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%)

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72.97

/freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py

import numpy as np
from sklearn.base import is_classifier
from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.parallel import Parallel, delayed
from sklearn.utils.validation import has_fit_parameter

from freqtrade.exceptions import OperationalException


class FreqaiMultiOutputClassifier(MultiOutputClassifier):

    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 classifier 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 is_classifier(self):
            check_classification_targets(y)

        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])
        )

        self.classes_ = []
        for estimator in self.estimators_:
            self.classes_.extend(estimator.classes_)
        if len(set(self.classes_)) != len(self.classes_):
            raise OperationalException(f"Class labels must be unique across targets: "
                                       f"{self.classes_}")

        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 self

    def predict_proba(self, X):
        """
        Get predict_proba and stack arrays horizontally
        """
        results = np.hstack(super().predict_proba(X))
        return np.squeeze(results)

    def predict(self, X):
        """
        Get predict and squeeze into 2D array
        """
        results = super().predict(X)
        return np.squeeze(results)

1	import numpy as np	1✔
2	from sklearn.base import is_classifier	1✔
3	from sklearn.multioutput import MultiOutputClassifier, _fit_estimator	1✔
4	from sklearn.utils.multiclass import check_classification_targets	1✔
5	from sklearn.utils.parallel import Parallel, delayed	1✔
6	from sklearn.utils.validation import has_fit_parameter	1✔
7
8	from freqtrade.exceptions import OperationalException	1✔
9
10
11	class FreqaiMultiOutputClassifier(MultiOutputClassifier):	1✔
12
13	def fit(self, X, y, sample_weight=None, fit_params=None):	1✔
14	"""Fit the model to data, separately for each output variable.
15	Parameters
16	----------
17	X : {array-like, sparse matrix} of shape (n_samples, n_features)
18	The input data.
19	y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
20	Multi-output targets. An indicator matrix turns on multilabel
21	estimation.
22	sample_weight : array-like of shape (n_samples,), default=None
23	Sample weights. If `None`, then samples are equally weighted.
24	Only supported if the underlying classifier supports sample
25	weights.
26	fit_params : A list of dicts for the fit_params
27	Parameters passed to the ``estimator.fit`` method of each step.
28	Each dict may contain same or different values (e.g. different
29	eval_sets or init_models)
30	.. versionadded:: 0.23
31	Returns
32	-------
33	self : object
34	Returns a fitted instance.
35	"""
36
37	if not hasattr(self.estimator, "fit"):	1✔
38	raise ValueError("The base estimator should implement a fit method")	×
39
40	y = self._validate_data(X="no_validation", y=y, multi_output=True)	1✔
41
42	if is_classifier(self):	1✔
43	check_classification_targets(y)	1✔
44
45	if y.ndim == 1:	1✔
46	raise ValueError(	×
47	"y must have at least two dimensions for "
48	"multi-output regression but has only one."
49	)
50
51	if sample_weight is not None and not has_fit_parameter(	1✔
52	self.estimator, "sample_weight"
53	):
54	raise ValueError("Underlying estimator does not support sample weights.")	×
55
56	if not fit_params:	1✔
57	fit_params = [None] * y.shape[1]	×
58
59	self.estimators_ = Parallel(n_jobs=self.n_jobs)(	1✔
60	delayed(_fit_estimator)(
61	self.estimator, X, y[:, i], sample_weight, **fit_params[i]
62	)
63	for i in range(y.shape[1])
64	)
65
66	self.classes_ = []	1✔
67	for estimator in self.estimators_:	1✔
68	self.classes_.extend(estimator.classes_)	1✔
69	if len(set(self.classes_)) != len(self.classes_):	1✔
70	raise OperationalException(f"Class labels must be unique across targets: "	×
71	f"{self.classes_}")
72
73	if hasattr(self.estimators_[0], "n_features_in_"):	1✔
74	self.n_features_in_ = self.estimators_[0].n_features_in_	1✔
75	if hasattr(self.estimators_[0], "feature_names_in_"):	1✔
76	self.feature_names_in_ = self.estimators_[0].feature_names_in_	×
77
78	return self	1✔
79
80	def predict_proba(self, X):	1✔
81	"""
82	Get predict_proba and stack arrays horizontally
83	"""
84	results = np.hstack(super().predict_proba(X))	×
85	return np.squeeze(results)	×
86
87	def predict(self, X):	1✔
88	"""
89	Get predict and squeeze into 2D array
90	"""
91	results = super().predict(X)	×
92	return np.squeeze(results)	×

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