def combine(self, prediction_groups: typing.Dict,
inputs: container.DataFrame):
all_results = []
only_one = 'only_one_time_series' in prediction_groups
for i, row in inputs.iterrows():
# date = pd.Timestamp(et.time_indicator.get_datetime(row))
date = self.time_indicator.get_datetime(row)
if only_one:
key = 'only_one_time_series'
else:
key = []
for x in self.categorical_indices:
key.append(row.iloc[x])
key = tuple(key)
predictions = prediction_groups[key]
all_results.append(predictions.loc[date, 0])
return np.array(all_results).T
def _convert_lists(dataframe: container.DataFrame) -> container.DataFrame:
# convert colum contents to numpy array of values similar to what extract semantic types would do
for index, row in dataframe.iterrows():
row["bravo"] = container.ndarray([int(i) for i in row["bravo"].split(",")])
return dataframe
