Ejemplo de Scikit_Learn: ejemplo de comparación de clasificador

Una comparación de varios clasificadores en scikit-learn en conjuntos de datos sintéticos. El objetivo de este ejemplo es ilustrar la naturaleza de los límites de decisión de diferentes clasificadores. Esto debe tomarse con un grano de sal, ya que la intuición transmitida por estos ejemplos no se traslada necesariamente a los conjuntos de datos reales.

Particularmente en espacios de alta dimensión, los datos se pueden separar más fácilmente de forma lineal y la simplicidad de los clasificadores, como los Bayes ingenuos y las SVM lineales, podría conducir a una mejor generalización que la que logran otros clasificadores.

Los gráficos muestran puntos de entrenamiento en colores sólidos y puntos de prueba semitransparentes. La parte inferior derecha muestra la precisión de clasificación en el equipo de prueba.

print(__doc__)# Code source: Gaël Varoquaux#              Andreas Müller# Modified for documentation by Jaques Grobler# License: BSD 3 clauseimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

h =.02# step size in the mesh

names =["Nearest Neighbors","Linear SVM","RBF SVM","Gaussian Process","Decision Tree","Random Forest","Neural Net","AdaBoost","Naive Bayes","QDA"]

classifiers =[
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0* RBF(1.0)),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1, max_iter=1000),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis()]

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X +=2* rng.uniform(size=X.shape)
linearly_separable =(X, y)

datasets =[make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]

figure = plt.figure(figsize=(27,9))
i =1# iterate over datasetsfor ds_cnt, ds inenumerate(datasets):# preprocess dataset, split into training and test part
    X, y = ds
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = 
        train_test_split(X, y, test_size=.4, random_state=42)

    x_min, x_max = X[:,0].min()-.5, X[:,0].max()+.5
    y_min, y_max = X[:,1].min()-.5, X[:,1].max()+.5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))# just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000','#0000FF'])
    ax = plt.subplot(len(datasets),len(classifiers)+1, i)if ds_cnt ==0:
        ax.set_title("Input data")# Plot the training points
    ax.scatter(X_train[:,0], X_train[:,1], c=y_train, cmap=cm_bright,
               edgecolors='k')# Plot the testing points
    ax.scatter(X_test[:,0], X_test[:,1], c=y_test, cmap=cm_bright, alpha=0.6,
               edgecolors='k')
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())
    i +=1# iterate over classifiersfor name, clf inzip(names, classifiers):
        ax = plt.subplot(len(datasets),len(classifiers)+1, i)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)# Plot the decision boundary. For that, we will assign a color to each# point in the mesh [x_min, x_max]x[y_min, y_max].ifhasattr(clf,"decision_function"):
            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])else:
            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1]# Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)# Plot the training points
        ax.scatter(X_train[:,0], X_train[:,1], c=y_train, cmap=cm_bright,
                   edgecolors='k')# Plot the testing points
        ax.scatter(X_test[:,0], X_test[:,1], c=y_test, cmap=cm_bright,
                   edgecolors='k', alpha=0.6)

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())if ds_cnt ==0:
            ax.set_title(name)
        ax.text(xx.max()-.3, yy.min()+.3,('%.2f'% score).lstrip('0'),
                size=15, horizontalalignment='right')
        i +=1

plt.tight_layout()
plt.show()

Tiempo total de ejecución del script: (0 minutos 7.329 segundos)

Download Python source code: plot_classifier_comparison.py

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