Multi-Output Calibrated Classifier
- class snapml.MultiOutputCalibratedClassifier(estimator, *, n_jobs=None)
Multi Output Calibrated Classifier used for multi-target classification.
This model fits one classifier per target. It can be used for classifiers that do not have native support for multi-target classification.
- Parameters
- estimatorestimator object
A scikit-learn CalibratedClassifierCV object.
- n_jobsint or None, optional (default=None)
The number of jobs to run in parallel.
fit()andpredict()will be parallelized for each target. When individual estimators are fast to train or predict, usingn_jobs > 1can result in slower performance due to the parallelism overhead.Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all available processes / threads.
- Attributes
- classes_ndarray of shape (n_classes,)
Class labels.
- estimators_list of
n_outputestimators Estimators used for predictions.
- n_features_in_int
Number of features seen during fit. Only defined if the underlying base_estimator of the CalibratedClassifierCV estimator exposes such an attribute when fit.
- feature_names_in_ndarray of shape (n_features_in_,)
Names of features seen during fit. Only defined if the underlying base_estimator of the CalibratedClassifierCV estimator exposes such an attribute when fit.
- fit(X, Y, sample_weight=None, optimize_for_inference=True, **fit_params)
Fit the model to the feature matrix X and labels Y.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The train data.
- Yarray-like of shape (n_samples, n_classes)
The train labels.
- sample_weightarray-like of shape (n_samples,), default=None
The sample weights. If None, all samples have the same weight. Only supported if the underlying classifier supports sample weights.
- optimize_for_inferencebool, default=True
If True, save the model and calibration coefficients as attributes to be used at predict_proba time. It is recommended to use the default setting for performance-optimized inference.
- **fit_paramsdict of string -> object
Parameters passed to the
estimator.fitmethod of each step.
- Returns
- selfobject
Returns a fitted instance.
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- partial_fit(X, y, classes=None, sample_weight=None)
Incrementally fit a separate model for each class output.
- 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.
- classeslist of ndarray of shape (n_outputs,), default=None
Each array is unique classes for one output in str/int. Can be obtained via
[np.unique(y[:, i]) for i in range(y.shape[1])], where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.- sample_weightarray-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.
- Returns
- selfobject
Returns a fitted instance.
- predict(X)
Predict multi-output variable using model for each target variable.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- Returns
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
- predict_proba(X)
Predict class probabilities for each output.
- Parameters
- Xarray-like of shape (n_samples, n_features)
The input data.
- Returns
- parray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
- score(X, y)
Return the mean accuracy on the given test data and labels.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples, n_outputs)
True values for X.
- Returns
- scoresfloat
Mean accuracy of predicted target versus true target.
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.