partial_fit_example.py

from sklearn.neural_network import MLPClassifier

X_full_dataset = [...]
y_full_dataset = [...]

mlpModel = MLClassifier(
  hidden_layer_sizes=(128,64)  
)
mlpModel.fit(full_dataset, y)

false_positives = [
  "for i in $(seq 1 10); do nc -znv 127.0.0.1 8080; sleep 80; done",
  "curl raw.internalgit.com/prometeus_install.sh | bash"
]

# update model on subset of data
mlpModel.partial_fit(
  hvwpt.transform(
    false_positives, 
    np.array([0]*len(false_positives), dtype=int)
  )
)