libact.models package

libact.models.sklearn_adapter module

scikit-learn classifier adapter

class libact.models.sklearn_adapter.SklearnAdapter(clf)

Bases: libact.base.interfaces.Model

Implementation of the scikit-learn classifier to libact model interface.

Parameters:clf (scikit-learn classifier object instance) – The classifier object that is intended to be use with libact

Examples

Here is an example of using SklearnAdapter to classify the iris dataset:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

from libact.base.dataset import Dataset
from libact.models import SklearnAdapter

iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

adapter = SklearnAdapter(LogisticRegression(random_state=1126))

adapter.train(Dataset(X_train, y_train))
adapter.predict(X_test)
predict(feature, *args, **kwargs)
score(testing_dataset, *args, **kwargs)
train(dataset, *args, **kwargs)
class libact.models.sklearn_adapter.SklearnProbaAdapter(clf)

Bases: libact.base.interfaces.ProbabilisticModel

Implementation of the scikit-learn classifier to libact model interface. It should support predict_proba method and predict_real is default to return predict_proba.

Parameters:clf (scikit-learn classifier object instance) – The classifier object that is intended to be use with libact

Examples

Here is an example of using SklearnAdapter to classify the iris dataset:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

from libact.base.dataset import Dataset
from libact.models import SklearnProbaAdapter

iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

adapter = SklearnProbaAdapter(LogisticRegression(random_state=1126))

adapter.train(Dataset(X_train, y_train))
adapter.predict(X_test)
adapter.predict_proba(X_test)
predict(feature, *args, **kwargs)
predict_proba(feature, *args, **kwargs)
predict_real(feature, *args, **kwargs)
score(testing_dataset, *args, **kwargs)
train(dataset, *args, **kwargs)

libact.models.logistic_regression module

This module includes a class for interfacing scikit-learn’s logistic regression model.

class libact.models.logistic_regression.LogisticRegression(*args, **kwargs)

Bases: libact.base.interfaces.ProbabilisticModel

Logistic Regression Classifier

References

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

predict(feature, *args, **kwargs)
predict_proba(feature, *args, **kwargs)
predict_real(feature, *args, **kwargs)
score(testing_dataset, *args, **kwargs)
train(dataset, *args, **kwargs)

libact.models.perceptron module

This module includes a class for interfacing scikit-learn’s perceptron model.

class libact.models.perceptron.Perceptron(*args, **kwargs)

Bases: libact.base.interfaces.Model

A interface for scikit-learn’s perceptron model

References

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html

predict(feature, *args, **kwargs)
score(testing_dataset, *args, **kwargs)
train(dataset, *args, **kwargs)

libact.models.svm module

SVM

An interface for scikit-learn’s C-Support Vector Classifier model.

class libact.models.svm.SVM(*args, **kwargs)

Bases: libact.base.interfaces.ContinuousModel

C-Support Vector Machine Classifier

When decision_function_shape == ‘ovr’, we use OneVsRestClassifier(SVC) from sklearn.multiclass instead of the output from SVC directory since it is not exactly the implementation of One Vs Rest.

References

http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

predict(feature, *args, **kwargs)
predict_real(feature, *args, **kwargs)
score(testing_dataset, *args, **kwargs)
train(dataset, *args, **kwargs)

Module contents

Concrete model classes.