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Merge branch 'develop' into add-spatial-markov

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This commit is contained in:
Andy Eschbacher
2016-06-28 10:19:36 -04:00
parent 4834ee2f42 faa899cf87
commit dd0ca9a24f
69 changed files with 3641 additions and 109 deletions
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src/pg/Makefile
+1 -6
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@@ -7,7 +7,6 @@ include ../../Makefile.global
# requires sudo. In additionof the current development version
# named 'dev', an alias 'current' is generating for ease of
# update (upgrade to 'current', then to 'dev').
# the python module is installed in a virtualenv in envs/dev/
# * test runs the tests for the currently generated Development
# extension.
@@ -18,11 +17,8 @@ DATA = $(EXTENSION)--dev.sql \
SOURCES_DATA_DIR = sql
SOURCES_DATA = $(wildcard $(SOURCES_DATA_DIR)/*.sql)
VIRTUALENV_PATH = $(realpath ../../envs)
ESC_VIRVIRTUALENV_PATH = $(subst /,\/,$(VIRTUALENV_PATH))
REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g' \
-e 's/@@VIRTUALENV_PATH@@/$(ESC_VIRVIRTUALENV_PATH)/g'
REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g'
$(DATA): $(SOURCES_DATA)
$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > $@
@@ -54,7 +50,6 @@ release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > ../../release/$(EXTENSION)--$(EXTVERSION).sql
# Install the current relese into the PostgreSQL extensions directory
# and the Python package in a virtual environment envs/X.Y.Z
deploy:
$(INSTALL_DATA) ../../release/$(EXTENSION).control '$(DESTDIR)$(datadir)/extension/'
$(INSTALL_DATA) ../../release/*.sql '$(DESTDIR)$(datadir)/extension/'
src/pg/crankshaft.control
+2 -2
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@@ -1,5 +1,5 @@
comment = 'CartoDB Spatial Analysis extension'
default_version = '0.0.2'
requires = 'plpythonu, postgis, cartodb'
default_version = '0.0.4'
requires = 'plpythonu, postgis'
superuser = true
schema = cdb_crankshaft
src/pg/sql/02_py.sql
-23
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@@ -1,23 +0,0 @@
CREATE OR REPLACE FUNCTION _cdb_crankshaft_virtualenvs_path()
RETURNS text
AS $$
BEGIN
-- RETURN '/opt/virtualenvs/crankshaft';
RETURN '@@VIRTUALENV_PATH@@';
END;
$$ language plpgsql IMMUTABLE STRICT;
-- Use the crankshaft python module
CREATE OR REPLACE FUNCTION _cdb_crankshaft_activate_py()
RETURNS VOID
AS $$
import os
# plpy.notice('%',str(os.environ))
# activate virtualenv
crankshaft_version = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_internal_version()')[0]['_cdb_crankshaft_internal_version']
base_path = plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_virtualenvs_path()')[0]['_cdb_crankshaft_virtualenvs_path']
default_venv_path = os.path.join(base_path, crankshaft_version)
venv_path = os.environ.get('CRANKSHAFT_VENV', default_venv_path)
activate_path = venv_path + '/bin/activate_this.py'
exec(open(activate_path).read(), dict(__file__=activate_path))
$$ LANGUAGE plpythonu;
src/pg/sql/03_random_seeds.sql
-1
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@@ -4,7 +4,6 @@
CREATE OR REPLACE FUNCTION
_cdb_random_seeds (seed_value INTEGER) RETURNS VOID
AS $$
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
from crankshaft import random_seeds
random_seeds.set_random_seeds(seed_value)
$$ LANGUAGE plpythonu;
src/pg/sql/08_interpolation.sql
+130
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@@ -0,0 +1,130 @@
-- 0: nearest neighbor
-- 1: barymetric
-- 2: IDW
CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
IN query text,
IN point geometry,
IN method integer DEFAULT 1,
IN p1 numeric DEFAULT 0,
IN p2 numeric DEFAULT 0
)
RETURNS numeric AS
$$
DECLARE
gs geometry[];
vs numeric[];
output numeric;
BEGIN
EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs;
SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a;
RETURN output;
END;
$$
language plpgsql IMMUTABLE;
CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
IN geomin geometry[],
IN colin numeric[],
IN point geometry,
IN method integer DEFAULT 1,
IN p1 numeric DEFAULT 0,
IN p2 numeric DEFAULT 0
)
RETURNS numeric AS
$$
DECLARE
gs geometry[];
vs numeric[];
gs2 geometry[];
vs2 numeric[];
g geometry;
vertex geometry[];
sg numeric;
sa numeric;
sb numeric;
sc numeric;
va numeric;
vb numeric;
vc numeric;
output numeric;
BEGIN
output := -999.999;
-- nearest
IF method = 0 THEN
WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v)
SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
RETURN output;
-- barymetric
ELSIF method = 1 THEN
WITH a as (SELECT unnest(geomin) AS e),
b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
c as (SELECT (ST_Dump(t)).geom as v FROM b),
d as (SELECT v FROM c WHERE ST_Within(point, v))
SELECT v INTO g FROM d;
IF g is null THEN
-- out of the realm of the input data
RETURN -888.888;
END IF;
-- vertex of the selected cell
WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
SELECT array_agg(v) INTO vertex FROM a;
-- retrieve the value of each vertex
WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc;
output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg);
RETURN output;
-- IDW
-- p1: limit the number of neighbors, 0->no limit
-- p2: order of distance decay, 0-> order 1
ELSIF method = 2 THEN
IF p2 = 0 THEN
p2 := 1;
END IF;
WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v),
b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g)
SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b;
IF p1::integer>0 THEN
gs2:=gs;
vs2:=vs;
FOR i IN 1..p1
LOOP
gs2 := gs2 || gs[i];
vs2 := vs2 || vs[i];
END LOOP;
ELSE
gs2:=gs;
vs2:=vs;
END IF;
WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v),
b as (
SELECT
(1/ST_distance(point, a.g)^p2::integer) as k,
(a.v/ST_distance(point, a.g)^p2::integer) as f
FROM a
)
SELECT sum(b.f)/sum(b.k) INTO output FROM b;
RETURN output;
END IF;
RETURN -777.777;
END;
$$
language plpgsql IMMUTABLE;
src/pg/sql/10_moran.sql
-4
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@@ -10,7 +10,6 @@ CREATE OR REPLACE FUNCTION
id_col TEXT DEFAULT 'cartodb_id')
RETURNS TABLE (moran NUMERIC, significance NUMERIC)
AS $$
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
from crankshaft.clustering import moran_local
# TODO: use named parameters or a dictionary
return moran(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
@@ -28,7 +27,6 @@ CREATE OR REPLACE FUNCTION
id_col TEXT)
RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
AS $$
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
from crankshaft.clustering import moran_local
# TODO: use named parameters or a dictionary
return moran_local(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
@@ -122,7 +120,6 @@ CREATE OR REPLACE FUNCTION
id_col TEXT DEFAULT 'cartodb_id')
RETURNS TABLE (moran FLOAT, significance FLOAT)
AS $$
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
from crankshaft.clustering import moran_local
# TODO: use named parameters or a dictionary
return moran_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
@@ -143,7 +140,6 @@ CREATE OR REPLACE FUNCTION
RETURNS
TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC)
AS $$
plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
from crankshaft.clustering import moran_local_rate
# TODO: use named parameters or a dictionary
return moran_local_rate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
src/pg/sql/11_kmeans.sql
+49
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@@ -0,0 +1,49 @@
CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer,no_init integer default 20)
RETURNS table (cartodb_id integer, cluster_no integer) as $$
from crankshaft.clustering import kmeans
return kmeans(query,no_clusters,no_init)
$$ language plpythonu;
CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
RETURNS Numeric[] AS
$$
DECLARE
newX NUMERIC;
newY NUMERIC;
newW NUMERIC;
BEGIN
IF weight IS NULL OR the_geom IS NULL THEN
newX = state[1];
newY = state[2];
newW = state[3];
ELSE
newX = state[1] + ST_X(the_geom)*weight;
newY = state[2] + ST_Y(the_geom)*weight;
newW = state[3] + weight;
END IF;
RETURN Array[newX,newY,newW];
END
$$ LANGUAGE plpgsql;
CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
RETURNS GEOMETRY AS
$$
BEGIN
IF state[3] = 0 THEN
RETURN ST_SetSRID(ST_MakePoint(state[1],state[2]), 4326);
ELSE
RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
END IF;
END
$$ LANGUAGE plpgsql;
CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC)(
SFUNC = CDB_WeightedMeanS,
FINALFUNC = CDB_WeightedMeanF,
STYPE = Numeric[],
INITCOND = "{0.0,0.0,0.0}"
);
src/pg/test/expected/01_install_test.out
-1
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@@ -1,6 +1,5 @@
-- Install dependencies
CREATE EXTENSION plpythonu;
CREATE EXTENSION postgis;
CREATE EXTENSION cartodb;
-- Install the extension
CREATE EXTENSION crankshaft VERSION 'dev';
src/pg/test/expected/05_kmeans_test.out
+10
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@@ -0,0 +1,10 @@
\pset format unaligned
\set ECHO all
SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
clusters
2
(1 row)
SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;
clusters
52
(1 row)
src/pg/test/expected/08_interpolation_test.out
+5
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@@ -0,0 +1,5 @@
SET client_min_messages TO WARNING;
\set ECHO none
cdb_spatialinterpolation
t
(1 row)
src/pg/test/sql/01_install_test.sql
-1
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@@ -1,7 +1,6 @@
-- Install dependencies
CREATE EXTENSION plpythonu;
CREATE EXTENSION postgis;
CREATE EXTENSION cartodb;
-- Install the extension
CREATE EXTENSION crankshaft VERSION 'dev';
src/pg/test/sql/05_kmeans_test.sql
+6
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@@ -0,0 +1,6 @@
\pset format unaligned
\set ECHO all
SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;
src/pg/test/sql/08_interpolation_test.sql
+10
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@@ -0,0 +1,10 @@
SET client_min_messages TO WARNING;
\set ECHO none
\pset format unaligned
WITH a AS (
SELECT
ARRAY[800, 700, 600, 500, 400, 300, 200, 100] AS vals,
ARRAY[ST_GeomFromText('POINT(2.1744 41.403)'),ST_GeomFromText('POINT(2.1228 41.380)'),ST_GeomFromText('POINT(2.1511 41.374)'),ST_GeomFromText('POINT(2.1528 41.413)'),ST_GeomFromText('POINT(2.165 41.391)'),ST_GeomFromText('POINT(2.1498 41.371)'),ST_GeomFromText('POINT(2.1533 41.368)'),ST_GeomFromText('POINT(2.131386 41.41399)')] AS g
)
SELECT (cdb_crankshaft.CDB_SpatialInterpolation(g, vals, ST_GeomFromText('POINT(2.154 41.37)'), 1) - 780.79470198683925288365) / 780.79470198683925288365 < 0.001 As cdb_spatialinterpolation FROM a;
src/pg/test/sql/90_permissions.sql
+1 -1
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@@ -4,7 +4,7 @@ SELECT cdb_crankshaft._cdb_random_seeds(1234);
SET ROLE test_regular_user;
-- Add to the search path the schema
SET search_path TO public,cartodb,cdb_crankshaft;
SET search_path TO public,cdb_crankshaft;
-- Exercise public functions
SELECT ppoints.code, m.quads
src/py/Makefile
+4 -9
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@@ -2,21 +2,16 @@ include ../../Makefile.global
# Install the package locally for development
install:
virtualenv --system-site-packages ../../envs/dev
# source ../../envs/dev/bin/activate
../../envs/dev/bin/pip install -I ./crankshaft
../../envs/dev/bin/pip install -I nose
pip install --upgrade ./crankshaft
# Test develpment install
test:
../../envs/dev/bin/nosetests crankshaft/test/
nosetests crankshaft/test/
release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
mkdir -p ../../release/python/$(EXTVERSION)
cp -r ./$(PACKAGE) ../../release/python/$(EXTVERSION)/
$(SED) -i -r 's/version='"'"'[0-9]+\.[0-9]+\.[0-9]+'"'"'/version='"'"'$(EXTVERSION)'"'"'/g' ../../release/python/$(EXTVERSION)/$(PACKAGE)/setup.py
deploy:
virtualenv --system-site-packages $(VIRTUALENV_PATH)/$(RELEASE_VERSION)
$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I -U ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)
$(VIRTUALENV_PATH)/$(RELEASE_VERSION)/bin/pip install -I nose
deploy:
pip install $(RUN_OPTIONS) --upgrade ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)
src/py/README.md
+1 -16
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@@ -10,7 +10,6 @@ nosetests test/
## Notes about Python dependencies
* This extension is targeted at production databases. Therefore certain restrictions must be assumed about the production environment vs other experimental environments.
* We're using `pip` and `virtualenv` to generate a suitable isolated environment for python code that has all the dependencies
* Every dependency should be:
- Added to the `setup.py` file
- Installed through it
@@ -30,21 +29,7 @@ PySAL 1.10 or later, so we'll stick to 1.9.1.
apt-get install -y python-scipy
```
We'll use virtual environments to install our packages,
but configued to use also system modules so that the
mentioned scipy and numpy are used.
# Create a virtual environment for python
$ virtualenv --system-site-packages dev
# Activate the virtualenv
$ source dev/bin/activate
# Install all the requirements
# expect this to take a while, as it will trigger a few compilations
(dev) $ pip install -I ./crankshaft
#### Test the libraries with that virtual env
#### Test the libraries
##### Test numpy library dependency:
src/py/crankshaft/crankshaft/clustering/__init__.py
+3 -2
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@@ -1,2 +1,3 @@
"""Import all functions from moran clustering"""
from crankshaft.clustering.moran import *
"""Import all functions from for clustering"""
from moran import *
from kmeans import *
src/py/crankshaft/crankshaft/clustering/kmeans.py
+18
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@@ -0,0 +1,18 @@
from sklearn.cluster import KMeans
import plpy
def kmeans(query, no_clusters, no_init=20):
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
array_agg(ST_X(the_geom) order by cartodb_id) xs,
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
where the_geom is not null
'''.format(query=query))
xs = data[0]['xs']
ys = data[0]['ys']
ids = data[0]['ids']
km = KMeans(n_clusters= no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs,ys))
return zip(ids,labels)
src/py/crankshaft/crankshaft/clustering/moran.py
+31 -29
View File
@@ -7,6 +7,7 @@ Moran's I geostatistics (global clustering & outliers presence)
import pysal as ps
import plpy
from collections import OrderedDict
# crankshaft module
import crankshaft.pysal_utils as pu
@@ -21,11 +22,11 @@ def moran(subquery, attr_name,
core clusters with PySAL.
Andy Eschbacher
"""
qvals = {"id_col": id_col,
"attr1": attr_name,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
qvals = OrderedDict([("id_col", id_col),
("attr1", attr_name),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
@@ -65,11 +66,11 @@ def moran_local(subquery, attr,
# geometries with attributes that are null are ignored
# resulting in a collection of not as near neighbors
qvals = {"id_col": id_col,
"attr1": attr,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
qvals = OrderedDict([("id_col", id_col),
("attr1", attr),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
@@ -101,12 +102,12 @@ def moran_rate(subquery, numerator, denominator,
Moran's I Rate (global)
Andy Eschbacher
"""
qvals = {"id_col": id_col,
"attr1": numerator,
"attr2": denominator,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
qvals = OrderedDict([("id_col", id_col),
("attr1", numerator),
("attr2", denominator)
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
@@ -145,13 +146,14 @@ def moran_local_rate(subquery, numerator, denominator,
# geometries with values that are null are ignored
# resulting in a collection of not as near neighbors
query = pu.construct_neighbor_query(w_type,
{"id_col": id_col,
"numerator": numerator,
"denominator": denominator,
"geom_col": geom_col,
"subquery": subquery,
"num_ngbrs": num_ngbrs})
qvals = OrderedDict([("id_col", id_col),
("numerator", numerator),
("denominator", denominator),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
try:
result = plpy.execute(query)
@@ -186,12 +188,12 @@ def moran_local_bv(subquery, attr1, attr2,
"""
plpy.notice('** Constructing query')
qvals = {"num_ngbrs": num_ngbrs,
"attr1": attr1,
"attr2": attr2,
"subquery": subquery,
"geom_col": geom_col,
"id_col": id_col}
qvals = OrderedDict([("id_col", id_col),
("attr1", attr1),
("attr2", attr2),
("geom_col", geom_col),
("subquery", subquery),
("num_ngbrs", num_ngbrs)])
query = pu.construct_neighbor_query(w_type, qvals)
src/py/crankshaft/setup.py
+2 -2
View File
@@ -40,9 +40,9 @@ setup(
# The choice of component versions is dictated by what's
# provisioned in the production servers.
install_requires=['pysal==1.9.1', 'numpy==1.11.0'],
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
requires=['pysal', 'numpy' ],
requires=['pysal', 'numpy', 'sklearn'],
test_suite='test'
)
src/py/crankshaft/test/fixtures/kmeans.json
Vendored
+1
View File
@@ -0,0 +1 @@
[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]␍
src/py/crankshaft/test/test_cluster_kmeans.py
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import unittest
import numpy as np
# from mock_plpy import MockPlPy
# plpy = MockPlPy()
#
# import sys
# sys.modules['plpy'] = plpy
from helper import plpy, fixture_file
import numpy as np
import crankshaft.clustering as cc
import crankshaft.pysal_utils as pu
from crankshaft import random_seeds
import json
class KMeansTest(unittest.TestCase):
"""Testing class for Moran's I functions"""
def setUp(self):
plpy._reset()
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
self.params = {"subquery": "select * from table",
"no_clusters": "10"
}
def test_kmeans(self):
data = self.cluster_data
plpy._define_result('select' ,data)
clusters = cc.kmeans('subquery', 2)
labels = [a[1] for a in clusters]
c1 = [a for a in clusters if a[1]==0]
c2 = [a for a in clusters if a[1]==1]
self.assertEqual(len(np.unique(labels)),2)
self.assertEqual(len(c1),20)
self.assertEqual(len(c2),20)