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formatting updates

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This commit is contained in:
Andy Eschbacher
2016-06-27 09:56:12 -04:00
parent a177bf5620
commit dae406927f
1 changed files with 17 additions and 20 deletions
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37
src/pg/sql/05_segmentation.sql
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@@ -9,32 +9,31 @@ CREATE OR REPLACE FUNCTION
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)
min_samples_leaf INTEGER DEFAULT 1)
RETURNS TABLE(cartodb_id NUMERIC, prediction NUMERIC, accuracy NUMERIC)
AS $$
import numpy as np
import plpy
import numpy as np
import plpy
from crankshaft.segmentation import create_and_predict_segment_agg
model_params = { 'n_estimators' : n_estimators,
'max_depth' : max_depth,
'subsample' : subsample,
'learning_rate' : learning_rate,
'min_samples_leaf' : min_samples_leaf}
model_params = {'n_estimators': n_estimators,
'max_depth': max_depth,
'subsample': subsample,
'learning_rate': learning_rate,
'min_samples_leaf': min_samples_leaf}
def unpack2D(data):
dimension = data.pop(0)
a = np.array(data, dtype=float)
return a.reshape(len(a)/dimension, dimension)
return create_and_predict_segment_agg( np.array(target, dtype=float),
unpack2D(features),
unpack2D(target_features),
return create_and_predict_segment_agg(np.array(target, dtype=float),
unpack2D(features),
unpack2D(target_features),
target_ids,
model_params)
$$ Language plpythonu;
$$ LANGUAGE plpythonu;
CREATE OR REPLACE FUNCTION
CDB_CreateAndPredictSegment (
@@ -45,12 +44,10 @@ CREATE OR REPLACE FUNCTION
max_depth INTEGER DEFAULT 3,
subsample DOUBLE PRECISION DEFAULT 0.5,
learning_rate DOUBLE PRECISION DEFAULT 0.01,
min_samples_leaf INTEGER DEFAULT 1
)
RETURNS TABLE (cartodb_id text, prediction Numeric,accuracy Numeric )
min_samples_leaf INTEGER DEFAULT 1)
RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
AS $$
from crankshaft.segmentation import create_and_predict_segment
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
return create_and_predict_segment(query,variable_name,target_table, model_params)
$$ LANGUAGE plpythonu;