mirror of
https://github.com/apachecn/ailearning.git
synced 2026-02-13 07:15:26 +08:00
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
#!/usr/bin/python
|
|
# coding:utf8
|
|
|
|
from math import sqrt
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from scipy.sparse.linalg import svds
|
|
from sklearn import cross_validation as cv
|
|
from sklearn.metrics import mean_squared_error
|
|
from sklearn.metrics.pairwise import pairwise_distances
|
|
|
|
# 加载数据集
|
|
header = ['user_id', 'item_id', 'rating', 'timestamp']
|
|
# http://files.grouplens.org/datasets/movielens/ml-100k.zip
|
|
dataFile = 'input/16.RecommenderSystems/ml-100k/u.data'
|
|
df = pd.read_csv(dataFile, sep='\t', names=header)
|
|
|
|
n_users = df.user_id.unique().shape[0]
|
|
n_items = df.item_id.unique().shape[0]
|
|
print 'Number of users = ' + str(n_users) + ' | Number of movies = ' + str(n_items)
|
|
|
|
# 拆分数据集
|
|
train_data, test_data = cv.train_test_split(df, test_size=0.25)
|
|
|
|
# 创建用户产品矩阵，针对测试数据和训练数据，创建两个矩阵：
|
|
train_data_matrix = np.zeros((n_users, n_items))
|
|
for line in train_data.itertuples():
|
|
train_data_matrix[line[1]-1, line[2]-1] = line[3]
|
|
test_data_matrix = np.zeros((n_users, n_items))
|
|
for line in test_data.itertuples():
|
|
test_data_matrix[line[1]-1, line[2]-1] = line[3]
|
|
# 使用sklearn的pairwise_distances函数来计算余弦相似性。
|
|
user_similarity = pairwise_distances(train_data_matrix, metric="cosine")
|
|
item_similarity = pairwise_distances(train_data_matrix.T, metric="cosine")
|
|
|
|
|
|
def predict(rating, similarity, type='user'):
|
|
if type == 'user':
|
|
mean_user_rating = rating.mean(axis=1)
|
|
rating_diff = (rating - mean_user_rating[:, np.newaxis])
|
|
pred = mean_user_rating[:, np.newaxis] + similarity.dot(rating_diff)/np.array([np.abs(similarity).sum(axis=1)]).T
|
|
elif type == 'item':
|
|
pred = rating.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])
|
|
return pred
|
|
|
|
|
|
user_prediction = predict(train_data_matrix, user_similarity, type='user')
|
|
item_prediction = predict(train_data_matrix, item_similarity, type='item')
|
|
|
|
|
|
def rmse(prediction, ground_truth):
|
|
prediction = prediction[ground_truth.nonzero()].flatten()
|
|
ground_truth = ground_truth[ground_truth.nonzero()].flatten()
|
|
return sqrt(mean_squared_error(prediction, ground_truth))
|
|
|
|
|
|
print 'User based CF RMSE: ' + str(rmse(user_prediction, test_data_matrix))
|
|
print 'Item based CF RMSe: ' + str(rmse(item_prediction, test_data_matrix))
|
|
|
|
sparsity = round(1.0 - len(df)/float(n_users*n_items), 3)
|
|
print 'The sparsity level of MovieLen100K is ' + str(sparsity * 100) + '%'
|
|
|
|
|
|
u, s, vt = svds(train_data_matrix, k=20)
|
|
s_diag_matrix = np.diag(s)
|
|
x_pred = np.dot(np.dot(u, s_diag_matrix), vt)
|
|
print 'User-based CF MSE: ' + str(rmse(x_pred, test_data_matrix))
|