Completing code and regenerating plots with clean attribute names

explainability/shap_plots.ipynb

@@ -4,7 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Libraries"
+    "**SHAP Explainability Plots** \\\n",
+    "_Author: Joaquín Torres Bravo_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Libraries"
    ]
   },
   {
@@ -25,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Data"
+    "### Data"
    ]
   },
   {
@@ -71,6 +79,7 @@
     "    \"UNDER\": \"XGB\"\n",
     "}\n",
     "\n",
+    "# Load names of social and individual attributes\n",
     "soc_var_names = np.load('../EDA/output/feature_names/social_factors.npy', allow_pickle=True)\n",
     "ind_var_names = np.load('../EDA/output/feature_names/individual_factors.npy', allow_pickle=True)"
    ]
@@ -79,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### SHAP Plots"
+    "### SHAP Plots"
    ]
   },
   {
@@ -89,18 +98,21 @@
    "outputs": [],
    "source": [
     "method_name = 'OVER'\n",
+    "\n",
     "plt.figure(figsize=(35, 75))\n",
     "for i, group in enumerate(['pre', 'post']):\n",
     "            X_test = data_dic['X_test_' + group]\n",
     "            y_test = data_dic['y_test_' + group]\n",
     "            model_name = model_choices[method_name]\n",
     "            shap_vals = np.load(f'./output/shap_values/{group}_{method_name}.npy')\n",
-    "            ax = plt.subplot(2,1,i+1)\n",
+    "            ax = plt.subplot(2,1,i+1) # 2 rows (pre - post) 1 column\n",
+    "            # show = False to modify plot before showing\n",
     "            shap.summary_plot(shap_vals, X_test, max_display=len(attribute_names), show=False)\n",
     "            plt.title(group.upper(), fontsize = 12, fontweight='bold')\n",
     "            plt.xlabel('SHAP Value')\n",
     "            plt.xlim(-3,5)\n",
     "            used_colors = {'purple': 'Social factor', 'green': 'Individual factor'}\n",
+    "            # Modify color of attributes\n",
     "            for label in ax.get_yticklabels():\n",
     "                label_text = label.get_text()  # Get the text of the label\n",
     "                label.set_fontsize(8)\n",
@@ -140,13 +152,12 @@
     "            plt.xlim(-0.5,0.5)\n",
     "            used_colors = {'purple': 'Social factor', 'green': 'Individual factor'}\n",
     "            for label in ax.get_yticklabels():\n",
-    "                label_text = label.get_text()  # Get the text of the label\n",
+    "                label_text = label.get_text()\n",
     "                label.set_fontsize(8)\n",
     "                if label_text in soc_var_names:\n",
     "                        label.set_color('purple')\n",
     "                else:\n",
     "                        label.set_color('green')\n",
-    "                # Create custom legend for each subplot\n",
     "                handles = [mpatches.Patch(color=color, label=label) for color, label in used_colors.items()]\n",
     "                ax.legend(handles=handles, loc='lower right', fontsize=8)\n",
     "\n",
@@ -161,7 +172,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### SHAP Interaction Plots"
+    "### SHAP Interaction Plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**IMPORTANT NOTE**\n",
+    "For the code to work as intended, the SHAP source code had to be modified:\n",
+    "* **File**: shap/plots/_beeswarm.py\n",
+    "* **Line**: 591\n",
+    "* **Modification**: "
    ]
   },
   {
@@ -253,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,17 +419,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Excel file has been created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define tolerance\n",
     "tolerance = np.median(dist_matrix)\n",
@@ -462,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,