calfcv.CalfCV

class calfcv.CalfCV(grid=(-1, 1), verbose=0)

Course approximation linear function with cross validation

CalfCV fits a linear model with coefficients w = (w1, …, wp) to maximize the AUC of the targets predicted by the linear function.

Parameters:

gridthe search grid. Default is (-1, 1).

verbose0 is silent. 1-3 are increasingly verbose

Notes

Only one processor is used due to a bug caused by “Python’s multiprocessing that does fork without exec”. See, https://scikit-learn.org/stable/faq.html#id27

Examples

>>> import numpy
>>> from calfcv import CalfCV
>>> from sklearn.datasets import make_classification as mc
>>> X, y = mc(n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1, random_state=42)
>>> numpy.round(X[0:3, :], 2)
array([[ 1.23, -0.76],
       [ 0.7 , -1.38],
       [ 2.55,  2.5 ]])

>>> y[0:3]
array([0, 0, 1])

>>> cls = CalfCV().fit(X, y)
>>> cls.score(X, y)
0.7

>>> numpy.round(cls.best_score_, 2)
0.82

>>> numpy.round(cls.best_auc_, 2)
array([0.53, 0.8 ])

>>> cls.best_coef_
[1, 1]

>>> numpy.round(cls.best_score_, 2)
0.82

>>> cls.fit_time_ > 0
True

>>> cls.predict(np.array([[3, 5]]))
array([0])

>>> cls.predict_proba(np.array([[3, 5]]))
array([[1., 0.]])

Attributes:

best_coef_array of shape (n_features, )

Estimated coefficients for the linear fit problem. Only one target should be passed, and this is a 1D array of length n_features.

best_score_float

The best auc score over the cross validation

best_auc_array of shape (n_features, )

The cumulative auc by feature.

n_features_in_int

Number of features seen during fit.

classes_list

The unique class labels

fit_time_float

The number of seconds to fit X to y

__init__(grid=(-1, 1), verbose=0)

Initialize CalfCV

decision_function(X)

Identify confidence scores for the samples

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples

Returns:

the decision vector (n_samples)

fit(X, y)

fit X and y

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples.

y : ground truth vector

Returns:

self

fit_transform(X, y)

Fit to the data, then reduce X to the features that contribute positive AUC.

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples

y : array-like of shape (n_samples,)

Returns:

X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features.

predict(X)

Predict class labels for each sample

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples

Returns:

the class prediction of X (n_samples)

predict_proba(X)

Identify the probability of each sample class label

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples

Returns:

the class probabilities of X (n_samples)

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → CalfCV

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

transform(X)

Reduce X to the features that contribute positive AUC.

Arguments:

Xarray-like, shape (n_samples, n_features)

The training input features and samples

Returns:

X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features.

Examples using calfcv.CalfCV

Plot the AUC curve

Plot the AUC curve

Plot the ROC curve

Plot the ROC curve

CalfCV comparison plot

CalfCV comparison plot

Decision boundaries

Decision boundaries