This is a Python framework for weakly supervised learning. This package can be used for weak supervised learning classification tasks.
You can install it by pip
method.
pip install weaklysupervised
We have implemented following weakly-supervised learning algorithm.
- bootstrapping
from examples_utils import get_data, DNN
from sklearn import metrics
from weaklysupervised import BootstrappingNeuralNetworkClassifier
if __name__ == "__main__":
X_train, X_test, y_train, y_test = get_data()
DNN = DNN()
clf = DNN.build_model(input_dim=30, output_dim=2)
model = BootstrappingNeuralNetworkClassifier(clf, batch_size=128, epochs=40, bootstrapping_type="soft",
beta=0.95, patience=5, best_model_name="model_check_point_best_model")
model.fit(X_train, y_train, validation_data=(X_test, y_test), )
predict = model.predict(X_test)
acc = metrics.accuracy_score(y_test, predict)
print("bootstrapping accuracy", acc)
Please see examples
folder for more examples.