def run_svm():
# 20NG
for i in range(2):
scope_name = ng20_scope_names[i]
scope = ng20_scopes[i]
with open('data/local/' + scope_name + '.dmp') as f:
hin = pk.load(f)
print scope_name + ' svm'
tf_param = {'word': True, 'entity': False, 'we_weight': 0.1}
X, doc_new_ids, entity_new_ids = GraphGenerator.getTFVectorX(
hin, param=tf_param, entity_types=None)
y = GraphGenerator.gety(hin)
result[i, 2] = svm_experiment(scope_name, X, y)
print scope_name + ' svm+entity'
tf_param = {'word': True, 'entity': True, 'we_weight': 0.1}
X, doc_new_ids, entity_new_ids = GraphGenerator.getTFVectorX(
hin, param=tf_param, entity_types=None)
y = GraphGenerator.gety(hin)
result[i, 3] = svm_experiment(scope_name, X, y)
# GCAT
for i in range(2):
scope_name = gcat_scope_names[i]
scope = gcat_scopes[i]
with open('data/local/' + scope_name + '.dmp') as f:
hin = pk.load(f)
print scope_name + ' svm'
tf_param = {'word': True, 'entity': False, 'we_weight': 0.1}
X, doc_new_ids, entity_new_ids = GraphGenerator.getTFVectorX(
hin, param=tf_param, entity_types=None)
y = GraphGenerator.gety(hin)
result[i + 2, 2] = svm_experiment(scope_name, X, y)
print scope_name + ' svm+entity'
with open('data/local/laplacian/' + scope_name + '.x') as f:
X = pk.load(f)
y = GraphGenerator.gety(hin)
result[i + 2, 3] = svm_experiment(scope_name, X, y)
def ensemble_gal_experiment(scope, scope_name, type_list, threshold):
# this section should be changed between different scopes
pred_path = 'data/local/metagraph/' + scope_name + '/'
lb_cand = [5]
repeats = 50
with open('data/local/' + scope_name + '.dmp') as f:
hin = pk.load(f)
#X, newIds = GraphGenerator.getTFVectorX(hin, param={'word': True, 'entity': False, 'we_weight': 0.1})
y = GraphGenerator.gety(hin)
if sys.platform == 'win32':
command_file = open('galm.bat', 'a')
else:
command_file = open('galm.sh', 'a')
for lb in lb_cand:
results = []
for r in range(repeats):
with open('data/local/split/' + scope_name + '/lb' +
str(lb).zfill(3) + '_' + str(r).zfill(3) +
'_train') as f:
trainLabel = pk.load(f)
with open('data/local/split/' + scope_name + '/lb' +
str(lb).zfill(3) + '_' + str(r).zfill(3) + '_test') as f:
testLabel = pk.load(f)
if not os.path.exists('data/local/gal/' + scope_name + '/'):
os.makedirs('data/local/gal/' + scope_name + '/')
label_file = open(
'data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) +
'_' + str(r).zfill(3) + '_label.txt', 'w')
gold_file = open(
'data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) +
'_' + str(r).zfill(3) + '_gold.txt', 'w')
eval_file = open(
'data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) +
'_' + str(r).zfill(3) + '_eval.txt', 'w')
# write get-another-label gold file
for k, v in trainLabel.items():
gold_file.write(str(k) + '\t' + v + '\n')
# write get-another-label eval file
for k, v in testLabel.items():
eval_file.write(str(k) + '\t' + v + '\n')
# write get-another-label label file
for t in type_list:
with open(pred_path + str(t) + '/lb' + str(lb).zfill(3) + '_' +
str(r).zfill(3) + '_train') as f:
trainPred = pk.load(f)
for i, k in enumerate(trainLabel.keys()):
v = scope[np.argmax(trainPred[i, :])]
label_file.write(
str(t) + '\t' + str(k) + '\t' + v + '\n')
with open(pred_path + str(t) + '/lb' + str(lb).zfill(3) + '_' +
str(r).zfill(3) + '_test') as f:
testPred = pk.load(f)
for i, k in enumerate(testLabel.keys()):
v = scope[np.argmax(testPred[i, :])]
max = np.max(testPred[i, :])
if max > threshold[str(t)]:
label_file.write(
str(t) + '\t' + str(k) + '\t' + v + '\n')
# run get-another-label batch
if sys.platform == 'win32':
command = r'call galm/bin/get-another-label.bat ' + \
'--categories galm/settings/' + scope_name + '_categories.txt ' + \
'--cost galm/settings/' + scope_name + '_costs.txt ' + \
'--gold data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_gold.txt ' + \
'--input data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_label.txt ' + \
'--eval data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_eval.txt ' + \
'> data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + '_' + \
str(r).zfill(3) + '_result.txt'
else:
command = r'galm/bin/get-another-label.sh ' + \
'--categories /home/hejiang/results/gal/' + scope_name + '_categories.txt ' + \
'--cost /home/hejiang/results/gal/' + scope_name + '_costs.txt ' + \
'--gold data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_gold.txt ' + \
'--input data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_label.txt ' + \
'--eval data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + \
'_' + str(r).zfill(3) + '_eval.txt ' + \
'> data/local/gal/' + scope_name + '/lb' + str(lb).zfill(3) + '_' + \
str(r).zfill(3) + '_result.txt'
command_file.write(command + '\r\n')
def ensemble_svm_experiment(scope, scope_name, type_list, threshold):
# this section should be changed between different scopes
experiment_path = 'data/local/metagraph/' + scope_name + '/'
lb_cand = [5]
repeats = 50
with open('data/local/' + scope_name + '.dmp') as f:
hin = pk.load(f)
#X, newIds = GraphGenerator.getTFVectorX(hin, param={'word': True, 'entity': False, 'we_weight': 0.1})
y = GraphGenerator.gety(hin)
for lb in lb_cand:
results = []
for r in range(repeats):
with open('data/local/split/' + scope_name + '/lb' +
str(lb).zfill(3) + '_' + str(r).zfill(3) +
'_train') as f:
trainLabel = pk.load(f)
with open('data/local/split/' + scope_name + '/lb' +
str(lb).zfill(3) + '_' + str(r).zfill(3) + '_test') as f:
testLabel = pk.load(f)
yTrain = y[trainLabel.keys()]
yTest = y[testLabel.keys()]
numTrain = len(trainLabel)
numTest = len(testLabel)
XTrain = np.zeros((numTrain, 0))
XTest = np.zeros((numTest, 0))
for t in type_list:
with open(experiment_path + str(t) + '/lb' + str(lb).zfill(3) +
'_' + str(r).zfill(3) + '_train') as f:
trainPred = pk.load(f)
with open(experiment_path + str(t) + '/lb' + str(lb).zfill(3) +
'_' + str(r).zfill(3) + '_test') as f:
testPred = pk.load(f)
# threshold each meta-graph
XTraint = np.zeros((numTrain, 3))
XTestt = np.zeros((numTest, 3))
for i, k in enumerate(trainLabel.items()):
v = np.argmax(trainPred[i, :])
max = np.max(trainPred[i, :])
if max > threshold[str(t)]:
# zero-one prediction
XTraint[i, v] = 1
# raw prediction
#XTraint[i, :] = trainPred[i, :]
for i, k in enumerate(testLabel.items()):
v = np.argmax(testPred[i, :])
max = np.max(testPred[i, :])
if max > threshold[str(t)]:
# zero-one prediction
XTestt[i, v] = 1
# raw prediction
#XTestt[i, :] = testPred[i, :]
XTrain = np.concatenate((XTrain, XTraint), axis=1)
XTest = np.concatenate((XTest, XTestt), axis=1)
# use raw input
#XTrain = np.concatenate((XTrain,trainPred),axis=1)
#XTest = np.concatenate((XTest,testPred),axis=1)
# train
clf = LinearSVC(C=0.1)
clf.fit(XTrain, yTrain)
# test
pred = clf.predict(XTest)
results.append(sum(pred == yTest) / float(yTest.shape[0]))
return np.mean(results)