/dairyML

Predict lactose content of various foods based on other nutritional information provided by ASA24 food recalls

Primary LanguageJupyter Notebook

Testing the model:

  1. Install an Anaconda distribution with Python 3

    The following has been tested using a full Anaconda distribution, but Miniconda is probably sufficient and will take less time to install.

  2. Clone or download the repository

    git clone https://github.com/g-simmons/dairyML.git

    or download using the green "clone or download" button on the right

  3. Set up an environment with the necessary packages

    cd DairyML

    conda env create -f environment_test_min.yml python=3.6

    conda activate dmltest

    (Note that this will only install the minimum requirements for testing the most recent model. The full development environment can be installed with conda env create -f environment_full.yml)

  4. Place your test data in data/ directory

    First create the directory:

    mkdir data

    Then place the testing csv file in the new directory.

    Make sure that the test data columns match the columns of data/training_for_GS_122118.csv

  5. Run the test script

    Test script usage is: python test.py <model_path> <data_path>

    For example, from the main directory:

    python src/test.py models/xgb_combined.model data/<test_data_filename>.csv

    Example output:

    (dmltest) C:\Users\Gabriel\DairyML>python src\test.py models\xgb_combined.model
data\training_for_GS_122118.csv
Loading modules...
Loading model at models\xgb_combined.model
Loading data at data\training_for_GS_122118.csv
Scaling input features...
Testing the model...

Results:
r2: 1.0
SRC: 1.0
PCC: 1.0
MI: 4.0
MAE: 0.0
classifier_accuracy: 1.0
classifier_f1: 1.0

Results saved to reports/test_results_2019-02-21-20-26-24.csv

Predictions saved to reports/test_predictions_2019-02-21-20-26-24.csv
  6. Results and predictions are stored to csv in reports/

Using the model in your own code

This starter code has not been tested, but this is what using the model would look like. It is stored as a binary object using pickle, and can be loaded using pickle.load.

Ex code

import pickle as pkl
from xgboost import XGBRegressor, XGBClassifier
from dairyml import XGBCombined
  
model_path = <specify model path>

with open(model_path, "rb" ) as f:
	model = pkl.load(f)

#do stuff with the model, e.g.
#X = features
#Y = target variable
predictions = model.predict(X_new)