LogisticRegression

Logistic Regression (aka logit, MaxEnt) classifier.

In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross-entropy loss if the ‘multi_class’ option is set to ‘multinomial’. (Currently the ‘multinomial’ option is supported only by the ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)

This class implements regularized logistic regression using the ‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. Note that regularization is applied by default. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).

The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation, or no regularization. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only supported by the ‘saga’ solver.

Constructors

constructor()

Signature

new LogisticRegression(opts?: object): LogisticRegression;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.C?`	`number`	Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. `Default Value` `1`
`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.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. `Default Value` `false`
`opts.fit_intercept?`	`boolean`	Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. `Default Value` `true`
`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. `Default Value` `1`
`opts.l1_ratio?`	`number`	The Elastic-Net mixing parameter, with `0 <= l1\_ratio <= 1`. Only used if `penalty='elasticnet'`. Setting `l1\_ratio=0` is equivalent to using `penalty='l2'`, while setting `l1\_ratio=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 taken for the solvers to converge. `Default Value` `100`
`opts.multi_class?`	`"auto"` \| `"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’. `Default Value` `'auto'`
`opts.n_jobs?`	`number`	Number of CPU cores used when parallelizing over classes if multi_class=’ovr’”. This parameter is ignored when the `solver` is set to ‘liblinear’ regardless of whether ‘multi_class’ is specified or not. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.penalty?`	`"l1"` \| `"l2"` \| `"elasticnet"`	Specify the norm of the penalty: `Default Value` `'l2'`
`opts.random_state?`	`number`	Used when `solver` == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data. See Glossary for details.
`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: `Default Value` `'lbfgs'`
`opts.tol?`	`number`	Tolerance for stopping criteria. `Default Value` `0.0001`
`opts.verbose?`	`number`	For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. `Default Value` `0`
`opts.warm_start?`	`boolean`	When set to `true`, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver. See the Glossary. `Default Value` `false`

Returns

LogisticRegression

Defined in: generated/linear_model/LogisticRegression.ts:29 (opens in a new tab)

Methods

decision_function()

Predict confidence scores for samples.

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

Signature

decision_function(opts: object): Promise<ArrayLike>;

Parameters

Name	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/LogisticRegression.ts:235 (opens in a new tab)

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.

Signature

densify(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/linear_model/LogisticRegression.ts:275 (opens in a new tab)

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/linear_model/LogisticRegression.ts:216 (opens in a new tab)

fit()

Fit the model according to the given training data.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vector, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.sample_weight?`	`any`	Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
`opts.y?`	`ArrayLike`	Target vector relative to X.

Returns

Promise<any>

Defined in: generated/linear_model/LogisticRegression.ts:303 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/linear_model/LogisticRegression.ts:354 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/linear_model/LogisticRegression.ts:156 (opens in a new tab)

predict()

Predict class labels for samples in X.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

Name	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/LogisticRegression.ts:392 (opens in a new tab)

predict_log_proba()

Predict logarithm of probability estimates.

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

Signature

predict_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

Name	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/LogisticRegression.ts:429 (opens in a new tab)

predict_proba()

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.

Signature

predict_proba(opts: object): Promise<ArrayLike[]>;

Parameters

Name	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/LogisticRegression.ts:471 (opens in a new tab)

score()

Return the mean accuracy on the given test 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.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True labels for `X`.

Returns

Promise<number>

Defined in: generated/linear_model/LogisticRegression.ts:510 (opens in a new tab)

set_fit_request()

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:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

Name	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/LogisticRegression.ts:563 (opens in a new tab)

set_score_request()

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:

Signature

set_score_request(opts: object): Promise<any>;

Parameters

Name	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/LogisticRegression.ts:605 (opens in a new tab)

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.

Signature

sparsify(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/linear_model/LogisticRegression.ts:647 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/LogisticRegression.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/LogisticRegression.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/LogisticRegression.ts:25 (opens in a new tab)

id

string

Defined in: generated/linear_model/LogisticRegression.ts:22 (opens in a new tab)

opts

any

Defined in: generated/linear_model/LogisticRegression.ts:23 (opens in a new tab)

Accessors

classes_

A list of class labels known to the classifier.

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LogisticRegression.ts:675 (opens in a new tab)

coef_

Coefficient of the features in the decision function.

coef\_ is of shape (1, n_features) when the given problem is binary. In particular, when multi\_class='multinomial', coef\_ corresponds to outcome 1 (true) and \-coef\_ corresponds to outcome 0 (false).

Signature

coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LogisticRegression.ts:704 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LogisticRegression.ts:787 (opens in a new tab)

intercept_

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 given problem is binary. In particular, when multi\_class='multinomial', intercept\_ corresponds to outcome 1 (true) and \-intercept\_ corresponds to outcome 0 (false).

Signature

intercept_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LogisticRegression.ts:733 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/LogisticRegression.ts:760 (opens in a new tab)

n_iter_

Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given.