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np.NaN -> np.nan (Error) #1688

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np.NaN -> np.nan (Error) #1688

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10 changes: 5 additions & 5 deletions neuralprophet/data/process.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ def _reshape_raw_predictions_to_forecst_df(
forecast = predicted[:, forecast_lag - 1, j]
pad_before = max_lags + forecast_lag - 1
pad_after = n_forecasts - forecast_lag
yhat = np.pad(forecast, (pad_before, pad_after), mode="constant", constant_values=np.NaN)
yhat = np.pad(forecast, (pad_before, pad_after), mode="constant", constant_values=np.nan)
if prediction_frequency is not None:
ds = df_forecast["ds"].iloc[pad_before : -pad_after if pad_after > 0 else None]
mask = df_utils.create_mask_for_prediction_frequency(
Expand All @@ -79,7 +79,7 @@ def _reshape_raw_predictions_to_forecst_df(
)
yhat = np.full((len(ds),), np.nan)
yhat[mask] = forecast
yhat = np.pad(yhat, (pad_before, pad_after), mode="constant", constant_values=np.NaN)
yhat = np.pad(yhat, (pad_before, pad_after), mode="constant", constant_values=np.nan)
# 0 is the median quantile index
if j == 0:
name = f"yhat{forecast_lag}"
Expand All @@ -104,7 +104,7 @@ def _reshape_raw_predictions_to_forecst_df(
forecast = components[comp][:, forecast_lag - 1, j] # 0 is the median quantile
pad_before = max_lags + forecast_lag - 1
pad_after = n_forecasts - forecast_lag
yhat = np.pad(forecast, (pad_before, pad_after), mode="constant", constant_values=np.NaN)
yhat = np.pad(forecast, (pad_before, pad_after), mode="constant", constant_values=np.nan)
if prediction_frequency is not None:
ds = df_forecast["ds"].iloc[pad_before : -pad_after if pad_after > 0 else None]
mask = df_utils.create_mask_for_prediction_frequency(
Expand All @@ -114,7 +114,7 @@ def _reshape_raw_predictions_to_forecst_df(
)
yhat = np.full((len(ds),), np.nan)
yhat[mask] = forecast
yhat = np.pad(yhat, (pad_before, pad_after), mode="constant", constant_values=np.NaN)
yhat = np.pad(yhat, (pad_before, pad_after), mode="constant", constant_values=np.nan)
if j == 0: # temporary condition to add only the median component
name = f"{comp}{forecast_lag}"
df_forecast[name] = yhat
Expand All @@ -126,7 +126,7 @@ def _reshape_raw_predictions_to_forecst_df(
forecast_0 = components[comp][0, :, j]
forecast_rest = components[comp][1:, n_forecasts - 1, j]
yhat = np.pad(
np.concatenate((forecast_0, forecast_rest)), (max_lags, 0), mode="constant", constant_values=np.NaN
np.concatenate((forecast_0, forecast_rest)), (max_lags, 0), mode="constant", constant_values=np.nan
)
if prediction_frequency is not None:
date_list = []
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2 changes: 1 addition & 1 deletion neuralprophet/df_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1117,7 +1117,7 @@ def get_freq_dist(ds_col):
tuple
numeric delta values (``ms``) and distribution of frequency counts
"""
converted_ds = pd.to_datetime(ds_col, utc=True).view(dtype=np.int64)
converted_ds = pd.to_datetime(ds_col, utc=True).astype(dtype=np.int64)
diff_ds = np.unique(converted_ds.diff(), return_counts=True)
return diff_ds

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