def test_create_input_nub_numerical(self):
iris_df = lib.load_iris()
# Zero variables
variable_type_dict = {'numerical_vars': []}
input_layers, input_nub = Automater()._create_input_nub(
variable_type_dict, iris_df)
self.assertEqual(list(), input_layers)
# One variable
iris_numerical_cols = ['sepal_length']
variable_type_dict = {'numerical_vars': iris_numerical_cols}
input_layers, input_nub = Automater(
numerical_vars=iris_numerical_cols)._create_input_nub(
variable_type_dict, iris_df)
self.assertEqual(1, len(input_layers))
# Multiple numeric variables
iris_numerical_cols = [
'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
]
variable_type_dict = {'numerical_vars': iris_numerical_cols}
input_layers, input_nub = Automater(
numerical_vars=iris_numerical_cols)._create_input_nub(
variable_type_dict, iris_df)
self.assertEqual(4, len(input_layers))
def test_fit(self):
iris_df = lib.load_iris()
# One variable
iris_numerical_cols = ['sepal_length']
auto = Automater(numerical_vars=iris_numerical_cols)
auto.fit(iris_df)
self.assertEqual(Automater, type(auto))
self.assertEqual(iris_numerical_cols, auto._user_provided_variables)
self.assertTrue(auto.fitted)
# Assert that transformation pipline has been built / trained
self.assertEqual([['sepal_length']],
list(
map(lambda x: x[0],
auto.input_mapper.built_features)))
def test_transform(self):
iris_df = lib.load_iris()
# Two numerical variables, df_out = False
iris_numerical_cols = ['sepal_length', 'sepal_width']
auto = Automater(numerical_vars=iris_numerical_cols, df_out=False)
auto.fit(iris_df)
(X, y) = auto.transform(iris_df)
self.assertEqual((150, ), X[0].shape)
# Two numerical variables, df_out = True
iris_numerical_cols = ['sepal_length', 'sepal_width']
auto = Automater(numerical_vars=iris_numerical_cols, df_out=True)
auto.fit(iris_df)
transformed = auto.transform(iris_df)
self.assertEqual(150, len(transformed.index))
self.assertEqual((150, 2), transformed.shape)
self.assertCountEqual(iris_numerical_cols, transformed.columns)
def test_numerical_whole(self):
# St up data set
iris = lib.load_iris()
iris_train = iris[:100]
iris_test = iris[101:]
iris_numerical_cols = ['sepal_length', 'petal_length']
# Create auto
auto = Automater(numerical_vars=iris_numerical_cols,
response_var='sepal_length')
# Train auto
auto.fit(iris_train)
X_train, y_train = auto.transform(iris_train)
# Extract input_nub from auto
input_nub = auto.input_nub
# Extract output_nub from auto
output_nub = auto.output_nub
# Create DL model
x = input_nub
x = Dense(30)(x)
x = output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss=auto.loss)
# Train DL model
model.fit(X_train, y_train)
# Transform test set
iris_test = iris_test.drop('sepal_length', axis=1)
X_test, y_test = auto.transform(iris_test)
model.predict(X_test)
pass