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evaluate.py
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evaluate.py
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from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from tqdm import tqdm
import math
import evaluate as eval_fnc
import tensorflow as tf
from tensorflow.keras import backend as K
def batch_encode(encoder,context,bs = 150,fully_connec=False):
batch_encoded = []
for i in range(len(context)//bs+1):
if fully_connec == False:
batch_encoded.append(encoder(context[(i*bs):((i+1)*bs)]).numpy())
else:
raise Exception
batch_encoded = np.concatenate(batch_encoded,0)
return np.array(batch_encoded)
def batch_encode_fc(encoder,context,fc_layer,bs=10):
batch_encoded = []
for i in range(len(context)//bs+1):
encdoed_context = encoder(context[(i*bs):((i+1)*bs)])
batch_encoded.append(fc_layer(encdoed_context))
batch_encoded_ = np.concatenate(batch_encoded,0)
return tf.convert_to_tensor(batch_encoded_)
def sim_search(question_encoded,doc_encoded):
query_map = np.full(doc_encoded.shape, question_encoded)
sim_score = np.array([*map(np.inner,query_map,doc_encoded)])
return np.argsort(sim_score)[::-1]
def evaluate(question_id,question_all,context_id,context_all,mrr_rank=10):
top_1 = 0; top_5 = 0; top_10 = 0;
mrr_score = 0
context_id = np.array(context_id)
for idx,question in enumerate(question_all):
index_ = sim_search(question,context_all)
index_edit = [context_id[x] for x in index_]
try:
idx_search = list(index_edit).index(question_id[idx])
except:
idx_search = 999999
if idx_search == 0:
top_1+=1
top_5+=1
top_10+=1
elif idx_search < 5:
top_5+=1
top_10+=1
elif idx_search < 10:
top_10+=1
if idx_search < mrr_rank:
mrr_score += (1/(idx_search+1))
mrr_score/=len(question_all)
return top_1,top_5,top_10,mrr_score
def evaluate_dot(question_id,question_all,context_id,context_all,mrr_rank=10):
top_1 = 0; top_5 = 0; top_10 = 0;
mrr_score = 0
context_id = np.array(context_id)
sim_score = 1-K.dot(question_all,tf.transpose(context_all)).numpy()
for idx,sim in enumerate(sim_score):
index = np.argsort(sim)
print(sim)
print('*'*50)
print(index)
raise Exception
index_edit = [context_id[x] for x in index]
idx_search = list(index_edit).index(question_id[idx])
if idx_search == 0:
top_1+=1
top_5+=1
top_10+=1
elif idx_search < 5:
top_5+=1
top_10+=1
elif idx_search < 10:
top_10+=1
if idx_search < mrr_rank:
mrr_score += (1/(idx_search+1))
mrr_score/=len(question_all)
return top_1,top_5,top_10,mrr_score
def evaluate_dot_normal(question_id,question_all,context_id,context_all,mrr_rank=10):
top_1 = 0; top_5 = 0; top_10 = 0;
mrr_score = 0
context_id = np.array(context_id)
sim_score = np.inner(question_all,context_all)
for idx,sim in enumerate(sim_score):
index = np.argsort(sim)[::-1]
index_edit = [context_id[x] for x in index]
try:
idx_search = list(index_edit).index(question_id[idx])
except:
idx_search = 999999
if idx_search == 0:
top_1+=1
top_5+=1
top_10+=1
elif idx_search < 5:
top_5+=1
top_10+=1
elif idx_search < 10:
top_10+=1
if idx_search < mrr_rank:
mrr_score += (1/(idx_search+1))
mrr_score/=len(question_all)
return top_1,top_5,top_10,mrr_score
def evaluate_inner(question_id,question_all,context_id,context_all,mrr_rank=10):
top_1 = 0; top_5 = 0; top_10 = 0;
mrr_score = 0
context_id = np.array(context_id)
sim_score = np.inner(question_all,context_all)
for idx,sim in enumerate(sim_score):
index = np.argsort(sim)[::-1]
index_edit = [context_id[x] for x in index]
try:
idx_search = list(index_edit).index(question_id[idx])
except:
idx_search = 999999
if idx_search == 0:
top_1+=1
top_5+=1
top_10+=1
elif idx_search < 5:
top_5+=1
top_10+=1
elif idx_search < 10:
top_10+=1
if idx_search < mrr_rank:
mrr_score += (1/(idx_search+1))
mrr_score/=len(question_all)
return top_1,top_5,top_10,mrr_score
def evaluate_para(question_id_,question_all,context_id,context_all,mrr_rank=10):
top_1 = 0; top_5 = 0; top_10 = 0;
mrr_score = 0
context_id = np.array(context_id)
for idx,question in enumerate(question_all):
index = sim_search(question,context_all)
index = list(index)
if context_id[index[0]] == question_id_[idx]:
top_1+=1
for i in range(0,5):
if context_id[index[i]] == question_id_[idx]:
top_5+=1
break
for i in range(0,10):
if context_id[index[i]] == question_id_[idx]:
mrr_score += (1/(i+1))
top_10+=1
break
mrr_score/=len(question_all)
return top_1,top_5,top_10,mrr_score