elasticnet_classifier

Module Contents

Classes Summary

ElasticNetClassifier

Elastic Net Classifier. Uses Logistic Regression with elasticnet penalty as the base estimator.

Contents

class evalml.pipelines.components.estimators.classifiers.elasticnet_classifier.ElasticNetClassifier(penalty='elasticnet', C=1.0, l1_ratio=0.15, multi_class='auto', solver='saga', n_jobs=- 1, random_seed=0, **kwargs)

Elastic Net Classifier. Uses Logistic Regression with elasticnet penalty as the base estimator.

Parameters

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

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

• l1_ratio (float) – The 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. Defaults to 0.15.

• 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 “saga”.

• 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	{ “C”: Real(0.01, 10), “l1_ratio”: Real(0, 1)}
model_family	ModelFamily.LINEAR_MODEL
modifies_features	True
modifies_target	False
name	Elastic Net Classifier
predict_uses_y	False
supported_problem_types	[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]

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	Returns importance associated with each feature.
fit	Fits component to data
load	Loads component at file path
needs_fitting	Returns boolean determining if component needs fitting before
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

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

prints and returns dictionary

Return type

None or dict

property feature_importance(self)

Returns importance associated with each feature.

Returns

Importance associated with each feature

Return type

np.ndarray

fit(self, X, y)

Fits component to data

Parameters

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

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

Returns

self

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.

property parameters(self)

Returns the parameters which were used to initialize the component

predict(self, X)

Make predictions using selected features.

Parameters

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

Returns

Predicted values

Return type

pd.Series

predict_proba(self, X)

Make probability estimates for labels.

Parameters

X (pd.DataFrame, or np.ndarray) – Features

Returns

Probability estimates

Return type

pd.Series

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.

Returns

None

decision_tree_classifier et_classifier