def get_train_instances(train, num_negatives):
user_input, item_input, labels = [], [], []
num_users = train.shape[0]
print(train.keys()[:10])
print(train.values()[:10])
for (u, i) in train.keys():
# positive instance
user_input.append(u)
item_input.append(i)
rating = train[u, i]
labels.append(rating)
# negative instances
for t in xrange(num_negatives):
j = np.random.randint(num_items)
while train.has_key((u, j)):
j = np.random.randint(num_items)
user_input.append(u)
item_input.append(j)
labels.append(0)
ratings = utils.load_ratings()
user_inputs = [x[0] for x in ratings]
item_inputs = [x[1] for x in ratings]
labels = [x[2] for x in ratings]
# labels = keras.utils.np_utils.to_categorical(labels, nb_classes=6)
return user_input, item_input, labels
def setUp(self):
#Load data
ratings = load_ratings('../data/ml-latest-small/ratings.csv')
self.movies = load_movies('../data/ml-latest-small/movies.csv')
#Train & Test data
train, test = ratings.split_train_test(percent=80)
self.train = train
self.test = test
self.svd = create_svd_model(self.train)
import numpy as np
from item2vec import Item2Vector
from metrics import n_precision
from metrics import n_recall
from utils import load_ratings
from utils import get_user_movie_dictionary
from utils import split_matrix
from utils import transform_binary_matrix
if __name__ == '__main__':
# Load and Preprocess Dataset
rating_df = load_ratings('ratings.csv')
user2idx, movie2idx = get_user_movie_dictionary(rating_df)
print(f'# of user: {len(user2idx)}\t# of movie: {len(movie2idx)}')
rating_matrix, stat =\
transform_binary_matrix(rating_df, user2idx, movie2idx)
print(f'Positive Feedback: {stat["pos"]}',
f'\tNegative Feedback: {stat["neg"]}')
rating_matrix_train, rating_matrix_val =\
split_matrix(rating_matrix, user2idx, movie2idx)
print(f'Train: {rating_matrix_train.count_nonzero()}\t',
f'Validation Size: {rating_matrix_val.count_nonzero()}')
# Train Item2Vec Model
model = Item2Vector(item_dim=len(movie2idx), embedding_dim=100)
model.train(rating_matrix_train)
6. Save & quit
"""
choice = input("Enter your choice: ")
return choice
if __name__ == "__main__":
#Load data
try:
ratings = Data()
ratings.load('../data/myratings.data')
except:
ratings = load_ratings('../data/ml-latest-small/ratings.csv')
movies = load_movies('../data/ml-latest-small/movies.csv')
tags = load_tags('../data/ml-latest-small/tags.csv')
os.system('clear')
print """
#####################################################
#### COMMAND LINE RECOMMENDER ####
#####################################################
A minimalistic command line recommender system using
SVD decomposistion.
"""
# Create a user
user_id = str(raw_input("Please enter your username: "))
def main(_):
print(FLAGS.__flags)
FLAGS.data = load_ratings('%s/ratings.dat' % FLAGS.input_dir, random_state=FLAGS.random_state)
model = AutoRec(**FLAGS.__flags)
model.train(n_epochs=FLAGS.n_epochs)
if __name__ == "__main__":
# Load data from custom path
try:
data_path = sys.argv[1]
except IndexError:
data_path = '/data'
#Load data
ratings = Data()
if os.path.isfile(data_path + '/myratings.data'):
ratings.load(data_path + '/myratings.data')
else:
try:
ratings = load_ratings(data_path + '/ratings.csv')
except IOError:
raise Exception('Data not found. Please specify it.'
% data_path)
movies = load_movies(data_path + '/movies.csv')
tags = load_tags(data_path + '/tags.csv')
os.system('clear')
print """
#####################################################
#### COMMAND LINE RECOMMENDER ####
#####################################################
A minimalistic command line recommender system using
SVD decomposistion.
"""