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