How to use get_root_dir method in autotest

Best Python code snippet using autotest_python Github


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...7fields_user_num = ['followers_count', 'friends_count', 'listed_count', 'statuses_count', 'favourites_count', 'len_name',8 'len_screen_name', 'len_description']9fields_user_cat = ['geo_enabled', 'verified', 'has_location']10fields_user = ['followers_count', 'friends_count', 'listed_count', 'statuses_count', 'favourites_count', 'geo_enabled', 'verified', 'has_location']11def get_root_dir():12 """13 Change this to the root data directory14 :return: root directory15 """16 root = osp.join("..", "fake_news_data")17 if == "posix":18 root = osp.join("..", "fake_news_data")19 else:20 root = osp.join("C:\\Workspace", "FakeNews", "fake_news_data")21 return root22def get_processed_dir(exp_name=None):23 if exp_name is not None:24 processed_dir = osp.join(get_root_dir(), "back", exp_name)25 if not osp.exists(processed_dir):26 os.mkdir(processed_dir)27 return processed_dir28 else:29 return get_root_dir()30def load_tweets(dataset):31 return torch.load(osp.join(get_root_dir(), f"{dataset}"))32def save_tweets(dataset, all_tweets_d, all_replies_d, all_tweets_score_d):33, all_replies_d, all_tweets_score_d), osp.join(get_root_dir(), f"{dataset}"))34def load_users(dataset):35 all_user_feat_d = torch.load(osp.join(get_root_dir(), f"{dataset}"))36 return all_user_feat_d37def save_users(dataset, all_user_feat_d):38, osp.join(get_root_dir(), f"{dataset}"))39def load_nx_graphs(dataset):40 all_Gu = torch.load(osp.join(get_root_dir(), f"{dataset}"))41 return all_Gu42def save_Gu(dataset, all_Gu):43, osp.join(get_root_dir(), f"{dataset}"))44def load_labels(dataset):45 processed_dir = get_root_dir()46 labels_d = torch.load(osp.join(processed_dir, f"{dataset}"))47 return labels_d48def save_labels(dataset, labels_d):49, osp.join(get_root_dir(), f"{dataset}"))50def load_user_feat(dataset):51 import torch52 all_Gu = torch.load(osp.join(get_root_dir(), f"{dataset}"))53 return all_Gu54def read_news_article_evidence(dataset):55 """56 format: claim_evi_pair, news_article, label57 :param dataset:58 :return:59 """60 data_dir = get_root_dir()61 path = osp.join(data_dir, f"{dataset}_news_article_evidence.pkl")62 with open(path, "rb") as f:63 examples = pickle.load(f)64 return examples65def read_tweets_and_scores(dataset):66 import torch67 all_tweets_d, all_replies_d, all_tweets_score_d = torch.load(osp.join(get_root_dir(), f"{dataset}"))68 return all_tweets_d, all_replies_d, all_tweets_score_d69def read_news_articles_text(global_news_article_d, dataset_name="politifact"):70 root = get_root_dir()71 with open(osp.join(root, f"{dataset_name}_news_articles.txt"), 'r', encoding='utf-8') as f:72 for line in f.readlines():73 filename, article = line.split("\t")74 article = article.strip()75 global_news_article_d[filename] = article76 f.close()77def read_news_articles_labels(dataset_name="politifact", n_samples=0):78 KEEP_EMPTY_RETWEETS_AND_REPLIES = 179 # if we only read the first `n_samples` samples in the dataframe80 if n_samples > 0:81 news_article_df = pd.read_csv(get_root_dir() + f"\\{dataset_name}_news_articles.tsv", sep='\t', iterator=True,82 header=None)83 news_article_df = news_article_df.get_chunk(n_samples)84 else:85 news_article_df = pd.read_csv(get_root_dir() + f"\\{dataset_name}_news_articles.tsv", sep='\t')86 if KEEP_EMPTY_RETWEETS_AND_REPLIES:87 news_article_cleaned_df = news_article_df[88 (news_article_df.has_tweets == 1) & (news_article_df.has_news_article == 1)]89 else:90 news_article_cleaned_df = news_article_df[91 (news_article_df.has_tweets == 1) & (news_article_df.has_news_article == 1) & (92 news_article_df.has_retweets == 1) & (news_article_df.has_replies == 1)]93 return news_article_cleaned_df94def only_directories(path):95 return [name for name in os.listdir(path) if osp.isdir(osp.join(path, name))]96def get_data_list():97 politifact_fake = only_directories("politifact_fake")98 politifact_real = only_directories("politifact_real")99 gossipcop_fake = only_directories("gossipcop_fake")100 gossipcop_real = only_directories("gossipcop_real")101 data_list = {102 "politifact_real": politifact_real,103 "politifact_fake": politifact_fake,104 "gossipcop_fake" : gossipcop_fake,105 "gossipcop_real" : gossipcop_real106 }107 return data_list108def get_dataset_names(dataset):109 if dataset == "politifact":110 dataset_names = {111 "politifact": ["politifact_real", "politifact_fake"]112 }113 elif dataset == "gossipcop":114 dataset_names = {115 "gossipcop": ["gossipcop_real", "gossipcop_fake"]116 }117 elif dataset == "both":118 dataset_names = {119 "politifact": ["politifact_real", "politifact_fake"],120 "gossipcop" : ["gossipcop_real", "gossipcop_fake"]121 }122 else:123 raise NotImplementedError124 return dataset_names125def filter_empty_dict_entry(d, filename, log=True):126 is_empty_json = False127 new_d = {}128 for k in d:129 if d[k] != []:130 new_d[k] = d[k]131 if new_d == {}:132 if log:133 print(f"\t{filename} json empty")134 is_empty_json = True135 return new_d, is_empty_json136# For a dict of dicts, filter empty entries, which are {}137def filter_empty_nested_dict(d):138 new_d, empty_li = {}, []139 for k, v in d.items():140 if v == {}:141 empty_li += [k]142 else:143 new_d[k] = d[k]144 return new_d, empty_li145def print_results(results, epoch, dataset_split_name="Train", enable_logging=True, args=None):146 log_str = f"\n[{dataset_split_name}] Epoch {epoch}\n\tPre: {results['pre']:.4f}, Rec: {results['rec']:.4f}\n\tAcc: {results['acc']:.4f}, F1: {results['f1']:.4f}\n"147 print(log_str)148 if enable_logging:149 f = open(f"{args.outdir}/{dataset_split_name}_{args.max_len}_{args.evi_num}_results.txt", "a+")150 f.write(log_str)151def load_tweet_df(filename, dataset):152 import pandas as pd153 import numpy as np154 pd.options.display.max_columns = 20155 pd.set_option('precision', 20)156 # NOTE: reading as int64 is super important157 dtypes = {158 'root_tweet_id': np.int64,159 'tweet_id' : np.int64,160 'root_user_id' : np.int64,161 'user_id' : np.int64,162 }163 path = osp.join(get_root_dir(), dataset, filename, "tweets_retweets_comments.tsv")164 if not osp.exists(path):165 print(f"\t SKIP {filename}: no tweet_retweet_comment.tsv")166 return None167 tweet_df = pd.read_csv(path, sep='\t', float_precision='high')168 return tweet_df169def print_heading(dataset):...

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...11 """Retrieves the pref value under the given name key from the settings file"""12 return load_settings(SETTINGS_FILENAME).get(key)13def open_config_rc(window):14 """Opens the default .jsbeautifyrc file for editing in a new tab"""15 file_path = join(get_root_dir(), '.jsbeautifyrc.defaults.json')16 window.open_file(file_path)17def open_u_config_rc(window):18 """Opens the user's .jsbeautifyrc file for editing in a new tab"""19 defaults = "{\n}"20 old_jsbeautifyrc_path = join(get_root_dir(), '.jsbeautifyrc')21 old_jsbeautifyrc = read_text_from_file(old_jsbeautifyrc_path, defaults)22 file_path = join(get_user_dir(), '.jsbeautifyrc')23 window.open_file(ensure_file(file_path, default_contents=old_jsbeautifyrc))24def open_sublime_settings(window):25 """Opens the default plugin settings file for editing in a new tab"""26 file_path = join(get_root_dir(), SETTINGS_FILENAME)27 window.open_file(file_path)28def open_u_sublime_settings(window):29 """Opens the user's plugin settings file for editing in a new tab"""30 file_path = join(get_user_dir(), SETTINGS_FILENAME)31 window.open_file(ensure_file(file_path, default_contents="{\n}"))32def open_sublime_keymap(window, platform):33 """Opens the default plugin keyboard bindings file for editing in a new tab"""34 file_name = KEYMAP_FILENAME.replace("$PLATFORM", platform)35 file_path = join(get_root_dir(), file_name)36 window.open_file(file_path)37def open_u_sublime_keymap(window, platform):38 """Opens the user's plugin keyboard bindings file for editing in a new tab"""39 file_name = KEYMAP_FILENAME.replace("$PLATFORM", platform)40 file_path = join(get_user_dir(), file_name)...

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