mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 04:33:57 +00:00
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
|
|
from joblib import Parallel
|
|
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
|
|
from sklearn.utils.fixes import delayed
|
|
from sklearn.utils.validation import has_fit_parameter
|
|
|
|
|
|
class FreqaiMultiOutputRegressor(MultiOutputRegressor):
|
|
|
|
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 regressor 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 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])
|
|
)
|
|
|
|
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
|