Add netron visualization.

docs/examples/example_onnx.ipynb

@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,9 +113,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<metalearners.xlearner.XLearner at 0x10e5b9520>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from metalearners import XLearner\n",
     "from lightgbm import LGBMRegressor, LGBMClassifier\n",
@@ -161,9 +172,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'propensity_model': [LGBMClassifier(n_estimators=5, verbose=-1)],\n",
+       " 'control_effect_model': [LGBMRegressor(n_estimators=5, verbose=-1)],\n",
+       " 'treatment_effect_model': [LGBMRegressor(n_estimators=5, verbose=-1)]}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "necessary_models = xlearner._necessary_onnx_models()\n",
     "necessary_models"
@@ -187,9 +211,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The maximum opset needed by this model is only 9.\n",
+      "The maximum opset needed by this model is only 8.\n",
+      "The maximum opset needed by this model is only 8.\n"
+     ]
+    }
+   ],
    "source": [
     "import onnx\n",
     "from onnxmltools import convert_lightgbm\n",
@@ -221,9 +255,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kevinklein/Code/metalearners/metalearners/xlearner.py:463: UserWarning: _build_onnx is an experimental feature. Use it at your own risk!\n",
+      "  warning_experimental_feature(\"_build_onnx\")\n"
+     ]
+    }
+   ],
    "source": [
     "onnx_model = xlearner._build_onnx(onnx_models)"
    ]
@@ -239,14 +282,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONNX model input:  [name: \"X\"\n",
+      "type {\n",
+      "  tensor_type {\n",
+      "    elem_type: 1\n",
+      "    shape {\n",
+      "      dim {\n",
+      "      }\n",
+      "      dim {\n",
+      "        dim_value: 11\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "]\n",
+      "ONNX model output:  [name: \"tau\"\n",
+      "type {\n",
+      "  tensor_type {\n",
+      "    elem_type: 1\n",
+      "    shape {\n",
+      "      dim {\n",
+      "      }\n",
+      "      dim {\n",
+      "        dim_value: 1\n",
+      "      }\n",
+      "      dim {\n",
+      "        dim_value: 1\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "]\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"ONNX model input: \", onnx_model.graph.input)\n",
     "print(\"ONNX model output: \", onnx_model.graph.output)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also visualize the ONNX model with, e.g. [netron](https://netron.app/):\n",
+    "<img src=\"imgs/onnx_netron.png\" alt=\"Drawing\" style=\"width: 400px;\"/>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -261,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,6 +387,15 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onnx.save_model(onnx_model, \"model.onnx\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -340,10 +438,24 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }