94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
import numpy as np
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from joblib import Parallel
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from sklearn.base import is_classifier
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from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
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from sklearn.utils.fixes import delayed
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.utils.validation import has_fit_parameter
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from freqtrade.exceptions import OperationalException
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class FreqaiMultiOutputClassifier(MultiOutputClassifier):
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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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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The input data.
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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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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 classifier supports sample
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weights.
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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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.. versionadded:: 0.23
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Returns
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-------
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self : object
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Returns a fitted instance.
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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 = self._validate_data(X="no_validation", y=y, multi_output=True)
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if is_classifier(self):
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check_classification_targets(y)
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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 "
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"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(
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self.estimator, "sample_weight"
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):
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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)(
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self.estimator, X, y[:, i], sample_weight, **fit_params[i]
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)
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for i in range(y.shape[1])
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)
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self.classes_ = []
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for estimator in self.estimators_:
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self.classes_.extend(estimator.classes_)
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if len(set(self.classes_)) != len(self.classes_):
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raise OperationalException(f"Class labels must be unique across targets: "
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f"{self.classes_}")
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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 self
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def predict_proba(self, X):
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"""
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Get predict_proba and stack arrays horizontally
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"""
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results = np.hstack(super().predict_proba(X))
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return np.squeeze(results)
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def predict(self, X):
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"""
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Get predict and squeeze into 2D array
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"""
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results = super().predict(X)
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return np.squeeze(results)
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