ankitdhall / predicting-peak-bloom

predicting-peak-bloom

Model Predictions and Results

If you are here to see the predictions and the model features (with corresponding feature importance) you can find it in this notebook.

Installation and Usage

1. You will need python3 (preferably python3.8) to run this code.
2. If you use Ubuntu/Linux you can run the below commands in the terminal to get started.
3. If you use another operating-system, you can use your preferred python IDE to set this project up with a virtual environment and install dependencies from the requirements.txt file.

Note regarding meteorological data used: The latest meteorological data is automatically extracted from the code. The weather forecast for March 1 - March 7, 2022 is prone to change and may slightly effect the predicted results. If you want to use the data we used, pass read_meteo_from_disk=True when calling generate_prediction_file() function.

1. Clone the repository:

If you use https:

git clone https://github.com/ankitdhall/predicting-peak-bloom.git
cd predicting-peak-bloom

If you use ssh:

git clone [e-mail]:ankitdhall/predicting-peak-bloom.git
cd predicting-peak-bloom

If you want to download a .zip: Alternatively, you can download the zip from the github website.

2. Install virtual environment for python3.

pip3 install virtualenv

3. Create a virtual environment to install dependencies:

virtualenv my_venv
source my_venv/bin/activate

4. Use the requirements.txt to install dependencies.

pip3 install -r requirements.txt

5. Make predictions:

Run this script to make predictions for 2022-2031.

cd utils
python3 make_predictions.py

You will find the predictions in the predictions folder as a predicted_bloom_doy.csv file.

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[Extra] Interactive Jupyter notebook demo:

When you are in the parent directory run the following:

cd notebooks
jupyter-lab

Your browser should open with the jupyter-lab interface.

1. Click on the "Folder" icon on the top-left and select the "Predictions.ipynb".
2. Once the notebook is open you can run the cells ("Kernel" > "Restart Kernel and run all cells...") to see the plots and predictions.
3. The features and their importance to predict the DOY for each model.