added dropna to avoid crash on nan values

explainerdashboard/explainer_methods.py

@@ -919,7 +919,7 @@ def get_pdp_df(
         else:
             first_row = X_sample.iloc[[0]]
         warnings.filterwarnings("ignore", category=UserWarning)
-        n_labels = model.predict_proba(first_row).shape[1]
+        n_labels = model.predict_proba(first_row.copy()).shape[1]
         warnings.filterwarnings("default", category=UserWarning)
         if multiclass:
             pdp_dfs = [pd.DataFrame() for i in range(n_labels)]

explainerdashboard/explainer_plots.py

@@ -2815,7 +2815,7 @@ def plotly_rf_trees(
                     "model": range(len(model.estimators_)),
                     "prediction": [
                         np.round(
-                            100 * m.predict_proba(observation)[0, pos_label], round
+                            100 * m.predict_proba(observation.copy())[0, pos_label], round
                         )
                         for m in model.estimators_
                     ],

explainerdashboard/explainers.py

@@ -240,8 +240,12 @@ def __init__(
             col for col in self.regular_cols if not is_numeric_dtype(self.X[col])
         ]
         self.categorical_dict = {
-            col: sorted(self.X[col].unique().tolist()) for col in self.categorical_cols
+            col: sorted(self.X[col].dropna().unique().tolist()) for col in self.categorical_cols
         }
+        #Add nan to list, as this is a valid option for encoders
+        for col in self.categorical_cols:
+            if self.X[col].isnull().values.any():
+                self.categorical_dict[col].append('NaN')
         self.cat_cols = self.onehot_cols + self.categorical_cols
         self.original_cols = self.X.columns
         self.merged_cols = pd.Index(self.regular_cols + self.onehot_cols)
@@ -757,6 +761,11 @@ def get_row_from_input(
             df_merged = pd.DataFrame(dict(zip(cols, inputs)), index=[0]).fillna(
                 self.na_fill
             )[self.merged_cols]
+            #Adjust categorical col to proper nan value instead of self.na_fill
+            for col, values in self.categorical_dict.items():
+                if 'NaN' in values:
+                    df_merged[col] = df_merged[col].replace(self.na_fill, np.nan)   #If the categorical feature comes from the existing data it will be nan
+                    df_merged[col] = df_merged[col].replace('NaN', np.nan)          #If the categorical feature is changed to NaN in the frontend it will be a string
             if return_merged:
                 return df_merged
             else:
@@ -765,6 +774,11 @@ def get_row_from_input(
         elif len(inputs) == len(self.columns):
             cols = self.columns
             df = pd.DataFrame(dict(zip(cols, inputs)), index=[0]).fillna(self.na_fill)
+            #unsure if this is okay here for categorical defined values
+            for col, values in self.categorical_dict.items():
+                if 'NaN' in values:
+                    df[col] = df[col].replace(self.na_fill, np.nan)         #If the categorical feature comes from the existing data it will be nan
+                    df[col] = df[col].replace('NaN', np.nan)                #If the categorical feature is changed to NaN in the frontend it will be a string
             if return_merged:
                 return merge_categorical_columns(df, self.onehot_dict, self.merged_cols)
             else:
@@ -2561,12 +2575,12 @@ def pred_probas_raw(self):
                 self.model, "predict_proba"
             ), "model does not have a predict_proba method!"
             if self.shap == "skorch":
-                self._pred_probas = self.model.predict_proba(self.X.values).astype(
+                self._pred_probas = self.model.predict_proba(self.X.copy().values).astype(
                     self.precision
                 )
             else:
                 warnings.filterwarnings("ignore", category=UserWarning)
-                self._pred_probas = self.model.predict_proba(self.X).astype(
+                self._pred_probas = self.model.predict_proba(self.X.copy()).astype(
                     self.precision
                 )
                 warnings.filterwarnings("default", category=UserWarning)
@@ -2768,7 +2782,7 @@ def shap_explainer(self):
 
                 def model_predict(data_asarray):
                     data_asframe = pd.DataFrame(data_asarray, columns=self.columns)
-                    return self.model.predict_proba(data_asframe)
+                    return self.model.predict_proba(data_asframe.copy())
 
                 self._shap_explainer = shap.KernelExplainer(
                     model_predict,
@@ -3251,7 +3265,7 @@ def get_cv_metrics(n_splits):
             ):
                 X_train, X_test = self.X.iloc[train_index], self.X.iloc[test_index]
                 y_train, y_test = self.y.iloc[train_index], self.y.iloc[test_index]
-                preds = clone(self.model).fit(X_train, y_train).predict_proba(X_test)
+                preds = clone(self.model).fit(X_train, y_train).predict_proba(X_test.copy())
                 for label in range(len(self.labels)):
                     for cut in np.linspace(1, 99, 99, dtype=int):
                         y_true = np.where(y_test == label, 1, 0)
@@ -3484,7 +3498,7 @@ def prediction_result_df(
                 X_row = X_cats_to_X(X_row, self.onehot_dict, self.X.columns)
             if self.shap == "skorch":
                 X_row = X_row.values.astype("float32")
-            pred_probas = self.model.predict_proba(X_row)[0, :].squeeze()
+            pred_probas = self.model.predict_proba(X_row.copy())[0, :].squeeze()
 
         preds_df = pd.DataFrame(dict(label=self.labels, probability=pred_probas))
         if logodds and all(preds_df.probability < 1 - np.finfo(np.float64).eps):

tests/test_datasets.py

@@ -0,0 +1,83 @@
+from sklearn.ensemble import RandomForestClassifier
+import pandas as pd
+from explainerdashboard import ClassifierExplainer, ExplainerDashboard
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+import os
+import numpy as np
+
+class CategoricalModelWrapper:
+    def __init__(self, model, categorical_label_test) -> None:
+        self._model = model
+        self._categorical_label_test = categorical_label_test
+        pass
+
+    def _perform_label_encoding(self, y):
+        label_enc = LabelEncoder()
+        label_enc.fit([["Survived"],["Not Survived"]])
+        return pd.Series(label_enc.transform(y.values), name=y.name, index=y.index)
+
+    def _perform_label_decoding(self, y):
+        label_enc = LabelEncoder()
+        label_enc.fit([["Survived"],["Not Survived"]])
+        return pd.Series(label_enc.inverse_transform(y), name=y.name)
+
+    def _preprocessor(self, X):
+        return X.drop(["Name"], axis=1)
+
+    def _postprocessor(self, y):
+        if self._categorical_label_test == True:
+            y = self._perform_label_decoding(y)
+        return y
+
+    def predict(self, X):
+        X = self._preprocessor(X)
+        y = self._model.predict(X)
+        return self._postprocessor(y)
+
+    def predict_proba(self, X):
+        X = self._preprocessor(X)
+        probabilities_raw = self._model.predict_proba(X)
+        return probabilities_raw
+
+def generate_categorical_dataset_model_wrapper(categorical_label_test=False):
+    model = RandomForestClassifier(n_estimators=5, max_depth=2)
+    wrapper = CategoricalModelWrapper(model, categorical_label_test)
+    df = pd.read_csv(os.path.join(os.getcwd(), "tests\\test_assets\\data.csv"))
+    if categorical_label_test == True:
+        #Test for categorical label, convert titanic binary numeric label to categorical ["Survived"],["Not Survived"]
+        df["Survival"] = wrapper._perform_label_decoding(df["Survival"])
+    else:
+        #We only test NaN in categorical features and numerical target
+        df["Name"][0] = np.nan
+        df["Name"][10] = np.nan
+        df["Name"][20] = np.nan
+        df["Name"][30] = np.nan
+        df["Name"][40] = np.nan
+        df["Name"][50] = np.nan
+        df["Name"][60] = np.nan
+        df["Name"][70] = np.nan
+        df["Name"][80] = np.nan
+    X_train, X_test, y_train, y_test = train_test_split(df.drop(["Survival"], axis=1), df["Survival"], test_size=0.2, random_state=42)
+
+    X_train = wrapper._preprocessor(X_train)
+
+    if categorical_label_test == True:
+        y_train = wrapper._perform_label_encoding(y_train)
+
+    model.fit(X_train, y_train)
+    return CategoricalModelWrapper(model, categorical_label_test), X_test, y_test
+
+def test_NaN_containing_categorical_dataset():
+    _wrapper, _test_X, _test_y = generate_categorical_dataset_model_wrapper()
+    explainer = ClassifierExplainer(
+                    _wrapper, _test_X, _test_y)
+    dashboard = ExplainerDashboard(explainer)
+    assert "NaN" in explainer.categorical_dict["Name"]
+
+def test_categorical_label():
+    _wrapper, _test_X, _test_y = generate_categorical_dataset_model_wrapper(True)
+    explainer = ClassifierExplainer(
+                    _wrapper, _test_X, _test_y)
+    dashboard = ExplainerDashboard(explainer)
+    assert "Survived" in explainer.labels