A library for training multiclass classifiers with weak labels
python3.7 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
See example of a call inside one of the add_queue scripts
Run the unit tests running the following script from a terminal
./runtests.shCurrent usage (may need updating)
Usage: testWLCkeras.py [options]
Options:
-h, --help show this help message and exit
-p PROBLEMS, --problems=PROBLEMS
List of datasets or toy examples totest separated by
with no spaces.
-s NS, --n-samples=NS
Number of samples if toy dataset.
-f NF, --n-features=NF
Number of features if toy dataset.
-c N_CLASSES, --n-classes=N_CLASSES
Number of classes if toy dataset.
-m N_SIM, --n-simulations=N_SIM
Number of times to run every model.
-l LOSS, --loss=LOSS Loss function to minimize between square (brier score)
or CE (cross entropy)
-u PATH_RESULTS, --path-results=PATH_RESULTS
Path to save the results
-r RHO, --rho=RHO Learning step for the Gradient Descent
-a ALPHA, --alpha=ALPHA
Alpha probability parameter
-b BETA, --beta=BETA Beta probability parameter
-g GAMMA, --gamma=GAMMA
Gamma probability parameter
-i N_IT, --n-iterations=N_IT
Number of iterations of Gradient Descent.
-e METHOD, --method=METHOD
Method to generate the matrix M.One of the following:
IPL, quasi_IPL, noisy, random_noise, random_weak
-t METHOD2, --method2=METHOD2
Method to impute the matrix M.One of the following:
Mproper