From 7509afa5a65e81821cef94599b85aa8a4669ef28 Mon Sep 17 00:00:00 2001 From: Andy Eschbacher Date: Mon, 9 Apr 2018 14:14:39 -0400 Subject: [PATCH] release feature name validation --- .gitignore | 1 + release/crankshaft--0.9.3--0.9.4.sql | 2421 +++++++++++++++++ release/crankshaft--0.9.4.sql | 2421 +++++++++++++++++ release/crankshaft.control | 2 +- .../crankshaft/.analysis_data_provider.py.swp | Bin 20480 -> 0 bytes .../crankshaft/.analysis_data_provider.py.swp | Bin 20480 -> 0 bytes .../segmentation/.segmentation.py.swp | Bin 57344 -> 0 bytes .../segmentation/.segmentation.py.swp | Bin 57344 -> 0 bytes .../crankshaft/model_storage/.core.py.swp | Bin 12288 -> 0 bytes .../segmentation/.segmentation.py.swp | Bin 24576 -> 0 bytes .../0.9.4/crankshaft/crankshaft/__init__.py | 7 + .../crankshaft/analysis_data_provider.py | 149 + .../crankshaft/clustering/__init__.py | 4 + .../crankshaft/crankshaft/clustering/getis.py | 50 + .../crankshaft/clustering/kmeans.py | 113 + .../crankshaft/crankshaft/clustering/moran.py | 341 +++ .../crankshaft/model_storage/__init__.py | 1 + .../crankshaft/model_storage/core.py | 90 + .../crankshaft/pysal_utils/__init__.py | 2 + .../crankshaft/pysal_utils/pysal_utils.py | 251 ++ .../crankshaft/crankshaft/random_seeds.py | 12 + .../crankshaft/regression/__init__.py | 3 + .../glm/GLM_validate_estimation.ipynb | 444 +++ .../crankshaft/regression/glm/__init__.py | 4 + .../crankshaft/regression/glm/base.py | 959 +++++++ .../crankshaft/regression/glm/family.py | 1845 +++++++++++++ .../crankshaft/regression/glm/glm.py | 326 +++ .../crankshaft/regression/glm/iwls.py | 84 + .../crankshaft/regression/glm/links.py | 953 +++++++ .../regression/glm/tests/test_glm.py | 993 +++++++ .../crankshaft/regression/glm/utils.py | 350 +++ .../crankshaft/regression/glm/varfuncs.py | 284 ++ .../crankshaft/regression/gwr/__init__.py | 1 + .../regression/gwr/base/__init__.py | 4 + .../regression/gwr/base/diagnostics.py | 81 + .../crankshaft/regression/gwr/base/gwr.py | 1086 ++++++++ .../crankshaft/regression/gwr/base/kernels.py | 120 + .../crankshaft/regression/gwr/base/search.py | 208 ++ .../crankshaft/regression/gwr/base/sel_bw.py | 286 ++ .../regression/gwr/base/tests/test_gwr.py | 853 ++++++ .../regression/gwr/base/tests/test_kernels.py | 84 + .../regression/gwr/base/tests/test_sel_bw.py | 139 + .../crankshaft/regression/gwr_cs.py | 202 ++ .../crankshaft/segmentation/__init__.py | 2 + .../crankshaft/segmentation/segmentation.py | 247 ++ .../space_time_dynamics/__init__.py | 2 + .../crankshaft/space_time_dynamics/markov.py | 194 ++ .../python/0.9.4/crankshaft/requirements.txt | 5 + release/python/0.9.4/crankshaft/setup.py | 49 + release/python/0.9.4/crankshaft/setup.py-r | 49 + .../0.9.4/crankshaft/test/fixtures/data.json | 1 + .../0.9.4/crankshaft/test/fixtures/getis.json | 1 + .../test/fixtures/gwr_packed_data.json | 1 + .../test/fixtures/gwr_packed_knowns.json | 1 + .../crankshaft/test/fixtures/kmeans.json | 1 + .../crankshaft/test/fixtures/markov.json | 1 + .../crankshaft/test/fixtures/model_data.json | 1 + .../0.9.4/crankshaft/test/fixtures/moran.json | 52 + .../crankshaft/test/fixtures/neighbors.json | 54 + .../test/fixtures/neighbors_getis.json | 1 + .../test/fixtures/neighbors_markov.json | 1 + .../test/fixtures/predict_data.json | 1 + .../test/fixtures/segmentation_result.json | 1 + .../crankshaft/test/fixtures/true_result.json | 1 + .../python/0.9.4/crankshaft/test/helper.py | 13 + .../python/0.9.4/crankshaft/test/mock_plpy.py | 58 + .../crankshaft/test/test_clustering_getis.py | 78 + .../crankshaft/test/test_clustering_kmeans.py | 87 + .../crankshaft/test/test_clustering_moran.py | 112 + .../0.9.4/crankshaft/test/test_pysal_utils.py | 83 + .../crankshaft/test/test_regression_gwr.py | 130 + .../crankshaft/test/test_segmentation.py | 139 + .../test/test_space_time_dynamics.py | 349 +++ src/pg/crankshaft.control | 2 +- 74 files changed, 16889 insertions(+), 2 deletions(-) create mode 100644 release/crankshaft--0.9.3--0.9.4.sql create mode 100644 release/crankshaft--0.9.4.sql delete mode 100644 release/python/0.8.0/crankshaft/crankshaft/.analysis_data_provider.py.swp delete mode 100644 release/python/0.8.1/crankshaft/crankshaft/.analysis_data_provider.py.swp delete mode 100644 release/python/0.9.0/crankshaft/crankshaft/segmentation/.segmentation.py.swp delete mode 100644 release/python/0.9.1/crankshaft/crankshaft/segmentation/.segmentation.py.swp delete mode 100644 release/python/0.9.3/crankshaft/crankshaft/model_storage/.core.py.swp delete mode 100644 release/python/0.9.3/crankshaft/crankshaft/segmentation/.segmentation.py.swp create mode 100644 release/python/0.9.4/crankshaft/crankshaft/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/analysis_data_provider.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/clustering/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/clustering/getis.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/clustering/kmeans.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/clustering/moran.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/model_storage/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/model_storage/core.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/pysal_utils/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/random_seeds.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/base.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/family.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/glm.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/iwls.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/links.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/tests/test_glm.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/utils.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/glm/varfuncs.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/diagnostics.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/gwr.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/kernels.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/search.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/sel_bw.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/regression/gwr_cs.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/segmentation/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/segmentation/segmentation.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/__init__.py create mode 100644 release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/markov.py create mode 100644 release/python/0.9.4/crankshaft/requirements.txt create mode 100644 release/python/0.9.4/crankshaft/setup.py create mode 100644 release/python/0.9.4/crankshaft/setup.py-r create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/data.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/getis.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/gwr_packed_data.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/gwr_packed_knowns.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/kmeans.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/markov.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/model_data.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/moran.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/neighbors.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/neighbors_getis.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/neighbors_markov.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/predict_data.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/segmentation_result.json create mode 100644 release/python/0.9.4/crankshaft/test/fixtures/true_result.json create mode 100644 release/python/0.9.4/crankshaft/test/helper.py create mode 100644 release/python/0.9.4/crankshaft/test/mock_plpy.py create mode 100644 release/python/0.9.4/crankshaft/test/test_clustering_getis.py create mode 100644 release/python/0.9.4/crankshaft/test/test_clustering_kmeans.py create mode 100644 release/python/0.9.4/crankshaft/test/test_clustering_moran.py create mode 100644 release/python/0.9.4/crankshaft/test/test_pysal_utils.py create mode 100644 release/python/0.9.4/crankshaft/test/test_regression_gwr.py create mode 100644 release/python/0.9.4/crankshaft/test/test_segmentation.py create mode 100644 release/python/0.9.4/crankshaft/test/test_space_time_dynamics.py diff --git a/.gitignore b/.gitignore index a09b4fe..c96dd42 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ envs/ *.pyc .DS_Store .idea/ +.*.sw[nop] diff --git a/release/crankshaft--0.9.3--0.9.4.sql b/release/crankshaft--0.9.3--0.9.4.sql new file mode 100644 index 0000000..f608c13 --- /dev/null +++ b/release/crankshaft--0.9.3--0.9.4.sql @@ -0,0 +1,2421 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.9.4'::text; +$$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT PARALLEL SAFE; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_pyagg' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) ( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + PARALLEL = SAFE, + INITCOND = "{}" + ); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=np.float64) + return a.reshape(int(len(a)/dimension), int(dimension)) + + return seg.create_and_predict_segment_agg( + np.array(target, dtype=np.float64), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ 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, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = { + 'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf + } + all_cols = list(plpy.execute(''' + select * from ({query}) as _w limit 0 + '''.format(query=query)).colnames()) + feature_cols = [a for a in all_cols + if a not in [variable_name, 'cartodb_id', ]] + return seg.create_and_predict_segment( + query, + variable_name, + feature_cols, + target_table, + model_params, + model_name=model_name + ) +$$ 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, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + +# get stored features if they exist to validate whether +# model matches input data +if model_name: + try: + stored_features = plpy.execute(''' + select feature_names + from model_storage + where name = \'{}\' + '''.format(model_name) + )[0]['feature_names'] + except plpy.SPIError: + stored_features = [] +else: + stored_features = [] + +if set(feature_columns) == set(stored_features): + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = { + 'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf + } + return seg.create_and_predict_segment( + query, + variable, + feature_columns, + target_query, + model_params, + model_name=model_name + ) +else: + raise plpy.SPIError( + 'Feature columns for stored model `{0}` does not match features ' + 'passed in this function.\n' + 'Stored model: {1}\n' + 'New model: {2}\n' + 'Pick a new model name or adjust features.'.format( + model_name, + ', '.join(sorted(feature_columns)), + ', '.join(sorted(stored_columns)) + )) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN target_query text, + IN weight_column text, + IN source_query text, + IN pop_column text, + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_id bigint[]; + t_geom geometry[]; + t_weight numeric[]; + s_id bigint[]; + s_geom geometry[]; + s_pop numeric[]; +BEGIN + EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight; + EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop; + RETURN QUERY + SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g; +END; +$$ language plpgsql VOLATILE PARALLEL UNSAFE; + +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN t_id bigint[], + IN t_geom geometry[], + IN t_weight numeric[], + IN s_id bigint[], + IN s_geom geometry[], + IN s_pop numeric[], + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_type text; + s_type text; + t_center geometry[]; + s_center geometry[]; +BEGIN + t_type := GeometryType(t_geom[1]); + s_type := GeometryType(s_geom[1]); + IF t_type = 'POINT' THEN + t_center := t_geom; + ELSE + WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp; + END IF; + IF s_type = 'POINT' THEN + s_center := s_geom; + ELSE + WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp; + END IF; + RETURN QUERY + with target0 as( + SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td + ), + source0 as( + SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp + ), + prev0 as( + SELECT + source0.sg, + source0.sd as sourc_id, + coalesce(source0.sp,0) as sp, + target.td as targ_id, + coalesce(target.tw,0) as tw, + GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance + FROM source0 + CROSS JOIN LATERAL + ( + SELECT + * + FROM target0 + WHERE tw > minval + AND ST_DWithin(geography(source0.sc), geography(tc), radius) + ) AS target + ), + deno as( + SELECT + sourc_id, + sum(tw/distance) as h_deno + FROM + prev0 + GROUP BY sourc_id + ) + SELECT + p.sg as the_geom, + p.sourc_id as source_id, + p.targ_id as target_id, + case when p.distance > 1 then p.distance else 0.0 end as dist, + 100*(p.tw/p.distance)/d.h_deno as h, + p.sp*(p.tw/p.distance)/d.h_deno as hpop + FROM + prev0 p, + deno d + WHERE + p.targ_id = target AND + p.sourc_id = d.sourc_id; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- 0: nearest neighbor(s) +-- 1: barymetric +-- 2: IDW +-- 3: krigin ---> TO DO + + +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 VOLATILE PARALLEL UNSAFE; + +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 neighbors + -- p1: limit the number of neighbors, 0-> closest one + IF method = 0 THEN + + IF p1 = 0 THEN + p1 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.v as v FROM a ORDER BY point<->a.g LIMIT p1::integer) + SELECT avg(b.v) INTO output FROM b; + 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(geomin) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + 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; + + -- krigin + ELSIF method = 3 THEN + + -- TO DO + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE PARALLEL SAFE; +-- ============================================================================================= +-- +-- CDB_Voronoi +-- +-- ============================================================================================= +CREATE OR REPLACE FUNCTION CDB_voronoi( + IN geomin geometry[], + IN buffer numeric DEFAULT 0.5, + IN tolerance numeric DEFAULT 1e-9 + ) +RETURNS geometry AS $$ +DECLARE + geomout geometry; +BEGIN + -- we need to make the geometry calculations in (pseudo)meters!!! + with a as ( + SELECT unnest(geomin) as g1 + ), + b as( + SELECT st_transform(g1, 3857) g2 from a + ) + SELECT array_agg(g2) INTO geomin from b; + + WITH + convexhull_1 as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as g, + buffer * |/ (st_area(ST_ConvexHull(ST_Collect(geomin)))/PI()) as r + ), + clipper as( + SELECT + st_buffer(ST_MinimumBoundingCircle(a.g), buffer*a.r) as g + FROM convexhull_1 a + ), + env0 as ( + SELECT + (st_dumppoints(st_expand(a.g, buffer*a.r))).geom as e + FROM convexhull_1 a + ), + env as ( + SELECT + array_agg(env0.e) as e + FROM env0 + ), + sample AS ( + SELECT + ST_Collect(geomin || env.e) as geom + FROM env + ), + convexhull as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as cg + ), + tin as ( + SELECT + ST_Dump(ST_DelaunayTriangles(geom, tolerance, 0)) as gd + FROM + sample + ), + tin_polygons as ( + SELECT + (gd).Path as id, + (gd).Geom as pg, + ST_Centroid(ST_MinimumBoundingCircle((gd).Geom, 180)) as ct + FROM tin + ), + tin_lines as ( + SELECT + id, + ST_ExteriorRing(pg) as lg + FROM tin_polygons + ), + tin_nodes as ( + SELECT + id, + ST_PointN(lg,1) p1, + ST_PointN(lg,2) p2, + ST_PointN(lg,3) p3 + FROM tin_lines + ), + tin_edges AS ( + SELECT + p.id, + UNNEST(ARRAY[ + ST_MakeLine(n.p1,n.p2) , + ST_MakeLine(n.p2,n.p3) , + ST_MakeLine(n.p3,n.p1)]) as Edge, + ST_Force2D(cdb_crankshaft._Find_Circle(n.p1,n.p2,n.p3)) as ct, + CASE WHEN st_distance(p.ct, ST_ExteriorRing(p.pg)) < tolerance THEN + TRUE + ELSE FALSE END AS ctx, + p.pg, + ST_within(p.ct, convexhull.cg) as ctin + FROM + tin_polygons p, + tin_nodes n, + convexhull + WHERE p.id = n.id + ), + voro_nodes as ( + SELECT + CASE WHEN x.ctx = TRUE THEN + ST_Centroid(x.edge) + ELSE + x.ct + END as xct, + CASE WHEN y.id is null THEN + CASE WHEN x.ctin = TRUE THEN + ST_SetSRID(ST_MakePoint( + ST_X(x.ct) + ((ST_X(ST_Centroid(x.edge)) - ST_X(x.ct)) * (1+buffer)), + ST_Y(x.ct) + ((ST_Y(ST_Centroid(x.edge)) - ST_Y(x.ct)) * (1+buffer)) + ), ST_SRID(x.ct)) + END + ELSE + y.ct + END as yct + FROM + tin_edges x + LEFT OUTER JOIN + tin_edges y + ON x.id <> y.id AND ST_Equals(x.edge, y.edge) + ), + voro_edges as( + SELECT + ST_LineMerge(ST_Collect(ST_MakeLine(xct, yct))) as v + FROM + voro_nodes + ), + voro_cells as( + SELECT + ST_Polygonize( + ST_Node( + ST_LineMerge( + ST_Union(v, ST_ExteriorRing( + ST_Convexhull(v) + ) + ) + ) + ) + ) as g + FROM + voro_edges + ), + voro_set as( + SELECT + (st_dump(v.g)).geom as g + FROM voro_cells v + ), + clipped_voro as( + SELECT + ST_intersection(c.g, v.g) as g + FROM + voro_set v, + clipper c + WHERE + ST_GeometryType(v.g) = 'ST_Polygon' + ) + SELECT + st_collect( + ST_Transform( + ST_ConvexHull(g), + 4326 + ) + ) + INTO geomout + FROM + clipped_voro; + RETURN geomout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +/** ---------------------------------------------------------------------------------------- + * @function : FindCircle + * @precis : Function that determines if three points form a circle. If so a table containing + * centre and radius is returned. If not, a null table is returned. + * @version : 1.0.1 + * @param : p_pt1 : First point in curve + * @param : p_pt2 : Second point in curve + * @param : p_pt3 : Third point in curve + * @return : geometry : In which X,Y ordinates are the centre X, Y and the Z being the radius of found circle + * or NULL if three points do not form a circle. + * @history : Simon Greener - Feb 2012 - Original coding. + * Rafa de la Torre - Aug 2016 - Small fix for type checking + * Raul Marin - Sept 2017 - Remove unnecessary NULL checks and set function categories + * @copyright : Simon Greener @ 2012 + * Licensed under a Creative Commons Attribution-Share Alike 2.5 Australia License. (http://creativecommons.org/licenses/by-sa/2.5/au/) +**/ +CREATE OR REPLACE FUNCTION _Find_Circle( + IN p_pt1 geometry, + IN p_pt2 geometry, + IN p_pt3 geometry) + RETURNS geometry AS +$BODY$ +DECLARE + v_Centre geometry; + v_radius NUMERIC; + v_CX NUMERIC; + v_CY NUMERIC; + v_dA NUMERIC; + v_dB NUMERIC; + v_dC NUMERIC; + v_dD NUMERIC; + v_dE NUMERIC; + v_dF NUMERIC; + v_dG NUMERIC; +BEGIN + IF ( ST_GeometryType(p_pt1) <> 'ST_Point' OR + ST_GeometryType(p_pt2) <> 'ST_Point' OR + ST_GeometryType(p_pt3) <> 'ST_Point' ) THEN + RAISE EXCEPTION 'All supplied geometries must be points.'; + RETURN NULL; + END IF; + v_dA := ST_X(p_pt2) - ST_X(p_pt1); + v_dB := ST_Y(p_pt2) - ST_Y(p_pt1); + v_dC := ST_X(p_pt3) - ST_X(p_pt1); + v_dD := ST_Y(p_pt3) - ST_Y(p_pt1); + v_dE := v_dA * (ST_X(p_pt1) + ST_X(p_pt2)) + v_dB * (ST_Y(p_pt1) + ST_Y(p_pt2)); + v_dF := v_dC * (ST_X(p_pt1) + ST_X(p_pt3)) + v_dD * (ST_Y(p_pt1) + ST_Y(p_pt3)); + v_dG := 2.0 * (v_dA * (ST_Y(p_pt3) - ST_Y(p_pt2)) - v_dB * (ST_X(p_pt3) - ST_X(p_pt2))); + -- If v_dG is zero then the three points are collinear and no finite-radius + -- circle through them exists. + IF ( v_dG = 0 ) THEN + RETURN NULL; + ELSE + v_CX := (v_dD * v_dE - v_dB * v_dF) / v_dG; + v_CY := (v_dA * v_dF - v_dC * v_dE) / v_dG; + v_Radius := SQRT(POWER(ST_X(p_pt1) - v_CX,2) + POWER(ST_Y(p_pt1) - v_CY,2) ); + END IF; + RETURN ST_SetSRID(ST_MakePoint(v_CX, v_CY, v_radius),ST_Srid(p_pt1)); +END; +$BODY$ + LANGUAGE plpgsql IMMUTABLE STRICT PARALLEL SAFE; + +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import Moran + # TODO: use named parameters or a dictionary + moran = Moran() + return moran.global_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (internal function) - DEPRECATED +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE ( + moran NUMERIC, + quads TEXT, + significance NUMERIC, + rowid INT, + vals NUMERIC) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + result = moran.local_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) + # remove spatial lag + return [(r[6], r[0], r[1], r[7], r[5]) for r in result] +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_MoransILocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE ( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + +from crankshaft.clustering import Moran +moran = Moran() +return moran.local_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +-- Moran's I Local (public-facing function) +-- Replaces CDB_AreasOfInterestLocal +CREATE OR REPLACE FUNCTION + CDB_MoransILocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE ( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + + SELECT + quads, significance, spatial_lag, spatial_lag_std, + orig_val, orig_val_std, moran_stat, rowid + FROM cdb_crankshaft._CDB_MoransILocal( + subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (public-facing function) - DEPRECATED +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + # TODO: use named parameters or a dictionary + return moran.global_rate_stat(subquery, numerator, denominator, w_type, + num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +-- Moran's I Local Rate (internal function) - DEPRECATED +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE( + moran NUMERIC, + quads TEXT, + significance NUMERIC, + rowid INT, + vals NUMERIC) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + # TODO: use named parameters or a dictionary + result = moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + # remove spatial lag + return [(r[6], r[0], r[1], r[7], r[4]) for r in result] +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate (public-facing function) - DEPRECATED +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Internal function +CREATE OR REPLACE FUNCTION + _CDB_MoransILocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ +from crankshaft.clustering import Moran +moran = Moran() +return moran.local_rate_stat( + subquery, + numerator, + denominator, + w_type, + num_ngbrs, + permutations, + geom_col, + id_col +) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Rate +-- Replaces CDB_AreasOfInterestLocalRate +CREATE OR REPLACE FUNCTION + CDB_MoransILocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + +SELECT + quads, significance, spatial_lag, spatial_lag_std, + orig_val, orig_val_std, moran_stat, rowid +FROM cdb_crankshaft._CDB_MoransILocalRate( + subquery, numerator, denominator, w_type, + num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; +-- Spatial k-means clustering + +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 $$ + +from crankshaft.clustering import Kmeans +kmeans = Kmeans() +return kmeans.spatial(query, no_clusters, no_init) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Non-spatial k-means clustering +-- query: sql query to retrieve all the needed data +-- colnames: text array of column names for doing the clustering analysis +-- no_clusters: number of requested clusters +-- standardize: whether to scale variables to a mean of zero and a standard +-- deviation of 1 +-- id_colname: name of the id column + +CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial( + query TEXT, + colnames TEXT[], + no_clusters INTEGER, + standardize BOOLEAN DEFAULT true, + id_col TEXT DEFAULT 'cartodb_id' +) +RETURNS TABLE( + cluster_label text, + cluster_center json, + silhouettes numeric, + inertia numeric, + rowid bigint +) AS $$ + +from crankshaft.clustering import Kmeans +kmeans = Kmeans() +return kmeans.nonspatial(query, colnames, no_clusters, + standardize=standardize, + id_col=id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +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 IMMUTABLE PARALLEL SAFE; + + +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 IMMUTABLE PARALLEL SAFE; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_weightedmean' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) ( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + PARALLEL = SAFE, + INITCOND = "{0.0,0.0,0.0}" + ); + END IF; +END +$$ LANGUAGE plpgsql; +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import Markov + markov = Markov() + + ## TODO: use named parameters or a dictionary + return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- Based on: +-- https://github.com/mapbox/polylabel/blob/master/index.js +-- https://sites.google.com/site/polesofinaccessibility/ +-- Requires: https://github.com/CartoDB/cartodb-postgresql + +-- Based on: +-- https://github.com/mapbox/polylabel/blob/master/index.js +-- https://sites.google.com/site/polesofinaccessibility/ +-- Requires: https://github.com/CartoDB/cartodb-postgresql + +CREATE OR REPLACE FUNCTION CDB_PIA( + IN polygon geometry, + IN tolerance numeric DEFAULT 1.0 + ) +RETURNS geometry AS $$ +DECLARE + env geometry[]; + cells geometry[]; + cell geometry; + best_c geometry; + best_d numeric; + test_d numeric; + test_mx numeric; + test_h numeric; + test_cells geometry[]; + width numeric; + height numeric; + h numeric; + i integer; + n integer; + sqr numeric; + p geometry; +BEGIN + sqr := 0.5*(|/2.0); + polygon := ST_Transform(polygon, 3857); + + -- grid #0 cell size + height := ST_YMax(polygon) - ST_YMin(polygon); + width := ST_XMax(polygon) - ST_XMin(polygon); + h := 0.5*LEAST(height, width); + + -- grid #0 + with c1 as( + SELECT cdb_crankshaft.CDB_RectangleGrid(polygon, h, h) as c + ) + SELECT array_agg(c) INTO cells FROM c1; + + -- 1st guess: centroid + best_c := polygon; + best_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(Polygon)); + + -- looping the loop + n := array_length(cells,1); + i := 1; + LOOP + + EXIT WHEN i > n; + + cell := cells[i]; + + i := i+1; + + -- cell side size, it's square + test_h := ST_XMax(cell) - ST_XMin(cell) ; + + -- check distance + test_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(cell)); + + IF test_d > best_d THEN + best_d := test_d; + best_c := cell; + END IF; + + -- longest distance within the cell + test_mx := test_d + (test_h * sqr); + + -- if the cell has no chance to contains the desired point, continue + CONTINUE WHEN test_mx - best_d <= tolerance; + + -- resample the cell + with c1 as( + SELECT cdb_crankshaft.CDB_RectangleGrid(cell, test_h/2, test_h/2) as c + ) + SELECT array_agg(c) INTO test_cells FROM c1; + + -- concat the new cells to the former array + cells := cells || test_cells; + + -- prepare next iteration + n := array_length(cells,1); + + END LOOP; + + RETURN ST_transform(ST_Centroid(best_c), 4326); + +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + + + +-- signed distance point to polygon with holes +-- negative is the point is out the polygon +-- rev 1. adding MULTIPOLYGON and GEOMETRYCOLLECTION support by @abelvm +CREATE OR REPLACE FUNCTION _Signed_Dist( + IN polygon geometry, + IN point geometry + ) +RETURNS numeric AS $$ +DECLARE + pols geometry[]; + pol geometry; + i integer; + j integer; + within integer; + w integer; + holes integer; + dist numeric; + d numeric; +BEGIN + dist := 1e999; + WITH collection as (SELECT (ST_dump(polygon)).geom as geom) SELECT array_agg(geom) into pols FROM collection; + FOR j in 1..array_length(pols, 1) + LOOP + pol := pols[j]; + d := dist; + SELECT LEAST(dist, ST_distance(point, ST_ExteriorRing(pol))::numeric) INTO d; + SELECT CASE WHEN ST_Within(point,pol) THEN 1 ELSE -1 END INTO w; + SELECT ST_NumInteriorRings(pol) INTO holes; + IF holes > 0 THEN + FOR i IN 1..holes + LOOP + SELECT LEAST(d, ST_distance(point, ST_InteriorRingN(pol, i))::numeric) INTO d; + END LOOP; + END IF; + IF d < dist THEN + dist:= d; + within := w; + END IF; + END LOOP; + dist := dist * within::numeric; + RETURN dist; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- +-- Iterative densification of a set of points using Delaunay triangulation +-- the new points have as assigned value the average value of the 3 vertex (centroid) +-- +-- @param geomin - array of geometries (points) +-- +-- @param colin - array of numeric values in that points +-- +-- @param iterations - integer, number of iterations +-- +-- +-- Returns: TABLE(geomout geometry, colout numeric) +-- +-- +CREATE OR REPLACE FUNCTION CDB_Densify( + IN geomin geometry[], + IN colin numeric[], + IN iterations integer + ) +RETURNS TABLE(geomout geometry, colout numeric) AS $$ +DECLARE + geotemp geometry[]; + coltemp numeric[]; + i integer; + gs geometry[]; + g geometry; + vertex geometry[]; + va numeric; + vb numeric; + vc numeric; + center geometry; + centerval numeric; + tmp integer; +BEGIN + geotemp := geomin; + coltemp := colin; + FOR i IN 1..iterations + LOOP + -- generate TIN + WITH a as (SELECT unnest(geotemp) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom AS v FROM b) + SELECT array_agg(v) INTO gs FROM c; + -- loop cells + FOREACH g IN ARRAY gs + LOOP + -- append centroid + SELECT ST_Centroid(g) INTO center; + geotemp := array_append(geotemp, center); + -- retrieve the value of each vertex + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + -- calc the value at the center + centerval := (va + vb + vc) / 3; + -- append the value + coltemp := array_append(coltemp, centerval); + END LOOP; + END LOOP; + RETURN QUERY SELECT unnest(geotemp ) as geomout, unnest(coltemp ) as colout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION CDB_TINmap( + IN geomin geometry[], + IN colin numeric[], + IN iterations integer + ) +RETURNS TABLE(geomout geometry, colout numeric) AS $$ +DECLARE + p geometry[]; + vals numeric[]; + gs geometry[]; + g geometry; + vertex geometry[]; + centerval numeric; + va numeric; + vb numeric; + vc numeric; + coltemp numeric[]; +BEGIN + SELECT array_agg(dens.geomout), array_agg(dens.colout) INTO p, vals FROM cdb_crankshaft.CDB_Densify(geomin, colin, iterations) dens; + WITH a as (SELECT unnest(p) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom AS v FROM b) + SELECT array_agg(v) INTO gs FROM c; + FOREACH g IN ARRAY gs + LOOP + -- retrieve the vertex of each triangle + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + -- calc the value at the center + centerval := (va + vb + vc) / 3; + -- append the value + coltemp := array_append(coltemp, centerval); + END LOOP; + RETURN QUERY SELECT unnest(gs) as geomout, unnest(coltemp ) as colout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- Getis-Ord's G +-- Hotspot/Coldspot Analysis tool +CREATE OR REPLACE FUNCTION + CDB_GetisOrdsG( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 999, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (z_score NUMERIC, p_value NUMERIC, p_z_sim NUMERIC, rowid BIGINT) +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 plpythonu VOLATILE PARALLEL UNSAFE; + +-- TODO: make a version that accepts the values as arrays + +-- Find outliers using a static threshold +-- +CREATE OR REPLACE FUNCTION CDB_StaticOutlier(column_value numeric, threshold numeric) +RETURNS boolean +AS $$ +BEGIN + + RETURN column_value > threshold; + +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE ; + +-- Find outliers by a percentage above the threshold +-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false` + +CREATE OR REPLACE FUNCTION CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[]) +RETURNS TABLE(is_outlier boolean, rowid int) +AS $$ +DECLARE + avg_val numeric; + out_vals boolean[]; +BEGIN + + SELECT avg(i) INTO avg_val + FROM unnest(column_values) As x(i); + + IF avg_val = 0 THEN + RAISE EXCEPTION 'Mean value is zero. Try another outlier method.'; + END IF; + + SELECT array_agg( + outlier_fraction < i / avg_val) INTO out_vals + FROM unnest(column_values) As x(i); + + RETURN QUERY + SELECT unnest(out_vals) As is_outlier, + unnest(ids) As rowid; + +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; + +-- Find outliers above a given number of standard deviations from the mean + +CREATE OR REPLACE FUNCTION CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true) +RETURNS TABLE(is_outlier boolean, rowid int) +AS $$ +DECLARE + stddev_val numeric; + avg_val numeric; + out_vals boolean[]; +BEGIN + + SELECT stddev(i), avg(i) INTO stddev_val, avg_val + FROM unnest(column_values) As x(i); + + IF stddev_val = 0 THEN + RAISE EXCEPTION 'Standard deviation of input data is zero'; + END IF; + + IF is_symmetric THEN + SELECT array_agg( + abs(i - avg_val) / stddev_val > num_deviations) INTO out_vals + FROM unnest(column_values) As x(i); + ELSE + SELECT array_agg( + (i - avg_val) / stddev_val > num_deviations) INTO out_vals + FROM unnest(column_values) As x(i); + END IF; + + RETURN QUERY + SELECT unnest(out_vals) As is_outlier, + unnest(ids) As rowid; +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION CDB_Contour( + IN geomin geometry[], + IN colin numeric[], + IN buffer numeric, + IN intmethod integer, + IN classmethod integer, + IN steps integer, + IN max_time integer DEFAULT 60000 + ) +RETURNS TABLE( + the_geom geometry, + bin integer, + min_value numeric, + max_value numeric, + avg_value numeric +) AS $$ +DECLARE + cell_count integer; + tin geometry[]; + resolution integer; +BEGIN + + -- nasty trick to override issue #121 + IF max_time = 0 THEN + max_time = -90; + END IF; + resolution := max_time; + max_time := -1 * resolution; + + -- calc the optimal number of cells for the current dataset + SELECT + CASE intmethod + WHEN 0 THEN round(3.7745903782 * max_time - 9.4399210051 * array_length(geomin,1) - 1350.8778213073) + WHEN 1 THEN round(2.2855592156 * max_time - 87.285217133 * array_length(geomin,1) + 17255.7085601797) + WHEN 2 THEN round(0.9799471999 * max_time - 127.0334085369 * array_length(geomin,1) + 22707.9579721218) + ELSE 10000 + END INTO cell_count; + + -- we don't have iterative barycentric interpolation in CDB_interpolation, + -- and it's a costy function, so let's make a custom one here till + -- we update the code + -- tin := ARRAY[]::geometry[]; + IF intmethod=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) + SELECT array_agg(v) INTO tin FROM c; + END IF; + -- Delaunay stuff performed just ONCE!! + + -- magic + RETURN QUERY + WITH + convexhull as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as g, + buffer * |/ st_area(ST_ConvexHull(ST_Collect(geomin)))/PI() as r + ), + envelope as ( + SELECT + st_expand(a.g, a.r) as e + FROM convexhull a + ), + envelope3857 as( + SELECT + ST_Transform(e, 3857) as geom + FROM envelope + ), + resolution as( + SELECT + CASE WHEN resolution <= 0 THEN + round(|/ ( + ST_area(geom) / abs(cell_count) + )) + ELSE + resolution + END AS cell + FROM envelope3857 + ), + grid as( + SELECT + ST_Transform(cdb_crankshaft.CDB_RectangleGrid(e.geom, r.cell, r.cell), 4326) as geom + FROM envelope3857 e, resolution r + ), + interp as( + SELECT + geom, + CASE + WHEN intmethod=1 THEN cdb_crankshaft._interp_in_tin(geomin, colin, tin, ST_Centroid(geom)) + ELSE cdb_crankshaft.CDB_SpatialInterpolation(geomin, colin, ST_Centroid(geom), intmethod) + END as val + FROM grid + ), + classes as( + SELECT CASE + WHEN classmethod = 0 THEN + cdb_crankshaft.CDB_EqualIntervalBins(array_agg(val), steps) + WHEN classmethod = 1 THEN + cdb_crankshaft.CDB_HeadsTailsBins(array_agg(val), steps) + WHEN classmethod = 2 THEN + cdb_crankshaft.CDB_JenksBins(array_agg(val), steps) + ELSE + cdb_crankshaft.CDB_QuantileBins(array_agg(val), steps) + END as b + FROM interp + where val is not null + ), + classified as( + SELECT + i.*, + width_bucket(i.val, c.b) as bucket + FROM interp i left join classes c + ON 1=1 + ), + classified2 as( + SELECT + geom, + val, + CASE + WHEN bucket = steps THEN bucket - 1 + ELSE bucket + END as b + FROM classified + ), + final as( + SELECT + st_union(geom) as the_geom, + b as bin, + min(val) as min_value, + max(val) as max_value, + avg(val) as avg_value + FROM classified2 + GROUP BY bin + ) + SELECT + * + FROM final + where final.bin is not null + ; +END; +$$ language plpgsql VOLATILE PARALLEL RESTRICTED; + + +-- ===================================================================== +-- Interp in grid, so we can use barycentric with a precalculated tin (NNI) +-- ===================================================================== +CREATE OR REPLACE FUNCTION _interp_in_tin( + IN geomin geometry[], + IN colin numeric[], + IN tin geometry[], + IN point geometry + ) +RETURNS numeric AS +$$ +DECLARE + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + -- get the cell the point is within + WITH + a as (SELECT unnest(tin) as v), + b as (SELECT v FROM a WHERE ST_Within(point, v)) + SELECT v INTO g FROM b; + + -- if we're out of the data realm, + -- return null + IF g is null THEN + RETURN null; + END IF; + + -- vertex of the selected cell + WITH a AS ( + SELECT (ST_DumpPoints(g)).geom AS v + ) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + -- calc the areas + SELECT + ST_area(g), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg,1); + RETURN output; +END; +$$ +language plpgsql IMMUTABLE PARALLEL SAFE; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql STABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION +CDB_GWR(subquery text, dep_var text, ind_vars text[], + bw numeric default null, fixed boolean default False, + kernel text default 'bisquare', geom_col text default 'the_geom', + id_col text default 'cartodb_id') +RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, + filtered_t_vals JSON, predicted numeric, + residuals numeric, r_squared numeric, bandwidth numeric, + rowid bigint) +AS $$ + +from crankshaft.regression import GWR + +gwr = GWR() + +return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +CREATE OR REPLACE FUNCTION +CDB_GWR_Predict(subquery text, dep_var text, ind_vars text[], + bw numeric default null, fixed boolean default False, + kernel text default 'bisquare', + geom_col text default 'the_geom', + id_col text default 'cartodb_id') +RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, + r_squared numeric, predicted numeric, rowid bigint) +AS $$ + +from crankshaft.regression import GWR +gwr = GWR() + +return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE PARALLEL RESTRICTED; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; +-- +-- Fill given extent with a rectangular coverage +-- +-- @param ext Extent to fill. Only rectangles with center point falling +-- inside the extent (or at the lower or leftmost edge) will +-- be emitted. The returned hexagons will have the same SRID +-- as this extent. +-- +-- @param width With of each rectangle +-- +-- @param height Height of each rectangle +-- +-- @param origin Optional origin to allow for exact tiling. +-- If omitted the origin will be 0,0. +-- The parameter is checked for having the same SRID +-- as the extent. +-- +-- +CREATE OR REPLACE FUNCTION CDB_RectangleGrid(ext GEOMETRY, width FLOAT8, height FLOAT8, origin GEOMETRY DEFAULT NULL) +RETURNS SETOF GEOMETRY +AS $$ +DECLARE + h GEOMETRY; -- rectangle cell + hstep FLOAT8; -- horizontal step + vstep FLOAT8; -- vertical step + hw FLOAT8; -- half width + hh FLOAT8; -- half height + vstart FLOAT8; + hstart FLOAT8; + hend FLOAT8; + vend FLOAT8; + xoff FLOAT8; + yoff FLOAT8; + xgrd FLOAT8; + ygrd FLOAT8; + x FLOAT8; + y FLOAT8; + srid INTEGER; +BEGIN + + srid := ST_SRID(ext); + + xoff := 0; + yoff := 0; + + IF origin IS NOT NULL THEN + IF ST_SRID(origin) != srid THEN + RAISE EXCEPTION 'SRID mismatch between extent (%) and origin (%)', srid, ST_SRID(origin); + END IF; + xoff := ST_X(origin); + yoff := ST_Y(origin); + END IF; + + --RAISE DEBUG 'X offset: %', xoff; + --RAISE DEBUG 'Y offset: %', yoff; + + hw := width/2.0; + hh := height/2.0; + + xgrd := hw; + ygrd := hh; + --RAISE DEBUG 'X grid size: %', xgrd; + --RAISE DEBUG 'Y grid size: %', ygrd; + + hstep := width; + vstep := height; + + -- Tweak horizontal start on hstep grid from origin + hstart := xoff + ceil((ST_XMin(ext)-xoff)/hstep)*hstep; + --RAISE DEBUG 'hstart: %', hstart; + + -- Tweak vertical start on vstep grid from origin + vstart := yoff + ceil((ST_Ymin(ext)-yoff)/vstep)*vstep; + --RAISE DEBUG 'vstart: %', vstart; + + hend := ST_XMax(ext); + vend := ST_YMax(ext); + + --RAISE DEBUG 'hend: %', hend; + --RAISE DEBUG 'vend: %', vend; + + x := hstart; + WHILE x < hend LOOP -- over X + y := vstart; + h := ST_MakeEnvelope(x-hw, y-hh, x+hw, y+hh, srid); + WHILE y < vend LOOP -- over Y + RETURN NEXT h; + h := ST_Translate(h, 0, vstep); + y := yoff + round(((y + vstep)-yoff)/ygrd)*ygrd; -- round to grid + END LOOP; + x := xoff + round(((x + hstep)-xoff)/xgrd)*xgrd; -- round to grid + END LOOP; + + RETURN; +END +$$ LANGUAGE 'plpgsql' IMMUTABLE PARALLEL SAFE; + +-- +-- Calculate the equal interval bins for a given column +-- +-- @param in_array A numeric array of numbers to determine the best +-- to determine the bin boundary +-- +-- @param breaks The number of bins you want to find. +-- +-- +-- Returns: upper edges of bins +-- +-- + +CREATE OR REPLACE FUNCTION CDB_EqualIntervalBins ( in_array NUMERIC[], breaks INT ) RETURNS NUMERIC[] as $$ +DECLARE + diff numeric; + min_val numeric; + max_val numeric; + tmp_val numeric; + i INT := 1; + reply numeric[]; +BEGIN + SELECT min(e), max(e) INTO min_val, max_val FROM ( SELECT unnest(in_array) e ) x WHERE e IS NOT NULL; + diff = (max_val - min_val) / breaks::numeric; + LOOP + IF i < breaks THEN + tmp_val = min_val + i::numeric * diff; + reply = array_append(reply, tmp_val); + i := i+1; + ELSE + reply = array_append(reply, max_val); + EXIT; + END IF; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +-- +-- Determine the Heads/Tails classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Heads/Tails method. +-- +-- @param breaks The number of bins you want to find. +-- +-- + +CREATE OR REPLACE FUNCTION CDB_HeadsTailsBins ( in_array NUMERIC[], breaks INT) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + arr_mean numeric; + i INT := 2; + reply numeric[]; +BEGIN + -- get the total size of our row + element_count := array_upper(in_array, 1) - array_lower(in_array, 1); + -- ensure the ordering of in_array + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e) x; + -- stop if no rows + IF element_count IS NULL THEN + RETURN NULL; + END IF; + -- stop if our breaks are more than our input array size + IF element_count < breaks THEN + RETURN in_array; + END IF; + + -- get our mean value + SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x; + + reply = Array[arr_mean]; + -- slice our bread + LOOP + IF i > breaks THEN EXIT; END IF; + SELECT avg(e) INTO arr_mean FROM ( SELECT unnest(in_array) e) x WHERE e > reply[i-1]; + IF arr_mean IS NOT NULL THEN + reply = array_append(reply, arr_mean); + END IF; + i := i+1; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +-- +-- Determine the Jenks classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Jenks method. +-- +-- @param breaks The number of bins you want to find. +-- +-- @param iterations The number of different starting positions to test. +-- +-- @param invert Optional wheter to return the top of each bin (default) +-- or the bottom. BOOLEAN, default=FALSE. +-- +-- + + +CREATE OR REPLACE FUNCTION CDB_JenksBins ( in_array NUMERIC[], breaks INT, iterations INT DEFAULT 5, invert BOOLEAN DEFAULT FALSE) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + arr_mean NUMERIC; + bot INT; + top INT; + tops INT[]; + classes INT[][]; + i INT := 1; j INT := 1; + curr_result NUMERIC[]; + best_result NUMERIC[]; + seedtarget TEXT; + quant NUMERIC[]; + shuffles INT; +BEGIN + -- get the total size of our row + element_count := array_length(in_array, 1); --array_upper(in_array, 1) - array_lower(in_array, 1); + -- ensure the ordering of in_array + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e) x; + -- stop if no rows + IF element_count IS NULL THEN + RETURN NULL; + END IF; + -- stop if our breaks are more than our input array size + IF element_count < breaks THEN + RETURN in_array; + END IF; + + shuffles := LEAST(GREATEST(floor(2500000.0/(element_count::float*iterations::float)), 1), 750)::int; + -- get our mean value + SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x; + + -- assume best is actually Quantile + SELECT cdb_crankshaft.CDB_QuantileBins(in_array, breaks) INTO quant; + + -- if data is very very large, just return quant and be done + IF element_count > 5000000 THEN + RETURN quant; + END IF; + + -- change quant into bottom, top markers + LOOP + IF i = 1 THEN + bot = 1; + ELSE + -- use last top to find this bot + bot = top+1; + END IF; + IF i = breaks THEN + top = element_count; + ELSE + SELECT count(*) INTO top FROM ( SELECT unnest(in_array) as v) x WHERE v <= quant[i]; + END IF; + IF i = 1 THEN + classes = ARRAY[ARRAY[bot,top]]; + ELSE + classes = ARRAY_CAT(classes,ARRAY[bot,top]); + END IF; + IF i > breaks THEN EXIT; END IF; + i = i+1; + END LOOP; + + best_result = cdb_crankshaft.CDB_JenksBinsIteration( in_array, breaks, classes, invert, element_count, arr_mean, shuffles); + + --set the seed so we can ensure the same results + SELECT setseed(0.4567) INTO seedtarget; + --loop through random starting positions + LOOP + IF j > iterations-1 THEN EXIT; END IF; + i = 1; + tops = ARRAY[element_count]; + LOOP + IF i = breaks THEN EXIT; END IF; + SELECT array_agg(distinct e) INTO tops FROM (SELECT unnest(array_cat(tops, ARRAY[floor(random()*element_count::float)::int])) as e ORDER BY e) x WHERE e != 1; + i = array_length(tops, 1); + END LOOP; + i = 1; + LOOP + IF i > breaks THEN EXIT; END IF; + IF i = 1 THEN + bot = 1; + ELSE + bot = top+1; + END IF; + top = tops[i]; + IF i = 1 THEN + classes = ARRAY[ARRAY[bot,top]]; + ELSE + classes = ARRAY_CAT(classes,ARRAY[bot,top]); + END IF; + i := i+1; + END LOOP; + curr_result = cdb_crankshaft.CDB_JenksBinsIteration( in_array, breaks, classes, invert, element_count, arr_mean, shuffles); + + IF curr_result[1] > best_result[1] THEN + best_result = curr_result; + j = j-1; -- if we found a better result, add one more search + END IF; + j = j+1; + END LOOP; + + RETURN (best_result)[2:array_upper(best_result, 1)]; +END; +$$ language plpgsql VOLATILE PARALLEL RESTRICTED; + + + +-- +-- Perform a single iteration of the Jenks classification +-- + +CREATE OR REPLACE FUNCTION CDB_JenksBinsIteration ( in_array NUMERIC[], breaks INT, classes INT[][], invert BOOLEAN, element_count INT4, arr_mean NUMERIC, max_search INT DEFAULT 50) RETURNS NUMERIC[] as $$ +DECLARE + tmp_val numeric; + new_classes int[][]; + tmp_class int[]; + i INT := 1; + j INT := 1; + side INT := 2; + sdam numeric; + gvf numeric := 0.0; + new_gvf numeric; + arr_gvf numeric[]; + class_avg numeric; + class_max_i INT; + class_min_i INT; + class_max numeric; + class_min numeric; + reply numeric[]; +BEGIN + + -- Calculate the sum of squared deviations from the array mean (SDAM). + SELECT sum((arr_mean - e)^2) INTO sdam FROM ( SELECT unnest(in_array) as e ) x; + --Identify the breaks for the lowest GVF + LOOP + i = 1; + LOOP + -- get our mean + SELECT avg(e) INTO class_avg FROM ( SELECT unnest(in_array[classes[i][1]:classes[i][2]]) as e) x; + -- find the deviation + SELECT sum((class_avg-e)^2) INTO tmp_val FROM ( SELECT unnest(in_array[classes[i][1]:classes[i][2]]) as e ) x; + IF i = 1 THEN + arr_gvf = ARRAY[tmp_val]; + -- init our min/max map for later + class_max = arr_gvf[i]; + class_min = arr_gvf[i]; + class_min_i = 1; + class_max_i = 1; + ELSE + arr_gvf = array_append(arr_gvf, tmp_val); + END IF; + i := i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + -- calculate our new GVF + SELECT sdam-sum(e) INTO new_gvf FROM ( SELECT unnest(arr_gvf) as e ) x; + -- if no improvement was made, exit + IF new_gvf < gvf THEN EXIT; END IF; + gvf = new_gvf; + IF j > max_search THEN EXIT; END IF; + j = j+1; + i = 1; + LOOP + --establish directionality (uppward through classes or downward) + IF arr_gvf[i] < class_min THEN + class_min = arr_gvf[i]; + class_min_i = i; + END IF; + IF arr_gvf[i] > class_max THEN + class_max = arr_gvf[i]; + class_max_i = i; + END IF; + i := i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + IF class_max_i > class_min_i THEN + class_min_i = class_max_i - 1; + ELSE + class_min_i = class_max_i + 1; + END IF; + --Move from higher class to a lower gid order + IF class_max_i > class_min_i THEN + classes[class_max_i][1] = classes[class_max_i][1] + 1; + classes[class_min_i][2] = classes[class_min_i][2] + 1; + ELSE -- Move from lower class UP into a higher class by gid + classes[class_max_i][2] = classes[class_max_i][2] - 1; + classes[class_min_i][1] = classes[class_min_i][1] - 1; + END IF; + END LOOP; + + i = 1; + LOOP + IF invert = TRUE THEN + side = 1; --default returns bottom side of breaks, invert returns top side + END IF; + reply = array_append(reply, in_array[classes[i][side]]); + i = i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + + RETURN array_prepend(gvf, reply); + +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + + +-- +-- Determine the Quantile classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Quantile method. +-- +-- @param breaks The number of bins you want to find. +-- +-- +CREATE OR REPLACE FUNCTION CDB_QuantileBins ( in_array NUMERIC[], breaks INT) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + break_size numeric; + tmp_val numeric; + i INT := 1; + reply numeric[]; +BEGIN + -- sort our values + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e ASC) x; + -- get the total size of our data + element_count := array_length(in_array, 1); + break_size := element_count::numeric / breaks; + -- slice our bread + LOOP + IF i < breaks THEN + IF break_size * i % 1 > 0 THEN + SELECT e INTO tmp_val FROM ( SELECT unnest(in_array) e LIMIT 1 OFFSET ceil(break_size * i) - 1) x; + ELSE + SELECT avg(e) INTO tmp_val FROM ( SELECT unnest(in_array) e LIMIT 2 OFFSET ceil(break_size * i) - 1 ) x; + END IF; + ELSIF i = breaks THEN + -- select the last value + SELECT max(e) INTO tmp_val FROM ( SELECT unnest(in_array) e ) x; + ELSE + EXIT; + END IF; + + reply = array_append(reply, tmp_val); + i := i+1; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE STRICT PARALLEL SAFE; diff --git a/release/crankshaft--0.9.4.sql b/release/crankshaft--0.9.4.sql new file mode 100644 index 0000000..f608c13 --- /dev/null +++ b/release/crankshaft--0.9.4.sql @@ -0,0 +1,2421 @@ +--DO NOT MODIFY THIS FILE, IT IS GENERATED AUTOMATICALLY FROM SOURCES +-- Complain if script is sourced in psql, rather than via CREATE EXTENSION +\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit +-- Version number of the extension release +CREATE OR REPLACE FUNCTION cdb_crankshaft_version() +RETURNS text AS $$ + SELECT '0.9.4'::text; +$$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE; + +-- Internal identifier of the installed extension instence +-- e.g. 'dev' for current development version +CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version() +RETURNS text AS $$ + SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL; +$$ language 'sql' STABLE STRICT PARALLEL SAFE; +-- Internal function. +-- Set the seeds of the RNGs (Random Number Generators) +-- used internally. +CREATE OR REPLACE FUNCTION +_cdb_random_seeds (seed_value INTEGER) RETURNS VOID +AS $$ + from crankshaft import random_seeds + random_seeds.set_random_seeds(seed_value) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +CREATE OR REPLACE FUNCTION + CDB_PyAggS(current_state Numeric[], current_row Numeric[]) + returns NUMERIC[] as $$ + BEGIN + if array_upper(current_state,1) is null then + RAISE NOTICE 'setting state %',array_upper(current_row,1); + current_state[1] = array_upper(current_row,1); + end if; + return array_cat(current_state,current_row) ; + END + $$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_pyagg' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) ( + SFUNC = CDB_PyAggS, + STYPE = Numeric[], + PARALLEL = SAFE, + INITCOND = "{}" + ); + END IF; +END +$$ LANGUAGE plpgsql; + +CREATE OR REPLACE FUNCTION + CDB_CreateAndPredictSegment( + target NUMERIC[], + features NUMERIC[], + target_features NUMERIC[], + target_ids NUMERIC[], + n_estimators INTEGER DEFAULT 1200, + max_depth INTEGER DEFAULT 3, + subsample DOUBLE PRECISION DEFAULT 0.5, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC) +AS $$ + import numpy as np + import plpy + + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = {'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf} + + def unpack2D(data): + dimension = data.pop(0) + a = np.array(data, dtype=np.float64) + return a.reshape(int(len(a)/dimension), int(dimension)) + + return seg.create_and_predict_segment_agg( + np.array(target, dtype=np.float64), + unpack2D(features), + unpack2D(target_features), + target_ids, + model_params) + +$$ 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, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = { + 'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf + } + all_cols = list(plpy.execute(''' + select * from ({query}) as _w limit 0 + '''.format(query=query)).colnames()) + feature_cols = [a for a in all_cols + if a not in [variable_name, 'cartodb_id', ]] + return seg.create_and_predict_segment( + query, + variable_name, + feature_cols, + target_table, + model_params, + model_name=model_name + ) +$$ 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, + learning_rate DOUBLE PRECISION DEFAULT 0.01, + min_samples_leaf INTEGER DEFAULT 1) +RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC) +AS $$ + +# get stored features if they exist to validate whether +# model matches input data +if model_name: + try: + stored_features = plpy.execute(''' + select feature_names + from model_storage + where name = \'{}\' + '''.format(model_name) + )[0]['feature_names'] + except plpy.SPIError: + stored_features = [] +else: + stored_features = [] + +if set(feature_columns) == set(stored_features): + from crankshaft.segmentation import Segmentation + seg = Segmentation() + model_params = { + 'n_estimators': n_estimators, + 'max_depth': max_depth, + 'subsample': subsample, + 'learning_rate': learning_rate, + 'min_samples_leaf': min_samples_leaf + } + return seg.create_and_predict_segment( + query, + variable, + feature_columns, + target_query, + model_params, + model_name=model_name + ) +else: + raise plpy.SPIError( + 'Feature columns for stored model `{0}` does not match features ' + 'passed in this function.\n' + 'Stored model: {1}\n' + 'New model: {2}\n' + 'Pick a new model name or adjust features.'.format( + model_name, + ', '.join(sorted(feature_columns)), + ', '.join(sorted(stored_columns)) + )) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN target_query text, + IN weight_column text, + IN source_query text, + IN pop_column text, + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_id bigint[]; + t_geom geometry[]; + t_weight numeric[]; + s_id bigint[]; + s_geom geometry[]; + s_pop numeric[]; +BEGIN + EXECUTE 'WITH foo as('+target_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || weight_column || ') FROM foo' INTO t_id, t_geom, t_weight; + EXECUTE 'WITH foo as('+source_query+') SELECT array_agg(cartodb_id), array_agg(the_geom), array_agg(' || pop_column || ') FROM foo' INTO s_id, s_geom, s_pop; + RETURN QUERY + SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g; +END; +$$ language plpgsql VOLATILE PARALLEL UNSAFE; + +CREATE OR REPLACE FUNCTION CDB_Gravity( + IN t_id bigint[], + IN t_geom geometry[], + IN t_weight numeric[], + IN s_id bigint[], + IN s_geom geometry[], + IN s_pop numeric[], + IN target bigint, + IN radius integer, + IN minval numeric DEFAULT -10e307 + ) +RETURNS TABLE( + the_geom geometry, + source_id bigint, + target_id bigint, + dist numeric, + h numeric, + hpop numeric) AS $$ +DECLARE + t_type text; + s_type text; + t_center geometry[]; + s_center geometry[]; +BEGIN + t_type := GeometryType(t_geom[1]); + s_type := GeometryType(s_geom[1]); + IF t_type = 'POINT' THEN + t_center := t_geom; + ELSE + WITH tmp as (SELECT unnest(t_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO t_center FROM tmp; + END IF; + IF s_type = 'POINT' THEN + s_center := s_geom; + ELSE + WITH tmp as (SELECT unnest(s_geom) as g) SELECT array_agg(ST_Centroid(g)) INTO s_center FROM tmp; + END IF; + RETURN QUERY + with target0 as( + SELECT unnest(t_center) as tc, unnest(t_weight) as tw, unnest(t_id) as td + ), + source0 as( + SELECT unnest(s_center) as sc, unnest(s_id) as sd, unnest (s_geom) as sg, unnest(s_pop) as sp + ), + prev0 as( + SELECT + source0.sg, + source0.sd as sourc_id, + coalesce(source0.sp,0) as sp, + target.td as targ_id, + coalesce(target.tw,0) as tw, + GREATEST(1.0,ST_Distance(geography(target.tc), geography(source0.sc)))::numeric as distance + FROM source0 + CROSS JOIN LATERAL + ( + SELECT + * + FROM target0 + WHERE tw > minval + AND ST_DWithin(geography(source0.sc), geography(tc), radius) + ) AS target + ), + deno as( + SELECT + sourc_id, + sum(tw/distance) as h_deno + FROM + prev0 + GROUP BY sourc_id + ) + SELECT + p.sg as the_geom, + p.sourc_id as source_id, + p.targ_id as target_id, + case when p.distance > 1 then p.distance else 0.0 end as dist, + 100*(p.tw/p.distance)/d.h_deno as h, + p.sp*(p.tw/p.distance)/d.h_deno as hpop + FROM + prev0 p, + deno d + WHERE + p.targ_id = target AND + p.sourc_id = d.sourc_id; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- 0: nearest neighbor(s) +-- 1: barymetric +-- 2: IDW +-- 3: krigin ---> TO DO + + +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 VOLATILE PARALLEL UNSAFE; + +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 neighbors + -- p1: limit the number of neighbors, 0-> closest one + IF method = 0 THEN + + IF p1 = 0 THEN + p1 := 1; + END IF; + + WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v), + b as (SELECT a.v as v FROM a ORDER BY point<->a.g LIMIT p1::integer) + SELECT avg(b.v) INTO output FROM b; + 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(geomin) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + SELECT ST_area(g), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + 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; + + -- krigin + ELSIF method = 3 THEN + + -- TO DO + + END IF; + + RETURN -777.777; + +END; +$$ +language plpgsql IMMUTABLE PARALLEL SAFE; +-- ============================================================================================= +-- +-- CDB_Voronoi +-- +-- ============================================================================================= +CREATE OR REPLACE FUNCTION CDB_voronoi( + IN geomin geometry[], + IN buffer numeric DEFAULT 0.5, + IN tolerance numeric DEFAULT 1e-9 + ) +RETURNS geometry AS $$ +DECLARE + geomout geometry; +BEGIN + -- we need to make the geometry calculations in (pseudo)meters!!! + with a as ( + SELECT unnest(geomin) as g1 + ), + b as( + SELECT st_transform(g1, 3857) g2 from a + ) + SELECT array_agg(g2) INTO geomin from b; + + WITH + convexhull_1 as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as g, + buffer * |/ (st_area(ST_ConvexHull(ST_Collect(geomin)))/PI()) as r + ), + clipper as( + SELECT + st_buffer(ST_MinimumBoundingCircle(a.g), buffer*a.r) as g + FROM convexhull_1 a + ), + env0 as ( + SELECT + (st_dumppoints(st_expand(a.g, buffer*a.r))).geom as e + FROM convexhull_1 a + ), + env as ( + SELECT + array_agg(env0.e) as e + FROM env0 + ), + sample AS ( + SELECT + ST_Collect(geomin || env.e) as geom + FROM env + ), + convexhull as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as cg + ), + tin as ( + SELECT + ST_Dump(ST_DelaunayTriangles(geom, tolerance, 0)) as gd + FROM + sample + ), + tin_polygons as ( + SELECT + (gd).Path as id, + (gd).Geom as pg, + ST_Centroid(ST_MinimumBoundingCircle((gd).Geom, 180)) as ct + FROM tin + ), + tin_lines as ( + SELECT + id, + ST_ExteriorRing(pg) as lg + FROM tin_polygons + ), + tin_nodes as ( + SELECT + id, + ST_PointN(lg,1) p1, + ST_PointN(lg,2) p2, + ST_PointN(lg,3) p3 + FROM tin_lines + ), + tin_edges AS ( + SELECT + p.id, + UNNEST(ARRAY[ + ST_MakeLine(n.p1,n.p2) , + ST_MakeLine(n.p2,n.p3) , + ST_MakeLine(n.p3,n.p1)]) as Edge, + ST_Force2D(cdb_crankshaft._Find_Circle(n.p1,n.p2,n.p3)) as ct, + CASE WHEN st_distance(p.ct, ST_ExteriorRing(p.pg)) < tolerance THEN + TRUE + ELSE FALSE END AS ctx, + p.pg, + ST_within(p.ct, convexhull.cg) as ctin + FROM + tin_polygons p, + tin_nodes n, + convexhull + WHERE p.id = n.id + ), + voro_nodes as ( + SELECT + CASE WHEN x.ctx = TRUE THEN + ST_Centroid(x.edge) + ELSE + x.ct + END as xct, + CASE WHEN y.id is null THEN + CASE WHEN x.ctin = TRUE THEN + ST_SetSRID(ST_MakePoint( + ST_X(x.ct) + ((ST_X(ST_Centroid(x.edge)) - ST_X(x.ct)) * (1+buffer)), + ST_Y(x.ct) + ((ST_Y(ST_Centroid(x.edge)) - ST_Y(x.ct)) * (1+buffer)) + ), ST_SRID(x.ct)) + END + ELSE + y.ct + END as yct + FROM + tin_edges x + LEFT OUTER JOIN + tin_edges y + ON x.id <> y.id AND ST_Equals(x.edge, y.edge) + ), + voro_edges as( + SELECT + ST_LineMerge(ST_Collect(ST_MakeLine(xct, yct))) as v + FROM + voro_nodes + ), + voro_cells as( + SELECT + ST_Polygonize( + ST_Node( + ST_LineMerge( + ST_Union(v, ST_ExteriorRing( + ST_Convexhull(v) + ) + ) + ) + ) + ) as g + FROM + voro_edges + ), + voro_set as( + SELECT + (st_dump(v.g)).geom as g + FROM voro_cells v + ), + clipped_voro as( + SELECT + ST_intersection(c.g, v.g) as g + FROM + voro_set v, + clipper c + WHERE + ST_GeometryType(v.g) = 'ST_Polygon' + ) + SELECT + st_collect( + ST_Transform( + ST_ConvexHull(g), + 4326 + ) + ) + INTO geomout + FROM + clipped_voro; + RETURN geomout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +/** ---------------------------------------------------------------------------------------- + * @function : FindCircle + * @precis : Function that determines if three points form a circle. If so a table containing + * centre and radius is returned. If not, a null table is returned. + * @version : 1.0.1 + * @param : p_pt1 : First point in curve + * @param : p_pt2 : Second point in curve + * @param : p_pt3 : Third point in curve + * @return : geometry : In which X,Y ordinates are the centre X, Y and the Z being the radius of found circle + * or NULL if three points do not form a circle. + * @history : Simon Greener - Feb 2012 - Original coding. + * Rafa de la Torre - Aug 2016 - Small fix for type checking + * Raul Marin - Sept 2017 - Remove unnecessary NULL checks and set function categories + * @copyright : Simon Greener @ 2012 + * Licensed under a Creative Commons Attribution-Share Alike 2.5 Australia License. (http://creativecommons.org/licenses/by-sa/2.5/au/) +**/ +CREATE OR REPLACE FUNCTION _Find_Circle( + IN p_pt1 geometry, + IN p_pt2 geometry, + IN p_pt3 geometry) + RETURNS geometry AS +$BODY$ +DECLARE + v_Centre geometry; + v_radius NUMERIC; + v_CX NUMERIC; + v_CY NUMERIC; + v_dA NUMERIC; + v_dB NUMERIC; + v_dC NUMERIC; + v_dD NUMERIC; + v_dE NUMERIC; + v_dF NUMERIC; + v_dG NUMERIC; +BEGIN + IF ( ST_GeometryType(p_pt1) <> 'ST_Point' OR + ST_GeometryType(p_pt2) <> 'ST_Point' OR + ST_GeometryType(p_pt3) <> 'ST_Point' ) THEN + RAISE EXCEPTION 'All supplied geometries must be points.'; + RETURN NULL; + END IF; + v_dA := ST_X(p_pt2) - ST_X(p_pt1); + v_dB := ST_Y(p_pt2) - ST_Y(p_pt1); + v_dC := ST_X(p_pt3) - ST_X(p_pt1); + v_dD := ST_Y(p_pt3) - ST_Y(p_pt1); + v_dE := v_dA * (ST_X(p_pt1) + ST_X(p_pt2)) + v_dB * (ST_Y(p_pt1) + ST_Y(p_pt2)); + v_dF := v_dC * (ST_X(p_pt1) + ST_X(p_pt3)) + v_dD * (ST_Y(p_pt1) + ST_Y(p_pt3)); + v_dG := 2.0 * (v_dA * (ST_Y(p_pt3) - ST_Y(p_pt2)) - v_dB * (ST_X(p_pt3) - ST_X(p_pt2))); + -- If v_dG is zero then the three points are collinear and no finite-radius + -- circle through them exists. + IF ( v_dG = 0 ) THEN + RETURN NULL; + ELSE + v_CX := (v_dD * v_dE - v_dB * v_dF) / v_dG; + v_CY := (v_dA * v_dF - v_dC * v_dE) / v_dG; + v_Radius := SQRT(POWER(ST_X(p_pt1) - v_CX,2) + POWER(ST_Y(p_pt1) - v_CY,2) ); + END IF; + RETURN ST_SetSRID(ST_MakePoint(v_CX, v_CY, v_radius),ST_Srid(p_pt1)); +END; +$BODY$ + LANGUAGE plpgsql IMMUTABLE STRICT PARALLEL SAFE; + +-- Moran's I Global Measure (public-facing) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, significance NUMERIC) +AS $$ + from crankshaft.clustering import Moran + # TODO: use named parameters or a dictionary + moran = Moran() + return moran.global_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (internal function) - DEPRECATED +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE ( + moran NUMERIC, + quads TEXT, + significance NUMERIC, + rowid INT, + vals NUMERIC) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + result = moran.local_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) + # remove spatial lag + return [(r[6], r[0], r[1], r[7], r[5]) for r in result] +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (internal function) +CREATE OR REPLACE FUNCTION + _CDB_MoransILocal( + subquery TEXT, + column_name TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS TABLE ( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + +from crankshaft.clustering import Moran +moran = Moran() +return moran.local_stat(subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +-- Moran's I Local (public-facing function) +-- Replaces CDB_AreasOfInterestLocal +CREATE OR REPLACE FUNCTION + CDB_MoransILocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE ( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + + SELECT + quads, significance, spatial_lag, spatial_lag_std, + orig_val, orig_val_std, moran_stat, rowid + FROM cdb_crankshaft._CDB_MoransILocal( + subquery, column_name, w_type, + num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local (public-facing function) - DEPRECATED +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocal( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspots( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspots( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliers( + subquery TEXT, + attr TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') + RETURNS TABLE (moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Global Rate (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestGlobalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (moran FLOAT, significance FLOAT) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + # TODO: use named parameters or a dictionary + return moran.global_rate_stat(subquery, numerator, denominator, w_type, + num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +-- Moran's I Local Rate (internal function) - DEPRECATED +CREATE OR REPLACE FUNCTION + _CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE( + moran NUMERIC, + quads TEXT, + significance NUMERIC, + rowid INT, + vals NUMERIC) +AS $$ + from crankshaft.clustering import Moran + moran = Moran() + # TODO: use named parameters or a dictionary + result = moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + # remove spatial lag + return [(r[6], r[0], r[1], r[7], r[4]) for r in result] +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate (public-facing function) - DEPRECATED +CREATE OR REPLACE FUNCTION + CDB_AreasOfInterestLocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Internal function +CREATE OR REPLACE FUNCTION + _CDB_MoransILocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT, + num_ngbrs INT, + permutations INT, + geom_col TEXT, + id_col TEXT) +RETURNS +TABLE( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ +from crankshaft.clustering import Moran +moran = Moran() +return moran.local_rate_stat( + subquery, + numerator, + denominator, + w_type, + num_ngbrs, + permutations, + geom_col, + id_col +) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Moran's I Rate +-- Replaces CDB_AreasOfInterestLocalRate +CREATE OR REPLACE FUNCTION + CDB_MoransILocalRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE( + quads TEXT, + significance NUMERIC, + spatial_lag NUMERIC, + spatial_lag_std NUMERIC, + orig_val NUMERIC, + orig_val_std NUMERIC, + moran_stat NUMERIC, + rowid INT) +AS $$ + +SELECT + quads, significance, spatial_lag, spatial_lag_std, + orig_val, orig_val_std, moran_stat, rowid +FROM cdb_crankshaft._CDB_MoransILocalRate( + subquery, numerator, denominator, w_type, + num_ngbrs, permutations, geom_col, id_col); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for HH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialHotspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HH', 'HL'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for LL and LH (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialColdspotsRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('LL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; + +-- Moran's I Local Rate only for LH and HL (public-facing function) +CREATE OR REPLACE FUNCTION + CDB_GetSpatialOutliersRate( + subquery TEXT, + numerator TEXT, + denominator TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS +TABLE(moran NUMERIC, quads TEXT, significance NUMERIC, rowid INT, vals NUMERIC) +AS $$ + + SELECT moran, quads, significance, rowid, vals + FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col) + WHERE quads IN ('HL', 'LH'); + +$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE; +-- Spatial k-means clustering + +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 $$ + +from crankshaft.clustering import Kmeans +kmeans = Kmeans() +return kmeans.spatial(query, no_clusters, no_init) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- Non-spatial k-means clustering +-- query: sql query to retrieve all the needed data +-- colnames: text array of column names for doing the clustering analysis +-- no_clusters: number of requested clusters +-- standardize: whether to scale variables to a mean of zero and a standard +-- deviation of 1 +-- id_colname: name of the id column + +CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial( + query TEXT, + colnames TEXT[], + no_clusters INTEGER, + standardize BOOLEAN DEFAULT true, + id_col TEXT DEFAULT 'cartodb_id' +) +RETURNS TABLE( + cluster_label text, + cluster_center json, + silhouettes numeric, + inertia numeric, + rowid bigint +) AS $$ + +from crankshaft.clustering import Kmeans +kmeans = Kmeans() +return kmeans.nonspatial(query, colnames, no_clusters, + standardize=standardize, + id_col=id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +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 IMMUTABLE PARALLEL SAFE; + + +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 IMMUTABLE PARALLEL SAFE; + +-- Create aggregate if it did not exist +DO $$ +BEGIN + IF NOT EXISTS ( + SELECT * + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE n.nspname = 'cdb_crankshaft' + AND p.proname = 'cdb_weightedmean' + AND p.proisagg) + THEN + CREATE AGGREGATE CDB_WeightedMean(geometry(Point, 4326), NUMERIC) ( + SFUNC = CDB_WeightedMeanS, + FINALFUNC = CDB_WeightedMeanF, + STYPE = Numeric[], + PARALLEL = SAFE, + INITCOND = "{0.0,0.0,0.0}" + ); + END IF; +END +$$ LANGUAGE plpgsql; +-- Spatial Markov + +-- input table format: +-- id | geom | date_1 | date_2 | date_3 +-- 1 | Pt1 | 12.3 | 13.1 | 14.2 +-- 2 | Pt2 | 11.0 | 13.2 | 12.5 +-- ... +-- Sample Function call: +-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate', +-- Array['date_1', 'date_2', 'date_3']) + +CREATE OR REPLACE FUNCTION + CDB_SpatialMarkovTrend ( + subquery TEXT, + time_cols TEXT[], + num_classes INT DEFAULT 7, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 99, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT) +AS $$ + + from crankshaft.space_time_dynamics import Markov + markov = Markov() + + ## TODO: use named parameters or a dictionary + return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col) +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + +-- input table format: identical to above but in a predictable format +-- Sample function call: +-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', +-- 'date_1') + + +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col_min text, +-- time_col_max text, +-- date_format text, -- '_YYYY_MM_DD' +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- +-- -- input table format: +-- -- id | geom | date | measurement +-- -- 1 | Pt1 | 12/3 | 13.2 +-- -- 2 | Pt2 | 11/5 | 11.3 +-- -- 3 | Pt1 | 11/13 | 12.9 +-- -- 4 | Pt3 | 12/19 | 10.1 +-- -- ... +-- +-- CREATE OR REPLACE FUNCTION +-- cdb_spatial_markov ( +-- subquery TEXT, +-- time_col text, +-- num_time_per_bin INT DEFAULT 1, +-- permutations INT DEFAULT 99, +-- geom_column TEXT DEFAULT 'the_geom', +-- id_col TEXT DEFAULT 'cartodb_id', +-- w_type TEXT DEFAULT 'knn', +-- num_ngbrs int DEFAULT 5) +-- RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT) +-- AS $$ +-- plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()') +-- from crankshaft.clustering import moran_local +-- # TODO: use named parameters or a dictionary +-- return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs) +-- $$ LANGUAGE plpythonu; +-- Based on: +-- https://github.com/mapbox/polylabel/blob/master/index.js +-- https://sites.google.com/site/polesofinaccessibility/ +-- Requires: https://github.com/CartoDB/cartodb-postgresql + +-- Based on: +-- https://github.com/mapbox/polylabel/blob/master/index.js +-- https://sites.google.com/site/polesofinaccessibility/ +-- Requires: https://github.com/CartoDB/cartodb-postgresql + +CREATE OR REPLACE FUNCTION CDB_PIA( + IN polygon geometry, + IN tolerance numeric DEFAULT 1.0 + ) +RETURNS geometry AS $$ +DECLARE + env geometry[]; + cells geometry[]; + cell geometry; + best_c geometry; + best_d numeric; + test_d numeric; + test_mx numeric; + test_h numeric; + test_cells geometry[]; + width numeric; + height numeric; + h numeric; + i integer; + n integer; + sqr numeric; + p geometry; +BEGIN + sqr := 0.5*(|/2.0); + polygon := ST_Transform(polygon, 3857); + + -- grid #0 cell size + height := ST_YMax(polygon) - ST_YMin(polygon); + width := ST_XMax(polygon) - ST_XMin(polygon); + h := 0.5*LEAST(height, width); + + -- grid #0 + with c1 as( + SELECT cdb_crankshaft.CDB_RectangleGrid(polygon, h, h) as c + ) + SELECT array_agg(c) INTO cells FROM c1; + + -- 1st guess: centroid + best_c := polygon; + best_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(Polygon)); + + -- looping the loop + n := array_length(cells,1); + i := 1; + LOOP + + EXIT WHEN i > n; + + cell := cells[i]; + + i := i+1; + + -- cell side size, it's square + test_h := ST_XMax(cell) - ST_XMin(cell) ; + + -- check distance + test_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(cell)); + + IF test_d > best_d THEN + best_d := test_d; + best_c := cell; + END IF; + + -- longest distance within the cell + test_mx := test_d + (test_h * sqr); + + -- if the cell has no chance to contains the desired point, continue + CONTINUE WHEN test_mx - best_d <= tolerance; + + -- resample the cell + with c1 as( + SELECT cdb_crankshaft.CDB_RectangleGrid(cell, test_h/2, test_h/2) as c + ) + SELECT array_agg(c) INTO test_cells FROM c1; + + -- concat the new cells to the former array + cells := cells || test_cells; + + -- prepare next iteration + n := array_length(cells,1); + + END LOOP; + + RETURN ST_transform(ST_Centroid(best_c), 4326); + +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + + + +-- signed distance point to polygon with holes +-- negative is the point is out the polygon +-- rev 1. adding MULTIPOLYGON and GEOMETRYCOLLECTION support by @abelvm +CREATE OR REPLACE FUNCTION _Signed_Dist( + IN polygon geometry, + IN point geometry + ) +RETURNS numeric AS $$ +DECLARE + pols geometry[]; + pol geometry; + i integer; + j integer; + within integer; + w integer; + holes integer; + dist numeric; + d numeric; +BEGIN + dist := 1e999; + WITH collection as (SELECT (ST_dump(polygon)).geom as geom) SELECT array_agg(geom) into pols FROM collection; + FOR j in 1..array_length(pols, 1) + LOOP + pol := pols[j]; + d := dist; + SELECT LEAST(dist, ST_distance(point, ST_ExteriorRing(pol))::numeric) INTO d; + SELECT CASE WHEN ST_Within(point,pol) THEN 1 ELSE -1 END INTO w; + SELECT ST_NumInteriorRings(pol) INTO holes; + IF holes > 0 THEN + FOR i IN 1..holes + LOOP + SELECT LEAST(d, ST_distance(point, ST_InteriorRingN(pol, i))::numeric) INTO d; + END LOOP; + END IF; + IF d < dist THEN + dist:= d; + within := w; + END IF; + END LOOP; + dist := dist * within::numeric; + RETURN dist; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- +-- Iterative densification of a set of points using Delaunay triangulation +-- the new points have as assigned value the average value of the 3 vertex (centroid) +-- +-- @param geomin - array of geometries (points) +-- +-- @param colin - array of numeric values in that points +-- +-- @param iterations - integer, number of iterations +-- +-- +-- Returns: TABLE(geomout geometry, colout numeric) +-- +-- +CREATE OR REPLACE FUNCTION CDB_Densify( + IN geomin geometry[], + IN colin numeric[], + IN iterations integer + ) +RETURNS TABLE(geomout geometry, colout numeric) AS $$ +DECLARE + geotemp geometry[]; + coltemp numeric[]; + i integer; + gs geometry[]; + g geometry; + vertex geometry[]; + va numeric; + vb numeric; + vc numeric; + center geometry; + centerval numeric; + tmp integer; +BEGIN + geotemp := geomin; + coltemp := colin; + FOR i IN 1..iterations + LOOP + -- generate TIN + WITH a as (SELECT unnest(geotemp) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom AS v FROM b) + SELECT array_agg(v) INTO gs FROM c; + -- loop cells + FOREACH g IN ARRAY gs + LOOP + -- append centroid + SELECT ST_Centroid(g) INTO center; + geotemp := array_append(geotemp, center); + -- retrieve the value of each vertex + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + -- calc the value at the center + centerval := (va + vb + vc) / 3; + -- append the value + coltemp := array_append(coltemp, centerval); + END LOOP; + END LOOP; + RETURN QUERY SELECT unnest(geotemp ) as geomout, unnest(coltemp ) as colout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION CDB_TINmap( + IN geomin geometry[], + IN colin numeric[], + IN iterations integer + ) +RETURNS TABLE(geomout geometry, colout numeric) AS $$ +DECLARE + p geometry[]; + vals numeric[]; + gs geometry[]; + g geometry; + vertex geometry[]; + centerval numeric; + va numeric; + vb numeric; + vc numeric; + coltemp numeric[]; +BEGIN + SELECT array_agg(dens.geomout), array_agg(dens.colout) INTO p, vals FROM cdb_crankshaft.CDB_Densify(geomin, colin, iterations) dens; + WITH a as (SELECT unnest(p) AS e), + b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a), + c as (SELECT (ST_Dump(t)).geom AS v FROM b) + SELECT array_agg(v) INTO gs FROM c; + FOREACH g IN ARRAY gs + LOOP + -- retrieve the vertex of each triangle + WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v) + SELECT array_agg(v) INTO vertex FROM a; + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + -- calc the value at the center + centerval := (va + vb + vc) / 3; + -- append the value + coltemp := array_append(coltemp, centerval); + END LOOP; + RETURN QUERY SELECT unnest(gs) as geomout, unnest(coltemp ) as colout; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; +-- Getis-Ord's G +-- Hotspot/Coldspot Analysis tool +CREATE OR REPLACE FUNCTION + CDB_GetisOrdsG( + subquery TEXT, + column_name TEXT, + w_type TEXT DEFAULT 'knn', + num_ngbrs INT DEFAULT 5, + permutations INT DEFAULT 999, + geom_col TEXT DEFAULT 'the_geom', + id_col TEXT DEFAULT 'cartodb_id') +RETURNS TABLE (z_score NUMERIC, p_value NUMERIC, p_z_sim NUMERIC, rowid BIGINT) +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 plpythonu VOLATILE PARALLEL UNSAFE; + +-- TODO: make a version that accepts the values as arrays + +-- Find outliers using a static threshold +-- +CREATE OR REPLACE FUNCTION CDB_StaticOutlier(column_value numeric, threshold numeric) +RETURNS boolean +AS $$ +BEGIN + + RETURN column_value > threshold; + +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE ; + +-- Find outliers by a percentage above the threshold +-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false` + +CREATE OR REPLACE FUNCTION CDB_PercentOutlier(column_values numeric[], outlier_fraction numeric, ids int[]) +RETURNS TABLE(is_outlier boolean, rowid int) +AS $$ +DECLARE + avg_val numeric; + out_vals boolean[]; +BEGIN + + SELECT avg(i) INTO avg_val + FROM unnest(column_values) As x(i); + + IF avg_val = 0 THEN + RAISE EXCEPTION 'Mean value is zero. Try another outlier method.'; + END IF; + + SELECT array_agg( + outlier_fraction < i / avg_val) INTO out_vals + FROM unnest(column_values) As x(i); + + RETURN QUERY + SELECT unnest(out_vals) As is_outlier, + unnest(ids) As rowid; + +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; + +-- Find outliers above a given number of standard deviations from the mean + +CREATE OR REPLACE FUNCTION CDB_StdDevOutlier(column_values numeric[], num_deviations numeric, ids int[], is_symmetric boolean DEFAULT true) +RETURNS TABLE(is_outlier boolean, rowid int) +AS $$ +DECLARE + stddev_val numeric; + avg_val numeric; + out_vals boolean[]; +BEGIN + + SELECT stddev(i), avg(i) INTO stddev_val, avg_val + FROM unnest(column_values) As x(i); + + IF stddev_val = 0 THEN + RAISE EXCEPTION 'Standard deviation of input data is zero'; + END IF; + + IF is_symmetric THEN + SELECT array_agg( + abs(i - avg_val) / stddev_val > num_deviations) INTO out_vals + FROM unnest(column_values) As x(i); + ELSE + SELECT array_agg( + (i - avg_val) / stddev_val > num_deviations) INTO out_vals + FROM unnest(column_values) As x(i); + END IF; + + RETURN QUERY + SELECT unnest(out_vals) As is_outlier, + unnest(ids) As rowid; +END; +$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION CDB_Contour( + IN geomin geometry[], + IN colin numeric[], + IN buffer numeric, + IN intmethod integer, + IN classmethod integer, + IN steps integer, + IN max_time integer DEFAULT 60000 + ) +RETURNS TABLE( + the_geom geometry, + bin integer, + min_value numeric, + max_value numeric, + avg_value numeric +) AS $$ +DECLARE + cell_count integer; + tin geometry[]; + resolution integer; +BEGIN + + -- nasty trick to override issue #121 + IF max_time = 0 THEN + max_time = -90; + END IF; + resolution := max_time; + max_time := -1 * resolution; + + -- calc the optimal number of cells for the current dataset + SELECT + CASE intmethod + WHEN 0 THEN round(3.7745903782 * max_time - 9.4399210051 * array_length(geomin,1) - 1350.8778213073) + WHEN 1 THEN round(2.2855592156 * max_time - 87.285217133 * array_length(geomin,1) + 17255.7085601797) + WHEN 2 THEN round(0.9799471999 * max_time - 127.0334085369 * array_length(geomin,1) + 22707.9579721218) + ELSE 10000 + END INTO cell_count; + + -- we don't have iterative barycentric interpolation in CDB_interpolation, + -- and it's a costy function, so let's make a custom one here till + -- we update the code + -- tin := ARRAY[]::geometry[]; + IF intmethod=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) + SELECT array_agg(v) INTO tin FROM c; + END IF; + -- Delaunay stuff performed just ONCE!! + + -- magic + RETURN QUERY + WITH + convexhull as ( + SELECT + ST_ConvexHull(ST_Collect(geomin)) as g, + buffer * |/ st_area(ST_ConvexHull(ST_Collect(geomin)))/PI() as r + ), + envelope as ( + SELECT + st_expand(a.g, a.r) as e + FROM convexhull a + ), + envelope3857 as( + SELECT + ST_Transform(e, 3857) as geom + FROM envelope + ), + resolution as( + SELECT + CASE WHEN resolution <= 0 THEN + round(|/ ( + ST_area(geom) / abs(cell_count) + )) + ELSE + resolution + END AS cell + FROM envelope3857 + ), + grid as( + SELECT + ST_Transform(cdb_crankshaft.CDB_RectangleGrid(e.geom, r.cell, r.cell), 4326) as geom + FROM envelope3857 e, resolution r + ), + interp as( + SELECT + geom, + CASE + WHEN intmethod=1 THEN cdb_crankshaft._interp_in_tin(geomin, colin, tin, ST_Centroid(geom)) + ELSE cdb_crankshaft.CDB_SpatialInterpolation(geomin, colin, ST_Centroid(geom), intmethod) + END as val + FROM grid + ), + classes as( + SELECT CASE + WHEN classmethod = 0 THEN + cdb_crankshaft.CDB_EqualIntervalBins(array_agg(val), steps) + WHEN classmethod = 1 THEN + cdb_crankshaft.CDB_HeadsTailsBins(array_agg(val), steps) + WHEN classmethod = 2 THEN + cdb_crankshaft.CDB_JenksBins(array_agg(val), steps) + ELSE + cdb_crankshaft.CDB_QuantileBins(array_agg(val), steps) + END as b + FROM interp + where val is not null + ), + classified as( + SELECT + i.*, + width_bucket(i.val, c.b) as bucket + FROM interp i left join classes c + ON 1=1 + ), + classified2 as( + SELECT + geom, + val, + CASE + WHEN bucket = steps THEN bucket - 1 + ELSE bucket + END as b + FROM classified + ), + final as( + SELECT + st_union(geom) as the_geom, + b as bin, + min(val) as min_value, + max(val) as max_value, + avg(val) as avg_value + FROM classified2 + GROUP BY bin + ) + SELECT + * + FROM final + where final.bin is not null + ; +END; +$$ language plpgsql VOLATILE PARALLEL RESTRICTED; + + +-- ===================================================================== +-- Interp in grid, so we can use barycentric with a precalculated tin (NNI) +-- ===================================================================== +CREATE OR REPLACE FUNCTION _interp_in_tin( + IN geomin geometry[], + IN colin numeric[], + IN tin geometry[], + IN point geometry + ) +RETURNS numeric AS +$$ +DECLARE + g geometry; + vertex geometry[]; + sg numeric; + sa numeric; + sb numeric; + sc numeric; + va numeric; + vb numeric; + vc numeric; + output numeric; +BEGIN + -- get the cell the point is within + WITH + a as (SELECT unnest(tin) as v), + b as (SELECT v FROM a WHERE ST_Within(point, v)) + SELECT v INTO g FROM b; + + -- if we're out of the data realm, + -- return null + IF g is null THEN + RETURN null; + END IF; + + -- vertex of the selected cell + WITH a AS ( + SELECT (ST_DumpPoints(g)).geom AS v + ) + SELECT array_agg(v) INTO vertex FROM a; + + -- retrieve the value of each vertex + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]); + + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]); + + WITH a AS(SELECT unnest(geomin) as geo, unnest(colin) as c) + SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]); + + -- calc the areas + SELECT + ST_area(g), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[2], vertex[3], point]))), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point, vertex[1], vertex[3], point]))), + ST_area(ST_MakePolygon(ST_MakeLine(ARRAY[point,vertex[1],vertex[2], point]))) INTO sg, sa, sb, sc; + + output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg,1); + RETURN output; +END; +$$ +language plpgsql IMMUTABLE PARALLEL SAFE; +-- Function by Stuart Lynn for a simple interpolation of a value +-- from a polygon table over an arbitrary polygon +-- (weighted by the area proportion overlapped) +-- Aereal weighting is a very simple form of aereal interpolation. +-- +-- Parameters: +-- * geom a Polygon geometry which defines the area where a value will be +-- estimated as the area-weighted sum of a given table/column +-- * target_table_name table name of the table that provides the values +-- * target_column column name of the column that provides the values +-- * schema_name optional parameter to defina the schema the target table +-- belongs to, which is necessary if its not in the search_path. +-- Note that target_table_name should never include the schema in it. +-- Return value: +-- Aereal-weighted interpolation of the column values over the geometry +CREATE OR REPLACE +FUNCTION cdb_overlap_sum(geom geometry, target_table_name text, target_column text, schema_name text DEFAULT NULL) + RETURNS numeric AS +$$ +DECLARE + result numeric; + qualified_name text; +BEGIN + IF schema_name IS NULL THEN + qualified_name := Format('%I', target_table_name); + ELSE + qualified_name := Format('%I.%s', schema_name, target_table_name); + END IF; + EXECUTE Format(' + SELECT sum(%I*ST_Area(St_Intersection($1, a.the_geom))/ST_Area(a.the_geom)) + FROM %s AS a + WHERE $1 && a.the_geom + ', target_column, qualified_name) + USING geom + INTO result; + RETURN result; +END; +$$ LANGUAGE plpgsql STABLE PARALLEL SAFE; +CREATE OR REPLACE FUNCTION +CDB_GWR(subquery text, dep_var text, ind_vars text[], + bw numeric default null, fixed boolean default False, + kernel text default 'bisquare', geom_col text default 'the_geom', + id_col text default 'cartodb_id') +RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, + filtered_t_vals JSON, predicted numeric, + residuals numeric, r_squared numeric, bandwidth numeric, + rowid bigint) +AS $$ + +from crankshaft.regression import GWR + +gwr = GWR() + +return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; + + +CREATE OR REPLACE FUNCTION +CDB_GWR_Predict(subquery text, dep_var text, ind_vars text[], + bw numeric default null, fixed boolean default False, + kernel text default 'bisquare', + geom_col text default 'the_geom', + id_col text default 'cartodb_id') +RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, + r_squared numeric, predicted numeric, rowid bigint) +AS $$ + +from crankshaft.regression import GWR +gwr = GWR() + +return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col) + +$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE; +-- +-- Creates N points randomly distributed arround the polygon +-- +-- @param g - the geometry to be turned in to points +-- +-- @param no_points - the number of points to generate +-- +-- @params max_iter_per_point - the function generates points in the polygon's bounding box +-- and discards points which don't lie in the polygon. max_iter_per_point specifies how many +-- misses per point the funciton accepts before giving up. +-- +-- Returns: Multipoint with the requested points +CREATE OR REPLACE FUNCTION cdb_dot_density(geom geometry , no_points Integer, max_iter_per_point Integer DEFAULT 1000) +RETURNS GEOMETRY AS $$ +DECLARE + extent GEOMETRY; + test_point Geometry; + width NUMERIC; + height NUMERIC; + x0 NUMERIC; + y0 NUMERIC; + xp NUMERIC; + yp NUMERIC; + no_left INTEGER; + remaining_iterations INTEGER; + points GEOMETRY[]; + bbox_line GEOMETRY; + intersection_line GEOMETRY; +BEGIN + extent := ST_Envelope(geom); + width := ST_XMax(extent) - ST_XMIN(extent); + height := ST_YMax(extent) - ST_YMIN(extent); + x0 := ST_XMin(extent); + y0 := ST_YMin(extent); + no_left := no_points; + + LOOP + if(no_left=0) THEN + EXIT; + END IF; + yp = y0 + height*random(); + bbox_line = ST_MakeLine( + ST_SetSRID(ST_MakePoint(yp, x0),4326), + ST_SetSRID(ST_MakePoint(yp, x0+width),4326) + ); + intersection_line = ST_Intersection(bbox_line,geom); + test_point = ST_LineInterpolatePoint(st_makeline(st_linemerge(intersection_line)),random()); + points := points || test_point; + no_left = no_left - 1 ; + END LOOP; + RETURN ST_Collect(points); +END; +$$ +LANGUAGE plpgsql VOLATILE PARALLEL RESTRICTED; +-- Make sure by default there are no permissions for publicuser +-- NOTE: this happens at extension creation time, as part of an implicit transaction. +-- REVOKE ALL PRIVILEGES ON SCHEMA cdb_crankshaft FROM PUBLIC, publicuser CASCADE; + +-- Grant permissions on the schema to publicuser (but just the schema) +GRANT USAGE ON SCHEMA cdb_crankshaft TO publicuser; + +-- Revoke execute permissions on all functions in the schema by default +-- REVOKE EXECUTE ON ALL FUNCTIONS IN SCHEMA cdb_crankshaft FROM PUBLIC, publicuser; +-- +-- Fill given extent with a rectangular coverage +-- +-- @param ext Extent to fill. Only rectangles with center point falling +-- inside the extent (or at the lower or leftmost edge) will +-- be emitted. The returned hexagons will have the same SRID +-- as this extent. +-- +-- @param width With of each rectangle +-- +-- @param height Height of each rectangle +-- +-- @param origin Optional origin to allow for exact tiling. +-- If omitted the origin will be 0,0. +-- The parameter is checked for having the same SRID +-- as the extent. +-- +-- +CREATE OR REPLACE FUNCTION CDB_RectangleGrid(ext GEOMETRY, width FLOAT8, height FLOAT8, origin GEOMETRY DEFAULT NULL) +RETURNS SETOF GEOMETRY +AS $$ +DECLARE + h GEOMETRY; -- rectangle cell + hstep FLOAT8; -- horizontal step + vstep FLOAT8; -- vertical step + hw FLOAT8; -- half width + hh FLOAT8; -- half height + vstart FLOAT8; + hstart FLOAT8; + hend FLOAT8; + vend FLOAT8; + xoff FLOAT8; + yoff FLOAT8; + xgrd FLOAT8; + ygrd FLOAT8; + x FLOAT8; + y FLOAT8; + srid INTEGER; +BEGIN + + srid := ST_SRID(ext); + + xoff := 0; + yoff := 0; + + IF origin IS NOT NULL THEN + IF ST_SRID(origin) != srid THEN + RAISE EXCEPTION 'SRID mismatch between extent (%) and origin (%)', srid, ST_SRID(origin); + END IF; + xoff := ST_X(origin); + yoff := ST_Y(origin); + END IF; + + --RAISE DEBUG 'X offset: %', xoff; + --RAISE DEBUG 'Y offset: %', yoff; + + hw := width/2.0; + hh := height/2.0; + + xgrd := hw; + ygrd := hh; + --RAISE DEBUG 'X grid size: %', xgrd; + --RAISE DEBUG 'Y grid size: %', ygrd; + + hstep := width; + vstep := height; + + -- Tweak horizontal start on hstep grid from origin + hstart := xoff + ceil((ST_XMin(ext)-xoff)/hstep)*hstep; + --RAISE DEBUG 'hstart: %', hstart; + + -- Tweak vertical start on vstep grid from origin + vstart := yoff + ceil((ST_Ymin(ext)-yoff)/vstep)*vstep; + --RAISE DEBUG 'vstart: %', vstart; + + hend := ST_XMax(ext); + vend := ST_YMax(ext); + + --RAISE DEBUG 'hend: %', hend; + --RAISE DEBUG 'vend: %', vend; + + x := hstart; + WHILE x < hend LOOP -- over X + y := vstart; + h := ST_MakeEnvelope(x-hw, y-hh, x+hw, y+hh, srid); + WHILE y < vend LOOP -- over Y + RETURN NEXT h; + h := ST_Translate(h, 0, vstep); + y := yoff + round(((y + vstep)-yoff)/ygrd)*ygrd; -- round to grid + END LOOP; + x := xoff + round(((x + hstep)-xoff)/xgrd)*xgrd; -- round to grid + END LOOP; + + RETURN; +END +$$ LANGUAGE 'plpgsql' IMMUTABLE PARALLEL SAFE; + +-- +-- Calculate the equal interval bins for a given column +-- +-- @param in_array A numeric array of numbers to determine the best +-- to determine the bin boundary +-- +-- @param breaks The number of bins you want to find. +-- +-- +-- Returns: upper edges of bins +-- +-- + +CREATE OR REPLACE FUNCTION CDB_EqualIntervalBins ( in_array NUMERIC[], breaks INT ) RETURNS NUMERIC[] as $$ +DECLARE + diff numeric; + min_val numeric; + max_val numeric; + tmp_val numeric; + i INT := 1; + reply numeric[]; +BEGIN + SELECT min(e), max(e) INTO min_val, max_val FROM ( SELECT unnest(in_array) e ) x WHERE e IS NOT NULL; + diff = (max_val - min_val) / breaks::numeric; + LOOP + IF i < breaks THEN + tmp_val = min_val + i::numeric * diff; + reply = array_append(reply, tmp_val); + i := i+1; + ELSE + reply = array_append(reply, max_val); + EXIT; + END IF; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +-- +-- Determine the Heads/Tails classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Heads/Tails method. +-- +-- @param breaks The number of bins you want to find. +-- +-- + +CREATE OR REPLACE FUNCTION CDB_HeadsTailsBins ( in_array NUMERIC[], breaks INT) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + arr_mean numeric; + i INT := 2; + reply numeric[]; +BEGIN + -- get the total size of our row + element_count := array_upper(in_array, 1) - array_lower(in_array, 1); + -- ensure the ordering of in_array + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e) x; + -- stop if no rows + IF element_count IS NULL THEN + RETURN NULL; + END IF; + -- stop if our breaks are more than our input array size + IF element_count < breaks THEN + RETURN in_array; + END IF; + + -- get our mean value + SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x; + + reply = Array[arr_mean]; + -- slice our bread + LOOP + IF i > breaks THEN EXIT; END IF; + SELECT avg(e) INTO arr_mean FROM ( SELECT unnest(in_array) e) x WHERE e > reply[i-1]; + IF arr_mean IS NOT NULL THEN + reply = array_append(reply, arr_mean); + END IF; + i := i+1; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + +-- +-- Determine the Jenks classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Jenks method. +-- +-- @param breaks The number of bins you want to find. +-- +-- @param iterations The number of different starting positions to test. +-- +-- @param invert Optional wheter to return the top of each bin (default) +-- or the bottom. BOOLEAN, default=FALSE. +-- +-- + + +CREATE OR REPLACE FUNCTION CDB_JenksBins ( in_array NUMERIC[], breaks INT, iterations INT DEFAULT 5, invert BOOLEAN DEFAULT FALSE) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + arr_mean NUMERIC; + bot INT; + top INT; + tops INT[]; + classes INT[][]; + i INT := 1; j INT := 1; + curr_result NUMERIC[]; + best_result NUMERIC[]; + seedtarget TEXT; + quant NUMERIC[]; + shuffles INT; +BEGIN + -- get the total size of our row + element_count := array_length(in_array, 1); --array_upper(in_array, 1) - array_lower(in_array, 1); + -- ensure the ordering of in_array + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e) x; + -- stop if no rows + IF element_count IS NULL THEN + RETURN NULL; + END IF; + -- stop if our breaks are more than our input array size + IF element_count < breaks THEN + RETURN in_array; + END IF; + + shuffles := LEAST(GREATEST(floor(2500000.0/(element_count::float*iterations::float)), 1), 750)::int; + -- get our mean value + SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x; + + -- assume best is actually Quantile + SELECT cdb_crankshaft.CDB_QuantileBins(in_array, breaks) INTO quant; + + -- if data is very very large, just return quant and be done + IF element_count > 5000000 THEN + RETURN quant; + END IF; + + -- change quant into bottom, top markers + LOOP + IF i = 1 THEN + bot = 1; + ELSE + -- use last top to find this bot + bot = top+1; + END IF; + IF i = breaks THEN + top = element_count; + ELSE + SELECT count(*) INTO top FROM ( SELECT unnest(in_array) as v) x WHERE v <= quant[i]; + END IF; + IF i = 1 THEN + classes = ARRAY[ARRAY[bot,top]]; + ELSE + classes = ARRAY_CAT(classes,ARRAY[bot,top]); + END IF; + IF i > breaks THEN EXIT; END IF; + i = i+1; + END LOOP; + + best_result = cdb_crankshaft.CDB_JenksBinsIteration( in_array, breaks, classes, invert, element_count, arr_mean, shuffles); + + --set the seed so we can ensure the same results + SELECT setseed(0.4567) INTO seedtarget; + --loop through random starting positions + LOOP + IF j > iterations-1 THEN EXIT; END IF; + i = 1; + tops = ARRAY[element_count]; + LOOP + IF i = breaks THEN EXIT; END IF; + SELECT array_agg(distinct e) INTO tops FROM (SELECT unnest(array_cat(tops, ARRAY[floor(random()*element_count::float)::int])) as e ORDER BY e) x WHERE e != 1; + i = array_length(tops, 1); + END LOOP; + i = 1; + LOOP + IF i > breaks THEN EXIT; END IF; + IF i = 1 THEN + bot = 1; + ELSE + bot = top+1; + END IF; + top = tops[i]; + IF i = 1 THEN + classes = ARRAY[ARRAY[bot,top]]; + ELSE + classes = ARRAY_CAT(classes,ARRAY[bot,top]); + END IF; + i := i+1; + END LOOP; + curr_result = cdb_crankshaft.CDB_JenksBinsIteration( in_array, breaks, classes, invert, element_count, arr_mean, shuffles); + + IF curr_result[1] > best_result[1] THEN + best_result = curr_result; + j = j-1; -- if we found a better result, add one more search + END IF; + j = j+1; + END LOOP; + + RETURN (best_result)[2:array_upper(best_result, 1)]; +END; +$$ language plpgsql VOLATILE PARALLEL RESTRICTED; + + + +-- +-- Perform a single iteration of the Jenks classification +-- + +CREATE OR REPLACE FUNCTION CDB_JenksBinsIteration ( in_array NUMERIC[], breaks INT, classes INT[][], invert BOOLEAN, element_count INT4, arr_mean NUMERIC, max_search INT DEFAULT 50) RETURNS NUMERIC[] as $$ +DECLARE + tmp_val numeric; + new_classes int[][]; + tmp_class int[]; + i INT := 1; + j INT := 1; + side INT := 2; + sdam numeric; + gvf numeric := 0.0; + new_gvf numeric; + arr_gvf numeric[]; + class_avg numeric; + class_max_i INT; + class_min_i INT; + class_max numeric; + class_min numeric; + reply numeric[]; +BEGIN + + -- Calculate the sum of squared deviations from the array mean (SDAM). + SELECT sum((arr_mean - e)^2) INTO sdam FROM ( SELECT unnest(in_array) as e ) x; + --Identify the breaks for the lowest GVF + LOOP + i = 1; + LOOP + -- get our mean + SELECT avg(e) INTO class_avg FROM ( SELECT unnest(in_array[classes[i][1]:classes[i][2]]) as e) x; + -- find the deviation + SELECT sum((class_avg-e)^2) INTO tmp_val FROM ( SELECT unnest(in_array[classes[i][1]:classes[i][2]]) as e ) x; + IF i = 1 THEN + arr_gvf = ARRAY[tmp_val]; + -- init our min/max map for later + class_max = arr_gvf[i]; + class_min = arr_gvf[i]; + class_min_i = 1; + class_max_i = 1; + ELSE + arr_gvf = array_append(arr_gvf, tmp_val); + END IF; + i := i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + -- calculate our new GVF + SELECT sdam-sum(e) INTO new_gvf FROM ( SELECT unnest(arr_gvf) as e ) x; + -- if no improvement was made, exit + IF new_gvf < gvf THEN EXIT; END IF; + gvf = new_gvf; + IF j > max_search THEN EXIT; END IF; + j = j+1; + i = 1; + LOOP + --establish directionality (uppward through classes or downward) + IF arr_gvf[i] < class_min THEN + class_min = arr_gvf[i]; + class_min_i = i; + END IF; + IF arr_gvf[i] > class_max THEN + class_max = arr_gvf[i]; + class_max_i = i; + END IF; + i := i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + IF class_max_i > class_min_i THEN + class_min_i = class_max_i - 1; + ELSE + class_min_i = class_max_i + 1; + END IF; + --Move from higher class to a lower gid order + IF class_max_i > class_min_i THEN + classes[class_max_i][1] = classes[class_max_i][1] + 1; + classes[class_min_i][2] = classes[class_min_i][2] + 1; + ELSE -- Move from lower class UP into a higher class by gid + classes[class_max_i][2] = classes[class_max_i][2] - 1; + classes[class_min_i][1] = classes[class_min_i][1] - 1; + END IF; + END LOOP; + + i = 1; + LOOP + IF invert = TRUE THEN + side = 1; --default returns bottom side of breaks, invert returns top side + END IF; + reply = array_append(reply, in_array[classes[i][side]]); + i = i+1; + IF i > breaks THEN EXIT; END IF; + END LOOP; + + RETURN array_prepend(gvf, reply); + +END; +$$ language plpgsql IMMUTABLE PARALLEL SAFE; + + +-- +-- Determine the Quantile classifications from a numeric array +-- +-- @param in_array A numeric array of numbers to determine the best +-- bins based on the Quantile method. +-- +-- @param breaks The number of bins you want to find. +-- +-- +CREATE OR REPLACE FUNCTION CDB_QuantileBins ( in_array NUMERIC[], breaks INT) RETURNS NUMERIC[] as $$ +DECLARE + element_count INT4; + break_size numeric; + tmp_val numeric; + i INT := 1; + reply numeric[]; +BEGIN + -- sort our values + SELECT array_agg(e) INTO in_array FROM (SELECT unnest(in_array) e ORDER BY e ASC) x; + -- get the total size of our data + element_count := array_length(in_array, 1); + break_size := element_count::numeric / breaks; + -- slice our bread + LOOP + IF i < breaks THEN + IF break_size * i % 1 > 0 THEN + SELECT e INTO tmp_val FROM ( SELECT unnest(in_array) e LIMIT 1 OFFSET ceil(break_size * i) - 1) x; + ELSE + SELECT avg(e) INTO tmp_val FROM ( SELECT unnest(in_array) e LIMIT 2 OFFSET ceil(break_size * i) - 1 ) x; + END IF; + ELSIF i = breaks THEN + -- select the last value + SELECT max(e) INTO tmp_val FROM ( SELECT unnest(in_array) e ) x; + ELSE + EXIT; + END IF; + + reply = array_append(reply, tmp_val); + i := i+1; + END LOOP; + RETURN reply; +END; +$$ language plpgsql IMMUTABLE STRICT PARALLEL SAFE; diff --git a/release/crankshaft.control b/release/crankshaft.control index c35edcd..980287f 100644 --- a/release/crankshaft.control +++ b/release/crankshaft.control @@ -1,5 +1,5 @@ comment = 'CartoDB Spatial Analysis extension' -default_version = '0.9.3' +default_version = '0.9.4' requires = 'plpythonu, postgis' superuser = true schema = cdb_crankshaft diff --git a/release/python/0.8.0/crankshaft/crankshaft/.analysis_data_provider.py.swp b/release/python/0.8.0/crankshaft/crankshaft/.analysis_data_provider.py.swp deleted file mode 100644 index ac1b6c3507d2b703ac5e94c837d53cbc178dbe1a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20480 zcmeHOO^h5z6>gIs2$1l9h;k_wnVuPD+Pe-B&W1&-?KP~hcg=e3{4DF4n(ms}_Vjdj 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zZI!iJE$_;jmeNdZPAtqcW*b`@+fboNo~;R_b82Jz;>~uHb6uD#9W`81w&o=bv5o&U z#P){Rv1rdFP@Q?Dy6|B1DWAB~2M#!LQg0_yfx6jb&B@q)A2V8lV?=%3u1uuOO25XG zWRclyL0LvsE$ENZ*`(tfN*WAaJ{rBTH47OA~FnaV*1jAzZ^&Ls>V7)L3#e zuT##=Nh3!%zwI*z6LhA-F-(oV7dETN`i~srM8R-@F^#+fBOGZ9M(xvD-*=kJBcy!Q zplw&uOX#XjTju-*JeE}^A1rCBNrRu+vj~Rdr9~!A`fwdl<%L;s1lETgt+P6ob7GW$1WM5brkF{mU3atN z-*-ZTXcS`Alzva0OCS#b(Q|3^v8GBA) zQ4SHJYg9kcJ7fQO%AywlWPyz?md~>>7uNXbxx&arx3`__ and the data providered provided in +the class instantiation (which defaults to PostgreSQL's plpy module's `database +access functions `__). +""" + +from collections import OrderedDict +import pysal as ps + +# crankshaft module +import crankshaft.pysal_utils as pu +from crankshaft.analysis_data_provider import AnalysisDataProvider + +# High level interface --------------------------------------- + + +class Moran(object): + """Class for calculation of Moran's I statistics (global, local, and local + rate) + + Parameters: + data_provider (:obj:`AnalysisDataProvider`): Class for fetching data. See + the `crankshaft.analysis_data_provider` module for more information. + """ + def __init__(self, data_provider=None): + if data_provider is None: + self.data_provider = AnalysisDataProvider() + else: + self.data_provider = data_provider + + def global_stat(self, subquery, attr_name, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (global) + Implementation building neighbors with a PostGIS database and Moran's I + core clusters with PySAL. + + Args: + + subquery (str): Query to give access to the data needed. This query + must give access to ``attr_name``, ``geom_col``, and ``id_col``. + attr_name (str): Column name of data to analyze + w_type (str): Type of spatial weight. Must be one of `knn` + or `queen`. See `PySAL documentation + `__ + for more information. + num_ngbrs (int): If using `knn` for ``w_type``, this + specifies the number of neighbors to be used to define the spatial + neighborhoods. + permutations (int): Number of permutations for performing + conditional randomization to find the p-value. Higher numbers + takes a longer time for getting results. + geom_col (str): Name of the geometry column in the dataset for + finding the spatial neighborhoods. + id_col (str): Row index for each value. Usually the database index. + + """ + params = OrderedDict([("id_col", id_col), + ("attr1", attr_name), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + result = self.data_provider.get_moran(w_type, params) + + # collect attributes + attr_vals = pu.get_attributes(result) + + # calculate weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate moran global + moran_global = ps.esda.moran.Moran(attr_vals, weight, + permutations=permutations) + + return zip([moran_global.I], [moran_global.EI]) + + def local_stat(self, subquery, attr, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I (local) + + Args: + + subquery (str): Query to give access to the data needed. This query + must give access to ``attr_name``, ``geom_col``, and ``id_col``. + attr (str): Column name of data to analyze + w_type (str): Type of spatial weight. Must be one of `knn` + or `queen`. See `PySAL documentation + `__ + for more information. + num_ngbrs (int): If using `knn` for ``w_type``, this + specifies the number of neighbors to be used to define the spatial + neighborhoods. + permutations (int): Number of permutations for performing + conditional randomization to find the p-value. Higher numbers + takes a longer time for getting results. + geom_col (str): Name of the geometry column in the dataset for + finding the spatial neighborhoods. + id_col (str): Row index for each value. Usually the database index. + + Returns: + list of tuples: Where each tuple consists of the following values: + - quadrants classification (one of `HH`, `HL`, `LL`, or `LH`) + - p-value + - spatial lag + - standardized spatial lag (centered on the mean, normalized by the + standard deviation) + - original value + - standardized value + - Moran's I statistic + - original row index + """ + + # geometries with attributes that are null are ignored + # resulting in a collection of not as near neighbors + + params = OrderedDict([("id_col", id_col), + ("attr1", attr), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + result = self.data_provider.get_moran(w_type, params) + + attr_vals = pu.get_attributes(result) + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local(attr_vals, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + # calculate spatial lag + lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y) + lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z) + + return zip( + quads, + lisa.p_sim, + lag, + lag_std, + lisa.y, + lisa.z, + lisa.Is, + weight.id_order + ) + + def global_rate_stat(self, subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Rate (global) + + Args: + + subquery (str): Query to give access to the data needed. This query + must give access to ``attr_name``, ``geom_col``, and ``id_col``. + numerator (str): Column name of numerator to analyze + denominator (str): Column name of the denominator + w_type (str): Type of spatial weight. Must be one of `knn` + or `queen`. See `PySAL documentation + `__ + for more information. + num_ngbrs (int): If using `knn` for ``w_type``, this + specifies the number of neighbors to be used to define the spatial + neighborhoods. + permutations (int): Number of permutations for performing + conditional randomization to find the p-value. Higher numbers + takes a longer time for getting results. + geom_col (str): Name of the geometry column in the dataset for + finding the spatial neighborhoods. + id_col (str): Row index for each value. Usually the database index. + """ + params = OrderedDict([("id_col", id_col), + ("attr1", numerator), + ("attr2", denominator), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + result = self.data_provider.get_moran(w_type, params) + + # collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate moran global rate + lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight, + permutations=permutations) + + return zip([lisa_rate.I], [lisa_rate.EI]) + + def local_rate_stat(self, subquery, numerator, denominator, + w_type, num_ngbrs, permutations, geom_col, id_col): + """ + Moran's I Local Rate + + Args: + + subquery (str): Query to give access to the data needed. This query + must give access to ``attr_name``, ``geom_col``, and ``id_col``. + numerator (str): Column name of numerator to analyze + denominator (str): Column name of the denominator + w_type (str): Type of spatial weight. Must be one of `knn` + or `queen`. See `PySAL documentation + `__ + for more information. + num_ngbrs (int): If using `knn` for ``w_type``, this + specifies the number of neighbors to be used to define the spatial + neighborhoods. + permutations (int): Number of permutations for performing + conditional randomization to find the p-value. Higher numbers + takes a longer time for getting results. + geom_col (str): Name of the geometry column in the dataset for + finding the spatial neighborhoods. + id_col (str): Row index for each value. Usually the database index. + + Returns: + list of tuples: Where each tuple consists of the following values: + - quadrants classification (one of `HH`, `HL`, `LL`, or `LH`) + - p-value + - spatial lag + - standardized spatial lag (centered on the mean, normalized by the + standard deviation) + - original value (roughly numerator divided by denominator) + - standardized value + - Moran's I statistic + - original row index + """ + # geometries with values that are null are ignored + # resulting in a collection of not as near neighbors + + params = OrderedDict([("id_col", id_col), + ("numerator", numerator), + ("denominator", denominator), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + result = self.data_provider.get_moran(w_type, params) + + # collect attributes + numer = pu.get_attributes(result, 1) + denom = pu.get_attributes(result, 2) + + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight, + permutations=permutations) + + # find quadrants for each geometry + quads = quad_position(lisa.q) + + # spatial lag + lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y) + lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z) + + return zip( + quads, + lisa.p_sim, + lag, + lag_std, + lisa.y, + lisa.z, + lisa.Is, + weight.id_order + ) + + def local_bivariate_stat(self, subquery, attr1, attr2, + permutations, geom_col, id_col, + w_type, num_ngbrs): + """ + Moran's I (local) Bivariate (untested) + """ + + params = OrderedDict([("id_col", id_col), + ("attr1", attr1), + ("attr2", attr2), + ("geom_col", geom_col), + ("subquery", subquery), + ("num_ngbrs", num_ngbrs)]) + + result = self.data_provider.get_moran(w_type, params) + + # collect attributes + attr1_vals = pu.get_attributes(result, 1) + attr2_vals = pu.get_attributes(result, 2) + + # create weights + weight = pu.get_weight(result, w_type, num_ngbrs) + + # calculate LISA values + lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight, + permutations=permutations) + + # find clustering of significance + lisa_sig = quad_position(lisa.q) + + return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order) + +# Low level functions ---------------------------------------- + + +def map_quads(coord): + """ + Map a quadrant number to Moran's I designation + HH=1, LH=2, LL=3, HL=4 + Args: + coord (int): quadrant of a specific measurement + Returns: + classification (one of 'HH', 'LH', 'LL', or 'HL') + """ + if coord == 1: + return 'HH' + elif coord == 2: + return 'LH' + elif coord == 3: + return 'LL' + elif coord == 4: + return 'HL' + return None + + +def quad_position(quads): + """ + Map all quads + + Args: + quads (:obj:`numpy.ndarray`): an array of quads classified by + 1-4 (PySAL default) + Returns: + list: an array of quads classied by 'HH', 'LL', etc. + """ + return [map_quads(q) for q in quads] diff --git a/release/python/0.9.4/crankshaft/crankshaft/model_storage/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/model_storage/__init__.py new file mode 100644 index 0000000..9cf2d58 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/model_storage/__init__.py @@ -0,0 +1 @@ +from core import set_model, get_model, create_model_table diff --git a/release/python/0.9.4/crankshaft/crankshaft/model_storage/core.py b/release/python/0.9.4/crankshaft/crankshaft/model_storage/core.py new file mode 100644 index 0000000..4db33da --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/model_storage/core.py @@ -0,0 +1,90 @@ +import time +import plpy +import pickle +# from petname import generate +import uuid + +def generate(): + return 'model_{}'.format(uuid.uuid4().hex) + +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() + 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() + 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())) diff --git a/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/__init__.py new file mode 100644 index 0000000..fdf073b --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions for pysal_utils""" +from crankshaft.pysal_utils.pysal_utils import * diff --git a/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py b/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py new file mode 100644 index 0000000..6b02f6d --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/pysal_utils/pysal_utils.py @@ -0,0 +1,251 @@ +""" + Utilities module for generic PySAL functionality, mainly centered on + translating queries into numpy arrays or PySAL weights objects +""" + +import numpy as np +import pysal as ps + + +def construct_neighbor_query(w_type, query_vals): + """Return query (a string) used for finding neighbors + @param w_type text: type of neighbors to calculate ('knn' or 'queen') + @param query_vals dict: values used to construct the query + """ + + if w_type.lower() == 'knn': + return knn(query_vals) + else: + return queen(query_vals) + + +# Build weight object +def get_weight(query_res, w_type='knn', num_ngbrs=5): + """ + Construct PySAL weight from return value of query + @param query_res dict-like: query results with attributes and neighbors + """ + + neighbors = {x['id']: x['neighbors'] for x in query_res} + print 'len of neighbors: %d' % len(neighbors) + + built_weight = ps.W(neighbors) + built_weight.transform = 'r' + + return built_weight + + +def query_attr_select(params, table_ref=True): + """ + Create portion of SELECT statement for attributes inolved in query. + Defaults to order in the params + @param params: dict of information used in query (column names, + table name, etc.) + Example: + OrderedDict([('numerator', 'price'), + ('denominator', 'sq_meters'), + ('subquery', 'SELECT * FROM interesting_data')]) + Output: + "i.\"price\"::numeric As attr1, " \ + "i.\"sq_meters\"::numeric As attr2, " + """ + + attr_string = "" + template = "\"%(col)s\"::numeric As attr%(alias_num)s, " + + if table_ref: + template = "i." + template + + if ('time_cols' in params) or ('ind_vars' in params): + # if markov or gwr analysis + attrs = (params['time_cols'] if 'time_cols' in params + else params['ind_vars']) + if 'ind_vars' in params: + template = "array_agg(\"%(col)s\"::numeric) As attr%(alias_num)s, " + + for idx, val in enumerate(attrs): + attr_string += template % {"col": val, "alias_num": idx + 1} + else: + # if moran's analysis + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', + 'num_ngbrs', 'subquery')] + + for idx, val in enumerate(attrs): + attr_string += template % {"col": params[val], + "alias_num": idx + 1} + + return attr_string + + +def query_attr_where(params, table_ref=True): + """ + Construct where conditions when building neighbors query + Create portion of WHERE clauses for weeding out NULL-valued geometries + Input: dict of params: + {'subquery': ..., + 'numerator': 'data1', + 'denominator': 'data2', + '': ...} + Output: + 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL' + Input: + {'subquery': ..., + 'time_cols': ['time1', 'time2', 'time3'], + 'etc': ...} + Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT + NULL AND idx_replace."time3" IS NOT NULL' + """ + attr_string = [] + template = "\"%s\" IS NOT NULL" + if table_ref: + template = "idx_replace." + template + + if ('time_cols' in params) or ('ind_vars' in params): + # markov or gwr where clauses + attrs = (params['time_cols'] if 'time_cols' in params + else params['ind_vars']) + # add values to template + for attr in attrs: + attr_string.append(template % attr) + else: + # moran where clauses + + # get keys + attrs = [k for k in params + if k not in ('id_col', 'geom_col', 'subquery', + 'num_ngbrs', 'subquery')] + + # add values to template + for attr in attrs: + attr_string.append(template % params[attr]) + + if 'denominator' in attrs: + attr_string.append( + "idx_replace.\"%s\" <> 0" % params['denominator']) + + out = " AND ".join(attr_string) + + return out + + +def knn(params): + """SQL query for k-nearest neighbors. + @param vars: dict of values to fill template + """ + + attr_select = query_attr_select(params, table_ref=True) + attr_where = query_attr_where(params, table_ref=True) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = ''' + SELECT + i."{id_col}" As id, + %(attr_select)s + (SELECT ARRAY(SELECT j."{id_col}" + FROM ({subquery}) As j + WHERE i."{id_col}" <> j."{id_col}" AND + %(attr_where_j)s AND + j."{geom_col}" IS NOT NULL + ORDER BY j."{geom_col}" <-> i."{geom_col}" ASC + LIMIT {num_ngbrs})) As neighbors + FROM ({subquery}) As i + WHERE %(attr_where_i)s AND i."{geom_col}" IS NOT NULL + ORDER BY i."{id_col}" ASC; + ''' % replacements + + return query.format(**params) + + +# SQL query for finding queens neighbors (all contiguous polygons) +def queen(params): + """SQL query for queen neighbors. + @param params dict: information to fill query + """ + attr_select = query_attr_select(params) + attr_where = query_attr_where(params) + + replacements = {"attr_select": attr_select, + "attr_where_i": attr_where.replace("idx_replace", "i"), + "attr_where_j": attr_where.replace("idx_replace", "j")} + + query = ''' + SELECT + i."{id_col}" As id, + %(attr_select)s + (SELECT ARRAY(SELECT j."{id_col}" + FROM ({subquery}) As j + WHERE i."{id_col}" <> j."{id_col}" AND + ST_Touches(i."{geom_col}", j."{geom_col}") AND + %(attr_where_j)s)) As neighbors + FROM ({subquery}) As i + WHERE + %(attr_where_i)s + ORDER BY i."{id_col}" ASC; + ''' % replacements + + return query.format(**params) + + +def gwr_query(params): + """ + GWR query + """ + + replacements = {"ind_vars_select": query_attr_select(params, + table_ref=None), + "ind_vars_where": query_attr_where(params, + table_ref=None)} + + query = ''' + SELECT + array_agg(ST_X(ST_Centroid("{geom_col}"))) As x, + array_agg(ST_Y(ST_Centroid("{geom_col}"))) As y, + array_agg("{dep_var}") As dep_var, + %(ind_vars_select)s + array_agg("{id_col}") As rowid + FROM ({subquery}) As q + WHERE + "{dep_var}" IS NOT NULL AND + %(ind_vars_where)s + ''' % replacements + + return query.format(**params).strip() + + +def gwr_predict_query(params): + """ + GWR query + """ + + replacements = {"ind_vars_select": query_attr_select(params, + table_ref=None), + "ind_vars_where": query_attr_where(params, + table_ref=None)} + + query = ''' + SELECT + array_agg(ST_X(ST_Centroid({geom_col}))) As x, + array_agg(ST_Y(ST_Centroid({geom_col}))) As y, + array_agg({dep_var}) As dep_var, + %(ind_vars_select)s + array_agg({id_col}) As rowid + FROM ({subquery}) As q + WHERE + %(ind_vars_where)s + ''' % replacements + + return query.format(**params).strip() +# to add more weight methods open a ticket or pull request + + +def get_attributes(query_res, attr_num=1): + """ + @param query_res: query results with attributes and neighbors + @param attr_num: attribute number (1, 2, ...) + """ + return np.array([x['attr' + str(attr_num)] for x in query_res], + dtype=np.float) diff --git a/release/python/0.9.4/crankshaft/crankshaft/random_seeds.py b/release/python/0.9.4/crankshaft/crankshaft/random_seeds.py new file mode 100644 index 0000000..c55ba14 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/random_seeds.py @@ -0,0 +1,12 @@ +"""Random seed generator used for non-deterministic functions in crankshaft""" +import random +import numpy + + +def set_random_seeds(value): + """ + Set the seeds of the RNGs (Random Number Generators) + used internally. + """ + random.seed(value) + numpy.random.seed(value) diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/regression/__init__.py new file mode 100644 index 0000000..f9d6d07 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/__init__.py @@ -0,0 +1,3 @@ +from crankshaft.regression.gwr import * +from crankshaft.regression.glm import * +from crankshaft.regression.gwr_cs import * diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb new file mode 100644 index 0000000..1b17831 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Import GLM and pysal\n", + "import os\n", + "import numpy as np\n", + "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n", + "from glm import GLM\n", + "import pysal\n", + "import pandas as pd\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.api as sm\n", + "from family import Gaussian, Binomial, Poisson, QuasiPoisson\n", + "\n", + "from statsmodels.api import families" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Prepare some test data - columbus example\n", + "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n", + "y = np.array(db.by_col(\"HOVAL\"))\n", + "y = np.reshape(y, (49,1))\n", + "X = []\n", + "#X.append(np.ones(len(y)))\n", + "X.append(db.by_col(\"INC\"))\n", + "X.append(db.by_col(\"CRIME\"))\n", + "X = np.array(X).T" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 46.42818268]\n", + " [ 0.62898397]\n", + " [ -0.48488854]]\n" + ] + } + ], + "source": [ + "#First fit pysal OLS model\n", + "from pysal.spreg import ols\n", + "OLS = ols.OLS(y, X)\n", + "print OLS.betas" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "[ 46.42818268 0.62898397 -0.48488854]\n", + "[ 46.42818268 0.62898397 -0.48488854]\n" + ] + } + ], + "source": [ + "#Then fit Gaussian GLM\n", + "\n", + "#create Gaussian GLM model object\n", + "model = GLM(y, X, Gaussian())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [46.4282, 0.6290, -0.4849]\n", + "print results.params\n", + "\n", + "# Gaussian GLM results from statsmodels\n", + "sm_model = smf.GLM(y, sm.add_constant(X), family=families.Gaussian())\n", + "sm_results = sm_model.fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 2\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "\n", + "\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "print results.df_model, sm_results.df_model\n", + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "[ 3.92159085 0.01183491 -0.01371397]\n", + "[ 3.92159085 0.01183491 -0.01371397]\n" + ] + } + ], + "source": [ + "#Now fit a Poisson GLM \n", + "\n", + "poisson_y = np.round(y).astype(int)\n", + "\n", + "#create Poisson GLM model object\n", + "model = GLM(poisson_y, X, Poisson())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [3.91926, 0.01198, -0.01371]\n", + "print results.params.T\n", + "\n", + "# Poisson GLM results from statsmodels\n", + "sm_results = smf.GLM(poisson_y, sm.add_constant(X), family=families.Poisson()).fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "\n", + "\n", + "\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "[ 0.13049161 0.00511599 0.00193769] [ 0.13049161 0.00511599 0.00193769]\n" + ] + } + ], + "source": [ + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n", + "print results.bse, sm_results.bse" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-5.33638276 0.0287754 ]\n", + "[-5.33638276 0.0287754 ]\n" + ] + } + ], + "source": [ + "#Now fit a binomial GLM\n", + "londonhp = pd.read_csv('/Users/toshan/projects/londonhp.csv')\n", + "#londonhp = pd.read_csv('/Users/qszhao/Dropbox/pysal/pysal/contrib/gwr/londonhp.csv')\n", + "y = londonhp['BATH2'].values\n", + "y = np.reshape(y, (316,1))\n", + "X = londonhp['FLOORSZ'].values\n", + "X = np.reshape(X, (316,1))\n", + "\n", + "#create logistic GLM model object\n", + "model = GLM(y, X, Binomial())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()\n", + "\n", + "#Check coefficients - R betas [-5.33638, 0.02878]\n", + "print results.params.T\n", + "\n", + "# Logistic GLM results from statsmodels\n", + "sm_results = smf.GLM(y, sm.add_constant(X), family=families.Binomial()).fit()\n", + "print sm_results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "print results.df_model, sm_results.df_model\n", + "print np.allclose(results.aic, sm_results.aic)\n", + "print np.allclose(results.bic, sm_results.bic)\n", + "print np.allclose(results.deviance, sm_results.deviance)\n", + "print np.allclose(results.df_model, sm_results.df_model)\n", + "print np.allclose(results.df_resid, sm_results.df_resid)\n", + "print np.allclose(results.llf, sm_results.llf)\n", + "print np.allclose(results.mu, sm_results.mu)\n", + "print np.allclose(results.n, sm_results.nobs)\n", + "print np.allclose(results.null, sm_results.null)\n", + "print np.allclose(results.null_deviance, sm_results.null_deviance)\n", + "print np.allclose(results.params, sm_results.params)\n", + "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n", + "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n", + "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n", + "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n", + "print np.allclose(results.resid_response, sm_results.resid_response)\n", + "print np.allclose(results.resid_working, sm_results.resid_working)\n", + "print np.allclose(results.scale, sm_results.scale)\n", + "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n", + "print np.allclose(results.cov_params(), sm_results.cov_params())\n", + "print np.allclose(results.bse, sm_results.bse)\n", + "print np.allclose(results.conf_int(), sm_results.conf_int())\n", + "print np.allclose(results.pvalues, sm_results.pvalues)\n", + "print np.allclose(results.tvalues, sm_results.tvalues)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "#create QUasiPoisson GLM model object\n", + "model = GLM(poisson_y, X, QuasiPoisson())\n", + "model\n", + "\n", + "#Fit model to estimate coefficients and return GLMResults object\n", + "results = model.fit()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/__init__.py new file mode 100644 index 0000000..4a468d5 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/__init__.py @@ -0,0 +1,4 @@ +import glm +import family +import utils +import iwls diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/base.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/base.py new file mode 100644 index 0000000..484c1c8 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/base.py @@ -0,0 +1,959 @@ + +from __future__ import print_function +import numpy as np +from scipy import stats +from utils import cache_readonly + +class Results(object): + """ + Class to contain model results + Parameters + ---------- + model : class instance + the previously specified model instance + params : array + parameter estimates from the fit model + """ + def __init__(self, model, params, **kwd): + self.__dict__.update(kwd) + self.initialize(model, params, **kwd) + self._data_attr = [] + + def initialize(self, model, params, **kwd): + self.params = params + self.model = model + if hasattr(model, 'k_constant'): + self.k_constant = model.k_constant + + def predict(self, exog=None, transform=True, *args, **kwargs): + """ + Call self.model.predict with self.params as the first argument. + Parameters + ---------- + exog : array-like, optional + The values for which you want to predict. + transform : bool, optional + If the model was fit via a formula, do you want to pass + exog through the formula. Default is True. E.g., if you fit + a model y ~ log(x1) + log(x2), and transform is True, then + you can pass a data structure that contains x1 and x2 in + their original form. Otherwise, you'd need to log the data + first. + args, kwargs : + Some models can take additional arguments or keywords, see the + predict method of the model for the details. + Returns + ------- + prediction : ndarray or pandas.Series + See self.model.predict + """ + if transform and hasattr(self.model, 'formula') and exog is not None: + from patsy import dmatrix + exog = dmatrix(self.model.data.design_info.builder, + exog) + + if exog is not None: + exog = np.asarray(exog) + if exog.ndim == 1 and (self.model.exog.ndim == 1 or + self.model.exog.shape[1] == 1): + exog = exog[:, None] + exog = np.atleast_2d(exog) # needed in count model shape[1] + + return self.model.predict(self.params, exog, *args, **kwargs) + + +#TODO: public method? +class LikelihoodModelResults(Results): + """ + Class to contain results from likelihood models + Parameters + ----------- + model : LikelihoodModel instance or subclass instance + LikelihoodModelResults holds a reference to the model that is fit. + params : 1d array_like + parameter estimates from estimated model + normalized_cov_params : 2d array + Normalized (before scaling) covariance of params. (dot(X.T,X))**-1 + scale : float + For (some subset of models) scale will typically be the + mean square error from the estimated model (sigma^2) + Returns + ------- + **Attributes** + mle_retvals : dict + Contains the values returned from the chosen optimization method if + full_output is True during the fit. Available only if the model + is fit by maximum likelihood. See notes below for the output from + the different methods. + mle_settings : dict + Contains the arguments passed to the chosen optimization method. + Available if the model is fit by maximum likelihood. See + LikelihoodModel.fit for more information. + model : model instance + LikelihoodResults contains a reference to the model that is fit. + params : ndarray + The parameters estimated for the model. + scale : float + The scaling factor of the model given during instantiation. + tvalues : array + The t-values of the standard errors. + Notes + ----- + The covariance of params is given by scale times normalized_cov_params. + Return values by solver if full_output is True during fit: + 'newton' + fopt : float + The value of the (negative) loglikelihood at its + minimum. + iterations : int + Number of iterations performed. + score : ndarray + The score vector at the optimum. + Hessian : ndarray + The Hessian at the optimum. + warnflag : int + 1 if maxiter is exceeded. 0 if successful convergence. + converged : bool + True: converged. False: did not converge. + allvecs : list + List of solutions at each iteration. + 'nm' + fopt : float + The value of the (negative) loglikelihood at its + minimum. + iterations : int + Number of iterations performed. + warnflag : int + 1: Maximum number of function evaluations made. + 2: Maximum number of iterations reached. + converged : bool + True: converged. False: did not converge. + allvecs : list + List of solutions at each iteration. + 'bfgs' + fopt : float + Value of the (negative) loglikelihood at its minimum. + gopt : float + Value of gradient at minimum, which should be near 0. + Hinv : ndarray + value of the inverse Hessian matrix at minimum. Note + that this is just an approximation and will often be + different from the value of the analytic Hessian. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + warnflag : int + 1: Maximum number of iterations exceeded. 2: Gradient + and/or function calls are not changing. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + 'lbfgs' + fopt : float + Value of the (negative) loglikelihood at its minimum. + gopt : float + Value of gradient at minimum, which should be near 0. + fcalls : int + Number of calls to loglike. + warnflag : int + Warning flag: + - 0 if converged + - 1 if too many function evaluations or too many iterations + - 2 if stopped for another reason + converged : bool + True: converged. False: did not converge. + 'powell' + fopt : float + Value of the (negative) loglikelihood at its minimum. + direc : ndarray + Current direction set. + iterations : int + Number of iterations performed. + fcalls : int + Number of calls to loglike. + warnflag : int + 1: Maximum number of function evaluations. 2: Maximum number + of iterations. + converged : bool + True : converged. False: did not converge. + allvecs : list + Results at each iteration. + 'cg' + fopt : float + Value of the (negative) loglikelihood at its minimum. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + warnflag : int + 1: Maximum number of iterations exceeded. 2: Gradient and/ + or function calls not changing. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + 'ncg' + fopt : float + Value of the (negative) loglikelihood at its minimum. + fcalls : int + Number of calls to loglike. + gcalls : int + Number of calls to gradient/score. + hcalls : int + Number of calls to hessian. + warnflag : int + 1: Maximum number of iterations exceeded. + converged : bool + True: converged. False: did not converge. + allvecs : list + Results at each iteration. + """ + + # by default we use normal distribution + # can be overwritten by instances or subclasses + use_t = False + + def __init__(self, model, params, normalized_cov_params=None, scale=1., + **kwargs): + super(LikelihoodModelResults, self).__init__(model, params) + self.normalized_cov_params = normalized_cov_params + self.scale = scale + + # robust covariance + # We put cov_type in kwargs so subclasses can decide in fit whether to + # use this generic implementation + if 'use_t' in kwargs: + use_t = kwargs['use_t'] + if use_t is not None: + self.use_t = use_t + if 'cov_type' in kwargs: + cov_type = kwargs.get('cov_type', 'nonrobust') + cov_kwds = kwargs.get('cov_kwds', {}) + + if cov_type == 'nonrobust': + self.cov_type = 'nonrobust' + self.cov_kwds = {'description' : 'Standard Errors assume that the ' + + 'covariance matrix of the errors is correctly ' + + 'specified.'} + else: + from statsmodels.base.covtype import get_robustcov_results + if cov_kwds is None: + cov_kwds = {} + use_t = self.use_t + # TODO: we shouldn't need use_t in get_robustcov_results + get_robustcov_results(self, cov_type=cov_type, use_self=True, + use_t=use_t, **cov_kwds) + + + def normalized_cov_params(self): + raise NotImplementedError + + + def _get_robustcov_results(self, cov_type='nonrobust', use_self=True, + use_t=None, **cov_kwds): + from statsmodels.base.covtype import get_robustcov_results + if cov_kwds is None: + cov_kwds = {} + + if cov_type == 'nonrobust': + self.cov_type = 'nonrobust' + self.cov_kwds = {'description' : 'Standard Errors assume that the ' + + 'covariance matrix of the errors is correctly ' + + 'specified.'} + else: + # TODO: we shouldn't need use_t in get_robustcov_results + get_robustcov_results(self, cov_type=cov_type, use_self=True, + use_t=use_t, **cov_kwds) + + @cache_readonly + def llf(self): + return self.model.loglike(self.params) + + @cache_readonly + def bse(self): + return np.sqrt(np.diag(self.cov_params())) + + @cache_readonly + def tvalues(self): + """ + Return the t-statistic for a given parameter estimate. + """ + return self.params / self.bse + + @cache_readonly + def pvalues(self): + if self.use_t: + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + return stats.t.sf(np.abs(self.tvalues), df_resid)*2 + else: + return stats.norm.sf(np.abs(self.tvalues))*2 + + + def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None, + other=None): + """ + Returns the variance/covariance matrix. + The variance/covariance matrix can be of a linear contrast + of the estimates of params or all params multiplied by scale which + will usually be an estimate of sigma^2. Scale is assumed to be + a scalar. + Parameters + ---------- + r_matrix : array-like + Can be 1d, or 2d. Can be used alone or with other. + column : array-like, optional + Must be used on its own. Can be 0d or 1d see below. + scale : float, optional + Can be specified or not. Default is None, which means that + the scale argument is taken from the model. + other : array-like, optional + Can be used when r_matrix is specified. + Returns + ------- + cov : ndarray + covariance matrix of the parameter estimates or of linear + combination of parameter estimates. See Notes. + Notes + ----- + (The below are assumed to be in matrix notation.) + If no argument is specified returns the covariance matrix of a model + ``(scale)*(X.T X)^(-1)`` + If contrast is specified it pre and post-multiplies as follows + ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T`` + If contrast and other are specified returns + ``(scale) * r_matrix (X.T X)^(-1) other.T`` + If column is specified returns + ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d + OR + ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d + """ + if (hasattr(self, 'mle_settings') and + self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): + dot_fun = nan_dot + else: + dot_fun = np.dot + + if (cov_p is None and self.normalized_cov_params is None and + not hasattr(self, 'cov_params_default')): + raise ValueError('need covariance of parameters for computing ' + '(unnormalized) covariances') + if column is not None and (r_matrix is not None or other is not None): + raise ValueError('Column should be specified without other ' + 'arguments.') + if other is not None and r_matrix is None: + raise ValueError('other can only be specified with r_matrix') + + if cov_p is None: + if hasattr(self, 'cov_params_default'): + cov_p = self.cov_params_default + else: + if scale is None: + scale = self.scale + cov_p = self.normalized_cov_params * scale + + if column is not None: + column = np.asarray(column) + if column.shape == (): + return cov_p[column, column] + else: + #return cov_p[column][:, column] + return cov_p[column[:, None], column] + elif r_matrix is not None: + r_matrix = np.asarray(r_matrix) + if r_matrix.shape == (): + raise ValueError("r_matrix should be 1d or 2d") + if other is None: + other = r_matrix + else: + other = np.asarray(other) + tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other))) + return tmp + else: # if r_matrix is None and column is None: + return cov_p + + #TODO: make sure this works as needed for GLMs + def t_test(self, r_matrix, cov_p=None, scale=None, + use_t=None): + """ + Compute a t-test for a each linear hypothesis of the form Rb = q + Parameters + ---------- + r_matrix : array-like, str, tuple + - array : If an array is given, a p x k 2d array or length k 1d + array specifying the linear restrictions. It is assumed + that the linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q). If q is given, + can be either a scalar or a length p row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + An optional `scale` to use. Default is the scale specified + by the model fit. + use_t : bool, optional + If use_t is None, then the default of the model is used. + If use_t is True, then the p-values are based on the t + distribution. + If use_t is False, then the p-values are based on the normal + distribution. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + The available results have the same elements as the parameter table + in `summary()`. + Examples + -------- + >>> import numpy as np + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> r = np.zeros_like(results.params) + >>> r[5:] = [1,-1] + >>> print(r) + [ 0. 0. 0. 0. 0. 1. -1.] + r tests that the coefficients on the 5th and 6th independent + variable are the same. + >>> T_test = results.t_test(r) + >>> print(T_test) + + >>> T_test.effect + -1829.2025687192481 + >>> T_test.sd + 455.39079425193762 + >>> T_test.tvalue + -4.0167754636411717 + >>> T_test.pvalue + 0.0015163772380899498 + Alternatively, you can specify the hypothesis tests using a string + >>> from statsmodels.formula.api import ols + >>> dta = sm.datasets.longley.load_pandas().data + >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' + >>> results = ols(formula, dta).fit() + >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1' + >>> t_test = results.t_test(hypotheses) + >>> print(t_test) + See Also + --------- + tvalues : individual t statistics + f_test : for F tests + patsy.DesignInfo.linear_constraint + """ + from patsy import DesignInfo + names = self.model.data.param_names + LC = DesignInfo(names).linear_constraint(r_matrix) + r_matrix, q_matrix = LC.coefs, LC.constants + num_ttests = r_matrix.shape[0] + num_params = r_matrix.shape[1] + + if (cov_p is None and self.normalized_cov_params is None and + not hasattr(self, 'cov_params_default')): + raise ValueError('Need covariance of parameters for computing ' + 'T statistics') + if num_params != self.params.shape[0]: + raise ValueError('r_matrix and params are not aligned') + if q_matrix is None: + q_matrix = np.zeros(num_ttests) + else: + q_matrix = np.asarray(q_matrix) + q_matrix = q_matrix.squeeze() + if q_matrix.size > 1: + if q_matrix.shape[0] != num_ttests: + raise ValueError("r_matrix and q_matrix must have the same " + "number of rows") + + if use_t is None: + #switch to use_t false if undefined + use_t = (hasattr(self, 'use_t') and self.use_t) + + _t = _sd = None + + _effect = np.dot(r_matrix, self.params) + # nan_dot multiplies with the convention nan * 0 = 0 + + # Perform the test + if num_ttests > 1: + _sd = np.sqrt(np.diag(self.cov_params( + r_matrix=r_matrix, cov_p=cov_p))) + else: + _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p)) + _t = (_effect - q_matrix) * recipr(_sd) + + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + + if use_t: + return ContrastResults(effect=_effect, t=_t, sd=_sd, + df_denom=df_resid) + else: + return ContrastResults(effect=_effect, statistic=_t, sd=_sd, + df_denom=df_resid, + distribution='norm') + + def f_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None): + """ + Compute the F-test for a joint linear hypothesis. + This is a special case of `wald_test` that always uses the F + distribution. + Parameters + ---------- + r_matrix : array-like, str, or tuple + - array : An r x k array where r is the number of restrictions to + test and k is the number of regressors. It is assumed + that the linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q), ``q`` can be + either a scalar or a length k row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + Default is 1.0 for no scaling. + invcov : array-like, optional + A q x q array to specify an inverse covariance matrix based on a + restrictions matrix. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + Examples + -------- + >>> import numpy as np + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> A = np.identity(len(results.params)) + >>> A = A[1:,:] + This tests that each coefficient is jointly statistically + significantly different from zero. + >>> print(results.f_test(A)) + + Compare this to + >>> results.fvalue + 330.2853392346658 + >>> results.f_pvalue + 4.98403096572e-10 + >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1])) + This tests that the coefficient on the 2nd and 3rd regressors are + equal and jointly that the coefficient on the 5th and 6th regressors + are equal. + >>> print(results.f_test(B)) + + Alternatively, you can specify the hypothesis tests using a string + >>> from statsmodels.datasets import longley + >>> from statsmodels.formula.api import ols + >>> dta = longley.load_pandas().data + >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR' + >>> results = ols(formula, dta).fit() + >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)' + >>> f_test = results.f_test(hypotheses) + >>> print(f_test) + See Also + -------- + statsmodels.stats.contrast.ContrastResults + wald_test + t_test + patsy.DesignInfo.linear_constraint + Notes + ----- + The matrix `r_matrix` is assumed to be non-singular. More precisely, + r_matrix (pX pX.T) r_matrix.T + is assumed invertible. Here, pX is the generalized inverse of the + design matrix of the model. There can be problems in non-OLS models + where the rank of the covariance of the noise is not full. + """ + res = self.wald_test(r_matrix, cov_p=cov_p, scale=scale, + invcov=invcov, use_f=True) + return res + + #TODO: untested for GLMs? + def wald_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None, + use_f=None): + """ + Compute a Wald-test for a joint linear hypothesis. + Parameters + ---------- + r_matrix : array-like, str, or tuple + - array : An r x k array where r is the number of restrictions to + test and k is the number of regressors. It is assumed that the + linear combination is equal to zero. + - str : The full hypotheses to test can be given as a string. + See the examples. + - tuple : A tuple of arrays in the form (R, q), ``q`` can be + either a scalar or a length p row vector. + cov_p : array-like, optional + An alternative estimate for the parameter covariance matrix. + If None is given, self.normalized_cov_params is used. + scale : float, optional + Default is 1.0 for no scaling. + invcov : array-like, optional + A q x q array to specify an inverse covariance matrix based on a + restrictions matrix. + use_f : bool + If True, then the F-distribution is used. If False, then the + asymptotic distribution, chisquare is used. If use_f is None, then + the F distribution is used if the model specifies that use_t is True. + The test statistic is proportionally adjusted for the distribution + by the number of constraints in the hypothesis. + Returns + ------- + res : ContrastResults instance + The results for the test are attributes of this results instance. + See also + -------- + statsmodels.stats.contrast.ContrastResults + f_test + t_test + patsy.DesignInfo.linear_constraint + Notes + ----- + The matrix `r_matrix` is assumed to be non-singular. More precisely, + r_matrix (pX pX.T) r_matrix.T + is assumed invertible. Here, pX is the generalized inverse of the + design matrix of the model. There can be problems in non-OLS models + where the rank of the covariance of the noise is not full. + """ + if use_f is None: + #switch to use_t false if undefined + use_f = (hasattr(self, 'use_t') and self.use_t) + + from patsy import DesignInfo + names = self.model.data.param_names + LC = DesignInfo(names).linear_constraint(r_matrix) + r_matrix, q_matrix = LC.coefs, LC.constants + + if (self.normalized_cov_params is None and cov_p is None and + invcov is None and not hasattr(self, 'cov_params_default')): + raise ValueError('need covariance of parameters for computing ' + 'F statistics') + + cparams = np.dot(r_matrix, self.params[:, None]) + J = float(r_matrix.shape[0]) # number of restrictions + if q_matrix is None: + q_matrix = np.zeros(J) + else: + q_matrix = np.asarray(q_matrix) + if q_matrix.ndim == 1: + q_matrix = q_matrix[:, None] + if q_matrix.shape[0] != J: + raise ValueError("r_matrix and q_matrix must have the same " + "number of rows") + Rbq = cparams - q_matrix + if invcov is None: + cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p) + if np.isnan(cov_p).max(): + raise ValueError("r_matrix performs f_test for using " + "dimensions that are asymptotically " + "non-normal") + invcov = np.linalg.inv(cov_p) + + if (hasattr(self, 'mle_settings') and + self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']): + F = nan_dot(nan_dot(Rbq.T, invcov), Rbq) + else: + F = np.dot(np.dot(Rbq.T, invcov), Rbq) + + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + if use_f: + F /= J + return ContrastResults(F=F, df_denom=df_resid, + df_num=invcov.shape[0]) + else: + return ContrastResults(chi2=F, df_denom=J, statistic=F, + distribution='chi2', distargs=(J,)) + + + def wald_test_terms(self, skip_single=False, extra_constraints=None, + combine_terms=None): + """ + Compute a sequence of Wald tests for terms over multiple columns + This computes joined Wald tests for the hypothesis that all + coefficients corresponding to a `term` are zero. + `Terms` are defined by the underlying formula or by string matching. + Parameters + ---------- + skip_single : boolean + If true, then terms that consist only of a single column and, + therefore, refers only to a single parameter is skipped. + If false, then all terms are included. + extra_constraints : ndarray + not tested yet + combine_terms : None or list of strings + Each string in this list is matched to the name of the terms or + the name of the exogenous variables. All columns whose name + includes that string are combined in one joint test. + Returns + ------- + test_result : result instance + The result instance contains `table` which is a pandas DataFrame + with the test results: test statistic, degrees of freedom and + pvalues. + Examples + -------- + >>> res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", + data).fit() + >>> res_ols.wald_test_terms() + + F P>F df constraint df denom + Intercept 279.754525 2.37985521351e-22 1 51 + C(Duration, Sum) 5.367071 0.0245738436636 1 51 + C(Weight, Sum) 12.432445 3.99943118767e-05 2 51 + C(Duration, Sum):C(Weight, Sum) 0.176002 0.83912310946 2 51 + >>> res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)", + data).fit(cov_type='HC0') + >>> wt = res_poi.wald_test_terms(skip_single=False, + combine_terms=['Duration', 'Weight']) + >>> print(wt) + chi2 P>chi2 df constraint + Intercept 15.695625 7.43960374424e-05 1 + C(Weight) 16.132616 0.000313940174705 2 + C(Duration) 1.009147 0.315107378931 1 + C(Weight):C(Duration) 0.216694 0.897315972824 2 + Duration 11.187849 0.010752286833 3 + Weight 30.263368 4.32586407145e-06 4 + """ + # lazy import + from collections import defaultdict + + result = self + if extra_constraints is None: + extra_constraints = [] + if combine_terms is None: + combine_terms = [] + design_info = getattr(result.model.data.orig_exog, 'design_info', None) + + if design_info is None and extra_constraints is None: + raise ValueError('no constraints, nothing to do') + + + identity = np.eye(len(result.params)) + constraints = [] + combined = defaultdict(list) + if design_info is not None: + for term in design_info.terms: + cols = design_info.slice(term) + name = term.name() + constraint_matrix = identity[cols] + + # check if in combined + for cname in combine_terms: + if cname in name: + combined[cname].append(constraint_matrix) + + k_constraint = constraint_matrix.shape[0] + if skip_single: + if k_constraint == 1: + continue + + constraints.append((name, constraint_matrix)) + + combined_constraints = [] + for cname in combine_terms: + combined_constraints.append((cname, np.vstack(combined[cname]))) + else: + # check by exog/params names if there is no formula info + for col, name in enumerate(result.model.exog_names): + constraint_matrix = identity[col] + + # check if in combined + for cname in combine_terms: + if cname in name: + combined[cname].append(constraint_matrix) + + if skip_single: + continue + + constraints.append((name, constraint_matrix)) + + combined_constraints = [] + for cname in combine_terms: + combined_constraints.append((cname, np.vstack(combined[cname]))) + + use_t = result.use_t + distribution = ['chi2', 'F'][use_t] + + res_wald = [] + index = [] + for name, constraint in constraints + combined_constraints + extra_constraints: + wt = result.wald_test(constraint) + row = [wt.statistic.item(), wt.pvalue, constraint.shape[0]] + if use_t: + row.append(wt.df_denom) + res_wald.append(row) + index.append(name) + + # distribution nerutral names + col_names = ['statistic', 'pvalue', 'df_constraint'] + if use_t: + col_names.append('df_denom') + # TODO: maybe move DataFrame creation to results class + from pandas import DataFrame + table = DataFrame(res_wald, index=index, columns=col_names) + res = WaldTestResults(None, distribution, None, table=table) + # TODO: remove temp again, added for testing + res.temp = constraints + combined_constraints + extra_constraints + return res + + + def conf_int(self, alpha=.05, cols=None, method='default'): + """ + Returns the confidence interval of the fitted parameters. + Parameters + ---------- + alpha : float, optional + The significance level for the confidence interval. + ie., The default `alpha` = .05 returns a 95% confidence interval. + cols : array-like, optional + `cols` specifies which confidence intervals to return + method : string + Not Implemented Yet + Method to estimate the confidence_interval. + "Default" : uses self.bse which is based on inverse Hessian for MLE + "hjjh" : + "jac" : + "boot-bse" + "boot_quant" + "profile" + Returns + -------- + conf_int : array + Each row contains [lower, upper] limits of the confidence interval + for the corresponding parameter. The first column contains all + lower, the second column contains all upper limits. + Examples + -------- + >>> import statsmodels.api as sm + >>> data = sm.datasets.longley.load() + >>> data.exog = sm.add_constant(data.exog) + >>> results = sm.OLS(data.endog, data.exog).fit() + >>> results.conf_int() + array([[-5496529.48322745, -1467987.78596704], + [ -177.02903529, 207.15277984], + [ -0.1115811 , 0.03994274], + [ -3.12506664, -0.91539297], + [ -1.5179487 , -0.54850503], + [ -0.56251721, 0.460309 ], + [ 798.7875153 , 2859.51541392]]) + >>> results.conf_int(cols=(2,3)) + array([[-0.1115811 , 0.03994274], + [-3.12506664, -0.91539297]]) + Notes + ----- + The confidence interval is based on the standard normal distribution. + Models wish to use a different distribution should overwrite this + method. + """ + bse = self.bse + + if self.use_t: + dist = stats.t + df_resid = getattr(self, 'df_resid_inference', self.df_resid) + q = dist.ppf(1 - alpha / 2, df_resid) + else: + dist = stats.norm + q = dist.ppf(1 - alpha / 2) + + if cols is None: + lower = self.params - q * bse + upper = self.params + q * bse + else: + cols = np.asarray(cols) + lower = self.params[cols] - q * bse[cols] + upper = self.params[cols] + q * bse[cols] + return np.asarray(lzip(lower, upper)) + + def save(self, fname, remove_data=False): + ''' + save a pickle of this instance + Parameters + ---------- + fname : string or filehandle + fname can be a string to a file path or filename, or a filehandle. + remove_data : bool + If False (default), then the instance is pickled without changes. + If True, then all arrays with length nobs are set to None before + pickling. See the remove_data method. + In some cases not all arrays will be set to None. + Notes + ----- + If remove_data is true and the model result does not implement a + remove_data method then this will raise an exception. + ''' + + from statsmodels.iolib.smpickle import save_pickle + + if remove_data: + self.remove_data() + + save_pickle(self, fname) + + @classmethod + def load(cls, fname): + ''' + load a pickle, (class method) + Parameters + ---------- + fname : string or filehandle + fname can be a string to a file path or filename, or a filehandle. + Returns + ------- + unpickled instance + ''' + + from statsmodels.iolib.smpickle import load_pickle + return load_pickle(fname) + + def remove_data(self): + '''remove data arrays, all nobs arrays from result and model + This reduces the size of the instance, so it can be pickled with less + memory. Currently tested for use with predict from an unpickled + results and model instance. + .. warning:: Since data and some intermediate results have been removed + calculating new statistics that require them will raise exceptions. + The exception will occur the first time an attribute is accessed + that has been set to None. + Not fully tested for time series models, tsa, and might delete too much + for prediction or not all that would be possible. + The list of arrays to delete is maintained as an attribute of the + result and model instance, except for cached values. These lists could + be changed before calling remove_data. + ''' + def wipe(obj, att): + #get to last element in attribute path + p = att.split('.') + att_ = p.pop(-1) + try: + obj_ = reduce(getattr, [obj] + p) + + #print(repr(obj), repr(att)) + #print(hasattr(obj_, att_)) + if hasattr(obj_, att_): + #print('removing3', att_) + setattr(obj_, att_, None) + except AttributeError: + pass + + model_attr = ['model.' + i for i in self.model._data_attr] + for att in self._data_attr + model_attr: + #print('removing', att) + wipe(self, att) + + data_in_cache = getattr(self, 'data_in_cache', []) + data_in_cache += ['fittedvalues', 'resid', 'wresid'] + for key in data_in_cache: + try: + self._cache[key] = None + except (AttributeError, KeyError): + pass + +def lzip(*args, **kwargs): + return list(zip(*args, **kwargs)) diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/family.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/family.py new file mode 100644 index 0000000..bad22c1 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/family.py @@ -0,0 +1,1845 @@ +''' +The one parameter exponential family distributions used by GLM. +''' +# TODO: quasi, quasibinomial, quasipoisson +# see http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html +# for comparison to R, and McCullagh and Nelder + +import numpy as np +from scipy import special +import links as L +import varfuncs as V +FLOAT_EPS = np.finfo(float).eps + + +class Family(object): + """ + The parent class for one-parameter exponential families. + + Parameters + ---------- + link : a link function instance + Link is the linear transformation function. + See the individual families for available links. + variance : a variance function + Measures the variance as a function of the mean probabilities. + See the individual families for the default variance function. + + See Also + -------- + :ref:`links` + + """ + # TODO: change these class attributes, use valid somewhere... + valid = [-np.inf, np.inf] + + links = [] + + def _setlink(self, link): + """ + Helper method to set the link for a family. + + Raises a ValueError exception if the link is not available. Note that + the error message might not be that informative because it tells you + that the link should be in the base class for the link function. + + See glm.GLM for a list of appropriate links for each family but note + that not all of these are currently available. + """ + # TODO: change the links class attribute in the families to hold + # meaningful information instead of a list of links instances such as + # [, + # , + # ] + # for Poisson... + self._link = link + if not isinstance(link, L.Link): + raise TypeError("The input should be a valid Link object.") + if hasattr(self, "links"): + validlink = link in self.links + validlink = max([isinstance(link, _) for _ in self.links]) + if not validlink: + errmsg = "Invalid link for family, should be in %s. (got %s)" + raise ValueError(errmsg % (repr(self.links), link)) + + def _getlink(self): + """ + Helper method to get the link for a family. + """ + return self._link + + # link property for each family is a pointer to link instance + link = property(_getlink, _setlink, doc="Link function for family") + + def __init__(self, link, variance): + self.link = link() + self.variance = variance + + def starting_mu(self, y): + r""" + Starting value for mu in the IRLS algorithm. + + Parameters + ---------- + y : array + The untransformed response variable. + + Returns + ------- + mu_0 : array + The first guess on the transformed response variable. + + Notes + ----- + .. math:: + + \mu_0 = (Y + \overline{Y})/2 + + Notes + ----- + Only the Binomial family takes a different initial value. + """ + return (y + y.mean())/2. + + def weights(self, mu): + r""" + Weights for IRLS steps + + Parameters + ---------- + mu : array-like + The transformed mean response variable in the exponential family + + Returns + ------- + w : array + The weights for the IRLS steps + + Notes + ----- + .. math:: + + w = 1 / (g'(\mu)^2 * Var(\mu)) + """ + return 1. / (self.link.deriv(mu)**2 * self.variance(mu)) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + The deviance function evaluated at (endog,mu,freq_weights,mu). + + Deviance is usually defined as twice the loglikelihood ratio. + + Parameters + ---------- + endog : array-like + The endogenous response variable + mu : array-like + The inverse of the link function at the linear predicted values. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + Deviance : array + The value of deviance function defined below. + + Notes + ----- + Deviance is defined + + .. math:: + + D = \sum_i (2 * freq\_weights_i * llf(Y_i, Y_i) - 2 * + llf(Y_i, \mu_i)) / scale + + where y is the endogenous variable. The deviance functions are + analytically defined for each family. + """ + raise NotImplementedError + + def resid_dev(self, endog, mu, freq_weights=1., scale=1.): + """ + The deviance residuals + + Parameters + ---------- + endog : array + The endogenous response variable + mu : array + The inverse of the link function at the linear predicted values. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + Deviance residuals. + + Notes + ----- + The deviance residuals are defined for each family. + """ + raise NotImplementedError + + def fitted(self, lin_pred): + """ + Fitted values based on linear predictors lin_pred. + + Parameters + ----------- + lin_pred : array + Values of the linear predictor of the model. + dot(X,beta) in a classical linear model. + + Returns + -------- + mu : array + The mean response variables given by the inverse of the link + function. + """ + fits = self.link.inverse(lin_pred) + return fits + + def predict(self, mu): + """ + Linear predictors based on given mu values. + + Parameters + ---------- + mu : array + The mean response variables + + Returns + ------- + lin_pred : array + Linear predictors based on the mean response variables. The value + of the link function at the given mu. + """ + return self.link(mu) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + """ + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + `endog` : array + Usually the endogenous response variable. + `mu` : array + Usually but not always the fitted mean response variable. + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood evaluated at + (endog,mu,freq_weights,scale) as defined below. + Notes + ----- + This is defined for each family. endog and mu are not restricted to + `endog` and `mu` respectively. For instance, the deviance function + calls both loglike(endog,endog) and loglike(endog,mu) to get the + likelihood ratio. + """ + raise NotImplementedError + + def resid_anscombe(self, endog, mu): + """ + The Anscome residuals. + + See also + -------- + statsmodels.families.family.Family docstring and the `resid_anscombe` + for the individual families for more information. + """ + raise NotImplementedError + + +class Poisson(Family): + """ + Poisson exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Poisson family is the log link. Available + links are log, identity, and sqrt. See statsmodels.family.links for + more information. + + Attributes + ---------- + Poisson.link : a link instance + The link function of the Poisson instance. + Poisson.variance : varfuncs instance + `variance` is an instance of + statsmodels.genmod.families.family.varfuncs.mu + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.sqrt] + variance = V.mu + valid = [0, np.inf] + safe_links = [L.Log, ] + + def __init__(self, link=L.log): + self.variance = Poisson.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def resid_dev(self, endog, mu, scale=1.): + r"""Poisson deviance residual + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 * + (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale + """ + endog_mu = self._clean(endog / mu) + return (np.sign(endog - mu) * + np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r''' + Poisson deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) as defined + below. + + Notes + ----- + If a constant term is included it is defined as + + .. math:: + + D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale + ''' + endog_mu = self._clean(endog / mu) + return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The scale parameter, defaults to 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + .. math:: + + llf = scale * \sum_i freq\_weights_i * (Y_i * \log(\mu_i) - \mu_i - + \ln \Gamma(Y_i + 1)) + """ + loglike = np.sum(freq_weights * (endog * np.log(mu) - mu - + special.gammaln(endog + 1))) + return scale * loglike + + def resid_anscombe(self, endog, mu): + r""" + Anscombe residuals for the Poisson exponential family distribution + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscome residuals for the Poisson family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6} + """ + return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.) + +class QuasiPoisson(Family): + """ + QuasiPoisson exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Poisson family is the log link. Available + links are log, identity, and sqrt. See statsmodels.family.links for + more information. + + Attributes + ---------- + Poisson.link : a link instance + The link function of the Poisson instance. + Poisson.variance : varfuncs instance + `variance` is an instance of + statsmodels.genmod.families.family.varfuncs.mu + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.sqrt] + variance = V.mu + valid = [0, np.inf] + safe_links = [L.Log, ] + + def __init__(self, link=L.log): + self.variance = Poisson.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def resid_dev(self, endog, mu, scale=1.): + r"""Poisson deviance residual + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{2 * + (Y_i * \log(Y_i / \mu_i) - (Y_i - \mu_i))} / scale + """ + endog_mu = self._clean(endog / mu) + return (np.sign(endog - mu) * + np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu))) / scale) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r''' + Poisson deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) as defined + below. + + Notes + ----- + If a constant term is included it is defined as + + .. math:: + + D = 2 * \sum_i (freq\_weights_i * Y_i * \log(Y_i / \mu_i))/ scale + ''' + endog_mu = self._clean(endog / mu) + return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Returns NaN for QuasiPoisson + + Returns + ------- + None: not applicable for QuasiPoisson + """ + return np.nan + + def resid_anscombe(self, endog, mu): + r""" + Anscombe residuals for the Poisson exponential family distribution + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscome residuals for the Poisson family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = (3/2) * (Y_i^{2/3} - \mu_i^{2/3}) / \mu_i^{1/6} + """ + return (3 / 2.) * (endog**(2/3.) - mu**(2 / 3.)) / mu**(1 / 6.) + +class Gaussian(Family): + """ + Gaussian exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Gaussian family is the identity link. + Available links are log, identity, and inverse. + See statsmodels.family.links for more information. + + Attributes + ---------- + Gaussian.link : a link instance + The link function of the Gaussian instance + Gaussian.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.constant + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.inverse_power] + variance = V.constant + safe_links = links + + def __init__(self, link=L.identity): + self.variance = Gaussian.variance + self.link = link() + + def resid_dev(self, endog, mu, scale=1.): + r""" + Gaussian deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + -------- + .. math:: + + resid\_dev_i = (Y_i - \mu_i) / \sqrt{Var(\mu_i)} / scale + """ + + return (endog - mu) / np.sqrt(self.variance(mu)) / scale + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Gaussian deviance function + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + The deviance function at (endog,mu,freq_weights,scale) + as defined below. + + Notes + -------- + .. math:: + + D = \sum_i freq\_weights_i * (Y_i - \mu_i)^2 / scale + """ + return np.sum((freq_weights * (endog - mu)**2)) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + Scales the loglikelihood function. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + If the link is the identity link function then the + loglikelihood function is the same as the classical OLS model. + + .. math:: + + llf = -nobs / 2 * (\log(SSR) + (1 + \log(2 \pi / nobs))) + + where + + .. math:: + SSR = \sum_i (Y_i - g^{-1}(\mu_i))^2 + + If the links is not the identity link then the loglikelihood + function is defined as + + .. math:: + + llf = \sum_i freq\_weights_i * ((Y_i * \mu_i - \mu_i^2 / 2) / scale- + Y^2 / (2 * scale) - (1/2) * \log(2 * \pi * scale)) + """ + if isinstance(self.link, L.Power) and self.link.power == 1: + # This is just the loglikelihood for classical OLS + nobs2 = endog.shape[0] / 2. + SSR = np.sum((endog-self.fitted(mu))**2, axis=0) + llf = -np.log(SSR) * nobs2 + llf -= (1+np.log(np.pi/nobs2))*nobs2 + return llf + else: + return np.sum(freq_weights * ((endog * mu - mu**2/2)/scale - + endog**2/(2 * scale) - .5*np.log(2 * np.pi * scale))) + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for the Gaussian exponential family distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the Gaussian family defined below + + Notes + -------- + .. math:: + + resid\_anscombe_i = Y_i - \mu_i + """ + return endog - mu + + +class Gamma(Family): + """ + Gamma exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Gamma family is the inverse link. + Available links are log, identity, and inverse. + See statsmodels.family.links for more information. + + Attributes + ---------- + Gamma.link : a link instance + The link function of the Gamma instance + Gamma.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.mu_squared + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + """ + + links = [L.log, L.identity, L.inverse_power] + variance = V.mu_squared + safe_links = [L.Log, ] + + def __init__(self, link=L.inverse_power): + self.variance = Gamma.variance + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Gamma deviance function + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + .. math:: + + D = 2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)/\mu_i - \log(Y_i / + \mu_i)) + """ + endog_mu = self._clean(endog/mu) + return 2*np.sum(freq_weights*((endog-mu)/mu-np.log(endog_mu))) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Gamma deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) \sqrt{-2 * + (-(Y_i - \mu_i) / \mu_i + \log(Y_i / \mu_i))} + """ + endog_mu = self._clean(endog / mu) + return np.sign(endog - mu) * np.sqrt(-2 * (-(endog - mu)/mu + + np.log(endog_mu))) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + -------- + .. math:: + + llf = -1 / scale * \sum_i *(Y_i / \mu_i+ \log(\mu_i)+ + (scale -1) * \log(Y) + \log(scale) + scale * + \ln \Gamma(1 / scale)) + """ + return - 1./scale * np.sum((endog/mu + np.log(mu) + (scale - 1) * + np.log(endog) + np.log(scale) + scale * + special.gammaln(1./scale)) * freq_weights) + + # in Stata scale is set to equal 1 for reporting llf + # in R it's the dispersion, though there is a loss of precision vs. + # our results due to an assumed difference in implementation + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for Gamma exponential family distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the Gamma family defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = 3 * (Y_i^{1/3} - \mu_i^{1/3}) / \mu_i^{1/3} + """ + return 3 * (endog**(1/3.) - mu**(1/3.)) / mu**(1/3.) + + +class Binomial(Family): + """ + Binomial exponential family distribution. + + Parameters + ---------- + link : a link instance, optional + The default link for the Binomial family is the logit link. + Available links are logit, probit, cauchy, log, and cloglog. + See statsmodels.family.links for more information. + + Attributes + ---------- + Binomial.link : a link instance + The link function of the Binomial instance + Binomial.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.binary + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + endog for Binomial can be specified in one of three ways. + + """ + + links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity] + variance = V.binary # this is not used below in an effort to include n + + # Other safe links, e.g. cloglog and probit are subclasses + safe_links = [L.Logit, L.CDFLink] + + def __init__(self, link=L.logit): # , n=1.): + # TODO: it *should* work for a constant n>1 actually, if freq_weights + # is equal to n + self.n = 1 + # overwritten by initialize if needed but always used to initialize + # variance since endog is assumed/forced to be (0,1) + self.variance = V.Binomial(n=self.n) + self.link = link() + + def starting_mu(self, y): + """ + The starting values for the IRLS algorithm for the Binomial family. + A good choice for the binomial family is :math:`\mu_0 = (Y_i + 0.5)/2` + """ + return (y + .5)/2 + + def initialize(self, endog, freq_weights): + ''' + Initialize the response variable. + + Parameters + ---------- + endog : array + Endogenous response variable + + Returns + -------- + If `endog` is binary, returns `endog` + + If `endog` is a 2d array, then the input is assumed to be in the format + (successes, failures) and + successes/(success + failures) is returned. And n is set to + successes + failures. + ''' + # if not np.all(np.asarray(freq_weights) == 1): + # self.variance = V.Binomial(n=freq_weights) + if (endog.ndim > 1 and endog.shape[1] > 1): + y = endog[:, 0] + # overwrite self.freq_weights for deviance below + self.n = endog.sum(1) + return y*1./self.n, self.n + else: + return endog, np.ones(endog.shape[0]) + + def deviance(self, endog, mu, freq_weights=1, scale=1., axis=None): + r''' + Deviance function for either Bernoulli or Binomial data. + + Parameters + ---------- + endog : array-like + Endogenous response variable (already transformed to a probability + if appropriate). + mu : array + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + -------- + deviance : float + The deviance function as defined below + + Notes + ----- + If the endogenous variable is binary: + + .. math:: + + D = -2 * \sum_i freq\_weights * (I_{1,i} * \log(\mu_i) + I_{0,i} * + \log(1 - \mu_i)) + + where :math:`I_{1,i}` is an indicator function that evalueates to 1 if + :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that + evaluates to 1 if :math:`Y_i = 0`. + + If the model is ninomial: + + .. math:: + + D = 2 * \sum_i freq\_weights * (\log(Y_i / \mu_i) + (n_i - Y_i) * + \log((n_i - Y_i) / n_i - \mu_i)) + + where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize. + ''' + if np.shape(self.n) == () and self.n == 1: + one = np.equal(endog, 1) + return -2 * np.sum((one * np.log(mu + 1e-200) + (1-one) * + np.log(1 - mu + 1e-200)) * freq_weights, axis=axis) + + else: + return 2 * np.sum(self.n * freq_weights * + (endog * np.log(endog/mu + 1e-200) + + (1 - endog) * np.log((1 - endog) / + (1 - mu) + 1e-200)), axis=axis) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Binomial deviance residuals + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + If the endogenous variable is binary: + + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{-2 * + \log(I_{1,i} * \mu_i + I_{0,i} * (1 - \mu_i))} + + where :math:`I_{1,i}` is an indicator function that evalueates to 1 if + :math:`Y_i = 1`. and :math:`I_{0,i}` is an indicator function that + evaluates to 1 if :math:`Y_i = 0`. + + If the endogenous variable is binomial: + + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) \sqrt{2 * n_i * + (Y_i * \log(Y_i / \mu_i) + (1 - Y_i) * + \log(1 - Y_i)/(1 - \mu_i))} + + where :math:`Y_i` and :math:`n` are as defined in Binomial.initialize. + """ + + mu = self.link._clean(mu) + if np.shape(self.n) == () and self.n == 1: + one = np.equal(endog, 1) + return np.sign(endog-mu)*np.sqrt(-2 * + np.log(one * mu + (1 - one) * + (1 - mu)))/scale + else: + return (np.sign(endog - mu) * + np.sqrt(2 * self.n * + (endog * np.log(endog/mu + 1e-200) + + (1 - endog) * np.log((1 - endog)/(1 - mu) + 1e-200)))/scale) + + def loglike(self, endog, mu, freq_weights=1, scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + Not used for the Binomial GLM. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + -------- + If the endogenous variable is binary: + + .. math:: + + llf = scale * \sum_i (y_i * \log(\mu_i/(1-\mu_i)) + \log(1-\mu_i)) * + freq\_weights_i + + If the endogenous variable is binomial: + + .. math:: + + llf = scale * \sum_i freq\_weights_i * (\ln \Gamma(n+1) - + \ln \Gamma(y_i + 1) - \ln \Gamma(n_i - y_i +1) + y_i * + \log(\mu_i / (1 - \mu_i)) + n * \log(1 - \mu_i)) + + where :math:`y_i = Y_i * n_i` with :math:`Y_i` and :math:`n_i` as + defined in Binomial initialize. This simply makes :math:`y_i` the + original number of successes. + """ + + if np.shape(self.n) == () and self.n == 1: + return scale * np.sum((endog * np.log(mu/(1 - mu) + 1e-200) + + np.log(1 - mu)) * freq_weights) + else: + y = endog * self.n # convert back to successes + return scale * np.sum((special.gammaln(self.n + 1) - + special.gammaln(y + 1) - + special.gammaln(self.n - y + 1) + y * + np.log(mu/(1 - mu)) + self.n * + np.log(1 - mu)) * freq_weights) + + def resid_anscombe(self, endog, mu): + ''' + The Anscombe residuals + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.)) + + where cox_snell is defined as + cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.) + where betainc is the incomplete beta function + + The name 'cox_snell' is idiosyncratic and is simply used for + convenience following the approach suggested in Cox and Snell (1968). + Further note that + cox_snell(x) = x**(2/3.)/(2/3.)*hyp2f1(2/3.,1/3.,5/3.,x) + where hyp2f1 is the hypergeometric 2f1 function. The Anscombe + residuals are sometimes defined in the literature using the + hyp2f1 formulation. Both betainc and hyp2f1 can be found in scipy. + + References + ---------- + Anscombe, FJ. (1953) "Contribution to the discussion of H. Hotelling's + paper." Journal of the Royal Statistical Society B. 15, 229-30. + + Cox, DR and Snell, EJ. (1968) "A General Definition of Residuals." + Journal of the Royal Statistical Society B. 30, 248-75. + + ''' + cox_snell = lambda x: (special.betainc(2/3., 2/3., x) + * special.beta(2/3., 2/3.)) + return np.sqrt(self.n) * ((cox_snell(endog) - cox_snell(mu)) / + (mu**(1/6.) * (1 - mu)**(1/6.))) + + +class InverseGaussian(Family): + """ + InverseGaussian exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the inverse Gaussian family is the + inverse squared link. + Available links are inverse_squared, inverse, log, and identity. + See statsmodels.family.links for more information. + + Attributes + ---------- + InverseGaussian.link : a link instance + The link function of the inverse Gaussian instance + InverseGaussian.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.mu_cubed + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + The inverse Guassian distribution is sometimes referred to in the + literature as the Wald distribution. + + """ + + links = [L.inverse_squared, L.inverse_power, L.identity, L.log] + variance = V.mu_cubed + safe_links = [L.inverse_squared, L.Log, ] + + def __init__(self, link=L.inverse_squared): + self.variance = InverseGaussian.variance + self.link = link() + + def resid_dev(self, endog, mu, scale=1.): + r""" + Returns the deviance residuals for the inverse Gaussian family. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + ------- + resid_dev : array + Deviance residuals as defined below + + Notes + ----- + .. math:: + + resid\_dev_i = sign(Y_i - \mu_i) * + \sqrt {(Y_i - \mu_i)^2 / (Y_i * \mu_i^2)} / scale + """ + return np.sign(endog-mu) * np.sqrt((endog-mu)**2/(endog*mu**2))/scale + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Inverse Gaussian deviance function + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + .. math:: + + D = \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i *\mu_i^2)) / + scale + """ + return np.sum(freq_weights*(endog-mu)**2/(endog*mu**2))/scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + .. math:: + + llf = -1/2 * \sum_i freq\_weights_i * ((Y_i - \mu_i)^2 / (Y_i * + \mu_i * scale) + \log(scale * Y_i^3) + \log(2 * \pi)) + """ + return -.5 * np.sum(((endog - mu)**2/(endog * mu**2 * scale) + + np.log(scale * endog**3) + np.log(2 * np.pi)) * + freq_weights) + + def resid_anscombe(self, endog, mu): + r""" + The Anscombe residuals for the inverse Gaussian distribution + + Parameters + ---------- + endog : array + Endogenous response variable + mu : array + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals for the inverse Gaussian distribution as + defined below + + Notes + ----- + .. math:: + + resid\_anscombe_i = \log(Y_i / \mu_i) / \sqrt{\mu_i} + """ + return np.log(endog / mu) / np.sqrt(mu) + + +class NegativeBinomial(Family): + """ + Negative Binomial exponential family. + + Parameters + ---------- + link : a link instance, optional + The default link for the negative binomial family is the log link. + Available links are log, cloglog, identity, nbinom and power. + See statsmodels.family.links for more information. + alpha : float, optional + The ancillary parameter for the negative binomial distribution. + For now `alpha` is assumed to be nonstochastic. The default value + is 1. Permissible values are usually assumed to be between .01 and 2. + + + Attributes + ---------- + NegativeBinomial.link : a link instance + The link function of the negative binomial instance + NegativeBinomial.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.nbinom + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + Power link functions are not yet supported. + + """ + links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power] + # TODO: add the ability to use the power links with an if test + # similar to below + variance = V.nbinom + safe_links = [L.Log, ] + + def __init__(self, link=L.log, alpha=1.): + self.alpha = 1. * alpha # make it at least float + self.variance = V.NegativeBinomial(alpha=self.alpha) + if isinstance(link, L.NegativeBinomial): + self.link = link(alpha=self.alpha) + else: + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, FLOAT_EPS, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Returns the value of the deviance function. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + :math:`D = \sum_i piecewise_i` where :math:`piecewise_i` is defined as: + + If :math:`Y_{i} = 0`: + + :math:`piecewise_i = 2* \log(1 + \alpha * \mu_i) / \alpha` + + If :math:`Y_{i} > 0`: + + :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) * + (1 + \alpha * Y_i) * \ln(1 + \alpha * Y_i) / (1 + \alpha * \mu_i)` + """ + iszero = np.equal(endog, 0) + notzero = 1 - iszero + endog_mu = self._clean(endog/mu) + tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha + tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha * + (1 + self.alpha * endog) * + np.log((1 + self.alpha * endog) / + (1 + self.alpha * mu))) + return np.sum(freq_weights * tmp) / scale + + def resid_dev(self, endog, mu, scale=1.): + r""" + Negative Binomial Deviance Residual + + Parameters + ---------- + endog : array-like + `endog` is the response variable + mu : array-like + `mu` is the fitted value of the model + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + -------- + resid_dev : array + The array of deviance residuals + + Notes + ----- + :math:`resid\_dev_i = sign(Y_i-\mu_i) * \sqrt{piecewise_i}` + + where :math:`piecewise_i` is defined as + + If :math:`Y_i = 0`: + + :math:`piecewise_i = 2 * \log(1 + \alpha * \mu_i)/ \alpha` + + If :math:`Y_i > 0`: + + :math:`piecewise_i = 2 * Y_i * \log(Y_i / \mu_i) - (2 / \alpha) * + (1 + \alpha * Y_i) * \log((1 + \alpha * Y_i) / (1 + \alpha * \mu_i))` + """ + iszero = np.equal(endog, 0) + notzero = 1 - iszero + endog_mu = self._clean(endog / mu) + tmp = iszero * 2 * np.log(1 + self.alpha * mu) / self.alpha + tmp += notzero * (2 * endog * np.log(endog_mu) - 2 / self.alpha * + (1 + self.alpha * endog) * + np.log((1 + self.alpha * endog) / + (1 + self.alpha * mu))) + return np.sign(endog - mu) * np.sqrt(tmp) / scale + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + The fitted mean response values + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + Defined as: + + .. math:: + + llf = \sum_i freq\_weights_i * (Y_i * \log{(\alpha * e^{\eta_i} / + (1 + \alpha * e^{\eta_i}))} - \log{(1 + \alpha * e^{\eta_i})}/ + \alpha + Constant) + + where :math:`Constant` is defined as: + + .. math:: + + Constant = \ln \Gamma{(Y_i + 1/ \alpha )} - \ln \Gamma(Y_i + 1) - + \ln \Gamma{(1/ \alpha )} + """ + lin_pred = self._link(mu) + constant = (special.gammaln(endog + 1 / self.alpha) - + special.gammaln(endog+1)-special.gammaln(1/self.alpha)) + exp_lin_pred = np.exp(lin_pred) + return np.sum((endog * np.log(self.alpha * exp_lin_pred / + (1 + self.alpha * exp_lin_pred)) - + np.log(1 + self.alpha * exp_lin_pred) / + self.alpha + constant) * freq_weights) + + def resid_anscombe(self, endog, mu): + """ + The Anscombe residuals for the negative binomial family + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + `resid_anscombe` = (hyp2f1(-alpha*endog)-hyp2f1(-alpha*mu)+\ + 1.5*(endog**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.) + + where hyp2f1 is the hypergeometric 2f1 function parameterized as + hyp2f1(x) = hyp2f1(2/3.,1/3.,5/3.,x) + """ + + hyp2f1 = lambda x : special.hyp2f1(2 / 3., 1 / 3., 5 / 3., x) + return ((hyp2f1(-self.alpha * endog) - hyp2f1(-self.alpha * mu) + + 1.5 * ( endog**(2 / 3.) - mu**(2 / 3.))) / + (mu + self.alpha * mu**2)**(1 / 6.)) + + +class Tweedie(Family): + """ + Tweedie family. + + Parameters + ---------- + link : a link instance, optional + The default link for the Tweedie family is the log link when the + link_power is 0. Otherwise, the power link is default. + Available links are log and Power. + var_power : float, optional + The variance power. + link_power : float, optional + The link power. + + Attributes + ---------- + Tweedie.link : a link instance + The link function of the Tweedie instance + Tweedie.variance : varfunc instance + `variance` is an instance of statsmodels.family.varfuncs.Power + Tweedie.link_power : float + The power of the link function, or 0 if its a log link. + Tweedie.var_power : float + The power of the variance function. + + See also + -------- + statsmodels.genmod.families.family.Family + :ref:`links` + + Notes + ----- + Logliklihood function not implemented because of the complexity of + calculating an infinite series of summations. The variance power can be + estimated using the `estimate_tweedie_power` function that is part of the + `GLM` class. + """ + links = [L.log, L.Power] + variance = V.Power + safe_links = [L.log, L.Power] + + def __init__(self, link=None, var_power=1., link_power=0): + self.var_power = var_power + self.link_power = link_power + self.variance = V.Power(power=var_power * 1.) + if link_power != 0 and not ((link is L.Power) or (link is None)): + msg = 'link_power of {} not supported specified link' + msg = msg.format(link_power) + raise ValueError(msg) + if (link_power == 0) and ((link is None) or (link is L.Log)): + self.link = L.log() + elif link_power != 0: + self.link = L.Power(power=link_power * 1.) + else: + self.link = link() + + def _clean(self, x): + """ + Helper function to trim the data so that is in (0,inf) + + Notes + ----- + The need for this function was discovered through usage and its + possible that other families might need a check for validity of the + domain. + """ + return np.clip(x, 0, np.inf) + + def deviance(self, endog, mu, freq_weights=1., scale=1.): + r""" + Returns the value of the deviance function. + + Parameters + ----------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float, optional + An optional scale argument. The default is 1. + + Returns + ------- + deviance : float + Deviance function as defined below + + Notes + ----- + When :math:`p = 1`, + + .. math:: + + resid\_dev_i = \mu + + when :math:`endog = 0` and + + .. math:: + + resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog) + + otherwise. + + When :math:`p = 2`, + + .. math:: + + resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu) + + For all other p, + + .. math:: + + resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) - + endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} / + (2 - p) + + Once :math:`resid\_dev_i` is calculated, then calculate deviance as + + .. math:: + + D = \sum{2 * freq\_weights * resid\_dev_i} + """ + p = self.var_power + if p == 1: + dev = np.where(endog == 0, + mu, + endog * np.log(endog / mu) + (mu - endog)) + elif p == 2: + endog1 = np.clip(endog, FLOAT_EPS, np.inf) + dev = ((endog - mu) / mu) - np.log(endog1 / mu) + else: + dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) - + endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p)) + return np.sum(2 * freq_weights * dev) + + def resid_dev(self, endog, mu, scale=1.): + r""" + Tweedie Deviance Residual + + Parameters + ---------- + endog : array-like + `endog` is the response variable + mu : array-like + `mu` is the fitted value of the model + scale : float, optional + An optional argument to divide the residuals by scale. The default + is 1. + + Returns + -------- + resid_dev : array + The array of deviance residuals + + Notes + ----- + When :math:`p = 1`, + + .. math:: + + resid\_dev_i = \mu + + when :math:`endog = 0` and + + .. math:: + + resid\_dev_i = endog * \log(endog / \mu) + (\mu - endog) + + otherwise. + + When :math:`p = 2`, + + .. math:: + + resid\_dev_i = (endog - \mu) / \mu - \log(endog / \mu) + + For all other p, + + .. math:: + + resid\_dev_i = endog ^{2 - p} / ((1 - p) * (2 - p)) - + endog * \mu ^{1 - p} / (1 - p) + \mu ^{2 - p} / + (2 - p) + """ + p = self.var_power + if p == 1: + dev = np.where(endog == 0, + mu, + endog * np.log(endog / mu) + (mu - endog)) + elif p == 2: + endog1 = np.clip(endog, FLOAT_EPS, np.inf) + dev = ((endog - mu) / mu) - np.log(endog1 / mu) + else: + dev = (endog ** (2 - p) / ((1 - p) * (2 - p)) - + endog * mu ** (1-p) / (1 - p) + mu ** (2 - p) / (2 - p)) + return np.sign(endog - mu) * np.sqrt(2 * dev) + + def loglike(self, endog, mu, freq_weights=1., scale=1.): + r""" + The log-likelihood function in terms of the fitted mean response. + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + The fitted mean response values + freq_weights : array-like + 1d array of frequency weights. The default is 1. + scale : float + The scale parameter. The default is 1. + + Returns + ------- + llf : float + The value of the loglikelihood function evaluated at + (endog,mu,freq_weights,scale) as defined below. + + Notes + ----- + This is not implemented because of the complexity of calculating an + infinite series of sums. + """ + return np.nan + + def resid_anscombe(self, endog, mu): + """ + The Anscombe residuals for the Tweedie family + + Parameters + ---------- + endog : array-like + Endogenous response variable + mu : array-like + Fitted mean response variable + + Returns + ------- + resid_anscombe : array + The Anscombe residuals as defined below. + + Notes + ----- + When :math:`p = 3`, then + + .. math:: + + resid\_anscombe_i = (\log(endog) - \log(\mu)) / \sqrt{mu} + + Otherwise, + + .. math:: + + c = (3 - p) / 3 + + .. math:: + + resid\_anscombe_i = (1 / c) * (endog ^ c - \mu ^ c) / \mu ^{p / 6} + """ + if self.var_power == 3: + return (np.log(endog) - np.log(mu)) / np.sqrt(mu) + else: + c = (3. - self.var_power) / 3. + return ((1. / c) * (endog ** c - mu ** c) / + mu ** (self.var_power / 6.)) diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/glm.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/glm.py new file mode 100644 index 0000000..f2fc17d --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/glm.py @@ -0,0 +1,326 @@ + +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 +import family +from iwls import iwls + +__all__ = ['GLM'] + +class GLM(RegressionPropsY): + """ + Generalised linear models. Can currently estimate Guassian, Poisson and + Logisitc regression coefficients. GLM object prepares model input and fit + method performs estimation which then returns a GLMResults object. + + Parameters + ---------- + y : array + n*1, dependent variable. + X : array + n*k, independent variable, exlcuding the constant. + family : string + Model type: 'Gaussian', 'Poisson', 'Binomial' + + Attributes + ---------- + y : array + n*1, dependent variable. + X : array + n*k, independent variable, including constant. + family : string + Model type: 'Gaussian', 'Poisson', 'logistic' + n : integer + Number of observations + k : integer + Number of independent variables + df_model : float + k-1, where k is the number of variables (including + intercept) + df_residual : float + observations minus variables (n-k) + mean_y : float + Mean of y + std_y : float + Standard deviation of y + fit_params : dict + Parameters passed into fit method to define estimation + routine. + normalized_cov_params : array + k*k, approximates [X.T*X]-1 + """ + def __init__(self, y, X, family=family.Gaussian(), constant=True): + """ + Initialize class + """ + self.n = USER.check_arrays(y, X) + USER.check_y(y, self.n) + self.y = y + if constant: + self.X = USER.check_constant(X) + else: + self.X = X + self.family = family + self.k = self.X.shape[1] + self.fit_params = {} + + def fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation. + tol: float + Tolerence for estimation convergence. + max_iter : integer + Maximum number of iterations if convergence not + achieved. + solve :string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + """ + self.fit_params['ini_betas'] = ini_betas + self.fit_params['tol'] = tol + self.fit_params['max_iter'] = max_iter + self.fit_params['solve']=solve + if solve.lower() == 'iwls': + params, predy, w, n_iter = iwls(self.y, self.X, self.family, + ini_betas=ini_betas, tol=tol, max_iter=max_iter) + self.fit_params['n_iter'] = n_iter + return GLMResults(self, params.flatten(), predy, w) + + @cache_readonly + def df_model(self): + return self.X.shape[1] - 1 + + @cache_readonly + def df_resid(self): + return self.n - self.df_model - 1 + +class GLMResults(LikelihoodModelResults): + """ + Results of estimated GLM and diagnostics. + + Parameters + ---------- + model : GLM object + Pointer to GLM object with estimation parameters. + params : array + k*1, estimared coefficients + mu : array + n*1, predicted y values. + w : array + n*1, final weight used for iwls + + Attributes + ---------- + model : GLM Object + Points to GLM object for which parameters have been + estimated. + y : array + n*1, dependent variable. + x : array + n*k, independent variable, including constant. + family : string + Model type: 'Gaussian', 'Poisson', 'Logistic' + n : integer + Number of observations + k : integer + Number of independent variables + df_model : float + k-1, where k is the number of variables (including + intercept) + df_residual : float + observations minus variables (n-k) + fit_params : dict + parameters passed into fit method to define estimation + routine. + scale : float + sigma squared used for subsequent computations. + params : array + n*k, estimared beta coefficients + w : array + n*1, final weight values of x + mu : array + n*1, predicted value of y (i.e., fittedvalues) + cov_params : array + Variance covariance matrix (kxk) of betas which has been + appropriately scaled by sigma-squared + bse : array + k*1, standard errors of betas + pvalues : array + k*1, two-tailed pvalues of parameters + tvalues : array + k*1, the tvalues of the standard errors + null : array + n*1, predicted values of y for null model + deviance : float + value of the deviance function evalued at params; + see family.py for distribution-specific deviance + null_deviance : float + value of the deviance function for the model fit with + a constant as the only regressor + llf : float + value of the loglikelihood function evalued at params; + see family.py for distribution-specific loglikelihoods + llnull : float + value of log-likelihood function evaluated at null + aic : float + AIC + bic : float + BIC + D2 : float + percent deviance explained + adj_D2 : float + adjusted percent deviance explained + pseudo_R2 : float + McFadden's pseudo R2 (coefficient of determination) + adj_pseudoR2 : float + adjusted McFadden's pseudo R2 + resid_response : array + response residuals; defined as y-mu + resid_pearson : array + Pearson residuals; defined as (y-mu)/sqrt(VAR(mu)) + where VAR is the distribution specific variance + function; see family.py and varfuncs.py for more information. + resid_working : array + Working residuals; the working residuals are defined as + resid_response/link'(mu); see links.py for the + derivatives of the link functions. + + resid_anscombe : array + Anscombe residuals; see family.py for + distribution-specific Anscombe residuals. + + resid_deviance : array + deviance residuals; see family.py for + distribution-specific deviance residuals. + + pearson_chi2 : float + chi-Squared statistic is defined as the sum + of the squares of the Pearson residuals + + normalized_cov_params : array + k*k, approximates [X.T*X]-1 + """ + def __init__(self, model, params, mu, w): + self.model = model + self.n = model.n + self.y = model.y.T.flatten() + self.X = model.X + self.k = model.k + self.family = model.family + self.fit_params = model.fit_params + self.params = params + self.w = w + self.mu = mu.flatten() + self._cache = {} + + @cache_readonly + def df_model(self): + return self.model.df_model + + @cache_readonly + def df_resid(self): + return self.model.df_resid + + @cache_readonly + def normalized_cov_params(self): + return la.inv(spdot(self.w.T, self.w)) + + @cache_readonly + def resid_response(self): + return (self.y-self.mu) + + @cache_readonly + def resid_pearson(self): + return ((self.y-self.mu) / + np.sqrt(self.family.variance(self.mu))) + + @cache_readonly + def resid_working(self): + return (self.resid_response / self.family.link.deriv(self.mu)) + + @cache_readonly + def resid_anscombe(self): + return (self.family.resid_anscombe(self.y, self.mu)) + + @cache_readonly + def resid_deviance(self): + return (self.family.resid_dev(self.y, self.mu)) + + @cache_readonly + def pearson_chi2(self): + chisq = (self.y - self.mu)**2 / self.family.variance(self.mu) + chisqsum = np.sum(chisq) + return chisqsum + + @cache_readonly + def null(self): + y = np.reshape(self.y, (-1,1)) + model = self.model + X = np.ones((len(y), 1)) + null_mod = GLM(y, X, family=self.family, constant=False) + return null_mod.fit().mu + + @cache_readonly + def scale(self): + if isinstance(self.family, (family.Binomial, family.Poisson)): + return 1. + else: + return (((np.power(self.resid_response, 2) / + self.family.variance(self.mu))).sum() / + (self.df_resid)) + @cache_readonly + def deviance(self): + return self.family.deviance(self.y, self.mu) + + @cache_readonly + def null_deviance(self): + return self.family.deviance(self.y, self.null) + + @cache_readonly + def llnull(self): + return self.family.loglike(self.y, self.null, scale=self.scale) + + @cache_readonly + def llf(self): + return self.family.loglike(self.y, self.mu, scale=self.scale) + + @cache_readonly + def aic(self): + if isinstance(self.family, family.QuasiPoisson): + return np.nan + else: + return -2 * self.llf + 2*(self.df_model+1) + + @cache_readonly + def bic(self): + return (self.deviance - + (self.model.n - self.df_model - 1) * + np.log(self.model.n)) + + @cache_readonly + def D2(self): + return 1 - (self.deviance / self.null_deviance) + + @cache_readonly + def adj_D2(self): + return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2) + + @cache_readonly + def pseudoR2(self): + return 1 - (self.llf/self.llnull) + + @cache_readonly + def adj_pseudoR2(self): + return 1 - ((self.llf-self.k)/self.llnull) + diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/iwls.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/iwls.py new file mode 100644 index 0000000..3ea6747 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/iwls.py @@ -0,0 +1,84 @@ +import numpy as np +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 + +def _compute_betas(y, x): + """ + compute MLE coefficients using iwls routine + + Methods: p189, Iteratively (Re)weighted Least Squares (IWLS), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + """ + xT = x.T + xtx = spdot(xT, x) + xtx_inv = la.inv(xtx) + xtx_inv = sp.csr_matrix(xtx_inv) + xTy = spdot(xT, y, array_out=False) + betas = spdot(xtx_inv, xTy) + return betas + +def _compute_betas_gwr(y, x, wi): + """ + compute MLE coefficients using iwls routine + + Methods: p189, Iteratively (Re)weighted Least Squares (IWLS), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + """ + xT = (x * wi).T + xtx = np.dot(xT, x) + xtx_inv = la.inv(xtx) + xtx_inv_xt = np.dot(xtx_inv, xT) + betas = np.dot(xtx_inv_xt, y) + return betas, xtx_inv_xt + +def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=None): + """ + Iteratively re-weighted least squares estimation routine + """ + n_iter = 0 + diff = 1.0e6 + if ini_betas is None: + betas = np.zeros((x.shape[1], 1), np.float) + else: + betas = ini_betas + if isinstance(family, Binomial): + y = family.link._clean(y) + if isinstance(family, Poisson): + y_off = y/offset + y_off = family.starting_mu(y_off) + v = family.predict(y_off) + mu = family.starting_mu(y) + else: + mu = family.starting_mu(y) + v = family.predict(mu) + + while diff > tol and n_iter < max_iter: + 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) + wx = spmultiply(x, w, array_out=False) + wz = spmultiply(z, w, array_out=False) + if wi is None: + n_betas = _compute_betas(wz, wx) + else: + n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi) + v = spdot(x, n_betas) + mu = family.fitted(v) + if isinstance(family, Poisson): + mu = mu * offset + diff = min(abs(n_betas-betas)) + betas = n_betas + + if wi is None: + return betas, mu, wx, n_iter + else: + return betas, mu, v, w, z, xtx_inv_xt, n_iter diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/links.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/links.py new file mode 100644 index 0000000..f45724d --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/links.py @@ -0,0 +1,953 @@ +''' +Defines the link functions to be used with GLM and GEE families. +''' + +import numpy as np +import scipy.stats +FLOAT_EPS = np.finfo(float).eps + + +class Link(object): + """ + A generic link function for one-parameter exponential family. + + `Link` does nothing, but lays out the methods expected of any subclass. + """ + + def __call__(self, p): + """ + Return the value of the link function. This is just a placeholder. + + Parameters + ---------- + p : array-like + Probabilities + + Returns + ------- + g(p) : array-like + The value of the link function g(p) = z + """ + return NotImplementedError + + def inverse(self, z): + """ + Inverse of the link function. Just a placeholder. + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor of the transformed variable + in the IRLS algorithm for GLM. + + Returns + ------- + g^(-1)(z) : array + The value of the inverse of the link function g^(-1)(z) = p + + + """ + return NotImplementedError + + def deriv(self, p): + """ + Derivative of the link function g'(p). Just a placeholder. + + Parameters + ---------- + p : array-like + + Returns + ------- + g'(p) : array + The value of the derivative of the link function g'(p) + """ + return NotImplementedError + + def deriv2(self, p): + """Second derivative of the link function g''(p) + + implemented through numerical differentiation + """ + from statsmodels.tools.numdiff import approx_fprime_cs + # TODO: workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(p, self.deriv)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse link function g^(-1)(z). + + Notes + ----- + This reference implementation gives the correct result but is + inefficient, so it can be overriden in subclasses. + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g'^(-1)(z) : array + The value of the derivative of the inverse of the link function + + """ + return 1 / self.deriv(self.inverse(z)) + + +class Logit(Link): + """ + The logit transform + + Notes + ----- + call and derivative use a private method _clean to make trim p by + machine epsilon so that p is in (0,1) + + Alias of Logit: + logit = Logit() + """ + + def _clean(self, p): + """ + Clip logistic values to range (eps, 1-eps) + + Parameters + ----------- + p : array-like + Probabilities + + Returns + -------- + pclip : array + Clipped probabilities + """ + return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS) + + def __call__(self, p): + """ + The logit transform + + Parameters + ---------- + p : array-like + Probabilities + + Returns + ------- + z : array + Logit transform of `p` + + Notes + ----- + g(p) = log(p / (1 - p)) + """ + p = self._clean(p) + return np.log(p / (1. - p)) + + def inverse(self, z): + """ + Inverse of the logit transform + + Parameters + ---------- + z : array-like + The value of the logit transform at `p` + + Returns + ------- + p : array + Probabilities + + Notes + ----- + g^(-1)(z) = exp(z)/(1+exp(z)) + """ + z = np.asarray(z) + t = np.exp(-z) + return 1. / (1. + t) + + def deriv(self, p): + + """ + Derivative of the logit transform + + Parameters + ---------- + p: array-like + Probabilities + + Returns + ------- + g'(p) : array + Value of the derivative of logit transform at `p` + + Notes + ----- + g'(p) = 1 / (p * (1 - p)) + + Alias for `Logit`: + logit = Logit() + """ + p = self._clean(p) + return 1. / (p * (1 - p)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the logit transform + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g'^(-1)(z) : array + The value of the derivative of the inverse of the logit function + + """ + t = np.exp(z) + return t/(1 + t)**2 + + + def deriv2(self, p): + """ + Second derivative of the logit function. + + Parameters + ---------- + p : array-like + probabilities + + Returns + ------- + g''(z) : array + The value of the second derivative of the logit function + """ + v = p * (1 - p) + return (2*p - 1) / v**2 + +class logit(Logit): + pass + + +class Power(Link): + """ + The power transform + + Parameters + ---------- + power : float + The exponent of the power transform + + Notes + ----- + Aliases of Power: + inverse = Power(power=-1) + sqrt = Power(power=.5) + inverse_squared = Power(power=-2.) + identity = Power(power=1.) + """ + + def __init__(self, power=1.): + self.power = power + + def __call__(self, p): + """ + Power transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array-like + Power transform of x + + Notes + ----- + g(p) = x**self.power + """ + + z = np.power(p, self.power) + return z + + def inverse(self, z): + """ + Inverse of the power transform link function + + Parameters + ---------- + `z` : array-like + Value of the transformed mean parameters at `p` + + Returns + ------- + `p` : array + Mean parameters + + Notes + ----- + g^(-1)(z`) = `z`**(1/`power`) + """ + + p = np.power(z, 1. / self.power) + return p + + def deriv(self, p): + """ + Derivative of the power transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + -------- + g'(p) : array + Derivative of power transform of `p` + + Notes + ----- + g'(`p`) = `power` * `p`**(`power` - 1) + """ + return self.power * np.power(p, self.power - 1) + + def deriv2(self, p): + """ + Second derivative of the power transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + -------- + g''(p) : array + Second derivative of the power transform of `p` + + Notes + ----- + g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2) + """ + return self.power * (self.power - 1) * np.power(p, self.power - 2) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the power transform + + Parameters + ---------- + z : array-like + `z` is usually the linear predictor for a GLM or GEE model. + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the power transform + function + """ + return np.power(z, (1 - self.power)/self.power) / self.power + + +class inverse_power(Power): + """ + The inverse transform + + Notes + ----- + g(p) = 1/p + + Alias of statsmodels.family.links.Power(power=-1.) + """ + def __init__(self): + super(inverse_power, self).__init__(power=-1.) + + +class sqrt(Power): + """ + The square-root transform + + Notes + ----- + g(`p`) = sqrt(`p`) + + Alias of statsmodels.family.links.Power(power=.5) + """ + def __init__(self): + super(sqrt, self).__init__(power=.5) + + +class inverse_squared(Power): + """ + The inverse squared transform + + Notes + ----- + g(`p`) = 1/(`p`\ \*\*2) + + Alias of statsmodels.family.links.Power(power=2.) + """ + def __init__(self): + super(inverse_squared, self).__init__(power=-2.) + + +class identity(Power): + """ + The identity transform + + Notes + ----- + g(`p`) = `p` + + Alias of statsmodels.family.links.Power(power=1.) + """ + def __init__(self): + super(identity, self).__init__(power=1.) + + +class Log(Link): + """ + The log transform + + Notes + ----- + call and derivative call a private method _clean to trim the data by + machine epsilon so that p is in (0,1). log is an alias of Log. + """ + + def _clean(self, x): + return np.clip(x, FLOAT_EPS, np.inf) + + def __call__(self, p, **extra): + """ + Log transform link function + + Parameters + ---------- + x : array-like + Mean parameters + + Returns + ------- + z : array + log(x) + + Notes + ----- + g(p) = log(p) + """ + x = self._clean(p) + return np.log(x) + + def inverse(self, z): + """ + Inverse of log transform link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + p : array + The mean probabilities given the value of the inverse `z` + + Notes + ----- + g^{-1}(z) = exp(z) + """ + return np.exp(z) + + def deriv(self, p): + """ + Derivative of log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + derivative of log transform of x + + Notes + ----- + g'(x) = 1/x + """ + p = self._clean(p) + return 1. / p + + def deriv2(self, p): + """ + Second derivative of the log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + Second derivative of log transform of x + + Notes + ----- + g''(x) = -1/x^2 + """ + p = self._clean(p) + return -1. / p**2 + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the log transform link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the log function, + the exponential function + """ + return np.exp(z) + + +class log(Log): + """ + The log transform + + Notes + ----- + log is a an alias of Log. + """ + pass + + +# TODO: the CDFLink is untested +class CDFLink(Logit): + """ + The use the CDF of a scipy.stats distribution + + CDFLink is a subclass of logit in order to use its _clean method + for the link and its derivative. + + Parameters + ---------- + dbn : scipy.stats distribution + Default is dbn=scipy.stats.norm + + Notes + ----- + The CDF link is untested. + """ + + def __init__(self, dbn=scipy.stats.norm): + self.dbn = dbn + + def __call__(self, p): + """ + CDF link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array + (ppf) inverse of CDF transform of p + + Notes + ----- + g(`p`) = `dbn`.ppf(`p`) + """ + p = self._clean(p) + return self.dbn.ppf(p) + + def inverse(self, z): + """ + The inverse of the CDF link + + Parameters + ---------- + z : array-like + The value of the inverse of the link function at `p` + + Returns + ------- + p : array + Mean probabilities. The value of the inverse of CDF link of `z` + + Notes + ----- + g^(-1)(`z`) = `dbn`.cdf(`z`) + """ + return self.dbn.cdf(z) + + def deriv(self, p): + """ + Derivative of CDF link + + Parameters + ---------- + p : array-like + mean parameters + + Returns + ------- + g'(p) : array + The derivative of CDF transform at `p` + + Notes + ----- + g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`)) + """ + p = self._clean(p) + return 1. / self.dbn.pdf(self.dbn.ppf(p)) + + def deriv2(self, p): + """ + Second derivative of the link function g''(p) + + implemented through numerical differentiation + """ + from statsmodels.tools.numdiff import approx_fprime + p = np.atleast_1d(p) + # Note: special function for norm.ppf does not support complex + return np.diag(approx_fprime(p, self.deriv, centered=True)) + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the CDF transformation link function + + Parameters + ---------- + z : array + The inverse of the link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the logit function + """ + return 1/self.deriv(self.inverse(z)) + + +class probit(CDFLink): + """ + The probit (standard normal CDF) transform + + Notes + -------- + g(p) = scipy.stats.norm.ppf(p) + + probit is an alias of CDFLink. + """ + pass + + +class cauchy(CDFLink): + """ + The Cauchy (standard Cauchy CDF) transform + + Notes + ----- + g(p) = scipy.stats.cauchy.ppf(p) + + cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy + """ + + def __init__(self): + super(cauchy, self).__init__(dbn=scipy.stats.cauchy) + + def deriv2(self, p): + """ + Second derivative of the Cauchy link function. + + Parameters + ---------- + p: array-like + Probabilities + + Returns + ------- + g''(p) : array + Value of the second derivative of Cauchy link function at `p` + """ + a = np.pi * (p - 0.5) + d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3 + return d2 + +class CLogLog(Logit): + """ + The complementary log-log transform + + CLogLog inherits from Logit in order to have access to its _clean method + for the link and its derivative. + + Notes + ----- + CLogLog is untested. + """ + def __call__(self, p): + """ + C-Log-Log transform link function + + Parameters + ---------- + p : array + Mean parameters + + Returns + ------- + z : array + The CLogLog transform of `p` + + Notes + ----- + g(p) = log(-log(1-p)) + """ + p = self._clean(p) + return np.log(-np.log(1 - p)) + + def inverse(self, z): + """ + Inverse of C-Log-Log transform link function + + + Parameters + ---------- + z : array-like + The value of the inverse of the CLogLog link function at `p` + + Returns + ------- + p : array + Mean parameters + + Notes + ----- + g^(-1)(`z`) = 1-exp(-exp(`z`)) + """ + return 1 - np.exp(-np.exp(z)) + + def deriv(self, p): + """ + Derivative of C-Log-Log transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + The derivative of the CLogLog transform link function + + Notes + ----- + g'(p) = - 1 / ((p-1)*log(1-p)) + """ + p = self._clean(p) + return 1. / ((p - 1) * (np.log(1 - p))) + + def deriv2(self, p): + """ + Second derivative of the C-Log-Log ink function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + The second derivative of the CLogLog link function + """ + p = self._clean(p) + fl = np.log(1 - p) + d2 = -1 / ((1 - p)**2 * fl) + d2 *= 1 + 1 / fl + return d2 + + def inverse_deriv(self, z): + """ + Derivative of the inverse of the C-Log-Log transform link function + + Parameters + ---------- + z : array-like + The value of the inverse of the CLogLog link function at `p` + + Returns + ------- + g^(-1)'(z) : array + The derivative of the inverse of the CLogLog link function + """ + return np.exp(z - np.exp(z)) + + +class cloglog(CLogLog): + """ + The CLogLog transform link function. + + Notes + ----- + g(`p`) = log(-log(1-`p`)) + + cloglog is an alias for CLogLog + cloglog = CLogLog() + """ + pass + + +class NegativeBinomial(object): + ''' + The negative binomial link function + + Parameters + ---------- + alpha : float, optional + Alpha is the ancillary parameter of the Negative Binomial link + function. It is assumed to be nonstochastic. The default value is 1. + Permissible values are usually assumed to be in (.01, 2). + ''' + + def __init__(self, alpha=1.): + self.alpha = alpha + + def _clean(self, x): + return np.clip(x, FLOAT_EPS, np.inf) + + def __call__(self, p): + ''' + Negative Binomial transform link function + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + z : array + The negative binomial transform of `p` + + Notes + ----- + g(p) = log(p/(p + 1/alpha)) + ''' + p = self._clean(p) + return np.log(p/(p + 1/self.alpha)) + + def inverse(self, z): + ''' + Inverse of the negative binomial transform + + Parameters + ----------- + z : array-like + The value of the inverse of the negative binomial link at `p`. + + Returns + ------- + p : array + Mean parameters + + Notes + ----- + g^(-1)(z) = exp(z)/(alpha*(1-exp(z))) + ''' + return -1/(self.alpha * (1 - np.exp(-z))) + + def deriv(self, p): + ''' + Derivative of the negative binomial transform + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g'(p) : array + The derivative of the negative binomial transform link function + + Notes + ----- + g'(x) = 1/(x+alpha*x^2) + ''' + return 1/(p + self.alpha * p**2) + + def deriv2(self,p): + ''' + Second derivative of the negative binomial link function. + + Parameters + ---------- + p : array-like + Mean parameters + + Returns + ------- + g''(p) : array + The second derivative of the negative binomial transform link + function + + Notes + ----- + g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2 + ''' + numer = -(1 + 2 * self.alpha * p) + denom = (p + self.alpha * p**2)**2 + return numer / denom + + def inverse_deriv(self, z): + ''' + Derivative of the inverse of the negative binomial transform + + Parameters + ----------- + z : array-like + Usually the linear predictor for a GLM or GEE model + + Returns + ------- + g^(-1)'(z) : array + The value of the derivative of the inverse of the negative + binomial link + ''' + t = np.exp(z) + return t / (self.alpha * (1-t)**2) + + +class nbinom(NegativeBinomial): + """ + The negative binomial link function. + + Notes + ----- + g(p) = log(p/(p + 1/alpha)) + + nbinom is an alias of NegativeBinomial. + nbinom = NegativeBinomial(alpha=1.) + """ + pass diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/tests/test_glm.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/tests/test_glm.py new file mode 100644 index 0000000..b86ad6a --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/tests/test_glm.py @@ -0,0 +1,993 @@ +""" +Tests for generalized linear models. Majority of code either directly borrowed +or closely adapted from statsmodels package. Model results verfiied using glm +function in R and GLM function in statsmodels. +""" + +__author__ = 'Taylor Oshan tayoshan@gmail.com' + +from pysal.contrib.glm.glm import GLM +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial, QuasiPoisson +import numpy as np +import pysal +import unittest +import math + + +class TestGaussian(unittest.TestCase): + """ + Tests for Poisson GLM + """ + + def setUp(self): + db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r') + y = np.array(db.by_col("HOVAL")) + self.y = np.reshape(y, (49,1)) + X = [] + X.append(db.by_col("INC")) + X.append(db.by_col("CRIME")) + self.X = np.array(X).T + + def testIWLS(self): + model = GLM(self.y, self.X, family=Gaussian()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertEqual(results.aic, 408.73548964604873) + self.assertEqual(results.bic, 10467.991340493107) + self.assertEqual(results.deviance, 10647.015074206196) + self.assertEqual(results.llf, -201.36774482302437) + self.assertEqual(results.null_deviance, 16367.794631703124) + self.assertEqual(results.scale, 231.45684943926514) + np.testing.assert_allclose(results.params, [ 46.42818268, 0.62898397, + -0.48488854]) + np.testing.assert_allclose(results.bse, [ 13.19175703, 0.53591045, + 0.18267291]) + np.testing.assert_allclose(results.cov_params(), + [[ 1.74022453e+02, -6.52060364e+00, -2.15109867e+00], + [ -6.52060364e+00, 2.87200008e-01, 6.80956787e-02], + [ -2.15109867e+00, 6.80956787e-02, 3.33693910e-02]]) + np.testing.assert_allclose(results.tvalues, [ 3.51948437, 1.17367365, + -2.65440864]) + np.testing.assert_allclose(results.pvalues, [ 0.00043239, 0.24052577, + 0.00794475], atol=1.0e-8) + np.testing.assert_allclose(results.conf_int(), + [[ 20.57281401, 72.28355135], + [ -0.42138121, 1.67934915], + [ -0.84292086, -0.12685622]]) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 7.51857004e-01, -2.81720055e-02, -9.29373521e-03], + [ -2.81720055e-02, 1.24083607e-03, 2.94204638e-04], + [ -9.29373521e-03, 2.94204638e-04, 1.44171110e-04]]) + np.testing.assert_allclose(results.mu, + [ 51.08752105, 50.66601521, 41.61367567, 33.53969014, + 28.90638232, 43.87074227, 51.64910882, 34.92671563, + 42.69267622, 38.49449134, 20.92815471, 25.25228436, + 29.78223486, 25.02403635, 29.07959539, 24.63352275, + 34.71372149, 33.40443052, 27.29864225, 65.86219802, + 33.69854751, 37.44976435, 50.01304928, 36.81219959, + 22.02674837, 31.64775955, 27.63563294, 23.7697291 , + 22.43119725, 21.76987089, 48.51169321, 49.05891819, + 32.31656426, 44.20550354, 35.49244888, 51.27811308, + 36.55047181, 27.37048914, 48.78812922, 57.31744163, + 51.22914162, 54.70515578, 37.06622277, 44.5075759 , + 41.24328983, 49.93821824, 44.85644299, 40.93838609, 47.32045464]) + self.assertEqual(results.pearson_chi2, 10647.015074206196) + np.testing.assert_allclose(results.resid_response, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_working, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_pearson, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_anscombe, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.resid_deviance, + [ 29.37948195, -6.09901421, -15.26367567, -0.33968914, + -5.68138232, -15.12074227, 23.35089118, 2.19828437, + 9.90732178, 57.90551066, -1.22815371, -5.35228436, + 11.91776614, 17.87596565, -11.07959539, -5.83352375, + 7.03627851, 26.59556948, 3.30135775, 15.40479998, + -13.72354751, -6.99976335, -2.28004728, 16.38780141, + -4.12674837, -11.34776055, 6.46436506, -0.9197291 , + 10.06880275, 0.73012911, -16.71169421, -8.75891919, + -8.71656426, -15.75550254, -8.49244888, -14.97811408, + 6.74952719, -4.67048814, -9.18813122, 4.63255937, + -9.12914362, -10.37215578, -11.36622177, -11.0075759 , + -13.51028983, 26.16177976, -2.35644299, -14.13838709, -11.52045564]) + np.testing.assert_allclose(results.null, + [ 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, + 38.43622447, 38.43622447, 38.43622447, 38.43622447, 38.43622447]) + self.assertAlmostEqual(results.D2, .349514377851) + self.assertAlmostEqual(results.adj_D2, 0.32123239427957673) + +class TestPoisson(unittest.TestCase): + + def setUp(self): + db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r') + y = np.array(db.by_col("HOVAL")) + y = np.reshape(y, (49,1)) + self.y = np.round(y).astype(int) + X = [] + X.append(db.by_col("INC")) + X.append(db.by_col("CRIME")) + self.X = np.array(X).T + + def testIWLS(self): + model = GLM(self.y, self.X, family=Poisson()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertAlmostEqual(results.aic, 500.85184179938756) + self.assertAlmostEqual(results.bic, 51.436404535087661) + self.assertAlmostEqual(results.deviance, 230.46013824817649) + self.assertAlmostEqual(results.llf, -247.42592089969378) + self.assertAlmostEqual(results.null_deviance, 376.97293610347361) + self.assertEqual(results.scale, 1.0) + np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491, + -0.01371397], atol=1.0e-8) + np.testing.assert_allclose(results.bse, [ 0.13049161, 0.00511599, + 0.00193769], atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.tvalues, [ 30.0524361 , 2.31331634, + -7.07748998]) + np.testing.assert_allclose(results.pvalues, [ 2.02901657e-198, + 2.07052532e-002, 1.46788805e-012]) + np.testing.assert_allclose(results.conf_int(), + [[ 3.66583199e+00, 4.17734972e+00], + [ 1.80774841e-03, 2.18620753e-02], + [ -1.75117666e-02, -9.91616901e-03]]) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.mu, + [ 51.26831574, 50.15022766, 40.06142973, 34.13799739, + 28.76119226, 42.6836241 , 55.64593703, 34.08277997, + 40.90389582, 37.19727958, 23.47459217, 26.12384057, + 29.78303507, 25.96888223, 29.14073823, 26.04369592, + 34.18996367, 32.28924005, 27.42284396, 72.69207879, + 33.05316347, 36.52276972, 49.2551479 , 35.33439632, + 24.07252457, 31.67153709, 27.81699478, 25.38021219, + 24.31759259, 23.13586161, 48.40724678, 48.57969818, + 31.92596006, 43.3679231 , 34.32925819, 51.78908089, + 34.49778584, 27.56236198, 48.34273194, 57.50829097, + 50.66038226, 54.68701352, 35.77103116, 43.21886784, + 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294]) + self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221) + np.testing.assert_allclose(results.resid_response, + [ 28.73168426, -5.15022766, -14.06142973, -1.13799739, + -5.76119226, -13.6836241 , 19.35406297, 2.91722003, + 12.09610418, 58.80272042, -3.47459217, -6.12384057, + 12.21696493, 17.03111777, -11.14073823, -7.04369592, + 7.81003633, 27.71075995, 3.57715604, 8.30792121, + -13.05316347, -6.52276972, -1.2551479 , 17.66560368, + -6.07252457, -11.67153709, 6.18300522, -2.38021219, + 7.68240741, -1.13586161, -16.40724678, -8.57969818, + -7.92596006, -15.3679231 , -7.32925819, -15.78908089, + 8.50221416, -4.56236198, -8.34273194, 4.49170903, + -8.66038226, -10.68701352, -9.77103116, -9.21886784, + -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294]) + np.testing.assert_allclose(results.resid_working, + [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192, + -165.69875817, -584.06666725, 1076.97496919, 99.42696848, + 494.77778514, 2187.30123163, -81.56463405, -159.97823479, + 363.858295 , 442.27909165, -324.64933645, -183.44387481, + 267.02485844, 894.75938 , 98.09579187, 603.9200634 , + -431.44834594, -238.2296165 , -61.82249568, 624.20344168, + -146.18099686, -369.65551968, 171.99262399, -60.41029031, + 186.81765356, -26.27913713, -794.22964417, -416.79914795, + -253.04388425, -666.47490701, -251.6079969 , -817.70198717, + 293.30756327, -125.74947222, -403.31045369, 258.31051005, + -438.73827602, -584.440853 , -349.51985996, -398.42903071, + -483.96599444, 1300.32189904, -48.89309853, -535.19735391, + -476.27334527]) + np.testing.assert_allclose(results.resid_pearson, + [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881, + -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836, + -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 , + -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591, + -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025, + -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708, + -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503, + -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634, + -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306, + 3.67934693, -0.17259375, -2.09312684, -1.51230062]) + np.testing.assert_allclose(results.resid_anscombe, + [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751, + -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452, + -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611, + -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923, + -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591, + -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278, + -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484, + -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202, + -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267, + 3.41729922, -0.17335867, -2.22921828, -1.57470549]) + np.testing.assert_allclose(results.resid_deviance, + [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765, + -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525, + -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376, + -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662, + -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865, + -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 , + -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892, + -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655, + -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949, + 3.41437156, -0.1733581 , -2.22765605, -1.57426046]) + np.testing.assert_allclose(results.null, + [ 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143]) + self.assertAlmostEqual(results.D2, .388656011675) + self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)#.375648692774) + + def testQuasi(self): + model = GLM(self.y, self.X, family=QuasiPoisson()) + results = model.fit() + self.assertEqual(results.n, 49) + self.assertEqual(results.df_model, 2) + self.assertEqual(results.df_resid, 46) + self.assertTrue(math.isnan(results.aic)) + self.assertAlmostEqual(results.bic, 51.436404535087661) + self.assertAlmostEqual(results.deviance, 230.46013824817649) + self.assertTrue(math.isnan(results.llf)) + self.assertAlmostEqual(results.null_deviance, 376.97293610347361) + self.assertAlmostEqual(results.scale, 5.7526658548022223) + np.testing.assert_allclose(results.params, [ 3.92159085, 0.01183491, + -0.01371397], atol=1.0e-8) + np.testing.assert_allclose(results.bse, [ 0.31298042, 0.01227057, + 0.00464749], atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 9.79567451e-02, -3.55876238e-03, -1.27356524e-03], + [ -3.55876238e-03, 1.50566777e-04, 3.89741067e-05], + [ -1.27356524e-03, 3.89741067e-05, 2.15991606e-05]]) + np.testing.assert_allclose(results.tvalues, [ 12.52982796, 0.96449604, + -2.95083339]) + np.testing.assert_allclose(results.pvalues, [ 5.12737770e-36, + 3.34797291e-01, 3.16917819e-03]) + np.testing.assert_allclose(results.conf_int(), + [[ 3.3081605 , 4.53502121], + [-0.01221495, 0.03588478], + [-0.02282288, -0.00460506]], atol=1.0e-8) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 1.70280610e-02, -6.18628383e-04, -2.21386966e-04], + [ -6.18628383e-04, 2.61733917e-05, 6.77496445e-06], + [ -2.21386966e-04, 6.77496445e-06, 3.75463502e-06]]) + np.testing.assert_allclose(results.mu, + [ 51.26831574, 50.15022766, 40.06142973, 34.13799739, + 28.76119226, 42.6836241 , 55.64593703, 34.08277997, + 40.90389582, 37.19727958, 23.47459217, 26.12384057, + 29.78303507, 25.96888223, 29.14073823, 26.04369592, + 34.18996367, 32.28924005, 27.42284396, 72.69207879, + 33.05316347, 36.52276972, 49.2551479 , 35.33439632, + 24.07252457, 31.67153709, 27.81699478, 25.38021219, + 24.31759259, 23.13586161, 48.40724678, 48.57969818, + 31.92596006, 43.3679231 , 34.32925819, 51.78908089, + 34.49778584, 27.56236198, 48.34273194, 57.50829097, + 50.66038226, 54.68701352, 35.77103116, 43.21886784, + 40.07615759, 49.98658004, 43.13352883, 40.28520774, 46.28910294]) + self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221) + np.testing.assert_allclose(results.resid_response, + [ 28.73168426, -5.15022766, -14.06142973, -1.13799739, + -5.76119226, -13.6836241 , 19.35406297, 2.91722003, + 12.09610418, 58.80272042, -3.47459217, -6.12384057, + 12.21696493, 17.03111777, -11.14073823, -7.04369592, + 7.81003633, 27.71075995, 3.57715604, 8.30792121, + -13.05316347, -6.52276972, -1.2551479 , 17.66560368, + -6.07252457, -11.67153709, 6.18300522, -2.38021219, + 7.68240741, -1.13586161, -16.40724678, -8.57969818, + -7.92596006, -15.3679231 , -7.32925819, -15.78908089, + 8.50221416, -4.56236198, -8.34273194, 4.49170903, + -8.66038226, -10.68701352, -9.77103116, -9.21886784, + -12.07615759, 26.01341996, -1.13352883, -13.28520774, -10.28910294]) + np.testing.assert_allclose(results.resid_working, + [ 1473.02506034, -258.28508941, -563.32097891, -38.84895192, + -165.69875817, -584.06666725, 1076.97496919, 99.42696848, + 494.77778514, 2187.30123163, -81.56463405, -159.97823479, + 363.858295 , 442.27909165, -324.64933645, -183.44387481, + 267.02485844, 894.75938 , 98.09579187, 603.9200634 , + -431.44834594, -238.2296165 , -61.82249568, 624.20344168, + -146.18099686, -369.65551968, 171.99262399, -60.41029031, + 186.81765356, -26.27913713, -794.22964417, -416.79914795, + -253.04388425, -666.47490701, -251.6079969 , -817.70198717, + 293.30756327, -125.74947222, -403.31045369, 258.31051005, + -438.73827602, -584.440853 , -349.51985996, -398.42903071, + -483.96599444, 1300.32189904, -48.89309853, -535.19735391, + -476.27334527]) + np.testing.assert_allclose(results.resid_pearson, + [ 4.01269878, -0.72726045, -2.221602 , -0.19477008, -1.07425881, + -2.09445239, 2.59451042, 0.49969118, 1.89131202, 9.64143836, + -0.71714142, -1.19813392, 2.23861212, 3.34207756, -2.0637814 , + -1.3802231 , 1.33568403, 4.87662684, 0.68309584, 0.97442591, + -2.27043598, -1.07931992, -0.17884182, 2.97186889, -1.23768025, + -2.07392709, 1.1723155 , -0.47246327, 1.55789092, -0.23614708, + -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503, + -2.19400568, 1.44755952, -0.8690235 , -1.19989348, 0.59230634, + -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306, + 3.67934693, -0.17259375, -2.09312684, -1.51230062]) + np.testing.assert_allclose(results.resid_anscombe, + [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751, + -2.22611959, 2.46352013, 0.49282126, 1.80857757, 8.06444452, + -0.73610811, -1.25061371, 2.10820431, 3.05467547, -2.22437611, + -1.45136173, 1.28939698, 4.35942058, 0.66904552, 0.95674923, + -2.45438937, -1.11429881, -0.17961012, 2.76715848, -1.29658591, + -2.22816691, 1.13269136, -0.48017382, 1.48562248, -0.23812278, + -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484, + -2.32398309, 1.39380683, -0.89495368, -1.23735395, 0.58485202, + -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267, + 3.41729922, -0.17335867, -2.22921828, -1.57470549]) + np.testing.assert_allclose(results.resid_deviance, + [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765, + -2.22466106, 2.46246446, 0.4928057 , 1.80799655, 8.02696525, + -0.73602255, -1.25021555, 2.10699958, 3.05084608, -2.22214376, + -1.45072221, 1.28913747, 4.35106213, 0.6689982 , 0.95669662, + -2.45171913, -1.11410444, -0.17960956, 2.76494217, -1.29609865, + -2.22612429, 1.13247453, -0.48015254, 1.48508549, -0.23812 , + -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892, + -2.32263069, 1.39348459, -0.89482132, -1.23715363, 0.58483655, + -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949, + 3.41437156, -0.1733581 , -2.22765605, -1.57426046]) + np.testing.assert_allclose(results.null, + [ 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, + 38.42857143, 38.42857143, 38.42857143, 38.42857143, 38.42857143]) + self.assertAlmostEqual(results.D2, .388656011675) + self.assertAlmostEqual(results.adj_D2, 0.36207583826952761) + +class TestBinomial(unittest.TestCase): + + def setUp(self): + #London house price data + #y: 'BATH2' + y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + self.y = y.reshape((316,1)) + #X: 'FLOORSZ' + X = np.array([ 77, 75, 64, 95, 107, 100, 81, 151, 98, 260, 171, 161, 91, + 80, 50, 85, 52, 69, 60, 84, 155, 97, 69, 126, 90, 43, + 51, 41, 140, 80, 52, 86, 66, 60, 40, 155, 138, 97, 115, + 148, 206, 60, 53, 96, 88, 160, 31, 43, 154, 60, 131, 60, + 46, 61, 125, 150, 76, 92, 96, 100, 105, 72, 48, 41, 72, + 65, 60, 65, 98, 33, 144, 111, 91, 108, 38, 48, 95, 63, + 98, 129, 108, 51, 131, 66, 48, 127, 76, 68, 52, 64, 57, + 121, 67, 76, 112, 96, 90, 53, 93, 64, 97, 58, 44, 157, + 53, 70, 71, 167, 47, 70, 96, 77, 75, 71, 67, 47, 71, + 90, 69, 64, 65, 95, 60, 60, 65, 54, 121, 105, 50, 85, + 69, 69, 62, 65, 93, 93, 70, 62, 155, 68, 117, 80, 80, + 75, 98, 114, 86, 70, 50, 51, 163, 124, 59, 95, 51, 63, + 85, 53, 46, 102, 114, 83, 47, 40, 63, 123, 100, 63, 110, + 79, 98, 99, 120, 52, 48, 37, 81, 30, 88, 50, 35, 116, + 67, 45, 80, 86, 109, 59, 75, 60, 71, 141, 121, 50, 168, + 90, 51, 133, 75, 133, 127, 37, 68, 105, 61, 123, 151, 110, + 77, 220, 94, 77, 70, 100, 98, 126, 55, 105, 60, 176, 104, + 68, 62, 70, 48, 102, 80, 97, 66, 80, 102, 160, 55, 60, + 71, 125, 85, 85, 190, 137, 48, 41, 42, 51, 57, 60, 114, + 88, 84, 108, 66, 85, 42, 98, 90, 127, 100, 55, 76, 82, + 63, 80, 71, 76, 121, 109, 92, 160, 109, 185, 100, 90, 90, + 86, 88, 95, 116, 135, 61, 74, 60, 235, 76, 66, 100, 49, + 50, 37, 100, 88, 90, 52, 95, 81, 79, 96, 75, 91, 86, + 83, 180, 108, 80, 96, 49, 117, 117, 86, 46, 66, 95, 57, + 120, 137, 68, 240]) + self.X = X.reshape((316,1)) + + def testIWLS(self): + model = GLM(self.y, self.X, family=Binomial()) + results = model.fit() + self.assertEqual(results.n, 316) + self.assertEqual(results.df_model, 1) + self.assertEqual(results.df_resid, 314) + self.assertEqual(results.aic, 155.19347530342466) + self.assertEqual(results.bic, -1656.1095797628657) + self.assertEqual(results.deviance, 151.19347530342466) + self.assertEqual(results.llf, -75.596737651712331) + self.assertEqual(results.null_deviance, 189.16038985881212) + self.assertEqual(results.scale, 1.0) + np.testing.assert_allclose(results.params, [-5.33638276, 0.0287754 ]) + np.testing.assert_allclose(results.bse, [ 0.64499904, 0.00518312], + atol=1.0e-8) + np.testing.assert_allclose(results.cov_params(), + [[ 4.16023762e-01, -3.14338457e-03], + [ -3.14338457e-03, 2.68646833e-05]]) + np.testing.assert_allclose(results.tvalues, [-8.27347396, 5.55175826]) + np.testing.assert_allclose(results.pvalues, [ 1.30111233e-16, + 2.82810512e-08]) + np.testing.assert_allclose(results.conf_int(), + [[-6.60055765, -4.07220787], + [ 0.01861668, 0.03893412]], atol=1.0e-8) + np.testing.assert_allclose(results.normalized_cov_params, + [[ 4.16023762e-01, -3.14338457e-03], + [ -3.14338457e-03, 2.68646833e-05]]) + np.testing.assert_allclose(results.mu, + [ 0.04226237, 0.03999333, 0.02946178, 0.0689636 , 0.09471181, + 0.07879431, 0.04717464, 0.27065598, 0.07471691, 0.89522144, + 0.39752487, 0.33102718, 0.06192993, 0.04589793, 0.01988679, + 0.0526265 , 0.02104007, 0.03386636, 0.02634295, 0.05121018, + 0.29396682, 0.07275173, 0.03386636, 0.15307528, 0.06027915, + 0.01631789, 0.02045547, 0.01541937, 0.2128508 , 0.04589793, + 0.02104007, 0.05407977, 0.0311527 , 0.02634295, 0.01498855, + 0.29396682, 0.20336776, 0.07275173, 0.11637537, 0.25395607, + 0.64367488, 0.02634295, 0.02164101, 0.07083428, 0.05710047, + 0.32468619, 0.01160845, 0.01631789, 0.28803008, 0.02634295, + 0.17267234, 0.02634295, 0.01776301, 0.02709115, 0.14938186, + 0.26501331, 0.04111287, 0.06362285, 0.07083428, 0.07879431, + 0.08989109, 0.03680743, 0.0187955 , 0.01541937, 0.03680743, + 0.03029581, 0.02634295, 0.03029581, 0.07471691, 0.01228768, + 0.23277197, 0.10505173, 0.06192993, 0.09720799, 0.01416217, + 0.0187955 , 0.0689636 , 0.02865003, 0.07471691, 0.16460503, + 0.09720799, 0.02045547, 0.17267234, 0.0311527 , 0.0187955 , + 0.15684317, 0.04111287, 0.03293737, 0.02104007, 0.02946178, + 0.02421701, 0.1353385 , 0.03203302, 0.04111287, 0.10778798, + 0.07083428, 0.06027915, 0.02164101, 0.06535882, 0.02946178, + 0.07275173, 0.02490638, 0.01678627, 0.30605146, 0.02164101, + 0.03482061, 0.03580075, 0.37030921, 0.0182721 , 0.03482061, + 0.07083428, 0.04226237, 0.03999333, 0.03580075, 0.03203302, + 0.0182721 , 0.03580075, 0.06027915, 0.03386636, 0.02946178, + 0.03029581, 0.0689636 , 0.02634295, 0.02634295, 0.03029581, + 0.02225873, 0.1353385 , 0.08989109, 0.01988679, 0.0526265 , + 0.03386636, 0.03386636, 0.02786 , 0.03029581, 0.06535882, + 0.06535882, 0.03482061, 0.02786 , 0.29396682, 0.03293737, + 0.12242534, 0.04589793, 0.04589793, 0.03999333, 0.07471691, + 0.11344884, 0.05407977, 0.03482061, 0.01988679, 0.02045547, + 0.34389327, 0.14576223, 0.02561486, 0.0689636 , 0.02045547, + 0.02865003, 0.0526265 , 0.02164101, 0.01776301, 0.08307425, + 0.11344884, 0.04982997, 0.0182721 , 0.01498855, 0.02865003, + 0.14221564, 0.07879431, 0.02865003, 0.10237696, 0.04465416, + 0.07471691, 0.07673078, 0.13200634, 0.02104007, 0.0187955 , + 0.01376599, 0.04717464, 0.01128289, 0.05710047, 0.01988679, + 0.01300612, 0.11936722, 0.03203302, 0.01726786, 0.04589793, + 0.05407977, 0.09976271, 0.02561486, 0.03999333, 0.02634295, + 0.03580075, 0.21771181, 0.1353385 , 0.01988679, 0.37704374, + 0.06027915, 0.02045547, 0.18104935, 0.03999333, 0.18104935, + 0.15684317, 0.01376599, 0.03293737, 0.08989109, 0.02709115, + 0.14221564, 0.27065598, 0.10237696, 0.04226237, 0.72991785, + 0.06713876, 0.04226237, 0.03482061, 0.07879431, 0.07471691, + 0.15307528, 0.02289366, 0.08989109, 0.02634295, 0.43243779, + 0.08756457, 0.03293737, 0.02786 , 0.03482061, 0.0187955 , + 0.08307425, 0.04589793, 0.07275173, 0.0311527 , 0.04589793, + 0.08307425, 0.32468619, 0.02289366, 0.02634295, 0.03580075, + 0.14938186, 0.0526265 , 0.0526265 , 0.53268924, 0.19874565, + 0.0187955 , 0.01541937, 0.01586237, 0.02045547, 0.02421701, + 0.02634295, 0.11344884, 0.05710047, 0.05121018, 0.09720799, + 0.0311527 , 0.0526265 , 0.01586237, 0.07471691, 0.06027915, + 0.15684317, 0.07879431, 0.02289366, 0.04111287, 0.04848506, + 0.02865003, 0.04589793, 0.03580075, 0.04111287, 0.1353385 , + 0.09976271, 0.06362285, 0.32468619, 0.09976271, 0.49676673, + 0.07879431, 0.06027915, 0.06027915, 0.05407977, 0.05710047, + 0.0689636 , 0.11936722, 0.18973955, 0.02709115, 0.03890304, + 0.02634295, 0.80625182, 0.04111287, 0.0311527 , 0.07879431, + 0.0193336 , 0.01988679, 0.01376599, 0.07879431, 0.05710047, + 0.06027915, 0.02104007, 0.0689636 , 0.04717464, 0.04465416, + 0.07083428, 0.03999333, 0.06192993, 0.05407977, 0.04982997, + 0.46087756, 0.09720799, 0.04589793, 0.07083428, 0.0193336 , + 0.12242534, 0.12242534, 0.05407977, 0.01776301, 0.0311527 , + 0.0689636 , 0.02421701, 0.13200634, 0.19874565, 0.03293737, + 0.82774282], atol=1.0e-8) + self.assertAlmostEqual(results.pearson_chi2, 271.21110541713801) + np.testing.assert_allclose(results.resid_response, + [-0.04226237, -0.03999333, -0.02946178, -0.0689636 , -0.09471181, + -0.07879431, -0.04717464, -0.27065598, -0.07471691, 0.10477856, + -0.39752487, 0.66897282, -0.06192993, -0.04589793, -0.01988679, + -0.0526265 , -0.02104007, -0.03386636, -0.02634295, -0.05121018, + -0.29396682, 0.92724827, -0.03386636, -0.15307528, -0.06027915, + -0.01631789, -0.02045547, -0.01541937, -0.2128508 , -0.04589793, + -0.02104007, -0.05407977, -0.0311527 , -0.02634295, -0.01498855, + -0.29396682, 0.79663224, -0.07275173, -0.11637537, 0.74604393, + -0.64367488, -0.02634295, -0.02164101, -0.07083428, -0.05710047, + -0.32468619, -0.01160845, -0.01631789, -0.28803008, -0.02634295, + -0.17267234, -0.02634295, -0.01776301, -0.02709115, 0.85061814, + 0.73498669, -0.04111287, -0.06362285, -0.07083428, -0.07879431, + 0.91010891, -0.03680743, -0.0187955 , -0.01541937, -0.03680743, + -0.03029581, -0.02634295, -0.03029581, -0.07471691, -0.01228768, + 0.76722803, -0.10505173, -0.06192993, -0.09720799, -0.01416217, + -0.0187955 , -0.0689636 , -0.02865003, -0.07471691, -0.16460503, + -0.09720799, -0.02045547, 0.82732766, -0.0311527 , -0.0187955 , + -0.15684317, -0.04111287, -0.03293737, -0.02104007, -0.02946178, + -0.02421701, -0.1353385 , -0.03203302, -0.04111287, -0.10778798, + -0.07083428, -0.06027915, -0.02164101, -0.06535882, -0.02946178, + -0.07275173, -0.02490638, -0.01678627, -0.30605146, -0.02164101, + -0.03482061, -0.03580075, 0.62969079, -0.0182721 , -0.03482061, + -0.07083428, -0.04226237, -0.03999333, -0.03580075, -0.03203302, + -0.0182721 , -0.03580075, -0.06027915, -0.03386636, -0.02946178, + -0.03029581, -0.0689636 , -0.02634295, -0.02634295, -0.03029581, + -0.02225873, -0.1353385 , -0.08989109, -0.01988679, -0.0526265 , + -0.03386636, -0.03386636, -0.02786 , -0.03029581, -0.06535882, + -0.06535882, -0.03482061, -0.02786 , -0.29396682, -0.03293737, + -0.12242534, -0.04589793, -0.04589793, -0.03999333, -0.07471691, + -0.11344884, -0.05407977, -0.03482061, -0.01988679, -0.02045547, + 0.65610673, 0.85423777, -0.02561486, -0.0689636 , -0.02045547, + -0.02865003, -0.0526265 , -0.02164101, -0.01776301, -0.08307425, + -0.11344884, -0.04982997, -0.0182721 , -0.01498855, -0.02865003, + -0.14221564, -0.07879431, -0.02865003, -0.10237696, -0.04465416, + -0.07471691, -0.07673078, -0.13200634, -0.02104007, -0.0187955 , + -0.01376599, -0.04717464, -0.01128289, 0.94289953, -0.01988679, + -0.01300612, -0.11936722, -0.03203302, -0.01726786, -0.04589793, + -0.05407977, -0.09976271, -0.02561486, -0.03999333, -0.02634295, + -0.03580075, -0.21771181, 0.8646615 , -0.01988679, 0.62295626, + -0.06027915, -0.02045547, -0.18104935, 0.96000667, -0.18104935, + -0.15684317, -0.01376599, -0.03293737, -0.08989109, -0.02709115, + -0.14221564, 0.72934402, -0.10237696, -0.04226237, -0.72991785, + -0.06713876, -0.04226237, -0.03482061, -0.07879431, -0.07471691, + -0.15307528, 0.97710634, 0.91010891, -0.02634295, -0.43243779, + -0.08756457, -0.03293737, -0.02786 , -0.03482061, -0.0187955 , + 0.91692575, -0.04589793, -0.07275173, -0.0311527 , -0.04589793, + -0.08307425, 0.67531381, -0.02289366, -0.02634295, -0.03580075, + -0.14938186, -0.0526265 , -0.0526265 , 0.46731076, -0.19874565, + -0.0187955 , -0.01541937, -0.01586237, -0.02045547, -0.02421701, + -0.02634295, -0.11344884, -0.05710047, -0.05121018, -0.09720799, + 0.9688473 , -0.0526265 , -0.01586237, -0.07471691, -0.06027915, + -0.15684317, -0.07879431, -0.02289366, -0.04111287, -0.04848506, + -0.02865003, -0.04589793, -0.03580075, -0.04111287, -0.1353385 , + -0.09976271, -0.06362285, 0.67531381, -0.09976271, -0.49676673, + -0.07879431, -0.06027915, -0.06027915, -0.05407977, -0.05710047, + -0.0689636 , -0.11936722, -0.18973955, -0.02709115, -0.03890304, + -0.02634295, 0.19374818, -0.04111287, -0.0311527 , -0.07879431, + -0.0193336 , 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-0.40240831, -0.2454118 , -0.2454118 , -0.26350118, + -0.22530448, -0.57618899, -0.46253505, -0.21280762, -0.34971301, + -0.27890356, -0.27890356, -0.25249702, -0.26350118, -0.39130036, + -0.39130036, -0.28288904, -0.25249702, -0.90063409, -0.27497271, + -0.5456246 , -0.3259044 , -0.3259044 , -0.3036605 , -0.41962951, + -0.52366614, -0.35467084, -0.28288904, -0.21280762, -0.2158664 , + 1.63703418, 2.30570989, -0.24194253, -0.40240831, -0.2158664 , + -0.25611424, -0.34971301, -0.22211425, -0.20099345, -0.44366892, + -0.52366614, -0.33999576, -0.20388497, -0.18447466, -0.25611424, + -0.59203547, -0.43149255, -0.25611424, -0.49563627, -0.32133344, + -0.41962951, -0.42552227, -0.56840788, -0.21896842, -0.20681747, + -0.17672552, -0.33053793, -0.15987433, 2.9768074 , -0.21280762, + -0.17173916, -0.53821445, -0.27109582, -0.19814236, -0.3259044 , + -0.35467084, -0.48884654, -0.24194253, -0.3036605 , -0.2454118 , + -0.28692985, -0.75249089, 2.35983933, -0.21280762, 1.55726719, + -0.37518169, -0.2158664 , -0.67712261, 3.23165236, -0.67712261, + -0.62488429, -0.17672552, -0.27497271, -0.46253505, -0.24892975, + -0.59203547, 1.83482464, -0.49563627, -0.31237627, -1.83652534, + -0.39681759, -0.31237627, -0.28288904, -0.43149255, -0.41962951, + -0.61652596, 3.63983609, 2.65398426, -0.2454118 , -1.16171662, + -0.45616505, -0.27497271, -0.25249702, -0.28288904, -0.20681747, + 2.71015945, -0.3259044 , -0.41381347, -0.2672722 , -0.3259044 , + -0.44366892, 1.68567947, -0.22853969, -0.2454118 , -0.28692985, + -0.60826548, -0.34971301, -0.34971301, 1.2290223 , -0.71397735, + -0.20681747, -0.18713159, -0.1898263 , -0.2158664 , -0.23514749, + -0.2454118 , -0.52366614, -0.3647888 , -0.34482158, -0.48214234, + 3.41271513, -0.34971301, -0.1898263 , -0.41962951, -0.37518169, + -0.62488429, -0.43149255, -0.22853969, -0.30798858, -0.3352348 , + -0.25611424, -0.3259044 , -0.28692985, -0.30798858, -0.57618899, + -0.48884654, -0.38585584, 1.68567947, -0.48884654, -1.28709718, + -0.43149255, -0.37518169, -0.37518169, -0.35467084, -0.3647888 , + -0.40240831, -0.53821445, -0.69534436, -0.24892975, -0.29939131, + -0.2454118 , 0.70366797, -0.30798858, -0.2672722 , -0.43149255, + -0.2097915 , -0.21280762, -0.17672552, -0.43149255, 2.9768074 , + -0.37518169, -0.21896842, -0.40240831, -0.33053793, -0.32133344, + 2.82351017, -0.3036605 , -0.38048321, -0.35467084, -0.33999576, + -1.21650102, -0.48214234, -0.3259044 , -0.40807333, -0.2097915 , + -0.5456246 , -0.5456246 , -0.35467084, -0.20099345, -0.2672722 , + -0.40240831, -0.23514749, -0.56840788, -0.71397735, -0.27497271, + -2.18250381]) + np.testing.assert_allclose(results.resid_deviance, + [-0.29387552, -0.2857098 , -0.24455876, -0.37803944, -0.44609851, + -0.40514674, -0.31088148, -0.79449324, -0.39409528, 0.47049798, + -1.00668653, 1.48698001, -0.35757692, -0.30654405, -0.20043547, + -0.32882173, -0.20622595, -0.26249995, -0.23106769, -0.32424676, + -0.83437766, 2.28941155, -0.26249995, -0.57644334, -0.35262564, + -0.18139734, -0.20331052, -0.17629229, -0.69186337, -0.30654405, + -0.20622595, -0.33345774, -0.251588 , -0.23106769, -0.17379306, + -0.83437766, 1.78479093, -0.38867448, -0.4974393 , 1.65565332, + -1.43660134, -0.23106769, -0.20918228, -0.38332275, -0.34291558, + -0.88609006, -0.15281596, -0.18139734, -0.82428104, -0.23106769, + -0.61571821, -0.23106769, -0.18932865, -0.234371 , 1.94999969, + 1.62970871, -0.2897651 , -0.36259328, -0.38332275, -0.40514674, + 2.19506559, -0.27386827, -0.19480442, -0.17629229, -0.27386827, + -0.24804925, -0.23106769, -0.24804925, -0.39409528, -0.15725009, + 1.7074519 , -0.47114617, -0.35757692, -0.4522457 , -0.16889886, + -0.19480442, -0.37803944, -0.24111595, -0.39409528, -0.59975102, + -0.4522457 , -0.20331052, 1.87422489, -0.251588 , -0.19480442, + -0.5841272 , -0.2897651 , -0.25881274, -0.20622595, -0.24455876, + -0.22142749, -0.53929061, -0.25517563, -0.2897651 , -0.47760126, + -0.38332275, -0.35262564, -0.20918228, -0.36767536, -0.24455876, + 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+ -0.16650295, -0.31088148, -0.15064545, 2.39288231, -0.20043547, + -0.16181126, -0.5042114 , -0.25517563, -0.18664773, -0.30654405, + -0.33345774, -0.45846897, -0.22780971, -0.2857098 , -0.23106769, + -0.27002708, -0.7007597 , 1.99998811, -0.20043547, 1.39670618, + -0.35262564, -0.20331052, -0.63203077, 2.53733821, -0.63203077, + -0.5841272 , -0.16650295, -0.25881274, -0.43402996, -0.234371 , + -0.55389988, 1.61672923, -0.46476893, -0.29387552, -1.61804148, + -0.37282386, -0.29387552, -0.26623785, -0.40514674, -0.39409528, + -0.57644334, 2.74841605, 2.19506559, -0.23106769, -1.06433539, + -0.42810736, -0.25881274, -0.23772023, -0.26623785, -0.19480442, + 2.23070414, -0.30654405, -0.38867448, -0.251588 , -0.30654405, + -0.41648237, 1.49993075, -0.21521982, -0.23106769, -0.27002708, + -0.5688444 , -0.32882173, -0.32882173, 1.12233423, -0.66569789, + -0.19480442, -0.17629229, -0.17882689, -0.20331052, -0.22142749, + -0.23106769, -0.49074728, -0.34291558, -0.32424676, -0.4522457 , + 2.63395309, -0.32882173, -0.17882689, -0.39409528, -0.35262564, + -0.5841272 , -0.40514674, -0.21521982, -0.2897651 , -0.3152773 , + -0.24111595, -0.30654405, -0.27002708, -0.2897651 , -0.53929061, + -0.45846897, -0.36259328, 1.49993075, -0.45846897, -1.17192274, + -0.40514674, -0.35262564, -0.35262564, -0.33345774, -0.34291558, + -0.37803944, -0.5042114 , -0.64869028, -0.234371 , -0.28170899, + -0.23106769, 0.65629132, -0.2897651 , -0.251588 , -0.40514674, + -0.19760028, -0.20043547, -0.16650295, -0.40514674, 2.39288231, + -0.35262564, -0.20622595, -0.37803944, -0.31088148, -0.30226435, + 2.30104857, -0.2857098 , -0.35757692, -0.33345774, -0.31973217, + -1.11158678, -0.4522457 , -0.30654405, -0.38332275, -0.19760028, + -0.51106408, -0.51106408, -0.33345774, -0.18932865, -0.251588 , + -0.37803944, -0.22142749, -0.53211065, -0.66569789, -0.25881274, + -1.87550882]) + np.testing.assert_allclose(results.null, + [ 0.08860759, 0.08860759, 0.08860759, 0.08860759, 0.08860759, + 0.08860759, 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__name__ == '__main__': + unittest.main() diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/utils.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/utils.py new file mode 100644 index 0000000..0789675 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/utils.py @@ -0,0 +1,350 @@ + +from __future__ import absolute_import, print_function +import numpy as np +import warnings + + +def _bit_length_26(x): + if x == 0: + return 0 + elif x == 1: + return 1 + else: + return len(bin(x)) - 2 + + +try: + from scipy.lib._version import NumpyVersion +except ImportError: + import re + string_types = basestring + + class NumpyVersion(): + """Parse and compare numpy version strings. + Numpy has the following versioning scheme (numbers given are examples; they + can be >9) in principle): + - Released version: '1.8.0', '1.8.1', etc. + - Alpha: '1.8.0a1', '1.8.0a2', etc. + - Beta: '1.8.0b1', '1.8.0b2', etc. + - Release candidates: '1.8.0rc1', '1.8.0rc2', etc. + - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended) + - Development versions after a1: '1.8.0a1.dev-f1234afa', + '1.8.0b2.dev-f1234afa', + '1.8.1rc1.dev-f1234afa', etc. + - Development versions (no git hash available): '1.8.0.dev-Unknown' + Comparing needs to be done against a valid version string or other + `NumpyVersion` instance. + Parameters + ---------- + vstring : str + Numpy version string (``np.__version__``). + Notes + ----- + All dev versions of the same (pre-)release compare equal. + Examples + -------- + >>> from scipy.lib._version import NumpyVersion + >>> if NumpyVersion(np.__version__) < '1.7.0': + ... print('skip') + skip + >>> NumpyVersion('1.7') # raises ValueError, add ".0" + """ + + def __init__(self, vstring): + self.vstring = vstring + ver_main = re.match(r'\d[.]\d+[.]\d+', vstring) + if not ver_main: + raise ValueError("Not a valid numpy version string") + + self.version = ver_main.group() + self.major, self.minor, self.bugfix = [int(x) for x in + self.version.split('.')] + if len(vstring) == ver_main.end(): + self.pre_release = 'final' + else: + alpha = re.match(r'a\d', vstring[ver_main.end():]) + beta = re.match(r'b\d', vstring[ver_main.end():]) + rc = re.match(r'rc\d', vstring[ver_main.end():]) + pre_rel = [m for m in [alpha, beta, rc] if m is not None] + if pre_rel: + self.pre_release = pre_rel[0].group() + else: + self.pre_release = '' + + self.is_devversion = bool(re.search(r'.dev-', vstring)) + + def _compare_version(self, other): + """Compare major.minor.bugfix""" + if self.major == other.major: + if self.minor == other.minor: + if self.bugfix == other.bugfix: + vercmp = 0 + elif self.bugfix > other.bugfix: + vercmp = 1 + else: + vercmp = -1 + elif self.minor > other.minor: + vercmp = 1 + else: + vercmp = -1 + elif self.major > other.major: + vercmp = 1 + else: + vercmp = -1 + + return vercmp + + def _compare_pre_release(self, other): + """Compare alpha/beta/rc/final.""" + if self.pre_release == other.pre_release: + vercmp = 0 + elif self.pre_release == 'final': + vercmp = 1 + elif other.pre_release == 'final': + vercmp = -1 + elif self.pre_release > other.pre_release: + vercmp = 1 + else: + vercmp = -1 + + return vercmp + + def _compare(self, other): + if not isinstance(other, (string_types, NumpyVersion)): + raise ValueError("Invalid object to compare with NumpyVersion.") + + if isinstance(other, string_types): + other = NumpyVersion(other) + + vercmp = self._compare_version(other) + if vercmp == 0: + # Same x.y.z version, check for alpha/beta/rc + vercmp = self._compare_pre_release(other) + if vercmp == 0: + # Same version and same pre-release, check if dev version + if self.is_devversion is other.is_devversion: + vercmp = 0 + elif self.is_devversion: + vercmp = -1 + else: + vercmp = 1 + + return vercmp + + def __lt__(self, other): + return self._compare(other) < 0 + + def __le__(self, other): + return self._compare(other) <= 0 + + def __eq__(self, other): + return self._compare(other) == 0 + + def __ne__(self, other): + return self._compare(other) != 0 + + def __gt__(self, other): + return self._compare(other) > 0 + + def __ge__(self, other): + return self._compare(other) >= 0 + + def __repr(self): + return "NumpyVersion(%s)" % self.vstring + + +def _next_regular(target): + """ + Find the next regular number greater than or equal to target. + Regular numbers are composites of the prime factors 2, 3, and 5. + Also known as 5-smooth numbers or Hamming numbers, these are the optimal + size for inputs to FFTPACK. + Target must be a positive integer. + """ + if target <= 6: + return target + + # Quickly check if it's already a power of 2 + if not (target & (target - 1)): + return target + + match = float('inf') # Anything found will be smaller + p5 = 1 + while p5 < target: + p35 = p5 + while p35 < target: + # Ceiling integer division, avoiding conversion to float + # (quotient = ceil(target / p35)) + quotient = -(-target // p35) + # Quickly find next power of 2 >= quotient + try: + p2 = 2 ** ((quotient - 1).bit_length()) + except AttributeError: + # Fallback for Python <2.7 + p2 = 2 ** _bit_length_26(quotient - 1) + + N = p2 * p35 + if N == target: + return N + elif N < match: + match = N + p35 *= 3 + if p35 == target: + return p35 + if p35 < match: + match = p35 + p5 *= 5 + if p5 == target: + return p5 + if p5 < match: + match = p5 + return match +if NumpyVersion(np.__version__) >= '1.7.1': + np_matrix_rank = np.linalg.matrix_rank +else: + def np_matrix_rank(M, tol=None): + """ + Return matrix rank of array using SVD method + Rank of the array is the number of SVD singular values of the array that are + greater than `tol`. + Parameters + ---------- + M : {(M,), (M, N)} array_like + array of <=2 dimensions + tol : {None, float}, optional + threshold below which SVD values are considered zero. If `tol` is + None, and ``S`` is an array with singular values for `M`, and + ``eps`` is the epsilon value for datatype of ``S``, then `tol` is + set to ``S.max() * max(M.shape) * eps``. + Notes + ----- + The default threshold to detect rank deficiency is a test on the magnitude + of the singular values of `M`. By default, we identify singular values less + than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with + the symbols defined above). This is the algorithm MATLAB uses [1]. It also + appears in *Numerical recipes* in the discussion of SVD solutions for linear + least squares [2]. + This default threshold is designed to detect rank deficiency accounting for + the numerical errors of the SVD computation. Imagine that there is a column + in `M` that is an exact (in floating point) linear combination of other + columns in `M`. Computing the SVD on `M` will not produce a singular value + exactly equal to 0 in general: any difference of the smallest SVD value from + 0 will be caused by numerical imprecision in the calculation of the SVD. + Our threshold for small SVD values takes this numerical imprecision into + account, and the default threshold will detect such numerical rank + deficiency. The threshold may declare a matrix `M` rank deficient even if + the linear combination of some columns of `M` is not exactly equal to + another column of `M` but only numerically very close to another column of + `M`. + We chose our default threshold because it is in wide use. Other thresholds + are possible. For example, elsewhere in the 2007 edition of *Numerical + recipes* there is an alternative threshold of ``S.max() * + np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe + this threshold as being based on "expected roundoff error" (p 71). + The thresholds above deal with floating point roundoff error in the + calculation of the SVD. However, you may have more information about the + sources of error in `M` that would make you consider other tolerance values + to detect *effective* rank deficiency. The most useful measure of the + tolerance depends on the operations you intend to use on your matrix. For + example, if your data come from uncertain measurements with uncertainties + greater than floating point epsilon, choosing a tolerance near that + uncertainty may be preferable. The tolerance may be absolute if the + uncertainties are absolute rather than relative. + References + ---------- + .. [1] MATLAB reference documention, "Rank" + http://www.mathworks.com/help/techdoc/ref/rank.html + .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, + "Numerical Recipes (3rd edition)", Cambridge University Press, 2007, + page 795. + Examples + -------- + >>> from numpy.linalg import matrix_rank + >>> matrix_rank(np.eye(4)) # Full rank matrix + 4 + >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix + >>> matrix_rank(I) + 3 + >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0 + 1 + >>> matrix_rank(np.zeros((4,))) + 0 + """ + M = np.asarray(M) + if M.ndim > 2: + raise TypeError('array should have 2 or fewer dimensions') + if M.ndim < 2: + return int(not all(M == 0)) + S = np.linalg.svd(M, compute_uv=False) + if tol is None: + tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps + return np.sum(S > tol) + + + +class CacheWriteWarning(UserWarning): + pass + +class CachedAttribute(object): + + def __init__(self, func, cachename=None, resetlist=None): + self.fget = func + self.name = func.__name__ + self.cachename = cachename or '_cache' + self.resetlist = resetlist or () + + def __get__(self, obj, type=None): + if obj is None: + return self.fget + # Get the cache or set a default one if needed + _cachename = self.cachename + _cache = getattr(obj, _cachename, None) + if _cache is None: + setattr(obj, _cachename, resettable_cache()) + _cache = getattr(obj, _cachename) + # Get the name of the attribute to set and cache + name = self.name + _cachedval = _cache.get(name, None) + # print("[_cachedval=%s]" % _cachedval) + if _cachedval is None: + # Call the "fget" function + _cachedval = self.fget(obj) + # Set the attribute in obj + # print("Setting %s in cache to %s" % (name, _cachedval)) + try: + _cache[name] = _cachedval + except KeyError: + setattr(_cache, name, _cachedval) + # Update the reset list if needed (and possible) + resetlist = self.resetlist + if resetlist is not (): + try: + _cache._resetdict[name] = self.resetlist + except AttributeError: + pass + # else: + # print("Reading %s from cache (%s)" % (name, _cachedval)) + return _cachedval + + def __set__(self, obj, value): + errmsg = "The attribute '%s' cannot be overwritten" % self.name + warnings.warn(errmsg, CacheWriteWarning) + + +class _cache_readonly(object): + """ + Decorator for CachedAttribute + """ + + def __init__(self, cachename=None, resetlist=None): + self.func = None + self.cachename = cachename + self.resetlist = resetlist or None + + def __call__(self, func): + return CachedAttribute(func, + cachename=self.cachename, + resetlist=self.resetlist) +cache_readonly = _cache_readonly() + + diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/glm/varfuncs.py b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/varfuncs.py new file mode 100644 index 0000000..af66d8c --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/glm/varfuncs.py @@ -0,0 +1,284 @@ +""" +Variance functions for use with the link functions in statsmodels.family.links +""" + +__docformat__ = 'restructuredtext' + +import numpy as np +FLOAT_EPS = np.finfo(float).eps + +class VarianceFunction(object): + """ + Relates the variance of a random variable to its mean. Defaults to 1. + + Methods + ------- + call + Returns an array of ones that is the same shape as `mu` + + Notes + ----- + After a variance function is initialized, its call method can be used. + + Alias for VarianceFunction: + constant = VarianceFunction() + + See also + -------- + statsmodels.family.family + """ + + def __call__(self, mu): + """ + Default variance function + + Parameters + ----------- + mu : array-like + mean parameters + + Returns + ------- + v : array + ones(mu.shape) + """ + mu = np.asarray(mu) + return np.ones(mu.shape, np.float64) + + + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs + # TODO: diag workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(mu, self)) + + +constant = VarianceFunction() +constant.__doc__ = """ +The call method of constant returns a constant variance, i.e., a vector of ones. + +constant is an alias of VarianceFunction() +""" + +class Power(object): + """ + Power variance function + + Parameters + ---------- + power : float + exponent used in power variance function + + Methods + ------- + call + Returns the power variance + + Formulas + -------- + V(mu) = numpy.fabs(mu)**power + + Notes + ----- + Aliases for Power: + mu = Power() + mu_squared = Power(power=2) + mu_cubed = Power(power=3) + """ + + def __init__(self, power=1.): + self.power = power + + def __call__(self, mu): + """ + Power variance function + + Parameters + ---------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + numpy.fabs(mu)**self.power + """ + return np.power(np.fabs(mu), self.power) + + + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime + #return approx_fprime_cs(mu, self) # TODO fix breaks in `fabs + # TODO: diag is workaround problem with numdiff for 1d + return np.diag(approx_fprime(mu, self)) + + +mu = Power() +mu.__doc__ = """ +Returns np.fabs(mu) + +Notes +----- +This is an alias of Power() +""" +mu_squared = Power(power=2) +mu_squared.__doc__ = """ +Returns np.fabs(mu)**2 + +Notes +----- +This is an alias of statsmodels.family.links.Power(power=2) +""" +mu_cubed = Power(power=3) +mu_cubed.__doc__ = """ +Returns np.fabs(mu)**3 + +Notes +----- +This is an alias of statsmodels.family.links.Power(power=3) +""" + +class Binomial(object): + """ + Binomial variance function + + Parameters + ---------- + n : int, optional + The number of trials for a binomial variable. The default is 1 for + p in (0,1) + + Methods + ------- + call + Returns the binomial variance + + Formulas + -------- + V(mu) = p * (1 - p) * n + + where p = mu / n + + Notes + ----- + Alias for Binomial: + binary = Binomial() + + A private method _clean trims the data by machine epsilon so that p is + in (0,1) + """ + + def __init__(self, n=1): + self.n = n + + def _clean(self, p): + return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS) + + def __call__(self, mu): + """ + Binomial variance function + + Parameters + ----------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + variance = mu/n * (1 - mu/n) * self.n + """ + p = self._clean(mu / self.n) + return p * (1 - p) * self.n + + #TODO: inherit from super + def deriv(self, mu): + """ + Derivative of the variance function v'(mu) + """ + from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime + # TODO: diag workaround proplem with numdiff for 1d + return np.diag(approx_fprime_cs(mu, self)) + + +binary = Binomial() +binary.__doc__ = """ +The binomial variance function for n = 1 + +Notes +----- +This is an alias of Binomial(n=1) +""" + +class NegativeBinomial(object): + ''' + Negative binomial variance function + + Parameters + ---------- + alpha : float + The ancillary parameter for the negative binomial variance function. + `alpha` is assumed to be nonstochastic. The default is 1. + + Methods + ------- + call + Returns the negative binomial variance + + Formulas + -------- + V(mu) = mu + alpha*mu**2 + + Notes + ----- + Alias for NegativeBinomial: + nbinom = NegativeBinomial() + + A private method _clean trims the data by machine epsilon so that p is + in (0,inf) + ''' + + def __init__(self, alpha=1.): + self.alpha = alpha + + def _clean(self, p): + return np.clip(p, FLOAT_EPS, np.inf) + + def __call__(self, mu): + """ + Negative binomial variance function + + Parameters + ---------- + mu : array-like + mean parameters + + Returns + ------- + variance : array + variance = mu + alpha*mu**2 + """ + p = self._clean(mu) + return p + self.alpha*p**2 + + def deriv(self, mu): + """ + Derivative of the negative binomial variance function. + """ + + p = self._clean(mu) + return 1 + 2 * self.alpha * p + +nbinom = NegativeBinomial() +nbinom.__doc__ = """ +Negative Binomial variance function. + +Notes +----- +This is an alias of NegativeBinomial(alpha=1.) +""" diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/__init__.py new file mode 100644 index 0000000..f7a77b2 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/__init__.py @@ -0,0 +1 @@ +from base import * diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/__init__.py new file mode 100644 index 0000000..eeb63b3 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/__init__.py @@ -0,0 +1,4 @@ +import gwr +import sel_bw +import diagnostics +import kernels diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/diagnostics.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/diagnostics.py new file mode 100644 index 0000000..7fbcdc4 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/diagnostics.py @@ -0,0 +1,81 @@ +""" +Diagnostics for estimated gwr modesl +""" +__author__ = "Taylor Oshan tayoshan@gmail.com" + +import numpy as np +from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial + +def get_AICc(gwr): + """ + Get AICc value + + Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002) + + GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36) + + """ + n = gwr.n + k = gwr.tr_S + if isinstance(gwr.family, Gaussian): + aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0) + elif isinstance(gwr.family, (Poisson, Binomial)): + aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0) + return aicc + +def get_AIC(gwr): + """ + Get AIC calue + + Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002) + + GWGLM: AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective + number of parameters in the model with bandwidth G, respectively. + + """ + k = gwr.tr_S + #deviance = -2*log-likelihood + y = gwr.y + mu = gwr.mu + if isinstance(gwr.family, Gaussian): + aic = -2.0 * gwr.llf + 2.0 * (k+1) + elif isinstance(gwr.family, (Poisson, Binomial)): + aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k + return aic + +def get_BIC(gwr): + """ + Get BIC value + + Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002) + BIC = -2log(L)+klog(n) + + GWGLM: BIC = dev + tr_S * log(n) + + """ + n = gwr.n # (scalar) number of observations + k = gwr.tr_S + y = gwr.y + mu = gwr.mu + if isinstance(gwr.family, Gaussian): + bic = -2.0 * gwr.llf + (k+1) * np.log(n) + elif isinstance(gwr.family, (Poisson, Binomial)): + bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n) + return bic + +def get_CV(gwr): + """ + Get CV value + + Gaussian only + + Methods: p60, (2.31) or p212 (9.4) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + Modification: sum of residual squared is divided by n according to GWR4 results + + """ + aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ) + cv = np.sum(aa**2)/gwr.n + return cv + diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/gwr.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/gwr.py new file mode 100644 index 0000000..6d5257f --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/gwr.py @@ -0,0 +1,1086 @@ +#Main GWR classes + +#Offset does not yet do anyhting and needs to be implemented + +__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 +import pysal.spreg.user_output as USER +from crankshaft.regression.glm.family import Gaussian, Binomial, Poisson +from crankshaft.regression.glm.glm import GLM, GLMResults +from crankshaft.regression.glm.iwls import iwls +from crankshaft.regression.glm.utils import cache_readonly + +fk = {'gaussian': fix_gauss, 'bisquare': fix_bisquare, 'exponential': fix_exp} +ak = {'gaussian': adapt_gauss, 'bisquare': adapt_bisquare, 'exponential': adapt_exp} + +class GWR(GLM): + """ + Geographically weighted regression. Can currently estimate Gaussian, + Poisson, and logistic models(built on a GLM framework). GWR object prepares + model input. Fit method performs estimation and returns a GWRResults object. + + Parameters + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bw : scalar + bandwidth value consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations; + only for Poisson models + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + Attributes + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + bw : scalar + bandwidth value consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept + + n : integer + number of observations + + k : integer + number of independent variables + + mean_y : float + mean of y + + std_y : float + standard deviation of y + + fit_params : dict + parameters passed into fit method to define estimation + routine + + W : array + n*n, spatial weights matrix for weighting all + observations from each calibration point + """ + def __init__(self, coords, y, X, bw, family=Gaussian(), offset=None, + sigma2_v1=False, kernel='bisquare', fixed=False, constant=True): + """ + Initialize class + """ + GLM.__init__(self, y, X, family, constant=constant) + self.constant = constant + self.sigma2_v1 = sigma2_v1 + self.coords = coords + self.bw = bw + self.kernel = kernel + self.fixed = fixed + if offset is None: + self.offset = np.ones((self.n, 1)) + else: + self.offset = offset * 1.0 + self.fit_params = {} + self.W = self._build_W(fixed, kernel, coords, bw) + self.points = None + self.exog_scale = None + self.exog_resid = None + self.P = None + + def _build_W(self, fixed, kernel, coords, bw, points=None): + if fixed: + try: + W = fk[kernel](coords, bw, points) + except: + raise TypeError('Unsupported kernel function ', kernel) + else: + try: + 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'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation + tol: float + Tolerence for estimation convergence + max_iter : integer + Maximum number of iterations if convergence not + achieved + solve : string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + """ + self.fit_params['ini_params'] = ini_params + self.fit_params['tol'] = tol + self.fit_params['max_iter'] = max_iter + self.fit_params['solve']= solve + if solve.lower() == 'iwls': + m = self.W.shape[0] + params = np.zeros((m, self.k)) + predy = np.zeros((m, 1)) + v = np.zeros((m, 1)) + w = np.zeros((m, 1)) + z = np.zeros((self.n, self.n)) + S = np.zeros((self.n, self.n)) + R = np.zeros((self.n, self.n)) + CCT = np.zeros((m, self.k)) + #f = np.zeros((n, n)) + 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) + params[i,:] = rslt[0].T + predy[i] = rslt[1][i] + v[i] = rslt[2][i] + w[i] = rslt[3][i] + z[i] = rslt[4].flatten() + R[i] = np.dot(self.X[i], rslt[5]) + ri = np.dot(self.X[i], rslt[5]) + S[i] = ri*np.reshape(rslt[4].flatten(), (1,-1)) + #dont need unless f is explicitly passed for + #prediction of non-sampled points + #cf = rslt[5] - np.dot(rslt[5], f) + #CCT[i] = np.diag(np.dot(cf, cf.T/rslt[3])) + CCT[i] = np.diag(np.dot(rslt[5], rslt[5].T)) + S = S * (1.0/z) + return GWRResults(self, params, predy, S, CCT, w) + + def predict(self, points, P, exog_scale=None, exog_resid=None, fit_params={}): + """ + Method that predicts values of the dependent variable at un-sampled + locations + + Parameters + ---------- + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration prediction locations + P : array + n*k, independent variables used to make prediction; + exlcuding the constant + exog_scale : scalar + estimated scale using sampled locations; defualt is None + which estimates a model using points from "coords" + exog_resid : array-like + estimated residuals using sampled locations; defualt is None + which estimates a model using points from "coords"; if + given it must be n*1 where n is the length of coords + 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) + self.exog_scale = train_gwr.scale + self.exog_resid = train_gwr.resid_response + elif (exog_scale is not None) & (exog_resid is not None): + self.exog_scale = exog_scale + self.exog_resid = exog_resid + else: + raise InputError('exog_scale and exog_resid must both either be' + 'None or specified') + self.points = points + if self.constant: + P = np.hstack([np.ones((len(P),1)), P]) + self.P = P + else: + self.P = P + self.W = self._build_W(self.fixed, self.kernel, self.coords, self.bw, points) + gwr = self.fit(**fit_params) + + return gwr + + @cache_readonly + def df_model(self): + raise NotImplementedError('Only computed for fitted model in GWRResults') + + @cache_readonly + def df_resid(self): + raise NotImplementedError('Only computed for fitted model in GWRResults') + +class GWRResults(GLMResults): + """ + Basic class including common properties for all GWR regression models + + Parameters + ---------- + model : GWR object + pointer to GWR object with estimation parameters + + params : array + n*k, estimated coefficients + + predy : array + n*1, predicted y values + + w : array + n*1, final weight used for iteratively re-weighted least + sqaures; default is None + + S : array + n*n, hat matrix + + CCT : array + n*k, scaled variance-covariance matrix + + Attributes + ---------- + model : GWR Object + points to GWR object for which parameters have been + estimated + + params : array + n*k, parameter estimates + + predy : array + n*1, predicted value of y + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, including constant + + family : family object + underlying probability model; provides + distribution-specific calculations + + n : integer + number of observations + + k : integer + number of independent variables + + df_model : integer + model degrees of freedom + + df_resid : integer + residual degrees of freedom + + offset : array + n*1, the offset variable at the ith location. + For Poisson model this term is often the size of + the population at risk or the expected size of + the outcome in spatial epidemiology; Default is + None where Ni becomes 1.0 for all locations + + scale : float + sigma squared used for subsequent computations + + w : array + n*1, final weights from iteratively re-weighted least + sqaures routine + + resid_response : array + n*1, residuals of the repsonse + + resid_ss : scalar + residual sum of sqaures + + W : array + n*n; spatial weights for each observation from each + calibration point + + S : array + n*n, hat matrix + + CCT : array + n*k, scaled variance-covariance matrix + + tr_S : float + trace of S (hat) matrix + + tr_STS : float + trace of STS matrix + + tr_SWSTW : float + trace of weighted STS matrix; weights are those output + from iteratively weighted least sqaures (not spatial + weights) + + y_bar : array + n*1, weighted mean value of y + + TSS : array + n*1, geographically weighted total sum of squares + + RSS : array + n*1, geographically weighted residual sum of squares + + localR2 : array + n*1, local R square + + sigma2_v1 : float + sigma squared, use (n-v1) as denominator + + sigma2_v1v2 : float + sigma squared, use (n-2v1+v2) as denominator + + sigma2_ML : float + sigma squared, estimated using ML + + std_res : array + n*1, standardised residuals + + bse : array + n*k, standard errors of parameters (betas) + + influ : array + n*1, leading diagonal of S matrix + + CooksD : array + n*1, Cook's D + + tvalues : array + n*k, local t-statistics + + adj_alpha : array + 3*1, corrected alpha values to account for multiple + hypothesis testing for the 90%, 95%, and 99% confidence + levels; tvalues with an absolute value larger than the + corrected alpha are considered statistically + significant. + + deviance : array + n*1, local model deviance for each calibration point + + resid_deviance : array + n*1, local sum of residual deviance for each + calibration point + + llf : scalar + log-likelihood of the full model; see + pysal.contrib.glm.family for damily-sepcific + log-likelihoods + + pDev : float + local percent of deviation accounted for; analogous to + r-squared for GLM's + + mu : array + n*, flat one dimensional array of predicted mean + response value from estimator + + fit_params : dict + parameters passed into fit method to define estimation + routine + """ + def __init__(self, model, params, predy, S, CCT, w=None): + GLMResults.__init__(self, model, params, predy, w) + self.W = model.W + self.offset = model.offset + if w is not None: + self.w = w + self.predy = predy + self.S = S + self.CCT = self.cov_params(CCT, model.exog_scale) + self._cache = {} + + @cache_readonly + def resid_ss(self): + u = self.resid_response.flatten() + return np.dot(u, u.T) + + @cache_readonly + def scale(self, scale=None): + if isinstance(self.family, Gaussian): + if self.model.sigma2_v1: + scale = self.sigma2_v1 + else: + scale = self.sigma2_v1v2 + else: + scale = 1.0 + return scale + + def cov_params(self, cov, exog_scale=None): + """ + Returns scaled covariance parameters + Parameters + ---------- + cov : array + estimated covariance parameters + + Returns + ------- + Scaled covariance parameters + + """ + if exog_scale is not None: + return cov*exog_scale + else: + return cov*self.scale + + @cache_readonly + def tr_S(self): + """ + trace of S (hat) matrix + """ + return np.trace(self.S*self.w) + + @cache_readonly + def tr_STS(self): + """ + trace of STS matrix + """ + return np.trace(np.dot(self.S.T*self.w,self.S*self.w)) + + @cache_readonly + def y_bar(self): + """ + weighted mean of y + """ + if self.model.points is not None: + n = len(self.model.points) + else: + n = self.n + off = self.offset.reshape((-1,1)) + arr_ybar = np.zeros(shape=(self.n,1)) + for i in range(n): + w_i= np.reshape(np.array(self.W[i]), (-1, 1)) + sum_yw = np.sum(self.y.reshape((-1,1)) * w_i) + arr_ybar[i] = 1.0 * sum_yw / np.sum(w_i*off) + return arr_ybar + + @cache_readonly + def TSS(self): + """ + geographically weighted total sum of squares + + Methods: p215, (9.9) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + """ + if self.model.points is not None: + 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) + return TSS + + @cache_readonly + def RSS(self): + """ + geographically weighted residual sum of squares + + Methods: p215, (9.10) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + if self.model.points is not None: + 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)) + for i in range(n): + RSS[i] = np.sum(np.reshape(np.array(self.W[i]), (-1,1)) + * resid**2) + return RSS + + @cache_readonly + def localR2(self): + """ + local R square + + Methods: p215, (9.8) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + if isinstance(self.family, Gaussian): + return (self.TSS - self.RSS)/self.TSS + else: + raise NotImplementedError('Only applicable to Gaussian') + + @cache_readonly + def sigma2_v1(self): + """ + residual variance + + Methods: p214, (9.6), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + only use v1 + """ + return (self.resid_ss/(self.n-self.tr_S)) + + @cache_readonly + def sigma2_v1v2(self): + """ + residual variance + + Methods: p55 (2.16)-(2.18) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + + use v1 and v2 #used in GWR4 + """ + 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 + else: + return self.resid_ss/(self.n - 2.0*self.tr_S + + self.tr_STS) #could be changed to SWSTW - nothing to test against + @cache_readonly + def sigma2_ML(self): + """ + residual variance + + Methods: maximum likelihood + """ + return self.resid_ss/self.n + + @cache_readonly + def std_res(self): + """ + standardized residuals + + Methods: p215, (9.7) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + return self.resid_response.reshape((-1,1))/(np.sqrt(self.scale * (1.0 - self.influ))) + + @cache_readonly + def bse(self): + """ + standard errors of Betas + + Methods: p215, (2.15) and (2.21) + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + """ + return np.sqrt(self.CCT) + + @cache_readonly + def influ(self): + """ + Influence: leading diagonal of S Matrix + """ + return np.reshape(np.diag(self.S),(-1,1)) + + @cache_readonly + def cooksD(self): + """ + Influence: leading diagonal of S Matrix + + Methods: p216, (9.11), + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying + relationships. + 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') + 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): + dev = self.family.deviance(self.y, self.y_bar, self.W, axis=1) + return dev.reshape((-1,1)) + + @cache_readonly + def resid_deviance(self): + if isinstance(self.family, Gaussian): + raise NotImplementedError('deviance not currently used for Gaussian') + else: + off = self.offset.reshape((-1,1)).T + y = self.y + ybar = self.y_bar + global_dev_res = ((self.family.resid_dev(self.y, self.mu))**2) + dev_res = np.repeat(global_dev_res.flatten(),self.n) + dev_res = dev_res.reshape((self.n, self.n)) + dev_res = np.sum(dev_res * self.W.T, axis=0) + return dev_res.reshape((-1,1)) + + @cache_readonly + def pDev(self): + """ + Local percentage of deviance accounted for. Described in the GWR4 + manual. Equivalent to 1 - (deviance/null deviance) + """ + if isinstance(self.family, Gaussian): + raise NotImplementedError('Not implemented for Gaussian') + else: + return 1.0 - (self.resid_deviance/self.deviance) + + @cache_readonly + def adj_alpha(self): + """ + Corrected alpha (critical) values to account for multiple testing during hypothesis + testing. Includes corrected value for 90% (.1), 95% (.05), and 99% + (.01) confidence levels. Correction comes from: + + da Silva, A. R., & Fotheringham, A. S. (2015). The Multiple Testing Issue in + Geographically Weighted Regression. Geographical Analysis. + + """ + alpha = np.array([.1, .05, .001]) + pe = (2.0 * self.tr_S) - self.tr_STS + p = self.k + return (alpha*p)/pe + + def filter_tvals(self, alpha): + """ + Utility function to set tvalues with an absolute value smaller than the + absolute value of the alpha (critical) value to 0 + + Parameters + ---------- + alpha : scalar + critical value to determine which tvalues are + associated with statistically significant parameter + estimates + + Returns + ------- + filtered : array + n*k; new set of n tvalues for each of k variables + where absolute tvalues less than the absolute value of + alpha have been set to 0. + """ + alpha = np.abs(alpha)/2.0 + n = self.n + critical = t.ppf(1-alpha, n-1) + subset = (self.tvalues < critical) & (self.tvalues > -1.0*critical) + tvalues = self.tvalues.copy() + tvalues[subset] = 0 + return tvalues + + @cache_readonly + def df_model(self): + return self.n - self.tr_S + + @cache_readonly + def df_resid(self): + return self.n - 2.0*self.tr_S + self.tr_STS + + @cache_readonly + def normalized_cov_params(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_pearson(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_working(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def resid_anscombe(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pearson_chi2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def null(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def llnull(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def null_deviance(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def aic(self): + return get_AIC(self) + + @cache_readonly + def aicc(self): + return get_AICc(self) + + @cache_readonly + def bic(self): + return get_BIC(self) + + @cache_readonly + def D2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def adj_D2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pseudoR2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def adj_pseudoR2(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def pvalues(self): + raise NotImplementedError('Not implemented for GWR') + + @cache_readonly + def predictions(self): + P = self.model.P + if P is None: + 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 + +class FBGWR(GWR): + """ + Parameters + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bws : array-like + collection of bandwidth values consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW with fb=True. Order of values should the same as + the order of columns associated with X + XB : array + n*k, product of temporary X and params obtained as through-put + from the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + err : array + n*1, temporary residuals associated with the predicted values from + the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + Attributes + ---------- + coords : array-like + n*2, collection of n sets of (x,y) coordinates of + observatons; also used as calibration locations is + 'points' is set to None + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, exlcuding the constant + + points : array-like + n*2, collection of n sets of (x,y) coordinates used for + calibration locations; default is set to None, which + uses every observation as a calibration point + + bws : array-like + collection of bandwidth values consisting of either a distance or N + nearest neighbors; user specified or obtained using + Sel_BW with fb=True. Order of values should the same as + the order of columns associated with X + XB : array + n*k, product of temporary X and params obtained as through-put + from the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + err : array + n*1, temporary residuals associated with the predicted values from + the backfitting algorithm used to select flexible + bandwidths; product of the Sel_BW class + + family : family object + underlying probability model; provides + distribution-specific calculations + + offset : array + n*1, the offset variable at the ith location. For Poisson model + this term is often the size of the population at risk or + the expected size of the outcome in spatial epidemiology + Default is None where Ni becomes 1.0 for all locations + + sigma2_v1 : boolean + specify sigma squared, True to use n as denominator; + default is False which uses n-k + + kernel : string + type of kernel function used to weight observations; + available options: + 'gaussian' + 'bisquare' + 'exponential' + + fixed : boolean + True for distance based kernel function and False for + adaptive (nearest neighbor) kernel function (default) + + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + + Examples + ------- + TODO + + """ + def __init__(self, coords, y, X, bws, XB, err, family=Gaussian(), offset=None, + sigma2_v1=False, kernel='bisquare', fixed=False, constant=True): + """ + Initialize class + """ + self.coords = coords + self.y = y + self.X = X + self.XB = XB + self.err = err + self.bws = bws + self.family = family + self.offset = offset + self.sigma2_v1 = sigma2_v1 + self.kernel = kernel + self.fixed = fixed + self.constant = constant + if constant: + self.X = USER.check_constant(self.X) + + def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'): + """ + Method that fits a model with a particular estimation routine. + + Parameters + ---------- + + ini_betas : array + k*1, initial coefficient values, including constant. + Default is None, which calculates initial values during + estimation + tol: float + Tolerence for estimation convergence + max_iter : integer + Maximum number of iterations if convergence not + achieved + solve : string + Technique to solve MLE equations. + 'iwls' = iteratively (re)weighted least squares (default) + + """ + params = np.zeros_like(self.X) + err = self.err + for i, bw in enumerate(self.bws): + W = self._build_W(self.fixed, self.kernel, self.coords, bw) + X = self.X[:,i].reshape((-1,1)) + y = self.XB[:,i].reshape((-1,1)) + err + model = GWR(self.coords, y, X, bw, self.family, self.offset, + self.sigma2_v1, self.kernel, self.fixed, constant=False) + results = model.fit(ini_params, tol, max_iter, solve) + params[:,i] = results.params.flatten() + err = results.resid_response.reshape((-1,1)) + return FBGWRResults(self, params) + +class FBGWRResults(object): + """ + Parameters + ---------- + model : GWR object + pointer to FBGWR object with estimation parameters + + params : array + n*k, estimated coefficients + + Attributes + ---------- + model : GWR Object + points to FBGWR object for which parameters have been + estimated + + params : array + n*k, parameter estimates + + predy : array + n*1, predicted value of y + + y : array + n*1, dependent variable + + X : array + n*k, independent variable, including constant + + : array + resid_response n*1, residuals of response + + resid_ss : scalar + residual sum of sqaures + + Examples + ------- + TODO + + """ + def __init__(self, model, params): + """ + Initialize class + """ + self.model = model + self.params = params + self.X = model.X + self.y = model.y + self._cache = {} + + @cache_readonly + def predy(self): + return np.sum(np.multiply(self.params, self.X), axis=1).reshape((-1,1)) + + @cache_readonly + def resid_response(self): + return (self.y - self.predy).reshape((-1,1)) + + @cache_readonly + def resid_ss(self): + u = self.resid_response.flatten() + return np.dot(u, u.T) diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/kernels.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/kernels.py new file mode 100644 index 0000000..bdf246d --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/kernels.py @@ -0,0 +1,120 @@ +# GWR kernel function specifications + +__author__ = "Taylor Oshan tayoshan@gmail.com" + +#from pysal.weights.Distance import Kernel +import scipy +from scipy.spatial.kdtree import KDTree +import numpy as np + +#adaptive specifications should be parameterized with nn-1 to match original gwr +#implementation. That is, pysal counts self neighbors with knn automatically. + +def fix_gauss(coords, bw, points=None): + w = _Kernel(coords, function='gwr_gaussian', bandwidth=bw, + truncate=False, points=points) + return w.kernel + +def adapt_gauss(coords, nn, points=None): + w = _Kernel(coords, fixed=False, k=nn-1, function='gwr_gaussian', + truncate=False, points=points) + return w.kernel + +def fix_bisquare(coords, bw, points=None): + w = _Kernel(coords, function='bisquare', bandwidth=bw, points=points) + return w.kernel + +def adapt_bisquare(coords, nn, points=None): + w = _Kernel(coords, fixed=False, k=nn-1, function='bisquare', points=points) + return w.kernel + +def fix_exp(coords, bw, points=None): + w = _Kernel(coords, function='exponential', bandwidth=bw, + truncate=False, points=points) + return w.kernel + +def adapt_exp(coords, nn, points=None): + w = _Kernel(coords, fixed=False, k=nn-1, function='exponential', + truncate=False, points=points) + return w.kernel + +from scipy.spatial.distance import cdist + +class _Kernel(object): + """ + + """ + def __init__(self, data, bandwidth=None, fixed=True, k=None, + function='triangular', eps=1.0000001, ids=None, truncate=True, + points=None): #Added truncate flag + if issubclass(type(data), scipy.spatial.KDTree): + self.data = data.data + data = self.data + else: + self.data = data + if k is not None: + self.k = int(k) + 1 + else: + self.k = k + if points is None: + self.dmat = cdist(self.data, self.data) + else: + self.points = points + self.dmat = cdist(self.points, self.data) + self.function = function.lower() + self.fixed = fixed + self.eps = eps + self.trunc = truncate + if bandwidth: + try: + bandwidth = np.array(bandwidth) + bandwidth.shape = (len(bandwidth), 1) + except: + bandwidth = np.ones((len(data), 1), 'float') * bandwidth + self.bandwidth = bandwidth + else: + self._set_bw() + self.kernel = self._kernel_funcs(self.dmat/self.bandwidth) + + if self.trunc: + mask = np.repeat(self.bandwidth, len(self.data), axis=1) + self.kernel[(self.dmat >= mask)] = 0 + + def _set_bw(self): + if self.k is not None: + dmat = np.sort(self.dmat)[:,:self.k] + else: + dmat = self.dmat + if self.fixed: + # use max knn distance as bandwidth + bandwidth = dmat.max() * self.eps + n = len(self.data) + self.bandwidth = np.ones((n, 1), 'float') * bandwidth + else: + # use local max knn distance + self.bandwidth = dmat.max(axis=1) * self.eps + self.bandwidth.shape = (self.bandwidth.size, 1) + + + def _kernel_funcs(self, zs): + # functions follow Anselin and Rey (2010) table 5.4 + if self.function == 'triangular': + return 1 - zs + elif self.function == 'uniform': + return np.ones(zi.shape) * 0.5 + elif self.function == 'quadratic': + return (3. / 4) * (1 - zs ** 2) + elif self.function == 'quartic': + return (15. / 16) * (1 - zs ** 2) ** 2 + elif self.function == 'gaussian': + c = np.pi * 2 + c = c ** (-0.5) + return c * np.exp(-(zs ** 2) / 2.) + elif self.function == 'gwr_gaussian': + return np.exp(-0.5*(zs)**2) + elif self.function == 'bisquare': + return (1-(zs)**2)**2 + elif self.function =='exponential': + return np.exp(-zs) + else: + print('Unsupported kernel function', self.function) diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/search.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/search.py new file mode 100644 index 0000000..97de4be --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/search.py @@ -0,0 +1,208 @@ +#Bandwidth optimization methods + +__author__ = "Taylor Oshan" + +import numpy as np + +def golden_section(a, c, delta, function, tol, max_iter, int_score=False): + """ + Golden section search routine + Method: p212, 9.6.4 + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + + Parameters + ---------- + a : float + initial max search section value + b : float + initial min search section value + delta : float + constant used to determine width of search sections + function : function + obejective function to be evaluated at different section + values + int_score : boolean + False for float score, True for integer score + tol : float + tolerance used to determine convergence + max_iter : integer + maximum iterations if no convergence to tolerance + + Returns + ------- + opt_val : float + optimal value + opt_score : kernel + optimal score + output : list of tuples + searching history + """ + b = a + delta * np.abs(c-a) + d = c - delta * np.abs(c-a) + score = 0.0 + diff = 1.0e9 + iters = 0 + output = [] + while np.abs(diff) > tol and iters < max_iter: + iters += 1 + if int_score: + 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 + c = d + d = b + b = a + delta * np.abs(c-a) + #if int_score: + #b = np.round(b) + else: + opt_val = d + opt_score = score_d + a = b + b = d + d = c - delta * np.abs(c-a) + #if int_score: + #d = np.round(b) + + #if int_score: + # opt_val = np.round(opt_val) + output.append((opt_val, opt_score)) + diff = score_b - score_d + score = opt_score + return np.round(opt_val, 2), opt_score, output + +def equal_interval(l_bound, u_bound, interval, function, int_score=False): + """ + Interval search, using interval as stepsize + + Parameters + ---------- + l_bound : float + initial min search section value + u_bound : float + initial max search section value + interval : float + constant used to determine width of search sections + function : function + obejective function to be evaluated at different section + values + int_score : boolean + False for float score, True for integer score + + Returns + ------- + opt_val : float + optimal value + opt_score : kernel + optimal score + output : list of tuples + searching history + """ + a = l_bound + c = u_bound + b = a + interval + if int_score: + a = np.round(a,0) + c = np.round(c,0) + b = np.round(b,0) + + output = [] + + score_a = function(a) + score_c = function(c) + + output.append((a,score_a)) + output.append((c,score_c)) + + if score_a < score_c: + opt_val = a + opt_score = score_a + else: + opt_val = c + opt_score = score_c + + while b < c: + score_b = function(b) + + output.append((b,score_b)) + + if score_b < opt_score: + opt_val = b + opt_score = score_b + b = b + interval + + return opt_val, opt_score, output + + +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 + optim_model = gwr_func(y, X, bw) + err = optim_model.resid_response.reshape((-1,1)) + est = optim_model.params + else: + model = GLM(y, X, family=self.family, constant=False).fit() + err = model.resid_response.reshape((-1,1)) + est = np.repeat(model.params.T, n, axis=0) + + + XB = np.multiply(est, X) + if rss_score: + rss = np.sum((err)**2) + iters = 0 + scores = [] + delta = 1e6 + BWs = [] + VALs = [] + + while delta > tol and iters < max_iter: + iters += 1 + new_XB = np.zeros_like(X) + bws = [] + vals = [] + ests = np.zeros_like(X) + f_XB = XB.copy() + f_err = err.copy() + for i in range(k): + temp_y = XB[:,i].reshape((-1,1)) + temp_y = temp_y + err + temp_X = X[:,i].reshape((-1,1)) + bw_class = bw_func(temp_y, temp_X) + bw = sel_func(bw_class) + optim_model = gwr_func(temp_y, temp_X, bw) + err = optim_model.resid_response.reshape((-1,1)) + est = optim_model.params.reshape((-1,)) + + new_XB[:,i] = np.multiply(est, temp_X.reshape((-1,))) + bws.append(bw) + ests[:,i] = est + vals.append(bw_class.bw[1]) + + predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1)) + num = np.sum((new_XB - XB)**2)/n + den = np.sum(np.sum(new_XB, axis=1)**2) + score = (num/den)**0.5 + XB = new_XB + + if rss_score: + new_rss = np.sum((y - predy)**2) + score = np.abs((new_rss - rss)/new_rss) + rss = new_rss + print score + scores.append(score) + delta = score + BWs.append(bws) + VALs.append(vals) + + opt_bws = BWs[-1] + return opt_bws, np.array(BWs), np.array(VALs), np.array(scores), f_XB, f_err diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/sel_bw.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/sel_bw.py new file mode 100644 index 0000000..9ab1263 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/sel_bw.py @@ -0,0 +1,286 @@ +# GWR Bandwidth selection class + +#Thinking about removing the search method and just having optimization begin in +#class __init__ + +#x_glob and offset parameters dont yet do anything; former is for semiparametric +#GWR and later is for offset variable for Poisson model + +__author__ = "Taylor Oshan Tayoshan@gmail.com" + +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 scipy.spatial.distance import pdist, squareform +from pysal.common import KDTree +import numpy as np + +kernels = {1: fix_gauss, 2: adapt_gauss, 3: fix_bisquare, 4: + adapt_bisquare, 5: fix_exp, 6:adapt_exp} +getDiag = {'AICc': get_AICc,'AIC':get_AIC, 'BIC': get_BIC, 'CV': get_CV} + +class Sel_BW(object): + """ + Select bandwidth for kernel + + Methods: p211 - p213, bandwidth selection + Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). + Geographically weighted regression: the analysis of spatially varying relationships. + + Parameters + ---------- + y : array + n*1, dependent variable. + x_glob : array + n*k1, fixed independent variable. + x_loc : array + n*k2, local independent variable, including constant. + coords : list of tuples + (x,y) of points used in bandwidth selection + family : string + GWR model type: 'Gaussian', 'logistic, 'Poisson'' + offset : array + n*1, offset variable for Poisson model + kernel : string + kernel function: 'gaussian', 'bisquare', 'exponetial' + fixed : boolean + True for fixed bandwidth and False for adaptive (NN) + fb : True for flexible (mutliple covaraite-specific) bandwidths + False for a traditional (same for all covariates) + bandwdith; defualt is False. + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + + + Attributes + ---------- + y : array + n*1, dependent variable. + x_glob : array + n*k1, fixed independent variable. + x_loc : array + n*k2, local independent variable, including constant. + coords : list of tuples + (x,y) of points used in bandwidth selection + family : string + GWR model type: 'Gaussian', 'logistic, 'Poisson'' + kernel : string + type of kernel used and wether fixed or adaptive + criterion : string + bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV' + search : string + bw search method: 'golden', 'interval' + bw_min : float + min value used in bandwidth search + bw_max : float + max value used in bandwidth search + interval : float + interval increment used in interval search + tol : float + tolerance used to determine convergence + max_iter : integer + max interations if no convergence to tol + fb : True for flexible (mutliple covaraite-specific) bandwidths + False for a traditional (same for all covariates) + bandwdith; defualt is False. + constant : boolean + True to include intercept (default) in model and False to exclude + intercept. + """ + def __init__(self, coords, y, x_loc, x_glob=None, family=Gaussian(), + offset=None, kernel='bisquare', fixed=False, fb=False, constant=True): + self.coords = coords + self.y = y + self.x_loc = x_loc + if x_glob is not None: + self.x_glob = x_glob + else: + self.x_glob = [] + self.family=family + self.fixed = fixed + self.kernel = kernel + if offset is None: + self.offset = np.ones((len(y), 1)) + else: + self.offset = offset * 1.0 + self.fb = fb + self.constant = constant + + def search(self, search='golden_section', criterion='AICc', bw_min=0.0, + bw_max=0.0, interval=0.0, tol=1.0e-6, max_iter=200, init_fb=True, + tol_fb=1.0e-5, rss_score=False, max_iter_fb=200): + """ + Parameters + ---------- + criterion : string + bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV' + search : string + bw search method: 'golden', 'interval' + bw_min : float + min value used in bandwidth search + bw_max : float + max value used in bandwidth search + interval : float + interval increment used in interval search + tol : float + tolerance used to determine convergence + max_iter : integer + max iterations if no convergence to tol + init_fb : True to initialize flexible bandwidth search with + esitmates from a traditional GWR and False to + initialize flexible bandwidth search with global + regression estimates + tol_fb : convergence tolerence for the flexible bandwidth + backfitting algorithm; a larger tolerance may stop the + algorith faster though it may result in a less optimal + model + max_iter_fb : max iterations if no convergence to tol for flexible + bandwidth backfittign algorithm + rss_score : True to use the residual sum of sqaures to evaluate + each iteration of the flexible bandwidth backfitting + routine and False to use a smooth function; default is + False + + Returns + ------- + bw : scalar or array + optimal bandwidth value or values; returns scalar for + fb=False and array for fb=True; ordering of bandwidths + matches the ordering of the covariates (columns) of the + designs matrix, X + """ + self.search = search + self.criterion = criterion + self.bw_min = bw_min + self.bw_max = bw_max + self.interval = interval + self.tol = tol + self.max_iter = max_iter + self.init_fb = init_fb + self.tol_fb = tol_fb + self.rss_score = rss_score + self.max_iter_fb = max_iter_fb + + + if self.fixed: + if self.kernel == 'gaussian': + ktype = 1 + elif self.kernel == 'bisquare': + ktype = 3 + elif self.kernel == 'exponential': + ktype = 5 + else: + raise TypeError('Unsupported kernel function ', self.kernel) + else: + if self.kernel == 'gaussian': + ktype = 2 + elif self.kernel == 'bisquare': + ktype = 4 + elif self.kernel == 'exponential': + ktype = 6 + else: + raise TypeError('Unsupported kernel function ', self.kernel) + + function = lambda bw: getDiag[criterion]( + GWR(self.coords, self.y, self.x_loc, bw, family=self.family, + kernel=self.kernel, fixed=self.fixed, offset=self.offset).fit()) + + if ktype % 2 == 0: + int_score = True + else: + int_score = False + self.int_score = int_score + + if self.fb: + self._fbw() + print self.bw[1] + self.XB = self.bw[4] + self.err = self.bw[5] + else: + self._bw() + + return self.bw[0] + + def _bw(self): + gwr_func = lambda bw: getDiag[self.criterion]( + GWR(self.coords, self.y, self.x_loc, bw, family=self.family, + kernel=self.kernel, fixed=self.fixed, constant=self.constant).fit()) + if self.search == 'golden_section': + a,c = self._init_section(self.x_glob, self.x_loc, self.coords, + self.constant) + delta = 0.38197 #1 - (np.sqrt(5.0)-1.0)/2.0 + self.bw = golden_section(a, c, delta, gwr_func, self.tol, + self.max_iter, self.int_score) + elif self.search == 'interval': + self.bw = equal_interval(self.bw_min, self.bw_max, self.interval, + gwr_func, self.int_score) + else: + raise TypeError('Unsupported computational search method ', search) + + def _fbw(self): + y = self.y + if self.constant: + X = USER.check_constant(self.x_loc) + else: + X = self.x_loc + n, k = X.shape + family = self.family + offset = self.offset + kernel = self.kernel + fixed = self.fixed + coords = self.coords + search = self.search + criterion = self.criterion + bw_min = self.bw_min + bw_max = self.bw_max + interval = self.interval + tol = self.tol + max_iter = self.max_iter + gwr_func = lambda y, X, bw: GWR(coords, y, X, bw, family=family, + kernel=kernel, fixed=fixed, offset=offset, constant=False).fit() + bw_func = lambda y, X: Sel_BW(coords, y, X, x_glob=[], family=family, + kernel=kernel, fixed=fixed, offset=offset, constant=False) + sel_func = lambda bw_func: bw_func.search(search=search, + criterion=criterion, bw_min=bw_min, bw_max=bw_max, + interval=interval, tol=tol, max_iter=max_iter) + self.bw = flexible_bw(self.init_fb, y, X, n, k, family, self.tol_fb, + self.max_iter_fb, self.rss_score, gwr_func, bw_func, sel_func) + + + + def _init_section(self, x_glob, x_loc, coords, constant): + if len(x_glob) > 0: + n_glob = x_glob.shape[1] + else: + n_glob = 0 + if len(x_loc) > 0: + n_loc = x_loc.shape[1] + else: + n_loc = 0 + if constant: + n_vars = n_glob + n_loc + 1 + else: + n_vars = n_glob + n_loc + n = np.array(coords).shape[0] + + if self.int_score: + a = 40 + 2 * n_vars + c = n + else: + nn = 40 + 2 * n_vars + sq_dists = squareform(pdist(coords)) + sort_dists = np.sort(sq_dists, axis=1) + min_dists = sort_dists[:,nn-1] + max_dists = sort_dists[:,-1] + a = np.min(min_dists)/2.0 + c = np.max(max_dists)/2.0 + + if a < self.bw_min: + a = self.bw_min + if c > self.bw_max and self.bw_max > 0: + c = self.bw_max + return a, c diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py new file mode 100644 index 0000000..7f12b7e --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py @@ -0,0 +1,853 @@ +""" +GWR is tested against results from GWR4 +""" + +import unittest +import pickle as pk +from crankshaft.regression.gwr.gwr import GWR, FBGWR +from crankshaft.regression.gwr.sel_bw import Sel_BW +from crankshaft.regression.gwr.diagnostics import get_AICc, get_AIC, get_BIC, get_CV +from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial +import numpy as np +import pysal + +class TestGWRGaussian(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('GData_utm.csv')) + 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)) + black = np.array(data.by_col('PctBlack')).reshape((-1,1)) + self.X = np.hstack([rural, pov, black]) + self.BS_F = pysal.open(pysal.examples.get_path('georgia_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('georgia_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('georgia_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('georgia_GS_NN_listwise.csv')) + self.FB = pk.load(open(pysal.examples.get_path('FB.p'), 'r')) + self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r')) + self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_rural = self.BS_F.by_col(' est_PctRural') + se_rural = self.BS_F.by_col(' se_PctRural') + t_rural = self.BS_F.by_col(' t_PctRural') + est_pov = self.BS_F.by_col(' est_PctPov') + se_pov = self.BS_F.by_col(' se_PctPov') + t_pov = self.BS_F.by_col(' t_PctPov') + est_black = self.BS_F.by_col(' est_PctBlack') + se_black = self.BS_F.by_col(' se_PctBlack') + t_black = self.BS_F.by_col(' t_PctBlack') + yhat = self.BS_F.by_col(' yhat') + res = np.array(self.BS_F.by_col(' residual')) + std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + 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) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_rural = self.BS_NN.by_col(' est_PctRural') + se_rural = self.BS_NN.by_col(' se_PctRural') + t_rural = self.BS_NN.by_col(' t_PctRural') + est_pov = self.BS_NN.by_col(' est_PctPov') + se_pov = self.BS_NN.by_col(' se_PctPov') + t_pov = self.BS_NN.by_col(' t_PctPov') + est_black = self.BS_NN.by_col(' est_PctBlack') + se_black = self.BS_NN.by_col(' se_PctBlack') + t_black = self.BS_NN.by_col(' t_PctBlack') + yhat = self.BS_NN.by_col(' yhat') + res = np.array(self.BS_NN.by_col(' residual')) + std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + 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) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_rural = self.GS_F.by_col(' est_PctRural') + se_rural = self.GS_F.by_col(' se_PctRural') + t_rural = self.GS_F.by_col(' t_PctRural') + est_pov = self.GS_F.by_col(' est_PctPov') + se_pov = self.GS_F.by_col(' se_PctPov') + t_pov = self.GS_F.by_col(' t_PctPov') + est_black = self.GS_F.by_col(' est_PctBlack') + se_black = self.GS_F.by_col(' se_PctBlack') + t_black = self.GS_F.by_col(' t_PctBlack') + yhat = self.GS_F.by_col(' yhat') + res = np.array(self.GS_F.by_col(' residual')) + std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=87308.298, + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + 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) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_rural = self.GS_NN.by_col(' est_PctRural') + se_rural = self.GS_NN.by_col(' se_PctRural') + t_rural = self.GS_NN.by_col(' t_PctRural') + est_pov = self.GS_NN.by_col(' est_PctPov') + se_pov = self.GS_NN.by_col(' se_PctPov') + t_pov = self.GS_NN.by_col(' t_PctPov') + est_black = self.GS_NN.by_col(' est_PctBlack') + se_black = self.GS_NN.by_col(' se_PctBlack') + t_black = self.GS_NN.by_col(' t_PctBlack') + yhat = self.GS_NN.by_col(' yhat') + res = np.array(self.GS_NN.by_col(' residual')) + std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1)) + localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1)) + inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1)) + cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=49.000, + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + CV = get_CV(rslt) + + 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) + np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04) + np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04) + np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04) + np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04) + np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04) + np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04) + np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05) + np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04) + np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00) + + def test_FBGWR(self): + model = FBGWR(self.coords, self.y, self.X, [157.0, 65.0, 52.0], + XB=self.XB, err=self.err, constant=False) + rslt = model.fit() + + np.testing.assert_allclose(rslt.predy, self.FB['predy'], atol=1e-07) + np.testing.assert_allclose(rslt.params, self.FB['params'], atol=1e-07) + np.testing.assert_allclose(rslt.resid_response, self.FB['u'], atol=1e-05) + np.testing.assert_almost_equal(rslt.resid_ss, 6339.3497144025841) + + def test_Prediction(self): + coords =np.array(self.coords) + index = np.arange(len(self.y)) + #train = index[0:-10] + test = index[-10:] + + #y_train = self.y[train] + #X_train = self.X[train] + #coords_train = list(coords[train]) + + #y_test = self.y[test] + X_test = self.X[test] + coords_test = list(coords[test]) + + + model = GWR(self.coords, self.y, self.X, 93, family=Gaussian(), + fixed=False, kernel='bisquare') + results = model.predict(coords_test, X_test) + + params = np.array([22.77198, -0.10254, -0.215093, -0.01405, + 19.10531, -0.094177, -0.232529, 0.071913, + 19.743421, -0.080447, -0.30893, 0.083206, + 17.505759, -0.078919, -0.187955, 0.051719, + 27.747402, -0.165335, -0.208553, 0.004067, + 26.210627, -0.138398, -0.360514, 0.072199, + 18.034833, -0.077047, -0.260556, 0.084319, + 28.452802, -0.163408, -0.14097, -0.063076, + 22.353095, -0.103046, -0.226654, 0.002992, + 18.220508, -0.074034, -0.309812, 0.108636]).reshape((10,4)) + np.testing.assert_allclose(params, results.params, rtol=1e-03) + + bse = np.array([2.080166, 0.021462, 0.102954, 0.049627, + 2.536355, 0.022111, 0.123857, 0.051917, + 1.967813, 0.019716, 0.102562, 0.054918, + 2.463219, 0.021745, 0.110297, 0.044189, + 1.556056, 0.019513, 0.12764, 0.040315, + 1.664108, 0.020114, 0.131208, 0.041613, + 2.5835, 0.021481, 0.113158, 0.047243, + 1.709483, 0.019752, 0.116944, 0.043636, + 1.958233, 0.020947, 0.09974, 0.049821, + 2.276849, 0.020122, 0.107867, 0.047842]).reshape((10,4)) + np.testing.assert_allclose(bse, results.bse, rtol=1e-03) + + tvalues = np.array([10.947193, -4.777659, -2.089223, -0.283103, + 7.532584, -4.259179, -1.877395, 1.385161, + 10.033179, -4.080362, -3.012133, 1.515096, + 7.106862, -3.629311, -1.704079, 1.17042, + 17.831878, -8.473156, -1.633924, 0.100891, + 15.750552, -6.880725, -2.74765, 1.734978, + 6.980774, -3.586757, -2.302575, 1.784818, + 16.644095, -8.273001, -1.205451, -1.445501, + 11.414933, -4.919384, -2.272458, 0.060064, + 8.00251, -3.679274, -2.872176, 2.270738]).reshape((10,4)) + np.testing.assert_allclose(tvalues, results.tvalues, rtol=1e-03) + + localR2 = np.array([[ 0.53068693], + [ 0.59582647], + [ 0.59700925], + [ 0.45769954], + [ 0.54634509], + [ 0.5494828 ], + [ 0.55159604], + [ 0.55634237], + [ 0.53903842], + [ 0.55884954]]) + np.testing.assert_allclose(localR2, results.localR2, rtol=1e-05) + +class TestGWRPoisson(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur') + 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)) + OWN = np.array(data.by_col('OWNH')).reshape((-1,1)) + POP = np.array(data.by_col('POP65')).reshape((-1,1)) + UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1)) + self.X = np.hstack([OCC,OWN,POP,UNEMP]) + self.BS_F = pysal.open(pysal.examples.get_path('tokyo_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('tokyo_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('tokyo_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('tokyo_GS_NN_listwise.csv')) + self.BS_NN_OFF = pysal.open(pysal.examples.get_path('tokyo_BS_NN_OFF_listwise.csv')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_OCC = self.BS_F.by_col(' est_OCC_TEC') + se_OCC = self.BS_F.by_col(' se_OCC_TEC') + t_OCC = self.BS_F.by_col(' t_OCC_TEC') + est_OWN = self.BS_F.by_col(' est_OWNH') + se_OWN = self.BS_F.by_col(' se_OWNH') + t_OWN = self.BS_F.by_col(' t_OWNH') + est_POP = self.BS_F.by_col(' est_POP65') + se_POP = self.BS_F.by_col(' se_POP65') + t_POP = self.BS_F.by_col(' t_POP65') + est_UNEMP = self.BS_F.by_col(' est_UNEMP') + se_UNEMP = self.BS_F.by_col(' se_UNEMP') + t_UNEMP = self.BS_F.by_col(' t_UNEMP') + yhat = self.BS_F.by_col(' yhat') + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), + kernel='bisquare', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_OCC = self.BS_NN.by_col(' est_OCC_TEC') + se_OCC = self.BS_NN.by_col(' se_OCC_TEC') + t_OCC = self.BS_NN.by_col(' t_OCC_TEC') + est_OWN = self.BS_NN.by_col(' est_OWNH') + se_OWN = self.BS_NN.by_col(' se_OWNH') + t_OWN = self.BS_NN.by_col(' t_OWNH') + est_POP = self.BS_NN.by_col(' est_POP65') + se_POP = self.BS_NN.by_col(' se_POP65') + t_POP = self.BS_NN.by_col(' t_POP65') + est_UNEMP = self.BS_NN.by_col(' est_UNEMP') + se_UNEMP = self.BS_NN.by_col(' se_UNEMP') + t_UNEMP = self.BS_NN.by_col(' t_UNEMP') + yhat = self.BS_NN.by_col(' yhat') + pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_BS_NN_Offset(self): + est_Int = self.BS_NN_OFF.by_col(' est_Intercept') + se_Int = self.BS_NN_OFF.by_col(' se_Intercept') + t_Int = self.BS_NN_OFF.by_col(' t_Intercept') + est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC') + se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC') + t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC') + est_OWN = self.BS_NN_OFF.by_col(' est_OWNH') + se_OWN = self.BS_NN_OFF.by_col(' se_OWNH') + t_OWN = self.BS_NN_OFF.by_col(' t_OWNH') + est_POP = self.BS_NN_OFF.by_col(' est_POP65') + se_POP = self.BS_NN_OFF.by_col(' se_POP65') + t_POP = self.BS_NN_OFF.by_col(' t_POP65') + est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP') + se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP') + t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP') + yhat = self.BS_NN_OFF.by_col(' yhat') + pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03, + atol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04, + atol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02, + atol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01, + atol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_OCC = self.GS_F.by_col(' est_OCC_TEC') + se_OCC = self.GS_F.by_col(' se_OCC_TEC') + t_OCC = self.GS_F.by_col(' t_OCC_TEC') + est_OWN = self.GS_F.by_col(' est_OWNH') + se_OWN = self.GS_F.by_col(' se_OWNH') + t_OWN = self.GS_F.by_col(' t_OWNH') + est_POP = self.GS_F.by_col(' est_POP65') + se_POP = self.GS_F.by_col(' se_POP65') + t_POP = self.GS_F.by_col(' t_POP65') + est_UNEMP = self.GS_F.by_col(' est_UNEMP') + se_UNEMP = self.GS_F.by_col(' se_UNEMP') + t_UNEMP = self.GS_F.by_col(' t_UNEMP') + yhat = self.GS_F.by_col(' yhat') + pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_OCC = self.GS_NN.by_col(' est_OCC_TEC') + se_OCC = self.GS_NN.by_col(' se_OCC_TEC') + t_OCC = self.GS_NN.by_col(' t_OCC_TEC') + est_OWN = self.GS_NN.by_col(' est_OWNH') + se_OWN = self.GS_NN.by_col(' se_OWNH') + t_OWN = self.GS_NN.by_col(' t_OWNH') + est_POP = self.GS_NN.by_col(' est_POP65') + se_POP = self.GS_NN.by_col(' se_POP65') + t_POP = self.GS_NN.by_col(' t_POP65') + est_UNEMP = self.GS_NN.by_col(' est_UNEMP') + se_UNEMP = self.GS_NN.by_col(' se_UNEMP') + t_UNEMP = self.GS_NN.by_col(' t_UNEMP') + yhat = self.GS_NN.by_col(' yhat') + pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03) + np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02) + np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02) + np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04) + np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02) + np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02) + np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02) + np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02) + np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02) + np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02) + np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02) + np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04) + np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + +class TestGWRBinomial(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('landslides.csv')) + 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)) + SIN = np.array(data.by_col('SinAspct')).reshape((-1,1)) + COS = np.array(data.by_col('CosAspct')).reshape((-1,1)) + SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1)) + DIST = np.array(data.by_col('DistStrm')).reshape((-1,1)) + self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST]) + self.BS_F = pysal.open(pysal.examples.get_path('clearwater_BS_F_listwise.csv')) + self.BS_NN = pysal.open(pysal.examples.get_path('clearwater_BS_NN_listwise.csv')) + self.GS_F = pysal.open(pysal.examples.get_path('clearwater_GS_F_listwise.csv')) + self.GS_NN = pysal.open(pysal.examples.get_path('clearwater_GS_NN_listwise.csv')) + + def test_BS_F(self): + est_Int = self.BS_F.by_col(' est_Intercept') + se_Int = self.BS_F.by_col(' se_Intercept') + t_Int = self.BS_F.by_col(' t_Intercept') + est_elev = self.BS_F.by_col(' est_Elev') + se_elev = self.BS_F.by_col(' se_Elev') + t_elev = self.BS_F.by_col(' t_Elev') + est_slope = self.BS_F.by_col(' est_Slope') + se_slope = self.BS_F.by_col(' se_Slope') + t_slope = self.BS_F.by_col(' t_Slope') + est_sin = self.BS_F.by_col(' est_SinAspct') + se_sin = self.BS_F.by_col(' se_SinAspct') + t_sin = self.BS_F.by_col(' t_SinAspct') + est_cos = self.BS_F.by_col(' est_CosAspct') + se_cos = self.BS_F.by_col(' se_CosAspct') + t_cos = self.BS_F.by_col(' t_CosAspct') + est_south = self.BS_F.by_col(' est_AbsSouth') + se_south = self.BS_F.by_col(' se_AbsSouth') + t_south = self.BS_F.by_col(' t_AbsSouth') + est_strm = self.BS_F.by_col(' est_DistStrm') + se_strm = self.BS_F.by_col(' se_DistStrm') + t_strm = self.BS_F.by_col(' t_DistStrm') + yhat = self.BS_F.by_col(' yhat') + pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1)) + + model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), + kernel='bisquare', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_BS_NN(self): + est_Int = self.BS_NN.by_col(' est_Intercept') + se_Int = self.BS_NN.by_col(' se_Intercept') + t_Int = self.BS_NN.by_col(' t_Intercept') + est_elev = self.BS_NN.by_col(' est_Elev') + se_elev = self.BS_NN.by_col(' se_Elev') + t_elev = self.BS_NN.by_col(' t_Elev') + est_slope = self.BS_NN.by_col(' est_Slope') + se_slope = self.BS_NN.by_col(' se_Slope') + t_slope = self.BS_NN.by_col(' t_Slope') + est_sin = self.BS_NN.by_col(' est_SinAspct') + se_sin = self.BS_NN.by_col(' se_SinAspct') + t_sin = self.BS_NN.by_col(' t_SinAspct') + est_cos = self.BS_NN.by_col(' est_CosAspct') + se_cos = self.BS_NN.by_col(' se_CosAspct') + t_cos = self.BS_NN.by_col(' t_CosAspct') + est_south = self.BS_NN.by_col(' est_AbsSouth') + se_south = self.BS_NN.by_col(' se_AbsSouth') + t_south = self.BS_NN.by_col(' t_AbsSouth') + est_strm = self.BS_NN.by_col(' est_DistStrm') + se_strm = self.BS_NN.by_col(' se_DistStrm') + t_strm = self.BS_NN.by_col(' t_DistStrm') + yhat = self.BS_NN.by_col(' yhat') + pdev = self.BS_NN.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), + kernel='bisquare', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_F(self): + est_Int = self.GS_F.by_col(' est_Intercept') + se_Int = self.GS_F.by_col(' se_Intercept') + t_Int = self.GS_F.by_col(' t_Intercept') + est_elev = self.GS_F.by_col(' est_Elev') + se_elev = self.GS_F.by_col(' se_Elev') + t_elev = self.GS_F.by_col(' t_Elev') + est_slope = self.GS_F.by_col(' est_Slope') + se_slope = self.GS_F.by_col(' se_Slope') + t_slope = self.GS_F.by_col(' t_Slope') + est_sin = self.GS_F.by_col(' est_SinAspct') + se_sin = self.GS_F.by_col(' se_SinAspct') + t_sin = self.GS_F.by_col(' t_SinAspct') + est_cos = self.GS_F.by_col(' est_CosAspct') + se_cos = self.GS_F.by_col(' se_CosAspct') + t_cos = self.GS_F.by_col(' t_CosAspct') + est_south = self.GS_F.by_col(' est_AbsSouth') + se_south = self.GS_F.by_col(' se_AbsSouth') + t_south = self.GS_F.by_col(' t_AbsSouth') + est_strm = self.GS_F.by_col(' est_DistStrm') + se_strm = self.GS_F.by_col(' se_DistStrm') + t_strm = self.GS_F.by_col(' t_DistStrm') + yhat = self.GS_F.by_col(' yhat') + pdev = self.GS_F.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), + kernel='gaussian', fixed=True) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + + def test_GS_NN(self): + est_Int = self.GS_NN.by_col(' est_Intercept') + se_Int = self.GS_NN.by_col(' se_Intercept') + t_Int = self.GS_NN.by_col(' t_Intercept') + est_elev = self.GS_NN.by_col(' est_Elev') + se_elev = self.GS_NN.by_col(' se_Elev') + t_elev = self.GS_NN.by_col(' t_Elev') + est_slope = self.GS_NN.by_col(' est_Slope') + se_slope = self.GS_NN.by_col(' se_Slope') + t_slope = self.GS_NN.by_col(' t_Slope') + est_sin = self.GS_NN.by_col(' est_SinAspct') + se_sin = self.GS_NN.by_col(' se_SinAspct') + t_sin = self.GS_NN.by_col(' t_SinAspct') + est_cos = self.GS_NN.by_col(' est_CosAspct') + se_cos = self.GS_NN.by_col(' se_CosAspct') + t_cos = self.GS_NN.by_col(' t_CosAspct') + est_south = self.GS_NN.by_col(' est_AbsSouth') + se_south = self.GS_NN.by_col(' se_AbsSouth') + t_south = self.GS_NN.by_col(' t_AbsSouth') + est_strm = self.GS_NN.by_col(' est_DistStrm') + se_strm = self.GS_NN.by_col(' se_DistStrm') + t_strm = self.GS_NN.by_col(' t_DistStrm') + yhat = self.GS_NN.by_col(' yhat') + pdev = self.GS_NN.by_col(' localpdev') + + model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), + kernel='gaussian', fixed=False) + rslt = model.fit() + + AICc = get_AICc(rslt) + AIC = get_AIC(rslt) + BIC = get_BIC(rslt) + + 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) + np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00) + np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00) + np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00) + np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00) + np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00) + np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00) + np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01) + np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01) + np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01) + np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01) + np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01) + np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01) + np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01) + np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01) + np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01) + np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02) + np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01) + np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02) + np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00) + #This test fails - likely due to compound rounding errors + #Has been tested using statsmodels.family calculations and + #code from Jing's python version, which both yield the same + #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05) + +if __name__ == '__main__': + unittest.main() diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py new file mode 100644 index 0000000..ea044b9 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py @@ -0,0 +1,84 @@ +import unittest +import numpy as np +import pysal +from pysal.contrib.gwr.kernels import * + +PEGP = pysal.examples.get_path + +class TestKernels(unittest.TestCase): + def setUp(self): + np.random.seed(1234) + x = np.arange(1,6) + y = np.arange(5,0, -1) + np.random.shuffle(x) + np.random.shuffle(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], + [ 0.48567179, 0.89483932, 1. , 0.89483932, 0.48567179], + [ 0.48567179, 0.64118039, 0.89483932, 1. , 0.38889556], + [ 0.89483932, 0.48567179, 0.48567179, 0.38889556, 1. ]]) + self.adapt_gauss_kern = np.array([ + [ 1. , 0.52004183, 0.60653072, 0.60653072, 0.92596109], + [ 0.34559083, 1. , 0.88249692, 0.60653072, 0.44374738], + [ 0.03877423, 0.60653072, 1. , 0.60653072, 0.03877423], + [ 0.44374738, 0.60653072, 0.88249692, 1. , 0.34559083], + [ 0.92596109, 0.60653072, 0.60653072, 0.52004183, 1. ]]) + self.fix_bisquare_kern = np.array([ + [ 1. , 0. , 0. , 0. , 0.60493827], + [ 0. , 1. , 0.60493827, 0.01234568, 0. ], + [ 0. , 0.60493827, 1. , 0.60493827, 0. ], + [ 0. , 0.01234568, 0.60493827, 1. , 0. ], + [ 0.60493827, 0. , 0. , 0. , 1. ]]) + self.adapt_bisquare_kern = np.array([ + [ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 3.99999881e-14, 7.15976383e-01], + [ 0.00000000e+00, 1.00000000e+00, 5.62500075e-01, + 3.99999881e-14, 0.00000000e+00], + [ 0.00000000e+00, 3.99999881e-14, 1.00000000e+00, + 3.99999881e-14, 0.00000000e+00], + [ 0.00000000e+00, 3.99999881e-14, 5.62500075e-01, + 1.00000000e+00, 0.00000000e+00], + [ 7.15976383e-01, 0.00000000e+00, 3.99999881e-14, + 0.00000000e+00, 1.00000000e+00]]) + self.fix_exp_kern = np.array([ + [ 1. , 0.2529993 , 0.30063739, 0.30063739, 0.62412506], + [ 0.2529993 , 1. , 0.62412506, 0.38953209, 0.30063739], + [ 0.30063739, 0.62412506, 1. , 0.62412506, 0.30063739], + [ 0.30063739, 0.38953209, 0.62412506, 1. , 0.2529993 ], + [ 0.62412506, 0.30063739, 0.30063739, 0.2529993 , 1. ]]) + self.adapt_exp_kern = np.array([ + [ 1. , 0.31868771, 0.36787948, 0.36787948, 0.67554721], + [ 0.23276223, 1. , 0.60653069, 0.36787948, 0.27949951], + [ 0.07811997, 0.36787948, 1. , 0.36787948, 0.07811997], + [ 0.27949951, 0.36787948, 0.60653069, 1. , 0.23276223], + [ 0.67554721, 0.36787948, 0.36787948, 0.31868771, 1. ]]) + + def test_fix_gauss(self): + kern = fix_gauss(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_gauss_kern) + + def test_adapt_gauss(self): + kern = adapt_gauss(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_gauss_kern) + + def test_fix_biqsquare(self): + kern = fix_bisquare(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_bisquare_kern, + atol=1e-01) + + def test_adapt_bisqaure(self): + kern = adapt_bisquare(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_bisquare_kern, atol=1e-012) + + def test_fix_exp(self): + kern = fix_exp(self.coords, 3) + np.testing.assert_allclose(kern, self.fix_exp_kern) + + def test_adapt_exp(self): + kern = adapt_exp(self.coords, 3) + np.testing.assert_allclose(kern, self.adapt_exp_kern) + +if __name__ == '__main__': + unittest.main() diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py new file mode 100644 index 0000000..47c6d9d --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py @@ -0,0 +1,139 @@ + +""" +GWR is tested against results from GWR4 +""" + +import unittest +import pickle as pk +from pysal.contrib.glm.family import Gaussian, Poisson, Binomial +from pysal.contrib.gwr.sel_bw import Sel_BW +import numpy as np +import pysal + +class TestSelBW(unittest.TestCase): + def setUp(self): + data = pysal.open(pysal.examples.get_path('GData_utm.csv')) + 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)) + black = np.array(data.by_col('PctBlack')).reshape((-1,1)) + self.X = np.hstack([rural, pov, black]) + self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r')) + self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r')) + + def test_golden_fixed_AICc(self): + bw1 = 211027.34 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=True).search(criterion='AICc') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_AICc(self): + bw1 = 93.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=False).search(criterion='AICc') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_AIC(self): + bw1 = 76169.15 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='AIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_AIC(self): + bw1 = 50.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='AIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_BIC(self): + bw1 = 279451.43 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='BIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_BIC(self): + bw1 = 62.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='BIC') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_fixed_CV(self): + bw1 = 130406.67 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='CV') + self.assertAlmostEqual(bw1, bw2) + + def test_golden_adapt_CV(self): + bw1 = 68.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='CV') + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_AICc(self): + bw1 = 211025.0#211027.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=True).search(criterion='AICc', search='interval', bw_min=211001., + bw_max=211035.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_AICc(self): + bw1 = 93.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare', + fixed=False).search(criterion='AICc', search='interval', + bw_min=90.0, bw_max=95.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_AIC(self): + bw1 = 76175.0#76169.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='AIC', search='interval', + bw_min=76161.0, bw_max=76175.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_AIC(self): + bw1 = 40.0#50.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='AIC', search='interval', bw_min=40.0, + bw_max=60.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_BIC(self): + bw1 = 279461.0#279451.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='BIC', search='interval', bw_min=279441.0, + bw_max=279461.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_BIC(self): + bw1 = 62.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='BIC', search='interval', + bw_min=52.0, bw_max=72.0, interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_fixed_CV(self): + bw1 = 130400.0#130406.00 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=True).search(criterion='CV', search='interval', bw_min=130400.0, + bw_max=130410.0, interval=1) + self.assertAlmostEqual(bw1, bw2) + + def test_interval_adapt_CV(self): + bw1 = 62.0#68.0 + bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian', + fixed=False).search(criterion='CV', search='interval', bw_min=60.0, + bw_max=76.0 , interval=2) + self.assertAlmostEqual(bw1, bw2) + + def test_FBGWR_AIC(self): + bw1 = [157.0, 65.0, 52.0] + sel = Sel_BW(self.coords, self.y, self.X, fb=True, kernel='bisquare', + constant=False) + bw2 = sel.search(tol_fb=1e-03) + np.testing.assert_allclose(bw1, bw2) + np.testing.assert_allclose(sel.XB, self.XB, atol=1e-05) + np.testing.assert_allclose(sel.err, self.err, atol=1e-05) + +if __name__ == '__main__': + unittest.main() diff --git a/release/python/0.9.4/crankshaft/crankshaft/regression/gwr_cs.py b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr_cs.py new file mode 100644 index 0000000..9ccaefb --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/regression/gwr_cs.py @@ -0,0 +1,202 @@ +""" + Geographically weighted regression +""" +import numpy as np +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 + + +class GWR: + def __init__(self, data_provider=None): + if data_provider: + self.data_provider = data_provider + else: + self.data_provider = AnalysisDataProvider() + + def gwr(self, subquery, dep_var, ind_vars, + bw=None, fixed=False, kernel='bisquare', + geom_col='the_geom', id_col='cartodb_id'): + """ + subquery: 'select * from demographics' + dep_var: 'pctbachelor' + ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack'] + bw: value of bandwidth, if None then select optimal + fixed: False (kNN) or True ('distance') + kernel: 'bisquare' (default), or 'exponential', 'gaussian' + """ + + params = {'geom_col': geom_col, + 'id_col': id_col, + 'subquery': subquery, + 'dep_var': dep_var, + 'ind_vars': ind_vars} + + # get data from data provider + query_result = self.data_provider.get_gwr(params) + + # exit if data to analyze is empty + if len(query_result) == 0: + plpy.error('No data passed to analysis or independent variables ' + 'are all null-valued') + + # unique ids and variable names list + rowid = np.array(query_result[0]['rowid'], dtype=np.int) + + # 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 = zip(x, y) + + # extract dependent variable + Y = np.array(query_result[0]['dep_var'], dtype=np.float).reshape((-1, 1)) + + n = Y.shape[0] + k = len(ind_vars) + X = np.zeros((n, k)) + + # extract query result + for attr in range(0, k): + attr_name = 'attr' + str(attr + 1) + X[:, attr] = np.array( + query_result[0][attr_name], dtype=np.float).flatten() + + # add intercept variable name + ind_vars.insert(0, 'intercept') + + # calculate bandwidth if none is supplied + if bw is None: + bw = Sel_BW(coords, Y, X, + fixed=fixed, kernel=kernel).search() + model = PySAL_GWR(coords, Y, X, bw, + fixed=fixed, kernel=kernel).fit() + + # containers for outputs + coeffs = [] + stand_errs = [] + t_vals = [] + filtered_t_vals = [] + + # extracted model information + c_alpha = model.adj_alpha + filtered_t = model.filter_tvals(c_alpha[1]) + predicted = model.predy.flatten() + residuals = model.resid_response + r_squared = model.localR2.flatten() + bw = np.repeat(float(bw), n) + + # create lists of json objs for model outputs + 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] + for k, var in enumerate(ind_vars)})) + t_vals.append(json.dumps({var: model.tvalues[idx, k] + for k, var in enumerate(ind_vars)})) + filtered_t_vals.append( + json.dumps({var: filtered_t[idx, k] + for k, var in enumerate(ind_vars)})) + + 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', + geom_col='the_geom', id_col='cartodb_id'): + """ + subquery: 'select * from demographics' + dep_var: 'pctbachelor' + ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack'] + bw: value of bandwidth, if None then select optimal + fixed: False (kNN) or True ('distance') + kernel: 'bisquare' (default), or 'exponential', 'gaussian' + """ + + params = {'geom_col': geom_col, + 'id_col': id_col, + 'subquery': subquery, + 'dep_var': dep_var, + 'ind_vars': ind_vars} + + # get data from data provider + query_result = self.data_provider.get_gwr_predict(params) + + # exit if data to analyze is empty + if len(query_result) == 0: + plpy.error('No data passed to analysis or independent variables ' + 'are all null-valued') + + # unique ids and variable names list + rowid = np.array(query_result[0]['rowid'], dtype=np.int) + + x = np.array(query_result[0]['x'], dtype=np.float) + y = np.array(query_result[0]['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)) + + n = Y.shape[0] + k = len(ind_vars) + X = np.empty((n, k), dtype=np.float) + + for attr in range(0, k): + attr_name = 'attr' + str(attr + 1) + X[:, attr] = np.array( + query_result[0][attr_name], dtype=np.float).flatten() + + # add intercept variable name + ind_vars.insert(0, 'intercept') + + # split data into "training" and "test" for predictions + # create index to split based on null y values + train = np.where(Y != np.array(None))[0] + test = np.where(Y == np.array(None))[0] + + # report error if there is no data to predict + if len(test) < 1: + plpy.error('No rows flagged for prediction: verify that rows ' + 'denoting prediction locations have a dependent ' + 'variable value of `null`') + + # split dependent variable (only need training which is non-Null's) + Y_train = Y[train].reshape((-1, 1)) + Y_train = Y_train.astype(np.float) + + # split coords + coords_train = coords[train] + coords_test = coords[test] + + # split explanatory variables + X_train = X[train] + X_test = X[test] + + # calculate bandwidth if none is supplied + if bw is None: + bw = Sel_BW(coords_train, Y_train, X_train, + fixed=fixed, kernel=kernel).search() + + # estimate model and predict at new locations + model = PySAL_GWR(coords_train, Y_train, X_train, + bw, fixed=fixed, + kernel=kernel).predict(coords_test, X_test) + + coeffs = [] + stand_errs = [] + t_vals = [] + r_squared = model.localR2.flatten() + predicted = model.predy.flatten() + + m = len(model.predy) + 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] + for k, var in enumerate(ind_vars)})) + t_vals.append(json.dumps({var: model.tvalues[idx, k] + for k, var in enumerate(ind_vars)})) + + return zip(coeffs, stand_errs, t_vals, + r_squared, predicted, rowid[test]) diff --git a/release/python/0.9.4/crankshaft/crankshaft/segmentation/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/segmentation/__init__.py new file mode 100644 index 0000000..628c887 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/segmentation/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions from for segmentation""" +from segmentation import * diff --git a/release/python/0.9.4/crankshaft/crankshaft/segmentation/segmentation.py b/release/python/0.9.4/crankshaft/crankshaft/segmentation/segmentation.py new file mode 100644 index 0000000..8137a0a --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/segmentation/segmentation.py @@ -0,0 +1,247 @@ +""" +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 + + +class Segmentation(object): + """ + Add docstring + """ + + def __init__(self, data_provider=None): + if data_provider is None: + self.data_provider = AnalysisDataProvider() + else: + self.data_provider = data_provider + + def create_and_predict_segment_agg(self, target, features, target_features, + target_ids, model_parameters): + """ + Version of create_and_predict_segment that works on arrays that come + straight form the SQL calling the function. + + Input: + @param target: The 1D array of length NSamples containing the + target variable we want the model to predict + @param features: The 2D array of size NSamples * NFeatures that + form the input to the model + @param target_ids: A 1D array of target_ids that will be used + to associate the results of the prediction with the rows which + they come from + @param model_parameters: A dictionary containing parameters for + the model. + """ + clean_target, _ = replace_nan_with_mean(target) + clean_features, _ = replace_nan_with_mean(features) + target_features, _ = replace_nan_with_mean(target_features) + + model, accuracy = train_model(clean_target, clean_features, + model_parameters, 0.2) + prediction = model.predict(target_features) + accuracy_array = [accuracy] * prediction.shape[0] + return zip(target_ids, prediction, 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 + Stuart Lynn + @param query: subquery that data is pulled from for packaging + @param variable: name of the target variable + @param feature_columns: list of column names + @target_query: The query to run to obtain the data to predict + @param model_params: A dictionary of model parameters, the full + specification can be found on the + scikit learn page for [GradientBoostingRegressor] + (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) + """ + params = {"subquery": target_query, + "id_col": id_col} + + (target, features, target_mean, + feature_means) = self.clean_data(query, variable, feature_columns) + + if model_name: + 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) + _, features_test, _, target_test = train_test_split( + features, target, test_size=0.2) + if model is not None: + accuracy = calculate_model_accuracy( + model, features_test, target_test) + + 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) + + # store the model for later use + if model_name: + 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): + """ + Use the provided model to predict the values for the new feature set + Input: + @param model: The pretrained model + @features_col: A list of features to use in the + model prediction (list of column names) + @target_query: The query to run to obtain the data to predict + on and the cartodb_ids associated with it. + """ + + batch_size = 1000 + params = {"subquery": target_query, + "feature_columns": feature_columns} + + results = [] + cursors = self.data_provider.get_segmentation_predict_data(params) + ''' + cursors = [{'features': [[m1[0],m2[0],m3[0]],[m1[1],m2[1],m3[1]], + [m1[2],m2[2],m3[2]]]}] + ''' + + while True: + rows = cursors.fetch(batch_size) + if not rows: + break + batch = np.row_stack([np.array(row['features']) + for row in rows]).astype(float) + + batch = replace_nan_with_mean(batch, feature_means)[0] + prediction = model.predict(batch) + results.append(prediction) + + # NOTE: we removed the cartodb_ids calculation in here + return np.concatenate(results) + + def clean_data(self, query, variable, feature_columns): + """ + Add docstring + """ + params = {"subquery": query, + "target": variable, + "features": feature_columns} + + data = self.data_provider.get_segmentation_model_data(params) + + ''' + data = [{'target': [2.9, 4.9, 4, 5, 6], + 'feature1': [1,2,3,4], 'feature2' : [2,3,4,5]}] + ''' + + # extract target data from data_provider object + target = np.array(data[0]['target'], dtype=float) + + # put n feature data arrays into an n x m array of arrays + features = np.column_stack([np.array(data[0][col]) + for col in feature_columns]).astype(float) + + features, feature_means = replace_nan_with_mean(features) + target, target_mean = replace_nan_with_mean(target) + return target, features, target_mean, feature_means + + +def replace_nan_with_mean(array, means=None): + """ + Input: + @param array: an array of floats which may have null-valued + entries + Output: + array with nans filled in with the mean of the dataset + """ + + # returns an array of rows and column indices + nanvals = np.isnan(array) + indices = np.where(nanvals) + + def loops(array, axis): + try: + return np.shape(array)[axis] + except IndexError: + return 1 + ran = loops(array, 1) + + if means is None: + means = {} + + if ran == 1: + array = np.array(array) + means[0] = np.mean(array[~np.isnan(array)]) + for row in zip(*indices): + array[row] = means[0] + else: + for col in range(ran): + means[col] = np.mean(array[~np.isnan(array[:, col]), col]) + for row, col in zip(*indices): + array[row, col] = means[col] + else: + if ran == 1: + for row in zip(*indices): + array[row] = means[0] + else: + for row, col in zip(*indices): + array[row, col] = means[col] + + return array, means + + +def train_model(target, features, model_params, test_split): + """ + Train the Gradient Boosting model on the provided data to calculate + the accuracy of the model + Input: + @param target: 1D Array of the variable that the model is to be + trained to predict + @param features: 2D Array NSamples *NFeatures to use in training + the model + @param model_params: A dictionary of model parameters, the full + specification can be found on the + scikit learn page for [GradientBoostingRegressor] + (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) + @parma test_split: The fraction of the data to be withheld for + testing the model / calculating the accuray + """ + features_train, features_test, \ + target_train, target_test = train_test_split(features, target, + test_size=test_split) + model = GradientBoostingRegressor(**model_params) + model.fit(features_train, target_train) + accuracy = calculate_model_accuracy(model, features_test, target_test) + return model, accuracy + + +def calculate_model_accuracy(model, features_test, target_test): + """ + Calculate the mean squared error of the model prediction + Input: + @param model: model trained from input features + @param features_test: test features set to make prediction from + @param target_test: test target set to compare predictions to + Output: + mean squared error of the model prection compared target_test + """ + prediction = model.predict(features_test) + return metrics.mean_squared_error(prediction, target_test) diff --git a/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/__init__.py b/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/__init__.py new file mode 100644 index 0000000..a439286 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/__init__.py @@ -0,0 +1,2 @@ +"""Import all functions from clustering libraries.""" +from markov import * diff --git a/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/markov.py b/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/markov.py new file mode 100644 index 0000000..20daaf1 --- /dev/null +++ b/release/python/0.9.4/crankshaft/crankshaft/space_time_dynamics/markov.py @@ -0,0 +1,194 @@ +""" +Spatial dynamics measurements using Spatial Markov +""" + +# TODO: remove all plpy dependencies + +import numpy as np +import pysal as ps +import plpy +import crankshaft.pysal_utils as pu +from crankshaft.analysis_data_provider import AnalysisDataProvider + + +class Markov(object): + def __init__(self, data_provider=None): + if data_provider is None: + self.data_provider = AnalysisDataProvider() + else: + self.data_provider = data_provider + + def spatial_trend(self, subquery, time_cols, num_classes=7, + w_type='knn', num_ngbrs=5, permutations=0, + geom_col='the_geom', id_col='cartodb_id'): + """ + Predict the trends of a unit based on: + 1. history of its transitions to different classes (e.g., 1st + quantile -> 2nd quantile) + 2. average class of its neighbors + + Inputs: + @param subquery string: e.g., SELECT the_geom, cartodb_id, + interesting_time_column FROM table_name + @param time_cols list of strings: list of strings of column names + @param num_classes (optional): number of classes to break + distribution of values into. Currently uses quantile bins. + @param w_type string (optional): weight type ('knn' or 'queen') + @param num_ngbrs int (optional): number of neighbors (if knn type) + @param permutations int (optional): number of permutations for test + stats + @param geom_col string (optional): name of column which contains + the geometries + @param id_col string (optional): name of column which has the ids + of the table + + Outputs: + @param trend_up float: probablity that a geom will move to a higher + class + @param trend_down float: probablity that a geom will move to a + lower class + @param trend float: (trend_up - trend_down) / trend_static + @param volatility float: a measure of the volatility based on + probability stddev(prob array) + """ + + if len(time_cols) < 2: + plpy.error('More than one time column needs to be passed') + + params = {"id_col": id_col, + "time_cols": time_cols, + "geom_col": geom_col, + "subquery": subquery, + "num_ngbrs": num_ngbrs} + + result = self.data_provider.get_markov(w_type, params) + + # build weight + weights = pu.get_weight(result, w_type) + weights.transform = 'r' + + # prep time data + t_data = get_time_data(result, time_cols) + + sp_markov_result = ps.Spatial_Markov(t_data, + weights, + k=num_classes, + fixed=False, + permutations=permutations) + + # get lag classes + lag_classes = ps.Quantiles( + ps.lag_spatial(weights, t_data[:, -1]), + k=num_classes).yb + + # look up probablity distribution for each unit according to class and + # lag class + prob_dist = get_prob_dist(sp_markov_result.P, + lag_classes, + sp_markov_result.classes[:, -1]) + + # find the ups and down and overall distribution of each cell + trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1]) + + # output the results + return zip(trend, trend_up, trend_down, volatility, weights.id_order) + + + +def get_time_data(markov_data, time_cols): + """ + Extract the time columns and bin appropriately + """ + num_attrs = len(time_cols) + return np.array([[x['attr' + str(i)] for x in markov_data] + for i in range(1, num_attrs+1)], dtype=float).transpose() + + +# not currently used +def rebin_data(time_data, num_time_per_bin): + """ + Convert an n x l matrix into an (n/m) x l matrix where the values are + reduced (averaged) for the intervening states: + 1 2 3 4 1.5 3.5 + 5 6 7 8 -> 5.5 7.5 + 9 8 7 6 8.5 6.5 + 5 4 3 2 4.5 2.5 + + if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix. + + This process effectively resamples the data at a longer time span n + units longer than the input data. + For cases when there is a remainder (remainder(5/3) = 2), the remaining + two columns are binned together as the last time period, while the + first three are binned together for the first period. + + Input: + @param time_data n x l ndarray: measurements of an attribute at + different time intervals + @param num_time_per_bin int: number of columns to average into a new + column + Output: + ceil(n / m) x l ndarray of resampled time series + """ + + if time_data.shape[1] % num_time_per_bin == 0: + # if fit is perfect, then use it + n_max = time_data.shape[1] / num_time_per_bin + else: + # fit remainders into an additional column + n_max = time_data.shape[1] / num_time_per_bin + 1 + + return np.array( + [time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1) + for i in range(n_max)]).T + + +def get_prob_dist(transition_matrix, lag_indices, unit_indices): + """ + Given an array of transition matrices, look up the probability + associated with the arrangements passed + + Input: + @param transition_matrix ndarray[k,k,k]: + @param lag_indices ndarray: + @param unit_indices ndarray: + + Output: + Array of probability distributions + """ + + return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] + for i in range(len(lag_indices))]) + + +def get_prob_stats(prob_dist, unit_indices): + """ + get the statistics of the probability distributions + + Outputs: + @param trend_up ndarray(float): sum of probabilities for upward + movement (relative to the unit index of that prob) + @param trend_down ndarray(float): sum of probabilities for downward + movement (relative to the unit index of that prob) + @param trend ndarray(float): difference of upward and downward + movements + """ + + num_elements = len(unit_indices) + trend_up = np.empty(num_elements, dtype=float) + trend_down = np.empty(num_elements, dtype=float) + trend = np.empty(num_elements, dtype=float) + + for i in range(num_elements): + trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum() + trend_down[i] = prob_dist[i, :unit_indices[i]].sum() + if prob_dist[i, unit_indices[i]] > 0.0: + trend[i] = (trend_up[i] - trend_down[i]) / ( + prob_dist[i, unit_indices[i]]) + else: + trend[i] = None + + # calculate volatility of distribution + volatility = prob_dist.std(axis=1) + + return trend_up, trend_down, trend, volatility diff --git a/release/python/0.9.4/crankshaft/requirements.txt b/release/python/0.9.4/crankshaft/requirements.txt new file mode 100644 index 0000000..88c0a9e --- /dev/null +++ b/release/python/0.9.4/crankshaft/requirements.txt @@ -0,0 +1,5 @@ +joblib==0.8.3 +numpy==1.6.1 +scipy==0.14.0 +pysal==1.14.3 +scikit-learn==0.14.1 diff --git a/release/python/0.9.4/crankshaft/setup.py b/release/python/0.9.4/crankshaft/setup.py new file mode 100644 index 0000000..2f04ad9 --- /dev/null +++ b/release/python/0.9.4/crankshaft/setup.py @@ -0,0 +1,49 @@ + +""" +CartoDB Spatial Analysis Python Library +See: +https://github.com/CartoDB/crankshaft +""" + +from setuptools import setup, find_packages + +setup( + name='crankshaft', + + version='0.0.0', + + 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' +) diff --git a/release/python/0.9.4/crankshaft/setup.py-r b/release/python/0.9.4/crankshaft/setup.py-r new file mode 100644 index 0000000..2f04ad9 --- /dev/null +++ b/release/python/0.9.4/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' +) diff --git a/release/python/0.9.4/crankshaft/test/fixtures/data.json b/release/python/0.9.4/crankshaft/test/fixtures/data.json new file mode 100644 index 0000000..2fbad72 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/fixtures/data.json @@ -0,0 +1 @@ +[{"ids": [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, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]}] \ No newline at end of file diff --git a/release/python/0.9.4/crankshaft/test/fixtures/getis.json b/release/python/0.9.4/crankshaft/test/fixtures/getis.json new file mode 100644 index 0000000..02566fc --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/fixtures/getis.json @@ -0,0 +1 @@ +[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 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[[4.1332723757858041]], [[3.9190386346055655]], [[3.3570061842111683]], [[4.3000992650570122]], [[3.2744982636432503]], [[3.4530052231252344]], [[2.9362664904878524]], [[4.5160823458017774]], [[3.2157763779380728]], [[2.1699109068357223]]] \ No newline at end of file diff --git a/release/python/0.9.4/crankshaft/test/helper.py b/release/python/0.9.4/crankshaft/test/helper.py new file mode 100644 index 0000000..7d28b94 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/helper.py @@ -0,0 +1,13 @@ +import unittest + +from mock_plpy import MockPlPy +plpy = MockPlPy() + +import sys +sys.modules['plpy'] = plpy + +import os + +def fixture_file(name): + dir = os.path.dirname(os.path.realpath(__file__)) + return os.path.join(dir, 'fixtures', name) diff --git a/release/python/0.9.4/crankshaft/test/mock_plpy.py b/release/python/0.9.4/crankshaft/test/mock_plpy.py new file mode 100644 index 0000000..95c8989 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/mock_plpy.py @@ -0,0 +1,58 @@ +import re + + +class MockCursor: + def __init__(self, data): + self.cursor_pos = 0 + self.data = data + + def fetch(self, batch_size): + batch = self.data[self.cursor_pos:self.cursor_pos + batch_size] + self.cursor_pos += batch_size + return batch + + +class MockPlPy: + def __init__(self): + self._reset() + + def _reset(self): + self.infos = [] + self.notices = [] + self.debugs = [] + self.logs = [] + self.warnings = [] + self.errors = [] + self.fatals = [] + self.executes = [] + self.results = [] + self.prepares = [] + self.results = [] + + def _define_result(self, query, result): + pattern = re.compile(query, re.IGNORECASE | re.MULTILINE) + self.results.append([pattern, result]) + + def notice(self, msg): + self.notices.append(msg) + + def debug(self, msg): + self.notices.append(msg) + + def info(self, msg): + self.infos.append(msg) + + def error(self, msg): + self.infos.append(msg) + self.notices.append(msg) + + def cursor(self, query): + data = self.execute(query) + return MockCursor(data) + + # TODO: additional arguments + def execute(self, query): + for result in self.results: + if result[0].match(query): + return result[1] + return [] diff --git a/release/python/0.9.4/crankshaft/test/test_clustering_getis.py b/release/python/0.9.4/crankshaft/test/test_clustering_getis.py new file mode 100644 index 0000000..61add11 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_clustering_getis.py @@ -0,0 +1,78 @@ +import unittest +import numpy as np + +from helper import fixture_file + +from crankshaft.clustering import Getis +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json +from crankshaft.analysis_data_provider import AnalysisDataProvider + +# Fixture files produced as follows +# +# import pysal as ps +# import numpy as np +# import random +# +# # setup variables +# f = ps.open(ps.examples.get_path("stl_hom.dbf")) +# y = np.array(f.by_col['HR8893']) +# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp")) +# +# out_queen = [{"id": index + 1, +# "neighbors": [x+1 for x in w_queen.neighbors[index]], +# "value": val} for index, val in enumerate(y)] +# +# with open('neighbors_queen_getis.json', 'w') as f: +# f.write(str(out_queen)) +# +# random.seed(1234) +# np.random.seed(1234) +# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True, +# permutations=999) +# +# with open('getis_queen.json', 'w') as f: +# f.write(str(zip(lgstar_queen.z_sim, +# lgstar_queen.p_sim, lgstar_queen.p_z_sim))) + + +class FakeDataProvider(AnalysisDataProvider): + def __init__(self, mock_data): + self.mock_result = mock_data + + def get_getis(self, w_type, param): + return self.mock_result + + +class GetisTest(unittest.TestCase): + """Testing class for Getis-Ord's G* funtion + This test replicates the work done in PySAL documentation: + https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g + """ + + def setUp(self): + # load raw data for analysis + self.neighbors_data = json.loads( + open(fixture_file('neighbors_getis.json')).read()) + + # load pre-computed/known values + self.getis_data = json.loads( + open(fixture_file('getis.json')).read()) + + def test_getis_ord(self): + """Test Getis-Ord's G*""" + data = [{'id': d['id'], + 'attr1': d['value'], + 'neighbors': d['neighbors']} for d in self.neighbors_data] + + random_seeds.set_random_seeds(1234) + getis = Getis(FakeDataProvider(data)) + + result = getis.getis_ord('subquery', 'value', + 'queen', None, 999, 'the_geom', + 'cartodb_id') + result = [(row[0], row[1]) for row in result] + expected = np.array(self.getis_data)[:, 0:2] + for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected): + self.assertAlmostEqual(res_z, exp_z, delta=1e-2) diff --git a/release/python/0.9.4/crankshaft/test/test_clustering_kmeans.py b/release/python/0.9.4/crankshaft/test/test_clustering_kmeans.py new file mode 100644 index 0000000..c118d34 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_clustering_kmeans.py @@ -0,0 +1,87 @@ +import unittest +import numpy as np + + +from helper import fixture_file +from crankshaft.clustering import Kmeans +from crankshaft.analysis_data_provider import AnalysisDataProvider +import crankshaft.clustering as cc +from crankshaft import random_seeds + +import json +from collections import OrderedDict + + +class FakeDataProvider(AnalysisDataProvider): + def __init__(self, mocked_result): + self.mocked_result = mocked_result + + def get_spatial_kmeans(self, query): + return self.mocked_result + + def get_nonspatial_kmeans(self, query): + return self.mocked_result + + +class KMeansTest(unittest.TestCase): + """Testing class for k-means spatial""" + + def setUp(self): + 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 = [{'xs': d['xs'], + 'ys': d['ys'], + 'ids': d['ids']} for d in self.cluster_data] + + random_seeds.set_random_seeds(1234) + kmeans = Kmeans(FakeDataProvider(data)) + clusters = kmeans.spatial('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) + + +class KMeansNonspatialTest(unittest.TestCase): + """Testing class for k-means non-spatial""" + + def setUp(self): + self.params = {"subquery": "SELECT * FROM TABLE", + "n_clusters": 5} + + def test_kmeans_nonspatial(self): + """ + test for k-means non-spatial + """ + # data from: + # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn-cluster-kmeans + data_raw = [OrderedDict([("arr_col1", [1, 1, 1, 4, 4, 4]), + ("arr_col2", [2, 4, 0, 2, 4, 0]), + ("rowid", [1, 2, 3, 4, 5, 6])])] + + random_seeds.set_random_seeds(1234) + kmeans = Kmeans(FakeDataProvider(data_raw)) + clusters = kmeans.nonspatial('subquery', ['col1', 'col2'], 2) + + cl1 = clusters[0][0] + cl2 = clusters[3][0] + + for idx, val in enumerate(clusters): + if idx < 3: + self.assertEqual(val[0], cl1) + else: + self.assertEqual(val[0], cl2) + + # raises exception for no data + with self.assertRaises(Exception): + kmeans = Kmeans(FakeDataProvider([])) + kmeans.nonspatial('subquery', ['col1', 'col2'], 2) diff --git a/release/python/0.9.4/crankshaft/test/test_clustering_moran.py b/release/python/0.9.4/crankshaft/test/test_clustering_moran.py new file mode 100644 index 0000000..a91c046 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_clustering_moran.py @@ -0,0 +1,112 @@ +import unittest +import numpy as np + +from helper import fixture_file +from crankshaft.clustering import Moran +from crankshaft.analysis_data_provider import AnalysisDataProvider +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +import json +from collections import OrderedDict + + +class FakeDataProvider(AnalysisDataProvider): + def __init__(self, mock_data): + self.mock_result = mock_data + + def get_moran(self, w_type, params): + return self.mock_result + + +class MoranTest(unittest.TestCase): + """Testing class for Moran's I functions""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "attr1": "andy", + "attr2": "jay_z", + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.params_markov = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", + "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads( + open(fixture_file('neighbors.json')).read()) + self.moran_data = json.loads( + open(fixture_file('moran.json')).read()) + + def test_map_quads(self): + """Test map_quads""" + from crankshaft.clustering import map_quads + self.assertEqual(map_quads(1), 'HH') + self.assertEqual(map_quads(2), 'LH') + self.assertEqual(map_quads(3), 'LL') + self.assertEqual(map_quads(4), 'HL') + self.assertEqual(map_quads(33), None) + self.assertEqual(map_quads('andy'), None) + + def test_quad_position(self): + """Test lisa_sig_vals""" + from crankshaft.clustering import quad_position + + quads = np.array([1, 2, 3, 4], np.int) + + ans = np.array(['HH', 'LH', 'LL', 'HL']) + test_ans = quad_position(quads) + + self.assertTrue((test_ans == ans).all()) + + def test_local_stat(self): + """Test Moran's I local""" + data = [OrderedDict([('id', d['id']), + ('attr1', d['value']), + ('neighbors', d['neighbors'])]) + for d in self.neighbors_data] + + moran = Moran(FakeDataProvider(data)) + random_seeds.set_random_seeds(1234) + result = moran.local_stat('subquery', 'value', + 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[6]) for row in result] + 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) + self.assertEqual(res_quad, exp_quad) + + def test_moran_local_rate(self): + """Test Moran's I rate""" + data = [{'id': d['id'], + 'attr1': d['value'], + 'attr2': 1, + 'neighbors': d['neighbors']} for d in self.neighbors_data] + + random_seeds.set_random_seeds(1234) + moran = Moran(FakeDataProvider(data)) + result = moran.local_rate_stat('subquery', 'numerator', 'denominator', + 'knn', 5, 99, 'the_geom', 'cartodb_id') + result = [(row[0], row[6]) for row in result] + + 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) + + def test_moran(self): + """Test Moran's I global""" + data = [{'id': d['id'], + 'attr1': d['value'], + 'neighbors': d['neighbors']} for d in self.neighbors_data] + random_seeds.set_random_seeds(1235) + moran = Moran(FakeDataProvider(data)) + result = moran.global_stat('table', 'value', + 'knn', 5, 99, 'the_geom', + 'cartodb_id') + + result_moran = result[0][0] + expected_moran = np.array([row[0] for row in self.moran_data]).mean() + self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2) diff --git a/release/python/0.9.4/crankshaft/test/test_pysal_utils.py b/release/python/0.9.4/crankshaft/test/test_pysal_utils.py new file mode 100644 index 0000000..be45164 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_pysal_utils.py @@ -0,0 +1,83 @@ +import unittest + +import crankshaft.pysal_utils as pu +from crankshaft import random_seeds +from collections import OrderedDict + + +class PysalUtilsTest(unittest.TestCase): + """Testing class for utility functions related to PySAL integrations""" + + def setUp(self): + self.params1 = OrderedDict([("id_col", "cartodb_id"), + ("attr1", "andy"), + ("attr2", "jay_z"), + ("subquery", "SELECT * FROM a_list"), + ("geom_col", "the_geom"), + ("num_ngbrs", 321)]) + + self.params2 = OrderedDict([("id_col", "cartodb_id"), + ("numerator", "price"), + ("denominator", "sq_meters"), + ("subquery", "SELECT * FROM pecan"), + ("geom_col", "the_geom"), + ("num_ngbrs", 321)]) + + self.params3 = OrderedDict([("id_col", "cartodb_id"), + ("numerator", "sq_meters"), + ("denominator", "price"), + ("subquery", "SELECT * FROM pecan"), + ("geom_col", "the_geom"), + ("num_ngbrs", 321)]) + + self.params_array = {"id_col": "cartodb_id", + "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + + def test_query_attr_select(self): + """Test query_attr_select""" + + ans1 = ("i.\"andy\"::numeric As attr1, " + "i.\"jay_z\"::numeric As attr2, ") + + ans2 = ("i.\"price\"::numeric As attr1, " + "i.\"sq_meters\"::numeric As attr2, ") + + ans3 = ("i.\"sq_meters\"::numeric As attr1, " + "i.\"price\"::numeric As attr2, ") + + ans_array = ("i.\"_2013_dec\"::numeric As attr1, " + "i.\"_2014_jan\"::numeric As attr2, " + "i.\"_2014_feb\"::numeric As attr3, ") + + self.assertEqual(pu.query_attr_select(self.params1), ans1) + self.assertEqual(pu.query_attr_select(self.params2), ans2) + self.assertEqual(pu.query_attr_select(self.params3), ans3) + self.assertEqual(pu.query_attr_select(self.params_array), ans_array) + + def test_query_attr_where(self): + """Test pu.query_attr_where""" + + ans1 = ("idx_replace.\"andy\" IS NOT NULL AND " + "idx_replace.\"jay_z\" IS NOT NULL") + + ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND " + "idx_replace.\"_2014_jan\" IS NOT NULL AND " + "idx_replace.\"_2014_feb\" IS NOT NULL") + + self.assertEqual(pu.query_attr_where(self.params1), ans1) + self.assertEqual(pu.query_attr_where(self.params_array), ans_array) + + def test_get_attributes(self): + """Test get_attributes""" + + # need to add tests + + self.assertEqual(True, True) + + def test_get_weight(self): + """Test get_weight""" + + self.assertEqual(True, True) diff --git a/release/python/0.9.4/crankshaft/test/test_regression_gwr.py b/release/python/0.9.4/crankshaft/test/test_regression_gwr.py new file mode 100644 index 0000000..57cd952 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_regression_gwr.py @@ -0,0 +1,130 @@ +import unittest +import json +import numpy as np + + +from crankshaft import random_seeds +from helper import fixture_file +from crankshaft.regression import GWR +from crankshaft.analysis_data_provider import AnalysisDataProvider + + +class FakeDataProvider(AnalysisDataProvider): + def __init__(self, mocked_result): + self.mocked_result = mocked_result + + def get_gwr(self, params): + return self.mocked_result + + def get_gwr_predict(self, params): + return self.mocked_result + + +class GWRTest(unittest.TestCase): + """Testing class for geographically weighted regression (gwr)""" + + def setUp(self): + """ + fixture packed from canonical GWR georgia dataset using the + following query: + SELECT array_agg(x) As x, + array_agg(y) As y, + array_agg(pctbach) As dep_var, + array_agg(pctrural) As attr1, + array_agg(pctpov) As attr2, + array_agg(pctblack) As attr3, + array_agg(areakey) As rowid + FROM g_utm + WHERE pctbach is not NULL AND + pctrural IS NOT NULL AND + pctpov IS NOT NULL AND + pctblack IS NOT NULL + """ + import copy + # data packed from https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/GData_utm.csv + self.data = json.loads( + open(fixture_file('gwr_packed_data.json')).read()) + + # data packed from https://github.com/TaylorOshan/pysal/blob/a44c5541e2e0d10a99ff05edc1b7f81b70f5a82f/pysal/examples/georgia/georgia_BS_NN_listwise.csv + self.knowns = json.loads( + open(fixture_file('gwr_packed_knowns.json')).read()) + + # data for GWR prediction + self.data_predict = copy.deepcopy(self.data) + self.ids_of_unknowns = [13083, 13009, 13281, 13115, 13247, 13169] + self.idx_ids_of_unknowns = [self.data_predict[0]['rowid'].index(idx) + for idx in self.ids_of_unknowns] + + for idx in self.idx_ids_of_unknowns: + self.data_predict[0]['dep_var'][idx] = None + + self.predicted_knowns = {13009: 10.879, + 13083: 4.5259, + 13115: 9.4022, + 13169: 6.0793, + 13247: 8.1608, + 13281: 13.886} + + # params, with ind_vars in same ordering as query above + self.params = {'subquery': 'select * from table', + 'dep_var': 'pctbach', + 'ind_vars': ['pctrural', 'pctpov', 'pctblack'], + 'bw': 90.000, + 'fixed': False, + 'geom_col': 'the_geom', + 'id_col': 'areakey'} + + def test_gwr(self): + """ + """ + gwr = GWR(FakeDataProvider(self.data)) + gwr_resp = gwr.gwr(self.params['subquery'], + self.params['dep_var'], + self.params['ind_vars'], + bw=self.params['bw'], + fixed=self.params['fixed']) + + # unpack response + coeffs, stand_errs, t_vals, t_vals_filtered, predicteds, \ + residuals, r_squareds, bws, rowids = zip(*gwr_resp) + + # prepare for comparision + coeff_known_pctpov = self.knowns['est_pctpov'] + tval_known_pctblack = self.knowns['t_pctrural'] + pctpov_se = self.knowns['se_pctpov'] + ids = self.knowns['area_key'] + resp_idx = None + + # test pctpov coefficient estimates + for idx, val in enumerate(coeff_known_pctpov): + resp_idx = rowids.index(ids[idx]) + 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.assertAlmostEquals(val, + json.loads(t_vals[resp_idx])['pctrural'], + places=4) + + def test_gwr_predict(self): + """Testing for GWR_Predict""" + gwr = GWR(FakeDataProvider(self.data_predict)) + gwr_resp = gwr.gwr_predict(self.params['subquery'], + self.params['dep_var'], + self.params['ind_vars'], + bw=self.params['bw'], + fixed=self.params['fixed']) + + # unpack response + coeffs, stand_errs, t_vals, \ + r_squareds, predicteds, rowid = zip(*gwr_resp) + threshold = 0.01 + + for i, idx in enumerate(self.idx_ids_of_unknowns): + + known_val = self.predicted_knowns[rowid[i]] + predicted_val = predicteds[i] + test_val = abs(known_val - predicted_val) / known_val + self.assertTrue(test_val < threshold) diff --git a/release/python/0.9.4/crankshaft/test/test_segmentation.py b/release/python/0.9.4/crankshaft/test/test_segmentation.py new file mode 100644 index 0000000..150dbe7 --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_segmentation.py @@ -0,0 +1,139 @@ +"""Tests for segmentation functionality""" +import unittest +import json +from collections import OrderedDict + +import numpy as np + +from crankshaft.analysis_data_provider import AnalysisDataProvider +from crankshaft.segmentation import Segmentation +from helper import fixture_file +from mock_plpy import MockCursor + + +class RawDataProvider(AnalysisDataProvider): + """Data Provider to overwrite the default SQL provider""" + def __init__(self, data, model, predict): + self.data = data + self.model = model + self.predict = predict + + def get_segmentation_data(self, params): # pylint: disable=unused-argument + """return data""" + return self.data + + def get_segmentation_model_data(self, params): # pylint: disable=W0613 + """return model data""" + return self.model + + def get_segmentation_predict_data(self, params): # pylint: disable=W0613 + """return predict data""" + return self.predict + + +class SegmentationTest(unittest.TestCase): + """Testing class for Segmentation functions""" + + def setUp(self): + self.params = { + "query": 'SELECT * FROM segmentation_data', + "variable": 'price', + "feature_columns": ['m1', 'm2', 'm3', 'm4', 'm5', 'm6'], + "target_query": 'SELECT * FROM segmentation_result', + "id_col": 'cartodb_id', + "model_params": { + 'n_estimators': 1200, + 'max_depth': 3, + 'subsample': 0.5, + 'learning_rate': 0.01, + 'min_samples_leaf': 1 + } + } + self.model_data = json.loads( + open(fixture_file('model_data.json')).read()) + self.data = json.loads( + open(fixture_file('data.json')).read()) + self.predict_data = json.loads( + open(fixture_file('predict_data.json')).read()) + self.result_seg = json.loads( + open(fixture_file('segmentation_result.json')).read()) + self.true_result = json.loads( + open(fixture_file('true_result.json')).read()) + + def test_replace_nan_with_mean(self): + """test segmentation.test_replace_nan_with_mean""" + from crankshaft.segmentation import replace_nan_with_mean + 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.assertItemsEqual(result, expectation) + + def test_create_and_predict_segment(self): + """test segmentation.test_create_and_predict""" + from crankshaft.segmentation import replace_nan_with_mean + results = [] + feature_columns = ['m1', 'm2'] + feat = np.column_stack([np.array(self.model_data[0][col]) + for col in feature_columns]).astype(float) + feature_means = replace_nan_with_mean(feat)[1] + + # data_model is of the form: + # [OrderedDict([('target', target), + # ('features', feat), + # ('target_mean', target_mean), + # ('feature_means', feature_means), + # ('feature_columns', feature_columns)])] + data_model = self.model_data + cursor = self.predict_data + batch = [] + + batches = np.row_stack([np.array(row['features']) + for row in cursor]).astype(float) + batches = replace_nan_with_mean(batches, feature_means)[0] + batch.append(batches) + + data_predict = [OrderedDict([('features', d['features']), + ('batch', batch)]) + for d in self.predict_data] + data_predict = MockCursor(data_predict) + + model_parameters = { + 'n_estimators': 1200, + 'max_depth': 3, + 'subsample': 0.5, + 'learning_rate': 0.01, + 'min_samples_leaf': 1 + } + data = [OrderedDict([('ids', d['ids'])]) + for d in self.data] + + seg = Segmentation(RawDataProvider(data, data_model, + data_predict)) + + result = seg.create_and_predict_segment( + 'SELECT * FROM segmentation_test', + 'x_value', + ['m1', 'm2'], + 'SELECT * FROM segmentation_result', + model_parameters, + id_col='cartodb_id') + results = [(row[1], row[2]) for row in result] + 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] + + # test values + for (res_pre, _), (exp_pre, _) in zipped_values: + diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre]) + self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff)) + diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre]) + self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff)) + prediction = [r[0] for r in results] + + accuracy = np.sqrt(np.mean( + (np.array(prediction) - np.array(pre_res))**2 + )) + + self.assertEqual(len(results), len(self.result_seg)) + self.assertTrue(accuracy < 0.3 * np.mean(pre_res)) + self.assertTrue(results[0][1] < 0.01) diff --git a/release/python/0.9.4/crankshaft/test/test_space_time_dynamics.py b/release/python/0.9.4/crankshaft/test/test_space_time_dynamics.py new file mode 100644 index 0000000..d14563e --- /dev/null +++ b/release/python/0.9.4/crankshaft/test/test_space_time_dynamics.py @@ -0,0 +1,349 @@ +import unittest +import numpy as np + +import unittest + + +from helper import fixture_file + +from crankshaft.space_time_dynamics import Markov +import crankshaft.space_time_dynamics as std +from crankshaft import random_seeds +from crankshaft.analysis_data_provider import AnalysisDataProvider +import json + + +class FakeDataProvider(AnalysisDataProvider): + def __init__(self, data): + self.mock_result = data + + def get_markov(self, w_type, params): + return self.mock_result + + +class SpaceTimeTests(unittest.TestCase): + """Testing class for Markov Functions.""" + + def setUp(self): + self.params = {"id_col": "cartodb_id", + "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'], + "subquery": "SELECT * FROM a_list", + "geom_col": "the_geom", + "num_ngbrs": 321} + self.neighbors_data = json.loads( + open(fixture_file('neighbors_markov.json')).read()) + self.markov_data = json.loads(open(fixture_file('markov.json')).read()) + + self.time_data = np.array([i * np.ones(10, dtype=float) + for i in range(10)]).T + + self.transition_matrix = np.array([ + [[0.96341463, 0.0304878, 0.00609756, 0., 0.], + [0.06040268, 0.83221477, 0.10738255, 0., 0.], + [0., 0.14, 0.74, 0.12, 0.], + [0., 0.03571429, 0.32142857, 0.57142857, 0.07142857], + [0., 0., 0., 0.16666667, 0.83333333]], + [[0.79831933, 0.16806723, 0.03361345, 0., 0.], + [0.0754717, 0.88207547, 0.04245283, 0., 0.], + [0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.], + [0., 0., 0.06372549, 0.90196078, 0.03431373], + [0., 0., 0., 0.19444444, 0.80555556]], + [[0.84693878, 0.15306122, 0., 0., 0.], + [0.08133971, 0.78947368, 0.1291866, 0., 0.], + [0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135], + [0., 0., 0.09411765, 0.87058824, 0.03529412], + [0., 0., 0., 0.10204082, 0.89795918]], + [[0.8852459, 0.09836066, 0., 0.01639344, 0.], + [0.03875969, 0.81395349, 0.13953488, 0., 0.00775194], + [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505], + [0., 0.02339181, 0.12865497, 0.75438596, 0.09356725], + [0., 0., 0., 0.09661836, 0.90338164]], + [[0.33333333, 0.66666667, 0., 0., 0.], + [0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.], + [0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.], + [0., 0.01036269, 0.06217617, 0.89637306, 0.03108808], + [0., 0., 0., 0.02352941, 0.97647059]]] + ) + + def test_spatial_markov(self): + """Test Spatial Markov.""" + data = [{'id': d['id'], + 'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009'], + 'neighbors': d['neighbors']} for d in self.neighbors_data] + # print(str(data[0])) + markov = Markov(FakeDataProvider(data)) + random_seeds.set_random_seeds(1234) + + result = markov.spatial_trend('subquery', + ['y1995', 'y1996', 'y1997', 'y1998', + 'y1999', 'y2000', 'y2001', 'y2002', + 'y2003', 'y2004', 'y2005', 'y2006', + 'y2007', 'y2008', 'y2009'], + 5, 'knn', 5, 0, 'the_geom', + 'cartodb_id') + + self.assertTrue(result is not None) + result = [(row[0], row[1], row[2], row[3], row[4]) for row in result] + print result[0] + expected = self.markov_data + for ([res_trend, res_up, res_down, res_vol, res_id], + [exp_trend, exp_up, exp_down, exp_vol, exp_id] + ) in zip(result, expected): + self.assertAlmostEqual(res_trend, exp_trend) + + def test_get_time_data(self): + """Test get_time_data""" + data = [{'attr1': d['y1995'], + 'attr2': d['y1996'], + 'attr3': d['y1997'], + 'attr4': d['y1998'], + 'attr5': d['y1999'], + 'attr6': d['y2000'], + 'attr7': d['y2001'], + 'attr8': d['y2002'], + 'attr9': d['y2003'], + 'attr10': d['y2004'], + 'attr11': d['y2005'], + 'attr12': d['y2006'], + 'attr13': d['y2007'], + 'attr14': d['y2008'], + 'attr15': d['y2009']} for d in self.neighbors_data] + + result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', + 'y1999', 'y2000', 'y2001', 'y2002', + 'y2003', 'y2004', 'y2005', 'y2006', + 'y2007', 'y2008', 'y2009']) + + # expected was prepared from PySAL example: + # f = ps.open(ps.examples.get_path("usjoin.csv")) + # pci = np.array([f.by_col[str(y)] + # for y in range(1995, 2010)]).transpose() + # rpci = pci / (pci.mean(axis = 0)) + + expected = np.array( + [[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, + 0.83271652, 0.83786314, 0.85012593, 0.85509656, 0.86416612, + 0.87119375, 0.86302631, 0.86148267, 0.86252252, 0.86746356], + [0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, + 0.90746978, 0.89830489, 0.89431991, 0.88924794, 0.89815176, + 0.91832091, 0.91706054, 0.90139505, 0.87897455, 0.86216858], + [0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, + 0.78964559, 0.80584442, 0.8084998, 0.82258551, 0.82668196, + 0.82373724, 0.81814804, 0.83675961, 0.83574199, 0.84647177], + [1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, + 1.14506948, 1.12151133, 1.11160697, 1.10888621, 1.11399806, + 1.12168029, 1.13164797, 1.12958508, 1.11371818, 1.09936775], + [1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, + 1.16898201, 1.17212488, 1.14752303, 1.11843284, 1.11024964, + 1.11943471, 1.11736468, 1.10863242, 1.09642516, 1.07762337], + [1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, + 1.44184737, 1.44782832, 1.41978227, 1.39092208, 1.4059372, + 1.40788646, 1.44052766, 1.45241216, 1.43306098, 1.4174431], + [1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, + 1.10888138, 1.11856629, 1.13062931, 1.11944984, 1.12446239, + 1.11671008, 1.10880034, 1.08401709, 1.06959206, 1.07875225], + [1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, + 0.99854316, 0.9880258, 0.99669587, 0.99327676, 1.01400905, + 1.03176742, 1.040511, 1.01749645, 0.9936394, 0.98279746], + [0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, + 0.99127006, 0.97925917, 0.9683482, 0.95335147, 0.93694787, + 0.94308213, 0.92232874, 0.91284091, 0.89689833, 0.88928858], + [0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, + 0.8578708, 0.86036185, 0.86107306, 0.8500772, 0.86981998, + 0.86837929, 0.87204141, 0.86633032, 0.84946077, 0.83287146], + [1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, + 1.14450183, 1.12349752, 1.12596664, 1.12213996, 1.1119989, + 1.10257792, 1.10491258, 1.11059842, 1.10509795, 1.10020097], + [0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, + 0.95831051, 0.94480909, 0.94804195, 0.95430286, 0.94103989, + 0.92122519, 0.91010201, 0.89280392, 0.89298243, 0.89165385], + [0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, + 0.9480927, 0.93539182, 0.95388718, 0.94597005, 0.96918424, + 0.94781281, 0.93466815, 0.94281559, 0.96520315, 0.96715441], + [0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, + 0.98687073, 0.99237486, 0.98209969, 0.9877653, 0.97399471, + 0.96910087, 0.98416665, 0.98423613, 0.99823861, 0.99545704], + [0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, + 0.86191535, 0.84981451, 0.85472102, 0.84564835, 0.83998883, + 0.83478547, 0.82803648, 0.8198736, 0.82265395, 0.8399404], + [0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, + 0.82785597, 0.86008789, 0.86776298, 0.86720209, 0.8676334, + 0.89179317, 0.94202108, 0.9422231, 0.93902708, 0.94479184], + [0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, + 0.90906632, 0.92693339, 0.93695966, 0.94242697, 0.94338265, + 0.91981796, 0.91108804, 0.90543476, 0.91737138, 0.94793657], + [1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, + 1.20172869, 1.21328691, 1.22624778, 1.22397075, 1.23857042, + 1.24419893, 1.23929384, 1.23418676, 1.23626739, 1.26754398], + [1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, + 1.34790023, 1.34399863, 1.32575181, 1.30795492, 1.30544841, + 1.30303302, 1.32107766, 1.32936244, 1.33001241, 1.33288462], + [1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, + 1.05059016, 1.03405057, 1.02747623, 1.03162734, 0.9961416, + 0.97356208, 0.94241549, 0.92754547, 0.92549227, 0.92138102], + [1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, + 1.13889622, 1.12442212, 1.13367018, 1.13982256, 1.14029944, + 1.11979401, 1.10905389, 1.10577769, 1.11166825, 1.09985155], + [0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, + 0.74480073, 0.76098396, 0.76156903, 0.76651952, 0.76533288, + 0.78205934, 0.76842416, 0.77487118, 0.77768683, 0.78801192], + [0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, + 0.97370819, 0.96419154, 0.97209861, 0.97441313, 0.96356162, + 0.94745352, 0.93965462, 0.93069645, 0.94020973, 0.94358232], + [0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, + 0.80071489, 0.83358256, 0.83451613, 0.85175032, 0.85954307, + 0.86790024, 0.87170334, 0.87863799, 0.87497981, 0.87888675], + [0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, + 0.98733195, 0.99644997, 0.99669587, 1.02559097, 1.01116651, + 0.99988024, 0.97906749, 0.99323123, 1.00204939, 0.99602148], + [1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, + 1.08312397, 1.05192626, 1.04230892, 1.05577278, 1.08569751, + 1.12443486, 1.08891079, 1.08603695, 1.05997314, 1.02160943], + [1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, + 1.18257029, 1.16226243, 1.16009196, 1.14467789, 1.14820235, + 1.12386598, 1.12680236, 1.12357937, 1.1159258, 1.12570828], + [1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, + 1.31210239, 1.29989156, 1.29203193, 1.27183516, 1.26830786, + 1.2617743, 1.28656675, 1.29734097, 1.29390205, 1.29345446], + [0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, + 0.78772975, 0.82848011, 0.8259679, 0.82435705, 0.83108634, + 0.84373784, 0.83891093, 0.84349247, 0.85637272, 0.86539395], + [1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, + 1.2256767, 1.21126648, 1.19377804, 1.18355337, 1.19674434, + 1.21536573, 1.23653297, 1.27962009, 1.27968392, 1.25907738], + [0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, + 0.96480308, 0.94686376, 0.93679073, 0.92540049, 0.92988835, + 0.93442917, 0.92100464, 0.91475304, 0.90249622, 0.9021363], + [0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, + 0.88937573, 0.894401, 0.90448993, 0.95495898, 0.92698333, + 0.94745352, 0.92562488, 0.96635366, 1.02520312, 1.0394296], + [1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, + 1.00759019, 0.99192968, 0.99747298, 0.99550759, 0.97583768, + 0.9610168, 0.94779638, 0.93759089, 0.93353431, 0.94121705], + [0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, + 0.83434854, 0.85813595, 0.84667961, 0.84374558, 0.85951183, + 0.87194227, 0.89455097, 0.88283929, 0.90349491, 0.90600675], + [1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, + 1.00581626, 0.98850522, 0.99291168, 0.98983209, 0.97511924, + 0.96134615, 0.96382634, 0.95011401, 0.9434686, 0.94637765], + [1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, + 1.04800023, 1.03024941, 1.04200483, 1.0402554, 1.03296979, + 1.02191682, 1.02476275, 1.02347523, 1.02517684, 1.04359571], + [1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, + 1.0531801, 1.07452771, 1.09383478, 1.1052447, 1.10322136, + 1.09167939, 1.08772756, 1.08859544, 1.09177338, 1.1096083], + [0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, + 0.86287327, 0.85169796, 0.85411285, 0.84886336, 0.84517414, + 0.84843858, 0.84488343, 0.83374329, 0.82812044, 0.82878599], + [0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, + 0.92652175, 0.94278865, 0.93682452, 0.98655146, 0.992237, + 0.9798497, 0.93869677, 0.96947771, 1.00362626, 0.98102351], + [0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, + 0.93092109, 0.92662519, 0.93412152, 0.93501274, 0.92879506, + 0.92110542, 0.91035556, 0.90430364, 0.89994694, 0.90073864], + [0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, + 0.98882205, 0.97662234, 0.95601578, 0.94905385, 0.94934888, + 0.97152609, 0.97163004, 0.9700702, 0.97158948, 0.95884908], + [0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, + 0.84818516, 0.85265681, 0.84502402, 0.82645665, 0.81743586, + 0.83550406, 0.83338919, 0.83511679, 0.82136617, 0.80921874], + [0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, + 0.95440787, 0.96364363, 0.96804412, 0.97136214, 0.97583768, + 0.95571724, 0.96895368, 0.97001634, 0.97082733, 0.98782366], + [1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, + 1.10558188, 1.1214086, 1.12292577, 1.13021031, 1.13342735, + 1.14686068, 1.14502975, 1.14474747, 1.14084037, 1.16142926], + [1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, + 1.11856702, 1.09764283, 1.08815849, 1.08044313, 1.09278827, + 1.07003204, 1.08398066, 1.09831768, 1.09298232, 1.09176125], + [0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, + 0.77751194, 0.79902974, 0.81437881, 0.80788828, 0.79603865, + 0.78966436, 0.79949807, 0.80172182, 0.82168155, 0.85587911], + [1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, + 1.00162979, 0.99860739, 1.00814981, 1.00574316, 0.99030032, + 0.97682565, 0.97292596, 0.96519561, 0.96173403, 0.95890284], + [0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, + 0.96608031, 0.99727185, 1.00781194, 1.03484236, 1.05333619, + 1.0983263, 1.1704974, 1.17025154, 1.18730553, 1.14242645]]) + + self.assertTrue(np.allclose(result, expected)) + self.assertTrue(type(result) == type(expected)) + self.assertTrue(result.shape == expected.shape) + + def test_rebin_data(self): + """Test rebin_data""" + # sample in double the time (even case since 10 % 2 = 0): + # (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2 + # = 0.5, 2.5, 4.5, 6.5, 8.5 + ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float) + for i in range(0, 10, 2)]).T + + self.assertTrue( + np.array_equal(std.rebin_data(self.time_data, 2), ans_even)) + + # sample in triple the time (uneven since 10 % 3 = 1): + # (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1 + # = 1, 4, 7, 9 + ans_odd = np.array([i * np.ones(10, dtype=float) + for i in (1, 4, 7, 9)]).T + self.assertTrue( + np.array_equal(std.rebin_data(self.time_data, 3), ans_odd)) + + def test_get_prob_dist(self): + """Test get_prob_dist""" + lag_indices = np.array([1, 2, 3, 4]) + unit_indices = np.array([1, 3, 2, 4]) + answer = np.array([ + [0.0754717, 0.88207547, 0.04245283, 0., 0.], + [0., 0., 0.09411765, 0.87058824, 0.03529412], + [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505], + [0., 0., 0., 0.02352941, 0.97647059] + ]) + result = std.get_prob_dist(self.transition_matrix, + lag_indices, unit_indices) + + self.assertTrue(np.array_equal(result, answer)) + + def test_get_prob_stats(self): + """Test get_prob_stats""" + + probs = np.array([ + [0.0754717, 0.88207547, 0.04245283, 0., 0.], + [0., 0., 0.09411765, 0.87058824, 0.03529412], + [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505], + [0., 0., 0., 0.02352941, 0.97647059] + ]) + unit_indices = np.array([1, 3, 2, 4]) + answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.]) + answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941]) + answer_trend = np.array([-0.03301887 / 0.88207547, + -0.05882353 / 0.87058824, + 0.02475248 / 0.77722772, + -0.02352941 / 0.97647059]) + answer_volatility = np.array([0.34221495, 0.33705421, + 0.29226542, 0.38834223]) + + result = std.get_prob_stats(probs, unit_indices) + result_up = result[0] + result_down = result[1] + result_trend = result[2] + result_volatility = result[3] + + self.assertTrue(np.allclose(result_up, answer_up)) + self.assertTrue(np.allclose(result_down, answer_down)) + self.assertTrue(np.allclose(result_trend, answer_trend)) + self.assertTrue(np.allclose(result_volatility, answer_volatility)) diff --git a/src/pg/crankshaft.control b/src/pg/crankshaft.control index c35edcd..980287f 100644 --- a/src/pg/crankshaft.control +++ b/src/pg/crankshaft.control @@ -1,5 +1,5 @@ comment = 'CartoDB Spatial Analysis extension' -default_version = '0.9.3' +default_version = '0.9.4' requires = 'plpythonu, postgis' superuser = true schema = cdb_crankshaft