cuml/nearest_neighbors_demo.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest Neighbors\n",
    "\n",
    "Nearest Neighbors allows the query of the k-nearest neighbors from a set of input samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can take array-like objects, either in host as NumPy arrays or in device (as Numba or cuda_array_interface-compliant), as well as cuDF DataFrames as the input. \n",
    "\n",
    "For information on converting your dataset to cuDF format, refer to the cuDF documentation: https://rapidsai.github.io/projects/cudf/en/latest/\n",
    "\n",
    "For additional information on cuML's Nearest Neighbors implementation: https://rapidsai.github.io/projects/cuml/en/latest/api.html#nearest-neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from cuml.datasets import make_blobs\n",
    "\n",
    "import pandas as pd\n",
    "import cudf as gd\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors as skNN\n",
    "from cuml.neighbors import NearestNeighbors as cumlNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 2**17\n",
    "n_features = 40\n",
    "\n",
    "n_query = 2**13\n",
    "\n",
    "n_neighbors = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Data\n",
    "\n",
    "### GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "device_data, _ = make_blobs(\n",
    "   n_samples=n_samples, n_features=n_features, centers=5, random_state=0)\n",
    "\n",
    "device_data = gd.DataFrame.from_gpu_matrix(device_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Host"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "host_data = np.asarray(device_data.as_gpu_matrix())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "knn_sk = skNN(metric = 'sqeuclidean', algorithm=\"brute\", n_jobs=-1)\n",
    "knn_sk.fit(host_data)\n",
    "\n",
    "D_sk, I_sk = knn_sk.kneighbors(host_data[:n_query], n_neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cuML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "knn_cuml = cumlNN()\n",
    "knn_cuml.fit(device_data)\n",
    "\n",
    "D_cuml, I_cuml = knn_cuml.kneighbors(device_data[:n_query], n_neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Results\n",
    "\n",
    "### Distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "passed = np.allclose(D_sk, D_cuml.as_gpu_matrix(), atol=1e-3)\n",
    "print('compare knn: cuml vs sklearn distances %s'%('equal'if passed else 'NOT equal'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_sorted = np.sort(I_sk, axis=1)\n",
    "cuml_sorted = np.sort(I_cuml.as_gpu_matrix(), axis=1)\n",
    "\n",
    "diff = sk_sorted - cuml_sorted\n",
    "\n",
    "passed = (len(diff[diff!=0]) / n_samples) < 1e-9\n",
    "print('compare knn: cuml vs sklearn indexes %s'%('equal'if passed else 'NOT equal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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