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onnx_sklearn_exp.ipynb
{
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"metadata": {
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"name": "python3",
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"name": "python"
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{
"cell_type": "code",
"execution_count": 1,
"metadata": {
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"id": "x-VA-SLtBiHb",
"outputId": "91b924fa-e677-4ae8-fb8c-d1d648cc6940"
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting onnxruntime\n",
" Downloading onnxruntime-1.13.1-cp37-cp37m-manylinux_2_27_x86_64.whl (4.5 MB)\n",
"\u001b[K |████████████████████████████████| 4.5 MB 5.4 MB/s \n",
"\u001b[?25hCollecting coloredlogs\n",
" Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
"\u001b[K |████████████████████████████████| 46 kB 1.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.21.6)\n",
"Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.7.1)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.19.6)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (21.3)\n",
"Collecting humanfriendly>=9.1\n",
" Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
"\u001b[K |████████████████████████████████| 86 kB 2.6 MB/s \n",
"\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxruntime) (3.0.9)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime) (1.2.1)\n",
"Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n",
"Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.13.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting skl2onnx\n",
" Downloading skl2onnx-1.13-py2.py3-none-any.whl (288 kB)\n",
"\u001b[K |████████████████████████████████| 288 kB 5.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: scikit-learn>=0.19 in /usr/local/lib/python3.7/dist-packages (from skl2onnx) (1.0.2)\n",
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from skl2onnx) (1.21.6)\n",
"Collecting onnxconverter-common>=1.7.0\n",
" Downloading onnxconverter_common-1.13.0-py2.py3-none-any.whl (83 kB)\n",
"\u001b[K |████████████████████████████████| 83 kB 1.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from skl2onnx) (1.7.3)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from skl2onnx) (3.19.6)\n",
"Collecting onnx>=1.2.1\n",
" Downloading onnx-1.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n",
"\u001b[K |████████████████████████████████| 13.1 MB 1.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.2.1->skl2onnx) (4.1.1)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxconverter-common>=1.7.0->skl2onnx) (21.3)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19->skl2onnx) (3.1.0)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19->skl2onnx) (1.2.0)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxconverter-common>=1.7.0->skl2onnx) (3.0.9)\n",
"Installing collected packages: onnx, onnxconverter-common, skl2onnx\n",
"Successfully installed onnx-1.12.0 onnxconverter-common-1.13.0 skl2onnx-1.13\n"
]
}
],
"source": [
"!pip install onnxruntime\n",
"!pip install skl2onnx"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import pickle\n",
"\n",
"from skl2onnx import to_onnx\n",
"from skl2onnx import convert_sklearn\n",
"from skl2onnx.common.data_types import FloatTensorType"
],
"metadata": {
"id": "AuiV6hi2Cogg"
},
"execution_count": 2,
"outputs": []
},
{
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"source": [
"raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
"raw_df.head()"
],
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"id": "IrQRKeyj6tKL",
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"outputs": [
{
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"raw_df[\"Class_bool\"] = raw_df[\"Class\"].astype(bool)"
],
"metadata": {
"id": "jkErb1MG61HK"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class', 'Class_bool']].head()"
],
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"data": {
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"\n",
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"0 -0.021053 149.62 0 False \n",
"1 0.014724 2.69 0 False \n",
"2 -0.059752 378.66 0 False \n",
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" <td>0.448154</td>\n",
" <td>0.060018</td>\n",
" <td>0.125895</td>\n",
" <td>-0.008983</td>\n",
" <td>0.014724</td>\n",
" <td>2.69</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>-1.358354</td>\n",
" <td>-1.340163</td>\n",
" <td>1.773209</td>\n",
" <td>0.379780</td>\n",
" <td>-0.503198</td>\n",
" <td>-0.139097</td>\n",
" <td>-0.055353</td>\n",
" <td>-0.059752</td>\n",
" <td>378.66</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>-0.966272</td>\n",
" <td>-0.185226</td>\n",
" <td>1.792993</td>\n",
" <td>-0.863291</td>\n",
" <td>-0.010309</td>\n",
" <td>-0.221929</td>\n",
" <td>0.062723</td>\n",
" <td>0.061458</td>\n",
" <td>123.50</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>-1.158233</td>\n",
" <td>0.877737</td>\n",
" <td>1.548718</td>\n",
" <td>0.403034</td>\n",
" <td>-0.407193</td>\n",
" <td>0.502292</td>\n",
" <td>0.219422</td>\n",
" <td>0.215153</td>\n",
" <td>69.99</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1602b06e-6f5a-4289-9d12-c0c9dc09612b')\"\n",
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"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-1602b06e-6f5a-4289-9d12-c0c9dc09612b');\n",
" const dataTable =\n",
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" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"classifier = RandomForestClassifier(\n",
" n_estimators = 5,\n",
" random_state = 42,\n",
" n_jobs = -1\n",
")"
],
"metadata": {
"id": "42igMctG7G_O"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"features = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount']"
],
"metadata": {
"id": "VcPZ3BmN7kOm"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x = raw_df[features]\n",
"y = raw_df['Class_bool']\n",
"classifier.fit(x, y)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s_x8nQ-S7TQ9",
"outputId": "5df57b0c-fdfa-40b4-b144-7e87f12ce4ce"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestClassifier(n_estimators=5, n_jobs=-1, random_state=42)"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"x1 = x[:1]\n",
"x1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "JyZL0x9M8OrI",
"outputId": "329bf5f8-5a41-423c-cfaf-81a2ff21cd0d"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Time V1 V2 V3 V4 V5 V26 V27 \\\n",
"0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 -0.189115 0.133558 \n",
"\n",
" V28 Amount \n",
"0 -0.021053 149.62 "
],
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" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V26</th>\n",
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" \n",
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" const buttonEl =\n",
" document.querySelector('#df-0a23a998-486a-4545-b538-4da30c0e2ae7 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-0a23a998-486a-4545-b538-4da30c0e2ae7');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"onx = to_onnx(classifier, x1.to_numpy().astype(np.float32), target_opset=12)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"id": "1gh5flMH_M5u",
"outputId": "4a27649d-3d88-420d-edd3-c7911b033e61"
},
"execution_count": 10,
"outputs": [
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-70444daf22cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0monx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_onnx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_opset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/convert.py\u001b[0m in \u001b[0;36mto_onnx\u001b[0;34m(model, X, name, initial_types, target_opset, options, white_op, black_op, final_types, dtype, naming, verbose)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0mwhite_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhite_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblack_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblack_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0mfinal_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m verbose=verbose, naming=naming)\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/convert.py\u001b[0m in \u001b[0;36mconvert_sklearn\u001b[0;34m(model, name, initial_types, doc_string, target_opset, custom_conversion_functions, custom_shape_calculators, custom_parsers, options, intermediate, white_op, black_op, final_types, dtype, naming, verbose)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mcustom_shape_calculators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_parsers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0mwhite_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhite_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblack_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblack_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m final_types=final_types, naming=naming)\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;31m# Convert our Topology object into ONNX. The outcome is an ONNX model.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36mparse_sklearn_model\u001b[0;34m(model, initial_types, target_opset, custom_conversion_functions, custom_shape_calculators, custom_parsers, options, white_op, black_op, final_types, naming)\u001b[0m\n\u001b[1;32m 787\u001b[0m outputs = parse_sklearn(scope, model, inputs,\n\u001b[1;32m 788\u001b[0m \u001b[0mcustom_parsers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_parsers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m final_types=final_types)\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0;31m# The object raw_model_container is a part of the topology we're\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36mparse_sklearn\u001b[0;34m(scope, model, inputs, custom_parsers, final_types)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m res = _parse_sklearn(\n\u001b[0;32m--> 708\u001b[0;31m scope, model, inputs, custom_parsers=custom_parsers)\n\u001b[0m\u001b[1;32m 709\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 710\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36m_parse_sklearn\u001b[0;34m(scope, model, inputs, custom_parsers, alias)\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msklearn_parsers_map\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 644\u001b[0m outputs = sklearn_parsers_map[tmodel](scope, model, inputs,\n\u001b[0;32m--> 645\u001b[0;31m custom_parsers=custom_parsers)\n\u001b[0m\u001b[1;32m 646\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipeline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 647\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msklearn_parsers_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipeline\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36m_parse_sklearn_classifier\u001b[0;34m(scope, model, inputs, custom_parsers)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m return _apply_zipmap(\n\u001b[0;32m--> 521\u001b[0;31m options['zipmap'], scope, model, inputs[0].type, probability_tensor)\n\u001b[0m\u001b[1;32m 522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36m_apply_zipmap\u001b[0;34m(zipmap_options, scope, model, input_type, probability_tensor)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mzipmap_operator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasslabels_int64s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 460\u001b[0m \u001b[0mzipmap_operator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasslabels_strings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0mlabel_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skl2onnx/_parse.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mzipmap_operator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasslabels_int64s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 460\u001b[0m \u001b[0mzipmap_operator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasslabels_strings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0mlabel_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringTensorType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.bool_' object has no attribute 'encode'"
]
}
]
},
{
"cell_type": "code",
"source": [
"# apply a workaround described at https://github.com/onnx/sklearn-onnx/issues/813\n",
"classifier.classes_ = classifier.classes_.astype(np.int32)\n",
"onx = to_onnx(classifier, x1.to_numpy().astype(np.float32), target_opset=12)"
],
"metadata": {
"id": "AcHnl9mDkGdK"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "J-9xRWtvIG5v"
},
"execution_count": null,
"outputs": []
}
]
}
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