{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 노트북의 코드에 대한 설명은 [Pipeline에서 캐싱을 사용하기](https://tensorflow.blog/2017/12/08/pipeline%ec%97%90%ec%84%9c-%ec%ba%90%ec%8b%b1%ec%9d%84-%ec%82%ac%ec%9a%a9%ed%95%98%ea%b8%b0/) 글을 참고하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.5.6\n",
      "IPython 6.5.0\n",
      "\n",
      "sklearn 0.20.1\n",
      "numpy 1.15.2\n",
      "scipy 1.1.0\n",
      "matplotlib 3.0.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p sklearn,numpy,scipy,matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "boston = load_boston()\n",
    "X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=0)\n",
    "param_grid = {'polynomialfeatures__degree': [1, 2, 3, 4, 5],\n",
    "                      'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(StandardScaler(), PolynomialFeatures(), Ridge())\n",
    "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1, iid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.35 s ± 1.44 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tempfile import mkdtemp\n",
    "from shutil import rmtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cache_dir = mkdtemp()\n",
    "pipe2 = make_pipeline(StandardScaler(), PolynomialFeatures(), Ridge(), memory=cache_dir)\n",
    "grid2 = GridSearchCV(pipe2, param_grid=param_grid, cv=5, n_jobs=-1, iid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.89 s ± 981 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit grid2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmtree(cache_dir)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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