Merge pull request #589 from guillaume-vignal/feature/smartplotter_si…

…mplification

Refactor DataFrame Column Transformation to Avoid Future Warning

shapash/explainer/smart_plotter.py

@@ -431,7 +431,6 @@ def contribution_plot(
         --------
         >>> xpl.plot.contribution_plot(0)
         """
-
         if self._explainer._case == "classification":
             label_num, _, label_value = self._explainer.check_label_name(label)
 
@@ -505,8 +504,13 @@ def contribution_plot(
         else:
             feature_values = self._explainer.x_init.loc[list_ind, col_name]
 
-        if self.explainer.x_init[col_name].dtype == 'bool':
-            feature_values = feature_values.astype(int)
+        if isinstance(col_name, list):
+            for el in col_name:
+                if feature_values[el].dtype == "bool":
+                    feature_values[el] = feature_values[el].astype(int)
+        else:
+            if feature_values.dtype == "bool":
+                feature_values = feature_values.astype(int)
 
         if col_is_group:
             feature_values = project_feature_values_1d(
@@ -1131,13 +1135,9 @@ def interactions_plot(
 
         # add break line to X label if necessary
         max_len_by_row = max([round(50 / self._explainer.features_desc[feature_values1.columns.values[0]]), 8])
-        feature_values1.iloc[:, 0] = feature_values1.iloc[:, 0].apply(
-            add_line_break,
-            args=(
-                max_len_by_row,
-                120,
-            ),
-        )
+        args = (max_len_by_row, 120)
+        feature_values_str = feature_values1.iloc[:, 0].apply(add_line_break, args=args)
+        feature_values1 = pd.DataFrame({feature_values1.columns[0]: feature_values_str})
 
         # selecting the best plot : Scatter, Violin?
         if col_value_count1 > violin_maxf:

shapash/plots/plot_contribution.py

@@ -85,7 +85,8 @@ def plot_scatter(
 
     # add break line to X label if necessary
     args = (max_len_by_row, 120)
-    feature_values.iloc[:, 0] = feature_values.iloc[:, 0].apply(add_line_break, args=args)
+    feature_values_str = feature_values.iloc[:, 0].apply(add_line_break, args=args)
+    feature_values = pd.DataFrame({column_name: feature_values_str})
 
     if pred is not None:
         hv_text = [f"Id: {x}<br />Predict: {y}" for x, y in zip(feature_values.index, pred.values.flatten())]
@@ -267,7 +268,8 @@ def plot_violin(
 
     # add break line to X label if necessary
     args = (max_len_by_row, 120)
-    feature_values.iloc[:, 0] = feature_values.iloc[:, 0].apply(add_line_break, args=args)
+    feature_values_str = feature_values.iloc[:, 0].apply(add_line_break, args=args)
+    feature_values = pd.DataFrame({column_name: feature_values_str})
 
     contributions = contributions.loc[feature_values.index]
     if pred is not None:

shapash/utils/utils.py

@@ -73,14 +73,14 @@ def is_nested_list(object_param):
     return any(isinstance(elem, list) for elem in object_param)
 
 
-def add_line_break(text, nbchar, maxlen=150):
+def add_line_break(value, nbchar, maxlen=150):
     """
     adding line break in string if necessary
 
     Parameters
     ----------
-    text : string
-        string to check in order to add line break
+    value : string or oither type
+        if string to check in order to add line break
     nbchar : int
         number of characters before line break
     maxlen : int
@@ -91,10 +91,10 @@ def add_line_break(text, nbchar, maxlen=150):
     string
         original text + line break
     """
-    if isinstance(text, str):
+    if isinstance(value, str):
         length = 0
         tot_length = 0
-        input_word = text.split()
+        input_word = value.split()
         final_sep = []
         for w in input_word[:-1]:
             length = length + len(w)
@@ -113,7 +113,7 @@ def add_line_break(text, nbchar, maxlen=150):
         new_string = "".join(sum(zip(input_word, final_sep + [""]), ())[:-1]) + last_char
         return new_string
     else:
-        return text
+        return value
 
 
 def truncate_str(text, maxlen=40):