def test_default_boston_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, regressor = \
_test_regressor_iterative_fit(dataset="boston",
Regressor=self.module)
score = sklearn.metrics.r2_score(targets, predictions)
fixture = self.res["default_boston_iterative"]
if score < -1e10:
score = np.log(-score)
fixture = np.log(-fixture)
self.assertAlmostEqual(
fixture,
score,
places=self.res.get("default_boston_iterative_places", 7),
)
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(regressor.estimator,
self.step_hyperparameter['name']),
self.step_hyperparameter['value'])
def test_default_configuration_iterative_fit_sparse(self):
for i in range(2):
predictions, targets = \
_test_regressor_iterative_fit(RandomForest, sparse=True)
self.assertAlmostEqual(
0.24225685933770469,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(2):
predictions, targets = _test_regressor_iterative_fit(
GradientBoosting)
self.assertAlmostEqual(
0.37192663934006487,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(2):
predictions, targets = \
_test_regressor_iterative_fit(RandomForest)
self.assertAlmostEqual(
0.41795829411621988,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_regressor_iterative_fit(ExtraTreesRegressor)
self.assertAlmostEqual(0.43258995365114405,
sklearn.metrics.r2_score(targets,
predictions))
def test_default_diabetes_iterative_sparse_fit(self):
if self.__class__ == BaseRegressionComponentTest:
return
if not hasattr(self.module, 'iterative_fit'):
return
if SPARSE not in self.module.get_properties()["input"]:
return
for i in range(2):
predictions, targets, regressor = \
_test_regressor_iterative_fit(dataset="diabetes",
Regressor=self.module,
sparse=True)
self.assertAlmostEqual(
self.res["default_diabetes_iterative_sparse"],
sklearn.metrics.r2_score(targets, predictions),
places=self.res.get("default_diabetes_iterative_sparse_places",
7))
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(regressor.estimator,
self.step_hyperparameter['name']),
self.res.get("diabetes_iterative_n_iter",
self.step_hyperparameter['value']))
def test_default_boston_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, regressor = \
_test_regressor_iterative_fit(dataset="boston",
Regressor=self.module)
score = sklearn.metrics.r2_score(targets, predictions)
fixture = self.res["default_boston_iterative"]
if score < -1e10:
score = np.log(-score)
fixture = np.log(-fixture)
self.assertAlmostEqual(
fixture,
score,
places=self.res.get("default_boston_iterative_places", 7),
)
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(regressor.estimator, self.step_hyperparameter['name']),
self.step_hyperparameter['value']
)
def test_default_diabetes_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, _ = \
_test_regressor_iterative_fit(dataset="diabetes",
Regressor=self.module)
self.assertAlmostEqual(
self.res["default_diabetes_iterative"],
sklearn.metrics.r2_score(targets, predictions),
places=self.res.get("default_diabetes_iterative_places", 7))
def test_default_diabetes_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets, _ = \
_test_regressor_iterative_fit(dataset="diabetes",
Regressor=self.module)
self.assertAlmostEqual(self.res["default_diabetes_iterative"],
sklearn.metrics.r2_score(targets,
predictions),
places=self.res.get(
"default_diabetes_iterative_places", 7))
def test_default_boston_iterative_sparse_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
if SPARSE not in self.module.get_properties()["input"]:
return
for i in range(2):
predictions, targets, _ = \
_test_regressor_iterative_fit(dataset="boston",
Regressor=self.module,
sparse=True)
self.assertAlmostEqual(
self.res["default_boston_iterative_sparse"],
sklearn.metrics.r2_score(targets, predictions),
places=self.res.get("default_boston_iterative_sparse_places",
7))
def test_default_boston_iterative_sparse_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
if SPARSE not in self.module.get_properties()["input"]:
return
for i in range(2):
predictions, targets, _ = \
_test_regressor_iterative_fit(dataset="boston",
Regressor=self.module,
sparse=True)
self.assertAlmostEqual(self.res["default_boston_iterative_sparse"],
sklearn.metrics.r2_score(targets,
predictions),
places=self.res.get(
"default_boston_iterative_sparse_places", 7))
def test_default_boston_iterative_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
for i in range(2):
predictions, targets = \
_test_regressor_iterative_fit(dataset="boston",
Regressor=self.module)
score = sklearn.metrics.r2_score(targets, predictions)
fixture = self.res["default_boston_iterative"]
if score < -1e10:
score = np.log(-score)
fixture = np.log(-fixture)
self.assertAlmostEqual(
fixture,
score,
places=self.res.get("default_boston_iterative_places", 7),
)
def test_default_diabetes_iterative_sparse_fit(self):
if not hasattr(self.module, 'iterative_fit'):
return
if SPARSE not in self.module.get_properties()["input"]:
return
for i in range(2):
predictions, targets, regressor = \
_test_regressor_iterative_fit(dataset="diabetes",
Regressor=self.module,
sparse=True)
self.assertAlmostEqual(self.res["default_diabetes_iterative_sparse"],
sklearn.metrics.r2_score(targets,
predictions),
places=self.res.get(
"default_diabetes_iterative_sparse_places", 7))
if self.step_hyperparameter is not None:
self.assertEqual(
getattr(regressor.estimator, self.step_hyperparameter['name']),
self.step_hyperparameter['value']
)
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = \
_test_regressor_iterative_fit(RandomForest)
self.assertAlmostEqual(0.41795829411621988,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit_sparse(self):
for i in range(10):
predictions, targets = \
_test_regressor_iterative_fit(RandomForest, sparse=True)
self.assertAlmostEqual(0.24225685933770469,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = _test_regressor_iterative_fit(GradientBoosting)
self.assertAlmostEqual(0.37192663934006487,
sklearn.metrics.r2_score(y_true=targets, y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = _test_regressor_iterative_fit(SGD)
self.assertAlmostEqual(0.066576586105546731,
sklearn.metrics.r2_score(y_true=targets,
y_pred=predictions))
def test_default_configuration_iterative_fit(self):
for i in range(10):
predictions, targets = _test_regressor_iterative_fit(ExtraTreesRegressor)
self.assertAlmostEqual(0.4269923975466271, sklearn.metrics.r2_score(targets, predictions))
