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import pandas as pd
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import pymysql
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# 中文问题
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from matplotlib.font_manager import FontProperties
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# 深度学习模块sklearn
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from sklearn.linear_model import LinearRegression
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# train_test_split是sklearn用于划分数据集的，即将原始数据集划分成测试集和训练集两部分的函数。
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from sklearn.model_selection import train_test_split
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# 设计字体
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font = FontProperties(size=10)
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# 加载数据集
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# data = pd.read_sql('select * from data05', con=db_pymysql)
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while True:
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db_pymysql = pymysql.connect(host='localhost', port=3306, user='root', password='12345678', db='movie',use_unicode=True, charset='utf8')
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data = pd.read_sql('select * from data05', con=db_pymysql)
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sql = "select * from data"
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mycursor = db_pymysql.cursor()
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mycursor.execute(sql)
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mydata = mycursor.fetchone()
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if mydata !=None:
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# 导演能力，编剧能力，演员能力，电影评分，票房
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data1 = data[['directorCapacity', 'screenwriterCapacity', 'starringCapacity', 'movie_rating', 'boxOffice']]
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data2 = data[['directorCapacity', 'screenwriterCapacity', 'starringCapacity', 'boxOffice']]
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x = data[['directorCapacity', 'screenwriterCapacity', 'starringCapacity']]
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y = data[['movie_rating']]
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y1 = data[['boxOffice']]
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# train_size；测试集大小，random-state:随机数种子（该组随机数的编号，再需要重复试验的时候，保证得到一组一样的随机数），主要是为了复现结果而设置的。
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
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x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y1, test_size=0.2, random_state=0)
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# 评分线性模型
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# 导入线性模型，模型的参数默认
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model = LinearRegression()
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# 训练模型
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model.fit(x_train, y_train)
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# 票房预测模型
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##导入线性模型，模型的参数默认
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model1 = LinearRegression()
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# 训练模型
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model1.fit(x_train1, y_train1)
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# 《爱情公寓》评分预测
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directorCapacity =6.266667
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starring = 6
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screen = 6.8
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X = [[directorCapacity, starring, screen]]
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# 打印评分线性模型的准确率accuracy
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print("评分预测模型准确率:")
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accuracy = model.score(x_test, y_test)
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print(accuracy)
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print("电影所获电影评分预测：")
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score = model.predict(X)[0][0]
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print(score)
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# 打印票房线性模型的准确率accuracy
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print("票房预测模型准确率：")
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office_accuracy = model1.score(x_test1, y_test1)
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print(office_accuracy)
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print("电影所获电影票房预测：")
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Boxoffice = model1.predict(X)[0][0]
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print(Boxoffice)
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# 最佳阵容导演、编剧、演员能力值参数
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director = float(mydata[1])
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actor = float(mydata[2])
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enditor = float(mydata[3])
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X1 = [[director, actor, enditor]]
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print("最佳阵容参演电影所获电影评分预测：")
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bestteamscore = model.predict(X1)[0][0]
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print(bestteamscore)
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print("最佳阵容参演电影所获电影票房预测：")
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bestteamBoxoffice = model1.predict(X1)[0][0]
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print(bestteamBoxoffice)
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data = (accuracy, score, office_accuracy, Boxoffice, bestteamscore, bestteamBoxoffice)
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mysql = "insert into result(scoreaccuracy,Scorepredicts,fficeaccuracy,boxoffice,bestteamscore,bestteamboxoffice) values(%s,%s,%s,%s,%s,%s)"
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mycursor.execute(mysql, data)
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db_pymysql.commit()
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print("成功")
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break
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