Plotting improvements - correlation and permutation importance

uncoverml/validate.py

@@ -680,11 +680,13 @@ def plot_feature_correlation_matrix(config: Config, x_all):
     corr_df = pd.DataFrame(x_all)
     corr_df.columns = features
     sns.heatmap(corr_df.corr(),
-                vmin=-1, vmax=1, annot=True,
+                vmin=-1, vmax=1, annot=False,
                 square=True, linewidths=.5, cbar_kws={"shrink": .5},
-                cmap='BrBG',
+                cmap='BrBG', xticklabels=True, yticklabels=True
                 )
     fig.suptitle('Feature Correlations')
+    plt.xticks(fontsize=5)
+    plt.yticks(fontsize=5)
     fig.tight_layout()
     save_path = Path(config.output_dir).joinpath(config.name + "_feature_correlation.png") \
         .as_posix()
@@ -772,20 +774,24 @@ def plot_permutation_feature_importance(model, x_all, targets_all, conf: Config,
                 score)).as_posix()
         df_picv.to_csv(csv, index=False)
 
-        x = np.arange(len(df_picv.index))
-        width = 0.35
-        fig, ax = plt.subplots()
-        ax.barh(x - width / 2, df_picv['weight'].values, width, label='Weight')
-        ax.barh(x + width / 2, df_picv['std'].values, width, label='Std')
-        ax.set_ylabel('Covariate')
-        ax.set_title('Permutation Feature Importance Weight and Std')
-        ax.set_xticks(x)
-        num_cov = np.arange(len(feature_names))
-        ax.set_yticks(num_cov)
-        ax.set_yticklabels(feature_names)
-        ax.set_xlabel('Score')
-        ax.legend()
+        # x = np.arange(len(df_picv.index))
+        # width = 0.35
+        # fig, ax = plt.subplots()
+        # ax.barh(x - width / 2, df_picv['weight'].values, width, label='Weight')
+        # ax.barh(x + width / 2, df_picv['std'].values, width, label='Std')
+        # ax.set_ylabel('Covariate')
+        # ax.set_title('Permutation Feature Importance Weight and Std')
+        # ax.set_xticks(x)
+        # num_cov = np.arange(len(feature_names))
+        # ax.set_yticks(num_cov)
+        # ax.set_yticklabels(feature_names)
+        # ax.set_xlabel('Score')
+        # ax.legend()
 
+        fig, ax = plt.subplots()
+        sns.barplot(data=df_picv, x='weight', y='feature', orient='h')
+        fig.suptitle('Permutation Importance')
+        plt.yticks(fontsize=5)
         fig.tight_layout()
         save_path = Path(conf.output_dir).joinpath(conf.name + "_feature_importance_bars_{}.png".format(score))\
             .as_posix()