def main(train_file, user_item_side_information_file, hierarchy_file,
test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_item_side_information_file)
hierarchy = json.loads(open(hierarchy_file).read())
lda = LDAHierarquical(B, hierarchy, topics=15)
#####REMOVE_IT
def important_topics(x, topics):
if not x:
return x
transf = [(i, j) for i, j in enumerate(x)]
transf = sorted(transf, cmp=lambda x, y: cmp(x[1], y[1]))
return [i[0] for i in transf[:topics]]
topics = 3
coincidencias = []
for user in range(1, 101):
# Topicos do usuario 10
user_topics = important_topics(lda.model['users'][user], topics)
# Topicos das cidades de teste do usuario 10
T = tsv_to_matrix(test_file)
cities = T[user].nonzero()[0]
cities_topics = [
important_topics(lda.model['cities'].get(city, []), topics)
for city in cities
]
total = 0
topics_compared = 0
coinc = 0
for city_topic in cities_topics:
if city_topic:
coinc += len(set(user_topics) & set(city_topic))
topics_compared += len(user_topics)
total += 1
else:
pass
if total:
perc = (coinc / float(topics_compared))
else:
perc = -1
coincidencias.append([coinc, topics_compared, perc])
aa = open('/tmp/coincidencias.json', 'w')
aa.write(json.dumps(coincidencias))
aa.close()
#####
W = slim_train(A)
recommendations = slim_lda_recommender(A, W, lda)
compute_precision(recommendations, test_file)
def main(train_file, user_item_side_information_file, hierarchy_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_item_side_information_file)
hierarchy = json.loads(open(hierarchy_file).read())
lda = LDAHierarquical(B, hierarchy, topics=15)
#####REMOVE_IT
def important_topics(x, topics):
if not x:
return x
transf = [ (i, j) for i, j in enumerate(x) ]
transf = sorted(transf, cmp=lambda x, y: cmp(x[1], y[1]))
return [ i[0] for i in transf[:topics] ]
topics = 3
coincidencias = []
for user in range(1, 101):
# Topicos do usuario 10
user_topics = important_topics(lda.model['users'][user], topics)
# Topicos das cidades de teste do usuario 10
T = tsv_to_matrix(test_file)
cities = T[user].nonzero()[0]
cities_topics = [ important_topics(lda.model['cities'].get(city, []), topics) for city in cities ]
total = 0
topics_compared = 0
coinc = 0
for city_topic in cities_topics:
if city_topic:
coinc += len(set(user_topics) & set(city_topic))
topics_compared += len(user_topics)
total += 1
else:
pass
if total:
perc = (coinc/float(topics_compared))
else:
perc = -1
coincidencias.append([coinc, topics_compared, perc])
aa = open('/tmp/coincidencias.json', 'w')
aa.write(json.dumps(coincidencias))
aa.close()
#####
W = slim_train(A)
recommendations = slim_lda_recommender(A, W, lda)
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)
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, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
recommendations = slim_recommender(A, W)
return compute_precision(recommendations, test_file)
def main(train_file, user_item_side_information_file, hierarchy_file,
test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_item_side_information_file)
hierarchy = json.loads(open(hierarchy_file).read())
W = slim_train(A)
## LDA
#lda = LDAHierarquical(B, hierarchy, topics=20)
#recommendations_lda = slim_lda_recommender(A, W, lda)
#compute_precision(recommendations_lda, test_file)
###
recommendations_slim = slim_recommender(A, W)
## HSLIM
from hslim import handle_user_bias, hierarchy_factory, normalize_wline, generate_subitem_hierarchy
hierarchy = hierarchy_factory('data/hierarchy.json')
K = slim_train(handle_user_bias(B))
Wline = generate_subitem_hierarchy(K, W, hierarchy)
WlineNorm = normalize_wline(Wline)
recommendations_other = slim_recommender(A, WlineNorm)
###
kendall_tau_values = []
differences_values = []
for u in recommendations_slim.iterkeys():
ranking_slim = recommendations_slim[u][:RANKING_UNTIL]
ranking_other = recommendations_other[u][:RANKING_UNTIL]
kendall_tau_values.append(kendalltau(ranking_slim, ranking_other))
differences_values.append(RANKING_UNTIL -
len(set(ranking_slim) & set(ranking_other)))
# Differences
plt.hist(differences_values)
plt.xlabel('Size of difference')
plt.ylabel('Amount of rankings')
plt.title('Differences (novelty) between rankings')
# Ranking comparison
show_matplot_fig()
plt.figure()
plt.hist([i[0] for i in kendall_tau_values])
plt.xlabel('KendallTau Distance SLIM/SLIM LDA')
plt.ylabel('Number of occurrences')
plt.title('Comparison between rankings')
show_matplot_fig()
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_item_side_information_file, hierarchy_file, test_file):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_item_side_information_file)
hierarchy = json.loads(open(hierarchy_file).read())
W = slim_train(A)
## LDA
#lda = LDAHierarquical(B, hierarchy, topics=20)
#recommendations_lda = slim_lda_recommender(A, W, lda)
#compute_precision(recommendations_lda, test_file)
###
recommendations_slim = slim_recommender(A, W)
## HSLIM
from hslim import handle_user_bias, hierarchy_factory, normalize_wline, generate_subitem_hierarchy
hierarchy = hierarchy_factory('data/hierarchy.json')
K = slim_train(handle_user_bias(B))
Wline = generate_subitem_hierarchy(K, W, hierarchy)
WlineNorm = normalize_wline(Wline)
recommendations_other = slim_recommender(A, WlineNorm)
###
kendall_tau_values = []
differences_values = []
for u in recommendations_slim.iterkeys():
ranking_slim = recommendations_slim[u][:RANKING_UNTIL]
ranking_other = recommendations_other[u][:RANKING_UNTIL]
kendall_tau_values.append(kendalltau(ranking_slim, ranking_other))
differences_values.append(RANKING_UNTIL-len(set(ranking_slim) & set(ranking_other)))
# Differences
plt.hist(differences_values)
plt.xlabel('Size of difference')
plt.ylabel('Amount of rankings')
plt.title('Differences (novelty) between rankings')
# Ranking comparison
show_matplot_fig()
plt.figure()
plt.hist([ i[0] for i in kendall_tau_values ])
plt.xlabel('KendallTau Distance SLIM/SLIM LDA')
plt.ylabel('Number of occurrences')
plt.title('Comparison between rankings')
show_matplot_fig()
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, user_sideinformation_file, test_file, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
save_matrix(W, 'sslim_oracle_wmatrix.tsv')
recommendations = slim_recommender(A, W)
precisions = compute_precision_as_an_oracle(recommendations, test_file)
return precisions
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, test_file):
A = tsv_to_matrix(train_file)
W = slim_train(A)
recommendations = slim_recommender(A, W)
return compute_precision(recommendations, test_file)
def main(train_file, user_sideinformation_file, test_file, normalize):
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
if normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
W = sslim_train(A, B)
save_matrix(W, 'sslim_oracle_wmatrix.tsv')
recommendations = slim_recommender(A, W)
precisions = compute_precision_as_an_oracle(recommendations, test_file)
return precisions
def main(train_file):
data = tsv_to_matrix(train_file, 942, 1682)
kmeans = KMeans(n_clusters=2, random_state=0).fit(data)
print (data.shape)
k = 4
# k-means picking the first k points as centroids
centroids = kmeans.cluster_centers
labels = kmeans.labels_
print(centroids)
def main(train_file):
data = tsv_to_matrix(train_file, 942, 1682).toarray()
k = 8
#shape[0] is 4, shape[1] is 1682
dim = data.shape[1]
# print("data shape0:" + str(data[:k].shape[0]))
# print("data shape1:" + str(data[:k].shape[1]))
iteration = 10
for i in range(0, iteration):
#call cuckoo search
if i == 0:
centroids = cuckoo_search(k, dim, data[:k])
else:
centroids = cuckoo_search(k, dim, new_centroids)
# k-means picking the first k points as centroids
#centroids = data[:k]
print("shape[0]: " + str(centroids.shape[0]))
print("shape[1]: " + str(centroids.shape[1]))
clusters, labels, new_centroids = kmeans(k, centroids, data, "first")
if i == iteration - 1:
train0 = np.array(clusters[0])
train1 = np.array(clusters[1])
train2 = np.array(clusters[2])
train3 = np.array(clusters[3])
train4 = np.array(clusters[4])
train5 = np.array(clusters[5])
train6 = np.array(clusters[6])
train7 = np.array(clusters[7])
file = open('./data/k8/k8_label.txt', 'w')
for i in range(len(labels)):
file.write("%s\n" % (str(labels[i])))
file.close()
print(len(train0))
print(len(train1))
print(len(train2))
print(len(train3))
print(len(train4))
print(len(train5))
print(len(train6))
print(len(train7))
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)
def main(train_file):
data = tsv_to_matrix(train_file, 942, 1682).toarray()
k = 4
# k-means picking the first k points as centroids
centroids = data[:k]
clusters, labels = kmeans(k, centroids, data, "first")
train0 = np.array(clusters[0])
train1 = np.array(clusters[1])
train2 = np.array(clusters[2])
train3 = np.array(clusters[3])
file = open('label.txt', 'w')
for i in range(len(labels)):
file.write("%s\n" %(str(labels[i])))
file.close()
print(train0.shape)
read "SLIM: Sparse LInear Methods for Top-N Recommender Systems".
"""
from sklearn.linear_model import SGDRegressor
from util import tsv_to_matrix, generate_slices
from util.metrics import compute_precision
from util.recommender import slim_recommender
import numpy as np
import multiprocessing
import ctypes
from util import parse_args
import simplejson as json
args = parse_args()
# Loading matrices
A = tsv_to_matrix(args.train)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1] ** 2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]
counter = 0
from util import (tsv_to_matrix, generate_slices,
make_compatible, normalize_values, save_matrix)
from util.metrics import compute_precision
import multiprocessing
import ctypes
from scipy.sparse import vstack
import datetime
from util import parse_args
import simplejson as json
print '>>> Start: %s' % datetime.datetime.now()
args = parse_args(side_information=True, beta=True)
# Loading matrices
A = tsv_to_matrix(args.train)
B = tsv_to_matrix(args.side_information)
if args.normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1] ** 2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
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))
read "SLIM: Sparse LInear Methods for Top-N Recommender Systems".
"""
from sklearn.linear_model import SGDRegressor
from util import tsv_to_matrix, generate_slices
from util.metrics import compute_precision
from util.recommender import slim_recommender
import numpy as np
import multiprocessing
import ctypes
from util import parse_args
import simplejson as json
args = parse_args()
# Loading matrices
A = tsv_to_matrix(args.train)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]
counter = 0
read "Sparse Linear Methods with Side Information for Top-N Recommendations"
"""
from sklearn.linear_model import SGDRegressor
import numpy as np
from recommender import slim_recommender
from util import tsv_to_matrix, split_train_test, generate_slices
from metrics import compute_precision
import multiprocessing
import ctypes
import sys
from scipy.sparse import vstack
train_file, user_sideinformation_file, test_file = sys.argv[1:]
# Loading matrices
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
SLIM Parallel implementation. To understand deeply how it works we encourage you to
read "SLIM: Sparse LInear Methods for Top-N Recommender Systems".
"""
from sklearn.linear_model import SGDRegressor
from util import tsv_to_matrix, generate_slices
from metrics import compute_precision
from recommender import slim_recommender
import numpy as np
import multiprocessing
import ctypes
import sys
train_file, test_file = sys.argv[1:]
# Loading matrices
A = tsv_to_matrix(train_file)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]
counter = 0
read "Sparse Linear Methods with Side Information for Top-N Recommendations"
"""
from sklearn.linear_model import SGDRegressor
import numpy as np
from recommender import slim_recommender
from util import tsv_to_matrix, split_train_test, generate_slices
from metrics import compute_precision
import multiprocessing
import ctypes
import sys
from scipy.sparse import vstack
train_file, user_sideinformation_file, test_file = sys.argv[1:]
# Loading matrices
A = tsv_to_matrix(train_file)
B = tsv_to_matrix(user_sideinformation_file)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
# parallel
def work(params, W=shared_array):
from_j = params[0]
to_j = params[1]
M = params[2]
model = params[3]
from util import (tsv_to_matrix, generate_slices, make_compatible,
normalize_values, save_matrix)
from util.metrics import compute_precision
import multiprocessing
import ctypes
from scipy.sparse import vstack
import datetime
from util import parse_args
import simplejson as json
print '>>> Start: %s' % datetime.datetime.now()
args = parse_args(side_information=True, beta=True)
# Loading matrices
A = tsv_to_matrix(args.train)
B = tsv_to_matrix(args.side_information)
if args.normalize:
B = normalize_values(B)
A, B = make_compatible(A, B)
# Loading shared array to be used in results
shared_array_base = multiprocessing.Array(ctypes.c_double, A.shape[1]**2)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(A.shape[1], A.shape[1])
# We create a work function to fit each one of the columns of our W matrix,
# because in SLIM each column is independent we can use make this work in
