🐛 Fixed outliers shown in the quantile plots.

RoyalHaskoningDHV · Nov 7, 2024 · 08b714a · 08b714a
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commit 08b714a
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -15,7 +15,7 @@ Version X.Y.Z stands for:
 - The `dump` and `load` functions are now inherited from the BaseTimeseriesRegressor.
 - Added abstract functions `dump_parameters` and `load_parameters` for dumping and loading model files.
 - Implemented `dump_parameters` and `load_parameters` for models.
-
+- Outliers in the `_interactive_quantile_plot` and `_static_quantile_plot` functions must now be within or *equal* to the quantile boundaries.
 ## Version 3.2.1
 
 ### Changes

diff --git a/sam/visualization/quantile_plot.py b/sam/visualization/quantile_plot.py
@@ -271,7 +271,7 @@ def _interactive_quantile_plot(
         if outlier_min_q is not None and outliers is None:
             valid_low = y_hat[these_cols[col_order[n_quants - 1 - (outlier_min_q - 1)]]]
             valid_high = y_hat[these_cols[col_order[n_quants + (outlier_min_q - 1)]]]
-            outliers = (y_true > valid_high) | (y_true < valid_low)
+            outliers = (y_true >= valid_high) | (y_true <= valid_low)
             outliers = outliers.astype(int)
             k = np.ones(outlier_window)
             outliers = (
@@ -379,7 +379,7 @@ def _static_quantile_plot(
         if outlier_min_q is not None and outliers is None:
             valid_low = y_hat[these_cols[col_order[n_quants - 1 - (outlier_min_q - 1)]]]
             valid_high = y_hat[these_cols[col_order[n_quants + (outlier_min_q - 1)]]]
-            outliers = (y_true > valid_high) | (y_true < valid_low)
+            outliers = (y_true >= valid_high) | (y_true <= valid_low)
             outliers = outliers.astype(int)
             k = np.ones(outlier_window)
             outliers = (