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ucalyptus/skearn-random-forest-example.ipynb

Created June 10, 2020 06:30
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skearn-random-forest-example.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6-final"
},
"colab": {
"name": "skearn-random-forest-example.ipynb",
"provenance": [],
"include_colab_link": true
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ucalyptus/bcf09b711009b87e94f989cb13035909/skearn-random-forest-example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pIbz1CDs6i0y",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 615
},
"outputId": "7433e265-65c0-4b1a-9f20-ee811cbd8775"
},
"source": [
"!pip install scikit-learn==0.21.3\n",
"!pip install hummingbird-ml"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting scikit-learn==0.21.3\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a0/c5/d2238762d780dde84a20b8c761f563fe882b88c5a5fb03c056547c442a19/scikit_learn-0.21.3-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n",
"\u001b[K |████████████████████████████████| 6.7MB 4.4MB/s \n",
"\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.3) (1.4.1)\n",
"Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.3) (1.18.5)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.3) (0.15.1)\n",
"Installing collected packages: scikit-learn\n",
" Found existing installation: scikit-learn 0.22.2.post1\n",
" Uninstalling scikit-learn-0.22.2.post1:\n",
" Successfully uninstalled scikit-learn-0.22.2.post1\n",
"Successfully installed scikit-learn-0.21.3\n",
"Collecting hummingbird-ml\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/07/fc/df69fb8cb958826487112832964a9014fa1338dcf17b706bc099701bd524/hummingbird_ml-0.0.1-py2.py3-none-any.whl (49kB)\n",
"\u001b[K |████████████████████████████████| 51kB 2.6MB/s \n",
"\u001b[?25hRequirement already satisfied: xgboost==0.90 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml) (0.90)\n",
"Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml) (1.5.0+cu101)\n",
"Collecting onnxconverter-common==1.6.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/77/3d/6112c19223d1eabbedf1b063567034e1463a11d7c82d1820f26b75d14e3c/onnxconverter_common-1.6.0-py2.py3-none-any.whl (43kB)\n",
"\u001b[K |████████████████████████████████| 51kB 5.7MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml) (1.18.5)\n",
"Requirement already satisfied: lightgbm>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml) (2.2.3)\n",
"Requirement already satisfied: scikit-learn==0.21.3 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml) (0.21.3)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost==0.90->hummingbird-ml) (1.4.1)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.0->hummingbird-ml) (0.16.0)\n",
"Collecting onnx\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/36/ee/bc7bc88fc8449266add978627e90c363069211584b937fd867b0ccc59f09/onnx-1.7.0-cp36-cp36m-manylinux1_x86_64.whl (7.4MB)\n",
"\u001b[K |████████████████████████████████| 7.4MB 12.1MB/s \n",
"\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from onnxconverter-common==1.6.0->hummingbird-ml) (1.12.0)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from onnxconverter-common==1.6.0->hummingbird-ml) (3.10.0)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.3->hummingbird-ml) (0.15.1)\n",
"Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.6/dist-packages (from onnx->onnxconverter-common==1.6.0->hummingbird-ml) (3.6.6)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnxconverter-common==1.6.0->hummingbird-ml) (47.1.1)\n",
"Installing collected packages: onnx, onnxconverter-common, hummingbird-ml\n",
"Successfully installed hummingbird-ml-0.0.1 onnx-1.7.0 onnxconverter-common-1.6.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wLSQXKMx6Z56",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"import torch\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.datasets import load_breast_cancer\n",
"from hummingbird.ml import convert\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DlnJvE556Z6G",
"colab_type": "code",
"colab": {}
},
"source": [
"# We are going to use the breast cancer dataset from scikit-learn for this example.\n",
"X, y = load_breast_cancer(return_X_y=True)\n",
"nrows=15000\n",
"X = X[0:nrows].astype('|f4')\n",
"y = y[0:nrows]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4_IoPJ3d6Z6P",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"outputId": "162f2ffc-0fd6-4701-c690-8e1f63de8541"
},
"source": [
"# Create and train a random forest model.\n",
"model = RandomForestClassifier(n_estimators=10, max_depth=10)\n",
"model.fit(X, y)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=10, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10,\n",
" n_jobs=None, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lNWLDmGF6Z6W",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "7c0b469e-c89a-4933-8e46-52cf031856b1"
},
"source": [
"%%timeit -r 3\n",
"\n",
"# Time for scikit-learn.\n",
"model.predict(X)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"1000 loops, best of 3: 1.27 ms per loop\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "E5725xaH6Z6f",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 163
},
"outputId": "177a9903-8e0c-41ca-d6d2-6c599d88ffa7"
},
"source": [
"model = convert(model, 'pytorch', extra_config={\"tree_implementation\":\"gemm\"})"
],
"execution_count": 6,
"outputs": [
{
"output_type": "error",
"ename": "TypeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-cdc139877306>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pytorch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"tree_implementation\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"gemm\"\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[0;31mTypeError\u001b[0m: 'module' object is not callable"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SM4ih8g26Z6m",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "36dce7f4-af29-4603-a87c-bed26c86564b"
},
"source": [
"%%timeit -r 3\n",
"\n",
"# Time for HB.\n",
"model.predict(X)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"1000 loops, best of 3: 1.25 ms per loop\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3jpof7vo6Z6u",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 340
},
"outputId": "2676971a-4244-464d-8da3-709e1b1be975"
},
"source": [
"model.to('cuda')"
],
"execution_count": 8,
"outputs": [
{
"output_type": "error",
"ename": "MissingBackend",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMissingBackend\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-46563f6e5343>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\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/python3.6/dist-packages/hummingbird/ml/convert.py\u001b[0m in \u001b[0;36m_to_sklearn\u001b[0;34m(self, backend, test_input, extra_config)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mUtility\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0mused\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcall\u001b[0m \u001b[0mthe\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mscikit\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \"\"\"\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0m_supported_backend_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\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 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_sklearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra_config\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/python3.6/dist-packages/hummingbird/ml/convert.py\u001b[0m in \u001b[0;36m_supported_backend_check\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m 29\u001b[0m \"\"\"\n\u001b[1;32m 30\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbackend_map\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMissingBackend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Backend: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\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 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMissingBackend\u001b[0m: Backend: cuda\nIt usually means the backend is not currently supported.\nPlease check the spelling or fill an issue at https://github.com/microsoft/hummingbird.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U_PbWOl86Z62",
"colab_type": "code",
"colab": {},
"outputId": "cbd51fed-7dfe-4e1f-a104-1700665b8d03"
},
"source": [
"%%timeit -r 3\n",
"\n",
"# Time for HB GPU.\n",
"model.predict(X)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"467 µs ± 2.29 µs per loop (mean ± std. dev. of 3 runs, 1000 loops each)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HrCBkNN_6Z6_",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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