{"id":17043489,"url":"https://github.com/rhsimplex/spectrm-challenge-ryan","last_synced_at":"2026-04-30T11:36:07.369Z","repository":{"id":77395099,"uuid":"67884096","full_name":"rhsimplex/spectrm-challenge-ryan","owner":"rhsimplex","description":"My work for the Spectrm challenge","archived":false,"fork":false,"pushed_at":"2016-09-23T11:13:59.000Z","size":3502,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-15T07:40:10.967Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/rhsimplex.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}},"created_at":"2016-09-10T17:15:55.000Z","updated_at":"2016-09-12T14:13:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"e8d5b118-0eea-4e66-af5a-e76207daed81","html_url":"https://github.com/rhsimplex/spectrm-challenge-ryan","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rhsimplex/spectrm-challenge-ryan","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhsimplex%2Fspectrm-challenge-ryan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhsimplex%2Fspectrm-challenge-ryan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhsimplex%2Fspectrm-challenge-ryan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhsimplex%2Fspectrm-challenge-ryan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rhsimplex","download_url":"https://codeload.github.com/rhsimplex/spectrm-challenge-ryan/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhsimplex%2Fspectrm-challenge-ryan/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32463892,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T22:27:22.272Z","status":"online","status_checked_at":"2026-04-30T02:00:05.929Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2024-10-14T09:29:40.906Z","updated_at":"2026-04-30T11:36:07.352Z","avatar_url":"https://github.com/rhsimplex.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Spectrm Challenge\n\nMy entry to the [Spectrm Challenge](https://github.com/cod3licious/spectrm-challenge).  Uses a very simple word-frequencey model and achieves recall rates around 5-8%. For an explanation on how it works, [see the Jupyter notebook.](https://github.com/rhsimplex/spectrm-challenge-ryan/blob/master/Word%20Frequency%20Model.ipynb)\n\n## Setup\n\n`spectrm-challenge-ryan` only requires basic scientific python dependencies (numpy, scipy, pandas, matplotlib, nltk).  I recommend using a pre-packaged distribution like Anaconda for multiprocessing/memory efficiency. However, you can use `pip` to set up the dependencies:\n\n```bash\n$ pip install -r requirements.txt\n```\n\n## Running the model\n\nNote: Though the matrices are pretty sparse, I used the regular `numpy` matrix implementation.  That means generating a model can be quite memory intensive.  All my tests were on a desktop with 10 cores and 16GB of memory.  A nice improvement would to have this code use the `scipy` sparse matrix implementation.\n\nTo run the model on the unlabeled examples, simply run:\n\n```bash\n$ python match_dialogs.py\n```\n\nIt knows the default locations.  If you want to run on other datasets, for instance the training set, you can specify:\n\n```bash\n$ python match_dialogs.py challenge_data/train_dialogs.txt challenge_data/train_missing.txt\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhsimplex%2Fspectrm-challenge-ryan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frhsimplex%2Fspectrm-challenge-ryan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhsimplex%2Fspectrm-challenge-ryan/lists"}