def evaluate(self, input_df, metric=None):
"""
Evaluate the model on a list of metrics.
:param input_df: The input time series data frame, Example:
datetime value "extra feature 1" "extra feature 2"
2019-01-01 1.9 1 2
2019-01-02 2.3 0 2
:param metric: A list of Strings Available string values are "mean_squared_error",
"r_square".
:return: a list of metric evaluation results.
"""
Evaluator.check_metric(metric)
return self.pipeline.evaluate(input_df, metric)
def _check_input(self, input_df, validation_df, metric):
input_is_list = self._check_input_format(input_df)
if not input_is_list:
self._check_missing_col(input_df)
if validation_df is not None:
self._check_missing_col(validation_df)
else:
for d in input_df:
self._check_missing_col(d)
if validation_df is not None:
for val_d in validation_df:
self._check_missing_col(val_d)
if not Evaluator.check_metric(metric):
raise ValueError("metric " + metric + " is not supported")
