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How to use annotate_file method in autotest

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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