def test_projecting_dataframe_from_hierarchical_to_one_hot_encoding_cross_product_hypergrid( self): adaptee = self.hierarchical_hypergrid adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=adaptee, merge_all_categorical_dimensions=True) self._test_projecting_dataframe_categorical_to_one_hot_encoding_point_from_adaptee( adapter=adapter, adaptee=adaptee, num_random_points=1000)
def test_projecting_dataframe_from_hierarchical_to_one_hot_encoding_drop_first_hypergrid( self): adaptee = self.hierarchical_hypergrid adapter = CategoricalToOneHotEncodedHypergridAdapter(adaptee=adaptee, drop='first') self._test_projecting_dataframe_categorical_to_one_hot_encoding_point_from_adaptee( adapter=adapter, adaptee=adaptee, num_random_points=1000)
def test_projecting_point_from_hierarchical_categorical_to_one_hot_encoding_hypergrid( self): hierarchical_adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=self.hierarchical_hypergrid) self._test_projecting_categorical_to_one_hot_encoding_point_from_adaptee( adaptee=self.hierarchical_hypergrid, adapter=hierarchical_adapter, num_random_points=500)
def test_projecting_point_from_categorical_to_one_hot_encoding_drop_first_simple_hypergrid( self): adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=self.simple_hypergrid, drop='first') self._test_projecting_categorical_to_one_hot_encoding_point_from_adaptee( adaptee=self.simple_hypergrid, adapter=adapter, num_random_points=500)
def test_projecting_point_from_categorical_to_one_hot_encoding_cross_product_simple_hypergrid( self): adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=self.simple_hypergrid, merge_all_categorical_dimensions=True) self._test_projecting_categorical_to_one_hot_encoding_point_from_adaptee( adaptee=self.simple_hypergrid, adapter=adapter, num_random_points=500)
def test_projecting_dataframe_from_flat_to_one_hot_encoded_cross_product_drop_first_hypergrid( self): adaptee = self.simple_hypergrid adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=adaptee, merge_all_categorical_dimensions=True, drop='first') self._test_projecting_dataframe_categorical_to_one_hot_encoding_point_from_adaptee( adapter=adapter, adaptee=adaptee, num_random_points=1000)
def test_projecting_point_from_hierarchical_categorical_to_one_hot_encoding_cross_product_drop_first_hypergrid( self): hierarchical_adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=self.hierarchical_hypergrid, merge_all_categorical_dimensions=True, drop='first') self._test_projecting_categorical_to_one_hot_encoding_point_from_adaptee( adaptee=self.hierarchical_hypergrid, adapter=hierarchical_adapter, num_random_points=100)
def test_projecting_dataframe_from_flat_to_one_hot_encoded_hypergrid_parameterized( self, drop, merge_all_categorical_dimensions): for adaptee in [self.simple_hypergrid, self.hierarchical_hypergrid]: adapter = CategoricalToOneHotEncodedHypergridAdapter( adaptee=adaptee, drop=drop, merge_all_categorical_dimensions= merge_all_categorical_dimensions) test_types_are_not_categorical = [ dimension.__class__ != CategoricalDimension for dimension in adapter.dimensions ] assert all(test_types_are_not_categorical) self._test_projecting_dataframe_categorical_to_one_hot_encoding_point_from_adaptee( adapter=adapter, adaptee=adaptee, num_random_points=1000)
def test_projecting_dataframe_from_flat_to_one_hot_encoded_hypergrid(self): adaptee = self.simple_hypergrid adapter = CategoricalToOneHotEncodedHypergridAdapter(adaptee=adaptee) self._test_projecting_dataframe_categorical_to_one_hot_encoding_point_from_adaptee( adapter=adapter, adaptee=adaptee, num_random_points=1000)