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52 lines (46 loc) · 1.68 KB
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#!/usr/bin/python3
#-*-coding:utf-8-*-
# BP神经网络 sklearn
# 表格中数据的位置：0-成绩；1-观看视频次数；2-单元测验次数；3-文档；４-随堂测验
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import xlrd
import xlwt
from xlrd import open_workbook
plt.rcParams['font.sans-serif']=['SimHei'] # 显示中文不乱码
plt.rcParams['axes.unicode_minus'] = False # 显示负数不乱码
file = '/home/hadoop/桌面/py/res/mooc后台数据/个人行为信息02.xlsx'
## 读取表数据
def read_data(filename,sheet):
wb = open_workbook(filename)
s = wb.sheet_by_index(sheet)
data = [[]for i in range(s.ncols)]
for r in range(s.nrows):
# s.row_values(0)[0] 均为 float数据类型
# if r == 0:
# continue
for c in range(s.ncols):
data[c].append(s.row_values(r)[c])
return data
s2017 = read_data(file,0)
x = np.array(s2017[1:4]).transpose()
y = np.array(s2017[0:1]).transpose()
X_train,X_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=100)
## 神经网络模型
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(14,14,30), random_state=1)
## 开始训练数据
clf.fit(X_train, y_train)
## 读取预测值
y_pred=clf.predict(X_test)
print (y_pred)
#做ROC曲线
plt.figure()
plt.plot(range(len(y_pred)),y_pred,'b',label='predict')
plt.plot(range(len(y_pred)),y_test,'r',label='test')
plt.legend(loc='upper right') #显示图中的标签
plt.xlabel(u'观看视频，单元测验，文档')
plt.ylabel(u'成绩')
plt.title(u'神经网络分析')
plt.show()