def _init_metrics(self):
""" Starts up the metrics and statistics watchers. One watcher is created
for each of the learners to be evaluated.
"""
self.mean_eval_measurements = []
self.current_eval_measurements = []
if self._task_type == constants.CLASSIFICATION:
for i in range(self.n_models):
self.mean_eval_measurements.append(
ClassificationMeasurements())
self.current_eval_measurements.append(
WindowClassificationMeasurements(
window_size=self.n_sliding))
elif self._task_type == constants.MULTI_TARGET_CLASSIFICATION:
for i in range(self.n_models):
self.mean_eval_measurements.append(
MultiTargetClassificationMeasurements())
self.current_eval_measurements.append(
WindowMultiTargetClassificationMeasurements(
window_size=self.n_sliding))
elif self._task_type == constants.REGRESSION:
for i in range(self.n_models):
self.mean_eval_measurements.append(RegressionMeasurements())
self.current_eval_measurements.append(
WindowRegressionMeasurements(window_size=self.n_sliding))
elif self._task_type == constants.MULTI_TARGET_REGRESSION:
for i in range(self.n_models):
self.mean_eval_measurements.append(
MultiTargetRegressionMeasurements())
self.current_eval_measurements.append(
WindowMultiTargetRegressionMeasurements(
window_size=self.n_sliding))
# Running time
self.running_time_measurements = []
for i in range(self.n_models):
self.running_time_measurements.append(RunningTimeMeasurements())
# Evaluation data buffer
self._data_dict = {}
for metric in self.metrics:
data_ids = [constants.MEAN, constants.CURRENT]
if metric == constants.TRUE_VS_PREDICTED:
data_ids = [constants.Y_TRUE, constants.Y_PRED]
elif metric == constants.DATA_POINTS:
data_ids = ['X', 'target_values', 'prediction']
elif metric == constants.RUNNING_TIME:
data_ids = [
'training_time', 'testing_time', 'total_running_time'
]
elif metric == constants.MODEL_SIZE:
data_ids = ['model_size']
self._data_dict[metric] = data_ids
self._data_buffer = EvaluationDataBuffer(data_dict=self._data_dict)
def test_running_time_measurements():
rtm = RunningTimeMeasurements()
for i in range(1000):
# Test training time
rtm.compute_training_time_begin()
time.sleep(0.0005)
rtm.compute_training_time_end()
# Test testing time
rtm.compute_testing_time_begin()
time.sleep(0.0002)
rtm.compute_testing_time_end()
# Update statistics
rtm.update_time_measurements()
expected_info = 'RunningTimeMeasurements: sample_count: 1000 - ' \
'Total running time: {} - ' \
'training_time: {} - ' \
'testing_time: {}'.format(
rtm.get_current_total_running_time(),
rtm.get_current_training_time(),
rtm.get_current_testing_time(),
)
assert expected_info == rtm.get_info()