Best Python code snippet using autotest_python
EvalModel.py
Source: EvalModel.py
1#!/usr/bin/env python2# -*- coding: utf-8 -*-3import sys4# from analysis import analysis5import csv6import scipy7from torch.utils.data import DataLoader8import math9from sentence_transformers import SentenceTransformer10from sentence_transformers import models11import logging12from datetime import datetime13import sys14import os15import argparse16import logging17import hashlib18sys.path.append("..")19from analysis import analysis20import argparse21logging.basicConfig(level=logging.DEBUG, format="[%(levelname).1s %(asctime)s] %(message)s", datefmt="%Y-%m-%d_%H:%M:%S")22def load_annotate_data(annotate_file, delimiter='\t'):23 annotate_dataset = []24 with open(annotate_file,"r") as fp:25 for line in fp:26 items = line.strip().split(delimiter)27 annotate_dataset.append(items)28 return annotate_dataset29def load_title_content_data(annotate_file, delimiter='\t'):30 annotate_dataset = []31 with open(annotate_file,"r", encoding="utf-8") as fp:32 for line in fp:33 line = line.encode("utf-8").decode("unicode_escape")34 items = line.rstrip('\n').split(delimiter)35 if len(items) != 5:36 print(line)37 kw, title, content, label, docid = items38 annotate_dataset.append([kw, title + ' ' + content, label])39 return annotate_dataset40def eval_model(annotate_file, model_name, eval_res_file):41 # annotate_dataset = load_annotate_data(annotate_file)42 annotate_dataset = load_title_content_data(annotate_file)43 results = []44 idindex = {}45 corpus = []46 count = 047 for query, sen, label in annotate_dataset:48 id_gen = hashlib.md5()49 id_gen.update(query.encode('utf-8'))50 query_id = id_gen.hexdigest()51 if query_id not in idindex:52 corpus.append(query)53 idindex[query_id] = count54 count += 155 id_gen = hashlib.md5()56 id_gen.update(sen.encode('utf-8'))57 sen_id = id_gen.hexdigest() 58 if sen_id not in idindex:59 corpus.append(sen)60 idindex[sen_id] = count61 count += 162 63 model = SentenceTransformer(model_name)64 #word_embedding_model = models.Transformer(model_name)65 #pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),66 # pooling_mode_mean_tokens=True,67 # pooling_mode_cls_token=False,68 # pooling_mode_max_tokens=False)69 #model = SentenceTransformer(modules=[word_embedding_model, pooling_model])70 corpus_embeddings = model.encode(corpus)71 for query, sen, label in annotate_dataset:72 label = int(label)73 id_gen = hashlib.md5()74 id_gen.update(query.encode('utf-8'))75 query_id = id_gen.hexdigest()76 id_gen = hashlib.md5()77 id_gen.update(sen.encode('utf-8'))78 sen_id = id_gen.hexdigest() 79 logging.debug('query:%s'%(query))80 logging.debug('idindex[query_id]:%d'%(idindex[query_id]))81 logging.debug('sen:%s'%(sen))82 logging.debug('idindex[sen_id]:%d'%(idindex[sen_id]))83 query_vec = corpus_embeddings[idindex[query_id]]84 sen_vec = corpus_embeddings[idindex[sen_id]]85 sim_score = scipy.spatial.distance.cdist([query_vec],[sen_vec], "cosine")[0] 86 results.append((label,query_id, sim_score, sen_id))87 fp = open(eval_res_file,"w", encoding="utf-8")88 writer = csv.writer(fp)89 ndcg = analysis.cal_NDCG(results,10)90 writer.writerow([model_path, ndcg])91 fp.close()92def model_predict(input_file, output_file):93 pass94if __name__ == "__main__":95 os.environ["CUDA_VISIBLE_DEVICES"] = "1"96 parser = argparse.ArgumentParser(description="Text Similarity")97 parser.add_argument('--annotate_file', action='store', type=str, required=True, help="annotate_file")98 parser.add_argument('--model_path', required=True, type=str, help="model_path")99 parser.add_argument('--eval_res_file', default="eval_res_file", type=str, help="eval_res_file")100 args = parser.parse_args()101 annotate_file = args.annotate_file102 model_path = args.model_path103 eval_res_file = args.eval_res_file104 eval_model(annotate_file, model_path, eval_res_file)...
annotate.py
Source: annotate.py
...27 """Run the report.28 See `coverage.report()` for arguments.29 """30 self.report_files(self.annotate_file, morfs, directory)31 def annotate_file(self, cu, analysis):32 """Annotate a single file.33 `cu` is the CodeUnit for the file to annotate.34 """35 if not cu.relative:36 return37 filename = cu.filename38 source = cu.source_file()39 if self.directory:40 dest_file = os.path.join(self.directory, cu.flat_rootname())41 dest_file += ".py,cover"42 else:43 dest_file = filename + ",cover"44 dest = open(dest_file, 'w')45 statements = sorted(analysis.statements)...
pretrain_custom.py
Source: pretrain_custom.py
1import torch2from PIL import Image3import pandas as pd4class CustomData(torch.utils.data.Dataset):5 def __init__(self , annotate_file , transform = None):6 self.annotate_file = pd.read_csv(annotate_file)7 self.transform = transform8 def __len__(self):9 return len(self.annotate_file)10 def __getitem__(self , index):11 label = int(self.annotate_file.iloc[index , 1])12 img = Image.open(self.annotate_file.iloc[index , 0])13 if self.transform:14 img = self.transform(img)...
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