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chore: rework docstring of MultiOutputRegressor

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closes #11309
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Matthias
2025-02-06 06:34:19 +01:00
parent 013006050f
commit 2cfb8834b6
1 changed files with 6 additions and 11 deletions
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17
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
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@@ -6,26 +6,21 @@ from sklearn.utils.validation import has_fit_parameter, validate_data
class FreqaiMultiOutputRegressor(MultiOutputRegressor): class FreqaiMultiOutputRegressor(MultiOutputRegressor):
def fit(self, X, y, sample_weight=None, fit_params=None): def fit(self, X, y, sample_weight=None, fit_params=None):
"""Fit the model to data, separately for each output variable. """Fit the model to data, separately for each output variable.
Parameters :param X: {array-like, sparse matrix} of shape (n_samples, n_features)
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data. The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs) :param y: {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel Multi-output targets. An indicator matrix turns on multilabel
estimation. estimation.
sample_weight : array-like of shape (n_samples,), default=None :param sample_weight: array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted. Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample Only supported if the underlying regressor supports sample
weights. weights.
fit_params : A list of dicts for the fit_params
:param fit_params: A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step. Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different Each dict may contain same or different values (e.g. different
eval_sets or init_models) eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
""" """
if not hasattr(self.estimator, "fit"): if not hasattr(self.estimator, "fit"):