def test_all_estimators_filter_estimator_types(estimator_type):
estimators = all_estimators(estimator_types=estimator_type,
return_names=False)
assert isinstance(estimators, list)
# assert len(estimators) > 0
if isinstance(estimator_type, str):
estimator_type = VALID_ESTIMATOR_BASE_TYPE_LOOKUP[estimator_type]
assert all(
[issubclass(estimator, estimator_type) for estimator in estimators])
def test_all_estimators_exclude_estimators(exclude_estimators):
estimators = all_estimators(return_names=True,
exclude_estimators=exclude_estimators)
assert isinstance(estimators, list)
assert len(estimators) > 0
names, estimators = list(zip(*estimators))
if not isinstance(exclude_estimators, list):
exclude_estimators = [exclude_estimators]
for estimator in exclude_estimators:
assert estimator not in names
def test_all_estimators_return_names(return_names):
estimators = all_estimators(return_names=return_names)
assert isinstance(estimators, list)
assert len(estimators) > 0
if return_names:
assert all([isinstance(estimator, tuple) for estimator in estimators])
names, estimators = list(zip(*estimators))
assert all([isinstance(name, str) for name in names])
assert all([
name == estimator.__name__
for name, estimator in zip(names, estimators)
])
assert all([isinstance(estimator, type) for estimator in estimators])
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from sktime.utils import all_estimators
from sktime.utils._testing.panel import make_transformer_problem
from sktime.tests._config import ESTIMATOR_TEST_PARAMS
PAIRWISE_TRANSFORMERS = all_estimators(estimator_types="transformer-pairwise",
return_names=False)
PAIRWISE_TRANSFORMERS_PANEL = all_estimators(
estimator_types="transformer-pairwise-panel", return_names=False)
EXPECTED_SHAPE = (4, 5)
X1_tab = make_transformer_problem(
n_instances=4,
n_columns=4,
n_timepoints=5,
random_state=1,
return_numpy=True,
panel=False,
)
X2_tab = make_transformer_problem(
n_instances=5,
n_columns=5,
n_timepoints=5,
random_state=2,
return_numpy=True,
panel=False,
)
from sktime.forecasting.tests._config import TEST_OOS_FHS
from sktime.forecasting.tests._config import TEST_STEP_LENGTHS
from sktime.forecasting.tests._config import TEST_WINDOW_LENGTHS
from sktime.forecasting.tests._config import TEST_YS
from sktime.forecasting.tests._config import VALID_INDEX_FH_COMBINATIONS
from sktime.performance_metrics.forecasting import smape_loss
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing.forecasting import _make_fh
from sktime.utils._testing.forecasting import assert_correct_pred_time_index
from sktime.utils._testing.forecasting import get_expected_index_for_update_predict
from sktime.utils._testing.forecasting import make_forecasting_problem
from sktime.utils.validation.forecasting import check_fh
# get all forecasters
FORECASTERS = all_estimators(estimator_types="forecaster", return_names=False)
FH0 = 1
# testing data
y = make_forecasting_problem()
y_train, y_test = temporal_train_test_split(y, train_size=0.75)
@pytest.mark.parametrize("Forecaster", FORECASTERS)
def test_fitted_params(Forecaster):
f = _construct_instance(Forecaster)
f.fit(y_train, fh=FH0)
try:
params = f.get_fitted_params()
assert isinstance(params, dict)
import pandas as pd
import pytest
from sktime.forecasting.base._sktime import _SktimeForecaster
from sktime.forecasting.base._sktime import _BaseWindowForecaster
from sktime.forecasting.base._sktime import _OptionalForecastingHorizonMixin
from sktime.forecasting.base._sktime import _RequiredForecastingHorizonMixin
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing.forecasting import make_forecasting_problem
# get all forecasters
FORECASTERS = [
forecaster
for (name, forecaster) in all_estimators(estimator_types="forecaster")
if issubclass(forecaster, _SktimeForecaster)
]
FH0 = 1
WINDOW_FORECASTERS = [
forecaster
for (name, forecaster) in all_estimators(estimator_types="forecaster")
if issubclass(forecaster, _BaseWindowForecaster)
]
# testing data
y = make_forecasting_problem()
y_train, y_test = temporal_train_test_split(y, train_size=0.75)
# -*- coding: utf-8 -*-
import numpy as np
from sktime.dists_kernels.compose_tab_to_panel import AggrDist
from sktime.dists_kernels.scipy_dist import ScipyDist
from sktime.utils._testing.panel import make_transformer_problem
from sktime.utils import all_estimators
PAIRWISE_TRANSFORMERS_TAB = all_estimators(
estimator_types="transformer-pairwise", return_names=False)
AGGFUNCS = [
np.mean,
np.std,
np.var,
np.sum,
np.prod,
np.min,
np.max,
np.argmin,
np.argmax,
np.any,
]
X1_num_pan = make_transformer_problem(n_instances=4,
n_columns=4,
n_timepoints=5,
random_state=1,
return_numpy=True)
X2_num_pan = make_transformer_problem(n_instances=5,
n_columns=5,
n_timepoints=5,
import pytest
from sktime.tests._config import VALID_TRANSFORMER_TYPES
from sktime.transformations.base import BaseTransformer
from sktime.transformations.base import _PanelToPanelTransformer
from sktime.transformations.base import _PanelToTabularTransformer
from sktime.transformations.base import _SeriesToPrimitivesTransformer
from sktime.transformations.base import _SeriesToSeriesTransformer
from sktime.utils import all_estimators
from sktime.utils import _has_tag
from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
from sktime.utils._testing.estimator_checks import _construct_instance
from sktime.utils._testing.estimator_checks import _make_args
from sktime.utils.data_processing import is_nested_dataframe
ALL_TRANSFORMERS = all_estimators(estimator_types="transformer",
return_names=False)
@pytest.mark.parametrize("Estimator", ALL_TRANSFORMERS)
def test_all_transformers(Estimator):
check_transformer(Estimator)
def check_transformer(Estimator):
for check in _yield_transformer_checks(Estimator):
check(Estimator)
def _construct_fit_transform(Estimator, **kwargs):
estimator = _construct_instance(Estimator)
]
import numpy as np
import pytest
from sktime.series_as_features.tests._config import ACCEPTED_OUTPUT_TYPES
from sktime.tests._config import EXCLUDE_ESTIMATORS
from sktime.tests._config import NON_STATE_CHANGING_METHODS
from sktime.transformations.base import _PanelToPanelTransformer
from sktime.transformations.base import _PanelToTabularTransformer
from sktime.utils import all_estimators
from sktime.utils._testing.estimator_checks import _construct_instance
from sktime.utils._testing.estimator_checks import _make_args
CLASSIFIERS = all_estimators("classifier",
return_names=False,
exclude_estimators=EXCLUDE_ESTIMATORS)
REGRESSORS = all_estimators("regressor",
return_names=False,
exclude_estimators=EXCLUDE_ESTIMATORS)
PANEL_TRANSFORMERS = all_estimators(
estimator_types=[_PanelToPanelTransformer, _PanelToTabularTransformer],
return_names=False,
exclude_estimators=EXCLUDE_ESTIMATORS,
)
PANEL_ESTIMATORS = CLASSIFIERS + REGRESSORS + PANEL_TRANSFORMERS
# We here only check the ouput for a single number of classes
N_CLASSES = 3
# coding: utf-8
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["Markus Löning"]
__all__ = []
import numpy as np
import pandas as pd
import pytest
from sktime.tests._config import EXCLUDED
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing import _make_args
ALL_CLASSIFIERS = [
e[1] for e in all_estimators(estimator_type="classifier")
if e[0] not in EXCLUDED
]
ALL_REGRESSORS = [
e[1] for e in all_estimators(estimator_type="regressor")
if e[0] not in EXCLUDED
]
N_CLASSES = 3
ACCEPTED_OUTPUT_TYPES = (np.ndarray, pd.Series)
@pytest.mark.parametrize("Estimator", [*ALL_CLASSIFIERS, *ALL_REGRESSORS])
def test_series_as_features_multivariate_input(Estimator):
# check if multivariate input is correctly handled
]
import numpy as np
import pandas as pd
import pytest
from sktime.forecasting.base._sktime import BaseSktimeForecaster
from sktime.forecasting.base._sktime import OptionalForecastingHorizonMixin
from sktime.forecasting.base._sktime import RequiredForecastingHorizonMixin
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing.forecasting import make_forecasting_problem
# get all forecasters
FORECASTERS = [forecaster for (name, forecaster) in
all_estimators(estimator_type="forecaster")
if issubclass(forecaster, BaseSktimeForecaster)]
FH0 = 1
# testing data
y = make_forecasting_problem()
y_train, y_test = temporal_train_test_split(y, train_size=0.75)
# test _y setting
@pytest.mark.parametrize("Forecaster", FORECASTERS)
def test_oh_setting(Forecaster):
# check _y and cutoff is None after construction
f = _construct_instance(Forecaster)
assert f._y is None
assert f.cutoff is None
#!/usr/bin/env python3 -u
# -*- coding: utf-8 -*-
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["Markus Löning"]
__all__ = ["test_estimator"]
import pytest
from sktime.tests._config import EXCLUDE_ESTIMATORS
from sktime.tests._config import EXCLUDED_TESTS
from sktime.utils import all_estimators
from sktime.utils._testing.estimator_checks import check_estimator
ALL_ESTIMATORS = all_estimators(return_names=False,
exclude_estimators=EXCLUDE_ESTIMATORS)
@pytest.mark.parametrize("Estimator", ALL_ESTIMATORS)
def test_estimator(Estimator):
# We run a number of basic checks on all estimators to ensure correct
# implementation of our framework and compatibility with scikit-learn.
check_estimator(Estimator, EXCLUDED_TESTS.get(Estimator.__name__, []))
#!/usr/bin/env python3 -u
# coding: utf-8
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["Markus Löning"]
__all__ = []
import numpy as np
import pytest
from sktime.tests.test_all_estimators import EXCLUDED
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing import _make_args
ALL_SERIES_AS_FEATURES_TRANSFORMERS = [
e[1] for e in all_estimators("series_as_features_transformer")
if e[0] not in EXCLUDED
]
@pytest.mark.parametrize("Transformer", ALL_SERIES_AS_FEATURES_TRANSFORMERS)
def test_transformed_data_has_same_index_as_input_data(Transformer):
transformer = _construct_instance(Transformer)
X, y = _make_args(transformer, "fit")
Xt = transformer.fit_transform(X, y)
np.testing.assert_array_equal(X.index, Xt.index)
# -*- coding: utf-8 -*-
import pandas as pd
import pytest
from sktime.utils._testing.estimator_checks import _construct_instance, _make_args
from sktime.utils import all_estimators
ALL_ANNOTATORS = all_estimators(estimator_types="series-annotator",
return_names=False)
@pytest.mark.parametrize("Estimator", ALL_ANNOTATORS)
def test_output_type(Estimator):
estimator = _construct_instance(Estimator)
args = _make_args(estimator, "fit")
estimator.fit(*args)
args = _make_args(estimator, "predict")
y_pred = estimator.predict(*args)
assert isinstance(y_pred, pd.Series)
__author__ = ["Markus Löning"]
__all__ = []
import numpy as np
import pandas as pd
import pytest
from sktime.tests._config import EXCLUDED_ESTIMATORS
from sktime.tests._config import NON_STATE_CHANGING_METHODS
from sktime.utils import all_estimators
from sktime.utils._testing import _construct_instance
from sktime.utils._testing import _make_args
CLASSIFIERS = [
e[1]
for e in all_estimators(estimator_type="classifier")
if e[0] not in EXCLUDED_ESTIMATORS
]
REGRESSORS = [
e[1]
for e in all_estimators(estimator_type="regressor")
if e[0] not in EXCLUDED_ESTIMATORS
]
SERIES_AS_FEATURES_TRANSFORMERS = [
e[1]
for e in all_estimators(estimator_type="series_as_features_transformer")
if e[0] not in EXCLUDED_ESTIMATORS
]
#!/usr/bin/env python3 -u
# coding: utf-8
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
__author__ = ["Markus Löning"]
__all__ = ["test_estimator"]
import pytest
from sktime.tests._config import EXCLUDED
from sktime.utils import all_estimators
from sktime.utils._testing.estimator_checks import check_estimator
ALL_ESTIMATORS = [e[1] for e in all_estimators() if e[0] not in EXCLUDED]
@pytest.mark.parametrize("Estimator", ALL_ESTIMATORS)
def test_estimator(Estimator):
# We run a number of basic checks on all estimators to ensure correct
# implementation of our framework and compatibility with scikit-learn
check_estimator(Estimator)
