首先我们先看数据集以及我们要解决的问题
数据集如下：
要解决的是分析priceUSB与其他因素的关系，做出关系式，也就是Y为priceUSD，X分别为其他5个因素，做出多元线性回归方程。
①分析下每个因素和Y的关系（这一步就是只是看个开心而已其实）
def display_lr():pd_data=pd.read_csv('eth.csv')#原始数表#画出单因素拟合情况print('pd_data.head(10)=\n{}'.format(pd_data.head(10)))# mpl.rcParams['font.sans-serif'] = ['SimHei'] #配置显示中文，否则乱码mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号，如果是plt画图，则将mlp换成pltsns.pairplot(pd_data, x_vars=['activeAddresses','adjustedVolume','paymentCount','exchangeVolume','priceBTC'], y_vars='priceUSD',kind="reg", size=5, aspect=0.7)plt.show()
②数据归一化（数据存在差异性过大，需要归一化统一处理）
def Normalization():#对数据进行归一化处理 并存储到eth2.csvpd_data=pd.read_csv('eth.csv')sam=[]a=['priceUSD','activeAddresses','adjustedVolume','paymentCount','exchangeVolume','priceBTC']for i in a:y = pd_data.loc[:, i]ys = list(preprocessing.scale(y)) # 归一化sam.append(ys)print len(sam)with open('eth2.csv', 'w') as file:writer = csv.writer(file)for i in range(len(sam[0])):writer.writerow([sam[0][i],sam[1][i],sam[2][i],sam[3][i],sam[4][i],sam[5][i]])print('完毕')
③利用sklearn的LinearRegression进行训练，训练集：测试集=8:2
from sklearn.model_selection import train_test_split #这里是引用了交叉验证from sklearn.linear_model import LinearRegression #线性回归def build_lr():X = pd_data.loc[:, ('activeAddresses','adjustedVolume','paymentCount','exchangeVolume','priceBTC')]y = pd_data.loc[:, 'priceUSD']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=532)#选择20%为测试集print('训练集测试及参数:')print('X_train.shape={}\n y_train.shape ={}\n X_test.shape={}\n, y_test.shape={}'.format(X_train.shape,y_train.shape,X_test.shape,y_test.shape))linreg = LinearRegression()#训练model = linreg.fit(X_train, y_train)print('模型参数:')print(model)# 训练后模型截距print('模型截距:')print linreg.intercept_# 训练后模型权重（特征个数无变化）print('参数权重:')print (linreg.coef_)y_pred = linreg.predict(X_test)sum_mean = 0for i in range(len(y_pred)):sum_mean += (y_pred[i] - y_test.values[i]) ** 2sum_erro = np.sqrt(sum_mean /len(y_pred)) # 测试级的数量# calculate RMSEprint ("RMSE by hand:", sum_erro)# 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("the number of sales")plt.ylabel('value of sales')plt.show()
输出如下
对于这份数据而言，由于priceUSD的价格不单单由上诉因素决定，以及波动性较大，所以RMSE为0.20507在此处算是一个较好的结果。
所以我们可以得出我们的结果（大致的过程就这样了）
④最后写一个测试程序看一下总体结果是怎样的
def Compared():#利用方程进行拟合 对比 并存储数据到eth3.csvpd_data = pd.read_csv('eth2.csv')sam=[]a=['priceUSD','activeAddresses','adjustedVolume','paymentCount','exchangeVolume','priceBTC']dic={}for i in a:y = pd_data.loc[:, i]dic[i] = list(y) # 归一化print(dic)for i in range(len(dic['priceUSD'])):x = 0.00406340113944 + float(dic['activeAddresses'][i])*0.49474868663194016+float(dic['adjustedVolume'][i])*0.42552157541384+float(dic['paymentCount'][i])*0.12214416604623446 +float(dic['exchangeVolume'][i])*(-0.23814049518276936) +float(dic['priceBTC'][i])* 0.21567132432245326sam.append(x)with open('eth3.csv', 'w') as file:writer = csv.writer(file)writer.writerow(['priceUSD','Predictive value'])for i in range(len(sam)):writer.writerow([dic['priceUSD'][i],sam[i]])print('完毕')pd_data=pd.read_csv('eth3.csv')pd_data.plot()plt.show()
得到结果如下：
⑤数据及相关的py文件：/download/weixin_39739342/11254555
