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Remove type conversion for _stride_lagged_features and _stride_future… #1331

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merged 1 commit into from
May 24, 2023
Merged

Remove type conversion for _stride_lagged_features and _stride_future… #1331

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16 changes: 4 additions & 12 deletions neuralprophet/time_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,20 +314,12 @@ def tabularize_univariate_datetime(
inputs = OrderedDict({})

def _stride_time_features_for_forecasts(x):
# only for case where n_lags > 0
if x.dtype != np.float64:
dtype = np.datetime64
else:
dtype = np.float64
return np.array([x[i + max_lags - n_lags : i + max_lags + n_forecasts] for i in range(n_samples)], dtype=dtype)
return np.array(
[x[i + max_lags - n_lags : i + max_lags + n_forecasts] for i in range(n_samples)], dtype=x.dtype
)

def _stride_future_time_features_for_forecasts(x):
# only for case where n_lags > 0
if x.dtype != np.float64:
dtype = np.datetime64
else:
dtype = np.float64
return np.array([x[max_lags + i : max_lags + i + n_forecasts] for i in range(n_samples)], dtype=dtype)
return np.array([x[max_lags + i : max_lags + i + n_forecasts] for i in range(n_samples)], dtype=x.dtype)

def _stride_lagged_features(df_col_name, feature_dims):
# only for case where max_lags > 0
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