版权声明:本文为博主原创文章,转载请注明出处。 https://blog.csdn.net/Yellow_python/article/details/84885979
import numpy as np, matplotlib.pyplot as mp
from sklearn.model_selection import train_test_split # 数据切分
from sklearn.preprocessing import StandardScaler # 数据标准化
from sklearn.datasets import make_moons, make_circles, make_classification # 数据集
from sklearn.neural_network import MLPClassifier # 神经网络
from sklearn.neighbors import KNeighborsClassifier # K最近邻
from sklearn.svm import SVC # 支持向量机
from sklearn.gaussian_process import GaussianProcessClassifier # 高斯过程
from sklearn.gaussian_process.kernels import RBF # 高斯核函数
from sklearn.tree import DecisionTreeClassifier # 决策树
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier # 集成方法
from sklearn.naive_bayes import GaussianNB # 朴素贝叶斯
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis # 判别分析
# 建模、设定参数
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process", "Decision Tree",
"Random Forest", "Neural Net", "AdaBoost", "Naive Bayes", "QDA"]
classifiers = [
KNeighborsClassifier(3), # K最近邻
SVC(kernel="linear", C=0.025), # 线性的支持向量机
SVC(gamma=2, C=1), # 径向基函数的支持向量机
GaussianProcessClassifier(1.0 * RBF(1.0)), # 基于拉普拉斯近似的高斯过程
DecisionTreeClassifier(max_depth=5), # 决策树
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1), # 随机森林
MLPClassifier(alpha=1), # 多层感知机
AdaBoostClassifier(), # 通过迭代弱分类器而产生最终的强分类器的算法
GaussianNB(), # 朴素贝叶斯
QuadraticDiscriminantAnalysis()] # 二次判别分析
# 创建随机样本集
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]
# 遍历样本集
figure = mp.figure(figsize=(20, 4))
i = 1 # 子图参数
h = .02 # 网眼步长(绘制等高线图的参数)
for ds_cnt, ds in enumerate(datasets):
# 数据预处理,切分训练集和测试集
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)
# 用于绘制等高线图
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))
# 绘制原始样本集
ax = mp.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data", size=10)
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,
edgecolors='k') # 绘制训练集散点图
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, alpha=0.5,
edgecolors='k') # 绘制测试集散点图
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1
# 遍历分类器
for name, clf in zip(names, classifiers):
ax = mp.subplot(len(datasets), len(classifiers) + 1, i)
clf.fit(X_train, y_train) # 训练
score = clf.score(X_test, y_test) # 模型评分
# 绘制决策边界
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, 2, alpha=.8) # 等高线图
# 散点图
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,
edgecolors='k') # 训练集
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test,
edgecolors='k', alpha=0.6) # 测试集
# 刻度、标签、标题等
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, size=10)
ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
size=9, horizontalalignment='right')
i += 1
mp.tight_layout()
mp.show()
En | Cn |
---|---|
boost | n. 推动;vt. 促进 |
preprocess | vt. 预处理 |
gamma | 希腊语的第三个字母: |
quadratic | 二次方程式;二次的 |
discriminant | 判别式 |
percept | n. 认知 |
radial | n. 射线,光线;adj. 半径的;放射状的 |
MLP | Multi Layered Perceptron |
RBF | Radial Basis Function |
linearly separable | 线性可分 |
tick | n. 滴答声;记号; |