9394559170

Committed 26 Apr 2024 06:36AM UTC coverage: 94.656% (-0.02%) from 94.674%

Build # 9394559170

Build Type

push

github

Committed by

xmatthias

Commit Message

Loader should be passed as kwarg for clarity

Run Details

20280 of 21425 relevant lines covered (94.66%)

0.95 hits per line

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80.0

/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py

from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.parallel import Parallel, delayed
from sklearn.utils.validation import has_fit_parameter


class FreqaiMultiOutputRegressor(MultiOutputRegressor):

    def fit(self, X, y, sample_weight=None, fit_params=None):
        """Fit the model to data, separately for each output variable.
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data.
        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
            Multi-output targets. An indicator matrix turns on multilabel
            estimation.
        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If `None`, then samples are equally weighted.
            Only supported if the underlying regressor supports sample
            weights.
        fit_params : A list of dicts for the fit_params
            Parameters passed to the ``estimator.fit`` method of each step.
            Each dict may contain same or different values (e.g. different
            eval_sets or init_models)
            .. versionadded:: 0.23
        Returns
        -------
        self : object
            Returns a fitted instance.
        """

        if not hasattr(self.estimator, "fit"):
            raise ValueError("The base estimator should implement a fit method")

        y = self._validate_data(X="no_validation", y=y, multi_output=True)

        if y.ndim == 1:
            raise ValueError(
                "y must have at least two dimensions for "
                "multi-output regression but has only one."
            )

        if sample_weight is not None and not has_fit_parameter(
            self.estimator, "sample_weight"
        ):
            raise ValueError("Underlying estimator does not support sample weights.")

        if not fit_params:
            fit_params = [None] * y.shape[1]

        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_estimator)(
                self.estimator, X, y[:, i], sample_weight, **fit_params[i]
            )
            for i in range(y.shape[1])
        )

        if hasattr(self.estimators_[0], "n_features_in_"):
            self.n_features_in_ = self.estimators_[0].n_features_in_
        if hasattr(self.estimators_[0], "feature_names_in_"):
            self.feature_names_in_ = self.estimators_[0].feature_names_in_

        return

1	from sklearn.multioutput import MultiOutputRegressor, _fit_estimator	1✔
2	from sklearn.utils.parallel import Parallel, delayed	1✔
3	from sklearn.utils.validation import has_fit_parameter	1✔
4
5
6	class FreqaiMultiOutputRegressor(MultiOutputRegressor):	1✔
7
8	def fit(self, X, y, sample_weight=None, fit_params=None):	1✔
9	"""Fit the model to data, separately for each output variable.
10	Parameters
11	----------
12	X : {array-like, sparse matrix} of shape (n_samples, n_features)
13	The input data.
14	y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
15	Multi-output targets. An indicator matrix turns on multilabel
16	estimation.
17	sample_weight : array-like of shape (n_samples,), default=None
18	Sample weights. If `None`, then samples are equally weighted.
19	Only supported if the underlying regressor supports sample
20	weights.
21	fit_params : A list of dicts for the fit_params
22	Parameters passed to the ``estimator.fit`` method of each step.
23	Each dict may contain same or different values (e.g. different
24	eval_sets or init_models)
25	.. versionadded:: 0.23
26	Returns
27	-------
28	self : object
29	Returns a fitted instance.
30	"""
31
32	if not hasattr(self.estimator, "fit"):	1✔
33	raise ValueError("The base estimator should implement a fit method")	×
34
35	y = self._validate_data(X="no_validation", y=y, multi_output=True)	1✔
36
37	if y.ndim == 1:	1✔
38	raise ValueError(	×
39	"y must have at least two dimensions for "
40	"multi-output regression but has only one."
41	)
42
43	if sample_weight is not None and not has_fit_parameter(	1✔
44	self.estimator, "sample_weight"
45	):
46	raise ValueError("Underlying estimator does not support sample weights.")	×
47
48	if not fit_params:	1✔
49	fit_params = [None] * y.shape[1]	×
50
51	self.estimators_ = Parallel(n_jobs=self.n_jobs)(	1✔
52	delayed(_fit_estimator)(
53	self.estimator, X, y[:, i], sample_weight, **fit_params[i]
54	)
55	for i in range(y.shape[1])
56	)
57
58	if hasattr(self.estimators_[0], "n_features_in_"):	1✔
59	self.n_features_in_ = self.estimators_[0].n_features_in_	1✔
60	if hasattr(self.estimators_[0], "feature_names_in_"):	1✔
61	self.feature_names_in_ = self.estimators_[0].feature_names_in_	1✔
62
63	return	1✔

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