def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]:
"""
Predict the value for each sample in X
Inputs:
X: array of shape [n_samples, n_features]
Outputs:
y: array of shape [n_samples, n_targets]
"""
result = d3m_ndarray(PolynomialKernel(inputs, self._Xtrain, self.hyperparams['sf'],
self.hyperparams['offset'], self.hyperparams['degree']).dot(self._coeffs).flatten())
return CallResult(d3m_ndarray(result, generate_metadata=True))
def produce(self,
*,
inputs: Inputs,
timeout: float = None,
iterations: int = None) -> CallResult[Outputs]:
if self.hyperparams['preprocess'] == 'YES':
inputs = tm_preprocess(inputs, colnorms=self._norms)
pred_test = d3m_ndarray(
tm_predict(self._weights, inputs, self.hyperparams['q'],
self.hyperparams['r'], 'regression').flatten())
#pred_test.metadata = self._ymetadata.set_for_value(pred_test)
return CallResult(d3m_ndarray(pred_test, generate_metadata=True))
def produce(self,
*,
inputs: Inputs,
timeout: float = None,
iterations: int = None) -> CallResult[Outputs]:
if self.hyperparams['preprocess'] == 'YES':
inputs = tm_preprocess(inputs, colnorms=self._norms)
pred_test = tm_predict(self._weights, inputs, self.hyperparams['q'],
self.hyperparams['r'], 'bc')
result = self.__map_binary_to_labels(pred_test.flatten()).astype(
self.LABELLIST.dtype)
return CallResult(
d3m_ndarray(input_array=result, generate_metadata=True))
def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]:
if self._fitted:
return CallResult(None)
if self._training_inputs is None or self._training_outputs is None:
raise ValueError("Missing training data.")
arima_training_output = d3m_ndarray(self._training_outputs)
shape = arima_training_output.shape
if len(shape) == 2 and shape[1] == 1:
sk_training_output = np.ravel(arima_training_output)
self._clf.fit(sk_training_output)
self._fitted = True
return CallResult(None)
def fit(self,
*,
timeout: float = None,
iterations: int = None) -> CallResult[None]:
if self._fitted:
return CallResult(None)
if self._training_inputs is None or self._training_outputs is None:
raise ValueError("Missing training data.")
sk_training_output = d3m_ndarray(self._training_outputs)
with stopit.ThreadingTimeout(timeout) as timer:
shape = sk_training_output.shape
if len(shape) == 2 and shape[1] == 1:
sk_training_output = np.ravel(sk_training_output)
self._model.fit(self._training_inputs, sk_training_output)
self._fitted = True
if timer.state == timer.EXECUTED:
return CallResult(None)
else:
raise TimeoutError('BBNMLPClassifier exceeded time limit')