def test_R2Score(generated_data):
r2_score = R2Score()
assert np.isnan(r2_score.result().numpy())
# test as single batch
r2_score.update_state(generated_data.y_true, generated_data.y_good)
good_single_batch = r2_score.result().numpy()
assert np.isreal(good_single_batch)
# test as two batches
r2_score.reset_states()
r2_score.update_state(generated_data.y_true[:SPLIT_POINT],
generated_data.y_good[:SPLIT_POINT])
r2_score.update_state(generated_data.y_true[SPLIT_POINT:],
generated_data.y_good[SPLIT_POINT:])
good_two_batch = r2_score.result().numpy()
assert np.isreal(good_two_batch)
# single batch and multi-batch should be very close
assert np.isclose(good_single_batch, good_two_batch)
# good predictions should be close to 1
assert np.abs(1.0 - good_two_batch) < 1e-3
# test for bad predictions
r2_score.reset_states()
r2_score.update_state(generated_data.y_true[:SPLIT_POINT],
generated_data.y_bad[:SPLIT_POINT])
r2_score.update_state(generated_data.y_true[SPLIT_POINT:],
generated_data.y_bad[SPLIT_POINT:])
bad_prediction_score = r2_score.result().numpy()
# r2 score for bad should be "far away" from 1
assert bad_prediction_score < 0.05
def _setup_metrics(self):
self.metric_functions[LOSS] = self.eval_loss_function
self.metric_functions[ERROR] = ErrorScore(name='metric_error')
self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric(
name='metric_mse')
self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric(
name='metric_mae')
self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self):
self.metric_functions = {} # needed to shadow class variable
self.metric_functions[LOSS] = self.eval_loss_function
self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric(
name='metric_mse'
)
self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric(
name='metric_mae'
)
self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self):
self.metric_functions = {} # needed to shadow class variable
if self.loss[TYPE] == 'mean_squared_error':
self.metric_functions[LOSS] = MSEMetric(name='eval_loss')
else:
self.metric_functions[LOSS] = MAEMetric(name='eval_loss')
self.metric_functions[ERROR] = ErrorScore(name='metric_error')
self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric(
name='metric_mse')
self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric(
name='metric_mae')
self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self):
self.metric_functions = {} # needed to shadow class variable
if self.loss[TYPE] == "mean_squared_error":
self.metric_functions[LOSS] = MSEMetric(name="eval_loss")
elif self.loss[TYPE] == "mean_absolute_error":
self.metric_functions[LOSS] = MAEMetric(name="eval_loss")
elif self.loss[TYPE] == "root_mean_squared_error":
self.metric_functions[LOSS] = RMSEMetric(name="eval_loss")
elif self.loss[TYPE] == "root_mean_squared_percentage_error":
self.metric_functions[LOSS] = RMSPEMetric(name="eval_loss")
self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric(
name="metric_mse"
)
self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric(
name="metric_mae"
)
self.metric_functions[
ROOT_MEAN_SQUARED_ERROR
] = RootMeanSquaredErrorMetric(name="metric_rmse")
self.metric_functions[
ROOT_MEAN_SQUARED_PERCENTAGE_ERROR
] = RMSPEMetric(name="metric_rmspe")
self.metric_functions[R2] = R2Score(name="metric_r2")
