notebooks/sklearn-random-forest-example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install hummingbird_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from hummingbird.ml import convert\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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]\n",
    "y = y[0:nrows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=10, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a random forest model.\n",
    "model = RandomForestClassifier(n_estimators=10, max_depth=10)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.07 ms ± 14.2 µs per loop (mean ± std. dev. of 3 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3\n",
    "\n",
    "# Time for scikit-learn.\n",
    "model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = convert(model, 'torch', extra_config={\"tree_implementation\":\"gemm\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "444 µs ± 16.9 µs per loop (mean ± std. dev. of 3 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3\n",
    "\n",
    "# Time for HB.\n",
    "model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyTorchBackendModelClassification(\n",
       "  (operator_map): ModuleDict(\n",
       "    (SklearnRandomForestClassifier): GEMMDecisionTreeImpl()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291 µs ± 16 µs per loop (mean ± std. dev. of 3 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 3\n",
    "\n",
    "# Time for HB GPU.\n",
    "model.predict(X)"
   ]
  }
 ],
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