print 'train lex raw ', len(train_lex_raw)
print 'train y raw', len(train_y_raw)
print train_lex_raw[1]
print train_y_raw[1]
test_toks_raw = []
test_lex_raw = []
test_y_raw = []
with open('./dataset/DDI13/DDI13_test_processed_drugbank.csv', 'rU') as f:
rd = csv.DictReader(f)
for row in rd:
test_toks_raw.append(parselist(row['lower_tokens']))
test_lex_raw.append([row['lower_text']])
test_y_raw.append(parselist(row['label']))
print 'test lex raw ', len(test_lex_raw)
# Convert each sentence of normalized tokens and labels into arrays of indices
train_lex = vectorize(train_toks_raw, tok2idx)
train_y = vectorize(train_y_raw, labels2idx)
valid_lex = vectorize(valid_toks_raw, tok2idx)
valid_y = vectorize(valid_y_raw, labels2idx)
test_lex = vectorize(test_toks_raw, tok2idx)
test_y = vectorize(test_y_raw, labels2idx)
print 'test lex ', len(test_lex)
# # Pickle the resulting data set
# with open('./dataset/DDI13_processed_10fold_testOnDrugbank.pkl','w') as fout:
# pkl.dump([[train_toks_raw,train_lex,train_y],[valid_toks_raw,valid_lex,valid_y],[test_toks_raw,test_lex,test_y],
# {'labels2idx':labels2idx, 'words2idx':tok2idx}], fout)
instances, classes, tags_cnt = build_data_set(user_id, FEATURES_LEVEL == 0)
print "Set built, %d(%d+%d) (ratio: %.2f)" % (
len(classes), classes.count(
Classes.INTERESTED), classes.count(Classes.UNKNOWN), ML_DATA_RATIO)
if FEATURES_LEVEL == 0:
stackoverflow_features = []
tot_features = sum([tags_cnt[k] for k in tags_cnt])
for k in tags_cnt:
if 1. * tags_cnt[k] / tot_features >= 0.002:
stackoverflow_features.append(k)
print "StackOverflow features:", stackoverflow_features
mapping, X = vectorize(instances)
if FEATURES_LEVEL == 0:
stackoverflow_tags = np.zeros(len(mapping))
for k in stackoverflow_features:
stackoverflow_tags[mapping[k]] = 1.
y = np.array(classes)
kf = KFold(len(classes), k=4)
if FEATURES_LEVEL > 0:
classifiers = {
'knn-15':
KNeighborsClassifier(15, weights='distance'),
'svc':
SVC(C=1.0,
print "Set built, %d(%d+%d) (ratio: %.2f)" % (len(classes),
classes.count(Classes.INTERESTED),
classes.count(Classes.UNKNOWN),
ML_DATA_RATIO)
if FEATURES_LEVEL == 0:
stackoverflow_features = []
tot_features = sum([tags_cnt[k] for k in tags_cnt])
for k in tags_cnt:
if 1.*tags_cnt[k]/tot_features >= 0.002:
stackoverflow_features.append(k)
print "StackOverflow features:", stackoverflow_features
mapping, X = vectorize(instances)
if FEATURES_LEVEL == 0:
stackoverflow_tags = np.zeros(len(mapping))
for k in stackoverflow_features:
stackoverflow_tags[mapping[k]] = 1.
y = np.array(classes)
kf = KFold(len(classes), k=4)
if FEATURES_LEVEL > 0:
classifiers = {
'knn-15': KNeighborsClassifier(15, weights='distance'),
'svc': SVC(C=1.0, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', probability=False,
shrinking=True, tol=0.001),
'tree': DecisionTreeClassifier(max_depth=10),
for k in range(markets):
for l in range(m):
for i in range(T):
dataset[l][k].append(float(data[k][l+i]))
###########################################################
###Set of Correlation Matrices as SET of Vectors
SET = []
D = markets*(markets-1)/2
for I in xrange(m):
matrix = []
matrix = dataset[I]
V = []
V = vectorize(correlation(matrix))
SET.append(V)
ASET = np.asarray(SET)
###########################################################
###########################################################
### RESULTADOS (Seccion PARALELIZABLE!)
###########################################################
###########################################################
###Parametros Globales
threshold = 0.1
lim = 20
nn = 40
print "RESULTADOS..."
