Predicting the optimal strategy for fueling for a given route (task description).
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── features <- Scripts to turn raw data into features for modeling
│ └── build_features.py
│
├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
│
└── visualization <- Scripts to create exploratory and results oriented visualizations
└── visualize.py
-
Clone the repository including submodules (to include the challenge data as well):
git clone --recursive [email protected]:WGierke/informatiCup2018.git
However, if you already downloaded the InformatiCup2018 repository, you can also create a symbolic link that shows fromdata/raw/input_datato the informatiCup2018 repository. A sanity check would be thatdata/raw/input_data/Eingabedaten/Fahrzeugrouten/Bertha\ Benz\ Memorial\ Route.csvis accessible. -
Install all dependencies
pip3 install -r requirements.txt
- To start the server:
python3 src/serving/server.py - To predict the gas prices given using training data up to a specified point in time for a given point in time:
python3 src/serving/price_prediction.py --input PATH_TO_PREDICTION_POINTS.CSV - To predict an optimal route given the path to an input file:
python3 src/serving/route_prediction.py --input PATH_TO_ROUTE.CSV