原数据
根据 ‘buy’, ‘maintain’, ‘doors’, ‘persons’, ‘boot’, ‘safety’, ‘accept’
购买”、“维护”、“门”、“人”、“系统”、“安全” 来判断顾客是否接受买车
# -*- coding:utf-8 -*-
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegressionCV
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
if __name__ == '__main__':
pd.set_option('display.width', 300)
pd.set_option('display.max_columns', 300)
data = pd.read_csv('car.data', header=None)
n_columns = len(data.columns)
columns = ['buy', 'maintain', 'doors', 'persons', 'boot', 'safety', 'accept']
new_columns = dict(list(zip(np.arange(n_columns), columns)))
data.rename(columns=new_columns, inplace=True)
print(data.head(10))
# one-hot编码
x = pd.DataFrame()
for col in columns[:-1]:
t = pd.get_dummies(data[col])
t = t.rename(columns=lambda x: col+'_'+str(x))
x = pd.concat((x, t), axis=1)
print(x.head(10))
print(x.shape)
y = np.array(pd.Categorical(data['accept']).codes)
x, x_test, y, y_test = train_test_split(x, y, test_size=0.3)
clf = LogisticRegressionCV(Cs=np.logspace(-3, 4, 8), cv=5)
# clf = RandomForestClassifier(n_estimators=50, max_depth=7)
clf.fit(x, y)
y_hat = clf.predict(x)
print('训练集精确度：', metrics.accuracy_score(y, y_hat))
y_test_hat = clf.predict(x_test)
print('测试集精确度：', metrics.accuracy_score(y_test, y_test_hat))
n_class = len(np.unique(y))
if n_class > 2:
y_test_one_hot = label_binarize(y_test, classes=np.arange(n_class))
y_test_one_hot_hat = clf.predict_proba(x_test)
fpr, tpr, _ = metrics.roc_curve(y_test_one_hot.ravel(), y_test_one_hot_hat.ravel())
print('Micro AUC:\t', metrics.auc(fpr, tpr))
auc = metrics.roc_auc_score(y_test_one_hot, y_test_one_hot_hat, average='micro')
print('Micro AUC(System):\t', auc)
auc = metrics.roc_auc_score(y_test_one_hot, y_test_one_hot_hat, average='macro')
print('Macro AUC:\t', auc)
else:
fpr, tpr, _ = metrics.roc_curve(y_test.ravel(), y_test_hat.ravel())
print('AUC:\t', metrics.auc(fpr, tpr))
auc = metrics.roc_auc_score(y_test, y_test_hat)
print('AUC(System):\t', auc)
mpl.rcParams['font.sans-serif'] = 'SimHei'
mpl.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(8, 7), facecolor='w')
plt.plot(fpr, tpr, 'r-', lw=2, label='AUC=%.4f' % auc)
plt.legend(loc='lower right', fontsize=14)
plt.xlim((-0.01, 1.02))
plt.ylim((-0.01, 1.02))
plt.xticks(np.arange(0, 1.1, 0.1))
plt.yticks(np.arange(0, 1.1, 0.1))
plt.xlabel('False Positive Rate', fontsize=14)
plt.ylabel('True Positive Rate', fontsize=14)
plt.grid(b=True, ls=':')
plt.title('ROC曲线和AUC', fontsize=16)
plt.show()
y
独热编码：x的数据
buy_high buy_low buy_med buy_vhigh maintain_high maintain_low maintain_med maintain_vhigh doors_2 doors_3 doors_4 doors_5more persons_2 persons_4 persons_more boot_big boot_med boot_small safety_high safety_low safety_med
0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0
1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 1
2 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0
3 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0
4 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1
5 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 1 0 0
6 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0
7 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1
8 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0
9 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0
LogisticRegressionCV
训练集精确度： 0.9081885856079405
测试集精确度： 0.8921001926782274
Micro AUC: 0.9834757073221438
Micro AUC(System): 0.9834757073221438
Macro AUC: 0.9668852150116471
RandomForestClassifier
训练集精确度： 0.9545078577336642
测试集精确度： 0.9075144508670521
Micro AUC: 0.9924203330598466
Micro AUC(System): 0.9924203330598466
Macro AUC: 0.988179483976249
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原文链接：汽车购买分类，转载请注明来源！