saealib.SklearnRFCClassificationSurrogate#

class saealib.SklearnRFCClassificationSurrogate(**kwargs)[ソース]#

Random Forest Classification surrogate.

Convenience wrapper around SklearnClassificationSurrogate using sklearn.ensemble.RandomForestClassifier.

パラメータ:: **kwargs (object) -- Keyword arguments forwarded to sklearn.ensemble.RandomForestClassifier.

Methods

`__init__`
`contract`	Return the surrogate model contract.
`fit`	Fit the classifier.
`post_fit`	Post-fit lifecycle hook; override to inject custom processing.
`predict`	Delegate to `predict_proba`.
`predict_proba`	Return class-1 probability estimates.
`with_post_fit`	Return a copy of this surrogate with `fn` appended to the post-fit hook.

Method Details

SklearnRFCClassificationSurrogate.__init__(**kwargs)[ソース]#

SklearnRFCClassificationSurrogate.contract()#

Return the surrogate model contract.

SklearnRFCClassificationSurrogate.fit(train_x, train_y)#

Fit the classifier.

パラメータ:

train_x (np.ndarray) -- Training input data. shape: (n_samples, n_features)
train_y (np.ndarray) -- Binary labels. shape: (n_samples,), values in {0, 1}.

戻り値の型:

None

SklearnRFCClassificationSurrogate.post_fit(train_x, train_y, ctx=None)#

Post-fit lifecycle hook; override to inject custom processing.

パラメータ:

train_x (np.ndarray) -- Training input data used for this fit. shape: (n_samples, n_features)
train_y (np.ndarray) -- Training output data used for this fit. shape: (n_samples, n_obj)
ctx (OptimizationState or None, optional) -- Current optimization context.

戻り値の型:

None

SklearnRFCClassificationSurrogate.predict(test_x)#

Delegate to predict_proba.

SklearnRFCClassificationSurrogate.predict_proba(test_x)#

Return class-1 probability estimates.

パラメータ:: test_x (np.ndarray) -- Input data. shape: (n_samples, n_features) or (n_features,)
戻り値:: value shape: (n_samples, 1), values in [0, 1].
戻り値の型:: SurrogatePrediction

SklearnRFCClassificationSurrogate.with_post_fit(fn)#

Return a copy of this surrogate with fn appended to the post-fit hook.