Add outputs

Quantco · Dec 13, 2024 · 2ef89f1 · 2ef89f1
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diff --git a/docs/examples/example_basic.ipynb b/docs/examples/example_basic.ipynb
diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb
diff --git a/docs/examples/example_feature_importance_shap.ipynb b/docs/examples/example_feature_importance_shap.ipynb
diff --git a/docs/examples/example_lime.ipynb b/docs/examples/example_lime.ipynb
diff --git a/docs/examples/example_onnx.ipynb b/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 0x16a753050>"
+      ]
+     },
+     "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 +255,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "_build_onnx is an experimental feature. Use it at your own risk!\n"
+     ]
+    }
+   ],
    "source": [
     "onnx_model = xlearner._build_onnx(onnx_models)"
    ]
@@ -239,9 +281,47 @@
   },
   {
    "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)"
@@ -269,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +452,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

diff --git a/docs/examples/example_propensity.ipynb b/docs/examples/example_propensity.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "vscode": {
      "languageId": "plaintext"
@@ -69,13 +69,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "plaintext"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3256664421133673"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[treatment_column].mean()"
    ]
@@ -111,13 +122,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "vscode": {
      "languageId": "plaintext"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<metalearners.rlearner.RLearner at 0x302d930d0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from metalearners import RLearner\n",
     "from metalearners.utils import FixedBinaryPropensity\n",
@@ -149,13 +171,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "vscode": {
      "languageId": "plaintext"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.7, 0.3],\n",
+       "       [0.7, 0.3],\n",
+       "       [0.7, 0.3],\n",
+       "       ...,\n",
+       "       [0.7, 0.3],\n",
+       "       [0.7, 0.3],\n",
+       "       [0.7, 0.3]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "rlearner.predict_nuisance(\n",
     "    X=df[feature_columns], model_kind=\"propensity_model\", model_ord=0, is_oos=False\n",
@@ -192,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,