asreview.models.classifiers.Logistic#

class asreview.models.classifiers.Logistic(penalty='deprecated', *, C=1.0, l1_ratio=0.0, dual=False, tol=0.0001, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, verbose=0, warm_start=False, n_jobs=None)[source]#

Bases: LogisticRegression

Logistic regression classifier.

Based on the sklearn implementation of the logistic regression sklearn.linear_model.LogisticRegression.

Methods

`__init__`([penalty, C, l1_ratio, dual, tol, ...])
`decision_function`(X)	Predict confidence scores for samples.
`densify`()	Convert coefficient matrix to dense array format.
`fit`(X, y[, sample_weight])	Fit the model according to the given training data.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict class labels for samples in X.
`predict_log_proba`(X)	Predict logarithm of probability estimates.
`predict_proba`(X)	Probability estimates.
`score`(X, y[, sample_weight])	Return accuracy on provided data and labels.
`set_callbacks`(*callbacks)	Set callbacks for the estimator.
`set_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.
`sparsify`()	Convert coefficient matrix to sparse format.

Attributes

`label`
`name`

decision_function(X)#

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data matrix for which we want to get the confidence scores.
Returns:: scores (ndarray of shape (n_samples,) or (n_samples, n_classes)) – Confidence scores per (n_samples, n_classes) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

densify()#

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns:: self – Fitted estimator.

fit(X, y, sample_weight=None)#

Fit the model according to the given training data.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.
y (array-like of shape (n_samples,)) – Target vector relative to X.
sample_weight (array-like of shape (n_samples,) default=None) –
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Added in version 0.17: sample_weight support to LogisticRegression.

Returns:

self – Fitted estimator.

Notes

The SAGA solver supports both float64 and float32 bit arrays.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing (MetadataRequest) – A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params (dict) – Parameter names mapped to their values.

label = 'Logistic regression'#

name = 'logistic'#

predict(X)#

Predict class labels for samples in X.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data matrix for which we want to get the predictions.
Returns:: y_pred (ndarray of shape (n_samples,)) – Vector containing the class labels for each sample.

predict_log_proba(X)#

Predict logarithm of probability estimates.

The returned estimates for all classes are ordered by the label of classes.

Parameters:: X (array-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 log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

predict_proba(X)#

Probability estimates.

The returned estimates for all classes are ordered by the label of classes.

For a multiclass / multinomial problem the softmax function is used to find the predicted probability of each class.

Parameters:: X (array-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_.

score(X, y, sample_weight=None)#

Return accuracy on provided data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score (float) – Mean accuracy of self.predict(X) w.r.t. y.

set_callbacks(*callbacks)#

Set callbacks for the estimator.

Parameters:: *callbacks (callback instances) – The callbacks to set.
Returns:: self (estimator instance) – The estimator instance itself.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Logistic#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit 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 fit.
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.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.
Returns:: self (object) – The updated object.

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:: **params (dict) – Estimator parameters.
Returns:: self (estimator instance) – Estimator instance.

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

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self (object) – The updated object.

sparsify()#

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Warning

This method is not supported for estimators fitted with array API inputs (i.e. when sklearn.config_context() is used with array_api_dispatch=True). The call may succeed but subsequent calls to predict() and other methods involving passing arrays may raise or return unexpected results.

Returns:: self – Fitted estimator.

Notes

For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.