lale.lib.sklearn.stacking_classifier module¶
- class lale.lib.sklearn.stacking_classifier.StackingClassifier(*, estimators, final_estimator=None, cv=5, stack_method='auto', n_jobs=None, passthrough=False)¶
Bases:
PlannedIndividualOp
Stacking classifier from scikit-learn for stacking ensemble.
This documentation is auto-generated from JSON schemas.
- Parameters
estimators (array) –
Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.
items : tuple
item 0 : string
item 1 : union type
operator
or None
final_estimator (union type, default None) –
A classifier which will be used to combine the base estimators. The default classifier is a ‘LogisticRegression’
operator
or None
cv (union type, default 5) –
Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator.
union type
integer, >=2, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 5
Number of folds for cross-validation.
or None, not for optimizer
to use the default 5-fold cross validation
or ‘prefit’, not for optimizer
”prefit” to assume the estimators are prefit. In this case, the estimators will not be refitted.
or CrossvalGenerator, not for optimizer
Object with split function: generator yielding (train, test) splits as arrays of indices. Can use any of the iterators from https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators
stack_method (‘auto’, ‘predict_proba’, ‘decision_function’, or ‘predict’, not for optimizer, default ‘auto’) – Methods called for each base estimator. If ‘auto’, it will try to invoke, for each estimator, ‘predict_proba’, ‘decision_function’ or ‘predict’ in that order. Otherwise, one of ‘predict_proba’, ‘decision_function’ or ‘predict’. If the method is not implemented by the estimator, it will raise an error.
n_jobs (union type, not for optimizer, default None) –
The number of jobs to run in parallel for
fit
.integer
or None
passthrough (boolean, default False) – When False, only the predictions of estimators will be used as training data for ‘final_estimator’. When True, the ‘final_estimator’ is trained on the predictions as well as the original training data.
- decision_function(X)¶
Confidence scores for all classes.
Note: The decision_function method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of features.
- Returns
result – The decision function computed by the final estimator.
- Return type
array of items : array of items : float
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of features.
y (union type) –
The target values (class labels).
array of items : float
or array of items : string
or array of items : boolean
sample_weight (union type, optional) –
Sample weights. If None, then samples are equally weighted.
array of items : float
or None
- predict(X, **predict_params)¶
Make predictions.
Note: The predict method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array, optional of items : array of items : float) – The input samples.
- Returns
result – Predicted targets.
- Return type
array of items : float
- predict_proba(X)¶
Probability estimates for all classes.
Note: The predict_proba method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array, optional of items : array of items : float) – The input samples.
- Returns
result – Class probabilities of the input samples.
- Return type
array of items : array of items : float
- transform(X, y=None)¶
Transform the data.
Note: The transform method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array, optional of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of features
- Returns
result – Transformed array
items : array
items : union type
float
or array of items : float
- Return type
array