Use separate fixture data for Python tests
This commit is contained in:
Javier Goizueta
parent
53a868be2e
commit
0833b2f226
@ -1,3 +1,4 @@
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-- Moran's I
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CREATE OR REPLACE FUNCTION
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CREATE OR REPLACE FUNCTION
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cdb_moran_local (
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cdb_moran_local (
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t TEXT,
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t TEXT,
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@ -7,12 +8,28 @@ CREATE OR REPLACE FUNCTION
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permutations INT DEFAULT 99,
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permutations INT DEFAULT 99,
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geom_column TEXT DEFAULT 'the_geom',
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geom_column TEXT DEFAULT 'the_geom',
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id_col TEXT DEFAULT 'cartodb_id',
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id_col TEXT DEFAULT 'cartodb_id',
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w_type TEXT DEFAULT 'knn',
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w_type TEXT DEFAULT 'knn')
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random_seed INTEGER DEFAULT NULL
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)
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RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
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RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
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AS $$
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AS $$
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from crankshaft.clustering import moran_local
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from crankshaft.clustering import moran_local
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# TODO: use named parameters or a dictionary
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# TODO: use named parameters or a dictionary
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return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type, random_seed)
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return moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
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$$ LANGUAGE plpythonu;
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-- Moran's I Local Rate
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CREATE OR REPLACE FUNCTION
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cdb_moran_local_rate(t TEXT,
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numerator TEXT,
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denominator TEXT,
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significance FLOAT DEFAULT 0.05,
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num_ngbrs INT DEFAULT 5,
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permutations INT DEFAULT 99,
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geom_column TEXT DEFAULT 'the_geom',
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id_col TEXT DEFAULT 'cartodb_id',
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w_type TEXT DEFAULT 'knn')
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RETURNS TABLE(moran FLOAT, quads TEXT, significance FLOAT, ids INT, y numeric)
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AS $$
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from crankshaft.clustering import moran_local_rate
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# TODO: use named parameters or a dictionary
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return moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type)
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$$ LANGUAGE plpythonu;
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$$ LANGUAGE plpythonu;
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@ -54,6 +54,95 @@ def moran_local(t, attr, significance, num_ngbrs, permutations, geom_column, id_
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
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def moran_local_rate(t, numerator, denominator, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
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"""
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Moran's I Local Rate
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Andy Eschbacher
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"""
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plpy.notice('** Constructing query')
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# geometries with attributes that are null are ignored
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# resulting in a collection of not as near neighbors
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qvals = {"id_col": id_col,
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"numerator": numerator,
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"denominator": denominator,
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"geom_col": geom_column,
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"table": t,
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"num_ngbrs": num_ngbrs}
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q = get_query(w_type, qvals)
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try:
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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except plpy.SPIError:
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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return zip([None], [None], [None], [None])
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plpy.notice('r.nrows() = %d' % r.nrows())
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## collect attributes
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numer = get_attributes(r, 1)
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denom = get_attributes(r, 2)
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w = get_weight(r, w_type, num_ngbrs)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, w, permutations=permutations)
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# find units of significance
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lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
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plpy.notice('** Finished calculations')
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## TODO: Decide on which return values here
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order, lisa.y)
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def moran_local_bv(t, attr1, attr2, significance, num_ngbrs, permutations, geom_column, id_col, w_type):
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plpy.notice('** Constructing query')
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qvals = {"num_ngbrs": num_ngbrs,
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"attr1": attr1,
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"attr2": attr2,
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"table": t,
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"geom_col": geom_column,
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"id_col": id_col}
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q = get_query(w_type, qvals)
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try:
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r = plpy.execute(q)
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plpy.notice('** Query returned with %d rows' % len(r))
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except plpy.SPIError:
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plpy.notice('** Query failed: "%s"' % q)
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plpy.notice('** Error: %s' % plpy.SPIError)
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plpy.notice('** Exiting function')
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return zip([None], [None], [None], [None])
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## collect attributes
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attr1_vals = get_attributes(r, 1)
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attr2_vals = get_attributes(r, 2)
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# create weights
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w = get_weight(r, w_type, num_ngbrs)
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# calculate LISA values
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lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, w)
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plpy.notice("len of Is: %d" % len(lisa.Is))
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# find clustering of significance
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lisa_sig = lisa_sig_vals(lisa.p_sim, lisa.q, significance)
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plpy.notice('** Finished calculations')
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return zip(lisa.Is, lisa_sig, lisa.p_sim, w.id_order)
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# Low level functions ----------------------------------------
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# Low level functions ----------------------------------------
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def map_quads(coord):
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def map_quads(coord):
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52
python/crankshaft/test/fixtures/moran.json
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52
python/crankshaft/test/fixtures/moran.json
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@ -0,0 +1,52 @@
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[[0.9319096128346788, "HH"],
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[-1.135787401862846, "HL"],
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[0.11732030672508517, "Not significant"],
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[0.6152779669180425, "Not significant"],
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[-0.14657336660125297, "Not significant"],
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[0.6967858120189607, "Not significant"],
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[0.07949310115714454, "Not significant"],
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[0.4703198759258987, "Not significant"],
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[0.4421125200498064, "Not significant"],
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[0.5724288737143592, "Not significant"],
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[0.8970743435692062, "LL"],
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[0.18327334401918674, "Not significant"],
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[-0.01466729201304962, "Not significant"],
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[0.3481559372544409, "Not significant"],
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[0.06547094736902978, "Not significant"],
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[0.15482141569329988, "HH"],
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[0.4373841193538136, "Not significant"],
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[0.15971286468915544, "Not significant"],
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[1.0543588860308968, "Not significant"],
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[1.7372866900020818, "HH"],
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[1.091998586053999, "LL"],
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[0.1171572584252222, "Not significant"],
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[0.08438455015300014, "Not significant"],
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[0.06547094736902978, "Not significant"],
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[0.15482141569329985, "HH"],
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[1.1627044812890683, "HH"],
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[0.06547094736902978, "Not significant"],
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[0.795275137550483, "Not significant"],
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[0.18562939195219, "LL"],
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[0.3010757406693439, "Not significant"],
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[2.8205795942839376, "HH"],
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[0.11259190602909264, "Not significant"],
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[-0.07116352791516614, "Not significant"],
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[-0.09945240794119009, "Not significant"],
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[0.18562939195219, "LL"],
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[0.1832733440191868, "Not significant"],
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[-0.39054253768447705, "Not significant"],
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[-0.1672071289487642, "HL"],
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[0.3337669247916343, "Not significant"],
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[0.2584386102554792, "Not significant"],
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[-0.19733845476322634, "HL"],
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[-0.9379282899805409, "LH"],
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[-0.028770969951095866, "Not significant"],
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[0.051367269430983485, "Not significant"],
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[-0.2172548045913472, "LH"],
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[0.05136726943098351, "Not significant"],
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[0.04191046803899837, "Not significant"],
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[0.7482357030403517, "HH"],
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[-0.014585767863118111, "Not significant"],
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[0.5410013139159929, "Not significant"],
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[1.0223932668429925, "LL"],
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[1.4179402898927476, "LL"]]
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54
python/crankshaft/test/fixtures/neighbors.json
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54
python/crankshaft/test/fixtures/neighbors.json
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[
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{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
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{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
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{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
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{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
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{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
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{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
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{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
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{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
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{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
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{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
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{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
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{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
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{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
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{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
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{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
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{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
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{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
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{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
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{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
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{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
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{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
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{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
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{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
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{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
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{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
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{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
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{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
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{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
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{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
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{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
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{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
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{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
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{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
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{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
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{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
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{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
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{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
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{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
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{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
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{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
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{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
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{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
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{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
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{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
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{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
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{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
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{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
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{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
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{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
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{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
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{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
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{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
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]
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@ -11,9 +11,9 @@ import unittest
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# sys.modules['plpy'] = plpy
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# sys.modules['plpy'] = plpy
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from helper import plpy
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from helper import plpy
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# import crankshaft.clustering as cc
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import crankshaft.clustering as cc
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import crankshaft.clustering as cc
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from crankshaft import random_seeds
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import json
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class MoranTest(unittest.TestCase):
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class MoranTest(unittest.TestCase):
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"""Testing class for Moran's I functions."""
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"""Testing class for Moran's I functions."""
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@ -26,6 +26,8 @@ class MoranTest(unittest.TestCase):
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"table": "a_list",
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"table": "a_list",
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"geom_col": "the_geom",
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"geom_col": "the_geom",
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"num_ngbrs": 321}
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"num_ngbrs": 321}
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self.neighbors_data = json.loads(open('test/fixtures/neighbors.json').read())
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self.moran_data = json.loads(open('test/fixtures/moran.json').read())
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def test_map_quads(self):
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def test_map_quads(self):
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"""Test map_quads."""
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"""Test map_quads."""
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@ -119,17 +121,24 @@ class MoranTest(unittest.TestCase):
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self.assertTrue((test_ans == ans).all())
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self.assertTrue((test_ans == ans).all())
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def test_moran_local(self):
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def test_moran_local(self):
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"""Test Moran's I local"""
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"""Test Moran's I local"""
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plpy._define_result('select', [
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data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
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{ 'id': 1, 'attr1': 100.0, 'neighbors': [2,4,5,7,8] },
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plpy._define_result('select', data)
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{ 'id': 2, 'attr1': 110.0, 'neighbors': [1,4,5,6,7] },
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random_seeds.set_random_seeds(1234)
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{ 'id': 3, 'attr1': 90.0, 'neighbors': [1,4,5,7,8] },
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result = cc.moran_local('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
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{ 'id': 4, 'attr1': 100.0, 'neighbors': [1,2,5,7,8] },
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result = [(row[0], row[1]) for row in result]
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{ 'id': 5, 'attr1': 100.0, 'neighbors': [1,2,3,7,8] },
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expected = self.moran_data
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{ 'id': 6, 'attr1': 105.0, 'neighbors': [1,2,3,7,8] },
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for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
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{ 'id': 7, 'attr1': 105.0, 'neighbors': [1,2,3,6,8] },
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self.assertAlmostEqual(res_val, exp_val)
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{ 'id': 8, 'attr1': 105.0, 'neighbors': [1,2,3,6,7] },
|
self.assertEqual(res_quad, exp_quad)
|
||||||
{ 'id': 9, 'attr1': 120.0, 'neighbors': [1,2,5,6,7] }
|
|
||||||
])
|
def test_moran_local_rate(self):
|
||||||
result = cc.moran_local('table', 'value', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
|
"""Test Moran's I rate"""
|
||||||
# TODO: check results!
|
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||||
|
plpy._define_result('select', data)
|
||||||
|
random_seeds.set_random_seeds(1234)
|
||||||
|
result = cc.moran_local_rate('table', 'numerator', 'denominator', 0.05, 5, 99, 'the_geom', 'cartodb_id', 'knn')
|
||||||
|
result = [(row[0], row[1]) for row in result]
|
||||||
|
expected = self.moran_data
|
||||||
|
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||||
|
self.assertAlmostEqual(res_val, exp_val)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user