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: {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)