在Frappe数据集上使用pyFM

Question

我试图在Frappe (code.zip)数据集上使用code.zip。以下是我的代码：

import numpy as np  
from sklearn.feature_extraction import DictVectorizer  
from pyfm import pylibfm  

def loadData(filename,path="datasets/"):  
    data = []  
    y = []  
    users=set()  
    items=set()  
    with open(path+filename) as f:  
            for line in f:  
            (user,movieid,rating,ts)=line.split('\t')  
            data.append({ "user_id": str(user), "movie_id": str(movieid)})  
            y.append(float(rating))  
            users.add(user)  
            items.add(movieid)  

    return (data, np.array(y), users, items)  

(train_data, y_train, train_users, train_items) = loadData("traindata.txt")  
(test_data, y_test, test_users, test_items) = loadData("testdata.txt")  
v = DictVectorizer()  
X_train = v.fit_transform(train_data)  
X_test = v.transform(test_data)  
fm = pylibfm.FM(num_factors=10, num_iter=100, verbose=True, task="regression", initial_learning_rate=0.001,learning_rate_schedule="optimal")  

fm.fit(X_train,y_train)  
preds = fm.predict(X_test)  
from sklearn.metrics import mean_squared_error  
print("FM MSE: %.4f" % mean_squared_error(y_test,preds))  

但是，在运行此代码时，我会收到反对的警告，并将FM作为0.0000运行。如何使代码正确运行？

Answer 1

由于隐式数据集仅包含正评等，因此FM将无法在该数据集上工作(它将给出错误0.00)。您需要有负面的实例(例如。FM的正确工作(在现实世界中)。