{"id":17949621,"url":"https://github.com/wgierke/informaticup2018","last_synced_at":"2026-01-21T18:01:54.270Z","repository":{"id":28439523,"uuid":"111892130","full_name":"WGierke/informatiCup2018","owner":"WGierke","description":"Predicting the optimal strategy for fueling for a given route","archived":false,"fork":false,"pushed_at":"2022-12-07T23:45:21.000Z","size":18099,"stargazers_count":1,"open_issues_count":36,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-08T09:30:46.823Z","etag":null,"topics":["convolutional-neural-networks","fbprophet","forcasting","informaticup","machine-learning","prediction","recurrent-neural-networks","tensorflow","timeseries-analysis"],"latest_commit_sha":null,"homepage":"http://tofill.dyndns.info:7080","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WGierke.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-11-24T08:03:53.000Z","updated_at":"2018-12-19T20:13:08.000Z","dependencies_parsed_at":"2022-08-07T13:15:51.496Z","dependency_job_id":null,"html_url":"https://github.com/WGierke/informatiCup2018","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WGierke/informatiCup2018","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGierke%2FinformatiCup2018","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGierke%2FinformatiCup2018/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGierke%2FinformatiCup2018/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGierke%2FinformatiCup2018/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WGierke","download_url":"https://codeload.github.com/WGierke/informatiCup2018/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGierke%2FinformatiCup2018/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28638492,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-21T17:36:33.271Z","status":"ssl_error","status_checked_at":"2026-01-21T17:36:30.617Z","response_time":86,"last_error":"SSL_read: 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":["convolutional-neural-networks","fbprophet","forcasting","informaticup","machine-learning","prediction","recurrent-neural-networks","tensorflow","timeseries-analysis"],"created_at":"2024-10-29T09:27:31.946Z","updated_at":"2026-01-21T18:01:54.254Z","avatar_url":"https://github.com/WGierke.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"informatiCup2018 [![CircleCI](https://circleci.com/gh/WGierke/informatiCup2018.svg?style=svg\u0026circle-token=00f4e65f31b3192e58b793d0282ba0af8c009b44)](https://circleci.com/gh/WGierke/informatiCup2018)\n==============================\n\nPredicting the optimal strategy for fueling for a given route ([task description](https://github.com/WGierke/informatiCup2018/blob/master/references/Intellitank.pdf)).\n\n[Report](https://github.com/WGierke/informatiCup2018/blob/master/reports/informaticup2018.pdf)  \n[Routes](https://github.com/WGierke/informatiCup2018/tree/master/routes)\n\nProject Organization\n------------\n\n    ├── LICENSE\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │\n    ├── models             \u003c- Trained and serialized models, model predictions, or model summaries\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\n    │\n    ├── references         \u003c- Data dictionaries, manuals, and all other explanatory materials.\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures        \u003c- Generated graphics and figures to be used in reporting\n    │\n    ├── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n    │                         generated with `pip freeze \u003e requirements.txt`\n    │\n    └── src                \u003c- Source code for use in this project.\n        ├── __init__.py    \u003c- Makes src a Python module\n        │\n        ├── features       \u003c- Scripts to turn raw data into features for modeling\n        │   └── build_features.py\n        │\n        ├── models         \u003c- Scripts to train models and then use trained models to make\n        │   │                 predictions\n        │   ├── predict_model.py\n        │   └── train_model.py\n        │\n        └── visualization  \u003c- Scripts to create exploratory and results oriented visualizations\n            └── visualize.py\n\n--------\n### Setup\n\n- Clone the repository including submodules (to include the challenge data as well):  \n`git clone --recursive git@github.com:WGierke/informatiCup2018.git`  \nHowever, if you already downloaded the [InformatiCup2018 repository](https://github.com/InformatiCup/InformatiCup2018), you can also create a symbolic link that shows from `data/raw/input_data` to the informatiCup2018 repository. A sanity check would be that `data/raw/input_data/Eingabedaten/Fahrzeugrouten/Bertha\\ Benz\\ Memorial\\ Route.csv` is accessible.\n\n- Install all dependencies  \n`pip3 install -r requirements.txt`  \n\n### Usage\n- To start the server:  \n`python3 src/serving/server.py`  \n- To predict the gas prices given using training data up to a specified point in time for a given point in time:  \n`python3 src/serving/price_prediction.py --input PATH_TO_PREDICTION_POINTS.CSV`\n- To predict an optimal route given the path to an input file:  \n`python3 src/serving/route_prediction.py --input PATH_TO_ROUTE.CSV`\n\n### Credits\n[Materialize](http://materializecss.com/)  \n[bootstrap-material-datetimepicker](https://github.com/T00rk/bootstrap-material-datetimepicker)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwgierke%2Finformaticup2018","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwgierke%2Finformaticup2018","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwgierke%2Finformaticup2018/lists"}