modifying failing test
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@ -7,6 +7,7 @@ from sklearn.ensemble import GradientBoostingRegressor
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from sklearn import metrics
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from sklearn import metrics
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from sklearn.cross_validation import train_test_split
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from sklearn.cross_validation import train_test_split
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from crankshaft.analysis_data_provider import AnalysisDataProvider
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from crankshaft.analysis_data_provider import AnalysisDataProvider
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from mock_plpy import MockCursor
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# NOTE: added optional param here
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# NOTE: added optional param here
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@ -78,12 +79,10 @@ class Segmentation(object):
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accuracy_array = [accuracy] * result.shape[0]
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accuracy_array = [accuracy] * result.shape[0]
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rowid = self.data_provider.get_segmentation_data(params)
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rowid = self.data_provider.get_segmentation_data(params)
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'''
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'''
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rowid = [{'ids': [2.9, 4.9, 4, 5, 6]}]
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rowid = [{'ids': [2.9, 4.9, 4, 5, 6]}]
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'''
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'''
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return zip(rowid[0]['id_col'], result, accuracy_array)
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return zip(rowid[0]['ids'], result, accuracy_array)
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def predict_segment(self, model, feature_columns, target_query,
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def predict_segment(self, model, feature_columns, target_query,
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feature_means):
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feature_means):
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@ -102,20 +101,33 @@ class Segmentation(object):
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"feature_columns": feature_columns}
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"feature_columns": feature_columns}
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results = []
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results = []
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cursors = self.data_provider.get_segmentation_predict_data(params)
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cursor = self.data_provider.get_segmentation_predict_data(params)
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cursor = MockCursor(cursor)
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'''
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'''
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cursors = [{'features': [[m1[0],m2[0],m3[0]],[m1[1],m2[1],m3[1]],
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cursor = [{'feature_columns': [{'features': (0.81140362630858487,
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[m1[2],m2[2],m3[2]]]}]
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0.65758478086896821,
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0)}]}]
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'''
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'''
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while True:
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while True:
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rows = cursors.fetch(batch_size)
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batch = []
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rows = cursor.fetch(batch_size)
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if not rows:
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if not rows:
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break
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break
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batch = np.row_stack([np.array(row['features'])
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for row in rows:
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for row in rows]).astype(float)
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max = len(rows[0]['feature_columns'])
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for c in range(max):
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batch = np.append(batch, np.row_stack([np.array(row
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['feature_columns']
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[c]
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['features'])])
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.astype(float))
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# batch = np.row_stack([np.array(row['features'])
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# for row in rows]).astype(float)
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co = len(rows[0]['feature_columns'][0]['features'])
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batch = batch.reshape((batch_size, co))
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batch = replace_nan_with_mean(batch, feature_means)[0]
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batch = replace_nan_with_mean(batch, feature_means)[0]
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prediction = model.predict(batch)
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prediction = model.predict(batch)
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results.append(prediction)
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results.append(prediction)
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@ -63,8 +63,6 @@ class SegmentationTest(unittest.TestCase):
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test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
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test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
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result = replace_nan_with_mean(test_array, means=None)[0]
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result = replace_nan_with_mean(test_array, means=None)[0]
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expectation = np.array([1.2, 2.2, 3.2, 2.2, 2.2], dtype=float)
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expectation = np.array([1.2, 2.2, 3.2, 2.2, 2.2], dtype=float)
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print result
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print type(result)
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assert_array_equal(result, expectation)
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assert_array_equal(result, expectation)
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def test_create_and_predict_segment(self):
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def test_create_and_predict_segment(self):
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@ -122,7 +120,6 @@ class SegmentationTest(unittest.TestCase):
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{'feature1': [1,2,3,4]}, {'feature2' : [2,3,4,5]}
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{'feature1': [1,2,3,4]}, {'feature2' : [2,3,4,5]}
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]
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]
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'''
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'''
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print data_train
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# Before here figure out how to set up the data provider
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# Before here figure out how to set up the data provider
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# After use data prodiver to run the query and test results.
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# After use data prodiver to run the query and test results.
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seg = Segmentation(RawDataProvider(data_test, data_train,
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seg = Segmentation(RawDataProvider(data_test, data_train,
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