From 9a46c39eb968f0d8524ca147b3c24cbaa0335385 Mon Sep 17 00:00:00 2001 From: Oleksandr Shchur Date: Sat, 17 Jan 2026 10:20:05 +0000 Subject: [PATCH] Rename variable --- src/chronos/df_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/chronos/df_utils.py b/src/chronos/df_utils.py index 9ab199f..223ad8a 100644 --- a/src/chronos/df_utils.py +++ b/src/chronos/df_utils.py @@ -294,7 +294,7 @@ def convert_df_input_to_list_of_dicts_input( if future_df is not None: # Use timestamps from future_df - prediction_timestamps_array = pd.DatetimeIndex(future_df[timestamp_column]) + prediction_timestamps_flat = pd.DatetimeIndex(future_df[timestamp_column]) for col in future_df.columns.drop([id_column, timestamp_column]): future_covariates_dict[col] = future_df[col].to_numpy() else: @@ -306,7 +306,7 @@ def convert_df_input_to_list_of_dicts_input( # Silence PerformanceWarning for non-vectorized offsets https://github.com/pandas-dev/pandas/blob/95624ca2e99b0/pandas/core/arrays/datetimes.py#L822 warnings.simplefilter("ignore", category=pd.errors.PerformanceWarning) # Generate all prediction timestamps at once by stacking offsets into shape (n_series * prediction_length) - prediction_timestamps_array = pd.DatetimeIndex( + prediction_timestamps_flat = pd.DatetimeIndex( np.dstack([last_ts + step * offset for step in range(1, prediction_length + 1)]).ravel() ) @@ -315,7 +315,7 @@ def convert_df_input_to_list_of_dicts_input( future_start_idx, future_end_idx = i * prediction_length, (i + 1) * prediction_length series_id = df[id_column].iloc[start_idx] - prediction_timestamps[series_id] = prediction_timestamps_array[future_start_idx:future_end_idx] + prediction_timestamps[series_id] = prediction_timestamps_flat[future_start_idx:future_end_idx] task: dict[str, np.ndarray | dict[str, np.ndarray]] = {"target": target_array[:, start_idx:end_idx]} if len(past_covariates_dict) > 0: