{"id":25827474,"url":"https://github.com/happyoung68/deeplog-log_anomaly_detection","last_synced_at":"2026-05-11T21:32:29.738Z","repository":{"id":278480340,"uuid":"935750075","full_name":"happyoung68/Deeplog-log_anomaly_detection","owner":"happyoung68","description":"日志异常检测，Used for log anomaly detection, including log processing, training, prediction, and output results.","archived":false,"fork":false,"pushed_at":"2026-03-22T13:22:50.000Z","size":131,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-22T18:03:53.932Z","etag":null,"topics":["anomaly-detection","deeplog","failure-detection","log-analysis","pytorch","sequence-prediction"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/happyoung68.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-20T00:39:08.000Z","updated_at":"2026-03-22T13:22:53.000Z","dependencies_parsed_at":"2025-02-21T09:34:06.447Z","dependency_job_id":null,"html_url":"https://github.com/happyoung68/Deeplog-log_anomaly_detection","commit_stats":null,"previous_names":["happyoung68/log_anomaly_detect_deeplog","happyoung68/deeplog-log_anomaly_detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/happyoung68/Deeplog-log_anomaly_detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/happyoung68%2FDeeplog-log_anomaly_detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/happyoung68%2FDeeplog-log_anomaly_detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/happyoung68%2FDeeplog-log_anomaly_detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/happyoung68%2FDeeplog-log_anomaly_detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/happyoung68","download_url":"https://codeload.github.com/happyoung68/Deeplog-log_anomaly_detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/happyoung68%2FDeeplog-log_anomaly_detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32913632,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-11T17:09:15.040Z","status":"ssl_error","status_checked_at":"2026-05-11T17:08:45.420Z","response_time":120,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection","deeplog","failure-detection","log-analysis","pytorch","sequence-prediction"],"created_at":"2025-02-28T16:26:22.320Z","updated_at":"2026-05-11T21:32:29.710Z","avatar_url":"https://github.com/happyoung68.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deeplog-log_anomaly_detection\n日志异常检测，Used for log anomaly detection, including log preprocessing, training, prediction, and output results.\n## Introduction\n***This work is developed on the basis of \u003chttps://github.com/d0ng1ee/logdeep\u003e, and use [Drain](https://github.com/logpai/logparser) for log parsing.***  \n## Major features  \n- Used for producing environment.  \n- Including complete process.  \n- Outputing anomaly logs, rather than precision, recall, F1-score and so on.  \n## Requirement  \n- python\u003e=3.6  \n- pytorch \u003e= 1.1.0  \n## Quick start  \n1. Preprocess logs\n\n   ```\n   cd demo  \n   python preprocess.py\n   ```\n   Then you will get the parsed log file at `../result/parse_result`, and `length of event_id_map` represents the count of log templates, `../data/demo_input.csv` is the file where the EventId has been mapped to numbers starting from 1  \n\n3. Train model\n\n   ```\n   python deeplog.py train\n   ```\n   It will tain using `../data/demo_input.csv` and the result, key parameters and train logs will be saved under `result/deeplog` path\n\n4. Predict and output anomaly result\n\n   ```\n   python deeplog.py predict\n   ```\n   It will predict using `name = 'demo_input.csv'` in `predict.py`, here I am using the same file for **training, validation, and prediction.** But you can change it as needed.😁   \n   The anomaly output will be saved as `../result/anomaly_output_for_xxx.csv`.\n\n5. DIY your own model   \n   Just try to modify parameters like `window_size`, `num_candidates`, `inputfile` to build your own model.😇\n## Contributing  \n**If you have any questions, please open an** ***[issue](https://github.com/happyoung68/Deeplog-log_anomaly_detection/issues).***    \n\n**Welcome to** ***[pull requests](https://github.com/happyoung68/Deeplog-log_anomaly_detection/pulls)*** **to improve this repo!**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhappyoung68%2Fdeeplog-log_anomaly_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhappyoung68%2Fdeeplog-log_anomaly_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhappyoung68%2Fdeeplog-log_anomaly_detection/lists"}