def _sanity_check_model_conversion(self, svm_model, nn_model, x_val):
#use flatten layer to vectorize multi-dim array
flatten = Flatten()
flatten.to_numpy()
y_pred_svm = svm_model.decision_function(flatten.forward(x_val))
y_pred_nn = nn_model.forward(x_val)
rtol = 1e-7
if y_pred_nn.shape[1] == 2:
numpy.testing.assert_allclose(
y_pred_svm,
-y_pred_nn[:, 0],
rtol,
err_msg=
'Predictions of Trained SVM model and converted NN model are NOT equal! (2-Class-Case)'
)
numpy.testing.assert_allclose(
y_pred_svm,
y_pred_nn[:, 1],
rtol,
err_msg=
'Predictions of Trained SVM model and converted NN model are NOT equal! (2-Class-Case)'
)
else:
numpy.testing.assert_allclose(
y_pred_svm,
y_pred_nn,
rtol,
err_msg=
'Predictions of Trained SVM model and converted NN model are NOT equal!'
)
def train_model(self, x_train, y_train, x_val, y_val, *args, **kwargs):
# train model using sklearn
print('training {} model'.format(self.__class__.__name__))
#use flatten layer to vectorize multi-dim array
flatten = Flatten()
flatten.to_numpy()
x_train = flatten.forward(x_train)
self.model.fit(x_train, y_train)
self.model = self._convert_to_nn(self.model, y_train, x_val)