def eval(input_x,input_y,test_x,test,label,write_folder = None):
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
grid_clf = GridSearchCV(SVR(C=1,epsilon=0.2), tuned_parameters)
grid_clf.fit(input_x,input_y)
print "params : \t"
print grid_clf.get_params()
result = grid_clf.predict(test_x)
#py_weka = python_weka(input_x,input_y,label)
#py_weka.train()
#result = py_weka.predict(test_x)
#py_weka.close()
#clf = SVR(C=1.0, epsilon=0.2)
#clf.fit(input_x,input_y)
#result = clf.predict(test_x)
score_index = 0
produce_set = []
for i in test:
produce_set.append([])
score_list = []
index_list = []
for j in i.thread:
for k in j.sentences:
k.predict_score = result[score_index]
score_index += 1
score_list.append(k.predict_score)
index_list.append(k.index)
sorted_index_array = sorted_index(score_list)
sen_length = 0
for j in range(len(index_list)):
if sen_length < float(len(index_list))*0.3:
produce_set[-1].append(index_list[sorted_index_array[len(index_list)-j-1]])
sen_length += 1
else:
break
score = weightRecall(test,produce_set,write_folder)
print score
rouge_eval = rouge(test,produce_set)
rouge_score = rouge_eval.eval()['rouge_l_f_score']
print rouge_score
return score,rouge_score
def rnn_test(self):
produce_set = []
for i in self.test:
produce_set.append([])
score_list = []
index_list = []
for j in i.thread:
input_ins = []
label_ins = []
index = []
for k in j.sentences:
input_ins.append(k.feature)
index.append(k.index)
input_ins = input_ins + input_ins
input_ins = numpy.asarray(numpy.float32(input_ins))
softmax_array = self.rnn_model.prob(input_ins)
count = 0
for i in softmax_array[(len(softmax_array)/2) :]:
score = i#(i[1] * 0.33) + (i[2] * 0.66) + (i[3] * 1)
score_list.append(score)
index_list.append(index[count])
count += 1
sorted_index_array = sorted_index(score_list)
sen_length = 0
for j in range(len(index_list)):
if sen_length < float(len(index_list))*0.3:
produce_set[-1].append(index_list[sorted_index_array[len(index_list)-j-1]])
sen_length += 1
else:
break
score = weightRecall(self.test,produce_set)
print score
rouge_eval = rouge(self.test,produce_set)
rouge_score = rouge_eval.eval()['rouge_l_f_score']
print rouge_score
return score,rouge_score
