i = i + 1
precision = func_eval._precision_score(y_pred, y_real)
precisions.append(precision)
print(precision)
print("recall:")
i = 0
recalls = []
while i < top_k:
y_pred = test_pred[:, 0:i + 1]
i = i + 1
recall = func_eval.new_recall(y_pred, y_real)
recalls.append(recall)
print(recall)
#----------------below is commentted------------------
#print("Test Precision, Recall and F1-Score...")
#print(metrics.classification_report(test_labels, test_pred, digits=4))
#print("Macro average Test Precision, Recall and F1-Score...")
#print(metrics.precision_recall_fscore_support(test_labels, test_pred, average='macro'))
#print("Micro average Test Precision, Recall and F1-Score...")
#print(metrics.precision_recall_fscore_support(test_labels, test_pred, average='micro'))
#
## doc and word embeddings
#print('embeddings:')
#-----then evaluate each label and append to recalls,ncdgs,precisions--------
for j,label in enumerate(new_sort_list):
indices = [i for i, l in enumerate(y_test) if l == label]
each_label_real=[y_test[i] for i in indices]
# if(each_label_real==[]):
# f1_score[j]=0
# print(f1_score[j])
# continue
each_label_predict=np.array([test_predict_top_k[i] for i in indices])
each_labels_to_eval=np.array([labels_to_eval[i] for i in indices])
precision=func_eval._precision_score(each_label_predict,each_labels_to_eval)
precisions[j]=precision
recall=func_eval.new_recall(each_label_predict,each_labels_to_eval)
recalls[j]=recall
ncdg=func_eval._NDCG_score(each_label_predict,each_labels_to_eval)
ncdgs[j]=ncdg
#print('\nrecalls:',recalls)
#print('precisions:',precisions)
#print('ncdgs:',ncdgs)
#----------3. split labels into group by label frequency-------------------------
#-----------get the evaluation of each group------------------------------
group_recalls=[]
print(ndcg_i)
#sys.exit(0)
print("precision:")
i = 0
precisions = []
while i < top_k:
y_pred = test_predict_top_k[:, 0:i + 1]
i = i + 1
precision = func_eval._precision_score(y_pred, labels_to_eval)
precisions.append(precision)
print(precision)
print("recall:")
i = 0
recalls = []
while i < top_k:
y_pred = test_predict_top_k[:, 0:i + 1]
i = i + 1
recall = func_eval.new_recall(y_pred, labels_to_eval)
recalls.append(recall)
print(recall)
i = i+1
precision = func_eval._precision_score(y_pred,real_labels)
precisions.append(precision)
print(precision)
print("recall:")
i = 0
recalls = []
while i < top_k:
y_pred = pred_labels[:, 0:i+1]
i = i+1
recall = func_eval.new_recall(y_pred, real_labels)
recalls.append(recall)
print(recall)
print("f1:")
f1s=[]
for i in range(len(precisions)):
if(precisions[i]+recalls[i]!=0):
f1=((precisions[i]*recalls[i])/(precisions[i]+recalls[i]))
print(f1)
else:
f1=0.0
print(0.0)
f1s.append(f1)
