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)