def main():
util = Util()
dir = args.data_dir
rows = args.rows
ratings, to_read, books = util.read_data(dir)
ratings = util.clean_subset(ratings, rows)
num_vis = len(ratings)
free_energy = args.free_energy
train = util.preprocess(ratings)
valid = None
if free_energy:
train, valid = util.split_data(train)
H = args.num_hid
user = args.user
alpha = args.alpha
w = np.random.normal(loc=0, scale=0.01, size=[num_vis, H])
rbm = RBM(alpha, H, num_vis)
epochs = args.epochs
batch_size = args.batch_size
v = args.verbose
reco, prv_w, prv_vb, prv_hb = rbm.training(train, valid, user, epochs,
batch_size, free_energy, v)
unread, read = rbm.calculate_scores(ratings, books, to_read, reco, user)
rbm.export(unread, read)
def get_recc(att_df, cat_rating):
util = Util()
epochs = 50
rows = 40000
alpha = 0.01
H = 128
batch_size = 16
dir = 'etl/'
ratings, attractions = util.read_data(dir)
ratings = util.clean_subset(ratings, rows)
rbm_att, train = util.preprocess(ratings)
num_vis = len(ratings)
rbm = RBM(alpha, H, num_vis)
joined = ratings.set_index('attraction_id').join(attractions[[
"attraction_id", "category"
]].set_index("attraction_id")).reset_index('attraction_id')
grouped = joined.groupby('user_id')
category_df = grouped['category'].apply(list).reset_index()
rating_df = grouped['rating'].apply(list).reset_index()
cat_rat_df = category_df.set_index('user_id').join(
rating_df.set_index('user_id'))
cat_rat_df['cat_rat'] = cat_rat_df.apply(f, axis=1)
cat_rat_df = cat_rat_df.reset_index()[['user_id', 'cat_rat']]
cat_rat_df['user_data'] = [cat_rating for i in range(len(cat_rat_df))]
cat_rat_df['sim_score'] = cat_rat_df.apply(sim_score, axis=1)
user = cat_rat_df.sort_values(['sim_score']).values[0][0]
print("Similar User: {u}".format(u=user))
filename = "e" + str(epochs) + "_r" + str(rows) + "_lr" + str(
alpha) + "_hu" + str(H) + "_bs" + str(batch_size)
reco, weights, vb, hb = rbm.load_predict(filename, train, user)
unseen, seen = rbm.calculate_scores(ratings, attractions, reco, user)
rbm.export(unseen, seen, 'rbm_models/' + filename, str(user))
return filename, user, rbm_att
