notebooks/LGBM-ONNXML-example.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook shows how to convert from LGBM to ONNX and provides an example of the ONNXML converter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment and run this cell if you need to install Hummingbird and ONNX\n",
    "#!python -m pip install hummingbird_ml[extra,onnx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "\n",
    "import onnxruntime as ort\n",
    "from onnxmltools.convert import convert_lightgbm\n",
    "from onnxconverter_common.data_types import FloatTensorType\n",
    "\n",
    "from hummingbird.ml import convert\n",
    "from hummingbird.ml import constants\n",
    "\n",
    "# Create some random data for binary classification.\n",
    "num_classes = 2\n",
    "X = np.array(np.random.rand(10000, 28), dtype=np.float32)\n",
    "y = np.random.randint(num_classes, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a model (LightGBM in this case).\n",
    "model = lgb.LGBMClassifier()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use ONNXMLTOOLS to convert the model to ONNXML.\n",
    "# Here, we set the first dimension to None, so that the model can accept arbitrary batch sizes\n",
    "# If you want to have a fixed batch size, set it to a constant.\n",
    "initial_types = [(\"input\", FloatTensorType([None, X.shape[1]]))] \n",
    "onnx_ml_model = convert_lightgbm(\n",
    "    model, initial_types=initial_types, target_opset=9\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Hummingbird to convert the ONNXML model to ONNX.\n",
    "onnx_model = convert(onnx_ml_model, \"onnx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 ms ± 624 µs per loop (mean ± std. dev. of 3 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3\n",
    "\n",
    "# Run the ONNX model on CPU \n",
    "onnx_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 1, 0, 0, 1, 1, 0, 1, 0], dtype=int64),\n",
       " array([0, 0], dtype=int64))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model accepts arbitrary batch sizes too\n",
    "onnx_model.predict(X[:10]), onnx_model.predict(X[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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