autoprognosis.plugins.prediction.classifiers.plugin_logistic_regression module

class LogisticRegressionPlugin(C: float = 1.0, solver: int = 1, multi_class: int = 0, class_weight: int = 0, max_iter: int = 10000, penalty: str = 'l2', calibration: int = 0, model: Optional[Any] = None, hyperparam_search_iterations: Optional[int] = None, random_state: int = 0, n_jobs: int = 2, **kwargs: Any)

Bases: autoprognosis.plugins.prediction.classifiers.base.ClassifierPlugin

Classification plugin based on the Logistic Regression classifier.

Method:

Logistic regression is a linear model for classification rather than regression. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.

Parameters

• C – float Inverse of regularization strength; must be a positive float.

• solver – int index Algorithm to use in the optimization problem: [‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’]

• multi_class – int 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’.

• class_weight – int index Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.

• max_iter – int Maximum number of iterations taken for the solvers to converge.

• penalty – str Specify the norm of the penalty:

• calibration – int Enable/disable calibration. 0: disabled, 1 : sigmoid, 2: isotonic.

• random_state – int, default 0 Random seed

Example

>>> from autoprognosis.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("logistic_regression")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y) # returns the probabilities for each class

change_output(output: str) → None

classes = ['auto', 'ovr', 'multinomial']

explain(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

fit(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → autoprognosis.plugins.core.base_plugin.Plugin

Train the plugin

Parameters

X – pd.DataFrame

fit_predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

Fit the model and predict the training data. Used by predictors.

fit_transform(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

Fit the model and transform the training data. Used by imputers and preprocessors.

classmethod fqdn() → str

The fully-qualified name of the plugin: type->subtype->name

get_args() → dict

static hyperparameter_space(*args: Any, **kwargs: Any) → List[autoprognosis.plugins.core.params.Params]

The hyperparameter search domain, used for tuning.

classmethod hyperparameter_space_fqdn(*args: Any, **kwargs: Any) → List[autoprognosis.plugins.core.params.Params]

The hyperparameter domain using they fully-qualified name.

is_fitted() → bool

Check if the model was trained

classmethod load(buff: bytes) → autoprognosis.plugins.prediction.classifiers.plugin_logistic_regression.LogisticRegressionPlugin

Load the plugin from bytes

static name() → str

The name of the plugin, e.g.: xgboost

predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

Run predictions for the input. Used by predictors.

Parameters

X – pd.DataFrame

predict_proba(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

classmethod sample_hyperparameters(trial: optuna.trial.Trial, *args: Any, **kwargs: Any) → Dict[str, Any]

Sample hyperparameters for Optuna.

classmethod sample_hyperparameters_fqdn(trial: optuna.trial.Trial, *args: Any, **kwargs: Any) → Dict[str, Any]

Sample hyperparameters using they fully-qualified name.

classmethod sample_hyperparameters_np(random_state: int = 0, *args: Any, **kwargs: Any) → Dict[str, Any]

Sample hyperparameters as a dict.

save() → bytes

Save the plugin to bytes

score(X: pandas.core.frame.DataFrame, y: pandas.core.frame.DataFrame, metric: str = 'aucroc') → float

solvers = ['newton-cg', 'lbfgs', 'sag', 'saga']

static subtype() → str

The type of the plugin, e.g.: classifier

transform(X: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Transform the input. Used by imputers and preprocessors.

Parameters

X – pd.DataFrame

static type() → str

The type of the plugin, e.g.: prediction

weights = ['balanced', None]

plugin

alias of autoprognosis.plugins.prediction.classifiers.plugin_logistic_regression.LogisticRegressionPlugin