sklearn获得某个参数的不同取值在训练集和测试集上的表现的曲线刻画

from sklearn.svm import SVC

from sklearn.datasets import make_classification

import numpy as np

X,y = make_classification()

def plot_validation_curve(estimator,X,y,param_name="gamma",

                          param_range=np.logspace(-6,-1,5),cv=5,scoring="accuracy"):

    """

    描述：获得某个参数的不同取值在训练集和测试集上的表现

    """

    from sklearn.model_selection import validation_curve

    import matplotlib.pyplot as plt

    train_scores,test_scores = validation_curve(estimator=estimator,

                                                X=X,

                                                y=y,

                                                cv=cv,

                                                scoring=scoring,

                                                param_name=param_name,

                                                param_range=param_range)

    train_scores_mean = np.mean(train_scores, axis=1)

    train_scores_std  = np.std(train_scores, axis=1)

    test_scores_mean  = np.mean(test_scores, axis=1)

    test_scores_std   = np.std(test_scores, axis=1)

    plt.title("Validation Curve")

    plt.xlabel("$\gamma$")

    plt.ylabel("Score")

    plt.ylim(0.0, 1.1)

    plt.semilogx(param_range,train_scores_mean,label="Training score",color="darkorange", lw=2)

    plt.fill_between(param_range,

                     train_scores_mean-train_scores_std,

                     train_scores_mean+train_scores_std,

                     alpha=0.2,

                     color="darkorange",

                     lw=2)

    plt.semilogx(param_range, test_scores_mean, label="Cross-validation score",color="navy", lw=2)

    plt.fill_between(param_range,

                     test_scores_mean - test_scores_std,

                     test_scores_mean + test_scores_std,

                     alpha=0.2,

                     color="navy",

                     lw=2)

    plt.legend(loc="best")

    plt.show()

plot_validation_curve(estimator=SVC(),

                      X=X,y=y,

                      param_name="gamma",

                      param_range=np.logspace(-6,-1,5),cv=5,scoring="accuracy")