Issue #571: Compatibility with >= Numpy 2.0.0 (update of ptp method) (#…

…572) * Replaced use of x.ptp() with np.ptp(x) * Replaced np.Inf with np.inf * Updated change log
ModelOriented · Oct 2, 2024 · 2f31550 · 2f31550
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diff --git a/python/dalex/NEWS.md b/python/dalex/NEWS.md
@@ -2,6 +2,7 @@
 
 ### development
 
+* replaced instances of x.ptp() (with np.ptp(x)) and np.Inf (with np.inf) to make dx compatible with Numpy>=2.0.0
 * added a way to pass `sample_weight` to loss functions in `model_parts()` (variable importance) using `weights` from `dx.Explainer` ([#563](https://github.com/ModelOriented/DALEX/issues/563))
 * fixed the visualization of `shap_wrapper` for `shap==0.45.0`
 

diff --git a/python/dalex/dalex/aspect/_model_aspect_importance/object.py b/python/dalex/dalex/aspect/_model_aspect_importance/object.py
@@ -246,7 +246,7 @@ def plot(
         dl = _result_df.loc[
             _result_df.aspect_name != "_baseline_", "dropout_loss"
         ].to_numpy()
-        min_max_margin = dl.ptp() * 0.15
+        min_max_margin = np.ptp(dl) * 0.15
         min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]
 
         # take out full model

diff --git a/python/dalex/dalex/aspect/_model_triplot/object.py b/python/dalex/dalex/aspect/_model_triplot/object.py
@@ -231,7 +231,7 @@ def plot(
         fig.data[0]["y"] = y_vals
 
         ## triplot
-        min_x_imp, max_x_imp = np.Inf, -np.Inf
+        min_x_imp, max_x_imp = np.inf, -np.inf
         for data in hierarchical_importance_dendrogram_plot["data"][::-1]:
             data["xaxis"] = "x2"
             data["hoverinfo"] = "text"
@@ -241,7 +241,7 @@ def plot(
             max_x_imp = np.max([max_x_imp, np.max(data["x"])])
         min_max_margin_imp = (max_x_imp - min_x_imp) * 0.15
 
-        min_x_clust, max_x_clust = np.Inf, -np.Inf
+        min_x_clust, max_x_clust = np.inf, -np.inf
         for data in hierarchical_clustering_dendrogram_plot["data"]:
             data["xaxis"] = "x3"
             data["hoverinfo"] = "text"

diff --git a/python/dalex/dalex/aspect/_model_triplot/plot.py b/python/dalex/dalex/aspect/_model_triplot/plot.py
@@ -148,8 +148,8 @@ def plot_single_aspects_importance(
         }
     )
 
-    temp_min_max = [np.Inf, -np.Inf]
-    min_max_margin = _result.dropout_loss.values.ptp() * 0.15
+    temp_min_max = [np.inf, -np.inf]
+    min_max_margin = np.ptp(_result.dropout_loss.values) * 0.15
     temp_min_max[0] = np.min(
         [temp_min_max[0], baseline - min_max_margin]
     )

diff --git a/python/dalex/dalex/aspect/_predict_aspect_importance/object.py b/python/dalex/dalex/aspect/_predict_aspect_importance/object.py
@@ -306,7 +306,7 @@ def plot(
             vcolors = _theme.get_aspect_importance_colors()
 
         if min_max is None:
-            temp_min_max = [np.Inf, -np.Inf]
+            temp_min_max = [np.inf, -np.inf]
         else:
             temp_min_max = min_max
 
@@ -404,7 +404,7 @@ def plot(
 
             if min_max is None:
                 cum = _result.importance.values + baseline
-                min_max_margin =  cum.ptp() * 0.15 
+                min_max_margin =  np.ptp(cum) * 0.15 
                 temp_min_max[0] = np.min(
                     [
                         temp_min_max[0],

diff --git a/python/dalex/dalex/aspect/_predict_triplot/object.py b/python/dalex/dalex/aspect/_predict_triplot/object.py
@@ -284,7 +284,7 @@ def plot(
             line={"color": "#371ea3", "width": 1.5, "dash": "dot"},
         )
 
-        min_x_imp, max_x_imp = np.Inf, -np.Inf
+        min_x_imp, max_x_imp = np.inf, -np.inf
         for data in hierarchical_importance_dendrogram_plot["data"][::-1]:
             data["xaxis"] = "x2"
             data["hoverinfo"] = "text"
@@ -294,7 +294,7 @@ def plot(
             max_x_imp = np.max([max_x_imp, np.max(data["x"])])
         min_max_margin_imp = (max_x_imp - min_x_imp) * 0.15
 
-        min_x_clust, max_x_clust = np.Inf, -np.Inf
+        min_x_clust, max_x_clust = np.inf, -np.inf
         for data in hierarchical_clustering_dendrogram_plot["data"]:
             data["xaxis"] = "x3"
             data["hoverinfo"] = "text"

diff --git a/python/dalex/dalex/aspect/_predict_triplot/plot.py b/python/dalex/dalex/aspect/_predict_triplot/plot.py
@@ -239,8 +239,8 @@ def plot_single_aspects_importance(
         }
     )
 
-    temp_min_max = [np.Inf, -np.Inf]
-    min_max_margin = _result.importance.values.ptp() * 0.15
+    temp_min_max = [np.inf, -np.inf]
+    min_max_margin = np.ptp(_result.importance.values) * 0.15
     temp_min_max[0] = np.min(
         [temp_min_max[0], _result.importance.values.min() - min_max_margin]
     )

diff --git a/python/dalex/dalex/model_explanations/_aggregated_profiles/object.py b/python/dalex/dalex/model_explanations/_aggregated_profiles/object.py
@@ -242,7 +242,7 @@ def plot(self,
 
         #  calculate y axis range to allow for fixedrange True
         dl = _result_df['_yhat_'].to_numpy()
-        min_max_margin = dl.ptp() * 0.10
+        min_max_margin = np.ptp(dl) * 0.10
         min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]
 
         is_x_numeric = False if geom == 'bars' else pd.api.types.is_numeric_dtype(_result_df['_x_'])

diff --git a/python/dalex/dalex/model_explanations/_variable_importance/object.py b/python/dalex/dalex/model_explanations/_variable_importance/object.py
@@ -198,7 +198,7 @@ def plot(self,
             _global_checks.global_raise_objects_class(objects, self.__class__)
 
         dl = _result_df.loc[_result_df.variable != '_baseline_', 'dropout_loss'].to_numpy()
-        min_max_margin = dl.ptp() * 0.15
+        min_max_margin = np.ptp(dl) * 0.15
         min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]
 
         # take out full model

diff --git a/python/dalex/dalex/predict_explanations/_break_down/object.py b/python/dalex/dalex/predict_explanations/_break_down/object.py
@@ -199,7 +199,7 @@ def plot(self,
             vcolors = _theme.get_break_down_colors()
 
         if min_max is None:
-            temp_min_max = [np.Inf, -np.Inf]
+            temp_min_max = [np.inf, -np.inf]
         else:
             temp_min_max = min_max
 
@@ -256,7 +256,7 @@ def plot(self,
 
             if min_max is None:
                 cum = df.cumulative.values
-                min_max_margin = cum.ptp() * 0.15
+                min_max_margin = np.ptp(cum) * 0.15
                 temp_min_max[0] = np.min([temp_min_max[0], cum.min() - min_max_margin])
                 temp_min_max[1] = np.max([temp_min_max[1], cum.max() + min_max_margin])
 

diff --git a/python/dalex/dalex/predict_explanations/_ceteris_paribus/object.py b/python/dalex/dalex/predict_explanations/_ceteris_paribus/object.py
@@ -262,7 +262,7 @@ def plot(self,
 
         #  calculate y axis range to allow for fixedrange True
         dl = _result_df['_yhat_'].to_numpy()
-        min_max_margin = dl.ptp() * 0.10
+        min_max_margin = np.ptp(dl) * 0.10
         min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]
 
         # create _x_

diff --git a/python/dalex/dalex/predict_explanations/_shap/object.py b/python/dalex/dalex/predict_explanations/_shap/object.py
@@ -205,7 +205,7 @@ def plot(self,
             vcolors = _theme.get_break_down_colors()
 
         if min_max is None:
-            temp_min_max = [np.Inf, -np.Inf]
+            temp_min_max = [np.inf, -np.inf]
         else:
             temp_min_max = min_max
 
@@ -263,7 +263,7 @@ def plot(self,
 
             if min_max is None:
                 cum = df.contribution.values + prediction_baseline
-                min_max_margin = cum.ptp() * 0.15
+                min_max_margin = np.ptp(cum) * 0.15
                 temp_min_max[0] = np.min([temp_min_max[0], cum.min() - min_max_margin])
                 temp_min_max[1] = np.max([temp_min_max[1], cum.max() + min_max_margin])