Class: LogisticRegressionCV
Logistic Regression CV (aka logit, MaxEnt) classifier.
See glossary entry for cross-validation estimator.
This class implements logistic regression using liblinear, newton-cg, sag or lbfgs optimizer. The newton-cg, sag and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. Elastic-Net penalty is only supported by the saga solver.
For the grid of Cs values and l1_ratios values, the best hyperparameter is selected by the cross-validator StratifiedKFold, but it can be changed using the cv parameter. The ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers can warm-start the coefficients (see Glossary).
Read more in the User Guide.
Constructors
new LogisticRegressionCV()
new LogisticRegressionCV(
opts?):LogisticRegressionCV
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.class_weight? | any | Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes \* np.bincount(y)). Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. |
opts.Cs? | number | Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. Like in support vector machines, smaller values specify stronger regularization. |
opts.cv? | number | The default cross-validation generator used is Stratified K-Folds. If an integer is provided, then it is the number of folds used. See the module sklearn.model_selection module for the list of possible cross-validation objects. |
opts.dual? | boolean | Dual (constrained) or primal (regularized, see also this equation) formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=false when n_samples > n_features. |
opts.fit_intercept? | boolean | Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. |
opts.intercept_scaling? | number | Useful only when the solver ‘liblinear’ is used and self.fit_intercept is set to true. In this case, x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling \* synthetic_feature_weight. Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. |
opts.l1_ratios? | any | The list of Elastic-Net mixing parameter, with 0 <= l1_ratio <= 1. Only used if penalty='elasticnet'. A value of 0 is equivalent to using penalty='l2', while 1 is equivalent to using penalty='l1'. For 0 < l1_ratio <1, the penalty is a combination of L1 and L2. |
opts.max_iter? | number | Maximum number of iterations of the optimization algorithm. |
opts.multi_class? | "ovr" | "multinomial" | If the option chosen is ‘ovr’, then a binary problem is fit for each label. For ‘multinomial’ the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. ‘multinomial’ is unavailable when solver=’liblinear’. ‘auto’ selects ‘ovr’ if the data is binary, or if solver=’liblinear’, and otherwise selects ‘multinomial’. |
opts.n_jobs? | number | Number of CPU cores used during the cross-validation loop. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.penalty? | "l1" | "l2" | "elasticnet" | Specify the norm of the penalty: |
opts.random_state? | number | Used when solver='sag', ‘saga’ or ‘liblinear’ to shuffle the data. Note that this only applies to the solver and not the cross-validation generator. See Glossary for details. |
opts.refit? | boolean | If set to true, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged. |
opts.scoring? | string | A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). For a list of scoring functions that can be used, look at sklearn.metrics. The default scoring option used is ‘accuracy’. |
opts.solver? | "lbfgs" | "newton-cholesky" | "liblinear" | "newton-cg" | "sag" | "saga" | Algorithm to use in the optimization problem. Default is ‘lbfgs’. To choose a solver, you might want to consider the following aspects: |
opts.tol? | number | Tolerance for stopping criteria. |
opts.verbose? | number | For the ‘liblinear’, ‘sag’ and ‘lbfgs’ solvers set verbose to any positive number for verbosity. |
Returns LogisticRegressionCV
Defined in generated/linear_model/LogisticRegressionCV.ts:29
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/LogisticRegressionCV.ts:27 |
_isInitialized | boolean | false | generated/linear_model/LogisticRegressionCV.ts:26 |
_py | PythonBridge | undefined | generated/linear_model/LogisticRegressionCV.ts:25 |
id | string | undefined | generated/linear_model/LogisticRegressionCV.ts:22 |
opts | any | undefined | generated/linear_model/LogisticRegressionCV.ts:23 |
Accessors
C_
Get Signature
get C_():
Promise<ArrayLike>
Array of C that maps to the best scores across every class. If refit is set to false, then for each class, the best C is the average of the C’s that correspond to the best scores for each fold. C_ is of shape(n_classes,) when the problem is binary.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:847
classes_
Get Signature
get classes_():
Promise<ArrayLike>
A list of class labels known to the classifier.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:654
coef_
Get Signature
get coef_():
Promise<ArrayLike[]>
Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary.
Returns Promise<ArrayLike[]>
Defined in generated/linear_model/LogisticRegressionCV.ts:683
coefs_paths_
Get Signature
get coefs_paths_():
Promise<ArrayLike[][]>
dict with classes as the keys, and the path of coefficients obtained during cross-validating across each fold and then across each Cs after doing an OvR for the corresponding class as values. If the ‘multi_class’ option is set to ‘multinomial’, then the coefs_paths are the coefficients corresponding to each class. Each dict value has shape (n_folds, n_cs, n_features) or (n_folds, n_cs, n_features + 1) depending on whether the intercept is fit or not. If penalty='elasticnet', the shape is (n_folds, n_cs, n_l1_ratios_, n_features) or (n_folds, n_cs, n_l1_ratios_, n_features + 1).
Returns Promise<ArrayLike[][]>
Defined in generated/linear_model/LogisticRegressionCV.ts:793
Cs_
Get Signature
get Cs_():
Promise<ArrayLike>
Array of C i.e. inverse of regularization parameter values used for cross-validation.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:739
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:955
intercept_
Get Signature
get intercept_():
Promise<ArrayLike>
Intercept (a.k.a. bias) added to the decision function.
If fit_intercept is set to false, the intercept is set to zero. intercept_ is of shape(1,) when the problem is binary.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:712
l1_ratio_
Get Signature
get l1_ratio_():
Promise<ArrayLike>
Array of l1_ratio that maps to the best scores across every class. If refit is set to false, then for each class, the best l1_ratio is the average of the l1_ratio’s that correspond to the best scores for each fold. l1_ratio_ is of shape(n_classes,) when the problem is binary.
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:874
l1_ratios_
Get Signature
get l1_ratios_():
Promise<ArrayLike>
Array of l1_ratios used for cross-validation. If no l1_ratio is used (i.e. penalty is not ‘elasticnet’), this is set to \[undefined\]
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:766
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/LogisticRegressionCV.ts:928
n_iter_
Get Signature
get n_iter_():
Promise<ArrayLike[][]>
Actual number of iterations for all classes, folds and Cs. In the binary or multinomial cases, the first dimension is equal to 1. If penalty='elasticnet', the shape is (n_classes, n_folds, n_cs, n_l1_ratios) or (1, n_folds, n_cs, n_l1_ratios).
Returns Promise<ArrayLike[][]>
Defined in generated/linear_model/LogisticRegressionCV.ts:901
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/LogisticRegressionCV.ts:153
scores_
Get Signature
get scores_():
Promise<any>
dict with classes as the keys, and the values as the grid of scores obtained during cross-validating each fold, after doing an OvR for the corresponding class. If the ‘multi_class’ option given is ‘multinomial’ then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, n_cs) or (n_folds, n_cs, n_l1_ratios) if penalty='elasticnet'.
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:820
Methods
decision_function()
decision_function(
opts):Promise<ArrayLike>
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data matrix for which we want to get the confidence scores. |
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:228
densify()
densify(
opts):Promise<any>
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.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:266
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/linear_model/LogisticRegressionCV.ts:209
fit()
fit(
opts):Promise<any>
Fit the model according to the given training data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters to pass to the underlying splitter and scorer. |
opts.sample_weight? | any | Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target vector relative to X. |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:294
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:345
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/linear_model/LogisticRegressionCV.ts:166
predict()
predict(
opts):Promise<ArrayLike>
Predict class labels for samples in X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data matrix for which we want to get the predictions. |
Returns Promise<ArrayLike>
Defined in generated/linear_model/LogisticRegressionCV.ts:381
predict_log_proba()
predict_log_proba(
opts):Promise<ArrayLike[]>
Predict logarithm of probability estimates.
The returned estimates for all classes are ordered by the label of classes.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Vector to be scored, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike[]>
Defined in generated/linear_model/LogisticRegressionCV.ts:417
predict_proba()
predict_proba(
opts):Promise<ArrayLike[]>
Probability estimates.
The returned estimates for all classes are ordered by the label of classes.
For a multi_class problem, if multi_class is set to be “multinomial” the softmax function is used to find the predicted probability of each class. Else use a one-vs-rest approach, i.e. calculate the probability of each class assuming it to be positive using the logistic function and normalize these values across all the classes.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Vector to be scored, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike[]>
Defined in generated/linear_model/LogisticRegressionCV.ts:457
score()
score(
opts):Promise<number>
Score using the scoring option on the given test data and labels.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.score_params? | any | Parameters to pass to the score method of the underlying scorer. |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike | True labels for X. |
Returns Promise<number>
Defined in generated/linear_model/LogisticRegressionCV.ts:493
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit 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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:546
set_score_request()
set_score_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:586
sparsify()
sparsify(
opts):Promise<any>
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.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/LogisticRegressionCV.ts:626