# 前言

`提示：以下是本篇文章正文内容，下面案例可供参考`

# 一、源码解析

## 1.引入库

``````import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
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 import svm  # svm分类器
from sklearn.neighbors import KNeighborsClassifier  # K近邻分类器
from sklearn.tree import DecisionTreeClassifier  # 决策树分类器
from sklearn.linear_model import LogisticRegressionCV  # 逻辑回归
``````

## 2.设置属性防止中文乱码

``````mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
``````

## 3.获得随机数生成器

``````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)  # 获得随机数生成器
X += 2 * rng.uniform(size=X.shape)  # 产生随机数加到X样本上
linearly_separable = (X, y)  # 生成线性的独立的数据

datasets = [make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.4, random_state=1),
linearly_separable
]
``````

## 4.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),
svm.SVC(C=1, kernel='rbf')
]
``````

## 5.画图

``````figure = plt.figure(figsize=(27, 9), facecolor='w')
i = 1
h = .02  # 步长

for ds in datasets:
X, y = ds
# 将数据标准化
X = StandardScaler().fit_transform(X)
# 测试集验证集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

# 用以区域绘制
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'])

# 原始点的绘制
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
plt.show()
# plt.savefig("cs.png")
``````

THE END