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.
For classifiers, a "default" set of parameters should be provided for
general testing, and a "results_comparison" set for comparing against
previously recorded results if the general set does not produce suitable
probabilities to compare against.
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 sktime.classification.dictionary_based import ContractableBOSS
from sktime.classification.interval_based import CanonicalIntervalForest
from sktime.classification.interval_based import (
TimeSeriesForestClassifier as TSFC,
)
if parameter_set == "results_comparison":
cboss = ContractableBOSS(
n_parameter_samples=4, max_ensemble_size=2, random_state=0
)
cif = CanonicalIntervalForest(
n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
)
return {"estimators": [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])]}
else:
return {
"estimators": [
("tsf1", TSFC(n_estimators=2), 0),
("tsf2", TSFC(n_estimators=2), 0),
]
}
check_inverse=False)
TRANSFORMERS = [
(
"transformer1",
SeriesToSeriesRowTransformer(SERIES_TO_SERIES_TRANSFORMER,
check_transformer=False),
),
(
"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: {