bsql_iris.ipynb

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     "text": [
      "BlazingContext ready\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cudf\n",
    "from cuml import neighbors\n",
    "from blazingsql import BlazingContext\n",
    "bc = BlazingContext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/winston/silent-disco/data/iris.csv'"
      ]
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     "execution_count": 2,
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   "source": [
    "# identify path to data\n",
    "path = os.getcwd()\n",
    "path = path + '/data/iris.csv'\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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   "source": [
    "# create table \n",
    "bc.create_table('iris', path, header=0)\n",
    "\n",
    "# pull x & y training data\n",
    "y = bc.sql('select target from iris')\n",
    "x = bc.sql('select sepal_length, sepal_width, petal_length, petal_width from iris')\n",
    "\n",
    "# set knn model\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# train model\n",
    "knn.fit(x, y)\n",
    "\n",
    "# make simple test case \n",
    "t = cudf.DataFrame({'sepal_length':5.1, 'sepal_width':3.5, 'petal_length':1.4, 'petal_width':.2})\n",
    "\n",
    "# predict flower type\n",
    "knn.predict(t)"
   ]
  }
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