Example Heterogeneous

In this example we show that the framework can also be used using different classifier models in the pool of classifiers. Such pool of classifiers are called Heterogeneous.

Here we consider a pool of classifiers composed of a Gaussian Naive Bayes, Perceptron, k-NN, Decision tree Linear SVM and Gaussian SVM

# Importing dynamic selection techniques:
from deslib.dcs.a_posteriori import APosteriori
from deslib.dcs.mcb import MCB
from deslib.dcs.lca import LCA
from deslib.des.probabilistic import RRC
from deslib.des.knop import KNOP
from deslib.des.knora_e import KNORAE

# Base classifier models:
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV

# Importing dataset
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler


if __name__ == "__main__":
    # Generate a classification dataset
    data = load_breast_cancer()
    X = data.data
    y = data.target

    # split the data into training and test data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

    # Scale the variables to have 0 mean and unit variance
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    # Split the data into training and DSEL for DS techniques
    X_train, X_dsel, y_train, y_dsel = train_test_split(X, y, test_size=0.5)

    model_perceptron = CalibratedClassifierCV(Perceptron(max_iter=100)).fit(X_train, y_train)
    model_linear_svm = CalibratedClassifierCV(LinearSVC()).fit(X_train, y_train)
    model_svc = SVC(probability=True).fit(X_train, y_train)
    model_bayes = GaussianNB().fit(X_train, y_train)
    model_tree = DecisionTreeClassifier().fit(X_train, y_train)
    model_knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)
    pool_classifiers = [model_perceptron, model_linear_svm, model_svc, model_bayes,  model_tree, model_knn]

    # Initializing the DS techniques
    knop = KNOP(pool_classifiers)
    rrc = RRC(pool_classifiers)
    lca = LCA(pool_classifiers)
    mcb = MCB(pool_classifiers)
    aposteriori = APosteriori(pool_classifiers)

    # Fitting the techniques
    knop.fit(X_dsel, y_dsel)
    rrc.fit(X_dsel, y_dsel)
    lca.fit(X_dsel, y_dsel)
    mcb.fit(X_dsel, y_dsel)
    aposteriori.fit(X_dsel, y_dsel)

    # Calculate classification accuracy of each technique
    print('Evaluating DS techniques:')
    print('Classification accuracy KNOP: ', knop.score(X_test, y_test))
    print('Classification accuracy RRC: ', rrc.score(X_test, y_test))
    print('Classification accuracy LCA: ', lca.score(X_test, y_test))
    print('Classification accuracy A posteriori: ', aposteriori.score(X_test, y_test))

Total running time of the script: ( 0 minutes 0.000 seconds)