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added the feature of being able to only process

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one repo through command line argument
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Karma Riuk
2025-03-20 12:50:40 +01:00
parent 07a3f4765f
commit adc62867ff
1 changed files with 9 additions and 4 deletions
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13
pull_requests.py
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@ -180,7 +180,7 @@ def process_repo(repo_name: str, stats_df: Optional[pd.DataFrame], dataset: Data
pbar.set_postfix({"pr": pr.number}) pbar.set_postfix({"pr": pr.number})
process_pull(repo, pr, dataset, repos_dir) process_pull(repo, pr, dataset, repos_dir)
def process_repos(csv_file: str, stats_csv: Optional[str], dataset: Dataset, repos_dir: str): def process_repos(df: pd.DataFrame, stats_df: Optional[pd.DataFrame], dataset: Dataset, repos_dir: str):
""" """
Processes the repos in the given csv file, extracting the good ones and Processes the repos in the given csv file, extracting the good ones and
creating the "triplets" for the dataset. creating the "triplets" for the dataset.
@ -191,8 +191,6 @@ def process_repos(csv_file: str, stats_csv: Optional[str], dataset: Dataset, rep
Passing it by reference in order have the latest information, in case of an error Passing it by reference in order have the latest information, in case of an error
verbose (bool): Whether to be verbose or not verbose (bool): Whether to be verbose or not
""" """
df = get_good_projects(csv_file)
stats_df = pd.read_csv(stats_csv) if stats_csv is not None else None
already_processed_repos = [] already_processed_repos = []
potentially_good_repos = [] potentially_good_repos = []
if stats_df is not None: if stats_df is not None:
@ -222,15 +220,22 @@ if __name__ == "__main__":
parser.add_argument('-r', '--repos', type=str, default="./results/", help='The directory in which the repos were cloned (will be cloned if they aren\'t there already). Default: "./results/"') parser.add_argument('-r', '--repos', type=str, default="./results/", help='The directory in which the repos were cloned (will be cloned if they aren\'t there already). Default: "./results/"')
parser.add_argument('-s', '--stats', type=str, help="The name of the output file from the stats_pull_requests.py. The stats file already knows which PRs are good (the ones with only 1 comment between two rounds of commits), so instead of going through all of PRs of a repo, we can fast-track using this. If the repo isn't in the stats file, we must go through each PR") parser.add_argument('-s', '--stats', type=str, help="The name of the output file from the stats_pull_requests.py. The stats file already knows which PRs are good (the ones with only 1 comment between two rounds of commits), so instead of going through all of PRs of a repo, we can fast-track using this. If the repo isn't in the stats file, we must go through each PR")
# parser.add_argument('-v', '--verbose', action='store_true', help='Prints the number of good projects.') # parser.add_argument('-v', '--verbose', action='store_true', help='Prints the number of good projects.')
parser.add_argument("--only-repo", type=str, help="If this argument is not provided, all the repos in the '--repos' csv will be processed. If instead you want to run the script on a single repo (for testing purposes mainly) provide a string of form 'XXX/YYY' to this argument, where XXX is the owner of the repo and YYY is the name of the repo")
args = parser.parse_args() args = parser.parse_args()
g = Github(os.environ["GITHUB_AUTH_TOKEN_CRAB"]) g = Github(os.environ["GITHUB_AUTH_TOKEN_CRAB"])
docker_client = docker.from_env() docker_client = docker.from_env()
move_github_logging_to_file() move_github_logging_to_file()
df = get_good_projects(args.csv_file)
if args.only_repo is not None:
df = df.loc[df["name"] == args.only_repo]
stats_df = pd.read_csv(args.stats) if args.stats is not None else None
dataset = Dataset() dataset = Dataset()
try: try:
# try and finally to save, regardless of an error occuring or the program finished correctly # try and finally to save, regardless of an error occuring or the program finished correctly
process_repos(args.csv_file, args.stats, dataset, args.repos) process_repos(df, stats_df, dataset, args.repos)
finally: finally:
dataset.to_json(args.output) dataset.to_json(args.output)