In this project, we developed an application to easily apply machine learning and deep learning models to build a HTP phenotyping regression/classification model.

https://share.streamlit.io/songwan/htpps/develop/app.py

1. Upload your own dataset (.csv) or use pre-loaded dataset (2016 IRRI)
2. Select Phenotype to predict (Y), predictors (X), and a goal (regression/classification)
3. Select machine learning model and its parameters
   • For regression, you can choose linear regression, neural network, and SVR
   • For classification, you can choose neural network, and SVC
4. Click Run button, then the app automatically displays the following results:
   • Visualization outputs
     • For regression, scatter plot of target vs predicted valeus
     • For classification, contingency matrix for the test data
     • For neural network, the history plot
   • Performance metrics (Accracy, F1 score, R-squared, MSE)
   • The time it took the model to train
   • Buttons for downloading model (.pkl/.h5) and model information (.csv)

Make sure you have pip installed with Python 3.

• install pipenv

pip install pipenv

• go inside the folder and install the dependencies

pipenv install
pipenv shell

• run the app

streamlit run app.py

• app.py : The main script to start the app
• utils/
  • ui.py: UI functions to display the different components of the app
  • functions.py: for data processing, training the model and building the plotly graphs
• models/: where each model's hyper-parameter selector is defined