python大数据：实战热水器用户行为识别（三、建立模型与模型检验）

五、模型构建

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
import joblib

1. 读取数据

# 读取数据
Xtrain = pd.read_excel('../tmp/sj_final.xlsx')
ytrain = pd.read_excel('../data/water_heater_log.xlsx')
test = pd.read_excel('../data/test_data.xlsx')

2. 训练集测试集区分

# 训练集测试集区分。
x_train, x_test, y_train, y_test = Xtrain.iloc[:,5:],test.iloc[:,4:-1],\
                                   ytrain.iloc[:,-1],test.iloc[:,-1]
# 标准化
stdScaler = StandardScaler().fit(x_train)
x_stdtrain = stdScaler.transform(x_train)
x_stdtest = stdScaler.transform(x_test)

3. 建立模型

# 建立模型
bpnn = MLPClassifier(hidden_layer_sizes=(15, 12),
                     max_iter=200,
                     solver='lbfgs',
                     random_state=50)
bpnn.fit(x_stdtrain, y_train)
# 保存模型
joblib.dump(bpnn, '../tmp/water_heater_nnet.m')
print('构建的模型为：\n', bpnn)

构建的模型为：
 MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
              beta_2=0.999, early_stopping=False, epsilon=1e-08,
              hidden_layer_sizes=(15, 12), learning_rate='constant',
              learning_rate_init=0.001, max_fun=15000, max_iter=200,
              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
              power_t=0.5, random_state=50, shuffle=True, solver='lbfgs',
              tol=0.0001, validation_fraction=0.1, verbose=False,
              warm_start=False)

六、模型检验

1. 检验

from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve
import joblib
import matplotlib.pyplot as plt

bpnn = joblib.load('../tmp/water_heater_nnet.m')  # 加载模型
y_pred = bpnn.predict(x_stdtest)  # 返回预测结果
print('神经网络预测结果评价报告：\n', classification_report(y_test, y_pred))

神经网络预测结果评价报告：
               precision    recall  f1-score   support

           0       0.45      0.42      0.43        12
           1       0.82      0.84      0.83        37

    accuracy                           0.73        49
   macro avg       0.64      0.63      0.63        49
weighted avg       0.73      0.73      0.73        49

2. 可视化

# 绘制roc曲线图
plt.rcParams['font.sans-serif'] = 'SimHei'  # 显示中文
plt.rcParams['axes.unicode_minus'] = False  # 显示负号
fpr, tpr, thresholds = roc_curve(y_pred,y_test)  # 求出TPR和FPR
plt.figure(figsize=(6,4))  # 创建画布
plt.plot(fpr,tpr)  # 绘制曲线
plt.title('用户用水事件识别ROC曲线')  # 标题
plt.xlabel('FPR')  # x轴标签
plt.ylabel('TPR')  # y轴标签
plt.savefig('../tmp/用户用水事件识别ROC曲线.png')  # 保存图片
plt.show()  # 显示图形