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Merge pull request #14 from boostcampaitech4lv23nlp2/feat/dashboard
Feat/dashboard add dashboard to analyze result
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name: PRs reviews reminder | ||
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on: | ||
schedule: | ||
- cron: "0 1 * * *" #KST: 10:00 | ||
- cron: "0 3 * * *" #KST: 12:00 | ||
- cron: "0 4 * * *" #KST: 13:00 | ||
- cron: "0 7 * * *" #KST: 16:00 | ||
- cron: "0 9 * * *" #KST: 18:00 | ||
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jobs: | ||
pr-reviews-reminder: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: davideviolante/[email protected] | ||
env: | ||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | ||
with: | ||
webhook-url: 'https://hooks.slack.com/services/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' # Required | ||
provider: 'slack' # Required (slack or msteams) | ||
channel: '#ecl-free-talking' # Optional, eg: #general | ||
github-provider-map: 'wbin0718:U041WE3RDMX,FacerAin:U041WE4P8GZ,ghlrobin:U041HN2FGMR,kyc3492:U041388FBM5,jinmyeongAN:U041HR962M8' # Optional, eg: DavideViolante:UEABCDEFG,foobar:UAABCDEFG | ||
ignore-label: '' # Optional, eg: no-reminder |
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import argparse | ||
import pickle as pickle | ||
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import pandas as pd | ||
import streamlit as st | ||
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from .utils import get_filtered_result, test | ||
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def app(args): | ||
"""Run streamlit app""" | ||
test_df = pd.read_csv(args.valid_data_path) | ||
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st.set_page_config(page_icon="❄️", page_title="Into the RE", layout="wide") | ||
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st.title("Into the Re") | ||
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result_df = test(args) | ||
filtered_df = get_filtered_result(result_df, test_df) | ||
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st.dataframe(filtered_df) | ||
st.text(f"전체 {len(test_df)} 중 {len(filtered_df)}개를 틀렸습니다.") | ||
st.text("실제 정답 분포") | ||
st.bar_chart(filtered_df["answer"].value_counts()) | ||
st.text("예측 라벨 분포") | ||
st.bar_chart(filtered_df["pred_label"].value_counts()) | ||
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parser = argparse.ArgumentParser() | ||
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parser.add_argument("--model_name", default="klue/bert-base", type=str) | ||
parser.add_argument( | ||
"--model_dir", | ||
default="src/best_model", | ||
type=str, | ||
) | ||
parser.add_argument( | ||
"--valid_data_path", | ||
default="dataset/train/dev.csv", | ||
type=str, | ||
) | ||
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args = parser.parse_args() | ||
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app(args) |
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import json | ||
import pickle as pickle | ||
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import numpy as np | ||
import pandas as pd | ||
import torch | ||
import torch.nn.functional as F | ||
from torch.utils.data import DataLoader | ||
from tqdm import tqdm | ||
from transformers import AutoModelForSequenceClassification, AutoTokenizer | ||
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DICT_NUM_TO_LABEL_PATH = "dashboard/dict_num_to_label.pkl" | ||
with open(DICT_NUM_TO_LABEL_PATH, "rb") as f: | ||
dict_num_to_label = pickle.load(f) | ||
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def inference(model, tokenized_sent, device): | ||
""" | ||
test dataset을 DataLoader로 만들어 준 후, | ||
batch_size로 나눠 model이 예측 합니다. | ||
""" | ||
dataloader = DataLoader(tokenized_sent, batch_size=16, shuffle=False) | ||
model.eval() | ||
output_pred = [] | ||
output_prob = [] | ||
for i, data in enumerate(tqdm(dataloader)): | ||
with torch.no_grad(): | ||
outputs = model( | ||
input_ids=data["input_ids"].to(device), | ||
attention_mask=data["attention_mask"].to(device), | ||
token_type_ids=data["token_type_ids"].to(device), | ||
) | ||
logits = outputs[0] | ||
prob = F.softmax(logits, dim=-1).detach().cpu().numpy() | ||
logits = logits.detach().cpu().numpy() | ||
result = np.argmax(logits, axis=-1) | ||
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output_pred.append(result) | ||
output_prob.append(prob) | ||
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return ( | ||
np.concatenate(output_pred).tolist(), | ||
np.concatenate(output_prob, axis=0).tolist(), | ||
) | ||
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def tokenized_dataset(dataset, tokenizer): | ||
"""tokenizer에 따라 sentence를 tokenizing 합니다.""" | ||
concat_entity = [] | ||
for e01, e02 in zip(dataset["subject_entity"], dataset["object_entity"]): | ||
temp = "" | ||
temp = e01 + "[SEP]" + e02 | ||
concat_entity.append(temp) | ||
tokenized_sentences = tokenizer( | ||
concat_entity, | ||
list(dataset["sentence"]), | ||
return_tensors="pt", | ||
padding=True, | ||
truncation=True, | ||
max_length=256, | ||
add_special_tokens=True, | ||
) | ||
return tokenized_sentences | ||
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def preprocessing_dataset(dataset): | ||
"""처음 불러온 csv 파일을 원하는 형태의 DataFrame으로 변경 시켜줍니다.""" | ||
subject_entity = [] | ||
object_entity = [] | ||
for i, j in zip(dataset["subject_entity"], dataset["object_entity"]): | ||
i = i[1:-1].split(",")[0].split(":")[1] | ||
j = j[1:-1].split(",")[0].split(":")[1] | ||
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subject_entity.append(i) | ||
object_entity.append(j) | ||
out_dataset = pd.DataFrame( | ||
{ | ||
"id": dataset["id"], | ||
"sentence": dataset["sentence"], | ||
"subject_entity": subject_entity, | ||
"object_entity": object_entity, | ||
"label": dataset["label"], | ||
} | ||
) | ||
return out_dataset | ||
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def load_data(dataset_dir): | ||
"""csv 파일을 경로에 맡게 불러 옵니다.""" | ||
pd_dataset = pd.read_csv(dataset_dir) | ||
dataset = preprocessing_dataset(pd_dataset) | ||
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return dataset | ||
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def load_test_dataset(dataset_dir, tokenizer): | ||
""" | ||
test dataset을 불러온 후, | ||
tokenizing 합니다. | ||
""" | ||
test_dataset = load_data(dataset_dir) | ||
test_label = [100 for i in range(len(test_dataset))] | ||
# tokenizing dataset | ||
tokenized_test = tokenized_dataset(test_dataset, tokenizer) | ||
return test_dataset["id"], tokenized_test, test_label | ||
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def num_to_label(label): | ||
""" | ||
숫자로 되어 있던 class를 원본 문자열 라벨로 변환 합니다. | ||
""" | ||
origin_label = [] | ||
for v in label: | ||
origin_label.append(dict_num_to_label[v]) | ||
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return origin_label | ||
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class RE_Dataset(torch.utils.data.Dataset): | ||
"""Dataset 구성을 위한 class.""" | ||
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def __init__(self, pair_dataset, labels): | ||
self.pair_dataset = pair_dataset | ||
self.labels = labels | ||
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def __getitem__(self, idx): | ||
item = {key: val[idx].clone().detach() for key, val in self.pair_dataset.items()} | ||
item["labels"] = torch.tensor(self.labels[idx]) | ||
return item | ||
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def __len__(self): | ||
return len(self.labels) | ||
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def get_topn_probs(probs, n=3): | ||
"""_summary_ | ||
Args: | ||
probs (_type_): _description_ | ||
n (int, optional): _description_. Defaults to 3. | ||
Returns: | ||
_type_: _description_ | ||
""" | ||
pairs = [] | ||
top_n_idxs = list(reversed(np.array(probs).argsort()))[:n] | ||
for idx in top_n_idxs: | ||
pairs.append((dict_num_to_label[idx], probs[idx])) | ||
return pairs | ||
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def get_entity_word(row): | ||
"""_summary_ | ||
Args: | ||
row (_type_): _description_ | ||
Returns: | ||
_type_: _description_ | ||
""" | ||
row = row.replace("'", '"') | ||
return json.loads(row)["word"] | ||
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def get_filtered_result(new_df, test_df): | ||
"""_summary_ | ||
Args: | ||
new_df (_type_): _description_ | ||
test_df (_type_): _description_ | ||
Returns: | ||
_type_: _description_ | ||
""" | ||
new_df["sentence"] = test_df["sentence"] | ||
new_df["answer"] = test_df["label"] | ||
new_df["subject"] = test_df["subject_entity"].apply(get_entity_word) | ||
new_df["object"] = test_df["object_entity"].apply(get_entity_word) | ||
new_df["probs"] = new_df["probs"].apply(get_topn_probs) | ||
new_df = new_df.loc[new_df["pred_label"] != new_df["answer"]] | ||
new_df = new_df[["sentence", "subject", "object", "pred_label", "answer", "probs"]] | ||
return new_df | ||
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def test(args): | ||
"""Perform a test using model of model_dir | ||
Returns: | ||
_type_: pd.DataFrame | ||
""" | ||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | ||
tokenizer = AutoTokenizer.from_pretrained(args.model_name) | ||
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model = AutoModelForSequenceClassification.from_pretrained(args.model_dir) | ||
model.to(device) | ||
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test_id, test_dataset, test_label = load_test_dataset(args.valid_data_path, tokenizer) | ||
Re_test_dataset = RE_Dataset(test_dataset, test_label) | ||
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pred_answer, output_prob = inference(model, Re_test_dataset, device) | ||
pred_answer = num_to_label(pred_answer) | ||
output = pd.DataFrame( | ||
{ | ||
"pred_label": pred_answer, | ||
"probs": output_prob, | ||
} | ||
) | ||
return output |
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