pep8 syntax
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@ -50,12 +50,23 @@ RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
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AS $$
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AS $$
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from crankshaft.segmentation import Segmentation
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from crankshaft.segmentation import Segmentation
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seg = Segmentation()
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seg = Segmentation()
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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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model_params = {
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return seg.create_and_predict_segment(query,variable_name,target_table, model_params)
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'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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}
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return seg.create_and_predict_segment(
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query,
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variable_name,
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target_table,
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model_params
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)
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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CREATE OR REPLACE FUNCTION
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CREATE OR REPLACE FUNCTION
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CDB_CreateAndPredictSegment (
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CDB_CreateAndPredictSegment(
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query TEXT,
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query TEXT,
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variable TEXT,
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variable TEXT,
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feature_columns TEXT[],
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feature_columns TEXT[],
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@ -69,6 +80,18 @@ RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
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AS $$
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AS $$
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from crankshaft.segmentation import Segmentation
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from crankshaft.segmentation import Segmentation
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seg = Segmentation()
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seg = Segmentation()
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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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model_params = {
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return seg.create_and_predict_segment(query, variable, feature_columns, target_query, model_params)
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'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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}
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return seg.create_and_predict_segment(
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query,
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variable,
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feature_columns,
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target_query,
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model_params
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)
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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