Upload app.py

app.py

@@ -124,7 +124,7 @@ x1_rf = x1_rf.astype(d1)
 
 #Assign hyperparameters.
 y1_xgb_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 3.540855010579091e-08, 'alpha': 4.005546508605542e-08, 'max_depth': 5, 'eta': 5.190362998186933e-08, 'gamma': 1.1458984717217304e-05, 'grow_policy': 'depthwise'}
-y1_lgb_params = {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.5873889067286373, 'lambda_l2': 0.0043364331356120405, 'num_leaves': 221, 'feature_fraction': 0.8036023624154648, 'bagging_fraction': 0.43028542431491096, 'bagging_freq': 5, 'min_child_samples': 7}
+y1_lgb_params = {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.5873889067286373, 'lambda_l2': 0.0043364331356120405, 'num_leaves': 221, 'feature_fraction': 0.8036023624154648, 'bagging_fraction': 0.43028542431491096, 'bagging_freq': 5, 'min_child_samples': 7, 'verbosity': -1}
 y1_cb_params = {'objective': 'Logloss', 'colsample_bylevel': 0.02893626365611705, 'depth': 9, 'boosting_type': 'Plain', 'bootstrap_type': 'Bayesian', 'bagging_temperature': 0.8396785798333539}
 y1_rf_params = {'criterion': 'entropy', 'max_features': None, 'max_depth': 71, 'n_estimators': 900, 'min_samples_leaf': 4, 'min_samples_split': 5}
 
@@ -195,31 +195,33 @@ def y1_interpret_xgb(*args):
     shap_values = y1_explainer_xgb.shap_values(xgb.DMatrix(df, enable_categorical=True))
     shap.bar_plot(shap_values[0], max_display = 10, show = False, feature_names = f_names)
     fig_y1 = plt.gcf()
-    fig_y1.set_figheight(6)
-    fig_y1.set_figwidth(8)
     ax_y1 = plt.gca()
+    wrap_labels(ax_y1, 30)
+    ax_y1.figure
     plt.tight_layout()
+    fig_y1.set_figheight(6)
+    fig_y1.set_figwidth(8)
     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)
-    wrap_labels(ax_y1, 30)
-    ax_y1.figure
     return fig_y1
 
 def y1_interpret_lgb(*args):
     df = pd.DataFrame([args], columns=x1_lgb.columns)
     df = df.astype({col: "category" for col in categorical_columns1})
     shap_values = y1_explainer_lgb.shap_values(df)
-    scores_desc = list(zip(shap_values[0][0], x1.columns))
-    scores_desc = sorted(scores_desc)
-    fig_m = plt.figure(facecolor='white')
-    fig_m.set_size_inches(4, 12)
-    plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
-    plt.title("Feature Shap Values", fontsize = 24, pad = 20, fontweight = 'bold')
-    plt.yticks(fontsize=12)
-    plt.xlabel("Shap Value", fontsize = 16, labelpad=8, fontweight = 'bold')
-    plt.ylabel("Feature", fontsize = 16, labelpad=14, fontweight = 'bold')
-    return fig_m
+    shap.bar_plot(shap_values[0][0], max_display = 10, show = False, feature_names = f_names)
+    fig_y1 = plt.gcf()
+    ax_y1 = plt.gca()
+    wrap_labels(ax_y1, 30)
+    ax_y1.figure
+    plt.tight_layout()
+    fig_y1.set_figheight(6)
+    fig_y1.set_figwidth(8)
+    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_y1
 
 def y1_interpret_cb(*args):
     df = pd.DataFrame([args], columns=x1.columns)