def train(reg):
logdir = 'logs/mf/numpy'
if not os.path.exists(logdir):
os.makedirs(logdir)
print("Loading data...")
movie_titles, ratings, rating_indices, n_users, n_items = get_netflix_data(n_samples=1000000)
print("number of users with ratings: {}".format(len(np.unique(rating_indices[:,0]))))
print("number of movies with ratings: {}".format(len(np.unique(rating_indices[:,1]))))
method = 'als'
if reg:
print("Performing cross validation with reg: {}.".format(reg))
else:
print("Finding optimal regularization penalty.")
reg_vals = [0.01, 0.1, 1, 10]
best_reg = 0
mean_loss = 0.0
n_splits = 5
n_features = 15
loss_path = np.zeros((len(reg_vals), n_splits))
kf = KFold(n_splits=n_splits, shuffle=True)
kf.get_n_splits(rating_indices)
for k, (train_index, test_index) in enumerate(kf.split(rating_indices)):
print("Fold {}".format(k))
train_indices, test_indices = rating_indices[train_index], rating_indices[test_index]
train_indices = (train_indices[:,0], train_indices[:,1], train_indices[:,2])
test_indices = (test_indices[:,0], test_indices[:,1], test_indices[:,2])
if reg:
start = time.time()
model = MF(n_users, n_items, n_features, method=method)
model.fit(train_indices, verbose=1)
acc, loss = model.predict(test_indices)
print("val_loss: {:.4f} - val_acc: {:.4f}".format(loss, acc))
mean_loss = (mean_loss*k + loss) / (k+1)
else:
for i, reg in enumerate(reg_vals):
print("lambda: {}".format(reg))
start = time.time()
model = MF(n_users, n_items, n_features, method=method)
model.fit(train_indices, verbose=1)
acc, loss = model.predict(test_indices)
print("val_loss: {:.4f} - val_acc: {:.4f}".format(loss, acc))
loss_path[i, k] = loss
if reg:
print("mean loss: {:.4f}".format(mean_loss))
else:
loss_means = np.mean(loss_path, axis=1)
print(loss_means)
best_reg = reg_vals[np.argmin(loss_means)]
best_loss = np.amin(loss_means)
print("best lambda: {} - loss: {}".format(best_reg, best_loss))
print("Successfully finished training MF. See logs directory.")
index2userid = {y: x for x, y in userid2index.items()}
index2itemid = {y: x for x, y in itemid2index.items()}
nonzero_row, nonzero_col = rating_matrix.nonzero()
inds = zip(nonzero_row.tolist(), nonzero_col.tolist())
import sys
sys.path.append('../tpmrec/')
from mf import MF
mf = MF(rating_matrix, inds, 10, 0.0001, 0.01)
mf.train(10)
for userindex in range(1000):
userid = index2userid[userindex]
if len(userid2itemindexes[userid]) > 20:
continue
pr = mf.predict()
user_predict = pr[userindex, :]
top_item_indexes = np.argsort(user_predict)[::-1][:10]
print "userid = ", userid
for itemid in userid2itemindexes[userid]:
print itemid, itemid2name[itemid]
print "recommend item"
for itemindex in top_item_indexes:
itemid = index2itemid[itemindex]
print itemid, itemid2name[itemid]