logistic_regression_classifier

Logistic Regression Classifier.

Module Contents

Classes Summary

LogisticRegressionClassifier

Logistic Regression Classifier.

Contents

class evalml.pipelines.components.estimators.classifiers.logistic_regression_classifier.LogisticRegressionClassifier(penalty='l2', C=1.0, multi_class='auto', solver='lbfgs', n_jobs=-1, random_seed=0, **kwargs)

Logistic Regression Classifier.

Parameters

• penalty ({"l1", "l2", "elasticnet", "none"}) – The norm used in penalization. Defaults to “l2”.

• C (float) – Inverse of regularization strength. Must be a positive float. Defaults to 1.0.

• 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”. Defaults to “auto”.

• solver ({"newton-cg", "lbfgs", "liblinear", "sag", "saga"}) –

  Algorithm to use in the optimization problem. For small datasets, “liblinear” is a good choice, whereas “sag” and “saga” are faster for large ones. For multiclass problems, only “newton-cg”, “sag”, “saga” and “lbfgs” handle multinomial loss; “liblinear” is limited to one-versus-rest schemes.

  • ”newton-cg”, “lbfgs”, “sag” and “saga” handle L2 or no penalty

  • ”liblinear” and “saga” also handle L1 penalty

  • ”saga” also supports “elasticnet” penalty

  • ”liblinear” does not support setting penalty=’none’

  Defaults to “lbfgs”.

• n_jobs (int) – Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to -1.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “penalty”: [“l2”], “C”: Real(0.01, 10),}
model_family	ModelFamily.LINEAR_MODEL
modifies_features	True
modifies_target	False
name	Logistic Regression Classifier
supported_problem_types	[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]
training_only	False

Methods

clone	Constructs a new component with the same parameters and random state.
default_parameters	Returns the default parameters for this component.
describe	Describe a component and its parameters.
feature_importance	Feature importance for fitted logistic regression classifier.
fit	Fits estimator to data.
get_prediction_intervals	Find the prediction intervals using the fitted regressor.
load	Loads component at file path.
needs_fitting	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
parameters	Returns the parameters which were used to initialize the component.
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves component at file path.
update_parameters	Updates the parameter dictionary of the component.

clone(self)

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)

Describe a component and its parameters.

Parameters

• print_name (bool, optional) – whether to print name of component

• return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

property feature_importance(self)

Feature importance for fitted logistic regression classifier.

fit(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None)

Fits estimator to data.

Parameters

• X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

• y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) → Dict[str, pandas.Series]

Find the prediction intervals using the fitted regressor.

This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation.

Parameters

• X (pd.DataFrame) – Data of shape [n_samples, n_features].

• y (pd.Series) – Target data. Ignored.

• coverage (list[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.

• predictions (pd.Series) – Optional list of predictions to use. If None, will generate predictions using X.

Returns

Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.

Return type

dict

Raises

MethodPropertyNotFoundError – If the estimator does not support Time Series Regression as a problem type.

static load(file_path)

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)

Returns the parameters which were used to initialize the component.

predict(self, X: pandas.DataFrame) → pandas.Series

Make predictions using selected features.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].

Returns

Predicted values.

Return type

pd.Series

Raises

MethodPropertyNotFoundError – If estimator does not have a predict method or a component_obj that implements predict.

predict_proba(self, X: pandas.DataFrame) → pandas.Series

Make probability estimates for labels.

Parameters

X (pd.DataFrame) – Features.

Returns

Probability estimates.

Return type

pd.Series

Raises

MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

Saves component at file path.

Parameters

• file_path (str) – Location to save file.

• pickle_protocol (int) – The pickle data stream format.

update_parameters(self, update_dict, reset_fit=True)

Updates the parameter dictionary of the component.

Parameters

• update_dict (dict) – A dict of parameters to update.

• reset_fit (bool, optional) – If True, will set _is_fitted to False.