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)

Predict the class labels for the input samples

Parameters:feature (array-like, shape (n_samples, n_features)) – The unlabeled samples whose labels are to be predicted.
Returns:y_pred – The class labels for samples in the feature array.
Return type:array-like, shape (n_samples,)
score(testing_dataset, *args, **kwargs)

Return the mean accuracy on the test dataset

Parameters:testing_dataset (Dataset object) – The testing dataset used to measure the perforance of the trained model.
Returns:score – Mean accuracy of self.predict(X) wrt. y.
Return type:float
train(dataset, *args, **kwargs)

Train a model according to the given training dataset.

Parameters:dataset (Dataset object) – The training dataset the model is to be trained on.
Returns:self – Returns self.
Return type:object
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 the class labels for the input samples

Parameters:feature (array-like, shape (n_samples, n_features)) – The unlabeled samples whose labels are to be predicted.
Returns:y_pred – The class labels for samples in the feature array.
Return type:array-like, shape (n_samples,)
predict_proba(feature, *args, **kwargs)

Predict probability estimate for samples.

Parameters:feature (array-like, shape (n_samples, n_features)) – The samples whose probability estimation are to be predicted.
Returns:X – Each entry is the prabablity estimate for each class.
Return type:array-like, shape (n_samples, n_classes)
predict_real(feature, *args, **kwargs)

Predict confidence scores for samples.

Returns the confidence score for each (sample, class) combination.

The larger the value for entry (sample=x, class=k) is, the more confident the model is about the sample x belonging to the class k.

Take Logistic Regression as example, the return value is the signed distance of that sample to the hyperplane.

Parameters:feature (array-like, shape (n_samples, n_features)) – The samples whose confidence scores are to be predicted.
Returns:X – Each entry is the confidence scores per (sample, class) combination.
Return type:array-like, shape (n_samples, n_classes)
score(testing_dataset, *args, **kwargs)

Return the mean accuracy on the test dataset

Parameters:testing_dataset (Dataset object) – The testing dataset used to measure the perforance of the trained model.
Returns:score – Mean accuracy of self.predict(X) wrt. y.
Return type:float
train(dataset, *args, **kwargs)

Train a model according to the given training dataset.

Parameters:dataset (Dataset object) – The training dataset the model is to be trained on.
Returns:self – Returns self.
Return type:object

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 the class labels for the input samples

Parameters:feature (array-like, shape (n_samples, n_features)) – The unlabeled samples whose labels are to be predicted.
Returns:y_pred – The class labels for samples in the feature array.
Return type:array-like, shape (n_samples,)
predict_proba(feature, *args, **kwargs)

Predict probability estimate for samples.

Parameters:feature (array-like, shape (n_samples, n_features)) – The samples whose probability estimation are to be predicted.
Returns:X – Each entry is the prabablity estimate for each class.
Return type:array-like, shape (n_samples, n_classes)
predict_real(feature, *args, **kwargs)

Predict confidence scores for samples.

Returns the confidence score for each (sample, class) combination.

The larger the value for entry (sample=x, class=k) is, the more confident the model is about the sample x belonging to the class k.

Take Logistic Regression as example, the return value is the signed distance of that sample to the hyperplane.

Parameters:feature (array-like, shape (n_samples, n_features)) – The samples whose confidence scores are to be predicted.
Returns:X – Each entry is the confidence scores per (sample, class) combination.
Return type:array-like, shape (n_samples, n_classes)
score(testing_dataset, *args, **kwargs)

Return the mean accuracy on the test dataset

Parameters:testing_dataset (Dataset object) – The testing dataset used to measure the perforance of the trained model.
Returns:score – Mean accuracy of self.predict(X) wrt. y.
Return type:float
train(dataset, *args, **kwargs)

Train a model according to the given training dataset.

Parameters:dataset (Dataset object) – The training dataset the model is to be trained on.
Returns:self – Returns self.
Return type:object

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)

Predict the class labels for the input samples

Parameters:feature (array-like, shape (n_samples, n_features)) – The unlabeled samples whose labels are to be predicted.
Returns:y_pred – The class labels for samples in the feature array.
Return type:array-like, shape (n_samples,)
score(testing_dataset, *args, **kwargs)

Return the mean accuracy on the test dataset

Parameters:testing_dataset (Dataset object) – The testing dataset used to measure the perforance of the trained model.
Returns:score – Mean accuracy of self.predict(X) wrt. y.
Return type:float
train(dataset, *args, **kwargs)

Train a model according to the given training dataset.

Parameters:dataset (Dataset object) – The training dataset the model is to be trained on.
Returns:self – Returns self.
Return type:object

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 the class labels for the input samples

Parameters:feature (array-like, shape (n_samples, n_features)) – The unlabeled samples whose labels are to be predicted.
Returns:y_pred – The class labels for samples in the feature array.
Return type:array-like, shape (n_samples,)
predict_real(feature, *args, **kwargs)

Predict confidence scores for samples.

Returns the confidence score for each (sample, class) combination.

The larger the value for entry (sample=x, class=k) is, the more confident the model is about the sample x belonging to the class k.

Take Logistic Regression as example, the return value is the signed distance of that sample to the hyperplane.

Parameters:feature (array-like, shape (n_samples, n_features)) – The samples whose confidence scores are to be predicted.
Returns:X – Each entry is the confidence scores per (sample, class) combination.
Return type:array-like, shape (n_samples, n_classes)
score(testing_dataset, *args, **kwargs)

Return the mean accuracy on the test dataset

Parameters:testing_dataset (Dataset object) – The testing dataset used to measure the perforance of the trained model.
Returns:score – Mean accuracy of self.predict(X) wrt. y.
Return type:float
train(dataset, *args, **kwargs)

Train a model according to the given training dataset.

Parameters:dataset (Dataset object) – The training dataset the model is to be trained on.
Returns:self – Returns self.
Return type:object

Module contents

Concrete model classes.