Assignment for COMP 472.
Uses scikit-learn to run machine learning algorithms on data to predict letters.
- Python 3.5+
Usage: python3 model.py [-h] [-d] [-n] [-nn] [-m model_file] [-te test_file] [-tr training_file] [-v validation_file] [-r result_file] [-s save_file]
To run this program specify the python3 interpreter and the model.py script name with arguments. The program expects multiple options that can be explained by running the -h or --help command.
The program expects all data files in a folder in the root of this project called DataSet.
The program outputs all result files in a folder in the root of this project called Results.
The program outputs and expects all model files in a folder in the root of this project called Models.
Example:
python3 model.py -d -tr 'ds1/ds1Train.csv' -v 'ds1/ds1Val.csv' -r 'ds1/ds1Val-dt.csv' -s 'ds1/ds1Model-dt.pkl'
The program trains the model using a decision tree and then validates and prints the accuracy. It uses input file ds1/ds1Train.csv for training and ds1/ds1Val.csv for validation, both are located in the DataSet folder. It will output the file ds1/ds1Val-dt.csv in folder Results and ds1/ds1Model-dt.pkl in folder Models.
python3 model.py -te 'ds1/ds1Test.csv' -r 'ds1/ds1Test-dt.csv' -m 'ds1/ds1Model-dt.pkl'
The program tests the model found in the file ds1/ds1Model-dt.pkl in the Models folder on the data found in file ds1/ds1Test.csv in the DataSet folder. It will output the file ds1/ds1Test-dt.csv in folder Results.
More examples can be found in the run_testing.sh and run_training.sh files.