基于随机梯度下降的矩阵分解算法
import pandas as pd
import numpy as np
import os
def difference(left,right,on): #求两个dataframe的差集
df = pd.merge(left,right,how='left',on=on) #参数on指的是用于连接的列索引名称
left_columns = left.columns
col_y = df.columns[-1] # 得到最后一列
df = df[df[col_y].isnull()]#得到boolean的list
df = df.iloc[:,0:left_columns.size]#得到的数据里面还有其他同列名的column
df.columns = left_columns # 重新定义columns
return df
def readfile(filepath): #读取文件，同时得到训练集和测试集
pwd = os.getcwd()#返回当前工程的工作目录
os.chdir(os.path.dirname(filepath))
#os.path.dirname()获得filepath文件的目录；chdir()切换到filepath目录下
initialData = pd.read_csv(os.path.basename(filepath))
#basename()获取指定目录的相对路径
os.chdir(pwd)#回到先前工作目录下
predData = initialData.iloc[:,0:3] #将最后一列数据去掉
newIndexData = predData.drop_duplicates()
trainData = newIndexData.sample(axis=0,frac = 0.1) #90%的数据作为训练集
testData = difference(newIndexData,trainData,['userId','movieId']).sample(axis=0,frac=0.1)
return trainData,testData
def getmodel(train):
slowRate = 0.99
preRmse = 10000000.0
max_iter = 100
features = 3
lamda = 0.2
gama = 0.01 #随机梯度下降中加入，防止更新过度
user = pd.DataFrame(train.userId.drop_duplicates(),columns=['userId']).reset_index(drop=True) #把在原来dataFrame中的索引重新设置，drop=True并抛弃
movie = pd.DataFrame(train.movieId.drop_duplicates(),columns=['movieId']).reset_index(drop=True)
userNum = user.count().loc['userId'] #671
movieNum = movie.count().loc['movieId']
userFeatures = np.random.rand(userNum,features) #构造user和movie的特征向量集合
movieFeatures = np.random.rand(movieNum,features)
#假设每个user和每个movie有3个feature
userFeaturesFrame =user.join(pd.DataFrame(userFeatures,columns = ['f1','f2','f3']))
movieFeaturesFrame =movie.join(pd.DataFrame(movieFeatures,columns= ['f1','f2','f3']))
userFeaturesFrame = userFeaturesFrame.set_index('userId')
movieFeaturesFrame = movieFeaturesFrame.set_index('movieId') #重新设置index
for i in range(max_iter):
rmse = 0
n = 0
for index,row in user.iterrows():
uId = row.userId
userFeature = userFeaturesFrame.loc[uId] #得到userFeatureFrame中对应uId的feature
u_m = train[train['userId'] == uId] #找到在train中userId点评过的movieId的data
for index,row in u_m.iterrows():
u_mId = int(row.movieId)
#realRating = row.sort_values(by=u'rating',ascending = False)
realRating = row.userId
movieFeature = movieFeaturesFrame.loc[u_mId]
eui = realRating-np.dot(userFeature,movieFeature)
rmse += pow(eui,2)
n += 1
userFeaturesFrame.loc[uId] += gama * (eui*movieFeature-lamda*userFeature)
movieFeaturesFrame.loc[u_mId] += gama*(eui*userFeature-lamda*movieFeature)
nowRmse = np.sqrt(rmse*1.0/n)
print('step:%f,rmse:%f'%((i+1),nowRmse))
if nowRmse测试数据表
运行结果
