modifications to accomodate numpy 2

neuralprophet/data/process.py

@@ -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(
@@ -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}"
@@ -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(
@@ -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
@@ -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 = []

neuralprophet/data/split.py

@@ -229,7 +229,7 @@ def _make_future_dataframe(
                     f"{nan_at_end + 1} missing values were detected at the end of df before df was extended into "
                     "the future. Please make sure there are no NaN values at the end of df."
                 )
-            df["y"].iloc[-(nan_at_end + 1) :].ffill(inplace=True)
+            df.loc[df.index[-(nan_at_end + 1) :], "y"] = df.loc[df.index[-(nan_at_end + 1) :], "y"].ffill()
             log.warning(
                 f"{nan_at_end + 1} missing values were forward-filled at the end of df before df was extended into the "
                 "future. Please make sure there are no NaN values at the end of df."
@@ -266,7 +266,7 @@ def _make_future_dataframe(
             regressors_df=regressors_df,
         )
         if len(df) > 0:
-            df = pd.concat([df, future_df])
+            df = pd.concat([df, future_df], ignore_index=True)
         else:
             df = future_df
     df = df.reset_index(drop=True)

neuralprophet/df_utils.py

@@ -430,7 +430,7 @@ def check_dataframe(
     if df["ds"].isnull().any():
         raise ValueError("Found NaN in column ds.")
     if not np.issubdtype(df["ds"].to_numpy().dtype, np.datetime64):
-        df["ds"] = pd.to_datetime(df.loc[:, "ds"], utc=True).dt.tz_convert(None)
+        df["ds"] = pd.to_datetime(df["ds"]).dt.tz_localize(None)
     if df.groupby("ID").apply(lambda x: x.duplicated("ds").any()).any():
         raise ValueError("Column ds has duplicate values. Please remove duplicates.")
 
@@ -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(np.int64)
     diff_ds = np.unique(converted_ds.diff(), return_counts=True)
     return diff_ds
 
@@ -1171,7 +1171,7 @@ def get_dist_considering_two_freqs(dist):
 
     Note
     ----
-    Useful for the frequency exceptions (i.e. ``M``, ``Y``, ``Q``, ``B``, and ``BH``).
+    Useful for the frequency exceptions (i.e. ``ME``, ``YE``, ``QE``, ``B``, and ``bh``).
 
     Parameters
     ----------
@@ -1252,18 +1252,18 @@ def _infer_frequency(df, freq, min_freq_percentage=0.7):
     if argmax_frequency in MONTHLY_FREQUENCIES:
         dominant_freq_percentage = _get_dominant_frequency_percentage(frequencies, distribution, MONTHLY_FREQUENCIES)
         num_freq = 2.6784e15
-        inferred_freq = "MS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "M"
+        inferred_freq = "MS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "ME"
     # exception - yearly df (365 days freq or 366 days freq)
     elif argmax_frequency == 3.1536e16 or argmax_frequency == 3.16224e16:
         dominant_freq_percentage = get_dist_considering_two_freqs(distribution) / len(df["ds"])
         num_freq = 3.1536e16
-        inferred_freq = "YS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "Y"
+        inferred_freq = "YS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "YE"
     # exception - quarterly df (most common == 92 days - 3rd,4th quarters and second most common == 91 days 2nd quarter
     # and 1st quarter in leap year)
     elif argmax_frequency == 7.9488e15 and frequencies[np.argsort(distribution, axis=0)[-2]] == 7.8624e15:
         dominant_freq_percentage = get_dist_considering_two_freqs(distribution) / len(df["ds"])
         num_freq = 7.9488e15
-        inferred_freq = "QS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "Q"
+        inferred_freq = "QS" if pd.to_datetime(df["ds"].iloc[0]).day < 15 else "QE"
     # exception - Business day (most common == day delta and second most common == 3 days delta and second most common
     # is at least 12% of the deltas)
     elif (
@@ -1283,7 +1283,7 @@ def _infer_frequency(df, freq, min_freq_percentage=0.7):
     ):
         dominant_freq_percentage = get_dist_considering_two_freqs(distribution) / len(df["ds"])
         num_freq = 3.6e12
-        inferred_freq = "BH"
+        inferred_freq = "bh"
     else:
         dominant_freq_percentage = distribution.max() / len(df["ds"])
         num_freq = argmax_frequency  # get value of most common diff