fixes release path copy error [ci skip]

release/crankshaft--0.8.1--0.9.0.sql

@@ -4,7 +4,7 @@
 -- Version number of the extension release
 CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
 RETURNS text AS $$
-  SELECT '0.8.1'::text;
+  SELECT '0.9.0'::text;
 $$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE;
 
 -- Internal identifier of the installed extension instence
@@ -71,7 +71,8 @@ AS $$
     import numpy as np
     import plpy
 
-    from crankshaft.segmentation import create_and_predict_segment_agg
+    from crankshaft.segmentation import Segmentation
+    seg = Segmentation()
     model_params = {'n_estimators': n_estimators,
                     'max_depth': max_depth,
                     'subsample': subsample,
@@ -80,22 +81,24 @@ AS $$
 
     def unpack2D(data):
         dimension = data.pop(0)
-        a = np.array(data, dtype=float)
-        return a.reshape(len(a)/dimension, dimension)
+        a = np.array(data, dtype=np.float64)
+        return a.reshape(int(len(a)/dimension), int(dimension))
 
-    return create_and_predict_segment_agg(np.array(target, dtype=float),
-            unpack2D(features),
-            unpack2D(target_features),
-            target_ids,
-            model_params)
+    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 (
+  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,
@@ -103,9 +106,92 @@ CREATE OR REPLACE FUNCTION
       min_samples_leaf INTEGER DEFAULT 1)
 RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
 AS $$
-  from crankshaft.segmentation import create_and_predict_segment
-  model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
-  return create_and_predict_segment(query,variable_name,target_table, model_params)
+    from crankshaft.segmentation import Segmentation
+    seg = Segmentation()
+    model_params = {
+        'n_estimators': n_estimators,
+        'max_depth': max_depth,
+        'subsample': subsample,
+        'learning_rate': learning_rate,
+        'min_samples_leaf': min_samples_leaf
+    }
+    all_cols = list(plpy.execute('''
+        select * from ({query}) as _w limit 0
+    '''.format(query=query)).colnames())
+    feature_cols = [a for a in all_cols
+                    if a not in [variable_name, 'cartodb_id', ]]
+    return seg.create_and_predict_segment(
+        query,
+        variable_name,
+        feature_cols,
+        target_table,
+        model_params,
+        model_name=model_name
+    )
+$$ 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 $$
+    from crankshaft.segmentation import Segmentation
+    seg = Segmentation()
+    model_params = {
+        'n_estimators': n_estimators,
+        'max_depth': max_depth,
+        'subsample': subsample,
+        'learning_rate': learning_rate,
+        'min_samples_leaf': min_samples_leaf
+    }
+    return seg.create_and_predict_segment(
+        query,
+        variable,
+        feature_columns,
+        target_query,
+        model_params,
+        model_name=model_name
+    )
 $$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
 CREATE OR REPLACE FUNCTION CDB_Gravity(
     IN target_query text,