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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F17_randomized_search.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/17_randomized_search.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #17 ([video](https://www.youtube.com/watch?v=Q5dH5mOQ_ik&list=PL5-da3qGB5ID7YYAqireYEew2mWVvgmj6&index=17))\n",
    "\n",
    "GridSearchCV taking too long? Try RandomizedSearchCV with a small number of iterations.\n",
    "\n",
    "Make sure to specify a distribution (instead of a list of values) for continuous parameters!\n",
    "\n",
    "See example 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('http://bit.ly/kaggletrain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use single brackets around \"Name\" because CountVectorizer expects 1-D input\n",
    "X = df['Name']\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(CountVectorizer(), MultinomialNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8001820350260498"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validate the pipeline using default parameters\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify parameter values to search (use a distribution for any continuous parameters)\n",
    "import scipy as sp\n",
    "params = {}\n",
    "params['countvectorizer__min_df'] = [1, 2, 3, 4]\n",
    "params['countvectorizer__lowercase'] = [True, False]\n",
    "params['multinomialnb__alpha'] = sp.stats.uniform(scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try \"n_iter\" random combinations of those parameter values\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rand = RandomizedSearchCV(pipe, params, n_iter=10, cv=5, scoring='accuracy', random_state=1)\n",
    "rand.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8080534806352395"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what was the best score found during the search?\n",
    "rand.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'countvectorizer__lowercase': False,\n",
       " 'countvectorizer__min_df': 3,\n",
       " 'multinomialnb__alpha': 0.1981014890848788}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which combination of parameters produced the best score?\n",
    "rand.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 2 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
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