def main(train_file, test_file):
A = tsv_to_matrix(train_file)
W = slim_train(A)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
def main(train_file, test_file):
A = tsv_to_matrix(train_file)
W = slim_train(A)
recommendations = slim_recommender(A, W)
compute_precision_as_an_oracle(recommendations, test_file)
def main(train_file, part_file, test_file):
AG = tsv_to_matrix(train_file, 942, 1682)
AP = tsv_to_matrix(part_file, 942, 1682)
W1 = slim_train(AG)
W2 = slim_train(AP)
for i in range(0, 11):
W = (i / 10) * W1 + (1 - i / 10) * W2
print(i / 10)
recommendations = slim_recommender(AP, W)
compute_precision(recommendations, test_file)
def main(train_file, part_file):
AG = tsv_to_matrix(train_file, 942, 1682)
AP = tsv_to_matrix(part_file, 942, 1682)
W1 = slim_train(AG)
W2 = slim_train(AP)
W = 0 * W1 + 1 * W2
recommendations = slim_recommender(AP, W)
compute_precision(recommendations, part_file)
def main(train_file, user_sideinformation_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
"""
from util import mm2csr
mm2csr(A, '/tmp/train.mat')
mm2csr(useritem_featureitem, '/tmp/train_feature.mat')
C = tsv_to_matrix(test_file)
mm2csr(C, '/tmp/test.mat')
"""
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
def main(train_file, user_sideinformation_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
"""
from util import mm2csr
mm2csr(A, '/tmp/train.mat')
mm2csr(useritem_featureitem, '/tmp/train_feature.mat')
C = tsv_to_matrix(test_file)
mm2csr(C, '/tmp/test.mat')
"""
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
def main(train_file, user_sideinformation_file, hierarchy_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file, A.shape[0], A.shape[1])
hierarchy = hierarchy_factory(hierarchy_file)
# Learning using SLIM
# We handle user bias only in B because in B we have explicit evaluations
K = slim_train(handle_user_bias(B))
W = slim_train(A)
Wline = generate_subitem_hierarchy(K, W, hierarchy)
WlineNorm = normalize_wline(Wline)
#recommendations = slim_recommender(A, W + 0.2 * WlineNorm)
import pdb;pdb.set_trace()
recommendations = slim_recommender(A, WlineNorm)
# See if the predictor is just of not
#user_cities = np.array([ map(hierarchy, B[i].nonzero()[0].tolist()) for i in range(B.shape[0]) ])
#G = tsv_to_matrix(test_file)
#print 'TEM QUE DAR VAZIO: ', set(G[1].nonzero()[0]) & set(user_cities[1])
### ---- FIM REMOVAME
compute_precision(recommendations, test_file)
Mline = vstack((A, Balpha), format='lil')
# Fit each column of W separately. We put something in each positions of W
# to allow us direct indexing of each position in parallel
total_columns = Mline.shape[1]
ranges = generate_slices(total_columns)
separated_tasks = []
for from_j, to_j in ranges:
separated_tasks.append([from_j, to_j, Mline, model])
pool = multiprocessing.Pool()
pool.map(work, separated_tasks)
pool.close()
pool.join()
return shared_array
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
"""
main('data/atracoes/10/usuarios_atracoes_train.tsv',
'data/atracoes/10/palavras_atracoes.tsv',
'data/atracoes/10/usuarios_atracoes_test.tsv')
"""
"""
alpha = l1_reg+l2_reg
l1_ratio = l1_reg/alpha
model = SGDRegressor(
penalty='elasticnet',
fit_intercept=False,
alpha=alpha,
l1_ratio=l1_ratio,
)
total_columns = A.shape[1]
ranges = generate_slices(total_columns)
separated_tasks = []
for from_j, to_j in ranges:
separated_tasks.append([from_j, to_j, A, model])
pool = multiprocessing.Pool()
pool.map(work, separated_tasks)
pool.close()
pool.join()
return shared_array
W = slim_train(A)
recommendations = slim_recommender(A, W)
compute_precision(recommendations, test_file)
def main(train_file, part_file, test_file):
AG = tsv_to_matrix(train_file, 942, 1682)
AP = tsv_to_matrix(part_file, 942, 1682)
W1 = slim_train(AG)
W2 = slim_train(AP)
# total_precision = []
k = 2
matrix_5 = np.zeros((21, k))
matrix_10 = np.zeros((21, k))
matrix_15 = np.zeros((21, k))
matrix_20 = np.zeros((21, k))
for i in range(0, 105, 5):
gu = i / 100
W = gu * W1 + (1 - gu) * W2
print("gu: " + str(gu))
recommendations = slim_recommender(AP, W)
top5, top10, top15, top20 = compute_precision(recommendations,
test_file)
for j in range(2):
matrix_5[int(i / 5)][j] = top5[j]
matrix_10[int(i / 5)][j] = top10[j]
matrix_15[int(i / 5)][j] = top15[j]
matrix_20[int(i / 5)][j] = top20[j]
hr_values = []
hr_values1 = []
index1, value1 = max(enumerate(matrix_5[:, 0]), key=operator.itemgetter(1))
index2, value2 = max(enumerate(matrix_10[:, 0]),
key=operator.itemgetter(1))
index3, value3 = max(enumerate(matrix_15[:, 0]),
key=operator.itemgetter(1))
index4, value4 = max(enumerate(matrix_20[:, 0]),
key=operator.itemgetter(1))
hr_values.append(index1 * 0.05)
hr_values.append(value1)
hr_values.append(index2 * 0.05)
hr_values.append(value2)
hr_values.append(index3 * 0.05)
hr_values.append(value3)
hr_values.append(index4 * 0.05)
hr_values.append(value4)
hr_values1.append(matrix_5[20][0])
hr_values1.append(matrix_10[20][0])
hr_values1.append(matrix_15[20][0])
hr_values1.append(matrix_20[20][0])
arhr_values = []
arhr_values1 = []
index1, value1 = max(enumerate(matrix_5[:, 1]), key=operator.itemgetter(1))
index2, value2 = max(enumerate(matrix_10[:, 1]),
key=operator.itemgetter(1))
index3, value3 = max(enumerate(matrix_15[:, 1]),
key=operator.itemgetter(1))
index4, value4 = max(enumerate(matrix_20[:, 1]),
key=operator.itemgetter(1))
arhr_values.append(index1 * 0.05)
arhr_values.append(value1)
arhr_values.append(index2 * 0.05)
arhr_values.append(value2)
arhr_values.append(index3 * 0.05)
arhr_values.append(value3)
arhr_values.append(index4 * 0.05)
arhr_values.append(value4)
arhr_values1.append(matrix_5[20][1])
arhr_values1.append(matrix_10[20][1])
arhr_values1.append(matrix_15[20][1])
arhr_values1.append(matrix_20[20][1])
print('k8 top5: %s' % (matrix_5))
print('k8 top10: %s' % (matrix_10))
print('k8 top15: %s' % (matrix_15))
print('k8 top20: %s' % (matrix_20))
print('Max HR: %s' % (hr_values))
print('HR at gu = 1: %s' % (hr_values1))
print('Max ARHR: %s' % (arhr_values))
print('ARHR at gu = 1: %s' % (arhr_values1))
