API CheatSheet¶

Full API Reference: (https://pycombo.readthedocs.io/en/latest/api.html). The following APIs are consistent for most of the models (API Cheatsheet: https://pycombo.readthedocs.io/en/latest/api_cc.html).

combo.models.base.BaseAggregator.fit(): Fit estimator. y is optional for unsupervised methods.
combo.models.base.BaseAggregator.predict(): Predict on a particular sample once the estimator is fitted.
combo.models.base.BaseAggregator.predict_proba(): Predict the probability of a sample belonging to each class once the estimator is fitted.
combo.models.base.BaseAggregator.fit_predict(): Fit estimator and predict on X. y is optional for unsupervised methods.

Helpful functions:

combo.models.base.BaseAggregator.get_params(): Get the parameters of the model.
combo.models.base.BaseAggregator.set_params(): Set the parameters of the model.
Each base estimator can be accessed by calling clf[i] where i is the estimator index.

For raw score combination (after the score matrix is generated), use individual methods from “score_comb.py” directly. Raw score combination API: (https://pycombo.readthedocs.io/en/latest/api.html#score-combination).

See base class definition below:

combo.models.base module¶

Base class for core models

class combo.models.base.BaseAggregator(base_estimators, pre_fitted=False)[source]¶

Bases: abc.ABC

Abstract class for all combination classes.

Parameters

base_estimators (list, length must be greater than 1) – A list of base estimators. Certain methods must be present, e.g., fit and predict.
pre_fitted (bool, optional (default=False)) – Whether the base estimators are trained. If True, fit process may be skipped.

abstract fit(X, y=None)[source]¶

Fit estimator. y is optional for unsupervised methods.

Parameters

X (numpy array of shape (n_samples, n_features)) – The input samples.
y (numpy array of shape (n_samples,), optional (default=None)) – The ground truth of the input samples (labels).

Returns

Return type

self

abstract fit_predict(X, y=None)[source]¶

Fit estimator and predict on X. y is optional for unsupervised methods.

Parameters

X (numpy array of shape (n_samples, n_features)) – The input samples.
y (numpy array of shape (n_samples,), optional (default=None)) – The ground truth of the input samples (labels).

Returns

labels – Class labels for each data sample.

Return type

numpy array of shape (n_samples,)

get_params(deep=True)[source]¶

Get parameters for this estimator.

See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html and sklearn/base.py for more information.

Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: mapping of string to any

abstract predict(X)[source]¶

Predict the class labels for the provided data.

Parameters: X (numpy array of shape (n_samples, n_features)) – The input samples.
Returns: labels – Class labels for each data sample.
Return type: numpy array of shape (n_samples,)

abstract predict_proba(X)[source]¶

Return probability estimates for the test data X.

Parameters: X (numpy array of shape (n_samples, n_features)) – The input samples.
Returns: p – The class probabilities of the input samples. Classes are ordered by lexicographic order.
Return type: numpy array of shape (n_samples,)

set_params(**params)[source]¶

Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html and sklearn/base.py for more information.

Returns: self
Return type: object