53 lines
2.2 KiB
Python
53 lines
2.2 KiB
Python
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
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from sklearn.utils.parallel import Parallel, delayed
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from sklearn.utils.validation import has_fit_parameter, validate_data
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class FreqaiMultiOutputRegressor(MultiOutputRegressor):
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def fit(self, X, y, sample_weight=None, fit_params=None):
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"""Fit the model to data, separately for each output variable.
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:param X: {array-like, sparse matrix} of shape (n_samples, n_features)
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The input data.
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:param y: {array-like, sparse matrix} of shape (n_samples, n_outputs)
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Multi-output targets. An indicator matrix turns on multilabel
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estimation.
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:param sample_weight: array-like of shape (n_samples,), default=None
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Sample weights. If `None`, then samples are equally weighted.
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Only supported if the underlying regressor supports sample
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weights.
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:param fit_params: A list of dicts for the fit_params
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Parameters passed to the ``estimator.fit`` method of each step.
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Each dict may contain same or different values (e.g. different
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eval_sets or init_models)
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"""
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if not hasattr(self.estimator, "fit"):
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raise ValueError("The base estimator should implement a fit method")
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y = validate_data(self, X="no_validation", y=y, multi_output=True)
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if y.ndim == 1:
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raise ValueError(
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"y must have at least two dimensions for multi-output regression but has only one."
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)
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if sample_weight is not None and not has_fit_parameter(self.estimator, "sample_weight"):
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raise ValueError("Underlying estimator does not support sample weights.")
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if not fit_params:
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fit_params = [None] * y.shape[1]
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self.estimators_ = Parallel(n_jobs=self.n_jobs)(
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delayed(_fit_estimator)(self.estimator, X, y[:, i], sample_weight, **fit_params[i])
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for i in range(y.shape[1])
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)
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if hasattr(self.estimators_[0], "n_features_in_"):
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self.n_features_in_ = self.estimators_[0].n_features_in_
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if hasattr(self.estimators_[0], "feature_names_in_"):
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self.feature_names_in_ = self.estimators_[0].feature_names_in_
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return
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