By FarukOzderim
This is a continuous calculations of confusion matrices with a concurrent simulated continuous data stream.
Whenever there is new incoming data, this program calculates the confusion matrices with moving windows and writes them to the database.
docker build -t py .
docker run py
- sqlite
- requirements.txt
pip3 install -r requirements.txt
rm database.db
python3 -m src.__init__ [input] [hard_stop_countdown] [confusion_matrix_window_length] [debug]
Examples:
rm database.db
python3 -m src.__init__ test/data/data_small.csv 20 3 True
rm database.db
python3 -m src.__init__ test/data/data_medium.csv 600 1000 True
rm database.db
python3 -m src.__init__ test/data/data.csv 1800 1000 True
pytest -v --cov=src --cov-report term-missing test/unit.py
Tests take around 30 seconds
id,given_label,model1_A,model1_B,model2_A,model2_B,model3_A,model3_B
1,A,0.6315429094436551,0.3684570905563449,0.9881789935400176,0.011821006459982408,0.7254980531654877,0.27450194683451234
There are 3 ML models, and each modelX_Y represents probability of model X predicting that the label is Y. We combine ML models' results and predict the label and create confusion matrices with that prediction.
