Add upgrade and downgrade files for 0.1.0
This commit is contained in:
Rafa de la Torre
parent
78ceb02c22
commit
3d99d1f9bf
258
release/crankshaft--0.0.4--0.1.0.sql
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258
release/crankshaft--0.0.4--0.1.0.sql
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@ -0,0 +1,258 @@
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--DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES
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-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
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\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
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--------------------------------------------------------------------------------
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-- Version number of the extension release
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CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
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RETURNS text AS $$
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SELECT '0.1.0'::text;
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$$ language 'sql' STABLE STRICT;
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--------------------------------------------------------------------------------
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-- PyAgg stuff
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CREATE OR REPLACE FUNCTION
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CDB_PyAggS(current_state Numeric[], current_row Numeric[])
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returns NUMERIC[] as $$
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BEGIN
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if array_upper(current_state,1) is null then
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RAISE NOTICE 'setting state %',array_upper(current_row,1);
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current_state[1] = array_upper(current_row,1);
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end if;
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return array_cat(current_state,current_row) ;
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END
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$$ LANGUAGE plpgsql;
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CREATE AGGREGATE CDB_PyAgg(NUMERIC[])(
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SFUNC = CDB_PyAggS,
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STYPE = Numeric[],
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INITCOND = "{}"
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);
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--------------------------------------------------------------------------------
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-- Segmentation stuff
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CREATE OR REPLACE FUNCTION
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CDB_CreateAndPredictSegment(
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target NUMERIC[],
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features NUMERIC[],
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target_features NUMERIC[],
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target_ids NUMERIC[],
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n_estimators INTEGER DEFAULT 1200,
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max_depth INTEGER DEFAULT 3,
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subsample DOUBLE PRECISION DEFAULT 0.5,
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learning_rate DOUBLE PRECISION DEFAULT 0.01,
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min_samples_leaf INTEGER DEFAULT 1)
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RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC)
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AS $$
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import numpy as np
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import plpy
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from crankshaft.segmentation import create_and_predict_segment_agg
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model_params = {'n_estimators': n_estimators,
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'max_depth': max_depth,
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'subsample': subsample,
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'learning_rate': learning_rate,
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'min_samples_leaf': min_samples_leaf}
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def unpack2D(data):
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dimension = data.pop(0)
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a = np.array(data, dtype=float)
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return a.reshape(len(a)/dimension, dimension)
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return create_and_predict_segment_agg(np.array(target, dtype=float),
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unpack2D(features),
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unpack2D(target_features),
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target_ids,
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model_params)
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$$ LANGUAGE plpythonu;
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CREATE OR REPLACE FUNCTION
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CDB_CreateAndPredictSegment (
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query TEXT,
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variable_name TEXT,
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target_table TEXT,
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n_estimators INTEGER DEFAULT 1200,
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max_depth INTEGER DEFAULT 3,
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subsample DOUBLE PRECISION DEFAULT 0.5,
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learning_rate DOUBLE PRECISION DEFAULT 0.01,
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min_samples_leaf INTEGER DEFAULT 1)
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RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
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AS $$
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from crankshaft.segmentation import create_and_predict_segment
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model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
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return create_and_predict_segment(query,variable_name,target_table, model_params)
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$$ LANGUAGE plpythonu;
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--------------------------------------------------------------------------------
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-- Spatial interpolation
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-- 0: nearest neighbor
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-- 1: barymetric
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-- 2: IDW
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CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
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IN query text,
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IN point geometry,
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IN method integer DEFAULT 1,
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IN p1 numeric DEFAULT 0,
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IN p2 numeric DEFAULT 0
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)
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RETURNS numeric AS
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$$
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DECLARE
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gs geometry[];
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vs numeric[];
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output numeric;
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BEGIN
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EXECUTE 'WITH a AS('||query||') SELECT array_agg(the_geom), array_agg(attrib) FROM a' INTO gs, vs;
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SELECT CDB_SpatialInterpolation(gs, vs, point, method, p1,p2) INTO output FROM a;
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RETURN output;
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END;
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$$
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language plpgsql IMMUTABLE;
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CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
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IN geomin geometry[],
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IN colin numeric[],
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IN point geometry,
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IN method integer DEFAULT 1,
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IN p1 numeric DEFAULT 0,
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IN p2 numeric DEFAULT 0
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)
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RETURNS numeric AS
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$$
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DECLARE
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gs geometry[];
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vs numeric[];
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gs2 geometry[];
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vs2 numeric[];
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g geometry;
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vertex geometry[];
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sg numeric;
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sa numeric;
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sb numeric;
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sc numeric;
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va numeric;
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vb numeric;
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vc numeric;
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output numeric;
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BEGIN
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output := -999.999;
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-- nearest
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IF method = 0 THEN
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WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v)
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SELECT a.v INTO output FROM a ORDER BY point<->a.g LIMIT 1;
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RETURN output;
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-- barymetric
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ELSIF method = 1 THEN
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WITH a as (SELECT unnest(geomin) AS e),
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b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
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c as (SELECT (ST_Dump(t)).geom as v FROM b),
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d as (SELECT v FROM c WHERE ST_Within(point, v))
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SELECT v INTO g FROM d;
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IF g is null THEN
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-- out of the realm of the input data
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RETURN -888.888;
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END IF;
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-- vertex of the selected cell
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WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
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SELECT array_agg(v) INTO vertex FROM a;
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-- retrieve the value of each vertex
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WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
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SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
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WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
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SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
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WITH a AS(SELECT unnest(vertex) as geo, unnest(colin) as c)
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SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
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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;
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output := (coalesce(sa,0) * coalesce(va,0) + coalesce(sb,0) * coalesce(vb,0) + coalesce(sc,0) * coalesce(vc,0)) / coalesce(sg);
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RETURN output;
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-- IDW
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-- p1: limit the number of neighbors, 0->no limit
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-- p2: order of distance decay, 0-> order 1
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ELSIF method = 2 THEN
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IF p2 = 0 THEN
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p2 := 1;
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END IF;
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WITH a as (SELECT unnest(geomin) as g, unnest(colin) as v),
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b as (SELECT a.g, a.v FROM a ORDER BY point<->a.g)
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SELECT array_agg(b.g), array_agg(b.v) INTO gs, vs FROM b;
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IF p1::integer>0 THEN
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gs2:=gs;
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vs2:=vs;
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FOR i IN 1..p1
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LOOP
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gs2 := gs2 || gs[i];
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vs2 := vs2 || vs[i];
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END LOOP;
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ELSE
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gs2:=gs;
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vs2:=vs;
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END IF;
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WITH a as (SELECT unnest(gs2) as g, unnest(vs2) as v),
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b as (
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SELECT
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(1/ST_distance(point, a.g)^p2::integer) as k,
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(a.v/ST_distance(point, a.g)^p2::integer) as f
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FROM a
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)
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SELECT sum(b.f)/sum(b.k) INTO output FROM b;
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RETURN output;
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END IF;
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RETURN -777.777;
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END;
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$$
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language plpgsql IMMUTABLE;
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--------------------------------------------------------------------------------
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-- Spatial Markov
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-- input table format:
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-- id | geom | date_1 | date_2 | date_3
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-- 1 | Pt1 | 12.3 | 13.1 | 14.2
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-- 2 | Pt2 | 11.0 | 13.2 | 12.5
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-- ...
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-- Sample Function call:
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-- SELECT CDB_SpatialMarkov('SELECT * FROM real_estate',
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-- Array['date_1', 'date_2', 'date_3'])
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CREATE OR REPLACE FUNCTION
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CDB_SpatialMarkovTrend (
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subquery TEXT,
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time_cols TEXT[],
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num_classes INT DEFAULT 7,
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w_type TEXT DEFAULT 'knn',
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num_ngbrs INT DEFAULT 5,
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permutations INT DEFAULT 99,
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geom_col TEXT DEFAULT 'the_geom',
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id_col TEXT DEFAULT 'cartodb_id')
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RETURNS TABLE (trend NUMERIC, trend_up NUMERIC, trend_down NUMERIC, volatility NUMERIC, rowid INT)
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AS $$
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from crankshaft.space_time_dynamics import spatial_markov_trend
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## TODO: use named parameters or a dictionary
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return spatial_markov_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
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$$ LANGUAGE plpythonu;
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81
release/crankshaft--0.1.0--0.0.4.sql
Normal file
81
release/crankshaft--0.1.0--0.0.4.sql
Normal file
@ -0,0 +1,81 @@
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--DO NOT MODIFY THIS FILE, IT IS GENERATED FROM SOURCES
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-- Complain if script is sourced in psql, rather than via CREATE EXTENSION
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\echo Use "CREATE EXTENSION crankshaft" to load this file. \quit
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--------------------------------------------------------------------------------
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-- Version number of the extension release
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CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
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RETURNS text AS $$
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SELECT '0.0.4'::text;
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$$ language 'sql' STABLE STRICT;
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--------------------------------------------------------------------------------
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-- PyAgg stuff
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DROP FUNCTION CDB_PyAggS(Numeric[], Numeric[]);
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DROP AGGREGATE CDB_PyAgg(NUMERIC[]);
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--------------------------------------------------------------------------------
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-- Segmentation stuff
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DROP FUNCTION
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CDB_CreateAndPredictSegment(
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target NUMERIC[],
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features NUMERIC[],
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target_features NUMERIC[],
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target_ids NUMERIC[],
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n_estimators INTEGER DEFAULT 1200,
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max_depth INTEGER DEFAULT 3,
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subsample DOUBLE PRECISION DEFAULT 0.5,
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learning_rate DOUBLE PRECISION DEFAULT 0.01,
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min_samples_leaf INTEGER DEFAULT 1);
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DROP FUNCTION
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CDB_CreateAndPredictSegment (
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query TEXT,
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variable_name TEXT,
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target_table TEXT,
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n_estimators INTEGER DEFAULT 1200,
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max_depth INTEGER DEFAULT 3,
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subsample DOUBLE PRECISION DEFAULT 0.5,
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learning_rate DOUBLE PRECISION DEFAULT 0.01,
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min_samples_leaf INTEGER DEFAULT 1);
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--------------------------------------------------------------------------------
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-- Spatial interpolation
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DROP FUNCTION CDB_SpatialInterpolation(
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IN query text,
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IN point geometry,
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IN method integer DEFAULT 1,
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IN p1 numeric DEFAULT 0,
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IN p2 numeric DEFAULT 0
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);
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DROP FUNCTION CDB_SpatialInterpolation(
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IN geomin geometry[],
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IN colin numeric[],
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IN point geometry,
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IN method integer DEFAULT 1,
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IN p1 numeric DEFAULT 0,
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IN p2 numeric DEFAULT 0
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);
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--------------------------------------------------------------------------------
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-- Spatial Markov
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DROP FUNCTION
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CDB_SpatialMarkovTrend (
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subquery TEXT,
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time_cols TEXT[],
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num_classes INT DEFAULT 7,
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w_type TEXT DEFAULT 'knn',
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num_ngbrs INT DEFAULT 5,
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permutations INT DEFAULT 99,
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geom_col TEXT DEFAULT 'the_geom',
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id_col TEXT DEFAULT 'cartodb_id');
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