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baselines.py
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baselines.py
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# Copyright 2020 The Q2 Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from collections import Counter
import numpy as np
import pandas as pd
import spacy
import sacrebleu
from bert_score import score
nlp = spacy.load("en_core_web_sm")
def get_tokens(text):
doc = nlp(text)
tokens = [tok.text.lower() for tok in doc if not tok.is_stop and not tok.is_punct]
return tokens
def f1_score(gold, pred):
gold_toks = get_tokens(gold)
pred_toks = get_tokens(pred)
common = Counter(gold_toks) & Counter(pred_toks)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def length_baseline(in_path):
df = pd.read_csv(in_path)
sentences_length = []
num_tokens = []
num_tokens_no_punct = []
for _, row in df.iterrows():
text = row['response']
sentences_length.append(len(text))
doc = nlp(text)
num_tokens.append(len(doc))
no_punct_tokens = 0
for tok in doc:
if tok.pos_ != 'PUNCT':
no_punct_tokens += 1
num_tokens_no_punct.append(no_punct_tokens)
print('Avg. sentence length (all chars):', np.mean(sentences_length))
print('Avg. number of tokens:', np.mean(num_tokens))
print('Avg. number of tokens, no punct', np.mean(num_tokens_no_punct))
def add_bertscore(pred, ref):
P, R, F1 = score(pred, ref, lang="en", verbose=False, rescale_with_baseline=True)
return F1.detach().numpy()
def add_baselines(df, out_path=''):
bleu = []
overlap = []
all_responses = []
all_knowledge = []
for _, row in df.iterrows():
response = row['response']
knowledge = row['knowledge'].lower()
# BLEU
bleu.append(sacrebleu.corpus_bleu([response], [[knowledge]]).score)
# Overlap
overlap.append(f1_score(knowledge, response))
all_responses.append(response)
all_knowledge.append(knowledge)
df['bleu'] = bleu
df['overlap'] = overlap
df['bertscore'] = add_bertscore(all_responses, all_knowledge)
if out_path != '':
df.to_csv(out_path)
return df
def cross_add_baselines(df, out_path=''):
dodeca_bleu = []
memnet_bleu = []
dodeca_overlap = []
memnet_overlap = []
dodeca_all = []
memnet_all = []
knowledge_all = []
for _, row in df.iterrows():
dodeca_response = row['response_x']
memnet_response = row['response_y']
knowledge = row['knowledge_x'].lower()
# BLEU
dodeca_bleu.append(sacrebleu.corpus_bleu([dodeca_response], [[knowledge]]).score)
memnet_bleu.append(sacrebleu.corpus_bleu([memnet_response], [[knowledge]]).score)
# Overlap
dodeca_overlap.append(f1_score(knowledge, dodeca_response))
memnet_overlap.append(f1_score(knowledge, memnet_response))
dodeca_all.append(dodeca_response)
memnet_all.append(memnet_response)
knowledge_all.append(knowledge)
df['bleu_x'] = dodeca_bleu
df['bleu_y'] = memnet_bleu
df['overlap_x'] = dodeca_overlap
df['overlap_y'] = memnet_overlap
df['bertscore_x'] = add_bertscore(dodeca_all, knowledge_all)
df['bertscore_y'] = add_bertscore(memnet_all, knowledge_all)
if out_path != '':
df.to_csv(out_path)
return df