def test_boxcox_transform():
y = load_airline()
t = TabularToSeriesAdaptor(PowerTransformer(method="box-cox", standardize=False))
actual = t.fit_transform(y)
expected, _ = boxcox(np.asarray(y)) # returns fitted lambda as second output
np.testing.assert_array_equal(actual, expected)
def compute_expected_y_pred(y_train, fh):
# fitting
yt = y_train.copy()
t1 = ExponentTransformer()
yt = t1.fit_transform(yt)
t2 = TabularToSeriesAdaptor(MinMaxScaler())
yt = t2.fit_transform(yt)
forecaster = NaiveForecaster()
forecaster.fit(yt, fh=fh)
# predicting
y_pred = forecaster.predict()
y_pred = t2.inverse_transform(y_pred)
y_pred = t1.inverse_transform(y_pred)
return y_pred
def test_pipeline():
"""Test results of TransformedTargetForecaster."""
y = load_airline()
y_train, y_test = temporal_train_test_split(y)
forecaster = TransformedTargetForecaster([
("t1", ExponentTransformer()),
("t2", TabularToSeriesAdaptor(MinMaxScaler())),
("forecaster", NaiveForecaster()),
])
fh = np.arange(len(y_test)) + 1
forecaster.fit(y_train, fh=fh)
actual = forecaster.predict()
def compute_expected_y_pred(y_train, fh):
# fitting
yt = y_train.copy()
t1 = ExponentTransformer()
yt = t1.fit_transform(yt)
t2 = TabularToSeriesAdaptor(MinMaxScaler())
yt = t2.fit_transform(yt)
forecaster = NaiveForecaster()
forecaster.fit(yt, fh=fh)
# predicting
y_pred = forecaster.predict()
y_pred = t2.inverse_transform(y_pred)
y_pred = t1.inverse_transform(y_pred)
return y_pred
expected = compute_expected_y_pred(y_train, fh)
np.testing.assert_array_equal(actual, expected)
def get_test_params(cls):
"""Return testing parameter settings for the estimator.
Returns
-------
params : dict or list of dict, default = {}
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`
"""
from sklearn.preprocessing import StandardScaler
from sktime.forecasting.naive import NaiveForecaster
from sktime.transformations.series.adapt import TabularToSeriesAdaptor
from sktime.transformations.series.boxcox import BoxCoxTransformer
STEPS1 = [
("transformer", TabularToSeriesAdaptor(StandardScaler())),
("forecaster", NaiveForecaster()),
]
params1 = {"steps": STEPS1}
STEPS2 = [
("transformer", BoxCoxTransformer()),
("forecaster", NaiveForecaster()),
]
params2 = {"steps": STEPS2}
return [params1, params2]
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return `"default"` set.
Returns
-------
params : dict or list of dict, default = {}
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`
"""
from sklearn.preprocessing import StandardScaler
from sktime.forecasting.arima import ARIMA
from sktime.forecasting.naive import NaiveForecaster
from sktime.transformations.series.adapt import TabularToSeriesAdaptor
from sktime.transformations.series.exponent import ExponentTransformer
# StandardScaler does not skip fit, NaiveForecaster is not probabilistic
STEPS1 = [
("transformer", TabularToSeriesAdaptor(StandardScaler())),
("forecaster", NaiveForecaster()),
]
params1 = {"steps": STEPS1}
# ARIMA has probabilistic methods, ExponentTransformer skips fit
STEPS2 = [
("transformer", ExponentTransformer()),
("forecaster", ARIMA()),
]
params2 = {"steps": STEPS2}
return [params1, params2]
def __rmul__(self, other):
"""Magic * method, return concatenated ClassifierPipeline, transformers on left.
Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
Parameters
----------
other: `sktime` transformer, must inherit from BaseTransformer
otherwise, `NotImplemented` is returned
Returns
-------
ClassifierPipeline object, concatenation of `other` (first) with `self` (last).
"""
from sktime.classification.compose import ClassifierPipeline
from sktime.transformations.base import BaseTransformer
from sktime.transformations.compose import TransformerPipeline
from sktime.transformations.series.adapt import TabularToSeriesAdaptor
# behaviour is implemented only if other inherits from BaseTransformer
# in that case, distinctions arise from whether self or other is a pipeline
# todo: this can probably be simplified further with "zero length" pipelines
if isinstance(other, BaseTransformer):
# ClassifierPipeline already has the dunder method defined
if isinstance(self, ClassifierPipeline):
return other * self
# if other is a TransformerPipeline but self is not, first unwrap it
elif isinstance(other, TransformerPipeline):
return ClassifierPipeline(classifier=self,
transformers=other.steps)
# if neither self nor other are a pipeline, construct a ClassifierPipeline
else:
return ClassifierPipeline(classifier=self,
transformers=[other])
elif is_sklearn_transformer(other):
return TabularToSeriesAdaptor(other) * self
else:
return NotImplemented
"transformer2",
SeriesToSeriesRowTransformer(SERIES_TO_SERIES_TRANSFORMER,
check_transformer=False),
),
]
REGRESSOR = LinearRegression()
ANOMALY_DETECTOR = KNN()
TIME_SERIES_CLASSIFIER = TSFC(n_estimators=3)
TIME_SERIES_CLASSIFIERS = [
("tsf1", TIME_SERIES_CLASSIFIER),
("tsf2", TIME_SERIES_CLASSIFIER),
]
FORECASTER = NaiveForecaster()
FORECASTERS = [("f1", FORECASTER), ("f2", FORECASTER)]
STEPS = [
("transformer", TabularToSeriesAdaptor(StandardScaler())),
("forecaster", NaiveForecaster()),
]
ESTIMATOR_TEST_PARAMS = {
ColumnEnsembleForecaster: {
"forecasters": FORECASTER
},
OnlineEnsembleForecaster: {
"forecasters": FORECASTERS
},
FeatureUnion: {
"transformer_list": TRANSFORMERS
},
DirectTabularRegressionForecaster: {
"estimator": REGRESSOR
},
