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_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())
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)
return compute_precision_as_an_oracle(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, 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)
