def make_from_setup(self, setup, data=None):
self.setup = defaults.add_defaults_to_setup(setup)
self.features = list(self.setup['features'].keys())
self.regressors = list(self.setup['regressors'].keys())
self.classifiers = list(self.setup['classifiers'].keys())
self._prepare_data(data=data)
self._make_preprocessors_from_setup()
self._make_model_from_setup()
def test_set_default_scaler_encoder(self):
setup = {
'default_scaler': 'MinMaxScaler',
'default_encoder': 'OrdinalEncoder',
'features': ['M1', 'qinit', 'Pinit', 'FeHinit'],
'regressors': ['Pfinal', 'qfinal'],
'classifiers': ['product', 'binary_type'],
}
setup_new = defaults.add_defaults_to_setup(setup)
assert setup_new['features']['M1']['processor'] == 'MinMaxScaler'
assert setup_new['classifiers']['product'][
'processor'] == 'OrdinalEncoder'
def test_add_defaults_to_setup(self):
setup = {
'features': ['M1', 'qinit', 'Pinit', 'FeHinit'],
'regressors': ['Pfinal', 'qfinal'],
'classifiers': ['product', 'binary_type'],
}
setup_new = defaults.add_defaults_to_setup(setup)
assert 'model' in setup_new
assert 'random_state' in setup_new
assert 'train_test_split' in setup_new
assert 'optimizer' in setup_new
assert 'optimizer_kwargs' in setup_new
def test_add_loss_defaults_to_setup(self):
#-- case where features, regressors and classifiers are lists
setup = {
'features': ['M1', 'qinit', 'Pinit', 'FeHinit'],
'regressors': ['Pfinal', 'qfinal'],
'classifiers': ['product', 'binary_type'],
}
setup_new = defaults.add_defaults_to_setup(setup)
assert 'loss' in setup_new['regressors']['Pfinal']
assert setup_new['regressors']['Pfinal'][
'loss'] == defaults.default_regressor_loss
assert 'loss' in setup_new['classifiers']['product']
assert setup_new['classifiers']['product'][
'loss'] == defaults.default_classifier_loss
#-- case where features, regressors and classifiers are dictionaries
setup = {
'features': {
'M1': {
'processor': 'StandardScaler'
},
'qinit': {
'processor': 'RobustScaler'
},
'Pinit': {
'processor': 'MinMaxScaler'
},
'FeHinit': {
'processor': 'MaxAbsScaler'
},
},
'regressors': {
'Pfinal': {
'processor': 'StandardScaler',
'loss': 'mafe'
},
'qfinal': {
'processor': 'RobustScaler'
},
},
'classifiers': {
'product': None,
},
}
setup_new = defaults.add_defaults_to_setup(setup)
assert 'loss' in setup_new['regressors']['Pfinal']
assert setup_new['regressors']['Pfinal']['loss'] == 'mafe'
assert setup_new['regressors']['qfinal'][
'loss'] == defaults.default_regressor_loss
assert 'loss' in setup_new['classifiers']['product']
assert setup_new['classifiers']['product'][
'loss'] == defaults.default_classifier_loss
def test_add_processor_defaults_to_setup(self):
#-- case where features, regressors and classifiers are lists
setup = {
'features': ['M1', 'qinit', 'Pinit', 'FeHinit'],
'regressors': ['Pfinal', 'qfinal'],
'classifiers': ['product', 'binary_type'],
}
setup_new = defaults.add_defaults_to_setup(setup)
assert 'processor' in setup_new['features']['M1']
assert setup_new['features']['M1'][
'processor'] == defaults.default_scaler
assert 'processor' in setup_new['regressors']['Pfinal']
assert setup_new['regressors']['Pfinal']['processor'] is None
assert 'processor' in setup_new['classifiers']['product']
assert setup_new['classifiers']['product'][
'processor'] == defaults.default_encoder
#-- case where features, regressors and classifiers are dictionaries
setup = {
'features': {
'M1': {
'processor': 'StandardScaler'
},
'qinit': {
'processor': 'RobustScaler'
},
'Pinit': {
'processor': 'MinMaxScaler'
},
'FeHinit': {
'processor': 'MaxAbsScaler'
},
},
'regressors': ['Pfinal', 'qfinal'],
'classifiers': {
'binary_type': {
'processor': None
},
'product': None,
},
}
setup_new = defaults.add_defaults_to_setup(setup)
for key in setup['features'].keys():
assert setup_new['features'][key]['processor'] == setup[
'features'][key]['processor']
for key in setup['regressors']:
assert setup_new['regressors'][key]['processor'] is None
assert setup_new['classifiers']['binary_type']['processor'] is None
assert setup_new['classifiers']['product'][
'processor'] == 'OneHotEncoder'