calfcv.Calf

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

Course approximation linear function

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

The feature matrix must be centered at 0. This can be accomplished with sklearn.preprocessing.StandardScaler, or similar. No intercept is calculated.

Examples

>>> import numpy
>>> from calfcv import Calf
>>> 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

>>> 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:

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.

auc_array of shape (n_features, )

The cumulative auc up to each 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 Calf

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 Calf to 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)

Probability estimates for samples in X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Vector to be scored, where n_samples is the number of samples and n_features is the number of features.

Returns:

T: array-like of shape (n_samples, n_classes)

Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

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

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.Calf

Calf runtime complexity

Calf runtime complexity

CalfCV comparison plot

CalfCV comparison plot

Compare Calf with LogisticRegression

Compare Calf with LogisticRegression