def test_bpmf_convergence(self):
n_user = 100
n_item = 200
n_feature = self.n_feature
ratings = make_ratings(n_user,
n_item,
20,
30,
self.rating_choices,
seed=self.seed)
bpmf1 = BPMF(n_user,
n_item,
n_feature,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat,
converge=1e-2)
bpmf1.fit(ratings, n_iters=5)
rmse_1 = RMSE(bpmf1.predict(ratings[:, :2]), ratings[:, 2])
bpmf2 = BPMF(n_user,
n_item,
n_feature,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat,
converge=1e-1)
bpmf2.fit(ratings, n_iters=5)
rmse_2 = RMSE(bpmf2.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse_1 < rmse_2)
def test_als_with_random_data(self):
n_user = 100
n_item = 200
n_feature = self.n_feature
ratings = make_ratings(n_user,
n_item,
20,
30,
self.rating_choices,
seed=self.seed)
als1 = ALS(n_user,
n_item,
n_feature,
reg=1e-2,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat)
als1.fit(ratings, n_iters=1)
rmse_1 = RMSE(als1.predict(ratings[:, :2]), ratings[:, 2])
als2 = ALS(n_user,
n_item,
n_feature,
reg=1e-2,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat)
als2.fit(ratings, n_iters=3)
rmse_2 = RMSE(als2.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse_1 > rmse_2)
def test_bpmf_with_random_data(self):
n_user = 1000
n_item = 2000
ratings = make_ratings(n_user,
n_item,
20,
30,
self.rating_choices,
seed=self.seed)
bpmf1 = BPMF(n_user,
n_item,
self.n_feature,
max_rating=self.max_rat,
min_rating=self.min_rat,
seed=self.seed)
bpmf1.fit(ratings, n_iters=1)
rmse_1 = RMSE(bpmf1.predict(ratings[:, :2]), ratings[:, 2])
bpmf2 = BPMF(n_user,
n_item,
self.n_feature,
max_rating=self.max_rat,
min_rating=self.min_rat,
seed=self.seed)
bpmf2.fit(ratings, n_iters=3)
rmse_2 = RMSE(bpmf2.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse_1 > rmse_2)
def test_als_convergence(self):
n_user = 100
n_item = 200
n_feature = self.n_feature
ratings = make_ratings(n_user,
n_item,
20,
30,
self.rating_choices,
seed=self.seed)
als1 = ALS(n_user,
n_item,
n_feature,
reg=1e-2,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat,
converge=1e-2)
als1.fit(ratings, n_iters=10)
rmse_1 = RMSE(als1.predict(ratings[:, :2]), ratings[:, 2])
als2 = ALS(n_user,
n_item,
n_feature,
reg=1e-2,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat,
converge=1e-1)
als2.fit(ratings, n_iters=10)
rmse_2 = RMSE(als2.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse_1 < rmse_2)
def test_pmf_with_random_data(self):
n_user = 1000
n_item = 2000
n_feature = self.n_feature
ratings = make_ratings(n_user,
n_item,
20,
30,
self.rating_choices,
seed=self.seed)
pmf1 = PMF(n_user,
n_item,
n_feature,
batch_size=1000.,
epsilon=10.,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat)
pmf1.fit(ratings, n_iters=1)
rmse_1 = RMSE(pmf1.predict(ratings[:, :2]), ratings[:, 2])
pmf2 = PMF(n_user,
n_item,
n_feature,
batch_size=1000.,
epsilon=10.,
seed=0,
max_rating=self.max_rat,
min_rating=self.min_rat)
pmf2.fit(ratings, n_iters=3)
rmse_2 = RMSE(pmf2.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse_1 > rmse_2)
def test_bpmf_with_ml_100k_rating(self):
n_user = 943
n_item = 1682
n_feature = 10
ratings = self.ratings
bpmf = BPMF(n_user,
n_item,
n_feature,
max_rating=5.,
min_rating=1.,
seed=self.seed)
bpmf.fit(ratings, n_iters=15)
rmse = RMSE(bpmf.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse < 0.85)
def test_als_with_ml_100k_rating(self):
n_user = 943
n_item = 1682
n_feature = 10
ratings = self.ratings
als = ALS(n_user,
n_item,
n_feature,
reg=1e-2,
max_rating=5.,
min_rating=1.,
seed=self.seed)
als.fit(ratings, n_iters=5)
rmse = RMSE(als.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse < 0.8)
def test_pmf_with_ml_100k_rating(self):
n_user = 943
n_item = 1682
n_feature = 10
ratings = self.ratings
pmf = PMF(n_user,
n_item,
n_feature,
batch_size=1e4,
epsilon=20.,
reg=1e-4,
max_rating=5.,
min_rating=1.,
seed=self.seed)
pmf.fit(ratings, n_iters=15)
rmse = RMSE(pmf.predict(ratings[:, :2]), ratings[:, 2])
self.assertTrue(rmse < 0.85)
validation = ratings[train_size:]
print(train.shape, validation.shape)
print(train[0, :], train[1, :])
# plt.imshow(train, cmap='jet', interpolation='nearest')
# plt.show()
# plt.imshow(validation, cmap='jet', interpolation='nearest')
# plt.show()
# models settings
n_feature = 10
eval_iters = 10
print(
"n_user: %d, n_item: %d, n_feature: %d, training size: %d, validation size: %d"
% (n_user, n_item, n_feature, train.shape[0], validation.shape[0]))
als = ALS(n_user=n_user,
n_item=n_item,
n_feature=n_feature,
reg=5e-2,
max_rating=5.,
min_rating=1.,
seed=0)
als.fit(train, n_iters=eval_iters)
train_preds = als.predict(train[:, :2])
train_rmse = RMSE(train_preds, train[:, 2])
val_preds = als.predict(validation[:, :2])
val_rmse = RMSE(val_preds, validation[:, 2])
print("after %d iterations, train RMSE: %.6f, validation RMSE: %.6f" % \
(eval_iters, train_rmse, val_rmse))
# -*- coding: utf-8 -*-
"""
Created on Sun May 26 17:34:22 2019
@author: dblab
"""
import numpy as np
from recommend.bpmf import BPMF
from recommend.utils.evaluation import RMSE
from recommend.utils.datasets import load_movielens_1m_ratings
# load user ratings
ratings = load_movielens_1m_ratings('ml-1m/ratings.dat')
n_user = max(ratings[:, 0])
n_item = max(ratings[:, 1])
ratings[:,
(0, 1)] -= 1 # shift ids by 1 to let user_id & movie_id start from 0
# fit model
bpmf = BPMF(n_user=n_user,
n_item=n_item,
n_feature=10,
max_rating=5.,
min_rating=1.,
seed=0).fit(ratings, n_iters=20)
RMSE(bpmf.predict(ratings[:, :2]), ratings[:, 2]) # training RMSE
# predict ratings for user 0 and item 0 to 9:
print(bpmf.predict(np.array([[0, i] for i in range(10)])))
ratings = load_movielens_1m_ratings('ml-1m/ratings.dat')
n_user = max(ratings[:, 0])
n_item = max(ratings[:, 1])
ratings[:, (0, 1)] -= 1 #shift ids by 1 to let user_id &movie_id start from 0
#fit model
bpmf = BPMF(n_user=n_user,
n_item=n_item,
n_feature=10,
max_rating=5.,
min_rating=1.,
seed=0).fit(ratings, n_iters=5)
#traing RMSE
rmse = RMSE(bpmf.predict(ratings[:, :2]), ratings[:, 2])
print("RMSE= 1 ---", rmse)
#predict rating for user 0 and item 0 to 9
#输入的用户id
userId = 5
#输入要推荐的电影集合item 0 to endmovieNum-1
endmovieNum = n_item
#输入要显示的前五个movie
topN = 5
array = bpmf.predict(np.array([[userId, i] for i in xrange(endmovieNum)]))
movies = load_movielens_movies('ml-1m/movies.dat')
dictMovies = {}
for i in movies[:endmovieNum]:
dictMovies[int(i[0]) - 1] = str(i[1])
def test_rmse(self):
np_test.assert_almost_equal(RMSE(np.ones(100), np.zeros(100)),
np.sqrt(100. / 99.))
def test_rmse_same_input(self):
rs = RandomState(0)
data = rs.randn(100)
np_test.assert_almost_equal(RMSE(data, data), 0.)