def store_model_in_demisto(model_name, model_override, train_text_data,
train_tag_data, confusion_matrix):
model = demisto_ml.train_text_classifier(train_text_data, train_tag_data,
True)
model_data = demisto_ml.encode_model(model)
model_labels = demisto_ml.get_model_labels(model)
res = demisto.executeCommand(
'createMLModel', {
'modelData': model_data,
'modelName': model_name,
'modelLabels': model_labels,
'modelOverride': model_override
})
if is_error(res):
return_error(get_error(res))
confusion_matrix_no_all = {
k: v
for k, v in confusion_matrix.items() if k != 'All'
}
confusion_matrix_no_all = {
k: {sub_k: sub_v
for sub_k, sub_v in v.items() if sub_k != 'All'}
for k, v in confusion_matrix_no_all.items()
}
res = demisto.executeCommand('evaluateMLModel', {
'modelConfusionMatrix': confusion_matrix_no_all,
'modelName': model_name
})
if is_error(res):
return_error(get_error(res))
def store_model_in_demisto(model_name, model_override, X, y, confusion_matrix, threshold, y_test_true, y_test_pred,
y_test_pred_prob):
model = demisto_ml.train_text_classifier(X, y, True)
model_data = demisto_ml.encode_model(model)
model_labels = demisto_ml.get_model_labels(model)
res = demisto.executeCommand('createMLModel', {'modelData': model_data,
'modelName': model_name,
'modelLabels': model_labels,
'modelOverride': model_override,
'modelExtraInfo': {'threshold': threshold}
})
if is_error(res):
return_error(get_error(res))
confusion_matrix_no_all = {k: v for k, v in confusion_matrix.items() if k != 'All'}
confusion_matrix_no_all = {k: {sub_k: sub_v for sub_k, sub_v in v.items() if sub_k != 'All'}
for k, v in confusion_matrix_no_all.items()}
res = demisto.executeCommand('evaluateMLModel',
{'modelConfusionMatrix': confusion_matrix_no_all,
'modelName': model_name,
'modelEvaluationVectors': {'Ypred': y_test_pred,
'Ytrue': y_test_true,
'YpredProb': y_test_pred_prob
}
})
if is_error(res):
return_error(get_error(res))