Upload app.py

app.py

@@ -176,15 +176,15 @@ def y1_interpret_xgb(*args):
     df = pd.DataFrame([args], columns=x1.columns)
     df = df.astype({col: "category" for col in categorical_columns1})
     shap_values = y1_explainer_xgb.shap_values(xgb.DMatrix(df, enable_categorical=True))
-    fig_m = shap.bar_plot(shap_values[0], max_display = 10, show = False)
-    fig_m = plt.gcf()
-    ax_m = plt.gca()
-    fig_m.set_figheight(6)
-    fig_m.set_figwidth(6)
+    fig_y1 = shap.bar_plot(shap_values[0], max_display = 10, show = False, feature_names = x1.columns)
+    fig_y1 = plt.gcf()
+    ax_y1 = plt.gca()
+    fig_y1.set_figheight(6)
+    fig_y1.set_figwidth(10)
     plt.xlabel("SHAP value (impact on model output)", fontsize =12, fontweight = 'heavy', labelpad = 8)
     plt.tick_params(axis="y",direction="out", labelsize = 12)
     plt.tick_params(axis="x",direction="out", labelsize = 12)
-    return fig_m
+    return fig_y1
 
 def y1_interpret_lgb(*args):
     df = pd.DataFrame([args], columns=x1_lgb.columns)