mirror of
https://github.com/firmai/financial-machine-learning.git
synced 2026-07-31 20:57:50 +00:00
+138
-15
@@ -1,10 +1,124 @@
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import os
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from typing import Dict
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from conf import PROJECT_ROOT_DIR
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import re
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import pandas as pd
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from github import Github, Repository
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# generic search functions
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def search_repo(search_term: str, qualifier_dict: Dict):
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github_token = os.environ.get('GIT_TOKEN')
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g = Github(github_token)
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qualifier_str = ' '.join(['{}:{}'.format(k, v) for k, v in iter(qualifier_dict.items())])
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if qualifier_str != '':
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final_search_term = '{} {}'.format(search_term, qualifier_str)
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else:
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final_search_term = search_term
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repo_result = g.search_repositories(final_search_term)
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return repo_result
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def search_repo_simple(search_term: str = None,
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min_stars_number: int = None,
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created_at: str = None,
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pushed_date: str = None
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):
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"""
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:param search_term:
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:param min_stars_number:
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:param created_at:
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:param pushed_date:
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usage:
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>>> search_term = '(deep learning) AND trading'
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>>> min_stars_number = 100
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>>> created_at = None
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>>> pushed_date = None
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"""
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if search_term is None:
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_search_term = ''
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else:
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_search_term = search_term
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qualifier_dict = {}
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if min_stars_number is not None:
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qualifier_dict['stars'] = '>={}'.format(min_stars_number)
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if created_at is not None:
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qualifier_dict['created'] = '>={}'.format(created_at)
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if pushed_date is not None:
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qualifier_dict['pushed'] = '>={}'.format(pushed_date)
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search_result = search_repo(_search_term, qualifier_dict)
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return search_result
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# *******
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# topic specific search functions
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# *******
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def convert_repo_list_to_df(repo_list, category):
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df_list = []
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for repo in repo_list:
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attr_dict = get_repo_attributes_dict(repo)
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attr_dict['name'] = repo.name
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attr_dict['comment'] = 'NEW'
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attr_dict['category'] = category
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attr_dict['repo_path'] = repo.full_name
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attr_dict['url'] = 'https://github.com/{}'.format(repo.full_name)
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df_list.append(attr_dict)
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result_df = pd.DataFrame(df_list)
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return result_df
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def search_new_repo_and_append(min_stars_number: int = 100):
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repo_df = get_repo_list()
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category_list = repo_df['category'].unique().tolist()
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new_repo_list = []
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for category in category_list:
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if category == 'Deep Learning':
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# github not yet support OR operator, issue here
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# https://github.com/isaacs/github/issues/660
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# hence run the search terms twice and combine
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search_term = 'deep learning trading'
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repo_list = search_repo_simple(search_term, min_stars_number)
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top_df = convert_repo_list_to_df(repo_list, category)
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search_term = 'deep learning finance'
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repo_list = search_repo_simple(search_term, min_stars_number)
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bottom_df = convert_repo_list_to_df(repo_list, category)
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combined_df = pd.concat([top_df, bottom_df]).reset_index(drop=True)
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# only find ones that need to be inserted
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combined_df = combined_df[~combined_df['repo_path'].str.lower().isin(repo_df['repo_path'].str.lower())]
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new_repo_list.append(combined_df)
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new_repo_df = pd.concat(new_repo_list).reset_index(drop=True)
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final_df = pd.concat([repo_df.drop('repo_path', axis=1), new_repo_df.drop('repo_path', axis=1)]).reset_index(
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drop=True)
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final_df = final_df.sort_values(by='category')
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final_df.to_csv(os.path.join(PROJECT_ROOT_DIR, 'raw_data', 'url_list.csv'), index=False)
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# *******
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# saved repo list, treat it as database for now
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# *******
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def get_repo_list():
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repo_df = pd.read_csv(os.path.join(PROJECT_ROOT_DIR, 'raw_data', 'url_list.csv'))
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repo_df['repo_path'] = repo_df['url'].apply(get_repo_path)
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return repo_df
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# *******
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# repo specific information
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# *******
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def get_repo_path(in_url):
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repo_path = None
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if 'https://github.com/' in in_url:
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url_query = in_url.replace('https://github.com/', '')
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repo_path = '/'.join(url_query.split('/')[:2])
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return repo_path
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def get_last_commit_date(input_repo: Repository):
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"""
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get latest commit from repo
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@@ -15,28 +129,36 @@ def get_last_commit_date(input_repo: Repository):
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return page.commit.author.date
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def get_repo_attributes_dict(input_repo: Repository):
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result_dict = {
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'created_at': input_repo.created_at,
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'last_commit': get_last_commit_date(input_repo),
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'last_update': input_repo.updated_at,
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'star_count': input_repo.stargazers_count,
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'fork_count': input_repo.forks_count,
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'contributors_count': input_repo.get_contributors().totalCount
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}
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return result_dict
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def get_repo_status():
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github_token = os.environ.get('GIT_TOKEN')
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g = Github(github_token)
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repo_df = pd.read_csv(os.path.join(PROJECT_ROOT_DIR, 'raw_data', 'url_list.csv'))
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repo_df = get_repo_list()
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for idx, row in repo_df.iterrows():
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url = row['url']
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if 'https://github.com/' in url:
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print('processing [{}]'.format(url))
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url_query = url.replace('https://github.com/', '')
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url_format = '/'.join(url_query.split('/')[:2])
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repo_path = row['repo_path']
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if repo_path is not None:
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try:
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repo = g.get_repo(url_format)
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repo_df.loc[idx, 'created_at'] = repo.created_at
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repo_df.loc[idx, 'last_commit'] = get_last_commit_date(repo)
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repo_df.loc[idx, 'last_update'] = repo.updated_at
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repo_df.loc[idx, 'star_count'] = repo.stargazers_count
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repo_df.loc[idx, 'fork_count'] = repo.forks_count
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repo_df.loc[idx, 'contributors_count'] = repo.get_contributors().totalCount
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print('processing [{}]'.format(repo_path))
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repo = g.get_repo(repo_path)
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repo_attr_dict = get_repo_attributes_dict(repo)
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except Exception as ex:
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print(ex)
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repo_df.loc[idx, 'last_update'] = None
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repo_attr_dict = {}
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for k, v in iter(repo_attr_dict.items()):
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repo_df.loc[idx, k] = v
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repo_df.to_csv(os.path.join(PROJECT_ROOT_DIR, 'raw_data', 'url_list.csv'), index=False)
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@@ -95,3 +217,4 @@ def parse_readme_md():
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if __name__ == '__main__':
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get_repo_status()
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search_new_repo_and_append(min_stars_number=100)
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