def test_update(self): count_col = FeatureRequestTotal.feature_name_from_class() mean_col = FeaturePathDepthAverage.feature_name_from_class() schema = T.StructType([ T.StructField(self.feature.current_features_column, T.MapType(T.StringType(), T.FloatType())), T.StructField(self.feature.past_features_column, T.MapType(T.StringType(), T.FloatType())), ]) sub_df = self.session.createDataFrame([{ self.feature.current_features_column: { self.feature.feature_name: 6., count_col: 3., mean_col: 5., }, self.feature.past_features_column: { self.feature.feature_name: 2., count_col: 1., mean_col: 4., } }], schema=schema) result_df = self.feature.update(sub_df) result_df.show() value = result_df.select( self.feature.updated_feature_col_name).collect()[0][ self.feature.updated_feature_col_name] from baskerville.features.helpers import update_variance expected_value = update_variance(2., 6., 1., 3., 4., 5.) print(expected_value) self.assertAlmostEqual(value, expected_value, places=2)
def update_row(cls, current, past, *args, **kwargs): return update_variance( past.get(cls.feature_name_from_class()), current[cls.feature_name_from_class()], past.get(FeatureRequestTotal.feature_name_from_class()), current[FeatureRequestTotal.feature_name_from_class()], past.get(FeaturePathDepthAverage.feature_name_from_class()), current[FeaturePathDepthAverage.feature_name_from_class()])
def test_update_row(self): requests = FeatureRequestTotal() ave = FeatureRequestIntervalAverage() test_current = {self.feature.feature_name: 6., requests.feature_name: 3., ave.feature_name: 5.} test_past = {self.feature.feature_name: 2., requests.feature_name: 1., ave.feature_name: 4.} value = self.feature.update_row( test_current, test_past ) from baskerville.features.helpers import update_variance expected_value = update_variance(2., 6., 1., 3., 4., 5.) self.assertAlmostEqual(value, expected_value, places=2)