release 0.9.5 [ci skip]

.brackets.json

@@ -0,0 +1,3 @@
+{
+    "sbruchmann.staticpreview.basepath": "/home/carto/Projects/crankshaft/"
+}

.gitignore

@@ -2,3 +2,4 @@ envs/
 *.pyc
 .DS_Store
 .idea/
+.*.sw[nop]

.travis.yml

@@ -1,48 +1,60 @@
 language: c
+dist: precise
 sudo: required
 
 env:
   global:
     - PAGER=cat
-    - PGUSER=postgres
-    - PGDATABASE=postgres
-    - PGOPTIONS='-c client_min_messages=NOTICE'
-
-jobs:
-  include:
-    - env: POSTGRESQL_VERSION="9.6" POSTGIS_VERSION="2.5"
-      dist: xenial
-    - env: POSTGRESQL_VERSION="10" POSTGIS_VERSION="2.5"
-      dist: xenial
-    - env: POSTGRESQL_VERSION="11" POSTGIS_VERSION="2.5"
-      dist: xenial
-    - env: POSTGRESQL_VERSION="12" POSTGIS_VERSION="3"
-      dist: bionic
 
 before_install:
+  - ./check-up-to-date-with-master.sh
+  - sudo apt-get -y install python-pip
 
-  - sudo apt-get install -y --allow-unauthenticated --no-install-recommends --no-install-suggests postgresql-$POSTGRESQL_VERSION postgresql-client-$POSTGRESQL_VERSION postgresql-server-dev-$POSTGRESQL_VERSION postgresql-common
-  - if [[ $POSTGRESQL_VERSION == '9.6' ]]; then sudo apt-get install -y postgresql-contrib-9.6; fi;
-  - sudo apt-get install -y --allow-unauthenticated postgresql-$POSTGRESQL_VERSION-postgis-$POSTGIS_VERSION postgresql-$POSTGRESQL_VERSION-postgis-$POSTGIS_VERSION-scripts postgis
+  - 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
 
-  # For pre12, install plpython2. For PG12 install plpython3
-  - if [[ $POSTGRESQL_VERSION != '12' ]]; then sudo apt-get install -y postgresql-plpython-$POSTGRESQL_VERSION python python-pip python-software-properties python-joblib python-nose python-setuptools; else sudo apt-get install -y postgresql-plpython3-12 python3 python3-pip python3-software-properties python3-joblib python3-nose python3-setuptools; fi;
-  - if [[ $POSTGRESQL_VERSION == '12' ]]; then echo -e "joblib==0.11\nnumpy==1.13.3\nscipy==0.19.1\npysal==1.14.3\nscikit-learn==0.19.1" > ./src/py/crankshaft/requirements.txt && sed -i -e "s/.*install_requires.*$/    install_requires=['joblib==0.11.0', 'numpy==1.13.3', 'scipy==0.19.1', 'pysal==1.14.3', 'scikit-learn==0.19.1'],/g" ./src/py/crankshaft/setup.py; fi;
+  - sudo apt-get -y install python-joblib=0.8.3-1-cdb1
+  - sudo apt-get -y install python-numpy=1:1.6.1-6ubuntu1
 
-  - sudo pg_dropcluster --stop $POSTGRESQL_VERSION main
-  - sudo rm -rf /etc/postgresql/$POSTGRESQL_VERSION /var/lib/postgresql/$POSTGRESQL_VERSION
-  - sudo pg_createcluster -u postgres $POSTGRESQL_VERSION main --start -- -A trust
-  - export PGPORT=$(pg_lsclusters | grep $POSTGRESQL_VERSION | awk '{print $3}')
+  - 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 postgresql-9.5
+  - sudo rm -rf /var/lib/postgresql/
+  - sudo rm -rf /var/log/postgresql/
+  - sudo rm -rf /etc/postgresql/
+  - sudo apt-get -y remove --purge postgis-2.2
+  - sudo apt-get -y autoremove
+
+  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
+  - sudo apt-get -y install postgresql-9.5-postgis-2.2=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
+  - make test || { cat src/pg/test/regression.diffs; false; }
   - ./check-compatibility.sh
-
-after_failure:
-  - pg_lsclusters
-  - cat src/pg/test/regression.diffs
-  - echo $PGPORT
-  - cat /var/log/postgresql/postgresql-$POSTGRESQL_VERSION-main.log

CONTRIBUTING.md

@@ -39,7 +39,9 @@ ALTER EXTENSION crankshaft UPDATE TO 'dev';
 If the extension has not previously been installed in a database,
 it can be installed directly with:
 ```sql
-CREATE EXTENSION crankshaft WITH VERSION 'dev' CASCADE;
+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

Makefile

@@ -23,7 +23,7 @@ test:   ## Run the tests for the development version of the extension
 	$(MAKE) -C $(EXT_DIR) test
 
 # Generate a new release into release
-release: ## Generate a new release of the extension.
+release: ## Generate a new release of the extension. Only for telease manager
 	$(MAKE) -C $(EXT_DIR) release
 	$(MAKE) -C $(PYP_DIR) release
 
@@ -31,7 +31,7 @@ release: ## Generate a new release of the extension.
 # Requires sudo.
 # Use the RELEASE_VERSION environment variable to deploy a specific version:
 #     sudo make deploy RELEASE_VERSION=1.0.0
-deploy:
+deploy: ## Deploy a released extension. Only for release manager. Requires sudo.
 	$(MAKE) -C $(EXT_DIR) deploy
 	$(MAKE) -C $(PYP_DIR) deploy
 

Makefile.global

@@ -3,21 +3,9 @@ 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
-AWK               = awk
-
-PG_CONFIG         = pg_config
-PG_VERSION_1000  := $(shell $(PG_CONFIG) --version | $(AWK) '{$$2*=1000; print $$2}')
-PG_PARALLEL      := $(shell [ $(PG_VERSION_1000) -ge 9600 ] && echo true)
-
-PG_12plus        := $(shell [ $(PG_VERSION_1000) -ge 12000 ] && echo true)
-PYTHON3          ?= $(PG_12plus)
-
-ifeq ($(PYTHON3), true)
-PIP              := python3 -m pip
-NOSETESTS         = nosetests3
-else
-PIP              := python2 -m pip
+PIP               = pip
 NOSETESTS         = nosetests
-endif
+AWK               = awk
+PG_CONFIG         = pg_config
+PG_PARALLEL      := $(shell $(PG_CONFIG) --version | ($(AWK) '{$$2*=1000; if ($$2 >= 9600) print 1; else print 0;}' 2> /dev/null || echo 0))

NEWS.md

@@ -1,14 +1,3 @@
-0.9.0 (2019-12-23)
-------------------
-* Compatibility with PG12.
-* Compatibility with python3 (enable with PYTHON3=true env variable, default in PG12+).
-
-0.8.2 (2019-02-07)
-------------------
-* Update dependencies to match what it's being used in production.
-* Update travis to xenial, PG10 and 11, and postgis 2.5
-* Compatibility with PG11
-
 0.8.1 (2018-03-12)
 ------------------
 * Adds improperly added version files

README.md

@@ -8,21 +8,28 @@ CARTO Spatial Analysis extension for PostgreSQL.
 * `src/` source code
  - `pg/` contains the PostgreSQL extension source code
  - `py/` Python module source code
-* `release` released versions
+* `release` reseleased versions
 
 ## Requirements
 
 * PostgreSQL
-* plpythonu (for PG12+, plpython3u) and postgis extensions
+* plpythonu and postgis extensions
 * python-scipy system package (see [src/py/README.md](https://github.com/CartoDB/crankshaft/blob/develop/src/py/README.md))
 
 # Development Process
 
+We distinguish two roles:
+
+* *developers* will implement new functionality and bugfixes into
+  the codebase.
+* A *release manager* will handle the release process.
+
 We use the branch `develop` as the main integration branch for development. The `master` is reserved to handle releases.
 
 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:
+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
@@ -32,6 +39,7 @@ The process is as follows:
 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.

RELEASE.md

@@ -1,6 +1,7 @@
 # Release & Deployment Process
 
-:warning: Do not forget about updating dependencies in `cartodb-platform` and `carto-postgres-artifacts` :warning:
+The release process of a new version of the extension
+shall be performed by the designated *Release Manager*.
 
 ## Release steps
 * Make sure `develop` branch passes all the tests.

carto-package.json

@@ -1,20 +0,0 @@
-{
-    "name": "crankshaft",
-    "current_version": {
-        "requires": {
-            "postgres": ">=9.5.0",
-            "postgis": ">=2.2.0.0",
-            "python": ">=2.7.0",
-            "joblib": "0.8.3",
-            "numpy": "1.6.1",
-            "scipy": "0.14.0",
-            "pysal": "1.14.3",
-            "scikit-learn": "0.14.1"
-        },
-        "works_with": {
-        }
-    },
-
-    "exceptional_versions": {
-    }
-}

check-compatibility.sh

@@ -25,6 +25,10 @@ psql -c "SELECT * FROM pg_available_extension_versions WHERE name LIKE 'cranksha
 
 # 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
 $$
@@ -40,53 +44,30 @@ END
 $$ LANGUAGE plpgsql;
 
 -- Install the default version
-CREATE EXTENSION crankshaft CASCADE;
+CREATE EXTENSION crankshaft;
 \dx
 EOF
 
-
-# Check PG version
-PG_VERSION=`psql -q -t -c "SELECT current_setting('server_version_num')"`
-
 # Save public function signatures
-if [[ "$PG_VERSION" -lt 110000 ]]; then
-    psql $DBNAME -c "
-    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;"
-else
-    psql $DBNAME -c "
-    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.prokind = 'a' THEN 'agg'
-            WHEN p.prokind = 'w' 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;"
-fi
+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"
@@ -95,42 +76,26 @@ 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"
 
-if [[ $PG_VERSION -lt 110000 ]]; then
-    psql $DBNAME -c "
-    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;"
-else
-    psql $DBNAME -c "
-    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.prokind = 'a' THEN 'agg'
-                        WHEN p.prokind = 'w' 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;"
-fi
-
+# 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;"

release/crankshaft--0.8.1--0.9.0.sql

@@ -4,7 +4,7 @@
 -- Version number of the extension release
 CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
 RETURNS text AS $$
-  SELECT '0.8.2'::text;
+  SELECT '0.9.0'::text;
 $$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE;
 
 -- Internal identifier of the installed extension instence
@@ -35,16 +35,25 @@ CREATE OR REPLACE FUNCTION
     $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
-        SFUNC = CDB_PyAggS,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{}"
-    );
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_pyagg'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
+            SFUNC = CDB_PyAggS,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -89,6 +98,7 @@ CREATE OR REPLACE FUNCTION
       query TEXT,
       variable_name TEXT,
       target_table TEXT,
+      model_name text DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -105,24 +115,59 @@ AS $$
         'learning_rate': learning_rate,
         'min_samples_leaf': min_samples_leaf
     }
-    feature_cols = set(plpy.execute('''
+    all_cols = list(plpy.execute('''
         select * from ({query}) as _w limit 0
-    '''.format(query=query)).colnames()) -  set([variable_name, 'cartodb_id', ])
+    '''.format(query=query)).colnames())
+    feature_cols = [a for a in all_cols
+                    if a not in [variable_name, 'cartodb_id', ]]
     return seg.create_and_predict_segment(
         query,
         variable_name,
         feature_cols,
         target_table,
-        model_params
+        model_params,
+        model_name=model_name
     )
 $$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
+CREATE OR REPLACE FUNCTION
+  CDB_RetrieveModelParams(
+    model_name text,
+    param_name text
+  )
+RETURNS TABLE(param numeric, feature_name text) AS $$
+
+import pickle
+from collections import Iterable
+
+plan = plpy.prepare('''
+    SELECT model, feature_names FROM model_storage
+    WHERE name = $1;
+''', ['text', ])
+
+try:
+    model_encoded = plpy.execute(plan, [model_name, ])
+except plpy.SPIError as err:
+    plpy.error('ERROR: {}'.format(err))
+plpy.notice(model_encoded[0]['feature_names'])
+model = pickle.loads(
+    model_encoded[0]['model']
+)
+
+res = getattr(model, param_name) 
+if not isinstance(res, Iterable):
+    raise Exception('Cannot return `{}` as a table'.format(param_name))
+return zip(res, model_encoded[0]['feature_names'])
+
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
+
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
       query TEXT,
       variable TEXT,
       feature_columns TEXT[],
       target_query TEXT,
+      model_name TEXT DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -144,7 +189,8 @@ AS $$
         variable,
         feature_columns,
         target_query,
-        model_params
+        model_params,
+        model_name=model_name
     )
 $$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION CDB_Gravity(
@@ -1104,19 +1150,27 @@ BEGIN
 END
 $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
-
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
-        SFUNC = CDB_WeightedMeanS,
-        FINALFUNC = CDB_WeightedMeanF,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{0.0,0.0,0.0}"
-);
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_weightedmean'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
+            SFUNC = CDB_WeightedMeanS,
+            FINALFUNC = CDB_WeightedMeanF,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{0.0,0.0,0.0}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 -- Spatial Markov
 
 -- input table format:

release/crankshaft--0.9.0--0.9.1.sql

@@ -4,7 +4,7 @@
 -- Version number of the extension release
 CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
 RETURNS text AS $$
-  SELECT '0.8.2'::text;
+  SELECT '0.9.1'::text;
 $$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE;
 
 -- Internal identifier of the installed extension instence
@@ -35,16 +35,25 @@ CREATE OR REPLACE FUNCTION
     $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
-        SFUNC = CDB_PyAggS,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{}"
-    );
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_pyagg'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
+            SFUNC = CDB_PyAggS,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -89,6 +98,7 @@ CREATE OR REPLACE FUNCTION
       query TEXT,
       variable_name TEXT,
       target_table TEXT,
+      model_name text DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -105,24 +115,59 @@ AS $$
         'learning_rate': learning_rate,
         'min_samples_leaf': min_samples_leaf
     }
-    feature_cols = set(plpy.execute('''
+    all_cols = list(plpy.execute('''
         select * from ({query}) as _w limit 0
-    '''.format(query=query)).colnames()) -  set([variable_name, 'cartodb_id', ])
+    '''.format(query=query)).colnames())
+    feature_cols = [a for a in all_cols
+                    if a not in [variable_name, 'cartodb_id', ]]
     return seg.create_and_predict_segment(
         query,
         variable_name,
         feature_cols,
         target_table,
-        model_params
+        model_params,
+        model_name=model_name
     )
 $$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
+CREATE OR REPLACE FUNCTION
+  CDB_RetrieveModelParams(
+    model_name text,
+    param_name text
+  )
+RETURNS TABLE(param numeric, feature_name text) AS $$
+
+import pickle
+from collections import Iterable
+
+plan = plpy.prepare('''
+    SELECT model, feature_names FROM model_storage
+    WHERE name = $1;
+''', ['text', ])
+
+try:
+    model_encoded = plpy.execute(plan, [model_name, ])
+except plpy.SPIError as err:
+    plpy.error('ERROR: {}'.format(err))
+plpy.notice(model_encoded[0]['feature_names'])
+model = pickle.loads(
+    model_encoded[0]['model']
+)
+
+res = getattr(model, param_name) 
+if not isinstance(res, Iterable):
+    raise Exception('Cannot return `{}` as a table'.format(param_name))
+return zip(res, model_encoded[0]['feature_names'])
+
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
+
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
       query TEXT,
       variable TEXT,
       feature_columns TEXT[],
       target_query TEXT,
+      model_name TEXT DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -144,7 +189,8 @@ AS $$
         variable,
         feature_columns,
         target_query,
-        model_params
+        model_params,
+        model_name=model_name
     )
 $$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION CDB_Gravity(
@@ -1104,19 +1150,27 @@ BEGIN
 END
 $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
-
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
-        SFUNC = CDB_WeightedMeanS,
-        FINALFUNC = CDB_WeightedMeanF,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{0.0,0.0,0.0}"
-);
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_weightedmean'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
+            SFUNC = CDB_WeightedMeanS,
+            FINALFUNC = CDB_WeightedMeanF,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{0.0,0.0,0.0}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 -- Spatial Markov
 
 -- input table format:

release/crankshaft--0.9.0.sql

@@ -21,7 +21,7 @@ _cdb_random_seeds (seed_value INTEGER) RETURNS VOID
 AS $$
   from crankshaft import random_seeds
   random_seeds.set_random_seeds(seed_value)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION
     CDB_PyAggS(current_state Numeric[], current_row Numeric[]) 
     returns NUMERIC[] as $$
@@ -35,16 +35,25 @@ CREATE OR REPLACE FUNCTION
     $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
-        SFUNC = CDB_PyAggS,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{}"
-    );
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_pyagg'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
+            SFUNC = CDB_PyAggS,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -82,13 +91,14 @@ AS $$
         target_ids,
         model_params)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL RESTRICTED;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL RESTRICTED;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
       query TEXT,
       variable_name TEXT,
       target_table TEXT,
+      model_name text DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -105,17 +115,51 @@ AS $$
         'learning_rate': learning_rate,
         'min_samples_leaf': min_samples_leaf
     }
-    feature_cols = set(plpy.execute('''
+    all_cols = list(plpy.execute('''
         select * from ({query}) as _w limit 0
-    '''.format(query=query)).colnames()) -  set([variable_name, 'cartodb_id', ])
+    '''.format(query=query)).colnames())
+    feature_cols = [a for a in all_cols
+                    if a not in [variable_name, 'cartodb_id', ]]
     return seg.create_and_predict_segment(
         query,
         variable_name,
         feature_cols,
         target_table,
-        model_params
+        model_params,
+        model_name=model_name
     )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
+
+CREATE OR REPLACE FUNCTION
+  CDB_RetrieveModelParams(
+    model_name text,
+    param_name text
+  )
+RETURNS TABLE(param numeric, feature_name text) AS $$
+
+import pickle
+from collections import Iterable
+
+plan = plpy.prepare('''
+    SELECT model, feature_names FROM model_storage
+    WHERE name = $1;
+''', ['text', ])
+
+try:
+    model_encoded = plpy.execute(plan, [model_name, ])
+except plpy.SPIError as err:
+    plpy.error('ERROR: {}'.format(err))
+plpy.notice(model_encoded[0]['feature_names'])
+model = pickle.loads(
+    model_encoded[0]['model']
+)
+
+res = getattr(model, param_name) 
+if not isinstance(res, Iterable):
+    raise Exception('Cannot return `{}` as a table'.format(param_name))
+return zip(res, model_encoded[0]['feature_names'])
+
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -123,6 +167,7 @@ CREATE OR REPLACE FUNCTION
       variable TEXT,
       feature_columns TEXT[],
       target_query TEXT,
+      model_name TEXT DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -144,9 +189,10 @@ AS $$
         variable,
         feature_columns,
         target_query,
-        model_params
+        model_params,
+        model_name=model_name
     )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION CDB_Gravity(
     IN target_query text,
     IN weight_column text,
@@ -656,7 +702,7 @@ AS $$
   moran = Moran()
   return moran.global_stat(subquery, column_name, w_type,
                            num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local (internal function) - DEPRECATED
 CREATE OR REPLACE FUNCTION
@@ -681,7 +727,7 @@ AS $$
                             num_ngbrs, permutations, geom_col, id_col)
   # remove spatial lag
   return [(r[6], r[0], r[1], r[7], r[5]) for r in result]
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local (internal function)
 CREATE OR REPLACE FUNCTION
@@ -709,7 +755,7 @@ moran = Moran()
 return moran.local_stat(subquery, column_name, w_type,
                         num_ngbrs, permutations, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 -- Moran's I Local (public-facing function)
@@ -836,7 +882,7 @@ AS $$
   # TODO: use named parameters or a dictionary
   return moran.global_rate_stat(subquery, numerator, denominator, w_type,
                                 num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 -- Moran's I Local Rate (internal function) - DEPRECATED
@@ -864,7 +910,7 @@ AS $$
   result = moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
   # remove spatial lag
   return [(r[6], r[0], r[1], r[7], r[4]) for r in result]
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local Rate (public-facing function) - DEPRECATED
 CREATE OR REPLACE FUNCTION
@@ -920,7 +966,7 @@ return moran.local_rate_stat(
     geom_col,
     id_col
 )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Rate
 -- Replaces CDB_AreasOfInterestLocalRate
@@ -1033,7 +1079,7 @@ from crankshaft.clustering import Kmeans
 kmeans = Kmeans()
 return kmeans.spatial(query, no_clusters, no_init)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Non-spatial k-means clustering
 -- query: sql query to retrieve all the needed data
@@ -1063,7 +1109,7 @@ kmeans = Kmeans()
 return kmeans.nonspatial(query, colnames, no_clusters,
                          standardize=standardize,
                          id_col=id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(
@@ -1104,19 +1150,27 @@ BEGIN
 END
 $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
-
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
-        SFUNC = CDB_WeightedMeanS,
-        FINALFUNC = CDB_WeightedMeanF,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{0.0,0.0,0.0}"
-);
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_weightedmean'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
+            SFUNC = CDB_WeightedMeanS,
+            FINALFUNC = CDB_WeightedMeanF,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{0.0,0.0,0.0}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 -- Spatial Markov
 
 -- input table format:
@@ -1146,7 +1200,7 @@ AS $$
 
   ## TODO: use named parameters or a dictionary
   return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- input table format: identical to above but in a predictable format
 -- Sample function call:
@@ -1172,7 +1226,7 @@ $$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
 --   from crankshaft.clustering import moran_local
 --   # TODO: use named parameters or a dictionary
 --   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpython3u;
+-- $$ LANGUAGE plpythonu;
 --
 -- -- input table format:
 -- -- id | geom | date  | measurement
@@ -1198,7 +1252,7 @@ $$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
 --   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)
--- $$ LANGUAGE plpython3u;
+-- $$ LANGUAGE plpythonu;
 -- Based on:
 -- https://github.com/mapbox/polylabel/blob/master/index.js
 -- https://sites.google.com/site/polesofinaccessibility/
@@ -1468,7 +1522,7 @@ AS $$
   from crankshaft.clustering import Getis
   getis = Getis()
   return getis.getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- TODO: make a version that accepts the values as arrays
 
@@ -1808,7 +1862,7 @@ gwr = GWR()
 
 return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 CREATE OR REPLACE FUNCTION
@@ -1826,7 +1880,7 @@ gwr = GWR()
 
 return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 --
 -- Creates N points randomly distributed arround the polygon
 --

release/crankshaft--0.9.1--0.9.2.sql

@@ -4,7 +4,7 @@
 -- Version number of the extension release
 CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
 RETURNS text AS $$
-  SELECT '0.9.0'::text;
+  SELECT '0.9.2'::text;
 $$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE;
 
 -- Internal identifier of the installed extension instence
@@ -21,7 +21,7 @@ _cdb_random_seeds (seed_value INTEGER) RETURNS VOID
 AS $$
   from crankshaft import random_seeds
   random_seeds.set_random_seeds(seed_value)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION
     CDB_PyAggS(current_state Numeric[], current_row Numeric[]) 
     returns NUMERIC[] as $$
@@ -35,16 +35,25 @@ CREATE OR REPLACE FUNCTION
     $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
-        SFUNC = CDB_PyAggS,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{}"
-    );
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_pyagg'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
+            SFUNC = CDB_PyAggS,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -82,13 +91,14 @@ AS $$
         target_ids,
         model_params)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL RESTRICTED;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL RESTRICTED;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
       query TEXT,
       variable_name TEXT,
       target_table TEXT,
+      model_name text DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -105,17 +115,51 @@ AS $$
         'learning_rate': learning_rate,
         'min_samples_leaf': min_samples_leaf
     }
-    feature_cols = set(plpy.execute('''
+    all_cols = list(plpy.execute('''
         select * from ({query}) as _w limit 0
-    '''.format(query=query)).colnames()) -  set([variable_name, 'cartodb_id', ])
+    '''.format(query=query)).colnames())
+    feature_cols = [a for a in all_cols
+                    if a not in [variable_name, 'cartodb_id', ]]
     return seg.create_and_predict_segment(
         query,
         variable_name,
         feature_cols,
         target_table,
-        model_params
+        model_params,
+        model_name=model_name
     )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
+
+CREATE OR REPLACE FUNCTION
+  CDB_RetrieveModelParams(
+    model_name text,
+    param_name text
+  )
+RETURNS TABLE(param numeric, feature_name text) AS $$
+
+import pickle
+from collections import Iterable
+
+plan = plpy.prepare('''
+    SELECT model, feature_names FROM model_storage
+    WHERE name = $1;
+''', ['text', ])
+
+try:
+    model_encoded = plpy.execute(plan, [model_name, ])
+except plpy.SPIError as err:
+    plpy.error('ERROR: {}'.format(err))
+plpy.notice(model_encoded[0]['feature_names'])
+model = pickle.loads(
+    model_encoded[0]['model']
+)
+
+res = getattr(model, param_name) 
+if not isinstance(res, Iterable):
+    raise Exception('Cannot return `{}` as a table'.format(param_name))
+return zip(res, model_encoded[0]['feature_names'])
+
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment(
@@ -123,6 +167,7 @@ CREATE OR REPLACE FUNCTION
       variable TEXT,
       feature_columns TEXT[],
       target_query TEXT,
+      model_name TEXT DEFAULT NULL,
       n_estimators INTEGER DEFAULT 1200,
       max_depth INTEGER DEFAULT 3,
       subsample DOUBLE PRECISION DEFAULT 0.5,
@@ -144,9 +189,10 @@ AS $$
         variable,
         feature_columns,
         target_query,
-        model_params
+        model_params,
+        model_name=model_name
     )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION CDB_Gravity(
     IN target_query text,
     IN weight_column text,
@@ -656,7 +702,7 @@ AS $$
   moran = Moran()
   return moran.global_stat(subquery, column_name, w_type,
                            num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local (internal function) - DEPRECATED
 CREATE OR REPLACE FUNCTION
@@ -681,7 +727,7 @@ AS $$
                             num_ngbrs, permutations, geom_col, id_col)
   # remove spatial lag
   return [(r[6], r[0], r[1], r[7], r[5]) for r in result]
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local (internal function)
 CREATE OR REPLACE FUNCTION
@@ -709,7 +755,7 @@ moran = Moran()
 return moran.local_stat(subquery, column_name, w_type,
                         num_ngbrs, permutations, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 -- Moran's I Local (public-facing function)
@@ -836,7 +882,7 @@ AS $$
   # TODO: use named parameters or a dictionary
   return moran.global_rate_stat(subquery, numerator, denominator, w_type,
                                 num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 -- Moran's I Local Rate (internal function) - DEPRECATED
@@ -864,7 +910,7 @@ AS $$
   result = moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
   # remove spatial lag
   return [(r[6], r[0], r[1], r[7], r[4]) for r in result]
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Local Rate (public-facing function) - DEPRECATED
 CREATE OR REPLACE FUNCTION
@@ -920,7 +966,7 @@ return moran.local_rate_stat(
     geom_col,
     id_col
 )
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Moran's I Rate
 -- Replaces CDB_AreasOfInterestLocalRate
@@ -1033,7 +1079,7 @@ from crankshaft.clustering import Kmeans
 kmeans = Kmeans()
 return kmeans.spatial(query, no_clusters, no_init)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- Non-spatial k-means clustering
 -- query: sql query to retrieve all the needed data
@@ -1063,7 +1109,7 @@ kmeans = Kmeans()
 return kmeans.nonspatial(query, colnames, no_clusters,
                          standardize=standardize,
                          id_col=id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(
@@ -1104,19 +1150,27 @@ BEGIN
 END
 $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
 
-
 -- Create aggregate if it did not exist
-DO $$ BEGIN
-    CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
-        SFUNC = CDB_WeightedMeanS,
-        FINALFUNC = CDB_WeightedMeanF,
-        STYPE = Numeric[],
-        PARALLEL = SAFE,
-        INITCOND = "{0.0,0.0,0.0}"
-);
-EXCEPTION
-    WHEN duplicate_function THEN NULL;
-END $$;
+DO $$
+BEGIN
+    IF NOT EXISTS (
+        SELECT *
+        FROM pg_catalog.pg_proc p
+            LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace
+        WHERE n.nspname = 'cdb_crankshaft'
+            AND p.proname = 'cdb_weightedmean'
+            AND p.proisagg)
+    THEN
+        CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) (
+            SFUNC = CDB_WeightedMeanS,
+            FINALFUNC = CDB_WeightedMeanF,
+            STYPE = Numeric[],
+            PARALLEL = SAFE,
+            INITCOND = "{0.0,0.0,0.0}"
+        );
+    END IF;
+END
+$$ LANGUAGE plpgsql;
 -- Spatial Markov
 
 -- input table format:
@@ -1146,7 +1200,7 @@ AS $$
 
   ## TODO: use named parameters or a dictionary
   return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- input table format: identical to above but in a predictable format
 -- Sample function call:
@@ -1172,7 +1226,7 @@ $$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
 --   from crankshaft.clustering import moran_local
 --   # TODO: use named parameters or a dictionary
 --   return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
--- $$ LANGUAGE plpython3u;
+-- $$ LANGUAGE plpythonu;
 --
 -- -- input table format:
 -- -- id | geom | date  | measurement
@@ -1198,7 +1252,7 @@ $$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
 --   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)
--- $$ LANGUAGE plpython3u;
+-- $$ LANGUAGE plpythonu;
 -- Based on:
 -- https://github.com/mapbox/polylabel/blob/master/index.js
 -- https://sites.google.com/site/polesofinaccessibility/
@@ -1468,7 +1522,7 @@ AS $$
   from crankshaft.clustering import Getis
   getis = Getis()
   return getis.getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 -- TODO: make a version that accepts the values as arrays
 
@@ -1808,7 +1862,7 @@ gwr = GWR()
 
 return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 
 
 CREATE OR REPLACE FUNCTION
@@ -1826,7 +1880,7 @@ gwr = GWR()
 
 return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
 
-$$ LANGUAGE plpython3u VOLATILE PARALLEL UNSAFE;
+$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 --
 -- Creates N points randomly distributed arround the polygon
 --

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.9.0'
-requires = 'plpython3u, postgis'
+default_version = '0.9.5'
+requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

release/python/0.5.2/crankshaft/requirements.txt

@@ -1,5 +1,5 @@
 joblib==0.8.3
-numpy==1.22.0
+numpy==1.6.1
 scipy==0.14.0
 pysal==1.11.2
 scikit-learn==0.14.1

release/python/0.5.2/crankshaft/setup.py

@@ -41,7 +41,7 @@ setup(
     # 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==1.22.0', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+    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'],
 

release/python/0.8.2/crankshaft/requirements.txt

@@ -1,5 +0,0 @@
-joblib==0.9.4
-numpy==1.11.0
-scipy==0.17.0
-pysal==1.14.3
-scikit-learn==0.17.0

release/python/0.9.0/crankshaft/crankshaft/__init__.py

@@ -4,4 +4,4 @@ import crankshaft.clustering
 import crankshaft.space_time_dynamics
 import crankshaft.segmentation
 import crankshaft.regression
-from . import analysis_data_provider
+import analysis_data_provider

release/python/0.9.0/crankshaft/crankshaft/analysis_data_provider.py

@@ -1,6 +1,6 @@
 """class for fetching data"""
 import plpy
-from . import pysal_utils as pu
+import pysal_utils as pu
 
 NULL_VALUE_ERROR = ('No usable data passed to analysis. Check your input rows '
                     'for null values and fill in appropriately.')

release/python/0.9.0/crankshaft/crankshaft/centers/median_center.py

@@ -0,0 +1,76 @@
+"""
+    Based on the Weiszfeld algorithm:
+      https://en.wikipedia.org/wiki/Geometric_median
+"""
+
+
+# import plpy
+import numpy as np
+from numpy.linalg import norm
+
+
+def median_center(tablename, geom_col, num_iters=50, tolerance=0.001):
+
+    query = '''
+        SELECT array_agg(ST_X({geom_col})) As x_coords,
+               array_agg(ST_Y({geom_col})) As y_coords
+          FROM {tablename}
+    '''.format(geom_col=geom_col, tablename=tablename)
+
+    try:
+        resp = plpy.execute(query)
+        data = np.vstack((resp['x_coords'][0],
+                          resp['y_coords'][0])).T
+
+        plpy.notice('coords: %s' % str(coords))
+    except Exception, err:
+        # plpy.error('Analysis failed: %s' % err)
+        print('No plpy')
+        data = np.array([[1.2 * np.random.random() + 10.,
+                          1.1 * (np.random.random() - 1.) + 3.]
+                        for i in range(1, 100)])
+
+    # initialize 'median center' to be the mean
+    coords_center_temp = data.mean(axis=0)
+
+    # plpy.notice('temp_center: %s' % str(coords_center_temp))
+    print('temp_center: %s' % str(coords_center_temp))
+
+    for i in range(0, num_iters):
+        old_coords_center = coords_center_temp.copy()
+        denom = denominator(coords_center_temp, data)
+        coords_center_temp = np.sum([data[j] * numerator(coords_center_temp,
+                                                         data[j])
+                                     for j in range(len(data))], axis=0)
+        coords_center_temp = coords_center_temp / denom
+
+        print("Pass #%d" % i)
+        print("max, min of data: %0.4f, %0.4f" % (data.max(), data.min()))
+        print('temp_center: %s' % str(coords_center_temp))
+        print("Change in center: %0.4f" % np.linalg.norm(old_coords_center -
+                                                         coords_center_temp))
+        print("Center coords: %s" % str(coords_center_temp))
+        print("Objective Function: %0.4f" % obj_func(coords_center_temp, data))
+
+    return coords_center_temp
+
+
+def obj_func(center_coords, data):
+    """
+
+    """
+    return np.linalg.norm(center_coords - data)
+
+
+def numerator(center_coords, data_i):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(center_coords - data_i))
+
+
+def denominator(center_coords, data):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(data - center_coords))

release/python/0.9.0/crankshaft/crankshaft/clustering/__init__.py

@@ -1,4 +1,4 @@
 """Import all functions from for clustering"""
-from .moran import *
-from .kmeans import *
-from .getis import *
+from moran import *
+from kmeans import *
+from getis import *

release/python/0.9.0/crankshaft/crankshaft/clustering/getis.py

@@ -47,4 +47,4 @@ class Getis(object):
         getis = ps.esda.getisord.G_Local(attr_vals, weight,
                                          star=True, permutations=permutations)
 
-        return list(zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order))
+        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)

release/python/0.9.0/crankshaft/crankshaft/clustering/kmeans.py

@@ -28,8 +28,8 @@ class Kmeans(object):
         ids = result[0]['ids']
 
         km = KMeans(n_clusters=no_clusters, n_init=no_init)
-        labels = km.fit_predict(list(zip(xs, ys)))
-        return list(zip(ids, labels))
+        labels = km.fit_predict(zip(xs, ys))
+        return zip(ids, labels)
 
     def nonspatial(self, subquery, colnames, no_clusters=5,
                    standardize=True, id_col='cartodb_id'):
@@ -75,18 +75,18 @@ class Kmeans(object):
         kmeans = KMeans(n_clusters=no_clusters,
                         random_state=0).fit(cluster_columns)
 
-        centers = [json.dumps(dict(list(zip(colnames, c))))
+        centers = [json.dumps(dict(zip(colnames, c)))
                    for c in kmeans.cluster_centers_[kmeans.labels_]]
 
         silhouettes = metrics.silhouette_samples(cluster_columns,
                                                  kmeans.labels_,
                                                  metric='sqeuclidean')
 
-        return list(zip(kmeans.labels_,
+        return zip(kmeans.labels_,
                    centers,
                    silhouettes,
                    [kmeans.inertia_] * kmeans.labels_.shape[0],
-                   data[0]['rowid']))
+                   data[0]['rowid'])
 
 
 # -- Preprocessing steps
@@ -99,7 +99,7 @@ def _extract_columns(data):
     # number of columns minus rowid column
     n_cols = len(data[0]) - 1
     return np.array([data[0]['arr_col{0}'.format(i+1)]
-                     for i in range(n_cols)],
+                     for i in xrange(n_cols)],
                     dtype=float).T
 
 

release/python/0.9.0/crankshaft/crankshaft/clustering/moran.py

@@ -75,7 +75,7 @@ class Moran(object):
         moran_global = ps.esda.moran.Moran(attr_vals, weight,
                                            permutations=permutations)
 
-        return list(zip([moran_global.I], [moran_global.EI]))
+        return zip([moran_global.I], [moran_global.EI])
 
     def local_stat(self, subquery, attr,
                    w_type, num_ngbrs, permutations, geom_col, id_col):
@@ -139,7 +139,7 @@ class Moran(object):
         lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
         lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
 
-        return list(zip(
+        return zip(
             quads,
             lisa.p_sim,
             lag,
@@ -148,7 +148,7 @@ class Moran(object):
             lisa.z,
             lisa.Is,
             weight.id_order
-        ))
+        )
 
     def global_rate_stat(self, subquery, numerator, denominator,
                          w_type, num_ngbrs, permutations, geom_col, id_col):
@@ -194,7 +194,7 @@ class Moran(object):
         lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
                                              permutations=permutations)
 
-        return list(zip([lisa_rate.I], [lisa_rate.EI]))
+        return zip([lisa_rate.I], [lisa_rate.EI])
 
     def local_rate_stat(self, subquery, numerator, denominator,
                         w_type, num_ngbrs, permutations, geom_col, id_col):
@@ -262,7 +262,7 @@ class Moran(object):
         lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
         lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
 
-        return list(zip(
+        return zip(
             quads,
             lisa.p_sim,
             lag,
@@ -271,7 +271,7 @@ class Moran(object):
             lisa.z,
             lisa.Is,
             weight.id_order
-        ))
+        )
 
     def local_bivariate_stat(self, subquery, attr1, attr2,
                              permutations, geom_col, id_col,
@@ -303,7 +303,7 @@ class Moran(object):
         # find clustering of significance
         lisa_sig = quad_position(lisa.q)
 
-        return list(zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order))
+        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
 
 # Low level functions ----------------------------------------
 

release/python/0.9.0/crankshaft/crankshaft/model_storage/__init__.py

@@ -0,0 +1 @@
+from core import set_model, get_model, create_model_table

release/python/0.9.0/crankshaft/crankshaft/model_storage/core.py

@@ -0,0 +1,86 @@
+import time
+import plpy
+import pickle
+from petname import generate
+
+def create_model_table():
+    q = '''
+        create table if not exists model_storage(
+            description text,
+            name text unique,
+            model bytea,
+            feature_names text[],
+            date_created timestamptz,
+            id serial primary key);
+        '''
+    plpy.notice(q)
+    plan = plpy.prepare(q)
+    resp = plpy.execute(plan)
+    plpy.notice('Model table successfully created')
+    plpy.notice(str(resp))
+
+def get_model(model_name):
+    """retrieve model if it exists"""
+    try:
+        plan = plpy.prepare('''
+            SELECT model FROM model_storage
+            WHERE name = $1;
+        ''', ['text', ])
+        model_encoded = plpy.execute(plan, [model_name, ])
+        if len(model_encoded) == 1:
+            model = pickle.loads(
+                model_encoded[0]['model']
+            )
+            plpy.notice('Model successfully loaded')
+        else:
+            plpy.notice('Model not found, or too many models '
+                        '({})'.format(len(model_encoded)))
+            model = None
+    except plpy.SPIError as err:
+        plpy.error('ERROR: {}'.format(err))
+
+    return model
+
+def set_model(model, model_name, feature_names):
+    """stores the model in the table model_storage"""
+    if model_name is None:
+	model_name = generate(words=2, separator='_', letters=8)
+	existing_names = plpy.execute('''
+	    SELECT array_agg(name) as name
+	    FROM model_storage
+	''')
+        plpy.notice('nrows: {}'.format(existing_names.nrows()))
+	plpy.notice('MODEL NAME: {}'.format(model_name))
+        plpy.notice('LEN of ms: {}'.format(len(existing_names)))
+        plpy.notice('existing_names: {}'.format(str(existing_names)))
+        plpy.notice('existing_names: {}'.format(str(existing_names[0]['name'])))
+        plpy.notice('type existing_names: {}'.format(type(existing_names[0]['name'])))
+        if existing_names[0]['name'] is not None:
+            while model_name in existing_names[0]['name']:
+                model_name = generate(words=2, separator='_', letters=10)
+                plpy.notice(model_name)
+
+    # store model
+    try:
+	plan = plpy.prepare('''
+	    INSERT INTO model_storage(description, name, model, feature_names, date_created)
+	    VALUES (
+	      $1,
+	      $2,
+	      $3,
+              $4::text[],
+	      to_timestamp($5));
+	''', ['text', 'text', 'bytea', 'text', 'numeric'])
+        plpy.notice('{%s}' % ','.join(feature_names))
+        plpy.notice(feature_names)
+	plpy.execute(
+	    plan,
+	    [' '.join(m.strip() for m in model.__repr__().split('\n')),
+	     model_name,
+	     pickle.dumps(model),
+             '{%s}' % ','.join(feature_names),
+	     time.time()]
+	)
+	plpy.notice('model successfully stored as {}'.format(model_name))
+    except plpy.SPIError as err:
+	plpy.notice('ERROR: {}\nt: {}'.format(err, time.time()))

release/python/0.9.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -27,7 +27,7 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
     """
 
     neighbors = {x['id']: x['neighbors'] for x in query_res}
-    print('len of neighbors: %d' % len(neighbors))
+    print 'len of neighbors: %d' % len(neighbors)
 
     built_weight = ps.W(neighbors)
     built_weight.transform = 'r'

release/python/0.9.0/crankshaft/crankshaft/regression/glm/__init__.py

@@ -1,4 +1,4 @@
-from . import glm
-from . import family
-from . import utils
-from . import iwls
+import glm
+import family
+import utils
+import iwls

release/python/0.9.0/crankshaft/crankshaft/regression/glm/base.py

@@ -1,9 +1,8 @@
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats
-from .utils import cache_readonly
-from functools import reduce
+from utils import cache_readonly
 
 class Results(object):
     """

release/python/0.9.0/crankshaft/crankshaft/regression/glm/family.py

@@ -7,8 +7,8 @@ The one parameter exponential family distributions used by GLM.
 
 import numpy as np
 from scipy import special
-from . import links as L
-from . import varfuncs as V
+import links as L
+import varfuncs as V
 FLOAT_EPS = np.finfo(float).eps
 
 

release/python/0.9.0/crankshaft/crankshaft/regression/glm/glm.py

@@ -3,10 +3,10 @@ import numpy as np
 import numpy.linalg as la
 from pysal.spreg.utils import RegressionPropsY, spdot
 import pysal.spreg.user_output as USER
-from .utils import cache_readonly
-from .base import LikelihoodModelResults
-from . import family
-from .iwls import iwls
+from utils import cache_readonly
+from base import LikelihoodModelResults
+import family
+from iwls import iwls
 
 __all__ = ['GLM']
 

release/python/0.9.0/crankshaft/crankshaft/regression/glm/iwls.py

@@ -3,7 +3,7 @@ import numpy.linalg as la
 from scipy import sparse as sp
 from scipy.sparse import linalg as spla
 from pysal.spreg.utils import spdot, spmultiply
-from .family import Binomial, Poisson
+from family import Binomial, Poisson
 
 def _compute_betas(y, x):
     """
@@ -49,7 +49,7 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
     if isinstance(family, Binomial):
         y = family.link._clean(y)
     if isinstance(family, Poisson):
-        y_off = y/offset
+    	y_off = y/offset
         y_off = family.starting_mu(y_off)
         v = family.predict(y_off)
         mu = family.starting_mu(y)
@@ -58,13 +58,13 @@ def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=
         v = family.predict(mu)
     
     while diff > tol and n_iter < max_iter:
-        n_iter += 1
+    	n_iter += 1
         w = family.weights(mu)
         z = v + (family.link.deriv(mu)*(y-mu))
         w = np.sqrt(w)
         if type(x) != np.ndarray:
-            w = sp.csr_matrix(w)
-            z = sp.csr_matrix(z)
+        	w = sp.csr_matrix(w)
+        	z = sp.csr_matrix(z)
         wx = spmultiply(x, w, array_out=False)
         wz = spmultiply(z, w, array_out=False)
         if wi is None:

release/python/0.9.0/crankshaft/crankshaft/regression/glm/utils.py

@@ -1,5 +1,5 @@
 
-
+from __future__ import absolute_import, print_function
 import numpy as np
 import warnings
 
@@ -17,7 +17,7 @@ try:
     from scipy.lib._version import NumpyVersion
 except ImportError:
     import re
-    string_types = str 
+    string_types = basestring 
 
     class NumpyVersion():
         """Parse and compare numpy version strings.

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/__init__.py

@@ -1 +1 @@
-from .base import *
+from base import *

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/__init__.py

@@ -1,4 +1,4 @@
-from . import gwr
-from . import sel_bw
-from . import diagnostics
-from . import kernels
+import gwr
+import sel_bw
+import diagnostics
+import kernels

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/gwr.py

@@ -7,8 +7,8 @@ __author__ = "Taylor Oshan Tayoshan@gmail.com"
 import numpy as np
 import numpy.linalg as la
 from scipy.stats import t
-from .kernels import *
-from .diagnostics import get_AIC, get_AICc, get_BIC
+from kernels import *
+from diagnostics import get_AIC, get_AICc, get_BIC
 import pysal.spreg.user_output as USER
 from crankshaft.regression.glm.family import Gaussian, Binomial, Poisson
 from crankshaft.regression.glm.glm import GLM, GLMResults
@@ -156,7 +156,7 @@ class GWR(GLM):
         self.kernel = kernel
         self.fixed = fixed
         if offset is None:
-            self.offset = np.ones((self.n, 1))
+        	self.offset = np.ones((self.n, 1))
         else:
             self.offset = offset * 1.0
         self.fit_params = {}
@@ -169,7 +169,7 @@ class GWR(GLM):
     def _build_W(self, fixed, kernel, coords, bw, points=None):
         if fixed:
             try:
-                W = fk[kernel](coords, bw, points)
+            	W = fk[kernel](coords, bw, points)
             except:
                 raise TypeError('Unsupported kernel function  ', kernel)
         else:
@@ -177,6 +177,7 @@ class GWR(GLM):
                 W = ak[kernel](coords, bw, points)
             except:
                 raise TypeError('Unsupported kernel function  ', kernel)
+        
         return W
 
     def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'):
@@ -217,7 +218,8 @@ class GWR(GLM):
             p = np.zeros((m, 1))
             for i in range(m):
                 wi = self.W[i].reshape((-1,1))
-                rslt = iwls(self.y, self.X, self.family, self.offset, ini_params, tol, max_iter, wi=wi)
+            	rslt = iwls(self.y, self.X, self.family, self.offset,
+            	        ini_params, tol, max_iter, wi=wi)
                 params[i,:] = rslt[0].T
                 predy[i] = rslt[1][i]
                 v[i] = rslt[2][i]
@@ -257,7 +259,7 @@ class GWR(GLM):
         fit_params    : dict
                         key-value pairs of parameters that will be passed into fit method to define estimation
                         routine; see fit method for more details
-
+                        
         """
         if (exog_scale is None) & (exog_resid is None):
             train_gwr = self.fit(**fit_params)
@@ -496,7 +498,7 @@ class GWRResults(GLMResults):
 
         """
         if exog_scale is not None:
-            return cov*exog_scale
+        	return cov*exog_scale
         else:
             return cov*self.scale
     
@@ -520,7 +522,7 @@ class GWRResults(GLMResults):
         weighted mean of y
         """
         if self.model.points is not None:
-            n = len(self.model.points)
+        	n = len(self.model.points)
         else:
             n = self.n
         off = self.offset.reshape((-1,1))
@@ -543,13 +545,13 @@ class GWRResults(GLMResults):
 
         """
         if self.model.points is not None:
-            n = len(self.model.points)
+        	n = len(self.model.points)
         else:
             n = self.n
         TSS = np.zeros(shape=(n,1))
         for i in range(n):
-            TSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) *
-                    (self.y.reshape((-1,1)) - self.y_bar[i])**2)
+	        TSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) *
+	                (self.y.reshape((-1,1)) - self.y_bar[i])**2)
         return TSS
 
     @cache_readonly
@@ -563,15 +565,15 @@ class GWRResults(GLMResults):
         relationships.
         """
         if self.model.points is not None:
-            n = len(self.model.points)
-            resid = self.model.exog_resid.reshape((-1,1))
+        	n = len(self.model.points)
+        	resid = self.model.exog_resid.reshape((-1,1))
         else:
             n = self.n
             resid = self.resid_response.reshape((-1,1))
-        RSS = np.zeros(shape=(n,1))
+    	RSS = np.zeros(shape=(n,1))
         for i in range(n):
             RSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1))
-                    * resid**2)
+	                * resid**2)
         return RSS
 
     @cache_readonly
@@ -617,10 +619,10 @@ class GWRResults(GLMResults):
         """
         if isinstance(self.family, (Poisson, Binomial)):
             return self.resid_ss/(self.n - 2.0*self.tr_S +
-                    self.tr_STS) #could be changed to SWSTW - nothing to test against
+	                self.tr_STS) #could be changed to SWSTW - nothing to test against
         else:
             return self.resid_ss/(self.n - 2.0*self.tr_S +
-                    self.tr_STS) #could be changed to SWSTW - nothing to test against
+	                self.tr_STS) #could be changed to SWSTW - nothing to test against
     @cache_readonly
     def sigma2_ML(self):
         """
@@ -673,14 +675,14 @@ class GWRResults(GLMResults):
         Note: in (9.11), p should be tr(S), that is, the effective number of parameters
         """
         return self.std_res**2 * self.influ / (self.tr_S * (1.0-self.influ))
-
+    
     @cache_readonly
     def deviance(self):
         off = self.offset.reshape((-1,1)).T
         y = self.y
         ybar = self.y_bar
         if isinstance(self.family, Gaussian):
-            raise NotImplementedError('deviance not currently used for Gaussian')
+        	raise NotImplementedError('deviance not currently used for Gaussian')
         elif isinstance(self.family, Poisson):
             dev = np.sum(2.0*self.W*(y*np.log(y/(ybar*off))-(y-ybar*off)),axis=1)
         elif isinstance(self.family, Binomial):
@@ -690,7 +692,7 @@ class GWRResults(GLMResults):
     @cache_readonly
     def resid_deviance(self):
         if isinstance(self.family, Gaussian):
-            raise NotImplementedError('deviance not currently used for Gaussian')
+        	raise NotImplementedError('deviance not currently used for Gaussian')
         else:
             off = self.offset.reshape((-1,1)).T
             y = self.y
@@ -708,7 +710,7 @@ class GWRResults(GLMResults):
         manual. Equivalent to 1 - (deviance/null deviance)
         """
         if isinstance(self.family, Gaussian):
-            raise NotImplementedError('Not implemented for Gaussian')
+        	raise NotImplementedError('Not implemented for Gaussian')
         else:
             return 1.0 - (self.resid_deviance/self.deviance)
 
@@ -831,8 +833,8 @@ class GWRResults(GLMResults):
     def predictions(self):
         P = self.model.P
         if P is None:
-            raise NotImplementedError('predictions only avaialble if predict'
-            'method called on GWR model')
+        	raise NotImplementedError('predictions only avaialble if predict'
+        	'method called on GWR model')
         else:
             predictions = np.sum(P*self.params, axis=1).reshape((-1,1)) 
         return predictions
@@ -985,7 +987,7 @@ class FBGWR(GWR):
         self.fixed = fixed
         self.constant = constant
         if constant:
-            self.X = USER.check_constant(self.X)
+        	self.X = USER.check_constant(self.X)
 
     def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'):
         """

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/search.py

@@ -47,14 +47,14 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
     while np.abs(diff) > tol and iters < max_iter:
         iters += 1
         if int_score:
-            b = np.round(b)
-            d = np.round(d)
+        	b = np.round(b)
+        	d = np.round(d)
 
         score_a = function(a)
         score_b = function(b)
         score_c = function(c)
         score_d = function(d)
-
+        
         if score_b <= score_d:
             opt_val = b
             opt_score = score_b
@@ -73,7 +73,7 @@ def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
                 #d = np.round(b)
         
         #if int_score:
-        #    opt_val = np.round(opt_val)
+        #	opt_val = np.round(opt_val)
         output.append((opt_val, opt_score))
         diff = score_b - score_d
         score = opt_score
@@ -146,7 +146,7 @@ def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
         gwr_func, bw_func, sel_func):
     if init:
         bw = sel_func(bw_func(y, X))
-        print(bw)
+        print bw
         optim_model = gwr_func(y, X, bw)
         err = optim_model.resid_response.reshape((-1,1))
         est = optim_model.params
@@ -198,7 +198,7 @@ def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
             new_rss = np.sum((y - predy)**2)
             score = np.abs((new_rss - rss)/new_rss)
             rss = new_rss
-        print(score)
+        print score
         scores.append(score)
         delta = score
         BWs.append(bws) 

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/sel_bw.py

@@ -8,12 +8,12 @@
 
 __author__ = "Taylor Oshan Tayoshan@gmail.com"
 
-from .kernels import *
-from .search import golden_section, equal_interval, flexible_bw
-from .gwr import GWR
+from kernels import *
+from search import golden_section, equal_interval, flexible_bw
+from gwr import GWR
 from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
 import pysal.spreg.user_output as USER
-from .diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from diagnostics import get_AICc, get_AIC, get_BIC, get_CV
 from scipy.spatial.distance import pdist, squareform
 from pysal.common import KDTree
 import numpy as np
@@ -197,7 +197,7 @@ class Sel_BW(object):
         
         if self.fb:
             self._fbw()
-            print(self.bw[1])
+            print self.bw[1]
             self.XB = self.bw[4]
             self.err = self.bw[5]
         else:

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py

@@ -14,7 +14,7 @@ import pysal
 class TestGWRGaussian(unittest.TestCase):
     def setUp(self):
         data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
-        self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
         self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
         rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
         pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 
@@ -56,10 +56,10 @@ class TestGWRGaussian(unittest.TestCase):
         BIC = get_BIC(rslt)
         CV = get_CV(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 894.0)
-        self.assertAlmostEqual(np.floor(AIC), 890.0)
-        self.assertAlmostEqual(np.floor(BIC), 944.0)
-        self.assertAlmostEqual(np.round(CV,2), 18.25)
+        self.assertAlmostEquals(np.floor(AICc), 894.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 944.0)
+        self.assertAlmostEquals(np.round(CV,2), 18.25)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@@ -107,10 +107,10 @@ class TestGWRGaussian(unittest.TestCase):
         BIC = get_BIC(rslt)
         CV = get_CV(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 896.0)
-        self.assertAlmostEqual(np.floor(AIC), 892.0)
-        self.assertAlmostEqual(np.floor(BIC), 941.0)
-        self.assertAlmostEqual(np.around(CV, 2), 19.19)
+        self.assertAlmostEquals(np.floor(AICc), 896.0)
+        self.assertAlmostEquals(np.floor(AIC), 892.0)
+        self.assertAlmostEquals(np.floor(BIC), 941.0)
+        self.assertAlmostEquals(np.around(CV, 2), 19.19)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@@ -159,10 +159,10 @@ class TestGWRGaussian(unittest.TestCase):
         BIC = get_BIC(rslt)
         CV = get_CV(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 895.0)
-        self.assertAlmostEqual(np.floor(AIC), 890.0)
-        self.assertAlmostEqual(np.floor(BIC), 943.0)
-        self.assertAlmostEqual(np.around(CV, 2), 18.21)
+        self.assertAlmostEquals(np.floor(AICc), 895.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 943.0)
+        self.assertAlmostEquals(np.around(CV, 2), 18.21)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@@ -211,10 +211,10 @@ class TestGWRGaussian(unittest.TestCase):
         BIC = get_BIC(rslt)
         CV = get_CV(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc),  896)
-        self.assertAlmostEqual(np.floor(AIC), 894.0)
-        self.assertAlmostEqual(np.floor(BIC), 922.0)
-        self.assertAlmostEqual(np.around(CV, 2), 17.91)
+        self.assertAlmostEquals(np.floor(AICc),  896)
+        self.assertAlmostEquals(np.floor(AIC), 894.0)
+        self.assertAlmostEquals(np.floor(BIC), 922.0)
+        self.assertAlmostEquals(np.around(CV, 2), 17.91)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@@ -314,7 +314,7 @@ class TestGWRGaussian(unittest.TestCase):
 class TestGWRPoisson(unittest.TestCase):
     def setUp(self):
         data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur')
-        self.coords = list(zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID')))
+        self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))
         self.y = np.array(data.by_col('db2564')).reshape((-1,1))
         self.off = np.array(data.by_col('eb2564')).reshape((-1,1))
         OCC  = np.array(data.by_col('OCC_TEC')).reshape((-1,1))
@@ -355,9 +355,9 @@ class TestGWRPoisson(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 13294.0)
-        self.assertAlmostEqual(np.floor(AIC), 13247.0)
-        self.assertAlmostEqual(np.floor(BIC), 13485.0)
+        self.assertAlmostEquals(np.floor(AICc), 13294.0)
+        self.assertAlmostEquals(np.floor(AIC), 13247.0)
+        self.assertAlmostEquals(np.floor(BIC), 13485.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03)
@@ -404,9 +404,9 @@ class TestGWRPoisson(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 13285)
-        self.assertAlmostEqual(np.floor(AIC), 13259.0)
-        self.assertAlmostEqual(np.floor(BIC), 13442.0)
+        self.assertAlmostEquals(np.floor(AICc), 13285)
+        self.assertAlmostEquals(np.floor(AIC), 13259.0)
+        self.assertAlmostEquals(np.floor(BIC), 13442.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@@ -452,9 +452,9 @@ class TestGWRPoisson(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 367.0)
-        self.assertAlmostEqual(np.floor(AIC), 361.0)
-        self.assertAlmostEqual(np.floor(BIC), 451.0)
+        self.assertAlmostEquals(np.floor(AICc), 367.0)
+        self.assertAlmostEquals(np.floor(AIC), 361.0)
+        self.assertAlmostEquals(np.floor(BIC), 451.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
                 atol=1e-02)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
@@ -511,9 +511,9 @@ class TestGWRPoisson(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 11283.0)
-        self.assertAlmostEqual(np.floor(AIC), 11211.0)
-        self.assertAlmostEqual(np.floor(BIC), 11497.0)
+        self.assertAlmostEquals(np.floor(AICc), 11283.0)
+        self.assertAlmostEquals(np.floor(AIC), 11211.0)
+        self.assertAlmostEquals(np.floor(BIC), 11497.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@@ -559,9 +559,9 @@ class TestGWRPoisson(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 21070.0)
-        self.assertAlmostEqual(np.floor(AIC), 21069.0)
-        self.assertAlmostEqual(np.floor(BIC), 21111.0)
+        self.assertAlmostEquals(np.floor(AICc), 21070.0)
+        self.assertAlmostEquals(np.floor(AIC), 21069.0)
+        self.assertAlmostEquals(np.floor(BIC), 21111.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@@ -583,7 +583,7 @@ class TestGWRPoisson(unittest.TestCase):
 class TestGWRBinomial(unittest.TestCase):
     def setUp(self):
         data = pysal.open(pysal.examples.get_path('landslides.csv'))
-        self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
         self.y = np.array(data.by_col('Landslid')).reshape((-1,1))
         ELEV  = np.array(data.by_col('Elev')).reshape((-1,1))
         SLOPE = np.array(data.by_col('Slope')).reshape((-1,1)) 
@@ -630,9 +630,9 @@ class TestGWRBinomial(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 275.0)
-        self.assertAlmostEqual(np.floor(AIC), 271.0)
-        self.assertAlmostEqual(np.floor(BIC), 349.0)
+        self.assertAlmostEquals(np.floor(AICc), 275.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 349.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@@ -693,9 +693,9 @@ class TestGWRBinomial(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 277.0)
-        self.assertAlmostEqual(np.floor(AIC), 271.0)
-        self.assertAlmostEqual(np.floor(BIC), 358.0)
+        self.assertAlmostEquals(np.floor(AICc), 277.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 358.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@@ -756,9 +756,9 @@ class TestGWRBinomial(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 276.0)
-        self.assertAlmostEqual(np.floor(AIC), 272.0)
-        self.assertAlmostEqual(np.floor(BIC), 341.0)
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 272.0)
+        self.assertAlmostEquals(np.floor(BIC), 341.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@@ -819,9 +819,9 @@ class TestGWRBinomial(unittest.TestCase):
         AIC = get_AIC(rslt)
         BIC = get_BIC(rslt)
         
-        self.assertAlmostEqual(np.floor(AICc), 276.0)
-        self.assertAlmostEqual(np.floor(AIC), 273.0)
-        self.assertAlmostEqual(np.floor(BIC), 331.0)
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 273.0)
+        self.assertAlmostEquals(np.floor(BIC), 331.0)
         np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
         np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
         np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py

@@ -12,7 +12,7 @@ class TestKernels(unittest.TestCase):
         y = np.arange(5,0, -1)
         np.random.shuffle(x)
         np.random.shuffle(y)
-        self.coords = np.array(list(zip(x, y)))
+        self.coords = np.array(zip(x, y))
         self.fix_gauss_kern = np.array([
         [ 1.        ,  0.38889556,  0.48567179,  0.48567179,  0.89483932],
         [ 0.38889556,  1.        ,  0.89483932,  0.64118039,  0.48567179],

release/python/0.9.0/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py

@@ -13,7 +13,7 @@ import pysal
 class TestSelBW(unittest.TestCase):
     def setUp(self):
         data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
-        self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
         self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
         rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
         pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 

release/python/0.9.0/crankshaft/crankshaft/regression/gwr_cs.py

@@ -2,8 +2,8 @@
     Geographically weighted regression
 """
 import numpy as np
-from .gwr.base.gwr import GWR as PySAL_GWR
-from .gwr.base.sel_bw import Sel_BW
+from gwr.base.gwr import GWR as PySAL_GWR
+from gwr.base.sel_bw import Sel_BW
 import json
 from crankshaft.analysis_data_provider import AnalysisDataProvider
 import plpy
@@ -48,7 +48,7 @@ class GWR:
         # x, y are centroids of input geometries
         x = np.array(query_result[0]['x'], dtype=np.float)
         y = np.array(query_result[0]['y'], dtype=np.float)
-        coords = list(zip(x, y))
+        coords = zip(x, y)
 
         # extract dependent variable
         Y = np.array(query_result[0]['dep_var'], dtype=np.float).reshape((-1, 1))
@@ -88,7 +88,7 @@ class GWR:
         bw = np.repeat(float(bw), n)
 
         # create lists of json objs for model outputs
-        for idx in range(n):
+        for idx in xrange(n):
             coeffs.append(json.dumps({var: model.params[idx, k]
                                       for k, var in enumerate(ind_vars)}))
             stand_errs.append(json.dumps({var: model.bse[idx, k]
@@ -99,8 +99,8 @@ class GWR:
                     json.dumps({var: filtered_t[idx, k]
                                 for k, var in enumerate(ind_vars)}))
 
-        return list(zip(coeffs, stand_errs, t_vals, filtered_t_vals,
-                   predicted, residuals, r_squared, bw, rowid))
+        return zip(coeffs, stand_errs, t_vals, filtered_t_vals,
+                   predicted, residuals, r_squared, bw, rowid)
 
     def gwr_predict(self, subquery, dep_var, ind_vars,
                     bw=None, fixed=False, kernel='bisquare',
@@ -133,7 +133,7 @@ class GWR:
 
         x = np.array(query_result[0]['x'], dtype=np.float)
         y = np.array(query_result[0]['y'], dtype=np.float)
-        coords = np.array(list(zip(x, y)), dtype=np.float)
+        coords = np.array(zip(x, y), dtype=np.float)
 
         # extract dependent variable
         Y = np.array(query_result[0]['dep_var']).reshape((-1, 1))
@@ -190,7 +190,7 @@ class GWR:
         predicted = model.predy.flatten()
 
         m = len(model.predy)
-        for idx in range(m):
+        for idx in xrange(m):
             coeffs.append(json.dumps({var: model.params[idx, k]
                                       for k, var in enumerate(ind_vars)}))
             stand_errs.append(json.dumps({var: model.bse[idx, k]
@@ -198,5 +198,5 @@ class GWR:
             t_vals.append(json.dumps({var: model.tvalues[idx, k]
                                       for k, var in enumerate(ind_vars)}))
 
-        return list(zip(coeffs, stand_errs, t_vals,
-                   r_squared, predicted, rowid[test]))
+        return zip(coeffs, stand_errs, t_vals,
+                   r_squared, predicted, rowid[test])

release/python/0.9.0/crankshaft/crankshaft/segmentation/__init__.py

@@ -1,2 +1,2 @@
 """Import all functions from for segmentation"""
-from .segmentation import *
+from segmentation import *

release/python/0.9.0/crankshaft/crankshaft/segmentation/segmentation.py

@@ -2,11 +2,14 @@
 Segmentation creation and prediction
 """
 
+import pickle
+import plpy
 import numpy as np
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn import metrics
 from sklearn.cross_validation import train_test_split
 from crankshaft.analysis_data_provider import AnalysisDataProvider
+from crankshaft import model_storage
 
 # NOTE: added optional param here
 
@@ -47,10 +50,11 @@ class Segmentation(object):
                                       model_parameters, 0.2)
         prediction = model.predict(target_features)
         accuracy_array = [accuracy] * prediction.shape[0]
-        return list(zip(target_ids, prediction, accuracy_array))
+        return zip(target_ids, prediction, accuracy_array)
 
     def create_and_predict_segment(self, query, variable, feature_columns,
                                    target_query, model_params,
+                                   model_name=None,
                                    id_col='cartodb_id'):
         """
         generate a segment with machine learning
@@ -70,16 +74,24 @@ class Segmentation(object):
         (target, features, target_mean,
             feature_means) = self.clean_data(query, variable, feature_columns)
 
-        model, accuracy = train_model(target, features, model_params, 0.2)
+        model_storage.create_model_table()
+
+        # find model if it exists and is specified
+        if model_name is not None:
+            model = model_storage.get_model(model_name)
+
+        if locals().get('model') is None:
+            model, accuracy = train_model(target, features, model_params, 0.2)
+
         result = self.predict_segment(model, feature_columns, target_query,
                                       feature_means)
         accuracy_array = [accuracy] * result.shape[0]
 
         rowid = self.data_provider.get_segmentation_data(params)
-        '''
-        rowid = [{'ids': [2.9, 4.9, 4, 5, 6]}]
-        '''
-        return list(zip(rowid[0]['ids'], result, accuracy_array))
+
+        # store the model for later use
+        model_storage.set_model(model, model_name, feature_columns)
+        return zip(rowid[0]['ids'], result, accuracy_array)
 
     def predict_segment(self, model, feature_columns, target_query,
                         feature_means):

release/python/0.9.0/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -1,2 +1,2 @@
 """Import all functions from clustering libraries."""
-from .markov import *
+from markov import *

release/python/0.9.0/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -91,7 +91,7 @@ class Markov(object):
         trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
 
         # output the results
-        return list(zip(trend, trend_up, trend_down, volatility, weights.id_order))
+        return zip(trend, trend_up, trend_down, volatility, weights.id_order)
 
 
 
@@ -140,7 +140,7 @@ def rebin_data(time_data, num_time_per_bin):
 
     return np.array(
       [time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
-       for i in range(int(n_max))]).T
+       for i in range(n_max)]).T
 
 
 def get_prob_dist(transition_matrix, lag_indices, unit_indices):

release/python/0.9.0/crankshaft/requirements.txt

@@ -1,5 +1,5 @@
-joblib==0.9.4
-numpy==1.11.0
-scipy==0.17.0
+joblib==0.8.3
+numpy==1.6.1
+scipy==0.14.0
 pysal==1.14.3
-scikit-learn==0.17.0
+scikit-learn==0.14.1

release/python/0.9.0/crankshaft/setup.py

@@ -10,7 +10,7 @@ from setuptools import setup, find_packages
 setup(
     name='crankshaft',
 
-    version='0.9.0',
+    version='0.0.0',
 
     description='CartoDB Spatial Analysis Python Library',
 
@@ -26,7 +26,7 @@ setup(
         'Intended Audience :: Mapping comunity',
         'Topic :: Maps :: Mapping Tools',
         'License :: OSI Approved :: MIT License',
-        'Programming Language :: Python',
+        'Programming Language :: Python :: 2.7',
     ],
 
     keywords='maps mapping tools spatial analysis geostatistics',
@@ -41,7 +41,7 @@ setup(
     # 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==0.9.4', 'numpy==1.11.0', 'scipy==0.17.0', 'pysal==1.14.3', 'scikit-learn==0.17.0'],
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.14.3', 'scikit-learn==0.14.1', 'petname==2.2'],
 
     requires=['pysal', 'numpy', 'sklearn'],
 

release/python/0.9.0/crankshaft/setup.py-r

@@ -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='0.0.0',
+
+    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==1.14.3', 'scikit-learn==0.14.1', 'petname==2.2'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.9.0/crankshaft/test/fixtures/optim.json

@@ -0,0 +1,6 @@
+{
+    "production_col": [10, 10, 10],
+    "capacity_col": [0.09, 0.31],
+    "marginal_col": [5, 5],
+    "pairwise": [[1, 2, 3], [3, 2, 1]]
+}

release/python/0.9.0/crankshaft/test/test_clustering_moran.py

@@ -72,7 +72,7 @@ class MoranTest(unittest.TestCase):
         result = moran.local_stat('subquery', 'value',
                                   'knn', 5, 99, 'the_geom', 'cartodb_id')
         result = [(row[0], row[6]) for row in result]
-        zipped_values = list(zip(result, self.moran_data))
+        zipped_values = zip(result, self.moran_data)
 
         for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
             self.assertAlmostEqual(res_val, exp_val)
@@ -91,7 +91,7 @@ class MoranTest(unittest.TestCase):
                                        'knn', 5, 99, 'the_geom', 'cartodb_id')
         result = [(row[0], row[6]) for row in result]
 
-        zipped_values = list(zip(result, self.moran_data))
+        zipped_values = zip(result, self.moran_data)
 
         for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
             self.assertAlmostEqual(res_val, exp_val)

release/python/0.9.0/crankshaft/test/test_regression_gwr.py

@@ -86,7 +86,7 @@ class GWRTest(unittest.TestCase):
 
         # unpack response
         coeffs, stand_errs, t_vals, t_vals_filtered, predicteds, \
-            residuals, r_squareds, bws, rowids = list(zip(*gwr_resp))
+            residuals, r_squareds, bws, rowids = zip(*gwr_resp)
 
         # prepare for comparision
         coeff_known_pctpov = self.knowns['est_pctpov']
@@ -98,13 +98,13 @@ class GWRTest(unittest.TestCase):
         # test pctpov coefficient estimates
         for idx, val in enumerate(coeff_known_pctpov):
             resp_idx = rowids.index(ids[idx])
-            self.assertAlmostEqual(val,
+            self.assertAlmostEquals(val,
                                     json.loads(coeffs[resp_idx])['pctpov'],
                                     places=4)
         # test pctrural tvals
         for idx, val in enumerate(tval_known_pctblack):
             resp_idx = rowids.index(ids[idx])
-            self.assertAlmostEqual(val,
+            self.assertAlmostEquals(val,
                                     json.loads(t_vals[resp_idx])['pctrural'],
                                     places=4)
 
@@ -119,7 +119,7 @@ class GWRTest(unittest.TestCase):
 
         # unpack response
         coeffs, stand_errs, t_vals, \
-            r_squareds, predicteds, rowid = list(zip(*gwr_resp))
+            r_squareds, predicteds, rowid = zip(*gwr_resp)
         threshold = 0.01
 
         for i, idx in enumerate(self.idx_ids_of_unknowns):

release/python/0.9.0/crankshaft/test/test_segmentation.py

@@ -66,7 +66,7 @@ class SegmentationTest(unittest.TestCase):
         test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
         result = replace_nan_with_mean(test_array, means=None)[0]
         expectation = np.array([1.2, 2.2, 3.2, 2.2, 2.2], dtype=float)
-        self.assertEqual(sorted(result), sorted(expectation))
+        self.assertItemsEqual(result, expectation)
 
     def test_create_and_predict_segment(self):
         """test segmentation.test_create_and_predict"""
@@ -118,7 +118,7 @@ class SegmentationTest(unittest.TestCase):
             model_parameters,
             id_col='cartodb_id')
         results = [(row[1], row[2]) for row in result]
-        zipped_values = list(zip(results, self.result_seg))
+        zipped_values = zip(results, self.result_seg)
         pre_res = [r[0] for r in self.true_result]
         acc_res = [r[1] for r in self.result_seg]
 

release/python/0.9.0/crankshaft/test/test_space_time_dynamics.py

@@ -98,7 +98,7 @@ class SpaceTimeTests(unittest.TestCase):
 
         self.assertTrue(result is not None)
         result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
-        print(result[0])
+        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]

release/python/0.9.0/crankshaft/tools/setup.py

@@ -0,0 +1,15 @@
+from test.helper import plpy, fixture_file
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import json
+import crankshaft
+
+class RawDataProvider(AnalysisDataProvider):
+    def __init__(self, fixturedata):
+        self.your_algo_data = fixturedata
+    def get_moran(self, params):
+        """
+          Replace this function name with the one used in your algorithm,
+          and make sure to use the same function signature that is written
+          for this algo in analysis_data_provider.py
+        """
+        return self.your_algo_data

release/python/0.9.1/crankshaft/crankshaft/centers/median_center.py

@@ -0,0 +1,76 @@
+"""
+    Based on the Weiszfeld algorithm:
+      https://en.wikipedia.org/wiki/Geometric_median
+"""
+
+
+# import plpy
+import numpy as np
+from numpy.linalg import norm
+
+
+def median_center(tablename, geom_col, num_iters=50, tolerance=0.001):
+
+    query = '''
+        SELECT array_agg(ST_X({geom_col})) As x_coords,
+               array_agg(ST_Y({geom_col})) As y_coords
+          FROM {tablename}
+    '''.format(geom_col=geom_col, tablename=tablename)
+
+    try:
+        resp = plpy.execute(query)
+        data = np.vstack((resp['x_coords'][0],
+                          resp['y_coords'][0])).T
+
+        plpy.notice('coords: %s' % str(coords))
+    except Exception, err:
+        # plpy.error('Analysis failed: %s' % err)
+        print('No plpy')
+        data = np.array([[1.2 * np.random.random() + 10.,
+                          1.1 * (np.random.random() - 1.) + 3.]
+                        for i in range(1, 100)])
+
+    # initialize 'median center' to be the mean
+    coords_center_temp = data.mean(axis=0)
+
+    # plpy.notice('temp_center: %s' % str(coords_center_temp))
+    print('temp_center: %s' % str(coords_center_temp))
+
+    for i in range(0, num_iters):
+        old_coords_center = coords_center_temp.copy()
+        denom = denominator(coords_center_temp, data)
+        coords_center_temp = np.sum([data[j] * numerator(coords_center_temp,
+                                                         data[j])
+                                     for j in range(len(data))], axis=0)
+        coords_center_temp = coords_center_temp / denom
+
+        print("Pass #%d" % i)
+        print("max, min of data: %0.4f, %0.4f" % (data.max(), data.min()))
+        print('temp_center: %s' % str(coords_center_temp))
+        print("Change in center: %0.4f" % np.linalg.norm(old_coords_center -
+                                                         coords_center_temp))
+        print("Center coords: %s" % str(coords_center_temp))
+        print("Objective Function: %0.4f" % obj_func(coords_center_temp, data))
+
+    return coords_center_temp
+
+
+def obj_func(center_coords, data):
+    """
+
+    """
+    return np.linalg.norm(center_coords - data)
+
+
+def numerator(center_coords, data_i):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(center_coords - data_i))
+
+
+def denominator(center_coords, data):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(data - center_coords))

release/python/0.9.1/crankshaft/crankshaft/model_storage/__init__.py

@@ -0,0 +1 @@
+from core import set_model, get_model, create_model_table

release/python/0.9.1/crankshaft/crankshaft/model_storage/core.py

@@ -0,0 +1,86 @@
+import time
+import plpy
+import pickle
+from petname import generate
+
+def create_model_table():
+    q = '''
+        create table if not exists model_storage(
+            description text,
+            name text unique,
+            model bytea,
+            feature_names text[],
+            date_created timestamptz,
+            id serial primary key);
+        '''
+    plpy.notice(q)
+    plan = plpy.prepare(q)
+    resp = plpy.execute(plan)
+    plpy.notice('Model table successfully created')
+    plpy.notice(str(resp))
+
+def get_model(model_name):
+    """retrieve model if it exists"""
+    try:
+        plan = plpy.prepare('''
+            SELECT model FROM model_storage
+            WHERE name = $1;
+        ''', ['text', ])
+        model_encoded = plpy.execute(plan, [model_name, ])
+        if len(model_encoded) == 1:
+            model = pickle.loads(
+                model_encoded[0]['model']
+            )
+            plpy.notice('Model successfully loaded')
+        else:
+            plpy.notice('Model not found, or too many models '
+                        '({})'.format(len(model_encoded)))
+            model = None
+    except plpy.SPIError as err:
+        plpy.error('ERROR: {}'.format(err))
+
+    return model
+
+def set_model(model, model_name, feature_names):
+    """stores the model in the table model_storage"""
+    if model_name is None:
+	model_name = generate(words=2, separator='_', letters=8)
+	existing_names = plpy.execute('''
+	    SELECT array_agg(name) as name
+	    FROM model_storage
+	''')
+        plpy.notice('nrows: {}'.format(existing_names.nrows()))
+	plpy.notice('MODEL NAME: {}'.format(model_name))
+        plpy.notice('LEN of ms: {}'.format(len(existing_names)))
+        plpy.notice('existing_names: {}'.format(str(existing_names)))
+        plpy.notice('existing_names: {}'.format(str(existing_names[0]['name'])))
+        plpy.notice('type existing_names: {}'.format(type(existing_names[0]['name'])))
+        if existing_names[0]['name'] is not None:
+            while model_name in existing_names[0]['name']:
+                model_name = generate(words=2, separator='_', letters=10)
+                plpy.notice(model_name)
+
+    # store model
+    try:
+	plan = plpy.prepare('''
+	    INSERT INTO model_storage(description, name, model, feature_names, date_created)
+	    VALUES (
+	      $1,
+	      $2,
+	      $3,
+              $4::text[],
+	      to_timestamp($5));
+	''', ['text', 'text', 'bytea', 'text', 'numeric'])
+        plpy.notice('{%s}' % ','.join(feature_names))
+        plpy.notice(feature_names)
+	plpy.execute(
+	    plan,
+	    [' '.join(m.strip() for m in model.__repr__().split('\n')),
+	     model_name,
+	     pickle.dumps(model),
+             '{%s}' % ','.join(feature_names),
+	     time.time()]
+	)
+	plpy.notice('model successfully stored as {}'.format(model_name))
+    except plpy.SPIError as err:
+	plpy.notice('ERROR: {}\nt: {}'.format(err, time.time()))