sklearn----SVM高斯核与多项式核函数

from sklearn import svm
import numpy as np
import time
from sklearn.model_selection import ShuffleSplit
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from matplotlib import pyplot as plt
from ROC import plot_learning_curve


cancer = load_breast_cancer()
X = cancer.data
y = cancer.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# C表示错误可接受的范围，kernel 表示所选择的核函数，gamma时对应选择高斯核的参数
# clf_rbf = svm.SVC(C=1.0, kernel='rbf', gamma=0.1)
# clf_rbf.fit(X_train, y_train)
#
# train_score = clf_rbf.score(X_train, y_train)
# test_score = clf_rbf.score(X_test, y_test)
#
# print("train score:{0}; test score:{1}".format(train_score, test_score))

# # 计算出最佳的gamma值
# gammas = np.linspace(0, 0.0003, 30)
# param_grid = {'gamma': gammas}
# clf = GridSearchCV(svm.SVC(), param_grid, cv=5)
# clf.fit(X, y)
#
# print("best param: {0}nbest score: {1}".format(clf.best_params_, clf.best_score_))
#
# cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
# title = 'Learning Curves for Gaussian Kernel'
#
#
# plt.figure(figsize=(10, 4), dpi=144)
# plot_learning_curve(svm.SVC(C=1.0, kernel='rbf', gamma=0.01), title, X, y, ylim=(0.5, 1.01), cv=cv)
# plt.show()

# # 多项式核函数 degree表示多项式的最高阶
# clf = svm.SVC(C=1.0, kernel='poly', degree=2)
# clf.fit(X_train, y_train)
# train_score = clf.score(X_train, y_train)
# test_score = clf.score(X_test, y_test)
# print("train score:{0}; test score:{1}".format(train_score, test_score))

cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
title = 'Learning Curves with degree={0}'
degree = [1, 2, 3]

plt.figure(figsize=(12, 4), dpi=144)
for i in range(len(degree)):
    plt.subplot(1, len(degree), i+1)
    plot_learning_curve(svm.SVC(C=1, kernel='poly', degree=degree[i]), title.format(degree[i]), X, y, ylim=(0.8, 1.01),
                        cv=cv, n_jobs=4)
plt.show()

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