Merge pull request #551 from guillaume-vignal/feature/change_apply_map

replace apply_map by map for pandas>=2.1.0

requirements.dev.txt

@@ -11,7 +11,7 @@ dash-html-components==2.0.0
 dash-renderer==1.8.3
 dash-table==5.0.0
 lightgbm==2.3.1
-pandas>1.0.2
+pandas>=2.1.0
 plotly==5.6.0
 shap>=0.38.1,<0.45.0
 Sphinx==4.5.0

setup.py

@@ -20,7 +20,7 @@
     "plotly>=5.0.0",
     "matplotlib>=3.2.0",
     "numpy>1.18.0",
-    "pandas>1.0.2",
+    "pandas>=2.1.0",
     "shap>=0.38.1,<0.45.0",
     "Flask<2.3.0",
     "dash>=2.3.1",

shapash/explainer/smart_plotter.py

@@ -1297,13 +1297,13 @@ def contribution_plot(
             y_pred = self.explainer.y_pred.loc[list_ind]
             # Add labels if exist
             if self.explainer._case == "classification" and self.explainer.label_dict is not None:
-                y_pred = y_pred.applymap(lambda x: self.explainer.label_dict[x])
+                y_pred = y_pred.map(lambda x: self.explainer.label_dict[x])
                 col_value = self.explainer.label_dict[col_value]
             # round predict
             elif self.explainer._case == "regression":
                 if self.round_digit is None:
                     self.tuning_round_digit()
-                y_pred = y_pred.applymap(lambda x: round(x, self.round_digit))
+                y_pred = y_pred.map(lambda x: round(x, self.round_digit))
         else:
             y_pred = None
 
@@ -3346,7 +3346,7 @@ def _prediction_regression_plot(
             # round predict
             if self.round_digit is None:
                 self.tuning_round_digit()
-            y_pred = y_pred.applymap(lambda x: round(x, self.round_digit))
+            y_pred = y_pred.map(lambda x: round(x, self.round_digit))
 
             hv_text = [
                 f"Id: {x}<br />True Values: {y:,.2f}<br />Predicted Values: {z:,.2f}<br />Prediction Error: {w:,.2f}"

shapash/manipulation/select_lines.py

@@ -57,7 +57,7 @@ def keep_right_contributions(y_pred, contributions, _case, _classes, label_dict,
             dtype=object,
         )
         if label_dict is not None:
-            y_pred = y_pred.applymap(lambda x: label_dict[x])
+            y_pred = y_pred.map(lambda x: label_dict[x])
         if proba_values is not None:
             y_proba = pd.DataFrame(
                 [proba[ind] for ind, proba in zip(indexclas, proba_values.values)],

shapash/manipulation/summarize.py

@@ -90,7 +90,7 @@ def summarize(s_contrib, var_dict, x_sorted, mask, columns_dict, features_dict):
         Result of the summarize step
     """
     contrib_sum = summarize_el(s_contrib, mask, "contribution_")
-    var_dict_sum = summarize_el(var_dict, mask, "feature_").applymap(
+    var_dict_sum = summarize_el(var_dict, mask, "feature_").map(
         lambda x: features_dict[columns_dict[x]] if not np.isnan(x) else x
     )
     x_sorted_sum = summarize_el(x_sorted, mask, "value_")

shapash/webapp/smart_app.py

@@ -116,12 +116,12 @@ def __init__(self, explainer, settings: dict = None):
             self.label = self.explainer.check_label_name(len(self.explainer._classes) - 1, "num")[1]
             self.selected_feature = self.explainer.features_imp[-1].idxmax()
             self.max_threshold = int(
-                max([x.applymap(lambda x: round_to_k(x, k=1)).max().max() for x in self.explainer.contributions])
+                max([x.map(lambda x: round_to_k(x, k=1)).max().max() for x in self.explainer.contributions])
             )
         else:
             self.label = None
             self.selected_feature = self.explainer.features_imp.idxmax()
-            self.max_threshold = int(self.explainer.contributions.applymap(lambda x: round_to_k(x, k=1)).max().max())
+            self.max_threshold = int(self.explainer.contributions.map(lambda x: round_to_k(x, k=1)).max().max())
         self.list_index = []
         self.subset = None
         self.last_click_data = None