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# Author:yifan

import numpy as np

import pandas as pd

import matplotlib as mpl

import matplotlib.pyplot as plt

import warnings

? ?

from sklearn import svm #svm导入

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

from sklearn.exceptions import ChangedBehaviorWarning

? ?

## 设置属性防止中文乱码

mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]

mpl.rcParams[‘axes.unicode_minus‘] = False

? ?

warnings.filterwarnings(‘ignore‘, category=ChangedBehaviorWarning)

? ?

## 读取数据

# ‘sepal length‘, ‘sepal width‘, ‘petal length‘, ‘petal width‘

iris_feature = u‘花萼长度‘, u‘花萼宽度‘, u‘花瓣长度‘, u‘花瓣宽度‘

path = ‘./datas/iris.data‘ # 数据文件路径

data = pd.read_csv(path, header=None)

x, y = data[list(range(4))], data[4]

y = pd.Categorical(y).codes #把文本数据进行编码，比如a b c编码为 0 1 2; 可以通过pd.Categorical(y).categories获取index对应的原始值

x = x[[0, 1]] # 获取第一列和第二列

? ?

## 数据分割

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, train_size=0.8)

## 数据SVM分类器构建

clf = svm.SVC(C=1,kernel=‘rbf‘,gamma=0.1)

#gamma值越大，训练集的拟合就越好，但是会造成过拟合，导致测试集拟合变差

#gamma值越小，模型的泛化能力越好，训练集和测试集的拟合相近，但是会导致训练集出现欠拟合问题，从而，准确率变低，导致测试集准确率也变低。

## 模型训练

#SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape=None, degree=3, gamma=0.1, kernel=‘rbf‘,

#max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)

clf.fit(x_train, y_train)

? ?

## 计算模型的准确率/精度

print (clf.score(x_train, y_train))

print (‘训练集准确率：‘, accuracy_score(y_train, clf.predict(x_train)))

print (clf.score(x_test, y_test))

print (‘测试集准确率：‘, accuracy_score(y_test, clf.predict(x_test)))

? ?

# 画图

N = 500

x1_min, x2_min = x.min()

x1_max, x2_max = x.max()

# print(x.max())

t1 = np.linspace(x1_min, x1_max, N)

t2 = np.linspace(x2_min, x2_max, N)

x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点

grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点

? ?

grid_hat = clf.predict(grid_show) # 预测分类值

grid_hat = grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

cm_light = mpl.colors.ListedColormap([‘#00FFCC‘, ‘#FFA0A0‘, ‘#A0A0FF‘])

cm_dark = mpl.colors.ListedColormap([‘g‘, ‘r‘, ‘b‘])

plt.figure(facecolor=‘w‘)

## 区域图

plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

plt.show()

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# Author:yifan

? ?

import numpy as np

import pandas as pd

import matplotlib as mpl

import matplotlib.pyplot as plt

from sklearn.svm import SVC

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

from sklearn.linear_model import LogisticRegression,RidgeClassifier

from sklearn.neighbors import KNeighborsClassifier

? ?

## 设置属性防止中文乱码

mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]

mpl.rcParams[‘axes.unicode_minus‘] = False

## 读取数据

# ‘sepal length‘, ‘sepal width‘, ‘petal length‘, ‘petal width‘

iris_feature = u‘花萼长度‘, u‘花萼宽度‘, u‘花瓣长度‘, u‘花瓣宽度‘

path = ‘./datas/iris.data‘ # 数据文件路径

data = pd.read_csv(path, header=None)

x, y = data[list(range(4))], data[4]

y = pd.Categorical(y).codes

x = x[[0, 1]]

? ?

## 数据分割

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6)

? ?

# 数据SVM分类器构建

svm = SVC(C=1, kernel=‘linear‘)

## Linear分类器构建

lr = LogisticRegression()

rc = RidgeClassifier()#ridge是为了解决特征大于样本，而导致分类效果较差的情况，而提出的

#svm有一个重要的瓶颈——当特征数大于样本数的时候，效果变差

knn = KNeighborsClassifier()

? ?

## 模型训练

svm.fit(x_train, y_train)

lr.fit(x_train, y_train)

rc.fit(x_train, y_train)

knn.fit(x_train, y_train)

? ?

## 效果评估

svm_score1 = accuracy_score(y_train, svm.predict(x_train))

svm_score2 = accuracy_score(y_test, svm.predict(x_test))

? ?

lr_score1 = accuracy_score(y_train, lr.predict(x_train))

lr_score2 = accuracy_score(y_test, lr.predict(x_test))

? ?

rc_score1 = accuracy_score(y_train, rc.predict(x_train))

rc_score2 = accuracy_score(y_test, rc.predict(x_test))

? ?

knn_score1 = accuracy_score(y_train, knn.predict(x_train))

knn_score2 = accuracy_score(y_test, knn.predict(x_test))

? ?

## 画图

x_tmp = [0,1,2,3]

y_score1 = [svm_score1, lr_score1, rc_score1, knn_score1]

y_score2 = [svm_score2, lr_score2, rc_score2, knn_score2]

? ?

plt.figure(facecolor=‘w‘)

plt.plot(x_tmp, y_score1, ‘r-‘, lw=2, label=u‘训练集准确率‘)

plt.plot(x_tmp, y_score2, ‘g-‘, lw=2, label=u‘测试集准确率‘)

plt.xlim(0, 3)

plt.ylim(np.min((np.min(y_score1), np.min(y_score2)))*0.9, np.max((np.max(y_score1), np.max(y_score2)))*1.1)

plt.legend(loc = ‘lower right‘)

plt.title(u‘鸢尾花数据不同分类器准确率比较‘, fontsize=16)

plt.xticks(x_tmp, [u‘SVM‘, u‘Logistic‘, u‘Ridge‘, u‘KNN‘], rotation=0)

plt.grid(b=True)

plt.show()

? ?

? ?

### 画图比较

N = 500

x1_min, x2_min = x.min()

x1_max, x2_max = x.max()

? ?

t1 = np.linspace(x1_min, x1_max, N)

t2 = np.linspace(x2_min, x2_max, N)

x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点

grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点

? ?

## 获取各个不同算法的测试值

svm_grid_hat = svm.predict(grid_show)

svm_grid_hat = svm_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

lr_grid_hat = lr.predict(grid_show)

lr_grid_hat = lr_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

rc_grid_hat = rc.predict(grid_show)

rc_grid_hat = rc_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

knn_grid_hat = knn.predict(grid_show)

knn_grid_hat = knn_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

## 画图

cm_light = mpl.colors.ListedColormap([‘#A0FFA0‘, ‘#FFA0A0‘, ‘#A0A0FF‘])

cm_dark = mpl.colors.ListedColormap([‘g‘, ‘r‘, ‘b‘])

plt.figure(facecolor=‘w‘, figsize=(14,7))

? ?

### svm 区域图

plt.subplot(221)

plt.pcolormesh(x1, x2, svm_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(222)

## 区域图

plt.pcolormesh(x1, x2, lr_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花Logistic特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(223)

## 区域图

plt.pcolormesh(x1, x2, rc_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花Ridge特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(224)

## 区域图

plt.pcolormesh(x1, x2, knn_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花KNN特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

plt.show()

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# Author:yifan

import time

import numpy as np

import pandas as pd

import matplotlib as mpl

import matplotlib.pyplot as plt

from sklearn.svm import SVC

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

? ?

## 设置属性防止中文乱码

mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]

mpl.rcParams[‘axes.unicode_minus‘] = False

## 读取数据

# ‘sepal length‘, ‘sepal width‘, ‘petal length‘, ‘petal width‘

iris_feature = u‘花萼长度‘, u‘花萼宽度‘, u‘花瓣长度‘, u‘花瓣宽度‘

path = ‘./datas/iris.data‘ # 数据文件路径

data = pd.read_csv(path, header=None)

x, y = data[list(range(4))], data[4]

y = pd.Categorical(y).codes

x = x[[0, 1]]

? ?

## 数据分割

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6)

? ?

## 数据SVM分类器构建

svm1 = SVC(C=1, kernel=‘linear‘)

svm2 = SVC(C=1, kernel=‘rbf‘)

svm3 = SVC(C=1, kernel=‘poly‘)

svm4 = SVC(C=1, kernel=‘sigmoid‘)

? ?

## 模型训练

t0=time.time()

svm1.fit(x_train, y_train)

t1=time.time()

svm2.fit(x_train, y_train)

t2=time.time()

svm3.fit(x_train, y_train)

t3=time.time()

svm4.fit(x_train, y_train)

t4=time.time()

? ?

### 效果评估

svm1_score1 = accuracy_score(y_train, svm1.predict(x_train))

svm1_score2 = accuracy_score(y_test, svm1.predict(x_test))

? ?

svm2_score1 = accuracy_score(y_train, svm2.predict(x_train))

svm2_score2 = accuracy_score(y_test, svm2.predict(x_test))

? ?

svm3_score1 = accuracy_score(y_train, svm3.predict(x_train))

svm3_score2 = accuracy_score(y_test, svm3.predict(x_test))

? ?

svm4_score1 = accuracy_score(y_train, svm4.predict(x_train))

svm4_score2 = accuracy_score(y_test, svm4.predict(x_test))

? ?

## 画图

x_tmp = [0,1,2,3]

t_score = [t1 - t0, t2-t1, t3-t2, t4-t3]

y_score1 = [svm1_score1, svm2_score1, svm3_score1, svm4_score1]

y_score2 = [svm1_score2, svm2_score2, svm3_score2, svm4_score2]

? ?

plt.figure(facecolor=‘w‘, figsize=(12,6))

? ?

? ?

plt.subplot(121)

plt.plot(x_tmp, y_score1, ‘r-‘, lw=2, label=u‘训练集准确率‘)

plt.plot(x_tmp, y_score2, ‘g-‘, lw=2, label=u‘测试集准确率‘)

plt.xlim(-0.3, 3.3)

plt.ylim(np.min((np.min(y_score1), np.min(y_score2)))*0.9, np.max((np.max(y_score1), np.max(y_score2)))*1.1)

plt.legend(loc = ‘lower left‘)

plt.title(u‘模型预测准确率‘, fontsize=13)

plt.xticks(x_tmp, [u‘linear-SVM‘, u‘rbf-SVM‘, u‘poly-SVM‘, u‘sigmoid-SVM‘], rotation=0)

plt.grid(b=True)

? ?

plt.subplot(122)

plt.plot(x_tmp, t_score, ‘b-‘, lw=2, label=u‘模型训练时间‘)

plt.title(u‘模型训练耗时‘, fontsize=13)

plt.xticks(x_tmp, [u‘linear-SVM‘, u‘rbf-SVM‘, u‘poly-SVM‘, u‘sigmoid-SVM‘], rotation=0)

plt.xlim(-0.3, 3.3)

plt.grid(b=True)

plt.suptitle(u‘鸢尾花数据SVM分类器不同内核函数模型比较‘, fontsize=16)

? ?

plt.show()

? ?

? ?

### 预测结果画图

### 画图比较

N = 500

x1_min, x2_min = x.min()

x1_max, x2_max = x.max()

? ?

t1 = np.linspace(x1_min, x1_max, N)

t2 = np.linspace(x2_min, x2_max, N)

x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点

grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点

? ?

## 获取各个不同算法的测试值

svm1_grid_hat = svm1.predict(grid_show)

svm1_grid_hat = svm1_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

svm2_grid_hat = svm2.predict(grid_show)

svm2_grid_hat = svm2_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

svm3_grid_hat = svm3.predict(grid_show)

svm3_grid_hat = svm3_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

svm4_grid_hat = svm4.predict(grid_show)

svm4_grid_hat = svm4_grid_hat.reshape(x1.shape) # 使之与输入的形状相同

? ?

## 画图

cm_light = mpl.colors.ListedColormap([‘#A0FFA0‘, ‘#FFA0A0‘, ‘#A0A0FF‘])

cm_dark = mpl.colors.ListedColormap([‘g‘, ‘r‘, ‘b‘])

plt.figure(facecolor=‘w‘, figsize=(14,7))

? ?

### svm

plt.subplot(221)

## 区域图

plt.pcolormesh(x1, x2, svm1_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花Linear-SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(222)

## 区域图

plt.pcolormesh(x1, x2, svm2_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花rbf-SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(223)

## 区域图

plt.pcolormesh(x1, x2, svm3_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花poly-SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

? ?

plt.subplot(224)

## 区域图

plt.pcolormesh(x1, x2, svm4_grid_hat, cmap=cm_light)

## 所以样本点

plt.scatter(x[0], x[1], c=y, edgecolors=‘k‘, s=50, cmap=cm_dark) # 样本

## 测试数据集

plt.scatter(x_test[0], x_test[1], s=120, facecolors=‘none‘, zorder=10) # 圈中测试集样本

## lable列表

plt.xlabel(iris_feature[0], fontsize=13)

plt.ylabel(iris_feature[1], fontsize=13)

plt.xlim(x1_min, x1_max)

plt.ylim(x2_min, x2_max)

plt.title(u‘鸢尾花sigmoid-SVM特征分类‘, fontsize=16)

plt.grid(b=True, ls=‘:‘)

plt.tight_layout(pad=1.5)

plt.show()

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# Author:yifan

import numpy as np

import matplotlib.pyplot as plt

import matplotlib as mpl

import matplotlib.font_manager

from sklearn import svm

## 设置属性防止中文乱码

mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]

mpl.rcParams[‘axes.unicode_minus‘] = False

? ?

# 模拟数据产生

xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))

# 产生训练数据

X = 0.3 * np.random.randn(100, 2)

X_train = np.r_[X + 2, X - 2]

# 产测试数据

X = 0.3 * np.random.randn(20, 2)

X_test = np.r_[X + 2, X - 2]

# 产生一些异常点数据

X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

? ?

# 模型训练

clf = svm.OneClassSVM(nu=0.01, kernel="rbf", gamma=0.1)

clf.fit(X_train)

? ?

# 预测结果获取

y_pred_train = clf.predict(X_train)

y_pred_test = clf.predict(X_test)

y_pred_outliers = clf.predict(X_outliers)

# 返回1表示属于这个类别，-1表示不属于这个类别

n_error_train = y_pred_train[y_pred_train == -1].size

n_error_test = y_pred_test[y_pred_test == -1].size

n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

? ?

# 获取绘图的点信息

Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])

Z = Z.reshape(xx.shape)

? ?

# 画图

plt.figure(facecolor=‘w‘)

plt.title("异常点检测")

# 画出区域图

plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 9), cmap=plt.cm.PuBu)

a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors=‘darkred‘)

plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors=‘palevioletred‘)

# 画出点图

s = 40

b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c=‘white‘, s=s, edgecolors=‘k‘)

b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c=‘blueviolet‘, s=s, edgecolors=‘k‘)

c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c=‘gold‘, s=s, edgecolors=‘k‘)

? ?

# 设置相关信息

plt.axis(‘tight‘)

plt.xlim((-5, 5))

plt.ylim((-5, 5))

plt.legend([a.collections[0], b1, b2, c],

["分割超平面", "训练样本", "测试样本", "异常点"],

loc="upper left",

prop=matplotlib.font_manager.FontProperties(size=11))

plt.xlabel("训练集错误率: %d/200 ; 测试集错误率: %d/40 ; 异常点错误率: %d/40" \

% (n_error_train, n_error_test, n_error_outliers))

plt.show()

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# Author:yifan

import numpy as np

import matplotlib.pyplot as plt

import matplotlib as mpl

from matplotlib.colors import ListedColormap

from sklearn import svm

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.neighbors import KNeighborsClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier

from sklearn.linear_model import LogisticRegressionCV

## 设置属性防止中文乱码

mpl.rcParams[‘font.sans-serif‘] = [u‘SimHei‘]

mpl.rcParams[‘axes.unicode_minus‘] = False

#构造数据

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=0.4, random_state=1),

linearly_separable]

#建模环节，用list把所有算法装起来

names = ["Nearest Neighbors", "Logistic","Decision Tree", "Random Forest", "AdaBoost", "GBDT","svm"]

classifiers = [

KNeighborsClassifier(3),

LogisticRegressionCV(),

DecisionTreeClassifier(max_depth=5),

RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),

AdaBoostClassifier(n_estimators=10,learning_rate=1.5),

GradientBoostingClassifier(n_estimators=10, learning_rate=1.5),

svm.SVC(C=1, kernel=‘rbf‘)

]

## 画图

figure = plt.figure(figsize=(27, 9), facecolor=‘w‘)

i = 1

h = .02 # 步长

? ?

for ds in datasets:

X, y = ds

X = StandardScaler().fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

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))

cm = plt.cm.RdBu

cm_bright = ListedColormap([‘r‘, ‘b‘, ‘y‘])

ax = plt.subplot(len(datasets), len(classifiers) + 1, i)

ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)

ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)

ax.set_xlim(xx.min(), xx.max())

ax.set_ylim(yy.min(), yy.max())

ax.set_xticks(())

ax.set_yticks(())

i += 1

# 画每个算法的图

for name, clf in zip(names, classifiers):

ax = plt.subplot(len(datasets), len(classifiers) + 1, i)

clf.fit(X_train, y_train)

score = clf.score(X_test, y_test)

# hasattr是判定某个模型中，有没有哪个参数，

# 判断clf模型中，有没有decision_function

# np.c_让内部数据按列合并

if hasattr(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]

? ?

Z = Z.reshape(xx.shape)

ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)

ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,

alpha=0.6)

? ?

ax.set_xlim(xx.min(), xx.max())

ax.set_ylim(yy.min(), yy.max())

ax.set_xticks(())

ax.set_yticks(())

ax.set_title(name)

ax.text(xx.max() - .3, yy.min() + .3, (‘%.2f‘ % score).lstrip(‘0‘),

size=25, horizontalalignment=‘right‘)

i += 1

## 展示图

figure.subplots_adjust(left=.02, right=.98)

plt.show()

# plt.savefig("cs.png")