'), +(161,0.906132042553,0.894978072798,0.105612356072,0,'train'), +(162,3.22766306171,0.204157590595,0.153314940931,3,'train'), +(163,1.56350600184,0.454133352705,0.33071535969,1,'train'), +(164,2.64217591247,0.726104057929,0.957116426846,1,'train'), +(165,1.60856928053,0.786138835293,0.906879509765,0,'train'), +(166,4.02384662579,0.492593147827,0.728871372714,3,'train'), +(167,3.69385120323,0.58902541735,0.323768105101,3,'train'), +(168,2.04441852581,0.875400416322,0.411118121089,1,'train'), +(169,2.39459275688,0.46138814577,0.966025160704,1,'train'), +(170,1.64761351845,0.497112314903,0.387944846006,1,'train'), +(171,2.68492168842,0.225544125445,0.677773976321,2,'train'), +(172,2.24044390692,0.205190156911,0.187759820009,2,'train'), +(173,0.236505988436,0.167122987329,0.263406532013,0,'train'), +(174,1.69740019481,0.64342808251,0.232318988243,1,'train'), +(175,1.42768204741,0.921523736652,0.711448037991,0,'train'), +(176,2.65464841219,0.410330229053,0.494285527947,2,'train'), +(177,0.220625300247,0.218783719264,0.0429136456521,0,'train'), +(178,0.902080497188,0.875728329944,0.162333506226,0,'train'), +(179,2.3734585601,0.373385586057,0.0085424848658,2,'train'), +(180,2.68418222177,0.498319457905,0.431118039365,2,'train'), +(181,1.65090360296,0.0582627105736,0.76983172992,1,'train'), +(182,2.70121647756,0.700836969263,0.0194809727219,2,'train'), +(183,3.64471767929,0.620811580137,0.154615973154,3,'train'), +(184,0.697598964398,0.35720278891,0.583434808259,0,'train'), +(185,2.09418732317,0.700734656898,0.627258053969,1,'train'), +(186,2.77565652503,0.534894620472,0.490674947966,2,'train'), +(187,2.99118156734,0.944799107597,0.215365874127,2,'train'), +(188,2.16246763706,0.985465980607,0.420715647983,1,'train'), +(189,0.792489856319,0.0470340536513,0.863397824104,0,'train'), +(190,0.687119116403,0.150185800385,0.732757337744,0,'train'), +(191,4.00045366032,0.772811734907,0.477118355766,3,'train'), +(192,4.00531986539,0.890961861059,0.338168603402,3,'train'), +(193,3.98787982655,0.917170421429,0.265912401226,3,'train'), +(194,0.800103167701,0.583836264582,0.465045054934,0,'train'), +(195,3.16136367592,0.157590380223,0.0614271576693,3,'train'), +(196,1.24032152242,0.222620887834,0.133043731843,1,'train'), +(197,3.94586252163,0.936739896945,0.0955124321134,3,'train'), +(198,0.724612971022,0.556639623191,0.409845517031,0,'train'), +(199,1.71914971653,0.486382998421,0.482459032575,1,'train'), +(200,0.701027027956,0.42292722954,0.527351683809,0,'train'), +(201,1.00451594424,0.213906286204,0.88916233503,0,'train'), +(202,1.28560098048,0.458749446911,0.909313770693,0,'train'), +(203,4.18130653509,0.872826563929,0.555409732681,3,'train'), +(204,3.70513653927,0.718920678846,0.993084014786,2,'train'), +(205,0.883517177383,0.836370992358,0.217131722751,0,'train'), +(206,2.41822052154,0.397240247753,0.144845689579,2,'train'), +(207,3.6881507522,0.134094496362,0.744349552182,3,'train'), +(208,2.94061893438,0.819973365795,0.347340709657,2,'train'), +(209,1.96736985525,0.932919137643,0.185609045059,1,'train'), +(210,3.55671188827,0.522939874975,0.183771633548,3,'train'), +(211,2.59491479328,0.569803336867,0.158465947167,2,'train'), +(212,0.845317716043,0.760524441834,0.291192847111,0,'train'), +(213,3.84865511262,0.953008053247,0.946386316139,2,'train'), +(214,1.79421500329,0.719882898171,0.272639148184,1,'train'), +(215,1.61283368743,0.580804023591,0.178968331944,1,'train'), +(216,1.69166880922,0.994233556172,0.835125890536,0,'train'), +(217,3.6663085942,0.558387950098,0.328512776159,3,'train'), +(218,4.14739895574,0.42903354328,0.847564400183,3,'train'), +(219,3.84834184259,0.829160865001,0.13849540639,3,'train'), +(220,3.70418573269,0.703884379256,0.0173595343695,3,'train'), +(221,2.32691481998,0.85344048504,0.68809471364,1,'train'), +(222,1.12861991727,0.639123203252,0.699640417657,0,'train'), +(223,3.3697979033,0.411858825745,0.978743621974,2,'train'), +(224,2.38363760199,0.38363455315,0.00174609204425,2,'train'), +(225,4.12587571793,0.945570023524,0.424624180199,3,'train'), +(226,1.64781419808,0.953412012914,0.833307977382,0,'train'), +(227,3.88605908504,0.743467379141,0.377613169665,3,'train'), +(228,3.85260504053,0.200479368566,0.807542984592,3,'train'), +(229,0.620538868538,0.5957157602,0.157553509444,0,'train'), +(230,2.96759001799,0.906294428571,0.247579460818,2,'train'), +(231,0.803879749102,0.359243472208,0.666810525482,0,'train'), +(232,1.21289265217,0.736679873124,0.690081719108,0,'train'), +(233,2.69090231294,0.624244952275,0.258180868115,2,'train'), +(234,2.06499182801,0.0439160520786,0.145174983831,2,'train'), +(235,1.91462241156,0.491150554019,0.65074715331,1,'train'), +(236,3.36652154268,0.53627297948,0.911179764482,2,'train'), +(237,0.984331075591,0.9624499175,0.14792281126,0,'train'), +(238,0.985365737701,0.683660823188,0.549276719435,0,'train'), +(239,0.480626074803,0.422862762119,0.240339993935,0,'train'), +(240,3.89794584279,0.0764720388433,0.906351920586,3,'train'), +(241,1.41850659471,0.396382097208,0.148743058659,1,'train'), +(242,2.76083817204,0.785713543335,0.987483989086,1,'train'), +(243,0.630662008259,0.0476826574982,0.763530844669,0,'train'), +(244,1.63198293695,0.630147700114,0.0428396642867,1,'train'), +(245,3.66212785948,0.0821554006103,0.761559228736,3,'train'), +(246,2.69689387455,0.210582393177,0.697360366938,2,'train'), +(247,3.83034962216,0.959005393505,0.93345820938,2,'train'), +(248,0.525457665501,0.524263722989,0.0345534732191,0,'train'), +(249,0.861481042856,0.162623542445,0.835976973613,0,'train'), +(250,1.18923074552,0.13365024941,0.235755161358,1,'train'), +(251,2.97758839764,0.395334103472,0.7630558919,2,'train'), +(252,1.94085977346,0.935959937907,0.0699988253456,1,'train'), +(253,2.27729296713,0.236608890328,0.201702941988,2,'train'), +(254,1.16865293427,0.262844805223,0.951739527942,0,'train'), +(255,0.876256070676,0.599286264016,0.526279209793,0,'train'), +(256,3.4820175379,0.637430802843,0.919014001558,2,'train'), +(257,2.62263559323,0.214770605542,0.638643083172,2,'train'), +(258,4.26774699502,0.678896999789,0.767365620305,3,'train'), +(259,1.92742959729,0.038076073495,0.943055419259,1,'train'), +(260,0.830310249039,0.336972363075,0.702380157724,0,'train'), +(261,0.190093286729,0.189345746521,0.027341181537,0,'train'), +(262,1.85856149645,0.350967827008,0.712456082468,1,'train'), +(263,2.19868876763,0.794310568466,0.635907382534,1,'train'), +(264,2.57775279682,0.282887761421,0.54301476536,2,'train'), +(265,3.20347370976,0.633154837581,0.75519459226,2,'train'), +(266,0.632469198504,0.270791595383,0.601396377709,0,'train'), +(267,0.922686896791,0.849793576252,0.269987630345,0,'train'), +(268,1.17592252313,0.793421991901,0.618466273316,0,'train'), +(269,1.16516284095,0.578767949852,0.765764252949,0,'train'), +(270,1.01311122453,0.888906154698,0.352427396545,0,'train'), +(271,2.65175162691,0.0944527639096,0.746524522709,2,'train'), +(272,1.45375342664,0.874605879696,0.761017441943,0,'train'), +(273,3.47583088126,0.253728465861,0.471277429331,3,'train'), +(274,0.589399547981,0.258841119105,0.574942109152,0,'train'), +(275,3.86372858515,0.891783117826,0.985872946849,2,'train'), +(276,1.06581404933,0.0593192118575,0.0805905545103,1,'train'), +(277,1.1873391595,0.0866856156474,0.317259426729,1,'train'), +(278,0.865600889401,0.0958516230905,0.877353558328,0,'train'), +(279,1.31207151088,0.99504370054,0.563052227007,0,'train'), +(280,3.27474268384,0.0126251383396,0.511974164877,3,'train'), +(281,1.47102201088,0.461570924759,0.0972166967251,1,'train'), +(282,2.29687679698,0.842379621346,0.674164056911,1,'train'), +(283,0.844185133675,0.275029507464,0.754424036077,0,'train'), +(284,2.07111091001,0.381008814631,0.830723838214,1,'train'), +(285,0.077539650653,0.0763769730851,0.0340980581244,0,'train'), +(286,1.8276930277,0.0952086879959,0.85585298954,1,'train'), +(287,4.23841977745,0.559781482165,0.823795056602,3,'train'), +(288,0.815109004853,0.341787559485,0.687983608357,0,'train'), +(289,3.46743270302,0.160450701172,0.55405956525,3,'train'), +(290,2.42266450219,0.961978440732,0.678738581085,1,'train'), +(291,2.99004689776,0.956935036248,0.18196664946,2,'train'), +(292,2.71323856164,0.692224597576,0.144961940055,2,'train'), +(293,2.05482704476,0.582246228265,0.68744513708,1,'train'), +(294,3.72152683971,0.665255175512,0.237216492248,3,'train'), +(295,1.81261247465,0.290164799032,0.722805420299,1,'train'), +(296,2.95071568645,0.301091012724,0.805992973746,2,'train'), +(297,3.60095700472,0.590015080655,0.104603652264,3,'train'), +(298,3.22976379154,0.170499829501,0.24344190691,3,'train'), +(299,2.53052994672,0.400046262715,0.361225253824,2,'train'), +(300,4.29248168564,0.714346239362,0.760352185683,3,'train'), +(301,3.63419301177,0.730822756503,0.950457918724,2,'train'), +(302,0.838103142117,0.736571494019,0.318640311477,0,'train'), +(303,2.31953053783,0.314612236636,0.0701306009595,2,'train'), +(304,2.63166163285,0.179836930722,0.67217906999,2,'train'), +(305,0.975304021572,0.296559450289,0.823859557985,0,'train'), +(306,1.75094033332,0.31909260818,0.657151219389,1,'train'), +(307,0.664783393542,0.169172615206,0.703996291422,0,'train'), +(308,2.04218742681,0.784692561315,0.507439519049,1,'train'), +(309,1.57636682723,0.383858484348,0.438757726866,1,'train'), +(310,1.2282706945,0.146782608227,0.285461181734,1,'train'), +(311,0.551187755918,0.144170303427,0.637979194404,0,'train'), +(312,2.56058324309,0.483713500532,0.277253931547,2,'train'), +(313,4.15764790974,0.958622990947,0.446122089557,3,'train'), +(314,2.79121629685,0.689782775695,0.318486296655,2,'train'), +(315,0.748431598562,0.137843689504,0.781401247156,0,'train'), +(316,1.66420987831,0.250719037897,0.64303253449,1,'train'), +(317,2.29475888839,0.0881384447859,0.454555215132,2,'train'), +(318,3.74719508348,0.236978994873,0.714294119121,3,'train'), +(319,2.78454298817,0.678070344718,0.326301460998,2,'train'), +(320,1.10371480513,0.0796255420211,0.155207161917,1,'train'), +(321,1.76641748403,0.863825876329,0.950048213354,0,'train'), +(322,2.49335475767,0.316214332717,0.42088053525,2,'train'), +(323,3.54333632187,0.172684341126,0.608811942016,3,'train'), +(324,2.22166069926,0.193473509034,0.167890411361,2,'train'), +(325,2.12581391522,0.117233470914,0.0926306876841,2,'train'), +(326,1.1106964371,0.8863218799,0.473681915635,0,'train'), +(327,2.15757621852,0.173216092038,0.992149246075,1,'train'), +(328,2.73658434122,0.358163646105,0.615159081143,2,'train'), +(329,2.59452938859,0.567128328148,0.165532656726,2,'train'), +(330,0.204381212023,0.145453208602,0.242750908178,0,'train'), +(331,2.566539304,0.0338857225982,0.729831200622,2,'train'), +(332,1.50047183779,0.150044407722,0.59196911243,1,'train'), +(333,1.20318271888,0.60453212288,0.773725142411,0,'train'), +(334,4.14756975063,0.997470494993,0.38742645191,3,'train'), +(335,2.54704842308,0.964634439528,0.763160522797,1,'train'), +(336,3.49783786504,0.987582572101,0.7143215613,2,'train'), +(337,2.46569832119,0.98128027012,0.696001473467,1,'train'), +(338,3.00370483816,0.801531906037,0.449636444387,2,'train'), +(339,3.34946013288,0.170840493359,0.422634167475,3,'train'), +(340,1.8134430762,0.76012023851,0.230917382812,1,'train'), +(341,4.01785111498,0.848006557061,0.412122018243,3,'train'), +(342,2.38847231817,0.156840694177,0.481281231702,2,'train'), +(343,4.21071602043,0.896237445362,0.560783893375,3,'train'), +(344,2.76188152894,0.194672158523,0.753133036332,2,'train'), +(345,1.39793034843,0.460185425697,0.968372305848,0,'train'), +(346,2.95426195892,0.0959496686049,0.926451450598,2,'train'), +(347,3.86987063976,0.868526979401,0.036655973104,3,'train'), +(348,0.563491456666,0.266468990277,0.544997675581,0,'train'), +(349,2.85618091544,0.855777109473,0.0200949238374,2,'train'), +(350,1.91344572093,0.725274473213,0.433787099525,1,'train'), +(351,3.56002399866,0.677283328575,0.939542798431,2,'train'), +(352,0.791124753553,0.769353271193,0.147551626086,0,'train'), +(353,1.30748850069,0.0113869481294,0.544152141007,1,'train'), +(354,1.51803181516,0.581141627833,0.967930879414,0,'train'), +(355,3.56002976317,0.142404802346,0.646239089516,3,'train'), +(356,0.596999512076,0.596434559096,0.0237687395507,0,'train'), +(357,2.10739518646,0.882980331155,0.473724450818,1,'train'), +(358,0.799164494823,0.788047867806,0.105435416331,0,'train'), +(359,1.64987834729,0.523003187071,0.356195396126,1,'train'), +(360,2.16786155357,0.150961121726,0.13000166092,2,'train'), +(361,3.61040687165,0.659965886629,0.974905628775,2,'train'), +(362,2.97545681733,0.982196849344,0.996624286269,1,'train'), +(363,4.29316606884,0.844843787562,0.669568727825,3,'train'), +(364,4.44648332608,0.835021494275,0.781960249507,3,'train'), +(365,2.25057031703,0.000773777407512,0.499796498213,2,'train'), +(366,1.25257530532,0.0909098245332,0.402076461366,1,'train'), +(367,3.67135736022,0.8949240159,0.881154551891,2,'train'), +(368,4.41622174539,0.664159254405,0.867215365977,3,'train'), +(369,2.18574155326,0.492151046784,0.832820812945,1,'train'), +(370,1.9143141796,0.868178728805,0.21479164508,1,'train'), +(371,2.6496939551,0.381294704864,0.518072630275,2,'train'), +(372,3.80102066555,0.149213176068,0.807345954028,3,'train'), +(373,2.7772675577,0.873441815607,0.950697502938,1,'train'), +(374,2.66383064066,0.210515315051,0.673286956364,2,'train'), +(375,1.18744758619,0.806568574132,0.61715396139,0,'train'), +(376,2.50432137301,0.504193972627,0.0112871777609,2,'train'), +(377,1.65232178342,0.190782640275,0.679366722135,1,'train'), +(378,0.216548289434,0.0350450971435,0.426031914639,0,'train'), +(379,1.89801398531,0.854429884034,0.208768056165,1,'train'), +(380,2.59459973115,0.498244828348,0.310410861276,2,'train'), +(381,1.49906884919,0.887433584663,0.782071137768,0,'train'), +(382,3.83949120738,0.776244945907,0.2514880941,3,'train'), +(383,0.996948415073,0.945722943258,0.226330448272,0,'train'), +(384,2.23674163998,0.934670617374,0.549609882198,1,'train'), +(385,3.86753529951,0.439466120143,0.65426995909,3,'train'), +(386,2.75218469685,0.266691198166,0.696773635184,2,'train'), +(387,3.67226558137,0.0359568909104,0.797689595305,3,'train'), +(388,1.18396975781,0.78847940941,0.628880233748,0,'train'), +(389,1.8281691901,0.636934297672,0.437304118921,1,'train'), +(390,3.39316302573,0.231497124561,0.402076984127,3,'train'), +(391,1.97156837706,0.810477779969,0.401360931191,1,'train'), +(392,0.326005646879,0.31542799544,0.102847709934,0,'train'), +(393,3.77529987287,0.193138447597,0.762995036207,3,'train'), +(394,0.404991080326,0.26462005965,0.374661207862,0,'train'), +(395,2.54244371023,0.246605747233,0.543909885,2,'train'), +(396,2.6684293602,0.549178196702,0.345327617626,2,'train'), +(397,1.34663375311,0.305396180334,0.203070364109,1,'train'), +(398,3.03821883597,0.718848856216,0.565128286107,2,'train'), +(399,3.30844448365,0.197294045161,0.333392319178,3,'train'), +(400,1.76708778971,0.351660952206,0.644536141346,1,'train'), +(401,2.19288933129,0.166187481641,0.163407006113,2,'train'), +(402,1.03244020978,0.030791397522,0.0406055693155,1,'train'), +(403,0.771574541234,0.444845689166,0.571602004955,0,'train'), +(404,3.79033185346,0.734240049438,0.236837083289,3,'train'), +(405,1.14478684595,0.102240151174,0.206268501653,1,'train'), +(406,3.40874657815,0.0865458594342,0.567627270941,3,'train'), +(407,1.06491450952,0.982979841473,0.286242323995,0,'train'), +(408,0.535434837721,0.418233058076,0.342347454561,0,'train'), +(409,2.41173077867,0.701821898754,0.842560905762,1,'train'), +(410,4.09289201995,0.544201347332,0.740736574377,3,'train'), +(411,3.44256370804,0.749248071362,0.83265577322,2,'train'), +(412,3.11232634313,0.0652220974382,0.217035125477,3,'train'), +(413,1.43287829588,0.970202680338,0.680202628298,0,'train'), +(414,3.74688543887,0.456671085777,0.538715465803,3,'train'), +(415,1.52774773516,0.40919367052,0.344316808541,1,'train'), +(416,3.51592792958,0.0347311982555,0.693683451816,3,'train'), +(417,1.15781412817,0.820426435015,0.580850835546,0,'train'), +(418,0.469889852652,0.436650793301,0.182315823095,0,'train'), +(419,2.58749479728,0.0534933012757,0.730754059861,2,'train'), +(420,4.1065431692,0.834869600156,0.521223147072,3,'train'), +(421,0.809284017219,0.524846417573,0.533326916296,0,'train'), +(422,2.58595893036,0.553485629255,0.180203499146,2,'train'), +(423,2.25777140465,0.72624304772,0.729059913125,1,'train'), +(424,1.72917055396,0.710125073451,0.13800536406,1,'train'), +(425,0.124649936754,0.124115071305,0.0231271582434,0,'train'), +(426,1.53509666829,0.998531920468,0.732505800537,0,'train'), +(427,3.02847113374,0.79440790934,0.483800810667,2,'train'), +(428,4.3851179319,0.783297574467,0.775770815016,3,'train'), +(429,3.27087178027,0.100197546736,0.413127381732,3,'train'), +(430,3.22844173875,0.835680707857,0.626706495012,2,'train'), +(431,2.74104331843,0.373032611942,0.606638860025,2,'train'), +(432,1.95049168742,0.752227208753,0.445268995854,1,'train'), +(433,1.29644698132,0.471302832527,0.908374454061,0,'train'), +(434,3.93537153239,0.733225365863,0.44960667981,3,'train'), +(435,3.81501278203,0.728951014591,0.293362859677,3,'train'), +(436,4.10866710473,0.207987014993,0.949041669127,3,'train'), +(437,2.7634739237,0.590973913842,0.415331205009,2,'train'), +(438,1.53152308102,0.117656420726,0.643324692742,1,'train'), +(439,0.652380533996,0.319926263427,0.576588475925,0,'train'), +(440,4.2112267977,0.762487307135,0.66988020613,3,'train'), +(441,1.62726294874,0.159706593681,0.683780926218,1,'train'), +(442,4.10543372071,0.223663519748,0.939026198232,3,'train'), +(443,2.90859925322,0.437374419854,0.686458180349,2,'train'), +(444,1.1195055957,0.998546959599,0.347791081106,0,'train'), +(445,1.16528637542,0.0825892555745,0.2875710692,1,'train'), +(446,2.69981869457,0.394716282032,0.552360763032,2,'train'), +(447,2.31472640151,0.466597210444,0.920939298255,1,'train'), +(448,0.840007432044,0.839287186984,0.0268373817632,0,'train'), +(449,3.37533076432,0.53220518824,0.918218697303,2,'train'), +(450,0.498196246143,0.494527456286,0.0605705362125,0,'train'), +(451,3.63867107652,0.604844737758,0.183919381144,3,'train'), +(452,2.44169783629,0.209551918468,0.48181523204,2,'train'), +(453,3.90584318551,0.905412754464,0.0207468321212,3,'train'), +(454,2.20274681323,0.317611550398,0.940816274751,1,'train'), +(455,1.07374375619,0.045673775221,0.167540982968,1,'train'), +(456,2.22789450557,0.406208007853,0.906469248081,1,'train'), +(457,0.738025792896,0.591130044355,0.383269811675,0,'train'), +(458,4.37758286643,0.403908240559,0.986749525398,3,'train'), +(459,4.18993686556,0.504331421886,0.82801294898,3,'train'), +(460,2.36744693265,0.291644989671,0.275321526539,2,'train'), +(461,4.27484787332,0.822147391766,0.672830202619,3,'train'), +(462,2.62893385742,0.0381880076456,0.768599928296,2,'train'), +(463,2.82103680329,0.267611552273,0.743925568196,2,'train'), +(464,3.33576384176,0.014899312033,0.566449053076,3,'train'), +(465,0.494069350277,0.424832132736,0.263129659181,0,'train'), +(466,4.33546872608,0.449962442518,0.941013434313,3,'train'), +(467,3.1657740975,0.164681194777,0.0330590792866,3,'train'), +(468,0.913594110102,0.106448948744,0.898412578584,0,'train'), +(469,0.0876071701603,0.0667651930665,0.144367507057,0,'train'), +(470,1.01627143078,0.972344146897,0.209588367727,0,'train'), +(471,2.99194992775,0.949094558881,0.207015383182,2,'train'), +(472,1.78094705038,0.255829485466,0.724649960263,1,'train'), +(473,3.54883347116,0.129841055614,0.647296234767,3,'train'), +(474,2.03002797557,0.959341658726,0.265868984358,1,'train'), +(475,3.29448636962,0.11285360726,0.426183953659,3,'train'), +(476,2.18606596886,0.129753823782,0.237301801664,2,'train'), +(477,3.5107261593,0.510192916321,0.0230920544285,3,'train'), +(478,1.15017973632,0.78358600181,0.605469846076,0,'train'), +(479,2.76224893245,0.738760418257,0.153259630026,2,'train'), +(480,2.31756997885,0.910168791138,0.638279866288,1,'train'), +(481,2.76038513132,0.982550038137,0.881949597868,1,'train'), +(482,4.01748906583,0.83510152367,0.427068545034,3,'train'), +(483,2.12787166797,0.372905103465,0.868888119669,1,'train'), +(484,2.57957368103,0.667245129304,0.955158914385,1,'train'), +(485,3.70415295095,0.495616433551,0.456657987337,3,'train'), +(486,1.64592760512,0.278527801341,0.606135136566,1,'train'), +(487,3.83709283289,0.641371840859,0.442403652818,3,'train'), +(488,2.56129870357,0.426296283084,0.367426755263,2,'train'), +(489,3.91279276709,0.934702510826,0.988984457039,2,'train'), +(490,4.20220011238,0.919698696644,0.531508622448,3,'train'), +(491,4.38469431267,0.490125275978,0.945816597811,3,'train'), +(492,1.56277743791,0.504768673882,0.240850086209,1,'train'), +(493,1.50934492129,0.619605815082,0.943259829639,0,'train'), +(494,2.18043863997,0.995838278886,0.429651441389,1,'train'), +(495,4.50005282664,0.825362772495,0.821395187561,3,'train'), +(496,0.959664747905,0.295857333997,0.814743771936,0,'train'), +(497,2.02829946359,0.00093705357447,0.165415869914,2,'train'), +(498,3.09189531627,0.747972936298,0.586448957684,2,'train'), +(499,0.431017371436,0.335701245336,0.308733098485,0,'train'), +(500,1.10440839723,0.552078386709,0.743189081273,0,'train'), +(501,0.945440898882,0.547411936875,0.63089536534,0,'train'), +(502,3.21316616106,0.1278379616,0.292109909896,3,'train'), +(503,1.84119994845,0.678165806943,0.403774864881,1,'train'), +(504,4.15718744193,0.693794586114,0.680729649579,3,'train'), +(505,0.692883161304,0.0272184267333,0.815882794628,0,'train'), +(506,1.71919116925,0.286770639048,0.657586899357,1,'train'), +(507,2.06057940992,0.642684745845,0.646447727255,1,'train'), +(508,0.734500135299,0.591537050228,0.378104595411,0,'train'), +(509,2.35886483681,0.355858321504,0.0548316998636,2,'train'), +(510,0.420694453626,0.007326589821,0.642936904996,0,'train'), +(511,2.86294289087,0.931893954384,0.964908771069,1,'train'), +(512,3.58832352341,0.349095720459,0.489109193284,3,'train'), +(513,2.85911785279,0.291534969988,0.753380967905,2,'train'), +(514,0.846780074256,0.680312260217,0.408004674041,0,'train'), +(515,3.30972718486,0.983222183524,0.571406161447,2,'train'), +(516,1.61022366452,0.582124087192,0.167629285416,1,'train'), +(517,1.46214580154,0.512692054312,0.974399172426,0,'train'), +(518,3.36788507401,0.300056049787,0.260440058799,3,'train'), +(519,3.86622353876,0.671197074989,0.441618006616,3,'train'), +(520,1.30602576682,0.034902869059,0.520694630053,1,'train'), +(521,1.3973559051,0.382415653242,0.122230322993,1,'train'), +(522,3.04765684433,0.0473962605573,0.0161426074989,3,'train'), +(523,1.53295789351,0.947557649786,0.765114529806,0,'train'), +(524,1.35895602915,0.74174013481,0.785630889377,0,'train'), +(525,3.79107471836,0.663054385947,0.357799290676,3,'train'), +(526,0.682890680332,0.68049614357,0.04893400415,0,'train'), +(527,2.68560513419,0.666283334171,0.139002877753,2,'train'), +(528,1.5138392269,0.52363126972,0.995091934035,0,'train'), +(529,2.29955146416,0.23688463329,0.250333439373,2,'train'), +(530,1.59246339626,0.228447971637,0.603336908059,1,'train'), +(531,3.64118876073,0.328684609331,0.559020707489,3,'train'), +(532,3.92236220744,0.89379113153,0.169029807748,3,'train'), +(533,3.02840062853,0.0167931789879,0.107737874204,3,'train'), +(534,3.05651013525,0.143665482385,0.95542904125,2,'train'), +(535,2.24584169722,0.320794807793,0.961793579427,1,'train'), +(536,2.4364261646,0.993502324935,0.66552523593,1,'train'), +(537,2.68993308109,0.446820334697,0.493064647277,2,'train'), +(538,4.3362925093,0.687250501777,0.805631434045,3,'train'), +(539,1.49375392384,0.372570856679,0.348113583706,1,'train'), +(540,3.82351336106,0.294574528184,0.727281811181,3,'train'), +(541,1.04094657631,0.277220124312,0.873914442034,0,'train'), +(542,2.31074431435,0.164069798301,0.38298109098,2,'train'), +(543,0.387592305402,0.353433661517,0.184820572137,0,'train'), +(544,1.30577223408,0.725768091129,0.761580030562,0,'train'), +(545,1.05124211258,0.326339499993,0.851412128518,0,'train'), +(546,1.16293518244,0.794492210847,0.606995034238,0,'train'), +(547,1.24059722178,0.237315759746,0.0572840469566,1,'train'), +(548,4.24004273423,0.670864672017,0.754438905556,3,'train'), +(549,1.94584105524,0.937247982949,0.0926988257174,1,'train'), +(550,0.906581414292,0.627344492022,0.528428729603,0,'train'), +(551,0.582423039372,0.577423399341,0.0707081327041,0,'train'), +(552,3.1744387083,0.976620911041,0.444767127,2,'train'), +(553,0.730856941869,0.454536615883,0.525661798104,0,'train'), +(554,2.33401498768,0.00919969253603,0.569925692655,2,'train'), +(555,3.31951115713,0.674448745998,0.803157774741,2,'train'), +(556,3.91313155555,0.469991735382,0.665687479353,3,'train'), +(557,2.2622878505,0.802162974088,0.67832505218,1,'train'), +(558,0.746195614582,0.439817436143,0.553514388647,0,'train'), +(559,1.39984555259,0.0984201123667,0.549022258407,1,'train'), +(560,3.06781140178,0.0161174641057,0.227363008578,3,'train'), +(561,3.06977099155,0.647397815513,0.649902435782,2,'train'), +(562,2.15437747815,0.0108508940941,0.378849025417,2,'train'), +(563,3.93255222969,0.906042385669,0.16281843881,3,'train'), +(564,1.52093726409,0.293162475924,0.477257570048,1,'train'), +(565,0.241088946455,0.189777524898,0.226520245359,0,'train'), +(566,1.22449638039,0.856499693862,0.606627304471,0,'train'), +(567,0.468568241538,0.457405975709,0.105651624827,0,'train'), +(568,2.14640700567,0.145085132675,0.0363575714233,2,'train'), +(569,1.82693783152,0.812944360193,0.118294003784,1,'train'), +(570,1.2685894001,0.173071530841,0.309059653232,1,'train'), +(571,1.85904737527,0.437692943801,0.6491181953,1,'train'), +(572,1.96354731002,0.194727840668,0.87682351095,1,'train'), +(573,2.17849619334,0.177828197415,0.025845617141,2,'train'), +(574,0.771941573176,0.736889769663,0.187221268859,0,'train'), +(575,3.16306686795,0.275838911088,0.941927787497,2,'train'), +(576,3.05990093593,0.998452693121,0.247887560811,2,'train'), +(577,2.11155741709,0.828257469462,0.532259286086,1,'train'), +(578,3.62719426631,0.116995754165,0.714281815631,3,'train'), +(579,0.771270150397,0.540639803068,0.480239885193,0,'train'), +(580,2.89355871232,0.663866396152,0.479262262408,2,'train'), +(581,1.66476389871,0.627070017213,0.194149121811,1,'train'), +(582,1.92564463876,0.353884908039,0.756147955575,1,'train'), +(583,3.06666120309,0.27155798321,0.891685605963,2,'train'), +(584,4.42952712424,0.818864583972,0.781449000427,3,'train'), +(585,0.753397387605,0.355818165957,0.630538834369,0,'train'), +(586,1.41371889414,0.396968172406,0.129424579343,1,'train'), +(587,2.0839171445,0.856548372034,0.476832017032,1,'train'), +(588,3.17150965715,0.318092789183,0.923805644046,2,'train'), +(589,1.4895290775,0.95476029651,0.731278866777,0,'train'), +(590,3.36473547392,0.490875136745,0.934804972802,2,'train'), +(591,2.99221426911,0.901284255985,0.301546038153,2,'train'), +(592,1.27960206088,0.0682052722718,0.459779065,1,'train'), +(593,3.38536561206,0.377904399177,0.0863783125523,3,'train'), +(594,0.644814164502,0.522337991631,0.349965959589,0,'train'), +(595,3.9654577968,0.812431656871,0.391185556898,3,'train'), +(596,1.9254902965,0.830963898738,0.307451455951,1,'train'), +(597,1.97701211335,0.883130426583,0.306401185967,1,'train'), +(598,3.4736588422,0.248167532959,0.474859252037,3,'train'), +(599,1.48362948566,0.539573277645,0.971625549281,0,'train'), +(600,3.3592534876,0.179340764215,0.424161199768,3,'train'), +(601,2.07128477674,0.0349609893738,0.190588004251,2,'train'), +(602,3.29067149956,0.432722131743,0.92625556291,2,'train'), +(603,1.5344838351,0.692619201057,0.917531816368,0,'train'), +(604,1.74572177974,0.285404197809,0.67846708242,1,'train'), +(605,3.9530290139,0.974574370864,0.989168662584,2,'train'), +(606,3.5518908479,0.602663068128,0.974283213326,2,'train'), +(607,4.9241038632,0.949510976834,0.98721471138,3,'train'), +(608,4.05888901245,0.207640753461,0.922631160861,3,'train'), +(609,3.5709155848,0.797356220433,0.879522236424,2,'train'), +(610,1.69327291681,0.138219735528,0.745018913372,1,'train'), +(611,2.31989251562,0.607549074802,0.844004408054,1,'train'), +(612,1.86762731819,0.616936454164,0.500690387391,1,'train'), +(613,1.38906829047,0.191578211182,0.444398559057,1,'train'), +(614,1.3504785947,0.308199348624,0.205619177313,1,'train'), +(615,0.782505977699,0.727772286602,0.233952326546,0,'train'), +(616,2.60291214904,0.0132415326267,0.767900134398,2,'train'), +(617,0.956542546629,0.231905032946,0.851256432389,0,'train'), +(618,1.01480229765,0.58596709406,0.654855101214,0,'train'), +(619,2.88052105394,0.837993350939,0.206222459987,2,'train'), +(620,1.97527237647,0.960567079291,0.121265399753,1,'train'), +(621,2.26801322237,0.200779342141,0.259294967618,2,'train'), +(622,2.37994972022,0.351892338036,0.167503379615,2,'train'), +(623,2.84054373779,0.823412755683,0.130885377737,2,'train'), +(624,3.88791650362,0.0559361677106,0.912129560925,3,'train'), +(625,1.00400302511,0.446928498162,0.746374253944,0,'train'), +(626,2.33488666829,0.257172000037,0.278773507084,2,'train'), +(627,0.893105656942,0.849557331986,0.208682354205,0,'train'), +(628,3.03238168333,0.00299493230483,0.171425642841,3,'train'), +(629,1.10807643915,0.649692903311,0.677040276378,0,'train'), +(630,2.26582203114,0.226634857035,0.197957505803,2,'train'), +(631,2.5443345758,0.171366693497,0.610710964617,2,'train'), +(632,1.49535083799,0.495134590279,0.0147053633082,1,'train'), +(633,4.28454765733,0.816343290208,0.684254606944,3,'train'), +(634,1.03194179244,0.409810231167,0.78875316879,0,'train'), +(635,3.64228965597,0.536740158877,0.32488382091,3,'train'), +(636,1.13005581937,0.11941513339,0.103153700781,1,'train'), +(637,3.1520887166,0.96198819152,0.436005189276,2,'train'), +(638,0.666405440181,0.103248254724,0.750437995744,0,'train'), +(639,3.30912819412,0.152626190352,0.395603341452,3,'train'), +(640,0.745269331126,0.742901399851,0.0486613940959,0,'train'), +(641,1.90255200165,0.897306888368,0.0724231542902,1,'train'), +(642,1.03137999187,0.238531157703,0.890420593968,0,'train'), +(643,2.59524544441,0.275224104957,0.565704286221,2,'train'), +(644,3.08882766013,0.155026540735,0.966333855038,2,'train'), +(645,0.534129987832,0.0340213072181,0.70718362581,0,'train'), +(646,2.37679211278,0.203334853448,0.416482003613,2,'train'), +(647,3.45486746732,0.376971875643,0.279097817392,3,'train'), +(648,2.76543758993,0.230114384821,0.731657847022,2,'train'), +(649,1.02591751794,0.951688544567,0.272449946541,0,'train'), +(650,0.359810570836,0.0800404459204,0.528933006075,0,'train'), +(651,0.486987204846,0.249618036951,0.487205467842,0,'train'), +(652,1.80869661167,0.511377476515,0.545269781991,1,'train'), +(653,1.45399626469,0.316677444251,0.37056554136,1,'train'), +(654,3.00908726103,0.987469441926,0.147029993904,2,'train'), +(655,3.88378285476,0.88318300657,0.0244917983512,3,'train'), +(656,2.06684700599,0.315941249135,0.866548184956,1,'train'), +(657,3.91911290956,0.108302168878,0.900450298842,3,'train'), +(658,1.57945249851,0.46517358655,0.338051640969,1,'train'), +(659,3.49677332069,0.371367013936,0.354127528941,3,'train'), +(660,2.40887402461,0.903523794162,0.710879898751,1,'train'), +(661,1.21741976737,0.0451816825109,0.415015764594,1,'train'), +(662,2.61079837947,0.972815709163,0.798738173816,1,'train'), +(663,3.84863884749,0.595770240395,0.502860425062,3,'train'), +(664,4.1374973138,0.717960727672,0.647716439597,3,'train'), +(665,2.55413334541,0.354314219794,0.447011326048,2,'train'), +(666,3.15647917932,0.869355115022,0.535839588211,2,'train'), +(667,1.17540927044,0.848011368526,0.572186946649,0,'train'), +(668,3.7415117792,0.816983132261,0.961524127072,2,'train'), +(669,3.68092030094,0.422499956808,0.508350611423,3,'train'), +(670,4.09798509889,0.705172088493,0.626747964013,3,'train'), +(671,0.722843358899,0.70617283972,0.129114364728,0,'train'), +(672,2.26556095716,0.930885697276,0.578511244387,1,'train'), +(673,2.85536438423,0.115947263352,0.859893668356,2,'train'), +(674,2.81607612378,0.74679673614,0.263209778767,2,'train'), +(675,1.36041377427,0.82540534443,0.731442704414,0,'train'), +(676,1.45759032149,0.147613684927,0.556755454903,1,'train'), +(677,2.85392551069,0.0727477746501,0.883842596866,2,'train'), +(678,3.31010759849,0.289122295372,0.144863049541,3,'train'), +(679,4.14334510553,0.92676282048,0.465384018906,3,'train'), +(680,3.48824310645,0.444564223256,0.208994935806,3,'train'), +(681,3.21491520598,0.701082007727,0.716821594436,2,'train'), +(682,0.724320902947,0.678767084014,0.213433406318,0,'train'), +(683,3.62177654488,0.899057752117,0.850128691882,2,'train'), +(684,2.10614898564,0.730593443211,0.612825866319,1,'train'), +(685,2.64537470486,0.653620476998,0.995868579616,1,'train'), +(686,0.215018532823,0.105306520581,0.331228036619,0,'train'), +(687,0.505516921954,0.379845580399,0.354501539566,0,'train'), +(688,1.82150462465,0.304048850706,0.719343988609,1,'train'), +(689,2.71847901632,0.358078703512,0.600333501319,2,'train'), +(690,4.11912569662,0.508067936207,0.781701836004,3,'train'), +(691,3.00897261869,0.25898246576,0.866019718555,2,'train'), +(692,0.782397713346,0.774348740496,0.0897160679632,0,'train'), +(693,0.938680550391,0.332386546162,0.77864883242,0,'train'), +(694,3.84065188299,0.0338781170005,0.898205859471,3,'train'), +(695,1.49121482249,0.858120050764,0.795672527943,0,'train'), +(696,2.10470668542,0.171925802401,0.965805820554,1,'train'), +(697,3.37281145241,0.661115820916,0.843620549476,2,'train'), +(698,3.38331967048,0.318783153871,0.254040383821,3,'train'), +(699,1.43129333879,0.163228324172,0.51774995376,1,'train'), +(700,2.52741414884,0.337829134484,0.435413612961,2,'train'), +(701,0.953970684609,0.202030491513,0.867144851277,0,'train'), +(702,0.543237745766,0.172624497115,0.608780131616,0,'train'), +(703,1.32376070114,0.722848761628,0.775185100161,0,'train'), +(704,0.72804023832,0.70565535173,0.149615796593,0,'train'), +(705,1.07860407567,0.953943477068,0.353073078277,0,'train'), +(706,0.898436369629,0.510123882364,0.623147243647,0,'train'), +(707,1.79328573793,0.23390972682,0.747914441037,1,'train'), +(708,1.20546817876,0.535504220336,0.818513260994,0,'train'), +(709,3.25849956264,0.182171722489,0.276274935808,3,'train'), +(710,3.36539973056,0.548944226377,0.903579273877,2,'train'), +(711,2.20768360824,0.221222906304,0.993207280446,1,'train'), +(712,0.763688671927,0.0762037898321,0.829147081099,0,'train'), +(713,2.31428605424,0.265557424375,0.220745622525,2,'train'), +(714,2.23260844404,0.160313071248,0.268877988673,2,'train'), +(715,1.41627690058,0.146814878869,0.519097314302,1,'train'), +(716,0.728696487379,0.510952711751,0.466630234369,0,'train'), +(717,3.56014747125,0.216810503711,0.585949628841,3,'train'), +(718,2.34117788408,0.0989076145477,0.492209578867,2,'train'), +(719,2.49427997317,0.193473445816,0.548458318702,2,'train'), +(720,1.12555716091,0.0922252252327,0.182570358143,1,'train'), +(721,1.10700886834,0.100248842948,0.0822193735651,1,'train'), +(722,2.48658116272,0.272913365175,0.462242141679,2,'train'), +(723,0.681839495212,0.681827802871,0.00341940659408,0,'train'), +(724,1.22270572491,0.21965998946,0.0551881821668,1,'train'), +(725,3.32333977248,0.87222251745,0.671652629737,2,'train'), +(726,2.47792619634,0.507703204666,0.984998980547,1,'train'), +(727,2.98220980684,0.59959737364,0.618556734019,2,'train'), +(728,2.1920753408,0.0083494343666,0.428632600756,2,'train'), +(729,2.85396274929,0.815287788457,0.196659504816,2,'train'), +(730,3.66500000528,0.658318029399,0.0817433537257,3,'train'), +(731,3.53685356519,0.784972026427,0.867111030237,2,'train'), +(732,3.0467056926,0.394635734334,0.807508488046,2,'train'), +(733,2.89203975883,0.86058012132,0.177368648623,2,'train'), +(734,0.431289845628,0.374520598288,0.238262979374,0,'train'), +(735,0.627621983958,0.571522481242,0.236853335877,0,'train'), +(736,1.76222405923,0.786213961527,0.987932233353,0,'train'), +(737,3.90108859988,0.892698590648,0.0915969935484,3,'train'), +(738,1.00824020786,0.809901655648,0.445352166504,0,'train'), +(739,0.573219436278,0.538727422752,0.1857202561,0,'train'), +(740,3.64895115275,0.307117889626,0.584665086285,3,'train'), +(741,0.236858711257,0.0114133571358,0.474810861419,0,'train'), +(742,2.81548278042,0.36006129573,0.67484923108,2,'train'), +(743,1.09889749697,0.0721705405948,0.163483810751,1,'train'), +(744,0.847468119179,0.108478274687,0.859645185232,0,'train'), +(745,3.01132259874,0.197499150101,0.902121637387,2,'train'), +(746,0.473285172799,0.385097036162,0.296964874417,0,'train'), +(747,1.51791738897,0.520437786654,0.998739006105,0,'train'), +(748,0.758146740412,0.741283589849,0.129858194055,0,'train'), +(749,0.813467576678,0.470118431819,0.585960019847,0,'train'), +(750,3.48360379109,0.839676713863,0.802450669652,2,'train'), +(751,2.37934769258,0.373447854177,0.0768104055884,2,'train'), +(752,0.485352842652,0.477219465024,0.0901852406362,0,'train'), +(753,4.56312660095,0.724907255871,0.91554319673,3,'train'), +(754,1.39606247302,0.314830889607,0.285011549607,1,'train'), +(755,1.07584378619,0.877489309046,0.445370045178,0,'train'), +(756,0.348907856813,0.326897100427,0.148360225079,0,'train'), +(757,0.541766102633,0.540661536382,0.0332350154282,0,'train'), +(758,3.00754864175,0.760057833149,0.49748448076,2,'train'), +(759,4.19085275321,0.753373744067,0.661421959983,3,'train'), +(760,3.75657559034,0.755226273152,0.036733053132,3,'train'), +(761,0.958281626211,0.933844468687,0.156323886606,0,'train'), +(762,2.84844220909,0.979059239209,0.932407083779,1,'train'), +(763,0.391096031332,0.0626595064201,0.573093818595,0,'train'), +(764,1.72778752786,0.727601196294,0.013650332081,1,'train'), +(765,1.75991628766,0.346177830203,0.64322504418,1,'train'), +(766,0.217757321845,0.214515467875,0.0569372810196,0,'train'), +(767,0.625162166518,0.103990282929,0.721922352881,0,'train'), +(768,2.99954339415,0.913919185581,0.292616145434,2,'train'), +(769,3.14408357105,0.970139957702,0.417065478493,2,'train'), +(770,4.45027168388,0.704262000876,0.863718520703,3,'train'), +(771,3.08313959596,0.243314613191,0.91641965429,2,'train'), +(772,0.564599092629,0.55497815543,0.0980863762156,0,'train'), +(773,2.03558008962,0.985957378349,0.222761556986,1,'train'), +(774,1.62188046085,0.272763752363,0.59086098914,1,'train'), +(775,2.90384386535,0.89761352196,0.0789325242619,2,'train'), +(776,3.94581453174,0.797879247105,0.38462356224,3,'train'), +(777,3.19184212652,0.0778032298582,0.337696456389,3,'train'), +(778,0.757984587615,0.101319032883,0.81034903266,0,'train'), +(779,3.17387699437,0.173871849162,0.00226830601557,3,'train'), +(780,3.10139259002,0.0378312159584,0.252113811721,3,'train'), +(781,3.49439249576,0.228316275025,0.51582576587,3,'train'), +(782,3.43715460165,0.164838244945,0.521839397421,3,'train'), +(783,4.91025144752,0.939925112887,0.985051437557,3,'train'), +(784,3.16781331966,0.489409175268,0.823652927142,2,'train'), +(785,3.36386096861,0.159963464883,0.45155011209,3,'train'), +(786,1.67673346419,0.547476303398,0.359523519115,1,'train'), +(787,2.7835685288,0.565778319948,0.46667998548,2,'train'), +(788,0.979752741281,0.939332186493,0.201048637867,0,'train'), +(789,2.51346807528,0.417686324694,0.30948626882,2,'train'), +(790,2.2346788067,0.690748410812,0.737516369912,1,'train'), +(791,2.40394378424,0.447863237165,0.977793713967,1,'train'), +(792,2.72654124556,0.671291357908,0.235052946489,2,'train'), +(793,1.08854116424,0.840075139264,0.498463664653,0,'train'), +(794,0.953122843546,0.396684214999,0.745948140655,0,'train'), +(795,3.23608545166,0.0666854960378,0.411582258631,3,'train'), +(796,1.32283272732,0.733806139302,0.76748067599,0,'train'), +(797,1.9722553054,0.957186626784,0.122754546229,1,'train'), +(798,0.627662918881,0.40124992114,0.475828748333,0,'train'), +(799,3.64775453138,0.220636671145,0.653542546616,3,'train'), +(800,2.98838440389,0.878915265432,0.330861207247,2,'train'), +(801,0.54336838096,0.126560701856,0.645606442892,0,'train'), +(802,3.7160641128,0.176307607664,0.734681226883,3,'train'), +(803,1.62344556238,0.0795330022565,0.73750427804,1,'train'), +(804,0.6678325499,0.570964245243,0.311236734107,0,'train'), +(805,0.906791817372,0.87719888792,0.172025955753,0,'train'), +(806,3.14677150065,0.856070899027,0.539166580587,2,'train'), +(807,0.933623237072,0.928531680834,0.0713551416389,0,'train'), +(808,2.46457532225,0.742823121166,0.849560004404,1,'train'), +(809,2.07854298038,0.763236801438,0.561521307645,1,'train'), +(810,1.05945527933,0.0571458100517,0.0480569379203,1,'train'), +(811,2.0753568234,0.328551775511,0.864178828649,1,'train'), +(812,1.4161183371,0.965560948376,0.671235717708,0,'train'), +(813,0.942869512223,0.803607393167,0.373178401111,0,'train'), +(814,0.434189233541,0.398336329103,0.189348631992,0,'train'), +(815,3.16421332872,0.469369158313,0.833573134408,2,'train'), +(816,0.877888510582,0.554817722226,0.568393163537,0,'train'), +(817,0.342256663894,0.0920928100983,0.500163826956,0,'train'), +(818,1.19437033781,0.958987036411,0.485163169873,0,'train'), +(819,2.4185222072,0.866575557093,0.7429311207,1,'train'), +(820,2.29702798937,0.508479232246,0.888002678557,1,'train'), +(821,2.27737701721,0.0426738921994,0.484461685802,2,'train'), +(822,1.13744938714,0.80327605942,0.578077267949,0,'train'), +(823,4.0991696931,0.586477571092,0.716025224419,3,'train'), +(824,1.34348757554,0.117170271196,0.475728183259,1,'train'), +(825,1.30440617617,0.375707588427,0.963690089054,0,'train'), +(826,2.2282483173,0.902165860634,0.571036300651,1,'train'), +(827,1.52656308845,0.5178091057,0.0935627209437,1,'train'), +(828,3.2429930153,0.43859738444,0.896881057255,2,'train'), +(829,3.48827157132,0.307373235648,0.425321449818,3,'train'), +(830,4.02685704625,0.132103837442,0.945913954231,3,'train'), +(831,1.05194933008,0.919265725091,0.364257608006,0,'train'), +(832,0.984672980151,0.952146351326,0.180351403726,0,'train'), +(833,2.95532888049,0.892889765465,0.249878200376,2,'train'), +(834,2.32095170827,0.872783732429,0.669453490424,1,'train'), +(835,3.75167335434,0.777071808185,0.987219097341,2,'train'), +(836,4.43624524123,0.478645228242,0.978570392453,3,'train'), +(837,0.493424617077,0.0498549496803,0.666010260729,0,'train'), +(838,3.9147927984,0.276291640244,0.799062674738,3,'train'), +(839,3.4198464471,0.396590191226,0.152500019265,3,'train'), +(840,1.27720483278,0.190638817152,0.29422103193,1,'train'), +(841,1.580936325,0.227904729719,0.594164619684,1,'train'), +(842,4.09466632998,0.936198268004,0.398080471731,3,'train'), +(843,1.30974316712,0.700471277387,0.780558703582,0,'train'), +(844,4.46191166039,0.881566201107,0.761804081953,3,'train'), +(845,0.0967506298265,0.0586957183066,0.195076681128,0,'train'), +(846,1.29916323675,0.291640023736,0.0867364572546,1,'train'), +(847,3.01170465403,0.944238471623,0.259742531,2,'train'), +(848,1.02612193445,0.652305745793,0.611405093748,0,'train'), +(849,3.39127260388,0.0446467757273,0.588749376346,3,'train'), +(850,2.40974411311,0.406408704746,0.0577529944119,2,'train'), +(851,2.17496825201,0.049944414591,0.353587100192,2,'train'), +(852,0.0449696231403,0.0380253394684,0.0833323686929,0,'train'), +(853,3.10792775531,0.107707836669,0.0148296540516,3,'train'), +(854,3.1009708213,0.571423454077,0.727700053059,2,'train'), +(855,2.66205273838,0.615416134198,0.215955097616,2,'train'), +(856,0.398343314672,0.194725706889,0.451240077767,0,'train'), +(857,3.42989580267,0.11396257019,0.562079382718,3,'train'), +(858,0.872845586455,0.872074811413,0.027762835634,0,'train'), +(859,1.01562878893,0.160300629277,0.924839531841,0,'train'), +(860,2.18750777785,0.614652514205,0.756872025938,1,'train'), +(861,3.6194255144,0.188497116264,0.656451367688,3,'train'), +(862,3.99363244841,0.989165241372,0.0668371681136,3,'train'), +(863,1.09300321982,0.199292331136,0.945362834408,0,'train'), +(864,3.42490120268,0.304694569109,0.346708283107,3,'train'), +(865,2.83813005817,0.769298134294,0.262358388227,2,'train'), +(866,3.22130563731,0.221304610449,0.00101334267253,3,'train'), +(867,2.02549033192,0.0240931491588,0.037378907932,2,'train'), +(868,4.12425029953,0.904176742613,0.469119981362,3,'train'), +(869,0.740432160855,0.0706798250473,0.81838397822,0,'train'), +(870,2.14564666895,0.964014894267,0.426182794922,1,'train'), +(871,3.68866819913,0.132065510642,0.746058099944,3,'train'), +(872,3.77130436563,0.763485018313,0.088427073443,3,'train'), +(873,0.703580918285,0.701962996215,0.0402234020203,0,'train'), +(874,1.83792292411,0.761602930498,0.276260734828,1,'train'), +(875,4.12029105794,0.833128182455,0.535875802297,3,'train'), +(876,2.82952596243,0.603048162809,0.475896837164,2,'train'), +(877,3.53876022216,0.582939761857,0.977660708174,2,'train'), +(878,3.49477215853,0.308148921783,0.431999116609,3,'train'), +(879,3.13048058934,0.575020109959,0.745292210732,2,'train'), +(880,3.1524980097,0.408512608084,0.862545883776,2,'train'), +(881,0.4229523324,0.365511850675,0.239667439851,0,'train'), +(882,2.20605366521,0.856995324245,0.590811595149,1,'train'), +(883,3.4892010668,0.767209072477,0.849701120584,2,'train'), +(884,0.160009943613,0.0916419043668,0.261472826974,0,'train'), +(885,4.71639652384,0.933767676698,0.884663126361,3,'train'), +(886,4.43421482904,0.992118536821,0.664903220189,3,'train'), +(887,3.32658636318,0.964516567288,0.601722357813,2,'train'), +(888,1.78519326735,0.761645439254,0.15345301592,1,'train'), +(889,2.21692608965,0.838661335479,0.615032319617,1,'train'), +(890,3.47516222055,0.744867712785,0.854572704787,2,'train'), +(891,1.52395923661,0.250633958142,0.522805201259,1,'train'), +(892,0.325735256557,0.236160325793,0.299290712793,0,'train'), +(893,2.81791079024,0.701856111712,0.340667988698,2,'train'), +(894,1.48728396068,0.394561566257,0.304503521192,1,'train'), +(895,0.397280198733,0.368601543974,0.169347733257,0,'train'), +(896,1.14982690429,0.948659432186,0.448516969699,0,'train'), +(897,2.22475700667,0.568393496392,0.810162644337,1,'train'), +(898,2.40712295578,0.222796613645,0.429332437785,2,'train'), +(899,3.21131611811,0.168476120319,0.20697825439,3,'train'), +(900,3.71464223054,0.452960586716,0.511548281029,3,'train'), +(901,0.222339523039,0.217049254224,0.0727342341283,0,'train'), +(902,3.99284107273,0.0906436446099,0.949840738292,3,'train'), +(903,1.6694185709,0.652064858526,0.131733489955,1,'train'), +(904,0.72648755406,0.71488655639,0.107707927611,0,'train'), +(905,1.0517420052,0.228148264941,0.907520655554,0,'train'), +(906,2.21657991343,0.844265982491,0.610175328031,1,'train'), +(907,4.02602526832,0.136026052682,0.943397697492,3,'train'), +(908,3.91164726022,0.611059059104,0.548259246264,3,'train'), +(909,4.05249309774,0.134708332182,0.958010837915,3,'train'), +(910,0.317620851239,0.0870424441656,0.480185804739,0,'train'), +(911,0.126737160202,0.105126235306,0.14700654712,0,'train'), +(912,1.07833625758,0.437347966474,0.800617443667,0,'train'), +(913,3.1593651061,0.426484055879,0.856084721403,2,'train'), +(914,3.51496843983,0.765842951482,0.865520356982,2,'train'), +(915,1.37401226506,0.956519981551,0.646136427937,0,'train'), +(916,4.24975475059,0.901133629716,0.590441462695,3,'train'), +(917,3.27510500703,0.313508101999,0.980610475688,2,'train'), +(918,2.87249433045,0.534970619834,0.580967908422,2,'train'), +(919,0.680590696151,0.460698101224,0.468927067812,0,'train'), +(920,4.06249482883,0.961230433943,0.318220670106,3,'train'), +(921,3.5208320043,0.809432340923,0.843445115808,2,'train'), +(922,3.38586140084,0.980409011669,0.636751434367,2,'train'), +(923,1.31270405892,0.199283633024,0.336779491507,1,'train'), +(924,1.08171591786,0.759752751913,0.56741798169,0,'train'), +(925,0.494048874062,0.493051635885,0.0315790781609,0,'train'), +(926,1.9541834616,0.883074702195,0.266662257184,1,'train'), +(927,3.28478298911,0.105363182792,0.423579752014,3,'train'), +(928,3.35596940015,0.198665784439,0.396615198536,3,'train'), +(929,1.43221235797,0.804595418616,0.792222783916,0,'train'), +(930,1.04151871739,0.0205844406083,0.144686823119,1,'train'), +(931,1.5573820282,0.864245592582,0.832548158138,0,'train'), +(932,0.850080657327,0.330153886806,0.721059477797,0,'train'), +(933,2.97850716878,0.619954263612,0.598792873341,2,'train'), +(934,0.57780127819,0.374295514667,0.451116130861,0,'train'), +(935,2.41942331853,0.0901423684633,0.573830070725,2,'train'), +(936,2.32338043422,0.384286695731,0.96906849009,1,'train'), +(937,1.0470787516,0.941459565527,0.324991055371,0,'train'), +(938,1.07923145778,0.317569591178,0.87273241409,0,'train'), +(939,2.33092538397,0.325371126098,0.0745268936308,2,'train'), +(940,2.60973559809,0.558275441315,0.226848312248,2,'train'), +(941,0.301873361397,0.210058481924,0.303009701945,0,'train'), +(942,1.92740318686,0.742962444954,0.42946564695,1,'train'), +(943,1.67324947457,0.673223362835,0.00510996446302,1,'train'), +(944,3.69688198197,0.442128635767,0.504730964177,3,'train'), +(945,1.93806386024,0.326367369678,0.782110280306,1,'train'), +(946,2.92587133413,0.465784473861,0.678297029528,2,'train'), +(947,1.59706726815,0.470594075229,0.355630697386,1,'train'), +(948,4.01042965214,0.781479433591,0.478487427786,3,'train'), +(949,4.34001007846,0.979308099385,0.600584697666,3,'train'), +(950,1.94568194003,0.286976987772,0.811606402303,1,'train'), +(951,2.48297192781,0.0703715354453,0.64233977953,2,'train'), +(952,3.43330486985,0.396866211666,0.190889125366,3,'train'), +(953,1.01049586623,0.759914861982,0.500580667078,0,'train'), +(954,0.606684884966,0.606410996736,0.0165495688711,0,'train'), +(955,4.516391481,0.932148046169,0.764358184903,3,'train'), +(956,1.45346440397,0.801876812536,0.807209756776,0,'train'), +(957,1.68322515944,0.387716339265,0.543607229693,1,'train'), +(958,3.52421319161,0.447168165065,0.277569858861,3,'train'), +(959,4.26284699331,0.688773154009,0.757676606015,3,'train'), +(960,1.20507542159,0.149018099633,0.236764275078,1,'train'), +(961,1.12653097139,0.6108689372,0.718096117658,0,'train'), +(962,2.16176705503,0.117254286354,0.21098049359,2,'train'), +(963,4.01356988893,0.672924387389,0.583648440019,3,'train'), +(964,3.86596494117,0.58173810517,0.533129286385,3,'train'), +(965,3.74667437944,0.544965521271,0.449120093254,3,'train'), +(966,2.74708319026,0.46598963354,0.530182569237,2,'train'), +(967,1.93197539131,0.790156105698,0.3765890142,1,'train'), +(968,3.38527452627,0.735576131329,0.806038705612,2,'train'), +(969,1.65966515441,0.656857808986,0.052984388504,1,'train'), +(970,2.50244248573,0.420062826889,0.287018568802,2,'train'), +(971,0.882958911742,0.37928633108,0.709698936636,0,'train'), +(972,1.4473343414,0.749503416286,0.835362750613,0,'train'), +(973,2.06038545094,0.908171444662,0.390146134519,1,'train'), +(974,0.70032257833,0.631457821076,0.262420954296,0,'train'), +(975,0.824186006975,0.7790660492,0.212414589366,0,'train'), +(976,4.94692718169,0.988679668696,0.978901176318,3,'train'), +(977,4.60296701793,0.865329679505,0.858858159666,3,'train'), +(978,4.50227138876,0.511692946573,0.995278072797,3,'train'), +(979,2.39104720558,0.537323210028,0.923971858635,1,'train'), +(980,3.77180068741,0.726794910228,0.212145650861,3,'train'), +(981,1.5247556069,0.460231751865,0.254015462195,1,'train'), +(982,1.68555720238,0.0739576495766,0.782048305929,1,'train'), +(983,2.91878772737,0.614469635115,0.551650335133,2,'train'), +(984,0.4676761178,0.330730590893,0.370061517734,0,'train'), +(985,2.31800820581,0.58701259216,0.854982814825,1,'train'), +(986,0.528420123049,0.527751394053,0.0258597949753,0,'train'), +(987,3.57257439721,0.569240335338,0.057741335945,3,'train'), +(988,1.49686453769,0.521534051101,0.987588217119,0,'train'), +(989,4.57689441388,0.65460205574,0.960360535496,3,'train'), +(990,2.94633319462,0.161663955217,0.885815578664,2,'train'), +(991,1.5877465381,0.534156901422,0.231494355609,1,'train'), +(992,2.65206272391,0.846418308035,0.897576969332,1,'train'), +(993,3.27111841261,0.244618976334,0.162786474474,3,'train'), +(994,4.09702326597,0.15086983814,0.972704183106,3,'train'), +(995,2.1538807163,0.425956851832,0.853184543033,1,'train'), +(996,3.83328066689,0.83031000015,0.0545038231579,3,'train'), +(997,1.08009466281,0.60012473769,0.692798618012,0,'train'), +(998,4.10648685932,0.70347100304,0.634835298543,3,'train'), +(999,1.43186970739,0.429642285891,0.0471955665007,1,'train'), +(1000,4.61585521712,0.777702410574,0.915506857725,3,'test'), +(1001,1.75829629844,0.237541220035,0.721633617848,1,'test'), +(1002,0.99861844769,0.824278532661,0.417540315453,0,'test'), +(1003,2.66485237446,0.965749198043,0.836123900158,1,'test'), +(1004,2.97260783117,0.972601113905,0.00259176786914,2,'test'), +(1005,3.98054771655,0.453449247417,0.726015474442,3,'test'), +(1006,4.18523414916,0.609042462761,0.759072912438,3,'test'), +(1007,3.8268713481,0.775526514605,0.226593983796,3,'test'), +(1008,1.94121836211,0.641613344759,0.547361870571,1,'test'), +(1009,1.67239363485,0.722018229517,0.97487199433,0,'test'), +(1010,3.03506699441,0.0350365241014,0.0055199916905,3,'test'), +(1011,3.30056773356,0.298449470889,0.0460245876972,3,'test'), +(1012,1.55263976535,0.0585124918821,0.70294187062,1,'test'), +(1013,3.79731586597,0.857060942587,0.969667429269,2,'test'), +(1014,1.10864822417,0.372854027875,0.857784469608,0,'test'), +(1015,3.80429985696,0.679847951578,0.352777416195,3,'test'), +(1016,2.64680289571,0.256279949327,0.62491835177,2,'test'), +(1017,1.57582576814,0.347581215152,0.477749466763,1,'test'), +(1018,3.651056129,0.00941277008097,0.801026440839,3,'test'), +(1019,3.55478111772,0.358333782705,0.443223797888,3,'test'), +(1020,1.35554435873,0.949094181678,0.637534451657,0,'test'), +(1021,0.861500842905,0.217899009132,0.802247987703,0,'test'), +(1022,4.0377106654,0.31939136638,0.847537196246,3,'test'), +(1023,2.15284892186,0.917772386001,0.484846920028,1,'test'), +(1024,1.04039166204,0.0319036664399,0.0921303185799,1,'test'), +(1025,2.42785443478,0.0650845370425,0.602303825105,2,'test'), +(1026,1.50775500547,0.629828999108,0.936977057542,0,'test'), +(1027,1.30984054026,0.87381344328,0.660323479048,0,'test'), +(1028,0.00942572686202,0.00871573230378,0.0266457230758,0,'test'), +(1029,4.30027100952,0.746577236994,0.744106022371,3,'test'), +(1030,1.60641384579,0.812841171003,0.890826961192,0,'test'), +(1031,1.11032930335,0.0757174461723,0.186042621927,1,'test'), +(1032,1.01926933899,0.656455334597,0.602340438952,0,'test'), +(1033,0.636798999146,0.509262200084,0.357122946704,0,'test'), +(1034,4.19933978491,0.479883391496,0.848207753684,3,'test'), +(1035,1.95689678229,0.955574144931,0.036368081527,1,'test'), +(1036,2.2859919064,1.20335694851e-05,0.534770860116,2,'test'), +(1037,1.33057587679,0.246978700992,0.289131762007,1,'test'), +(1038,3.71164928992,0.712232677912,0.999708263449,2,'test'), +(1039,3.32484243213,0.324582049787,0.0161363671181,3,'test'), +(1040,1.34057906726,0.276996356384,0.252156124011,1,'test'), +(1041,2.94037513323,0.695445452553,0.494903708486,2,'test'), +(1042,2.9335306374,0.918551748146,0.122388272526,2,'test'), +(1043,1.42860689936,0.244475702198,0.429105112017,1,'test'), +(1044,0.477310600576,0.458085817277,0.138653464793,0,'test'), +(1045,2.80452510659,0.252992682822,0.742652289953,2,'test'), +(1046,4.23954214907,0.379333291483,0.927474451176,3,'test'), +(1047,0.612695832598,0.604538828632,0.0903161334791,0,'test'), +(1048,2.57152996012,0.772378759681,0.893952571695,1,'test'), +(1049,1.64690797811,0.0679174967944,0.760914240444,1,'test'), +(1050,3.78951570565,0.686085079187,0.321606322179,3,'test'), +(1051,1.09922322062,0.548260097396,0.742268902234,0,'test'), +(1052,1.02307381137,0.137986052758,0.940791028132,0,'test'), +(1053,1.89794283363,0.0987532192049,0.893974056909,1,'test'), +(1054,2.43634085555,0.245559105325,0.436785702862,2,'test'), +(1055,2.45877421392,0.151786662584,0.554064573255,2,'test'), +(1056,2.06008699874,0.92599447949,0.366186454212,1,'test'), +(1057,2.71120700724,0.680105015605,0.176357567561,2,'test'), +(1058,0.413559844959,0.23765892188,0.419405439973,0,'test'), +(1059,2.74834334585,0.568885252557,0.423624944139,2,'test'), +(1060,2.63585770775,0.556632051434,0.281470524771,2,'test'), +(1061,3.36239358484,0.0727372108843,0.538197337377,3,'test'), +(1062,1.40698576923,0.839708510416,0.753178105641,0,'test'), +(1063,2.46385947845,0.405319492698,0.241950378694,2,'test'), +(1064,2.2960820441,0.144870989046,0.388858656909,2,'test'), +(1065,1.87946335431,0.190920058667,0.829785090033,1,'test'), +(1066,3.098338964,0.490640137065,0.779550400507,2,'test'), +(1067,1.28500053528,0.71202437425,0.756951888187,0,'test'), +(1068,2.06369395299,0.984938457701,0.280634095024,1,'test'), +(1069,2.68403288496,0.874786501735,0.899581226584,1,'test'), +(1070,0.593604832636,0.499041683901,0.307511217251,0,'test'), +(1071,1.24313989366,0.106779993994,0.36926941339,1,'test'), +(1072,3.81991947344,0.91321280699,0.952211460995,2,'test'), +(1073,3.11905267979,0.364915960886,0.868410455316,2,'test'), +(1074,3.58367910147,0.226587876899,0.5975711042,3,'test'), +(1075,2.46689941224,0.872431862345,0.771017217638,1,'test'), +(1076,0.141327622382,0.136358351759,0.0704930537199,0,'test'), +(1077,3.20567114734,0.236380160153,0.984525767659,2,'test'), +(1078,2.65720902691,0.595399245352,0.248615730707,2,'test'), +(1079,3.56919073809,0.563922609079,0.0725818779634,3,'test'), +(1080,4.54973724354,0.958934731983,0.768636787798,3,'test'), +(1081,0.983117317815,0.453239333224,0.727927183578,0,'test'), +(1082,2.14647982707,0.128958074579,0.132369756695,2,'test'), +(1083,0.983825690341,0.760567676767,0.472501866211,0,'test'), +(1084,2.29076748513,0.201634075114,0.298552189761,2,'test'), +(1085,1.91463577385,0.175729863213,0.85959636495,1,'test'), +(1086,3.82025184454,0.437118012651,0.618978054447,3,'test'), +(1087,2.47304170663,0.340260802848,0.364391141198,2,'test'), +(1088,1.25455496441,0.967253108561,0.536005462523,0,'test'), +(1089,3.63760498945,0.143026077088,0.703263046348,3,'test'), +(1090,3.84460234516,0.844558532806,0.00661909043169,3,'test'), +(1091,3.45392732798,0.669406139981,0.885732006873,2,'test'), +(1092,2.93495535693,0.10930490808,0.908653095989,2,'test'), +(1093,1.98331749135,0.0882535400293,0.946078195143,1,'test'), +(1094,3.9680856845,0.966462041277,0.0402944564596,3,'test'), +(1095,0.290659179246,0.194297485213,0.310421800189,0,'test'), +(1096,2.09120944355,0.0819000600183,0.0964851466769,2,'test'), +(1097,1.90966647423,0.26938469528,0.800176092462,1,'test'), +(1098,0.667947740593,0.650130518013,0.133481169382,0,'test'), +(1099,0.598799981248,0.546777245345,0.228084931339,0,'test'), +(1100,3.67714598263,0.696724843853,0.99016217802,2,'test'), +(1101,4.39350943143,0.963098077703,0.656057431733,3,'test'), +(1102,3.28503531757,0.277721529731,0.0855206866113,3,'test'), +(1103,1.99887667359,0.233626461848,0.874785809066,1,'test'), +(1104,1.91101832426,0.845672054479,0.255629164566,1,'test'), +(1105,0.291380317685,0.224212470625,0.259167604187,0,'test'), +(1106,1.82320660134,0.688906299559,0.366470055779,1,'test'), +(1107,0.157900239981,0.153494636978,0.0663747165885,0,'test'), +(1108,4.15240458413,0.203598136156,0.974066962779,3,'test'), +(1109,0.383914139369,0.0675988936036,0.56241910153,0,'test'), +(1110,2.17957886796,0.749485695435,0.655814891968,1,'test'), +(1111,3.2171592172,0.180079395418,0.192561215685,3,'test'), +(1112,0.124323413038,0.12426071025,0.00791850918942,0,'test'), +(1113,3.25825751938,0.224807273909,0.182894082671,3,'test'), +(1114,3.36455193881,0.279459553649,0.291705990956,3,'test'), +(1115,3.12351587595,0.0338217310438,0.299489807687,3,'test'), +(1116,0.340572580458,0.254399185041,0.293553053837,0,'test'), +(1117,0.965535481318,0.668854706421,0.544684105603,0,'test'), +(1118,3.76336951688,0.759919080621,0.058740414206,3,'test'), +(1119,3.81255575275,0.226014414267,0.765859868699,3,'test'), +(1120,0.719538347694,0.611849589326,0.328159653779,0,'test'), +(1121,2.14733862851,0.912256084319,0.484853116101,1,'test'), +(1122,2.17903018255,0.54161062828,0.798385592475,1,'test'), +(1123,3.41391817369,0.373945089576,0.199932698972,3,'test'), +(1124,2.07139789396,0.0132006255649,0.241241100133,2,'test'), +(1125,1.27396567499,0.829317694521,0.666819301212,0,'test'), +(1126,1.92785730459,0.816561527714,0.33361021698,1,'test'), +(1127,2.08022965716,0.0797201177562,0.022572979491,2,'test'), +(1128,2.61639878089,0.171675297677,0.666875912903,2,'test'), +(1129,2.97960364108,0.838149032221,0.376104518537,2,'test'), +(1130,1.57755412389,0.564160278862,0.115731780525,1,'test'), +(1131,1.36998934682,0.349782213601,0.142151796385,1,'test'), +(1132,0.774067742417,0.145002962855,0.793136041018,0,'test'), +(1133,1.43169489753,0.098519075917,0.577213843921,1,'test'), +(1134,3.92624777008,0.560181418316,0.605034174049,3,'test'), +(1135,0.854150998656,0.710065890063,0.379585443073,0,'test'), +(1136,2.89559998754,0.950871032626,0.971971684216,1,'test'), +(1137,1.63687681376,0.748051332126,0.942775414207,0,'test'), +(1138,1.19599509042,0.372246012424,0.907606235102,0,'test'), +(1139,3.79314854023,0.758477305598,0.186202133797,3,'test'), +(1140,2.10599171883,0.0710735124624,0.18686413879,2,'test'), +(1141,0.958750475639,0.621845368951,0.58043527347,0,'test'), +(1142,1.35358596509,0.720714030623,0.795532484861,0,'test'), +(1143,0.299947334585,0.0418086191212,0.50807353352,0,'test'), +(1144,2.44076885688,0.395241818264,0.213370660167,2,'test'), +(1145,2.56301090109,0.161338823455,0.633776046908,2,'test'), +(1146,0.644772710713,0.276083084978,0.607198176657,0,'test'), +(1147,1.34836924744,0.226503323313,0.349093002113,1,'test'), +(1148,1.31978983306,0.304683930305,0.122906072896,1,'test'), +(1149,1.25746696237,0.136233381122,0.348186130179,1,'test'), +(1150,0.724149758437,0.694250057599,0.172915299608,0,'test'), +(1151,0.126370676909,0.0528662019835,0.271117087115,0,'test'), +(1152,3.78411746958,0.581438300813,0.450199032397,3,'test'), +(1153,0.231055303897,0.179728128632,0.226555015978,0,'test'), +(1154,1.55875331413,0.506691350559,0.228170908691,1,'test'), +(1155,2.57306109804,0.281518836868,0.539946535474,2,'test'), +(1156,2.45440389177,0.370076877649,0.290391139881,2,'test'), +(1157,4.05697698306,0.504528912975,0.743268504706,3,'test'), +(1158,1.93630539213,0.122331211291,0.902205176686,1,'test'), +(1159,3.52153051984,0.130664199039,0.625193026833,3,'test'), +(1160,4.36008831853,0.844414256488,0.718104492427,3,'test'), +(1161,2.78206832155,0.392198546748,0.624395527536,2,'test'), +(1162,2.36404799147,0.169562478659,0.441005116534,2,'test'), +(1163,1.00210531406,0.801179068421,0.448247973384,0,'test'), +(1164,3.08259763962,0.910128630062,0.415293883359,2,'test'), +(1165,1.91244788438,0.829577477087,0.28787220654,1,'test'), +(1166,1.74761520298,0.714050926034,0.183205559255,1,'test'), +(1167,0.454729661982,0.0993676539884,0.596122477343,0,'test'), +(1168,4.0365271028,0.913301244027,0.351035409574,3,'test'), +(1169,0.620358436199,0.577979346166,0.20586182267,0,'test'), +(1170,2.31095426377,0.900275451751,0.640842267659,1,'test'), +(1171,0.768983511146,0.563570171869,0.453225483923,0,'test'), +(1172,1.42909600208,0.00984544079969,0.647495607154,1,'test'), +(1173,3.15797605105,0.994239362797,0.404643903028,2,'test'), +(1174,1.02791089586,0.132397836382,0.946315517931,0,'test'), +(1175,2.55667921757,0.692636257741,0.929539111512,1,'test'), +(1176,2.34241536018,0.334288252381,0.0901504730846,2,'test'), +(1177,0.204166144697,0.180203757609,0.154797891096,0,'test'), +(1178,2.37895024297,0.140597665172,0.488213659985,2,'test'), +(1179,1.08384168597,0.669247410639,0.643889955919,0,'test'), +(1180,3.34351137358,0.303854272831,0.199140906778,3,'test'), +(1181,2.62720499801,0.613654533898,0.116406460788,2,'test'), +(1182,2.99146169129,0.935599385282,0.236352080601,2,'test'), +(1183,3.24806489965,0.202904892888,0.212508839262,3,'test'), +(1184,1.43885986293,0.119610146156,0.565021872828,1,'test'), +(1185,1.4348463434,0.392190662913,0.206532516774,1,'test'), +(1186,3.70326035736,0.694539226642,0.0933869943682,3,'test'), +(1187,2.7397163059,0.944967033134,0.891487113066,1,'test'), +(1188,1.22574418918,0.163382494405,0.24972323635,1,'test'), +(1189,4.00783992763,0.458713305726,0.741030783371,3,'test'), +(1190,3.34707492133,0.278863551306,0.261173065267,3,'test'), +(1191,0.881470582996,0.204600073996,0.822721404243,0,'test'), +(1192,0.577733373564,0.308336030001,0.519035011886,0,'test'), +(1193,2.80079821395,0.795609570411,0.0720322395761,2,'test'), +(1194,3.64219009838,0.0928460319066,0.741177486486,3,'test'), +(1195,3.06211405139,0.869019178142,0.439425617419,2,'test'), +(1196,0.845842517265,0.833715381087,0.110123277184,0,'test'), +(1197,3.5701116727,0.681793553209,0.942506296791,2,'test'), +(1198,2.71554872782,0.295814257494,0.647869176858,2,'test'), +(1199,0.58950128452,0.166119796657,0.650677714282,0,'test'), +(1200,3.98897638804,0.918941290605,0.264641450712,3,'test'), +(1201,1.4716039368,0.0714251337186,0.632596872488,1,'test'), +(1202,1.47447298872,0.47446807109,0.00221757416388,1,'test'), +(1203,1.67611192053,0.360229419769,0.562034252301,1,'test'), +(1204,3.30759487651,0.159731082506,0.384530615173,3,'test'), +(1205,3.97180824048,0.967915759296,0.062389752216,3,'test'), +(1206,2.38910927864,0.218951478527,0.412501878917,2,'test'), +(1207,2.17012080457,0.711780406383,0.677008418105,1,'test'), +(1208,2.02993557098,0.879075493697,0.38840710252,1,'test'), +(1209,1.98899305597,0.668903793097,0.565764317427,1,'test'), +(1210,0.191586802293,0.143520548369,0.219240174067,0,'test'), +(1211,2.81419332381,0.79288798093,0.14596349844,2,'test'), +(1212,0.703780315308,0.215512697152,0.698761488747,0,'test'), +(1213,3.47054884462,0.955950030688,0.717355430683,2,'test'), +(1214,1.0943515289,0.0828580101206,0.107207829838,1,'test'), +(1215,2.66158930888,0.64103008465,0.143384881455,2,'test'), +(1216,1.53831351249,0.613446245406,0.961700196053,0,'test'), +(1217,3.85699038297,0.480071816039,0.613936940518,3,'test'), +(1218,2.17270037192,0.16519309665,0.0866445340065,2,'test'), +(1219,3.26285277099,0.25198515521,0.104247857426,3,'test'), +(1220,1.88256064237,0.73041733256,0.39005552144,1,'test'), +(1221,3.13328300134,0.958700681403,0.417830491876,2,'test'), +(1222,1.22650604174,0.925802092674,0.548364795614,0,'test'), +(1223,1.00675360567,0.737399898836,0.518992973782,0,'test'), +(1224,1.3615548729,0.233681859821,0.357593362747,1,'test'), +(1225,2.45467810597,0.764502777197,0.830767915109,1,'test'), +(1226,0.393679497682,0.297312219564,0.31043079441,0,'test'), +(1227,3.58904622434,0.281357942249,0.554696567586,3,'test'), +(1228,1.77479434836,0.767401505119,0.0859816447722,1,'test'), +(1229,2.74964077249,0.68578759901,0.252691854787,2,'test'), +(1230,1.99666814403,0.273093960118,0.850631638204,1,'test'), +(1231,4.13284274333,0.163533078832,0.984535253049,3,'test'), +(1232,3.42471704219,0.102916366172,0.567274779994,3,'test'), +(1233,4.13447772653,0.489381560617,0.803178788261,3,'test'), +(1234,2.9247275543,0.833084665274,0.302725765389,2,'test'), +(1235,0.738359633976,0.323256676485,0.644284841892,0,'test'), +(1236,2.6058091408,0.595940065806,0.0993432181666,2,'test'), +(1237,0.320510201713,0.181641380151,0.372651072133,0,'test'), +(1238,3.72081077407,0.321739411837,0.631720952824,3,'test'), +(1239,4.47977446688,0.695313719619,0.885697887126,3,'test'), +(1240,2.25405290657,0.501562771297,0.867461892691,1,'test'), +(1241,1.65875074005,0.870641166862,0.887755356608,0,'test'), +(1242,1.93317879651,0.828160596917,0.324065116282,1,'test'), +(1243,3.56270774508,0.297753740292,0.51473683061,3,'test'), +(1244,1.24104607157,0.0290940964598,0.460382422681,1,'test'), +(1245,3.15506517674,0.643180288949,0.715461311176,2,'test'), +(1246,3.75175346116,0.869188923198,0.939449060868,2,'test'), +(1247,1.90526292672,0.850537456241,0.233934756876,1,'test'), +(1248,2.37027682459,0.0147867353095,0.596229896336,2,'test'), +(1249,1.40269041443,0.151480914026,0.501208041041,1,'test'), +(1250,0.757007724335,0.1823519434,0.758060539096,0,'test'), +(1251,1.28247007315,0.281921722233,0.0234168937502,1,'test'), +(1252,1.60497610596,0.317435213414,0.536228395876,1,'test'), +(1253,2.67195972778,0.0816579363709,0.768310999146,2,'test'), +(1254,0.428524625858,0.366168532859,0.249712020134,0,'test'), +(1255,2.31394364157,0.903973719292,0.640288936559,1,'test'), +(1256,1.70577561334,0.12272157624,0.763579751633,1,'test'), +(1257,2.83897871889,0.782376779319,0.237911621351,2,'test'), +(1258,2.14208843001,0.0568528555986,0.291951321982,2,'test'), +(1259,2.0714639003,0.0632513807487,0.0906229526898,2,'test'), +(1260,2.57995965861,0.428950779716,0.388598608969,2,'test'), +(1261,2.03559304085,0.966559451599,0.26274243899,1,'test'), +(1262,2.27304377019,0.216750927674,0.237261127265,2,'test'), +(1263,1.97879204432,0.948072951627,0.175268630093,1,'test'), +(1264,1.277199669,0.0430766280946,0.483862626066,1,'test'), +(1265,1.41229561405,0.888170989997,0.723964518502,0,'test'), +(1266,0.574801683415,0.565321189889,0.0973678259298,0,'test'), +(1267,4.68160152313,0.822241809901,0.927016565779,3,'test'), +(1268,1.22208292076,0.481814886242,0.860388304501,0,'test'), +(1269,0.529250350219,0.251412080326,0.52710366143,0,'test'), +(1270,3.85163778206,0.844104169881,0.0867963834646,3,'test'), +(1271,3.33412206529,0.34095605773,0.996577145814,2,'test'), +(1272,2.53415835247,0.730357151657,0.896549608674,1,'test'), +(1273,2.06255615285,0.955123741626,0.327768838097,1,'test'), +(1274,2.12921198229,0.914937264844,0.462898171789,1,'test'), +(1275,2.59943560245,0.509604216146,0.299718845432,2,'test'), +(1276,1.27084968419,0.240408956325,0.174472713803,1,'test'), +(1277,3.34053207774,0.791311761713,0.741093999451,2,'test'), +(1278,1.10364889319,0.0455326456546,0.241073116566,1,'test'), +(1279,3.66585060482,0.899968761359,0.875146755384,2,'test'), +(1280,1.1983005446,0.565711657017,0.79535456721,0,'test'), +(1281,1.46069944727,0.0161590361331,0.6667386378,1,'test'), +(1282,2.543283049,0.519909397083,0.152884439756,2,'test'), +(1283,1.67919771186,0.493754211537,0.430631513387,1,'test'), +(1284,0.492359754106,0.212901970472,0.528637667627,0,'test'), +(1285,2.63114297184,0.618117087966,0.114130994369,2,'test'), +(1286,2.06166089723,0.001573158601,0.245128004586,2,'test'), +(1287,1.47550590996,0.2339404212,0.49149312178,1,'test'), +(1288,2.36276007409,0.526961430027,0.91422023827,1,'test'), +(1289,2.10782813881,0.199142919269,0.953249820113,1,'test'), +(1290,3.74400785924,0.243702527993,0.70732265003,3,'test'), +(1291,0.882649103026,0.436403237689,0.66801636607,0,'test'), +(1292,2.03295299136,0.0556422086427,0.988590300741,1,'test'), +(1293,3.45506379811,0.208198380538,0.49685552988,3,'test'), +(1294,3.09014356797,0.0736411275096,0.1284618249,3,'test'), +(1295,4.17939306833,0.798425752628,0.617225498263,3,'test'), +(1296,4.47118556718,0.502491850114,0.984222392078,3,'test'), +(1297,2.46151269575,0.261543140284,0.447179556186,2,'test'), +(1298,1.83094064397,0.796778755943,0.184829348403,1,'test'), +(1299,0.365691676425,0.15507878897,0.458925797331,0,'test'), +(1300,2.02190680521,0.865977719831,0.394878570424,1,'test'), +(1301,1.53553829482,0.46102322759,0.272974480921,1,'test'), +(1302,3.38048029725,0.561745241279,0.904839795752,2,'test'), +(1303,3.19654781993,0.312246265825,0.940373093036,2,'test'), +(1304,2.29463568429,0.243079768063,0.227059279098,2,'test'), +(1305,0.339802343327,0.276684527293,0.251232593495,0,'test'), +(1306,1.5865684912,0.359923450175,0.476072516564,1,'test'), +(1307,0.701085140088,0.569972338199,0.362095017763,0,'test'), +(1308,1.03096397264,0.0307467583864,0.0147381903925,1,'test'), +(1309,0.891796818972,0.385754614549,0.711366434703,0,'test'), +(1310,0.456770772011,0.341207154023,0.33994649283,0,'test'), +(1311,3.63666939478,0.629839703033,0.0826419490526,3,'test'), +(1312,2.20830394584,0.868598773777,0.582842321788,1,'test'), +(1313,0.824860825032,0.772639439844,0.228519988597,0,'test'), +(1314,1.70698780313,0.991370981023,0.845941382194,0,'test'), +(1315,2.17382087932,0.415932460556,0.870567871427,1,'test'), +(1316,4.07187743183,0.214645745578,0.925868071733,3,'test'), +(1317,3.12740137005,0.921886587932,0.453337382215,2,'test'), +(1318,2.338680043,0.814487580638,0.724011368948,1,'test'), +(1319,1.95389357975,0.91389798792,0.199988979263,1,'test'), +(1320,3.21344601883,0.210700325869,0.0523993603574,3,'test'), +(1321,3.01854619866,0.469201578501,0.741177860007,2,'test'), +(1322,3.32791618369,0.323516874982,0.0663272848058,3,'test'), +(1323,1.89279540395,0.451275656791,0.664469523121,1,'test'), +(1324,1.73789215873,0.727425050793,0.102308884959,1,'test'), +(1325,0.990387514373,0.724882441042,0.515271844109,0,'test'), +(1326,4.5587556684,0.637269775392,0.959940567435,3,'test'), +(1327,3.8761078728,0.33920152135,0.732738938128,3,'test'), +(1328,0.281425715506,0.224163025681,0.239296238635,0,'test'), +(1329,0.474572505793,0.467710763062,0.0828356368396,0,'test'), +(1330,1.31027542983,0.748721763941,0.74936884502,0,'test'), +(1331,2.12667327735,0.1834242421,0.971210088111,1,'test'), +(1332,3.6764181282,0.0241589571152,0.807625637956,3,'test'), +(1333,0.11832934962,0.105029491717,0.115325009876,0,'test'), +(1334,2.17413719646,0.128955187511,0.212560600656,2,'test'), +(1335,0.875691486585,0.464505759432,0.641237652632,0,'test'), +(1336,2.35236620121,0.356305333052,0.998028490657,1,'test'), +(1337,4.63970443221,0.919459622908,0.848672380431,3,'test'), +(1338,1.35393602606,0.208138030363,0.381835037287,1,'test'), +(1339,0.647229675545,0.38075324792,0.516213548471,0,'test'), +(1340,3.39366216362,0.0213976734131,0.610134813138,3,'test'), +(1341,3.25008743845,0.0598442103692,0.436168806861,3,'test'), +(1342,4.7515862723,0.837774322496,0.955935117988,3,'test'), +(1343,3.67693301178,0.70216938192,0.987301184976,2,'test'), +(1344,0.275465297145,0.0932686731653,0.426844964805,0,'test'), +(1345,1.68248287677,0.791342675649,0.944002225167,0,'test'), +(1346,3.6148719524,0.396384545945,0.467426364744,3,'test'), +(1347,0.477380071233,0.145294776376,0.576268422575,0,'test'), +(1348,3.98569147707,0.984927272003,0.0276442592572,3,'test'), +(1349,2.60142474933,0.118998491211,0.694569116873,2,'test'), +(1350,2.1721118122,0.1410042582,0.176373336991,2,'test'), +(1351,2.03016208102,0.853919425439,0.419812643431,1,'test'), +(1352,1.74230735132,0.727540444007,0.121519164396,1,'test'), +(1353,2.47135155804,0.0691293250639,0.63420992816,2,'test'), +(1354,2.82020757028,0.62143002917,0.445844750011,2,'test'), +(1355,0.613966487941,0.432989925339,0.425413402001,0,'test'), +(1356,2.40055937688,0.887380945086,0.716364733775,1,'test'), +(1357,2.64700403364,0.618009912313,0.170276602419,2,'test'), +(1358,4.46946835081,0.660810481504,0.899254062714,3,'test'), +(1359,2.95072158373,0.474449990642,0.690124331613,2,'test'), +(1360,0.00815006331396,0.00512158959855,0.0550315701702,0,'test'), +(1361,3.45066018514,0.384338254184,0.257530446658,3,'test'), +(1362,3.30404043722,0.296345506301,0.0877207553364,3,'test'), +(1363,2.92980160315,0.549542598706,0.616651444858,2,'test'), +(1364,2.65290680653,0.480474207679,0.41525004377,2,'test'), +(1365,1.36447623834,0.889405180168,0.68925398669,0,'test'), +(1366,1.15446424543,0.14754400922,0.0831879571144,1,'test'), +(1367,1.11943297648,0.070021245433,0.222287496389,1,'test'), +(1368,1.52291515234,0.479160465729,0.209176209481,1,'test'), +(1369,1.12766723816,0.124767133131,0.0538526232303,1,'test'), +(1370,0.733871102408,0.721084395581,0.113078321647,0,'test'), +(1371,2.65657986158,0.21557092683,0.66408503578,2,'test'), +(1372,1.29795781165,0.242955365473,0.234526003191,1,'test'), +(1373,3.63219710322,0.612258741415,0.141203264152,3,'test'), +(1374,3.26454367003,0.788756869533,0.689773006504,2,'test'), +(1375,2.73699925603,0.681801573434,0.234941870665,2,'test'), +(1376,2.27768333658,0.531895060638,0.863590340347,1,'test'), +(1377,2.38408958671,0.438972120416,0.972171521025,1,'test'), +(1378,0.998419426861,0.989180277753,0.0961204926521,0,'test'), +(1379,1.30049919107,0.622832201038,0.823205314626,0,'test'), +(1380,1.73494754526,0.91869165111,0.903468811943,0,'test'), +(1381,0.329668467737,0.14937217432,0.424613110275,0,'test'), +(1382,3.20272380062,0.79506940588,0.638478186582,2,'test'), +(1383,2.36371059483,0.7371050566,0.791584195285,1,'test'), +(1384,3.11048659347,0.15402470861,0.977988693627,2,'test'), +(1385,4.81931847515,0.924403658299,0.945999374655,3,'test'), +(1386,1.91917529702,0.994131725023,0.961791854819,0,'test'), +(1387,3.22757545871,0.216760922866,0.103992960549,3,'test'), +(1388,0.195293938248,0.195042427678,0.0158590847959,0,'test'), +(1389,3.0039181557,0.0479802256018,0.977720783303,2,'test'), +(1390,4.41574218091,0.489752456341,0.962283598828,3,'test'), +(1391,1.16963911588,0.800229136472,0.607791065591,0,'test'), +(1392,4.11525877853,0.657539649705,0.67654942822,3,'test'), +(1393,3.14322190348,0.131992690114,0.10596798272,3,'test'), +(1394,4.14853599561,0.944543778975,0.451654975216,3,'test'), +(1395,3.49739249244,0.340510121689,0.396083792592,3,'test'), +(1396,1.56825324975,0.478242712724,0.300017561188,1,'test'), +(1397,0.706547642228,0.378243823541,0.572978026356,0,'test'), +(1398,2.39467309839,0.512877944766,0.939039484593,1,'test'), +(1399,2.95932969243,0.589070319108,0.608489419238,2,'test'), +(1400,2.62885622685,0.621595168872,0.0852118417569,2,'test'), +(1401,2.86870853823,0.858388400591,0.10158807821,2,'test'), +(1402,0.964194876582,0.950172677803,0.118415365468,0,'test'), +(1403,4.01100893719,0.805528572939,0.453299420086,3,'test'), +(1404,4.29775508173,0.875049943754,0.650157779294,3,'test'), +(1405,0.661819831854,0.344787756992,0.563056013965,0,'test'), +(1406,2.36697129079,0.318923095973,0.219198984523,2,'test'), +(1407,1.88820855292,0.881654235208,0.0809587407693,1,'test'), +(1408,3.06098619228,0.925783964604,0.367698555438,2,'test'), +(1409,3.7465763021,0.379349377902,0.605992511668,3,'test'), +(1410,0.416554918272,0.241108210561,0.418863590816,0,'test'), +(1411,3.74206510803,0.254901975546,0.697970724661,3,'test'), +(1412,1.91245664193,0.678446469362,0.483745979378,1,'test'), +(1413,4.31216696476,0.682051579131,0.793798076107,3,'test'), +(1414,2.35424340265,0.766143014977,0.766877035561,1,'test'), +(1415,1.83671177296,0.467076150947,0.607976662389,1,'test'), +(1416,1.15074423186,0.671910471959,0.691978149871,0,'test'), +(1417,2.97995417382,0.85992022255,0.346459162486,2,'test'), +(1418,4.08871031675,0.190541755137,0.947717553709,3,'test'), +(1419,1.25676754536,0.0232760898759,0.483209535792,1,'test'), +(1420,0.842997741857,0.837092882606,0.076843082002,0,'test'), +(1421,2.27813484128,0.277712832971,0.0205428407532,2,'test'), +(1422,3.9791479257,0.974427896913,0.0687024656253,3,'test'), +(1423,3.83243454805,0.758217233476,0.272428549483,3,'test'), +(1424,0.632189067936,0.4385639833,0.440028504346,0,'test'), +(1425,2.08441624869,0.0390492147784,0.212995384722,2,'test'), +(1426,3.8379406474,0.780146969764,0.240403156462,3,'test'), +(1427,2.86745889377,0.356949145123,0.714499649158,2,'test'), +(1428,2.95906467116,0.569683886413,0.624003833923,2,'test'), +(1429,3.58005793997,0.992580785253,0.766470583074,2,'test'), +(1430,2.82511252446,0.591256540306,0.48358658393,2,'test'), +(1431,2.57137291,0.524899482178,0.215576964956,2,'test'), +(1432,1.31483575761,0.326459275903,0.994171253709,0,'test'), +(1433,2.65316143843,0.619064951233,0.184652341428,2,'test'), +(1434,2.07594293689,0.994126481547,0.286035758851,1,'test'), +(1435,2.42667854583,0.564059550602,0.928772843718,1,'test'), +(1436,2.61536806245,0.862818143626,0.867496350897,1,'test'), +(1437,3.26986007756,0.867091385877,0.634640600408,2,'test'), +(1438,4.65301034684,0.98471680316,0.817492228515,3,'test'), +(1439,2.32087082668,0.291267228567,0.172056961827,2,'test'), +(1440,0.200554068382,0.182112537713,0.135799597455,0,'test'), +(1441,2.48476719644,0.314766136151,0.412311848344,2,'test'), +(1442,1.04540189843,0.0246166548689,0.144170883187,1,'test'), +(1443,2.79685818111,0.219086959211,0.760112637637,2,'test'), +(1444,3.38523715707,0.584855399465,0.894640574536,2,'test'), +(1445,1.79918318673,0.687326545131,0.334449759449,1,'test'), +(1446,3.9347666664,0.647356851473,0.536106160129,3,'test'), +(1447,3.97831526983,0.855245434679,0.350813105728,3,'test'), +(1448,4.07145051009,0.701107099092,0.608558469659,3,'test'), +(1449,3.78394537246,0.831460355603,0.975953388669,2,'test'), +(1450,2.91066955665,0.0139322679342,0.946962136895,2,'test'), +(1451,1.14287226297,0.115377602742,0.165815138728,1,'test'), +(1452,3.68999913963,0.188577746597,0.708111144547,3,'test'), +(1453,4.72071234267,0.880361840279,0.916706333781,3,'test'), +(1454,2.36225213228,0.293483465932,0.262237804952,2,'test'), +(1455,0.523159257972,0.21654628481,0.553726442535,0,'test'), +(1456,1.48898747774,0.0886172599137,0.632748147231,1,'test'), +(1457,3.48876828423,0.485687105484,0.0555083664135,3,'test'), +(1458,3.50009742398,0.130599104536,0.60786373427,3,'test'), +(1459,2.64676669265,0.646389865677,0.0194120316653,2,'test'), +(1460,2.27171787071,0.270936352166,0.0279556530962,2,'test'), +(1461,4.43449899578,0.630181669692,0.896837402255,3,'test'), +(1462,1.26649493352,0.81287090887,0.673516165098,0,'test'), +(1463,1.74578964612,0.580766913692,0.406229900952,1,'test'), +(1464,3.90220611864,0.0061199497858,0.946618280436,3,'test'), +(1465,3.44537744926,0.201696850468,0.493640151119,3,'test'), +(1466,2.3839438305,0.300648907208,0.288608598782,2,'test'), +(1467,4.51356272689,0.83184911983,0.825659498255,3,'test'), +(1468,1.24909121211,0.209699220295,0.198474159073,1,'test'), +(1469,2.10750879184,0.987161367285,0.346911263233,1,'test'), +(1470,1.150124527,0.509949622305,0.80010930797,0,'test'), +(1471,2.17355636484,0.823064969715,0.59202313732,1,'test'), +(1472,3.50605665288,0.440596072316,0.25585265401,3,'test'), +(1473,3.35620335289,0.979705438418,0.613594258831,2,'test'), +(1474,3.06830411396,0.567505554032,0.707671223046,2,'test'), +(1475,3.76988881871,0.294283995297,0.68964108304,3,'test'), +(1476,0.924975257128,0.858271962097,0.258269810529,0,'test'), +(1477,3.99310749854,0.705350968011,0.53642942735,3,'test'), +(1478,0.909789949935,0.713724764642,0.442792485588,0,'test'), +(1479,0.361108733766,0.241412375267,0.345971615164,0,'test'), +(1480,3.24179447396,0.219700577144,0.148640158816,3,'test'), +(1481,1.3628060111,0.345599448738,0.13117378686,1,'test'), +(1482,0.878603234302,0.57479959972,0.551183848259,0,'test'), +(1483,0.185735390719,0.127154031716,0.242035863053,0,'test'), +(1484,2.18534709441,0.756256656931,0.655049950366,1,'test'), +(1485,2.84401395501,0.432793600259,0.641264652661,2,'test'), +(1486,2.28558545412,0.751462054727,0.730837464414,1,'test'), +(1487,1.17442990989,0.675373923581,0.706438947331,0,'test'), +(1488,4.31189174067,0.998864464079,0.55948840613,3,'test'), +(1489,1.12014578234,0.375917507714,0.862686660746,0,'test'), +(1490,2.52926938436,0.100396949661,0.654883527583,2,'test'), +(1491,1.05905893768,0.872174273476,0.432301589404,0,'test'), +(1492,2.16949267795,0.132034370457,0.193541487786,2,'test'), +(1493,0.446472220334,0.44445027549,0.0449660410119,0,'test'), +(1494,0.665225568085,0.464220582231,0.448335795865,0,'test'), +(1495,0.541144913282,0.242134306969,0.546818622866,0,'test'), +(1496,1.33012777671,0.999276236259,0.575196957964,0,'test'), +(1497,1.56473652153,0.913330207841,0.807097462325,0,'test'), +(1498,4.19676024731,0.657504575947,0.734340296703,3,'test'), +(1499,0.835467872155,0.659799890799,0.419127643273,0,'test'), +(1500,2.70865795836,0.540929153579,0.409547072733,2,'test'), +(1501,3.26260979484,0.254910804421,0.0877438910359,3,'test'), +(1502,0.889868271277,0.218983321615,0.819075667849,0,'test'), +(1503,1.96769735566,0.70266182348,0.514816017793,1,'test'), +(1504,0.566148817332,0.0609481423607,0.710774700571,0,'test'), +(1505,2.52049939635,0.510638166928,0.0993037231085,2,'test'), +(1506,4.55212801831,0.890399077342,0.813467234106,3,'test'), +(1507,1.82921855792,0.799079268281,0.173606709667,1,'test'), +(1508,3.39289383796,0.0172944108764,0.612861670428,3,'test'), +(1509,2.61247204649,0.212388730374,0.632521395778,2,'test'), +(1510,1.06584948888,0.341906551177,0.850848363516,0,'test'), +(1511,3.75085155098,0.0408938283573,0.842589889939,3,'test'), +(1512,0.990922298326,0.571555656449,0.647585239082,0,'test'), +(1513,3.19958843423,0.0614388891608,0.371684738818,3,'test'), +(1514,2.58174874069,0.0144896605169,0.753166037583,2,'test'), +(1515,2.36847257066,0.0566666919384,0.558395808293,2,'test'), +(1516,4.39900164489,0.505284801053,0.945365984073,3,'test'), +(1517,4.43842147192,0.464487877168,0.986880739884,3,'test'), +(1518,3.84621445141,0.305542973964,0.735303663424,3,'test'), +(1519,1.4594292163,0.65288607624,0.898077468854,0,'test'), +(1520,3.08665631068,0.0480890599736,0.196385464599,3,'test'), +(1521,3.39272458114,0.122283508484,0.520039491439,3,'test'), +(1522,4.43841751988,0.931381265562,0.712064782388,3,'test'), +(1523,3.87614204186,0.357459884607,0.720195915884,3,'test'), +(1524,2.93792866883,0.226753327174,0.843312125878,2,'test'), +(1525,1.28144785978,0.218520843148,0.250852579472,1,'test'), +(1526,1.9509532394,0.934140979064,0.129662100613,1,'test'), +(1527,3.08213839947,0.462185660547,0.787370776014,2,'test'), +(1528,1.60092145616,0.550737798236,0.224017092921,1,'test'), +(1529,0.323206292027,0.198345990549,0.353355771819,0,'test'), +(1530,3.76974123886,0.228435789598,0.735734632363,3,'test'), +(1531,3.72388835756,0.60542145578,0.344190211632,3,'test'), +(1532,2.14114024993,0.793264561218,0.589809875057,1,'test'), +(1533,4.29720899005,0.530157120162,0.875814974687,3,'test'), +(1534,2.3439754441,0.00343302276215,0.583560126581,2,'test'), +(1535,3.15912615908,0.48401211986,0.821653235389,2,'test'), +(1536,0.449348934631,0.397332282074,0.228071595245,0,'test'), +(1537,1.75740722659,0.754507890594,0.0538454826154,1,'test'), +(1538,1.63555245169,0.814944429756,0.905874175554,0,'test'), +(1539,0.571064665106,0.461805691162,0.330543452429,0,'test'), +(1540,0.413318305461,0.410329600956,0.0546690452121,0,'test'), +(1541,2.7244603296,0.647161462204,0.278026738627,2,'test'), +(1542,3.97477912165,0.93443400301,0.200860943542,3,'test'), +(1543,1.81179677187,0.327932291415,0.695603680595,1,'test'), +(1544,3.61736266921,0.607356932238,0.100028680739,3,'test'), +(1545,1.79707452445,0.796952682539,0.011038202415,1,'test'), +(1546,1.17043253206,0.812666158682,0.598135748288,0,'test'), +(1547,1.83655843581,0.265980507038,0.755366089237,1,'test'), +(1548,3.23361222466,0.509077566747,0.851196016155,2,'test'), +(1549,0.456097778164,0.357275612767,0.314359929694,0,'test'), +(1550,3.86271799265,0.766083473996,0.310860931376,3,'test'), +(1551,3.80101606604,0.677649232722,0.351236150364,3,'test'), +(1552,3.75890463033,0.910129513378,0.921289920142,2,'test'), +(1553,3.94547523294,0.863614035006,0.286113959699,3,'test'), +(1554,3.03580791913,0.228113202088,0.898718374711,2,'test'), +(1555,4.30371799782,0.585490805266,0.847482856792,3,'test'), +(1556,2.13568278029,0.101578974723,0.184672156978,2,'test'), +(1557,1.94791551676,0.831820467495,0.340727235879,1,'test'), +(1558,0.597812849661,0.31595698549,0.530900992814,0,'test'), +(1559,4.23437046496,0.824150173806,0.640484419132,3,'test'), +(1560,3.99108162181,0.816309927342,0.418057046911,3,'test'), +(1561,3.38422310773,0.37392770809,0.101466248757,3,'test'), +(1562,2.93972996129,0.662088839313,0.526916617669,2,'test'), +(1563,2.76607070171,0.996998416606,0.876967664799,1,'test'), +(1564,0.861818920637,0.54027791888,0.567045855075,0,'test'), +(1565,1.6868238595,0.464422167735,0.471594838566,1,'test'), +(1566,2.90221260755,0.73549405724,0.408311829741,2,'test'), +(1567,4.12434029169,0.738421765487,0.621223410864,3,'test'), +(1568,3.95335309201,0.929171302377,0.155504950501,3,'test'), +(1569,3.41808667635,0.407239817666,0.104148253371,3,'test'), +(1570,1.17902989152,0.663388718053,0.718081592484,0,'test'), +(1571,3.48353565064,0.917877881679,0.752102233052,2,'test'), +(1572,1.45991533119,0.575817436245,0.940264800441,0,'test'), +(1573,3.24275352434,0.242212321869,0.0232637587221,3,'test'), +(1574,1.17225707488,0.0056040898992,0.408231533542,1,'test'), +(1575,2.71119073458,0.707304839499,0.0623369479294,2,'test'), +(1576,3.56813894353,0.880823798299,0.829044718475,2,'test'), +(1577,2.6584577102,0.602501836267,0.236549939609,2,'test'), +(1578,1.03845001944,0.75506541143,0.532338809419,0,'test'), +(1579,2.36007366794,0.894865555576,0.682061663169,1,'test'), +(1580,3.85149734008,0.315429445323,0.732166575826,3,'test'), +(1581,2.24537478405,0.0170172926968,0.477867650453,2,'test'), +(1582,3.22052828059,0.216597281101,0.0626976832806,3,'test'), +(1583,1.87476209063,0.532113907379,0.585361583336,1,'test'), +(1584,3.55223354584,0.487485173777,0.254457014172,3,'test'), +(1585,3.30097105816,0.289539726115,0.106917407571,3,'test'), +(1586,1.5293992334,0.964479486204,0.751611433652,0,'test'), +(1587,4.42000676502,0.949432690618,0.685984019059,3,'test'), +(1588,3.55294388957,0.105832514133,0.668663873289,3,'test'), +(1589,1.66239060901,0.191442907578,0.686256294277,1,'test'), +(1590,1.70077079072,0.727590296825,0.986499109931,0,'test'), +(1591,2.7419894099,0.147804344761,0.770834006216,2,'test'), +(1592,4.24674331258,0.868790505144,0.614778665408,3,'test'), +(1593,4.06825845072,0.992879960521,0.274551434519,3,'test'), +(1594,1.77586448769,0.366385782605,0.639905231328,1,'test'), +(1595,1.23715362614,0.211352754462,0.160626497423,1,'test'), +(1596,1.51580162898,0.503152504265,0.112468327607,1,'test'), +(1597,4.49116862012,0.941377523832,0.741478992476,3,'test'), +(1598,2.7054810231,0.705424492381,0.00751869154257,2,'test'), +(1599,0.270922165976,0.17736872293,0.305865073269,0,'test'), +(1600,2.96633220473,0.933322489613,0.181685759259,2,'test'), +(1601,2.19001488054,0.169482570651,0.143290997239,2,'test'), +(1602,0.747823566306,0.135683525238,0.782393789001,0,'test'), +(1603,3.32489207851,0.594397103572,0.85468998762,2,'test'), +(1604,2.92863754321,0.634107127557,0.542706564957,2,'test'), +(1605,0.536297753681,0.0731327683116,0.680562256792,0,'test'), +(1606,2.67452994544,0.109213737371,0.751875127979,2,'test'), +(1607,3.87350842347,0.210084392625,0.81450845965,3,'test'), +(1608,1.42025407563,0.425636505835,0.9973051538,0,'test'), +(1609,1.54164925613,0.485199693756,0.237591166443,1,'test'), +(1610,2.74902508578,0.748412988859,0.0247405926202,2,'test'), +(1611,2.92141232836,0.888103351227,0.182507471446,2,'test'), +(1612,0.289326942832,0.279335982542,0.0999547912331,0,'test'), +(1613,2.49001494084,0.483143680121,0.082893067963,2,'test'), +(1614,1.22009569255,0.467576902471,0.867478408999,0,'test'), +(1615,0.288866064119,0.022766953088,0.515847953404,0,'test'), +(1616,0.860867631911,0.279112797932,0.762728545407,0,'test'), +(1617,2.08282086073,0.0607585660903,0.148533816475,2,'test'), +(1618,0.713294688192,0.533212213216,0.424361255272,0,'test'), +(1619,2.37411573284,0.372688539977,0.0377782062045,2,'test'), +(1620,1.96993409848,0.818295155511,0.389408452614,1,'test'), +(1621,2.9059984755,0.775787536549,0.36084752867,2,'test'), +(1622,2.96981944661,0.547982422486,0.649489818341,2,'test'), +(1623,2.5638614972,0.830552634531,0.856334550667,1,'test'), +(1624,3.53789475038,0.342783070854,0.441714477384,3,'test'), +(1625,1.85602874213,0.00880749223138,0.920446223252,1,'test'), +(1626,2.93362092688,0.348335217166,0.765039678526,2,'test'), +(1627,1.70901204009,0.658524619586,0.224694059778,1,'test'), +(1628,1.43504247123,0.326918752548,0.328821712603,1,'test'), +(1629,2.45234097623,0.908815253942,0.737241969975,1,'test'), +(1630,3.61420400988,0.394692710512,0.468520329727,3,'test'), +(1631,2.89316951849,0.774306833241,0.344764680982,2,'test'), +(1632,2.23387070772,0.184010109726,0.223294867809,2,'test'), +(1633,0.489833673332,0.238252730504,0.501578451319,0,'test'), +(1634,2.5526313095,0.146261645156,0.637471304719,2,'test'), +(1635,2.73348952494,0.685787240345,0.218408526826,2,'test'), +(1636,1.13954525892,0.745731226639,0.627546039969,0,'test'), +(1637,4.1493937775,0.789465319979,0.599940378301,3,'test'), +(1638,4.81972139041,0.999115351509,0.905873081012,3,'test'), +(1639,3.57665609327,0.492388060899,0.290289566418,3,'test'), +(1640,2.68871742243,0.679060046373,0.0982719494827,2,'test'), +(1641,0.656964324486,0.433009016914,0.473239165297,0,'test'), +(1642,0.963409882427,0.245165866767,0.847492782069,0,'test'), +(1643,3.73861449566,0.659641082575,0.281022086473,3,'test'), +(1644,3.37463206756,0.276540731458,0.313195364106,3,'test'), +(1645,3.72139453411,0.682908974392,0.196177367999,3,'test'), +(1646,2.67581025881,0.921807893633,0.868333095753,1,'test'), +(1647,2.58589280752,0.678071078511,0.952796793135,1,'test'), +(1648,1.13654166705,0.133765349811,0.0526907699719,1,'test'), +(1649,1.51451292679,0.276624911714,0.487737649848,1,'test'), +(1650,2.06600251447,0.906350696482,0.39956453545,1,'test'), +(1651,3.74995995177,0.726547901281,0.153009968584,3,'test'), +(1652,3.29036970971,0.954482936281,0.579557394421,2,'test'), +(1653,2.33825457982,0.67927828752,0.811773547423,1,'test'), +(1654,0.471733645097,0.471276251414,0.0213867642039,0,'test'), +(1655,0.263933955397,0.211596539186,0.228773722728,0,'test'), +(1656,1.91573093667,0.49994860155,0.644811860252,1,'test'), +(1657,3.14291571084,0.127191282221,0.125397083762,3,'test'), +(1658,4.6299665354,0.928187769357,0.837722368117,3,'test'), +(1659,1.1647310818,0.12128712615,0.208432136808,1,'test'), +(1660,2.91255411972,0.319180017713,0.770307796926,2,'test'), +(1661,4.1671723445,0.271813785709,0.946233881657,3,'test'), +(1662,2.35972208311,0.335668872305,0.155090975905,2,'test'), +(1663,3.44886039035,0.190327932596,0.508460871411,3,'test'), +(1664,1.6854013827,0.245746271652,0.663064937279,1,'test'), +(1665,1.11480401495,0.108459909015,0.0796498960008,1,'test'), +(1666,2.91361090391,0.900344100576,0.11518161021,2,'test'), +(1667,1.76099980496,0.148779639813,0.782444991771,1,'test'), +(1668,2.19197753767,0.170030474708,0.148145411552,2,'test'), +(1669,4.04315608788,0.622300513685,0.648733823842,3,'test'), +(1670,2.46888797528,0.398621184455,0.265078838892,2,'test'), +(1671,3.77590936542,0.762511316548,0.115749941144,3,'test'), +(1672,0.820028193775,0.541441511375,0.527813113137,0,'test'), +(1673,2.23996887164,0.105275677621,0.367005713877,2,'test'), +(1674,4.02761138177,0.200410805874,0.909505676672,3,'test'), +(1675,0.229908684095,0.227389653559,0.0501899445654,0,'test'), +(1676,3.21548458331,0.206813875671,0.0931166345981,3,'test'), +(1677,1.14795471295,0.497061376987,0.806779608049,0,'test'), +(1678,3.49978245485,0.29482946887,0.452717335632,3,'test'), +(1679,2.60888430925,0.604075952755,0.0693423138931,2,'test'), +(1680,4.21282674517,0.736723804402,0.690002130985,3,'test'), +(1681,3.30517612917,0.254046950244,0.226117621876,3,'test'), +(1682,3.56976694598,0.561926394697,0.0885468875018,3,'test'), +(1683,3.99272493381,0.89945113472,0.305407595011,3,'test'), +(1684,3.18091640361,0.0608536753063,0.346500690192,3,'test'), +(1685,0.671507882251,0.0303024440781,0.800753044436,0,'test'), +(1686,1.77955605306,0.418383474586,0.600976354337,1,'test'), +(1687,0.41555182918,0.275619917873,0.374074740269,0,'test'), +(1688,3.20700372164,0.24256799556,0.98205688536,2,'test'), +(1689,1.26237424562,0.254421751955,0.0891767552007,1,'test'), +(1690,2.67120147576,0.641951677363,0.171025724371,2,'test'), +(1691,0.178374711104,0.0590424311297,0.345445046244,0,'test'), +(1692,1.18431838703,0.523538497784,0.812883687406,0,'test'), +(1693,4.12197207834,0.949213844037,0.415641954459,3,'test'), +(1694,2.46412849559,0.442853491844,0.145859534298,2,'test'), +(1695,2.59115893553,0.128285443267,0.680348066997,2,'test'), +(1696,4.31460330092,0.943584547559,0.609113087501,3,'test'), +(1697,0.789470507455,0.535311213569,0.50414213659,0,'test'), +(1698,3.78527130955,0.256689739066,0.727036154865,3,'test'), +(1699,0.643137854192,0.203417539813,0.663114103589,0,'test'), +(1700,2.94078908041,0.265690618882,0.821643755853,2,'test'), +(1701,4.23846197683,0.350530670375,0.942301069962,3,'test'), +(1702,3.2038058852,0.0820447493392,0.348942883375,3,'test'), +(1703,0.617590562487,0.615423018897,0.0465568855285,0,'test'), +(1704,0.989064221351,0.762224683677,0.476276744838,0,'test'), +(1705,1.22618882229,0.789801583865,0.66059612353,0,'test'), +(1706,2.43213106016,0.734381937401,0.835313787001,1,'test'), +(1707,1.53393304123,0.0622412994636,0.68679818125,1,'test'), +(1708,0.743045047602,0.546492003664,0.443343031905,0,'test'), +(1709,2.88086462972,0.03770205273,0.918238845284,2,'test'), +(1710,3.77088030912,0.888264879644,0.939476146303,2,'test'), +(1711,2.46785387913,0.486187249976,0.990790910919,1,'test'), +(1712,1.94495089022,0.810321800007,0.366918369963,1,'test'), +(1713,4.03099632821,0.304242742719,0.852498437237,3,'test'), +(1714,2.1144015869,0.688656765499,0.652491242391,1,'test'), +(1715,2.94977856581,0.918701796198,0.176286044869,2,'test'), +(1716,1.34001251393,0.338289139515,0.0415135449808,1,'test'), +(1717,1.68328543143,0.313432340856,0.608155482234,1,'test'), +(1718,2.61732667826,0.593270483022,0.155100597165,2,'test'), +(1719,3.26196964154,0.0108037465141,0.501164538874,3,'test'), +(1720,3.43838347983,0.628091497166,0.900162197974,2,'test'), +(1721,3.33417331914,0.393567421692,0.969848388897,2,'test'), +(1722,4.6673290871,0.994461621143,0.820284990691,3,'test'), +(1723,0.422923721433,0.0401198482079,0.618711462012,0,'test'), +(1724,3.20699240376,0.0318538602635,0.41849557166,3,'test'), +(1725,2.77182196291,0.657159553558,0.338618383061,2,'test'), +(1726,1.19016265701,0.148127473749,0.205024835709,1,'test'), +(1727,0.816059663607,0.68726294123,0.3588826025,0,'test'), +(1728,1.09433988149,0.485579506493,0.780230975415,0,'test'), +(1729,2.64782692464,0.344643075771,0.550621329837,2,'test'), +(1730,0.531982379412,0.473165075717,0.242522790052,0,'test'), +(1731,1.02589339229,0.0725825915045,0.976376362264,0,'test'), +(1732,4.00787431006,0.244717795658,0.873588298002,3,'test'), +(1733,3.23885619004,0.518096790207,0.848975500135,2,'test'), +(1734,3.39541556299,0.748589301914,0.804255097017,2,'test'), +(1735,0.532748665383,0.24935556593,0.532346784956,0,'test'), +(1736,2.36414176029,0.475425182979,0.94271765514,1,'test'), +(1737,2.24073176008,0.807582466299,0.658140785685,1,'test'), +(1738,3.63136785581,0.965939506792,0.81573791687,2,'test'), +(1739,3.6652858561,0.639055277399,0.161958570941,3,'test'), +(1740,2.09827237984,0.588768024152,0.713795738069,1,'test'), +(1741,0.639717357528,0.509223341277,0.361239555214,0,'test'), +(1742,2.50872462901,0.423193451715,0.292457137534,2,'test'), +(1743,1.77981554586,0.710426371711,0.263418249462,1,'test'), +(1744,1.16585948607,0.155153244061,0.103470971803,1,'test'), +(1745,4.30839182541,0.894612223583,0.643257026254,3,'test'), +(1746,2.84079967185,0.837524210816,0.0572316436296,2,'test'), +(1747,4.40199759628,0.416507957968,0.992718307633,3,'test'), +(1748,1.4672330318,0.843609699478,0.78969825397,0,'test'), +(1749,2.73034660114,0.0829004991766,0.804640355663,2,'test'), +(1750,1.71027641877,0.807359823948,0.950219235136,0,'test'), +(1751,3.58888108249,0.689954179036,0.948117557822,2,'test'), +(1752,1.28381182606,0.213726109066,0.264737071432,1,'test'), +(1753,2.01980799022,0.743826873708,0.525339049098,1,'test'), +(1754,2.20142024592,0.341579674998,0.92727588717,1,'test'), +(1755,0.428307176323,0.36591565984,0.249782938735,0,'test'), +(1756,0.140735886642,0.140722739867,0.00362584810863,0,'test'), +(1757,3.0690557193,0.858234088606,0.459153166922,2,'test'), +(1758,1.81350813736,0.738297503772,0.274245571686,1,'test'), +(1759,0.166514145262,0.145989479601,0.143264320961,0,'test'), +(1760,1.57570537806,0.660475356691,0.956676550024,0,'test'), +(1761,3.50126983243,0.460686234233,0.201453712295,3,'test'), +(1762,1.12297886623,0.460412063488,0.813982065371,0,'test'), +(1763,1.93372993449,0.234592353115,0.836144473985,1,'test'), +(1764,1.27051172896,0.632818269548,0.798557110927,0,'test'), +(1765,1.61340027728,0.574646439104,0.196859945583,1,'test'), +(1766,2.11203469392,0.628871592859,0.695099346178,1,'test'), +(1767,1.58825500593,0.127452182723,0.678824589425,1,'test'), +(1768,0.744064434421,0.340818006107,0.635016872464,0,'test'), +(1769,2.80398856699,0.55888460786,0.495079750276,2,'test'), +(1770,0.951480291297,0.592940465471,0.598781951821,0,'test'), +(1771,0.975966657375,0.909664288069,0.257492464562,0,'test'), +(1772,2.98207798086,0.850071427344,0.363327061361,2,'test'), +(1773,1.31283141629,0.358971079624,0.976657737729,0,'test'), +(1774,4.052084222,0.0673736003422,0.992325864652,3,'test'), +(1775,0.950822247591,0.555789747568,0.628516109597,0,'test'), +(1776,2.38373306553,0.381813884674,0.0438084563916,2,'test'), +(1777,3.96631312043,0.761024177408,0.453088228741,3,'test'), +(1778,2.44644177003,0.42868997534,0.133235861114,2,'test'), +(1779,3.22024648946,0.539023698303,0.825362218153,2,'test'), +(1780,0.429475749976,0.380105708629,0.222193702311,0,'test'), +(1781,0.890019023714,0.877201498176,0.113214511164,0,'test'), +(1782,1.51868440783,0.765615671358,0.867795330982,0,'test'), +(1783,2.98909538058,0.90469039619,0.290525359285,2,'test'), +(1784,0.530304314167,0.300221092496,0.479669909073,0,'test'), +(1785,4.43865708545,0.753793824688,0.827564656547,3,'test'), +(1786,2.62652776039,0.0750218387013,0.742634446875,2,'test'), +(1787,3.57241567385,0.264791484852,0.554638791461,3,'test'), +(1788,3.02156413748,0.515237436426,0.7115663715,2,'test'), +(1789,2.00933317658,0.968676531412,0.201634930421,1,'test'), +(1790,1.57805674373,0.872349948731,0.840063566046,0,'test'), +(1791,3.82401844575,0.785991441443,0.195005139193,3,'test'), +(1792,1.25966250061,0.766691656905,0.702118824491,0,'test'), +(1793,4.02384674617,0.0578943846026,0.982828754953,3,'test'), +(1794,1.47026272634,0.469891048644,0.0192789443652,1,'test'), +(1795,3.00656396136,0.251103751529,0.869172140503,2,'test'), +(1796,2.45519130761,0.929647644789,0.724943903222,1,'test'), +(1797,3.10755441621,0.73584357845,0.609680931109,2,'test'), +(1798,2.18944213568,0.527796903638,0.813415780544,1,'test'), +(1799,0.66579588502,0.583030339454,0.287690016453,0,'test'), +(1800,2.12321244523,0.412880077063,0.842812178465,1,'test'), +(1801,0.99495652818,0.795156550994,0.446989907253,0,'test'), +(1802,4.35361280121,0.660027674234,0.832817583254,3,'test'), +(1803,2.57499780129,0.574549227493,0.0211795609136,2,'test'), +(1804,2.15822546556,0.134772660297,0.15314308755,2,'test'), +(1805,4.15816039344,0.248544264213,0.95373797724,3,'test'), +(1806,3.84971033768,0.369875165781,0.692701358381,3,'test'), +(1807,1.45063397298,0.348663873919,0.319327573284,1,'test'), +(1808,2.41817572053,0.103775892312,0.560713677578,2,'test'), +(1809,1.25917054046,0.954816004318,0.551683365837,0,'test'), +(1810,1.68635286017,0.408771331235,0.526860065802,1,'test'), +(1811,3.31125263094,0.610213533449,0.837280775779,2,'test'), +(1812,2.58561142764,0.920951745784,0.815266632368,1,'test'), +(1813,1.5716639192,0.473925355803,0.312631673696,1,'test'), +(1814,1.38321791837,0.315365349329,0.260485256859,1,'test'), +(1815,3.31527325627,0.967490695255,0.58973092255,2,'test'), +(1816,3.31574190404,0.889694370964,0.652723167256,2,'test'), +(1817,2.8280488642,0.78324851792,0.211660922888,2,'test'), +(1818,1.81556805429,0.878734495831,0.967901626438,0,'test'), +(1819,3.56992455607,0.488490231805,0.285366999255,3,'test'), +(1820,1.91418146868,0.870581882259,0.208805139826,1,'test'), +(1821,3.15239689399,0.906835177216,0.495541841597,2,'test'), +(1822,3.53250651291,0.429684492724,0.320658728529,3,'test'), +(1823,2.23934701947,0.20202637587,0.193185516019,2,'test'), +(1824,0.874851176848,0.284333425165,0.768451528519,0,'test'), +(1825,3.06439022494,0.498897517615,0.751992491536,2,'test'), +(1826,4.2582310202,0.955898550518,0.549847678615,3,'test'), +(1827,2.06735179772,0.181027797698,0.941447821187,1,'test'), +(1828,1.99322246037,0.955578660123,0.194020102685,1,'test'), +(1829,1.86725039431,0.0727639735394,0.891339677547,1,'test'), +(1830,2.12440093648,0.949347929034,0.418393364488,1,'test'), +(1831,2.56954018097,0.185832243103,0.619441633948,2,'test'), +(1832,2.967162917,0.404835789642,0.749884742719,2,'test'), +(1833,2.56227642267,0.0682428764691,0.702875199593,2,'test'), +(1834,0.533517180301,0.410112550476,0.351289951216,0,'test'), +(1835,2.01522799775,0.734948802656,0.529414011044,1,'test'), +(1836,1.77347563817,0.350110028397,0.650665512977,1,'test'), +(1837,1.72394694155,0.770235603582,0.976581454855,0,'test'), +(1838,1.52114420823,0.449934413968,0.266851633431,1,'test'), +(1839,1.75892480269,0.727830780903,0.176334970412,1,'test'), +(1840,1.33784471407,0.538841769629,0.893869646227,0,'test'), +(1841,2.59197595391,0.514174596597,0.278928946715,2,'test'), +(1842,4.46812489257,0.919540126441,0.740665083642,3,'test'), +(1843,3.1391116531,0.118966648717,0.141933098269,3,'test'), +(1844,3.07160004706,0.682104273454,0.624095965059,2,'test'), +(1845,1.81300668824,0.624794258533,0.43383456491,1,'test'), +(1846,2.77234205334,0.0336048843039,0.859498207699,2,'test'), +(1847,1.09149294457,0.0913320233632,0.0126854722709,1,'test'), +(1848,0.906410245527,0.678292028422,0.477617228651,0,'test'), +(1849,3.24655684712,0.198551567163,0.219101072475,3,'test'), +(1850,0.512011613356,0.415064782756,0.311362860021,0,'test'), +(1851,3.01505863092,0.920392409647,0.307678763118,2,'test'), +(1852,1.07168445462,0.399877177274,0.819638504068,0,'test'), +(1853,4.33665747697,0.993405931028,0.585876732719,3,'test'), +(1854,3.59656382845,0.14738282132,0.670209674006,3,'test'), +(1855,1.61577189586,0.601241729431,0.120541140006,1,'test'), +(1856,2.87270553719,0.190563050479,0.825919176863,2,'test'), +(1857,3.45690880724,0.386880264207,0.264629066866,3,'test'), +(1858,2.52466612947,0.610379878273,0.956183168227,1,'test'), +(1859,0.282226806598,0.278845746407,0.0581468846181,0,'test'), +(1860,3.9272823452,0.660832493148,0.516187806962,3,'test'), +(1861,1.29118962022,0.117852155891,0.416338161035,1,'test'), +(1862,3.63599865119,0.578217878229,0.240376315307,3,'test'), +(1863,0.643180023509,0.631687089896,0.107205100686,0,'test'), +(1864,3.39830172075,0.335670495646,0.25026231259,3,'test'), +(1865,3.08725331685,0.0829035161016,0.0659530192418,3,'test'), +(1866,3.56649805444,0.510683089389,0.236251910147,3,'test'), +(1867,2.9148248541,0.04878102631,0.93061475799,2,'test'), +(1868,0.772816815381,0.203531384933,0.754510059872,0,'test'), +(1869,3.9943472789,0.0184627979976,0.987868655693,3,'test'), +(1870,3.55205019704,0.583199520132,0.984302126846,2,'test'), +(1871,2.62960873343,0.587550543029,0.205080936224,2,'test'), +(1872,0.296860753433,0.22190350855,0.273783207817,0,'test'), +(1873,3.59301999509,0.396932556958,0.442817612719,3,'test'), +(1874,1.43112899885,0.991501607544,0.663044034215,0,'test'), +(1875,3.58324545314,0.808137348996,0.880402239971,2,'test'), +(1876,4.26226613232,0.40733751791,0.924623498731,3,'test'), +(1877,1.54909356185,0.543466450613,0.0750140735925,1,'test'), +(1878,0.481378829381,0.377889588782,0.32169743642,0,'test'), +(1879,2.55777137191,0.531804823133,0.161141393733,2,'test'), +(1880,0.830503856349,0.722299506142,0.328944296511,0,'test'), +(1881,4.13030895958,0.915665328446,0.463296482972,3,'test'), +(1882,2.09997484994,0.171653243927,0.963494476379,1,'test'), +(1883,0.580041502404,0.375908635537,0.451810653777,0,'test'), +(1884,3.77965047085,0.779558763098,0.0095764163488,3,'test'), +(1885,1.01450272452,0.839165050242,0.418733416725,0,'test'), +(1886,0.793008165755,0.622390143409,0.413059344823,0,'test'), +(1887,1.60178197497,0.508089302973,0.3060925873,1,'test'), +(1888,1.24841338579,0.0778431423252,0.413001505397,1,'test'), +(1889,2.40331373503,0.313684167518,0.299381975929,2,'test'), +(1890,1.50430715803,0.524233358709,0.989986767243,0,'test'), +(1891,3.63386269817,0.511300916934,0.35008824778,3,'test'), +(1892,1.70309712414,0.383261258423,0.565540330763,1,'test'), +(1893,3.31470762181,0.0615360151699,0.503161610861,3,'test'), +(1894,0.90762802197,0.778166806954,0.359807191445,0,'test'), +(1895,3.12897336919,0.128115412922,0.0292908904713,3,'test'), +(1896,0.316575566382,0.264321449497,0.228591594081,0,'test'), +(1897,3.80856892667,0.78734912918,0.145670166773,3,'test'), +(1898,0.202416441487,0.108914952777,0.30578013132,0,'test'), +(1899,3.0519453895,0.412263384389,0.799801228503,2,'test'), +(1900,1.72167749455,0.80401165088,0.957948768813,0,'test'), +(1901,3.18616212117,0.970546137106,0.464344682386,2,'test'), +(1902,1.06480111828,0.701032111689,0.603132660853,0,'test'), +(1903,1.22087548687,0.013450720439,0.455439091904,1,'test'), +(1904,1.56110722627,0.154926334418,0.637323224003,1,'test'), +(1905,0.845281388391,0.842336799226,0.0542640688227,0,'test'), +(1906,1.45770584801,0.294279931398,0.404259714305,1,'test'), +(1907,2.54473099894,0.925446300536,0.786946439349,1,'test'), +(1908,0.920019959942,0.919192698564,0.0287621518304,0,'test'), +(1909,2.59058206881,0.839900558356,0.866418784686,1,'test'), +(1910,3.49982162654,0.246260551634,0.50354848317,3,'test'), +(1911,3.72397200939,0.528035121419,0.442647588911,3,'test'), +(1912,3.27348204603,0.999684288645,0.523256875147,2,'test'), +(1913,2.17554874814,0.302843942269,0.934186708252,1,'test'), +(1914,2.30428222973,0.991373397452,0.559382545563,1,'test'), +(1915,3.81636769532,0.0255093043848,0.88930219326,3,'test'), +(1916,2.58202267207,0.257164860612,0.569962991305,2,'test'), +(1917,1.45978945987,0.133290832492,0.571400583981,1,'test'), +(1918,1.90525769744,0.132046833686,0.879324094831,1,'test'), +(1919,3.57785224284,0.572946082307,0.0700439899496,3,'test'), +(1920,4.48792216584,0.824553555107,0.814474438358,3,'test'), +(1921,0.642658807062,0.331427273232,0.557881290087,0,'test'), +(1922,1.93596222633,0.150516874745,0.886253548137,1,'test'), +(1923,0.836681823737,0.74897599821,0.296151693438,0,'test'), +(1924,0.0997209269147,0.0128509258339,0.294737172886,0,'test'), +(1925,3.00108577109,0.487481814063,0.716661675431,2,'test'), +(1926,0.177437614564,0.0769605187886,0.316981223065,0,'test'), +(1927,2.37683859581,0.0911150504603,0.534531145349,2,'test'), +(1928,0.242662108023,0.140789354709,0.31917511387,0,'test'), +(1929,2.94549723169,0.82287091288,0.350180408943,2,'test'), +(1930,0.9572773328,0.895988371518,0.247566074577,0,'test'), +(1931,4.49091232242,0.959209411196,0.729179615201,3,'test'), +(1932,0.315089572492,0.161806799396,0.391513439228,0,'test'), +(1933,3.49132905665,0.0198419518134,0.686649186149,3,'test'), +(1934,3.00112265453,0.951181525607,0.223475119237,2,'test'), +(1935,1.28571235608,0.147935028469,0.37118368446,1,'test'), +(1936,3.25703774402,0.553406557909,0.838827268339,2,'test'), +(1937,0.109872010282,0.102110906041,0.088097129582,0,'test'), +(1938,1.60261009975,0.601178781342,0.037832768992,1,'test'), +(1939,1.61253128986,0.27035172698,0.584961163564,1,'test'), +(1940,2.9931489255,0.0876261225681,0.951589618972,2,'test'), +(1941,2.31519171933,0.185954788371,0.359495383786,2,'test'), +(1942,3.71870129659,0.71470669129,0.0632028899412,3,'test'), +(1943,0.966130879678,0.738540194092,0.477064655561,0,'test'), +(1944,2.75531025075,0.471953434885,0.532312704961,2,'test'), +(1945,1.21280887784,0.18855272932,0.155743855478,1,'test'), +(1946,1.61281191429,0.612012358779,0.0282764126251,1,'test'), +(1947,4.66311561586,0.892511485737,0.877840606329,3,'test'), +(1948,2.06761163408,0.722214718191,0.587704786343,1,'test'), +(1949,4.31885566533,0.92842568248,0.624843966806,3,'test'), +(1950,0.991633719754,0.987935199597,0.0608154598515,0,'test'), +(1951,3.53047693046,0.135939888,0.628121837272,3,'test'), +(1952,0.808258374729,0.437176558169,0.609164851711,0,'test'), +(1953,4.68418768294,0.915593636552,0.876694956292,3,'test'), +(1954,0.604170759826,0.55145382796,0.229601680888,0,'test'), +(1955,2.26908090741,0.246761227421,0.149397724181,2,'test'), +(1956,0.580859904882,0.542238598322,0.196523043329,0,'test'), +(1957,1.72547859391,0.621955518682,0.321750019778,1,'test'), +(1958,2.83773054571,0.83504584089,0.0518141372296,2,'test'), +(1959,2.05442006097,0.914189764609,0.374473358677,1,'test'), +(1960,2.57345707287,0.655667638752,0.958013274502,1,'test'), +(1961,4.1230147686,0.551619483355,0.755906928957,3,'test'), +(1962,0.222690643459,0.219726692105,0.0544421835888,0,'test'), +(1963,0.744276778807,0.288878954802,0.674831700505,0,'test'), +(1964,2.16698560453,0.869970054018,0.544991330675,1,'test'), +(1965,3.2854290557,0.238157763797,0.217419621717,3,'test'), +(1966,3.75542631771,0.580238334609,0.418554635745,3,'test'), +(1967,2.01549948959,0.6292398158,0.621497927423,1,'test'), +(1968,2.79652846435,0.780581573159,0.126281000926,2,'test'), +(1969,1.96778109881,0.501755816748,0.68266044419,1,'test'), +(1970,0.0493477033576,0.04925887161,0.00942505955309,0,'test'), +(1971,1.52012270872,0.0851256599412,0.659543060593,1,'test'), +(1972,2.16470723813,0.828280082698,0.58002340938,1,'test'), +(1973,3.08855181657,0.829953378098,0.508525750061,2,'test'), +(1974,2.21861872737,0.0853745580489,0.365026258406,2,'test'), +(1975,2.09043802902,0.451247940358,0.799493645168,1,'test'), +(1976,3.10208578256,0.101779209716,0.0175092216619,3,'test'), +(1977,1.9140333904,0.432805735554,0.693705740819,1,'test'), +(1978,1.51344393524,0.444334238151,0.262887232644,1,'test'), +(1979,0.378488692208,0.353390532075,0.158423988499,0,'test'), +(1980,0.530527156737,0.454401343061,0.275909067766,0,'test'), +(1981,3.58540324832,0.799741074321,0.886375864971,2,'test'), +(1982,1.38864960164,0.020642043746,0.606636264899,1,'test'), +(1983,1.86705427687,0.846018409714,0.145037468123,1,'test'), +(1984,0.75531736757,0.54544203487,0.458121526125,0,'test'), +(1985,3.32803839577,0.19236356018,0.368340651562,3,'test'), +(1986,2.63135685292,0.497690204239,0.365604497626,2,'test'), +(1987,1.61306902879,0.612978207831,0.0095300028424,1,'test'), +(1988,2.69688358862,0.521792214845,0.41843921157,2,'test'), +(1989,1.19374842338,0.588523622104,0.777961953617,0,'test'), +(1990,1.55052182524,0.535838324388,0.121175496079,1,'test'), +(1991,0.521803046661,0.324592897743,0.444083493183,0,'test'), +(1992,1.61824698903,0.534816460534,0.288843432488,1,'test'), +(1993,2.75195464745,0.630966918201,0.347832904212,2,'test'), +(1994,0.642550668278,0.640947783993,0.040036037329,0,'test'), +(1995,0.199940949396,0.18066076746,0.138853094803,0,'test'), +(1996,2.28505531639,0.186840748362,0.313392035682,2,'test'), +(1997,1.21174260286,0.0752060834306,0.369508483575,1,'test'), +(1998,2.37858881946,0.372735963003,0.0765039636542,2,'test'), +(1999,1.6766399934,0.622827029704,0.231976213636,1,'test'); + diff --git a/src/pg/test/sql/01_install_test.sql b/src/pg/test/sql/01_install_test.sql index fc3ea80..c90ea59 100644 --- a/src/pg/test/sql/01_install_test.sql +++ b/src/pg/test/sql/01_install_test.sql @@ -1,7 +1,20 @@ -- Install dependencies CREATE EXTENSION plpythonu; -CREATE EXTENSION postgis; -CREATE EXTENSION cartodb; +CREATE EXTENSION postgis VERSION '2.2.2'; + +-- Create role publicuser if it does not exist +DO +$$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_user + WHERE usename = 'publicuser') THEN + + CREATE ROLE publicuser LOGIN; + END IF; +END +$$ LANGUAGE plpgsql; -- Install the extension CREATE EXTENSION crankshaft VERSION 'dev'; diff --git a/src/pg/test/sql/05_kmeans_test.sql b/src/pg/test/sql/05_kmeans_test.sql new file mode 100644 index 0000000..2298b85 --- /dev/null +++ b/src/pg/test/sql/05_kmeans_test.sql @@ -0,0 +1,6 @@ +\pset format unaligned +\set ECHO all + +SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2); + +SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p; diff --git a/src/pg/test/sql/05_markov_test.sql b/src/pg/test/sql/05_markov_test.sql new file mode 100644 index 0000000..2d7e667 --- /dev/null +++ b/src/pg/test/sql/05_markov_test.sql @@ -0,0 +1,33 @@ +SET client_min_messages TO WARNING; +\set ECHO none +\pset format unaligned +\i test/fixtures/markov_usjoin_example.sql + +-- Areas of Interest functions perform some nondeterministic computations +-- (to estimate the significance); we will set the seeds for the RNGs +-- that affect those results to have repeatable results +SELECT cdb_crankshaft._cdb_random_seeds(1234); + +SELECT + m1.cartodb_id, + CASE WHEN m1.cartodb_id = 1 THEN abs(m2.trend - 0.069767441860465115) / 0.069767441860465115 < 0.05 + WHEN m1.cartodb_id = 2 THEN abs(m2.trend - 0.15151515151515152) / 0.15151515151515152 < 0.05 + WHEN m1.cartodb_id = 3 THEN abs(m2.trend - 0.069767441860465115) / 0.069767441860465115 < 0.05 + ELSE NULL END As trend_test, + CASE WHEN m1.cartodb_id = 1 THEN abs(m2.trend_up - 0.065217391304347824) / 0.065217391304347824 < 0.05 + WHEN m1.cartodb_id = 2 THEN abs(m2.trend_up - 0.13157894736842105) / 0.13157894736842105 < 0.05 + WHEN m1.cartodb_id = 3 THEN abs(m2.trend_up - 0.065217391304347824) / 0.065217391304347824 < 0.05 + ELSE NULL END As trend_up_test, + CASE WHEN m1.cartodb_id = 1 THEN m2.trend_down = 0.0 + WHEN m1.cartodb_id = 2 THEN m2.trend_down = 0.0 + WHEN m1.cartodb_id = 3 THEN m2.trend_down = 0.0 + ELSE NULL END As trend_down_test, + CASE WHEN m1.cartodb_id = 1 THEN abs(m2.volatility - 0.367574633389) / 0.367574633389 < 0.1 + WHEN m1.cartodb_id = 2 THEN abs(m2.volatility - 0.33807340742635211) / 0.33807340742635211 < 0.1 + WHEN m1.cartodb_id = 3 THEN abs(m2.volatility - 0.3682585596149513) / 0.3682585596149513 < 0.1 + ELSE NULL END As volatility_test + FROM markov_usjoin_example As m1 + JOIN cdb_crankshaft.CDB_SpatialMarkovTrend('SELECT * FROM markov_usjoin_example ORDER BY cartodb_id DESC', Array['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009']::text[], 5::int, 'knn'::text, 5::int, 0::int, 'the_geom'::text, 'cartodb_id'::text) As m2 + ON m1.cartodb_id = m2.rowid +ORDER BY m1.cartodb_id +LIMIT 3; diff --git a/src/pg/test/sql/06_segmentation_test.sql b/src/pg/test/sql/06_segmentation_test.sql new file mode 100644 index 0000000..932cb04 --- /dev/null +++ b/src/pg/test/sql/06_segmentation_test.sql @@ -0,0 +1,33 @@ +\pset format unaligned +\set ECHO none +\i test/fixtures/ml_values.sql +SELECT cdb_crankshaft._cdb_random_seeds(1234); + +WITH expected AS ( + SELECT generate_series(1000,1020) AS id, unnest(ARRAY[ + 4.5656517130822492, + 1.7928053473230694, + 1.0283378773916563, + 2.6586517814904593, + 2.9699056242935944, + 3.9550646059951347, + 4.1662572444459745, + 3.8126334839264162, + 1.8809821053623488, + 1.6349065129019873, + 3.0391288591472954, + 3.3035970359672553, + 1.5835471589451968, + 3.7530378537263638, + 1.0833589653009252, + 3.8104965452882897, + 2.665217959294802, + 1.5850334252802472, + 3.679401198805563, + 3.5332033186588636 + ]) AS expected LIMIT 20 +), prediction AS ( + SELECT cartodb_id::integer id, prediction + FROM cdb_crankshaft.CDB_CreateAndPredictSegment('SELECT target, x1, x2, x3 FROM ml_values WHERE class = $$train$$','target','SELECT cartodb_id, target, x1, x2, x3 FROM ml_values WHERE class = $$test$$') + LIMIT 20 +) SELECT abs(e.expected - p.prediction) <= 1e-9 AS within_tolerance FROM expected e, prediction p WHERE e.id = p.id; diff --git a/src/pg/test/sql/07_gravity_test.sql b/src/pg/test/sql/07_gravity_test.sql new file mode 100644 index 0000000..c0db940 --- /dev/null +++ b/src/pg/test/sql/07_gravity_test.sql @@ -0,0 +1,24 @@ +SET client_min_messages TO WARNING; +\set ECHO none + +WITH t AS ( + SELECT + ARRAY[1,2,3] AS id, + ARRAY[7.0,8.0,3.0] AS w, + ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)')] AS g +), +s AS ( + SELECT + ARRAY[10,20,30,40,50,60,70,80] AS id, + ARRAY[800, 700, 600, 500, 400, 300, 200, 100] AS p, + ARRAY[ST_GeomFromText('POINT(2.1744 41.403)'),ST_GeomFromText('POINT(2.1228 41.380)'),ST_GeomFromText('POINT(2.1511 41.374)'),ST_GeomFromText('POINT(2.1528 41.413)'),ST_GeomFromText('POINT(2.165 41.391)'),ST_GeomFromText('POINT(2.1498 41.371)'),ST_GeomFromText('POINT(2.1533 41.368)'),ST_GeomFromText('POINT(2.131386 41.41399)')] AS g +) +SELECT + g.the_geom, + g.h, + g.hpop, + g.dist +FROM + t, + s, + cdb_crankshaft.CDB_Gravity(t.id, t.g, t.w, s.id, s.g, s.p, 2, 100000, 3) g; diff --git a/src/pg/test/sql/08_interpolation_test.sql b/src/pg/test/sql/08_interpolation_test.sql index cb0d2e4..89062f3 100644 --- a/src/pg/test/sql/08_interpolation_test.sql +++ b/src/pg/test/sql/08_interpolation_test.sql @@ -1,12 +1,21 @@ SET client_min_messages TO WARNING; \set ECHO none +<<<<<<< HEAD +======= +\pset format unaligned + +>>>>>>> ebbf68af5700f33737ac18516e2bdc22ea330b24 WITH a AS ( SELECT ARRAY[800, 700, 600, 500, 400, 300, 200, 100] AS vals, ARRAY[ST_GeomFromText('POINT(2.1744 41.403)'),ST_GeomFromText('POINT(2.1228 41.380)'),ST_GeomFromText('POINT(2.1511 41.374)'),ST_GeomFromText('POINT(2.1528 41.413)'),ST_GeomFromText('POINT(2.165 41.391)'),ST_GeomFromText('POINT(2.1498 41.371)'),ST_GeomFromText('POINT(2.1533 41.368)'),ST_GeomFromText('POINT(2.131386 41.41399)')] AS g ) +<<<<<<< HEAD SELECT CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'),0) as NN, CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'),1) as NNI, CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'),2) as IDW FROM a; +======= +SELECT (cdb_crankshaft.CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'), 1) - 780.79470198683925288365) / 780.79470198683925288365 < 0.001 As cdb_spatialinterpolation FROM a; +>>>>>>> ebbf68af5700f33737ac18516e2bdc22ea330b24 diff --git a/src/pg/test/sql/90_permissions.sql b/src/pg/test/sql/90_permissions.sql index 187f795..1e9ea99 100644 --- a/src/pg/test/sql/90_permissions.sql +++ b/src/pg/test/sql/90_permissions.sql @@ -4,7 +4,7 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234); SET ROLE test_regular_user; -- Add to the search path the schema -SET search_path TO public,cartodb,cdb_crankshaft; +SET search_path TO public,cdb_crankshaft; -- Exercise public functions SELECT ppoints.code, m.quads diff --git a/src/py/Makefile b/src/py/Makefile index 90b22b8..cc3c67e 100644 --- a/src/py/Makefile +++ b/src/py/Makefile @@ -2,21 +2,16 @@ include ../../Makefile.global # Install the package locally for development install: - virtualenv --system-site-packages ../../envs/dev - # source ../../envs/dev/bin/activate - ../../envs/dev/bin/pip install -I ./crankshaft - ../../envs/dev/bin/pip install -I nose + pip install --upgrade ./crankshaft # Test develpment install test: - ../../envs/dev/bin/nosetests crankshaft/test/ + nosetests crankshaft/test/ release: ../../release/$(EXTENSION).control $(SOURCES_DATA) mkdir -p ../../release/python/$(EXTVERSION) cp -r ./$(PACKAGE) ../../release/python/$(EXTVERSION)/ $(SED) -i -r 's/version='"'"'[0-9]+\.[0-9]+\.[0-9]+'"'"'/version='"'"'$(EXTVERSION)'"'"'/g' ../../release/python/$(EXTVERSION)/$(PACKAGE)/setup.py -deploy: - virtualenv --system-site-packages $(VIRTUALENV_PATH)/$(RELEASE_VERSION) - $(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I -U ../../release/python/$(RELEASE_VERSION)/$(PACKAGE) - $(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I nose +deploy: + pip install $(RUN_OPTIONS) --upgrade ../../release/python/$(RELEASE_VERSION)/$(PACKAGE) diff --git a/src/py/README.md b/src/py/README.md index 29a3145..8fcfcb7 100644 --- a/src/py/README.md +++ b/src/py/README.md @@ -10,7 +10,6 @@ nosetests test/ ## Notes about Python dependencies * This extension is targeted at production databases. Therefore certain restrictions must be assumed about the production environment vs other experimental environments. -* We're using `pip` and `virtualenv` to generate a suitable isolated environment for python code that has all the dependencies * Every dependency should be: - Added to the `setup.py` file - Installed through it @@ -30,21 +29,7 @@ PySAL 1.10 or later, so we'll stick to 1.9.1. apt-get install -y python-scipy ``` -We'll use virtual environments to install our packages, -but configued to use also system modules so that the -mentioned scipy and numpy are used. - - # Create a virtual environment for python - $ virtualenv --system-site-packages dev - - # Activate the virtualenv - $ source dev/bin/activate - - # Install all the requirements - # expect this to take a while, as it will trigger a few compilations - (dev) $ pip install -I ./crankshaft - -#### Test the libraries with that virtual env +#### Test the libraries ##### Test numpy library dependency: diff --git a/src/py/crankshaft/crankshaft/__init__.py b/src/py/crankshaft/crankshaft/__init__.py index d07e330..4e06bc5 100644 --- a/src/py/crankshaft/crankshaft/__init__.py +++ b/src/py/crankshaft/crankshaft/__init__.py @@ -1,2 +1,5 @@ -import random_seeds -import clustering +"""Import all modules""" +import crankshaft.random_seeds +import crankshaft.clustering +import crankshaft.space_time_dynamics +import crankshaft.segmentation diff --git a/src/py/crankshaft/crankshaft/clustering/__init__.py b/src/py/crankshaft/crankshaft/clustering/__init__.py index 0df080f..ed34fe0 100644 --- a/src/py/crankshaft/crankshaft/clustering/__init__.py +++ b/src/py/crankshaft/crankshaft/clustering/__init__.py @@ -1 +1,3 @@ +"""Import all functions from for clustering""" from moran import * +from kmeans import * diff --git a/src/py/crankshaft/crankshaft/clustering/kmeans.py b/src/py/crankshaft/crankshaft/clustering/kmeans.py new file mode 100644 index 0000000..4134062 --- /dev/null +++ b/src/py/crankshaft/crankshaft/clustering/kmeans.py @@ -0,0 +1,18 @@ +from sklearn.cluster import KMeans +import plpy + +def kmeans(query, no_clusters, no_init=20): + data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids, + array_agg(ST_X(the_geom) order by cartodb_id) xs, + array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a + where the_geom is not null + '''.format(query=query)) + + xs = data[0]['xs'] + ys = data[0]['ys'] + ids = data[0]['ids'] + + km = KMeans(n_clusters= no_clusters, n_init=no_init) + labels = km.fit_predict(zip(xs,ys)) + return zip(ids,labels) + diff --git a/src/py/crankshaft/crankshaft/clustering/moran.py b/src/py/crankshaft/crankshaft/clustering/moran.py index 39b3ff6..3282f5f 100644 --- a/src/py/crankshaft/crankshaft/clustering/moran.py +++ b/src/py/crankshaft/crankshaft/clustering/moran.py @@ -7,6 +7,7 @@ Moran's I geostatistics (global clustering & outliers presence) import pysal as ps import plpy +from collections import OrderedDict # crankshaft module import crankshaft.pysal_utils as pu @@ -21,11 +22,11 @@ def moran(subquery, attr_name, core clusters with PySAL. Andy Eschbacher """ - qvals = {"id_col": id_col, - "attr1": attr_name, - "geom_col": geom_col, - "subquery": subquery, - "num_ngbrs": num_ngbrs} + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr_name), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) query = pu.construct_neighbor_query(w_type, qvals) @@ -65,11 +66,11 @@ def moran_local(subquery, attr, # geometries with attributes that are null are ignored # resulting in a collection of not as near neighbors - qvals = {"id_col": id_col, - "attr1": attr, - "geom_col": geom_col, - "subquery": subquery, - "num_ngbrs": num_ngbrs} + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) query = pu.construct_neighbor_query(w_type, qvals) @@ -101,12 +102,12 @@ def moran_rate(subquery, numerator, denominator, Moran's I Rate (global) Andy Eschbacher """ - qvals = {"id_col": id_col, - "attr1": numerator, - "attr2": denominator, - "geom_col": geom_col, - "subquery": subquery, - "num_ngbrs": num_ngbrs} + qvals = OrderedDict([("id_col", id_col), + ("attr1", numerator), + ("attr2", denominator) + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) query = pu.construct_neighbor_query(w_type, qvals) @@ -145,13 +146,14 @@ def moran_local_rate(subquery, numerator, denominator, # geometries with values that are null are ignored # resulting in a collection of not as near neighbors - query = pu.construct_neighbor_query(w_type, - {"id_col": id_col, - "numerator": numerator, - "denominator": denominator, - "geom_col": geom_col, - "subquery": subquery, - "num_ngbrs": num_ngbrs}) + qvals = OrderedDict([("id_col", id_col), + ("numerator", numerator), + ("denominator", denominator), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + query = pu.construct_neighbor_query(w_type, qvals) try: result = plpy.execute(query) @@ -174,7 +176,7 @@ def moran_local_rate(subquery, numerator, denominator, lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, permutations=permutations) - # find units of significance + # find quadrants for each geometry quads = quad_position(lisa.q) return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y) @@ -186,12 +188,12 @@ def moran_local_bv(subquery, attr1, attr2, """ plpy.notice('** Constructing query') - qvals = {"num_ngbrs": num_ngbrs, - "attr1": attr1, - "attr2": attr2, - "subquery": subquery, - "geom_col": geom_col, - "id_col": id_col} + qvals = OrderedDict([("id_col", id_col), + ("attr1", attr1), + ("attr2", attr2), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) query = pu.construct_neighbor_query(w_type, qvals) diff --git a/src/py/crankshaft/crankshaft/pysal_utils/__init__.py b/src/py/crankshaft/crankshaft/pysal_utils/__init__.py index 835880d..fdf073b 100644 --- a/src/py/crankshaft/crankshaft/pysal_utils/__init__.py +++ b/src/py/crankshaft/crankshaft/pysal_utils/__init__.py @@ -1 +1,2 @@ -from pysal_utils import * +"""Import all functions for pysal_utils""" +from crankshaft.pysal_utils.pysal_utils import * diff --git a/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py index 02b5e35..4622925 100644 --- a/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py +++ b/src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -1,5 +1,6 @@ """ - Utilities module for generic PySAL functionality, mainly centered on translating queries into numpy arrays or PySAL weights objects + Utilities module for generic PySAL functionality, mainly centered on + translating queries into numpy arrays or PySAL weights objects """ import numpy as np @@ -20,19 +21,23 @@ def construct_neighbor_query(w_type, query_vals): def get_weight(query_res, w_type='knn', num_ngbrs=5): """ Construct PySAL weight from return value of query - @param query_res: query results with attributes and neighbors + @param query_res dict-like: query results with attributes and neighbors """ - if w_type.lower() == 'knn': - row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs - weights = {x['id']: row_normed_weights for x in query_res} - else: - weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) - if len(x['neighbors']) > 0 - else [] for x in query_res} + # if w_type.lower() == 'knn': + # row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs + # weights = {x['id']: row_normed_weights for x in query_res} + # else: + # weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors']) + # if len(x['neighbors']) > 0 + # else [] for x in query_res} neighbors = {x['id']: x['neighbors'] for x in query_res} + print 'len of neighbors: %d' % len(neighbors) - return ps.W(neighbors, weights) + built_weight = ps.W(neighbors) + built_weight.transform = 'r' + + return built_weight def query_attr_select(params): """ @@ -41,32 +46,63 @@ def query_attr_select(params): table name, etc.) """ - attrs = [k for k in params - if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')] - - template = "i.\"{%(col)s}\"::numeric As attr%(alias_num)s, " - attr_string = "" + template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, " - for idx, val in enumerate(sorted(attrs)): - attr_string += template % {"col": val, "alias_num": idx + 1} + if 'time_cols' in params: + ## if markov analysis + attrs = params['time_cols'] + + for idx, val in enumerate(attrs): + attr_string += template % {"col": val, "alias_num": idx + 1} + else: + ## if moran's analysis + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')] + + for idx, val in enumerate(sorted(attrs)): + attr_string += template % {"col": params[val], "alias_num": idx + 1} return attr_string def query_attr_where(params): """ + Construct where conditions when building neighbors query Create portion of WHERE clauses for weeding out NULL-valued geometries + Input: dict of params: + {'subquery': ..., + 'numerator': 'data1', + 'denominator': 'data2', + '': ...} + Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL' + Input: + {'subquery': ..., + 'time_cols': ['time1', 'time2', 'time3'], + 'etc': ...} + Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT + NULL AND idx_replace."time3" IS NOT NULL' """ - attrs = sorted([k for k in params - if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs')]) - attr_string = [] + template = "idx_replace.\"%s\" IS NOT NULL" - for attr in attrs: - attr_string.append("idx_replace.\"{%s}\" IS NOT NULL" % attr) + if 'time_cols' in params: + ## markov where clauses + attrs = params['time_cols'] + # add values to template + for attr in attrs: + attr_string.append(template % attr) + else: + ## moran where clauses - if len(attrs) == 2: - attr_string.append("idx_replace.\"{%s}\" <> 0" % attrs[1]) + # get keys + attrs = sorted([k for k in params + if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]) + # add values to template + for attr in attrs: + attr_string.append(template % params[attr]) + + if len(attrs) == 2: + attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]]) out = " AND ".join(attr_string) diff --git a/src/py/crankshaft/crankshaft/random_seeds.py b/src/py/crankshaft/crankshaft/random_seeds.py index b7c8eed..31958cb 100644 --- a/src/py/crankshaft/crankshaft/random_seeds.py +++ b/src/py/crankshaft/crankshaft/random_seeds.py @@ -1,3 +1,4 @@ +"""Random seed generator used for non-deterministic functions in crankshaft""" import random import numpy diff --git a/src/py/crankshaft/crankshaft/segmentation/__init__.py b/src/py/crankshaft/crankshaft/segmentation/__init__.py new file mode 100644 index 0000000..b825e85 --- /dev/null +++ b/src/py/crankshaft/crankshaft/segmentation/__init__.py @@ -0,0 +1 @@ +from segmentation import * diff --git a/src/py/crankshaft/crankshaft/segmentation/segmentation.py b/src/py/crankshaft/crankshaft/segmentation/segmentation.py new file mode 100644 index 0000000..ed61139 --- /dev/null +++ b/src/py/crankshaft/crankshaft/segmentation/segmentation.py @@ -0,0 +1,176 @@ +""" +Segmentation creation and prediction +""" + +import sklearn +import numpy as np +import plpy +from sklearn.ensemble import GradientBoostingRegressor +from sklearn import metrics +from sklearn.cross_validation import train_test_split + +# Lower level functions +#---------------------- + +def replace_nan_with_mean(array): + """ + Input: + @param array: an array of floats which may have null-valued entries + Output: + array with nans filled in with the mean of the dataset + """ + # returns an array of rows and column indices + indices = np.where(np.isnan(array)) + + # iterate through entries which have nan values + for row, col in zip(*indices): + array[row, col] = np.mean(array[~np.isnan(array[:, col]), col]) + + return array + +def get_data(variable, feature_columns, query): + """ + Fetch data from the database, clean, and package into + numpy arrays + Input: + @param variable: name of the target variable + @param feature_columns: list of column names + @param query: subquery that data is pulled from for the packaging + Output: + prepared data, packaged into NumPy arrays + """ + + columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns]) + + try: + data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format( + variable=variable, + columns=columns, + query=query)) + except Exception, e: + plpy.error('Failed to access data to build segmentation model: %s' % e) + + # extract target data from plpy object + target = np.array(data[0]['target']) + + # put n feature data arrays into an n x m array of arrays + features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns]) + + return replace_nan_with_mean(target), replace_nan_with_mean(features) + +# High level interface +# -------------------- + +def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters): + """ + Version of create_and_predict_segment that works on arrays that come stright form the SQL calling + the function. + + Input: + @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict + @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model + @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from + @param model_parameters: A dictionary containing parameters for the model. + """ + + clean_target = replace_nan_with_mean(target) + clean_features = replace_nan_with_mean(features) + target_features = replace_nan_with_mean(target_features) + + model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2) + prediction = model.predict(target_features) + accuracy_array = [accuracy]*prediction.shape[0] + return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array)) + + + +def create_and_predict_segment(query, variable, target_query, model_params): + """ + generate a segment with machine learning + Stuart Lynn + """ + + ## fetch column names + try: + columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys() + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + ## extract column names to be used in building the segmentation model + feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator']) + ## get data from database + target, features = get_data(variable, feature_columns, query) + + model, accuracy = train_model(target, features, model_params, 0.2) + cartodb_ids, result = predict_segment(model, feature_columns, target_query) + accuracy_array = [accuracy]*result.shape[0] + return zip(cartodb_ids, result, accuracy_array) + + +def train_model(target, features, model_params, test_split): + """ + Train the Gradient Boosting model on the provided data and calculate the accuracy of the model + Input: + @param target: 1D Array of the variable that the model is to be trianed to predict + @param features: 2D Array NSamples * NFeatures to use in trining the model + @param model_params: A dictionary of model parameters, the full specification can be found on the + scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) + @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray + """ + features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split) + model = GradientBoostingRegressor(**model_params) + model.fit(features_train, target_train) + accuracy = calculate_model_accuracy(model, features, target) + return model, accuracy + +def calculate_model_accuracy(model, features, target): + """ + Calculate the mean squared error of the model prediction + Input: + @param model: model trained from input features + @param features: features to make a prediction from + @param target: target to compare prediction to + Output: + mean squared error of the model prection compared to the target + """ + prediction = model.predict(features) + return metrics.mean_squared_error(prediction, target) + +def predict_segment(model, features, target_query): + """ + Use the provided model to predict the values for the new feature set + Input: + @param model: The pretrained model + @features: A list of features to use in the model prediction (list of column names) + @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it. + """ + + batch_size = 1000 + joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features]) + + try: + cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format( + joined_features=joined_features, + target_query=target_query)) + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + results = [] + + while True: + rows = cursor.fetch(batch_size) + if not rows: + break + batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows]) + + #Need to fix this. Should be global mean. This will cause weird effects + batch = replace_nan_with_mean(batch) + prediction = model.predict(batch) + results.append(prediction) + + try: + cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids'] + except Exception, e: + plpy.error('Failed to build segmentation model: %s' % e) + + return cartodb_ids, np.concatenate(results) diff --git a/src/py/crankshaft/crankshaft/space_time_dynamics/__init__.py b/src/py/crankshaft/crankshaft/space_time_dynamics/__init__.py new file mode 100644 index 0000000..a439286 --- /dev/null +++ b/src/py/crankshaft/crankshaft/space_time_dynamics/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions from clustering libraries.""" +from markov import * diff --git a/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py b/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py new file mode 100644 index 0000000..bbf524d --- /dev/null +++ b/src/py/crankshaft/crankshaft/space_time_dynamics/markov.py @@ -0,0 +1,189 @@ +""" +Spatial dynamics measurements using Spatial Markov +""" + + +import numpy as np +import pysal as ps +import plpy +import crankshaft.pysal_utils as pu + +def spatial_markov_trend(subquery, time_cols, num_classes=7, + w_type='knn', num_ngbrs=5, permutations=0, + geom_col='the_geom', id_col='cartodb_id'): + """ + Predict the trends of a unit based on: + 1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile) + 2. average class of its neighbors + + Inputs: + @param subquery string: e.g., SELECT the_geom, cartodb_id, + interesting_time_column FROM table_name + @param time_cols list of strings: list of strings of column names + @param num_classes (optional): number of classes to break distribution + of values into. Currently uses quantile bins. + @param w_type string (optional): weight type ('knn' or 'queen') + @param num_ngbrs int (optional): number of neighbors (if knn type) + @param permutations int (optional): number of permutations for test + stats + @param geom_col string (optional): name of column which contains the + geometries + @param id_col string (optional): name of column which has the ids of + the table + + Outputs: + @param trend_up float: probablity that a geom will move to a higher + class + @param trend_down float: probablity that a geom will move to a lower + class + @param trend float: (trend_up - trend_down) / trend_static + @param volatility float: a measure of the volatility based on + probability stddev(prob array) + """ + + if len(time_cols) < 2: + plpy.error('More than one time column needs to be passed') + + qvals = {"id_col": id_col, + "time_cols": time_cols, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + try: + query_result = plpy.execute( + pu.construct_neighbor_query(w_type, qvals) + ) + if len(query_result) == 0: + return zip([None], [None], [None], [None], [None]) + except plpy.SPIError, err: + plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals))) + plpy.error('Query failed, check the input parameters') + return zip([None], [None], [None], [None], [None]) + + ## build weight + weights = pu.get_weight(query_result, w_type) + weights.transform = 'r' + + ## prep time data + t_data = get_time_data(query_result, time_cols) + + plpy.debug('shape of t_data %d, %d' % t_data.shape) + plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape) + plpy.debug('first num elements: %f' % t_data[0, 0]) + + sp_markov_result = ps.Spatial_Markov(t_data, + weights, + k=num_classes, + fixed=False, + permutations=permutations) + + ## get lag classes + lag_classes = ps.Quantiles( + ps.lag_spatial(weights, t_data[:, -1]), + k=num_classes).yb + + ## look up probablity distribution for each unit according to class and lag class + prob_dist = get_prob_dist(sp_markov_result.P, + lag_classes, + sp_markov_result.classes[:, -1]) + + ## find the ups and down and overall distribution of each cell + trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, + sp_markov_result.classes[:, -1]) + + ## output the results + return zip(trend, trend_up, trend_down, volatility, weights.id_order) + +def get_time_data(markov_data, time_cols): + """ + Extract the time columns and bin appropriately + """ + num_attrs = len(time_cols) + return np.array([[x['attr' + str(i)] for x in markov_data] + for i in range(1, num_attrs+1)], dtype=float).transpose() + +## not currently used +def rebin_data(time_data, num_time_per_bin): + """ + Convert an n x l matrix into an (n/m) x l matrix where the values are + reduced (averaged) for the intervening states: + 1 2 3 4 1.5 3.5 + 5 6 7 8 -> 5.5 7.5 + 9 8 7 6 8.5 6.5 + 5 4 3 2 4.5 2.5 + + if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix. + + This process effectively resamples the data at a longer time span n + units longer than the input data. + For cases when there is a remainder (remainder(5/3) = 2), the remaining + two columns are binned together as the last time period, while the + first three are binned together for the first period. + + Input: + @param time_data n x l ndarray: measurements of an attribute at + different time intervals + @param num_time_per_bin int: number of columns to average into a new + column + Output: + ceil(n / m) x l ndarray of resampled time series + """ + + if time_data.shape[1] % num_time_per_bin == 0: + ## if fit is perfect, then use it + n_max = time_data.shape[1] / num_time_per_bin + else: + ## fit remainders into an additional column + n_max = time_data.shape[1] / num_time_per_bin + 1 + + return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1) + for i in range(n_max)]).T + +def get_prob_dist(transition_matrix, lag_indices, unit_indices): + """ + Given an array of transition matrices, look up the probability + associated with the arrangements passed + + Input: + @param transition_matrix ndarray[k,k,k]: + @param lag_indices ndarray: + @param unit_indices ndarray: + + Output: + Array of probability distributions + """ + + return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] + for i in range(len(lag_indices))]) + +def get_prob_stats(prob_dist, unit_indices): + """ + get the statistics of the probability distributions + + Outputs: + @param trend_up ndarray(float): sum of probabilities for upward + movement (relative to the unit index of that prob) + @param trend_down ndarray(float): sum of probabilities for downward + movement (relative to the unit index of that prob) + @param trend ndarray(float): difference of upward and downward + movements + """ + + num_elements = len(unit_indices) + trend_up = np.empty(num_elements, dtype=float) + trend_down = np.empty(num_elements, dtype=float) + trend = np.empty(num_elements, dtype=float) + + for i in range(num_elements): + trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum() + trend_down[i] = prob_dist[i, :unit_indices[i]].sum() + if prob_dist[i, unit_indices[i]] > 0.0: + trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]] + else: + trend[i] = None + + ## calculate volatility of distribution + volatility = prob_dist.std(axis=1) + + return trend_up, trend_down, trend, volatility diff --git a/src/py/crankshaft/setup.py b/src/py/crankshaft/setup.py index 8d5e622..cd8ad99 100644 --- a/src/py/crankshaft/setup.py +++ b/src/py/crankshaft/setup.py @@ -40,9 +40,10 @@ setup( # The choice of component versions is dictated by what's # provisioned in the production servers. - install_requires=['pysal==1.9.1'], + # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation. + install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'], - requires=['pysal', 'numpy' ], + requires=['pysal', 'numpy', 'sklearn'], test_suite='test' ) diff --git a/src/py/crankshaft/test/fixtures/kmeans.json b/src/py/crankshaft/test/fixtures/kmeans.json new file mode 100644 index 0000000..8f31c79 --- /dev/null +++ b/src/py/crankshaft/test/fixtures/kmeans.json @@ -0,0 +1 @@ +[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}] \ No newline at end of file diff --git a/src/py/crankshaft/test/fixtures/markov.json b/src/py/crankshaft/test/fixtures/markov.json new file mode 100644 index 0000000..d60e4e0 --- /dev/null +++ b/src/py/crankshaft/test/fixtures/markov.json @@ -0,0 +1 @@ +[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]] diff --git a/src/py/crankshaft/test/fixtures/neighbors_markov.json b/src/py/crankshaft/test/fixtures/neighbors_markov.json new file mode 100644 index 0000000..45a20e7 --- /dev/null +++ b/src/py/crankshaft/test/fixtures/neighbors_markov.json @@ -0,0 +1 @@ +[{"neighbors": [10, 7, 21, 23, 1], "y1995": 0.87654416055651474, "y1997": 0.85637566664752718, "y1996": 0.8631470006766887, "y1999": 0.84461540228037335, "y1998": 0.84811668329242784, "y2006": 0.86302631339545688, "y2007": 0.86148266513456728, "y2004": 0.86416611731111015, "y2005": 0.87119374831581786, "y2002": 0.85012592862683589, "y2003": 0.8550965633336135, "y2000": 0.83271652434603094, "y2001": 0.83786313566577242, "id": 0, "y2008": 0.86252252380501315, "y2009": 0.86746356478544273}, {"neighbors": [5, 7, 22, 29, 3], "y1995": 0.91889509774542122, "y1997": 0.92333257900976462, "y1996": 0.91757931190043385, "y1999": 0.92552387732371888, "y1998": 0.92517289327379471, "y2006": 0.91706053906277052, "y2007": 0.90139504820726424, "y2004": 0.89815175749309051, "y2005": 0.91832090781161113, "y2002": 0.89431990798552208, "y2003": 0.88924793576523797, "y2000": 0.90746978227271013, "y2001": 0.89830489127332913, "id": 1, "y2008": 0.87897455159080617, "y2009": 0.86216858051752643}, {"neighbors": [11, 8, 13, 18, 17], "y1995": 0.82591007476914713, "y1997": 0.81989792988843901, "y1996": 0.82548595539161707, "y1999": 0.81731522200916285, "y1998": 0.81503235035017918, "y2006": 0.81814804358939286, "y2007": 0.83675961003285626, "y2004": 0.82668195534569056, "y2005": 0.82373723764184559, "y2002": 0.80849979516360859, "y2003": 0.82258550658074148, "y2000": 0.78964559168205917, "y2001": 0.8058444152731008, "id": 2, "y2008": 0.8357419865626442, "y2009": 0.84647177436289112}, {"neighbors": [4, 14, 9, 5, 12], "y1995": 1.0908817638059434, "y1997": 1.0845641754849344, "y1996": 1.0853768890893893, "y1999": 1.098988414417104, "y1998": 1.0841540389418189, "y2006": 1.1316479722785828, "y2007": 1.1295850763954971, "y2004": 1.1139980568106316, "y2005": 1.1216802898290368, "y2002": 1.1116069731657288, "y2003": 1.1088862051501811, "y2000": 1.1450694824791507, "y2001": 1.1215113292620285, "id": 3, "y2008": 1.1137181812756343, "y2009": 1.0993677488645406}, {"neighbors": [14, 3, 9, 31, 12], "y1995": 1.1073144618319228, "y1997": 1.1328363804627946, "y1996": 1.1137394350312471, "y1999": 1.1591002514611153, "y1998": 1.144725587086376, "y2006": 1.1173646811350333, "y2007": 1.1086324218539598, "y2004": 1.1102496406140896, "y2005": 1.11943471361418, "y2002": 1.1475230282561595, "y2003": 1.1184328424005199, "y2000": 1.1689820101690329, "y2001": 1.1721248787169682, "id": 4, "y2008": 1.0964251552643696, "y2009": 1.0776233718455337}, {"neighbors": [29, 1, 22, 7, 4], "y1995": 1.422697571371182, "y1997": 1.4427350196405593, "y1996": 1.4211843379728528, "y1999": 1.4440068434166562, "y1998": 1.4357757095632602, "y2006": 1.4405276647793266, "y2007": 1.4524121586440921, "y2004": 1.4059372049179741, "y2005": 1.4078864636665769, "y2002": 1.4197822680667809, "y2003": 1.3909220829548647, "y2000": 1.4418473669388905, "y2001": 1.4478283203013527, "id": 5, "y2008": 1.4330609762040207, "y2009": 1.4174430982377491}, {"neighbors": [12, 47, 9, 25, 20], "y1995": 1.1307388498039153, "y1997": 1.1107470843142355, "y1996": 1.1311051255854685, "y1999": 1.130881491772973, "y1998": 1.1336463608751246, "y2006": 1.1088003408832796, "y2007": 1.0840170924825394, "y2004": 1.1244623853593112, "y2005": 1.1167100811401538, "y2002": 1.1306293052597198, "y2003": 1.1194498381213465, "y2000": 1.1088813841947593, "y2001": 1.1185662918783175, "id": 6, "y2008": 1.0695920556329086, "y2009": 1.0787522517402164}, {"neighbors": [21, 1, 22, 10, 0], "y1995": 1.0470612357366649, "y1997": 1.0425337165747406, "y1996": 1.0451683097376836, "y1999": 1.0207254480945218, "y1998": 1.0323998680588111, "y2006": 1.0405109962442973, "y2007": 1.0174964540280445, "y2004": 1.0140090547678748, "y2005": 1.0317674181861733, "y2002": 0.99669586934394627, "y2003": 0.99327675611171373, "y2000": 0.99854316295509526, "y2001": 0.98802579761429143, "id": 7, "y2008": 0.9936394033949828, "y2009": 0.98279746069218921}, {"neighbors": [11, 13, 17, 18, 15], "y1995": 0.98996985668705595, "y1997": 0.99491000469481983, "y1996": 1.0014356415938011, "y1999": 1.0045584503565237, "y1998": 1.0018840754492748, "y2006": 0.92232873520447411, "y2007": 0.91284090705064902, "y2004": 0.93694786512729977, "y2005": 0.94308212820743131, "y2002": 0.96834820215592055, "y2003": 0.95335147249088092, "y2000": 0.99127006477048718, "y2001": 0.97925917470464008, "id": 8, "y2008": 0.89689832627117483, "y2009": 0.88928857608264111}, {"neighbors": [12, 6, 4, 3, 14], "y1995": 0.87418390853652306, "y1997": 0.84425695187978567, "y1996": 0.86416601430334228, "y1999": 0.83903043942542854, "y1998": 0.8404493987171674, "y2006": 0.87204140839730271, "y2007": 0.86633032299764789, "y2004": 0.86981997840756087, "y2005": 0.86837929279319737, "y2002": 0.86107306112852877, "y2003": 0.85007719735663123, "y2000": 0.85787080050645603, "y2001": 0.86036185149249467, "id": 9, "y2008": 0.84946077011565357, "y2009": 0.83287145944123797}, {"neighbors": [0, 7, 21, 23, 22], "y1995": 1.1419611801631209, "y1997": 1.1489271154554144, "y1996": 1.146602624490825, "y1999": 1.1443662376135306, "y1998": 1.1490959392942743, "y2006": 1.1049125811637337, "y2007": 1.1105984164317646, "y2004": 1.1119989015058092, "y2005": 1.1025779214946556, "y2002": 1.1259666377127024, "y2003": 1.1221399558345004, "y2000": 1.144501826035474, "y2001": 1.1234975172649961, "id": 10, "y2008": 1.1050979494645479, "y2009": 1.1002009697391872}, {"neighbors": [8, 13, 18, 17, 2], "y1995": 0.97282462974938089, "y1997": 0.96252588061647382, "y1996": 0.96700147279313231, "y1999": 0.96057686787383312, "y1998": 0.96538780087103548, "y2006": 0.91010201260822066, "y2007": 0.89280392121658247, "y2004": 0.94103988614185807, "y2005": 0.9212251863828258, "y2002": 0.94804194711420009, "y2003": 0.9543028555845573, "y2000": 0.95831051250950716, "y2001": 0.94480908623936988, "id": 11, "y2008": 0.89298242828382146, "y2009": 0.89165384824292859}, {"neighbors": [33, 9, 6, 25, 31], "y1995": 0.94325467991401402, "y1997": 0.96455242154753429, "y1996": 0.96436902092427723, "y1999": 0.94117647058823528, "y1998": 0.95243008993884537, "y2006": 0.9346681464882507, "y2007": 0.94281559150403071, "y2004": 0.96918424441756057, "y2005": 0.94781280876672958, "y2002": 0.95388717527096822, "y2003": 0.94597005193649519, "y2000": 0.94809269652332606, "y2001": 0.93539181553564288, "id": 12, "y2008": 0.965203150896216, "y2009": 0.967154410723015}, {"neighbors": [18, 17, 11, 8, 19], "y1995": 0.97478408425654373, "y1997": 0.98712808751954773, "y1996": 0.98169225257738801, "y1999": 0.985598971191053, "y1998": 0.98474769442356791, "y2006": 0.98416665248276058, "y2007": 0.98423613480079708, "y2004": 0.97399471186978948, "y2005": 0.96910087128357136, "y2002": 0.9820996926750224, "y2003": 0.98776529543110569, "y2000": 0.98687072733199255, "y2001": 0.99237486444837619, "id": 13, "y2008": 0.99823861244053191, "y2009": 0.99545704236827348}, {"neighbors": [4, 31, 3, 29, 12], "y1995": 0.85570268988941878, "y1997": 0.85986131704895119, "y1996": 0.85575915188345031, "y1999": 0.85380119644969055, "y1998": 0.85693406055397725, "y2006": 0.82803647591954255, "y2007": 0.81987360180979219, "y2004": 0.83998883284341452, "y2005": 0.83478547261894065, "y2002": 0.85472102128186755, "y2003": 0.84564834502399988, "y2000": 0.86191535266765262, "y2001": 0.84981450830432048, "id": 14, "y2008": 0.82265395167873867, "y2009": 0.83994039782937002}, {"neighbors": [19, 8, 17, 16, 13], "y1995": 0.87022046646521634, "y1997": 0.85961813213722393, "y1996": 0.85996258309339635, "y1999": 0.8394713575455558, "y1998": 0.85689572413110093, "y2006": 0.94202108334913126, "y2007": 0.94222309998743192, "y2004": 0.86763340229291142, "y2005": 0.89179316746010362, "y2002": 0.86776297543511893, "y2003": 0.86720209304280604, "y2000": 0.82785596604704892, "y2001": 0.86008789452656809, "id": 15, "y2008": 0.93902708112840494, "y2009": 0.94479183757120588}, {"neighbors": [28, 26, 15, 19, 32], "y1995": 0.90134907329491731, "y1997": 0.90403990934606904, "y1996": 0.904077381347274, "y1999": 0.90399237579083946, "y1998": 0.90201769385650832, "y2006": 0.91108803862404764, "y2007": 0.90543476309316473, "y2004": 0.94338264626469681, "y2005": 0.91981795862151561, "y2002": 0.93695966482853577, "y2003": 0.94242697007039, "y2000": 0.90906631602055099, "y2001": 0.92693339421265908, "id": 16, "y2008": 0.91737137682250491, "y2009": 0.94793657442067902}, {"neighbors": [13, 18, 11, 19, 8], "y1995": 1.1977611005602815, "y1997": 1.1843915817489725, "y1996": 1.1822256425225894, "y1999": 1.1928672308275252, "y1998": 1.1826786457339149, "y2006": 1.2392938410349985, "y2007": 1.2341867605077472, "y2004": 1.2385704217423759, "y2005": 1.2441989281116201, "y2002": 1.2262477774195681, "y2003": 1.2239707531714479, "y2000": 1.2017286912636342, "y2001": 1.2132869128474402, "id": 17, "y2008": 1.2362673914436095, "y2009": 1.2675439750795283}, {"neighbors": [13, 17, 11, 8, 19], "y1995": 1.2491967813733067, "y1997": 1.2699116090397236, "y1996": 1.2575477330927329, "y1999": 1.3062566740535762, "y1998": 1.2802065055312271, "y2006": 1.3210776560048689, "y2007": 1.329362443219563, "y2004": 1.3054484140490119, "y2005": 1.3030330249408666, "y2002": 1.3257518058685978, "y2003": 1.3079549159235695, "y2000": 1.3479002255103918, "y2001": 1.3439986302151703, "id": 18, "y2008": 1.3300124123891741, "y2009": 1.3328846185074705}, {"neighbors": [26, 17, 28, 15, 16], "y1995": 1.0676800411188558, "y1997": 1.0363730321443168, "y1996": 1.0379927554499979, "y1999": 1.0329609259280523, "y1998": 1.027684488045026, "y2006": 0.94241549375546196, "y2007": 0.92754546923532677, "y2004": 0.99614160423102482, "y2005": 0.97356208269708677, "y2002": 1.0274762326434594, "y2003": 1.0316273366809443, "y2000": 1.0505901631347052, "y2001": 1.0340505678899605, "id": 19, "y2008": 0.92549226593721745, "y2009": 0.92138101880290568}, {"neighbors": [30, 25, 24, 37, 47], "y1995": 1.0947561397632881, "y1997": 1.1165429913770684, "y1996": 1.1152679554712275, "y1999": 1.1314326394231322, "y1998": 1.1310394841195361, "y2006": 1.1090538904302065, "y2007": 1.1057776900012568, "y2004": 1.1402994437897009, "y2005": 1.1197940058085571, "y2002": 1.133670175399079, "y2003": 1.139822558851451, "y2000": 1.1388962186541665, "y2001": 1.1244221220249986, "id": 20, "y2008": 1.1116682481010467, "y2009": 1.0998515545336902}, {"neighbors": [23, 22, 7, 10, 34], "y1995": 0.76530058421804126, "y1997": 0.76542450966153397, "y1996": 0.76612841163904621, "y1999": 0.76014283909933289, "y1998": 0.7672268310234307, "y2006": 0.76842416021983684, "y2007": 0.77487117798086069, "y2004": 0.76533287692895391, "y2005": 0.78205934309410463, "y2002": 0.76156903267949927, "y2003": 0.76651951668098528, "y2000": 0.74480073263159763, "y2001": 0.76098396210261965, "id": 21, "y2008": 0.77768682781054099, "y2009": 0.78801192267396702}, {"neighbors": [21, 34, 5, 7, 29], "y1995": 0.98391336093764348, "y1997": 0.98295341320156315, "y1996": 0.98075815675295552, "y1999": 0.96913802803963667, "y1998": 0.97386015032669815, "y2006": 0.93965462091114671, "y2007": 0.93069644684632924, "y2004": 0.9635616201227476, "y2005": 0.94745351657235244, "y2002": 0.97209860866113018, "y2003": 0.97441312580606143, "y2000": 0.97370819354423843, "y2001": 0.96419154157867693, "id": 22, "y2008": 0.94020973488297466, "y2009": 0.94358232339833159}, {"neighbors": [21, 10, 22, 34, 7], "y1995": 0.83561828119099946, "y1997": 0.81738501913392403, "y1996": 0.82298088022609361, "y1999": 0.80904800725677739, "y1998": 0.81748588141426259, "y2006": 0.87170334233473346, "y2007": 0.8786379876833581, "y2004": 0.85954307066870839, "y2005": 0.86790023653402792, "y2002": 0.83451612857812574, "y2003": 0.85175031934895873, "y2000": 0.80071489233375537, "y2001": 0.83358255807316928, "id": 23, "y2008": 0.87497981001981484, "y2009": 0.87888675419592222}, {"neighbors": [27, 20, 30, 32, 47], "y1995": 0.98845573274970278, "y1997": 0.99665282989553183, "y1996": 1.0209242772035507, "y1999": 0.99386618594343845, "y1998": 0.99141823200404444, "y2006": 0.97906748937234156, "y2007": 0.9932312332800689, "y2004": 1.0111665058188304, "y2005": 0.9998802359352077, "y2002": 0.99669586934394627, "y2003": 1.0255909749831356, "y2000": 0.98733194819247994, "y2001": 0.99644997431653437, "id": 24, "y2008": 1.0020493856497013, "y2009": 0.99602148231561483}, {"neighbors": [20, 33, 6, 30, 12], "y1995": 1.1493091345649815, "y1997": 1.143009615936718, "y1996": 1.1524194939429724, "y1999": 1.1398468268822266, "y1998": 1.1426554202510555, "y2006": 1.0889107875354573, "y2007": 1.0860369499254896, "y2004": 1.0856975145267398, "y2005": 1.1244348633192611, "y2002": 1.0423089214343333, "y2003": 1.0557727834721793, "y2000": 1.0831239730629278, "y2001": 1.0519262599166714, "id": 25, "y2008": 1.0599731384290745, "y2009": 1.0216094265950888}, {"neighbors": [28, 19, 16, 32, 17], "y1995": 1.1136826889802023, "y1997": 1.1189343096757198, "y1996": 1.1057147027213501, "y1999": 1.1432271991365353, "y1998": 1.1377866945457653, "y2006": 1.1268023587150906, "y2007": 1.1235793669317915, "y2004": 1.1482023546040769, "y2005": 1.1238659840114973, "y2002": 1.1600919581655105, "y2003": 1.1446778932605579, "y2000": 1.1825702862895446, "y2001": 1.1622624279436105, "id": 26, "y2008": 1.115925801617498, "y2009": 1.1257082797404696}, {"neighbors": [32, 24, 36, 16, 28], "y1995": 1.303794309231981, "y1997": 1.3120636604057812, "y1996": 1.3075218596998686, "y1999": 1.3062566740535762, "y1998": 1.3153226688859194, "y2006": 1.2865667454509278, "y2007": 1.2973409698906584, "y2004": 1.2683078569016086, "y2005": 1.2617743046198988, "y2002": 1.2920319347677043, "y2003": 1.2718351646774422, "y2000": 1.3121023910310281, "y2001": 1.2998915587009874, "id": 27, "y2008": 1.2939020510829768, "y2009": 1.2934544564717687}, {"neighbors": [26, 16, 19, 32, 27], "y1995": 0.83953719020532513, "y1997": 0.82006005316292385, "y1996": 0.82701447583159737, "y1999": 0.80294863992835086, "y1998": 0.8118887636743225, "y2006": 0.8389109342655191, "y2007": 0.84349246817602375, "y2004": 0.83108634437662732, "y2005": 0.84373783646216949, "y2002": 0.82596790474192727, "y2003": 0.82435704751379402, "y2000": 0.78772975118465016, "y2001": 0.82848010958278628, "id": 28, "y2008": 0.85637272428125033, "y2009": 0.86539395164519117}, {"neighbors": [5, 39, 22, 14, 31], "y1995": 1.2345008725695852, "y1997": 1.2353793515744536, "y1996": 1.2426021999018138, "y1999": 1.2452262575926329, "y1998": 1.2358129278404693, "y2006": 1.2365329681906834, "y2007": 1.2796200872578414, "y2004": 1.1967443443492951, "y2005": 1.2153657295128597, "y2002": 1.1937780418204111, "y2003": 1.1835533748469893, "y2000": 1.2256766974812463, "y2001": 1.2112664802237314, "id": 29, "y2008": 1.2796839248335934, "y2009": 1.2590773758694083}, {"neighbors": [37, 20, 24, 25, 27], "y1995": 0.97696620404861145, "y1997": 0.98035944080980575, "y1996": 0.9740071914763756, "y1999": 0.95543282313901556, "y1998": 0.97581530789338955, "y2006": 0.92100464312607799, "y2007": 0.9147530387633086, "y2004": 0.9298883479571457, "y2005": 0.93442917452618346, "y2002": 0.93679072759857129, "y2003": 0.92540049332494034, "y2000": 0.96480308308405971, "y2001": 0.9468637634838194, "id": 30, "y2008": 0.90249622070947177, "y2009": 0.90213630440783921}, {"neighbors": [35, 14, 33, 12, 4], "y1995": 0.84986885942491119, "y1997": 0.84295996568390696, "y1996": 0.89868510090623221, "y1999": 0.85659367787716301, "y1998": 0.87280533962476625, "y2006": 0.92562487931452408, "y2007": 0.96635366357254426, "y2004": 0.92698332540482575, "y2005": 0.94745351657235244, "y2002": 0.90448992922937876, "y2003": 0.95495898185605821, "y2000": 0.88937573313051443, "y2001": 0.89440100450887505, "id": 31, "y2008": 1.025203118044723, "y2009": 1.0394296020754366}, {"neighbors": [36, 27, 28, 16, 26], "y1995": 1.0192280751235561, "y1997": 1.0097442843101825, "y1996": 1.0025820319237864, "y1999": 0.99765073314119712, "y1998": 1.0030341681355639, "y2006": 0.94779637858468868, "y2007": 0.93759089358493275, "y2004": 0.97583768316642261, "y2005": 0.96101679691008712, "y2002": 0.99747298060178258, "y2003": 0.99550758543481688, "y2000": 1.0075901875261932, "y2001": 0.99192968437874551, "id": 32, "y2008": 0.93353431146829191, "y2009": 0.94121705123804411}, {"neighbors": [44, 25, 12, 35, 31], "y1995": 0.86367410708901315, "y1997": 0.85544345781923936, "y1996": 0.85558931627900803, "y1999": 0.84336613427334628, "y1998": 0.85103025143102673, "y2006": 0.89455097373003656, "y2007": 0.88283929116469462, "y2004": 0.85951183386707053, "y2005": 0.87194227372077004, "y2002": 0.84667960913556228, "y2003": 0.84374557883664714, "y2000": 0.83434853662160158, "y2001": 0.85813595114434105, "id": 33, "y2008": 0.90349490610221961, "y2009": 0.9060067497610369}, {"neighbors": [22, 39, 21, 29, 23], "y1995": 1.0094753356447226, "y1997": 1.0069881886439402, "y1996": 1.0041105523637666, "y1999": 0.99291086334982948, "y1998": 0.99513686502304577, "y2006": 0.96382634438484593, "y2007": 0.95011400973122428, "y2004": 0.975119236728752, "y2005": 0.96134614808826613, "y2002": 0.99291167539274383, "y2003": 0.98983209318633369, "y2000": 1.0058162611397035, "y2001": 0.98850522230466298, "id": 34, "y2008": 0.94346860300667812, "y2009": 0.9463776450423077}, {"neighbors": [31, 38, 44, 33, 14], "y1995": 1.0571257066143651, "y1997": 1.0575301194645879, "y1996": 1.0545941857842291, "y1999": 1.0510385688532684, "y1998": 1.0488078570498685, "y2006": 1.0247627521629479, "y2007": 1.0234752320591773, "y2004": 1.0329697933620496, "y2005": 1.0219168238570018, "y2002": 1.0420048344203974, "y2003": 1.0402553971511816, "y2000": 1.0480002306104303, "y2001": 1.030249414987729, "id": 35, "y2008": 1.0251768368501768, "y2009": 1.0435957064486703}, {"neighbors": [32, 43, 27, 28, 42], "y1995": 1.070841888164505, "y1997": 1.0793762307014196, "y1996": 1.0666949726007404, "y1999": 1.0794043012481198, "y1998": 1.0738798776109699, "y2006": 1.087727556316465, "y2007": 1.0885954360198933, "y2004": 1.1032213602455734, "y2005": 1.0916793915985508, "y2002": 1.0938347765734742, "y2003": 1.1052447043433509, "y2000": 1.0531800956589803, "y2001": 1.0745277096056161, "id": 36, "y2008": 1.0917733838297285, "y2009": 1.1096083021948762}, {"neighbors": [30, 40, 20, 42, 41], "y1995": 0.8671922185905101, "y1997": 0.86675155621455668, "y1996": 0.86628895935887062, "y1999": 0.86511809486628932, "y1998": 0.86425631732335095, "y2006": 0.84488343470424199, "y2007": 0.83374328958471722, "y2004": 0.84517414191529749, "y2005": 0.84843857600526962, "y2002": 0.85411284725399572, "y2003": 0.84886336375435456, "y2000": 0.86287327291635718, "y2001": 0.8516979624450659, "id": 37, "y2008": 0.82812044014430564, "y2009": 0.82878598934619596}, {"neighbors": [35, 31, 45, 39, 44], "y1995": 0.8838921149583755, "y1997": 0.90282398478743275, "y1996": 0.92288667453925455, "y1999": 0.92023285988219217, "y1998": 0.91229185518735723, "y2006": 0.93869676706720051, "y2007": 0.96947770975097391, "y2004": 0.99223700402629367, "y2005": 0.97984969609868555, "y2002": 0.93682451504456421, "y2003": 0.98655146182882891, "y2000": 0.92652175166361039, "y2001": 0.94278865361566122, "id": 38, "y2008": 1.0036262573224608, "y2009": 0.98102350657197357}, {"neighbors": [29, 34, 38, 22, 35], "y1995": 0.970820642185237, "y1997": 0.94534081352108112, "y1996": 0.95320232993219844, "y1999": 0.93967000034446724, "y1998": 0.94215592860799646, "y2006": 0.91035556215514757, "y2007": 0.90430364292511256, "y2004": 0.92879505989982103, "y2005": 0.9211054223180335, "y2002": 0.93412151936513388, "y2003": 0.93501274320242933, "y2000": 0.93092108910210503, "y2001": 0.92662519262599163, "id": 39, "y2008": 0.89994694483851023, "y2009": 0.9007386435858511}, {"neighbors": [41, 37, 42, 30, 45], "y1995": 0.95861858457245008, "y1997": 0.98254810501535106, "y1996": 0.95774543235102894, "y1999": 0.98684823919808018, "y1998": 0.98919471947721893, "y2006": 0.97163003599581876, "y2007": 0.97007020126757271, "y2004": 0.9493488753775261, "y2005": 0.97152609359561659, "y2002": 0.95601578436851964, "y2003": 0.94905384541254967, "y2000": 0.98882204635713133, "y2001": 0.97662233890759653, "id": 40, "y2008": 0.97158948117089283, "y2009": 0.95884908006927827}, {"neighbors": [40, 45, 44, 37, 42], "y1995": 0.83980438854721107, "y1997": 0.85746999875029983, "y1996": 0.84726737166133714, "y1999": 0.85567509846023126, "y1998": 0.85467221160427542, "y2006": 0.8333891885768886, "y2007": 0.83511679264592342, "y2004": 0.81743586206088703, "y2005": 0.83550405700769481, "y2002": 0.84502402428191115, "y2003": 0.82645665158259707, "y2000": 0.84818516243622177, "y2001": 0.85265681182580899, "id": 41, "y2008": 0.82136617314598481, "y2009": 0.80921873783836296}, {"neighbors": [43, 40, 46, 37, 36], "y1995": 0.95118156405662746, "y1997": 0.94688098462868708, "y1996": 0.9466212002600608, "y1999": 0.95124410099780687, "y1998": 0.95085829660091703, "y2006": 0.96895367966714574, "y2007": 0.9700163384024274, "y2004": 0.97583768316642261, "y2005": 0.95571723704302525, "y2002": 0.96804411514198463, "y2003": 0.97136213864358201, "y2000": 0.95440787445922959, "y2001": 0.96364362764682376, "id": 42, "y2008": 0.97082732652905901, "y2009": 0.9878236640328002}, {"neighbors": [36, 42, 32, 27, 46], "y1995": 1.0891004415267045, "y1997": 1.0849289528525252, "y1996": 1.0824896838138709, "y1999": 1.0945424900391545, "y1998": 1.0865692335830259, "y2006": 1.1450297539219478, "y2007": 1.1447474729339102, "y2004": 1.1334273474293739, "y2005": 1.1468606844516303, "y2002": 1.1229257675733433, "y2003": 1.1302103089739621, "y2000": 1.1055818811158884, "y2001": 1.1214085953998059, "id": 43, "y2008": 1.1408403740471014, "y2009": 1.1614292649793569}, {"neighbors": [33, 41, 45, 35, 40], "y1995": 1.0633603345917013, "y1997": 1.0869149629649646, "y1996": 1.0736582323828732, "y1999": 1.1166986255755473, "y1998": 1.0976484597942771, "y2006": 1.0839806574563229, "y2007": 1.0983176831786272, "y2004": 1.0927882684985315, "y2005": 1.0700320368873319, "y2002": 1.0881584856466706, "y2003": 1.0804431312806149, "y2000": 1.1185670222649935, "y2001": 1.0976428286056732, "id": 44, "y2008": 1.0929823187788443, "y2009": 1.0917612486217978}, {"neighbors": [41, 44, 40, 35, 33], "y1995": 0.79772064970019041, "y1997": 0.7858115114280021, "y1996": 0.78829195801876151, "y1999": 0.77035744221561353, "y1998": 0.77615921755360906, "y2006": 0.79949806580432425, "y2007": 0.80172181625581262, "y2004": 0.79603865293896003, "y2005": 0.78966436120841943, "y2002": 0.81437881076636964, "y2003": 0.80788827809912023, "y2000": 0.77751193519846906, "y2001": 0.79902973574567659, "id": 45, "y2008": 0.82168154748053679, "y2009": 0.85587910681858015}, {"neighbors": [42, 43, 40, 36, 37], "y1995": 1.0052446952315301, "y1997": 1.0047589936197736, "y1996": 1.0000769567582628, "y1999": 1.0063956091903872, "y1998": 1.0061394183885444, "y2006": 0.97292595590233411, "y2007": 0.96519561197191939, "y2004": 0.99030032232474696, "y2005": 0.97682565346267858, "y2002": 1.0081498135355325, "y2003": 1.0057431552702318, "y2000": 1.0016297948675874, "y2001": 0.99860738542320637, "id": 46, "y2008": 0.9617340332161447, "y2009": 0.95890283625473927}, {"neighbors": [20, 6, 24, 25, 30], "y1995": 0.95808418788867844, "y1997": 0.9654440995572009, "y1996": 0.93825679674127938, "y1999": 0.96987289157318213, "y1998": 0.95561201303757848, "y2006": 1.1704973973021624, "y2007": 1.1702515395802287, "y2004": 1.0533361880299275, "y2005": 1.0983262971945267, "y2002": 1.0078119390756035, "y2003": 1.0348423554112989, "y2000": 0.96608031008233231, "y2001": 0.99727184521431422, "id": 47, "y2008": 1.1873055260044207, "y2009": 1.1424264534188653}] diff --git a/src/py/crankshaft/test/mock_plpy.py b/src/py/crankshaft/test/mock_plpy.py index 63c88f6..a982ebe 100644 --- a/src/py/crankshaft/test/mock_plpy.py +++ b/src/py/crankshaft/test/mock_plpy.py @@ -1,5 +1,16 @@ import re +class MockCursor: + def __init__(self, data): + self.cursor_pos = 0 + self.data = data + + def fetch(self, batch_size): + batch = self.data[self.cursor_pos : self.cursor_pos + batch_size] + self.cursor_pos += batch_size + return batch + + class MockPlPy: def __init__(self): self._reset() @@ -24,9 +35,16 @@ class MockPlPy: def notice(self, msg): self.notices.append(msg) + def debug(self, msg): + self.notices.append(msg) + def info(self, msg): self.infos.append(msg) + def cursor(self, query): + data = self.execute(query) + return MockCursor(data) + def execute(self, query): # TODO: additional arguments for result in self.results: if result[0].match(query): diff --git a/src/py/crankshaft/test/test_cluster_kmeans.py b/src/py/crankshaft/test/test_cluster_kmeans.py new file mode 100644 index 0000000..aba8e07 --- /dev/null +++ b/src/py/crankshaft/test/test_cluster_kmeans.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file +import numpy as np +import crankshaft.clustering as cc +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json + +class KMeansTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read()) + self.params = {"subquery": "select * from table", + "no_clusters": "10" + } + + def test_kmeans(self): + data = self.cluster_data + plpy._define_result('select' ,data) + clusters = cc.kmeans('subquery', 2) + labels = [a[1] for a in clusters] + c1 = [a for a in clusters if a[1]==0] + c2 = [a for a in clusters if a[1]==1] + + self.assertEqual(len(np.unique(labels)),2) + self.assertEqual(len(c1),20) + self.assertEqual(len(c2),20) + diff --git a/src/py/crankshaft/test/test_clustering_moran.py b/src/py/crankshaft/test/test_clustering_moran.py index 393e93b..2b683cf 100644 --- a/src/py/crankshaft/test/test_clustering_moran.py +++ b/src/py/crankshaft/test/test_clustering_moran.py @@ -25,6 +25,11 @@ class MoranTest(unittest.TestCase): "subquery": "SELECT * FROM a_list", "geom_col": "the_geom", "num_ngbrs": 321} + self.params_markov = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read()) self.moran_data = json.loads(open(fixture_file('moran.json')).read()) diff --git a/src/py/crankshaft/test/test_pysal_utils.py b/src/py/crankshaft/test/test_pysal_utils.py index 4ea0d9b..171fdbc 100644 --- a/src/py/crankshaft/test/test_pysal_utils.py +++ b/src/py/crankshaft/test/test_pysal_utils.py @@ -15,22 +15,38 @@ class PysalUtilsTest(unittest.TestCase): "geom_col": "the_geom", "num_ngbrs": 321} + self.params_array = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + def test_query_attr_select(self): """Test query_attr_select""" - ans = "i.\"{attr1}\"::numeric As attr1, " \ - "i.\"{attr2}\"::numeric As attr2, " + ans = "i.\"andy\"::numeric As attr1, " \ + "i.\"jay_z\"::numeric As attr2, " + + ans_array = "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " self.assertEqual(pu.query_attr_select(self.params), ans) + self.assertEqual(pu.query_attr_select(self.params_array), ans_array) def test_query_attr_where(self): """Test pu.query_attr_where""" - ans = "idx_replace.\"{attr1}\" IS NOT NULL AND " \ - "idx_replace.\"{attr2}\" IS NOT NULL AND " \ - "idx_replace.\"{attr2}\" <> 0" + ans = "idx_replace.\"andy\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" IS NOT NULL AND " \ + "idx_replace.\"jay_z\" <> 0" + + ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \ + "idx_replace.\"_2014_jan\" IS NOT NULL AND " \ + "idx_replace.\"_2014_feb\" IS NOT NULL" self.assertEqual(pu.query_attr_where(self.params), ans) + self.assertEqual(pu.query_attr_where(self.params_array), ans_array) def test_knn(self): """Test knn neighbors constructor""" @@ -53,8 +69,27 @@ class PysalUtilsTest(unittest.TestCase): "i.\"jay_z\" IS NOT NULL AND " \ "i.\"jay_z\" <> 0 " \ "ORDER BY i.\"cartodb_id\" ASC;" + + ans_array = "SELECT i.\"cartodb_id\" As id, " \ + "i.\"_2013_dec\"::numeric As attr1, " \ + "i.\"_2014_jan\"::numeric As attr2, " \ + "i.\"_2014_feb\"::numeric As attr3, " \ + "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \ + "FROM (SELECT * FROM a_list) As j " \ + "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \ + "j.\"_2013_dec\" IS NOT NULL AND " \ + "j.\"_2014_jan\" IS NOT NULL AND " \ + "j.\"_2014_feb\" IS NOT NULL " \ + "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \ + "LIMIT 321)) As neighbors " \ + "FROM (SELECT * FROM a_list) As i " \ + "WHERE i.\"_2013_dec\" IS NOT NULL AND " \ + "i.\"_2014_jan\" IS NOT NULL AND " \ + "i.\"_2014_feb\" IS NOT NULL "\ + "ORDER BY i.\"cartodb_id\" ASC;" self.assertEqual(pu.knn(self.params), ans) + self.assertEqual(pu.knn(self.params_array), ans_array) def test_queen(self): """Test queen neighbors constructor""" diff --git a/src/py/crankshaft/test/test_segmentation.py b/src/py/crankshaft/test/test_segmentation.py new file mode 100644 index 0000000..d02e8b1 --- /dev/null +++ b/src/py/crankshaft/test/test_segmentation.py @@ -0,0 +1,64 @@ +import unittest +import numpy as np +from helper import plpy, fixture_file +import crankshaft.segmentation as segmentation +import json + +class SegmentationTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + plpy._reset() + + def generate_random_data(self,n_samples,random_state, row_type=False): + x1 = random_state.uniform(size=n_samples) + x2 = random_state.uniform(size=n_samples) + x3 = random_state.randint(0, 4, size=n_samples) + + y = x1+x2*x2+x3 + cartodb_id = range(len(x1)) + + if row_type: + return [ {'features': vals} for vals in zip(x1,x2,x3)], y + else: + return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))] + + def test_replace_nan_with_mean(self): + test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan]) + + def test_create_and_predict_segment(self): + n_samples = 1000 + + random_state_train = np.random.RandomState(13) + random_state_test = np.random.RandomState(134) + training_data = self.generate_random_data(n_samples, random_state_train) + test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True) + + + ids = [{'cartodb_ids': range(len(test_data))}] + rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}] + + plpy._define_result('select \* from \(select \* from training\) a limit 1',rows) + plpy._define_result('.*from \(select \* from training\) as a' ,training_data) + plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids) + plpy._define_result('.*select \* from test.*' ,test_data) + + model_parameters = {'n_estimators': 1200, + 'max_depth': 3, + 'subsample' : 0.5, + 'learning_rate': 0.01, + 'min_samples_leaf': 1} + + result = segmentation.create_and_predict_segment( + 'select * from training', + 'target', + 'select * from test', + model_parameters) + + prediction = [r[1] for r in result] + + accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y)))) + + self.assertEqual(len(result),len(test_data)) + self.assertTrue( result[0][2] < 0.01) + self.assertTrue( accuracy < 0.5*np.mean(test_y) ) diff --git a/src/py/crankshaft/test/test_space_time_dynamics.py b/src/py/crankshaft/test/test_space_time_dynamics.py new file mode 100644 index 0000000..54ffc9d --- /dev/null +++ b/src/py/crankshaft/test/test_space_time_dynamics.py @@ -0,0 +1,324 @@ +import unittest +import numpy as np + +import unittest + + +# from mock_plpy import MockPlPy +# plpy = MockPlPy() +# +# import sys +# sys.modules['plpy'] = plpy +from helper import plpy, fixture_file + +import crankshaft.space_time_dynamics as std +from crankshaft import random_seeds +import json + +class SpaceTimeTests(unittest.TestCase): + """Testing class for Markov Functions.""" + + def setUp(self): + plpy._reset() + self.params = {"id_col": "cartodb_id", + "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read()) + self.markov_data = json.loads(open(fixture_file('markov.json')).read()) + + self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T + + self.transition_matrix = np.array([ + [[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ], + [ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ], + [ 0. , 0.14 , 0.74 , 0.12 , 0. ], + [ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857], + [ 0. , 0. , 0. , 0.16666667, 0.83333333]], + [[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ], + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ], + [ 0. , 0. , 0.06372549, 0.90196078, 0.03431373], + [ 0. , 0. , 0. , 0.19444444, 0.80555556]], + [[ 0.84693878, 0.15306122, 0. , 0. , 0. ], + [ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ], + [ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0. , 0. , 0. , 0.10204082, 0.89795918]], + [[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ], + [ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725], + [ 0. , 0. , 0. , 0.09661836, 0.90338164]], + [[ 0.33333333, 0.66666667, 0. , 0. , 0. ], + [ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ], + [ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ], + [ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808], + [ 0. , 0. , 0. , 0.02352941, 0.97647059]]] + ) + + def test_spatial_markov(self): + """Test Spatial Markov.""" + data = [ { 'id': d['id'], + 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'], + 'neighbors': d['neighbors'] } for d in self.neighbors_data] + print(str(data[0])) + plpy._define_result('select', data) + random_seeds.set_random_seeds(1234) + + result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id') + + self.assertTrue(result != None) + result = [(row[0], row[1], row[2], row[3], row[4]) for row in result] + print result[0] + expected = self.markov_data + for ([res_trend, res_up, res_down, res_vol, res_id], + [exp_trend, exp_up, exp_down, exp_vol, exp_id] + ) in zip(result, expected): + self.assertAlmostEqual(res_trend, exp_trend) + + def test_get_time_data(self): + """Test get_time_data""" + data = [ { 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'] } for d in self.neighbors_data] + + result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009']) + + ## expected was prepared from PySAL example: + ### f = ps.open(ps.examples.get_path("usjoin.csv")) + ### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose() + ### rpci = pci / (pci.mean(axis = 0)) + + expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652 + , 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631 + , 0.86148267, 0.86252252, 0.86746356], + [ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978 + , 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054 + , 0.90139505, 0.87897455, 0.86216858], + [ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559 + , 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804 + , 0.83675961, 0.83574199, 0.84647177], + [ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948 + , 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797 + , 1.12958508, 1.11371818, 1.09936775], + [ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201 + , 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468 + , 1.10863242, 1.09642516, 1.07762337], + [ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737 + , 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766 + , 1.45241216, 1.43306098, 1.4174431 ], + [ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138 + , 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034 + , 1.08401709, 1.06959206, 1.07875225], + [ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316 + , 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511 + , 1.01749645, 0.9936394, 0.98279746], + [ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006 + , 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874 + , 0.91284091, 0.89689833, 0.88928858], + [ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708 + , 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141 + , 0.86633032, 0.84946077, 0.83287146], + [ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183 + , 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258 + , 1.11059842, 1.10509795, 1.10020097], + [ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051 + , 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201 + , 0.89280392, 0.89298243, 0.89165385], + [ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927 + , 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815 + , 0.94281559, 0.96520315, 0.96715441], + [ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073 + , 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665 + , 0.98423613, 0.99823861, 0.99545704], + [ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535 + , 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648 + , 0.8198736, 0.82265395, 0.8399404 ], + [ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597 + , 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108 + , 0.9422231, 0.93902708, 0.94479184], + [ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632 + , 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804 + , 0.90543476, 0.91737138, 0.94793657], + [ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869 + , 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384 + , 1.23418676, 1.23626739, 1.26754398], + [ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023 + , 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766 + , 1.32936244, 1.33001241, 1.33288462], + [ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016 + , 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549 + , 0.92754547, 0.92549227, 0.92138102], + [ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622 + , 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389 + , 1.10577769, 1.11166825, 1.09985155], + [ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073 + , 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416 + , 0.77487118, 0.77768683, 0.78801192], + [ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819 + , 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462 + , 0.93069645, 0.94020973, 0.94358232], + [ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489 + , 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334 + , 0.87863799, 0.87497981, 0.87888675], + [ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195 + , 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749 + , 0.99323123, 1.00204939, 0.99602148], + [ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397 + , 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079 + , 1.08603695, 1.05997314, 1.02160943], + [ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029 + , 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236 + , 1.12357937, 1.1159258, 1.12570828], + [ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239 + , 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675 + , 1.29734097, 1.29390205, 1.29345446], + [ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975 + , 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093 + , 0.84349247, 0.85637272, 0.86539395], + [ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767 + , 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297 + , 1.27962009, 1.27968392, 1.25907738], + [ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308 + , 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464 + , 0.91475304, 0.90249622, 0.9021363 ], + [ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573 + , 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488 + , 0.96635366, 1.02520312, 1.0394296 ], + [ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019 + , 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638 + , 0.93759089, 0.93353431, 0.94121705], + [ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854 + , 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097 + , 0.88283929, 0.90349491, 0.90600675], + [ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626 + , 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634 + , 0.95011401, 0.9434686, 0.94637765], + [ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023 + , 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275 + , 1.02347523, 1.02517684, 1.04359571], + [ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801 + , 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756 + , 1.08859544, 1.09177338, 1.1096083 ], + [ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327 + , 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343 + , 0.83374329, 0.82812044, 0.82878599], + [ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175 + , 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677 + , 0.96947771, 1.00362626, 0.98102351], + [ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109 + , 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556 + , 0.90430364, 0.89994694, 0.90073864], + [ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205 + , 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004 + , 0.9700702, 0.97158948, 0.95884908], + [ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516 + , 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919 + , 0.83511679, 0.82136617, 0.80921874], + [ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787 + , 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368 + , 0.97001634, 0.97082733, 0.98782366], + [ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188 + , 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975 + , 1.14474747, 1.14084037, 1.16142926], + [ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702 + , 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066 + , 1.09831768, 1.09298232, 1.09176125], + [ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194 + , 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807 + , 0.80172182, 0.82168155, 0.85587911], + [ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979 + , 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596 + , 0.96519561, 0.96173403, 0.95890284], + [ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031 + , 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974 + , 1.17025154, 1.18730553, 1.14242645]]) + + self.assertTrue(np.allclose(result, expected)) + self.assertTrue(type(result) == type(expected)) + self.assertTrue(result.shape == expected.shape) + + def test_rebin_data(self): + """Test rebin_data""" + ## sample in double the time (even case since 10 % 2 = 0): + ## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2 + ## = 0.5, 2.5, 4.5, 6.5, 8.5 + ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float) + for i in range(0, 10, 2)]).T + + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even)) + + ## sample in triple the time (uneven since 10 % 3 = 1): + ## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1 + ## = 1, 4, 7, 9 + ans_odd = np.array([i * np.ones(10, dtype=float) + for i in (1, 4, 7, 9)]).T + self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd)) + + def test_get_prob_dist(self): + """Test get_prob_dist""" + lag_indices = np.array([1, 2, 3, 4]) + unit_indices = np.array([1, 3, 2, 4]) + answer = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices) + + self.assertTrue(np.array_equal(result, answer)) + + def test_get_prob_stats(self): + """Test get_prob_stats""" + + probs = np.array([ + [ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ], + [ 0. , 0. , 0.09411765, 0.87058824, 0.03529412], + [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ], + [ 0. , 0. , 0. , 0.02352941, 0.97647059] + ]) + unit_indices = np.array([1, 3, 2, 4]) + answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.]) + answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941]) + answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059]) + answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223]) + + result = std.get_prob_stats(probs, unit_indices) + result_up = result[0] + result_down = result[1] + result_trend = result[2] + result_volatility = result[3] + + self.assertTrue(np.allclose(result_up, answer_up)) + self.assertTrue(np.allclose(result_down, answer_down)) + self.assertTrue(np.allclose(result_trend, answer_trend)) + self.assertTrue(np.allclose(result_volatility, answer_volatility))