170508_p53.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "housing=load_housing_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index() # adds an `index` column\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16362 train + 4278 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16267 train + 4373 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "housing[\"income_err\"] = housing[\"median_income\"] /1.5 - np.ceil(housing[\"median_income\"] / 1.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14262b86b00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t3wP3AkeS3AG8DNwGUFWnkhwBnmX+yqK7q+rN1u8u4BCwEXi8bZKkgSwaAlX1P4C3up7/\n5rfocwA4cJH6CeD65QxQkrR2/MSwJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOG\ngCR1zBCQpI4ZApLUsb6+71jr0rb9XxnkeQ/NbBrkeaXV5JmAJHXMEJCkjhkCktQxQ0CSOmYISFLH\nDAFJ6pghIEkdMwQkqWOLhkCSzyc5n+TbY7WrkjyR5IV2e+XYffckOZ3k+SS3jNVvTHKy3Xd/krf6\n8XpJ0oQs5UzgEDCzoLYfOF5V24HjbZ8k1wK7getanweTbGh9HgLuBLa3beFjSpImbNEQqKo/B/5m\nQXkXcLi1DwO3jtUfrao3qupF4DRwU5LNwBVV9WRVFfDwWB9J0kDe7ncHjarqXGu/Coxaewvw5Nhx\nZ1rtx629sH5RSfYCewFGoxGzs7Nvc5jDGG2EfTsuDD2MiepxznNzc5fca3OlnPP6s+IvkKuqSlKr\nMZixxzwIHASYmpqq6enp1Xz4NffAI0e572Rf3823b8eF7uZ8aGYTl9prc6VmZ2ed8zrzdq8Oeq0t\n8dBuz7f6WeDqseO2ttrZ1l5YlyQN6O2GwDFgT2vvAY6O1XcnuTzJNcy/AfxUWzp6PcnOdlXQ7WN9\nJEkDWfT8PckXgGngfUnOAP8BuBc4kuQO4GXgNoCqOpXkCPAscAG4u6rebA91F/NXGm0EHm+bJGlA\ni4ZAVf3WW9x181scfwA4cJH6CeD6ZY1uhYb6sZF9OwZ5WklaNj8xLEkdMwQkqWOGgCR1zBCQpI4Z\nApLUMUNAkjpmCEhSxwwBSepYX9/4Ja2ik2d/yCcG+kDiS/d+dJDn1frjmYAkdcwQkKSOGQKS1DFD\nQJI6ZghIUscMAUnqmCEgSR0zBCSpY35YTLoEDfWreYdmNg3yvFo7nglIUscmHgJJZpI8n+R0kv2T\nfn5J0k9NdDkoyQbgvwAfAc4A30hyrKqeneQ4JL09Q31fkt+VtHYm/Z7ATcDpqvouQJJHgV2AISDp\nLQ31HgjAvh0X1nXwTXo5aAvwytj+mVaTJA0gVTW5J0v+JTBTVf+67X8c+EdV9bsLjtsL7G27HwCe\nn9ggV8f7gL8eehAT5pz74JwvHf+wqt6/2EGTXg46C1w9tr+11f4/VXUQODipQa22JCeqamrocUyS\nc+6Dc15/Jr0c9A1ge5Jrkrwb2A0cm/AYJEnNRM8EqupCkt8F/hTYAHy+qk5NcgySpJ+a+CeGq+pP\ngD+Z9PNO2CW7lLUCzrkPznmdmegbw5Kkdxa/NkKSOmYIrKIkVyf5WpJnk5xK8qmhxzQJSTYk+VaS\nLw89lklJ8t4kjyX5TpLnkvzq0GNaS0n+XXtNfzvJF5K8Z+gxrYUkn09yPsm3x2pXJXkiyQvt9soh\nx7jaDIHVdQHYV1XXAjuBu5NcO/CYJuFTwHNDD2LCPgN8tap+Gfgg63j+SbYA/xaYqqrrmb+oY/ew\no1ozh4CZBbX9wPGq2g4cb/vrhiGwiqrqXFV9s7X/lvl/GNb1J6KTbAU+Cnx26LFMSpJfAn4N+BxA\nVf1dVf1g2FGtucuAjUkuA34e+N8Dj2dNVNWfA3+zoLwLONzah4FbJzqoNWYIrJEk24APAV8fdiRr\n7g+A3wP+79ADmaBrgO8Bf9iWwT6bZN1+0X5VnQX+M/BXwDngh1X134Yd1USNqupca78KjIYczGoz\nBNZAkl8A/hj4dFW9PvR41kqS3wTOV9XTQ49lwi4DfgV4qKo+BPyIdbZEMK6tge9iPvz+AbApye8M\nO6ph1PzllOvqkkpDYJUleRfzAfBIVX1x6PGssQ8DH0vyEvAo8OtJ/mjYIU3EGeBMVf3kLO8x5kNh\nvfpnwItV9b2q+jHwReAfDzymSXotyWaAdnt+4PGsKkNgFSUJ8+vEz1XV7w89nrVWVfdU1daq2sb8\nG4V/VlXr/n+IVfUq8EqSD7TSzazvr0P/K2Bnkp9vr/GbWcdvhF/EMWBPa+8Bjg44llVnCKyuDwMf\nZ/5/xM+07TeGHpTWxCeBR5L8JXAD8J8GHs+aaWc8jwHfBE4y/+/GuvwUbZIvAH8BfCDJmSR3APcC\nH0nyAvNnRfcOOcbV5ieGJaljnglIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOvb/\nAAqmTfwH+mOWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14262a07fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     0.350581\n",
       "2.0     0.318847\n",
       "4.0     0.176308\n",
       "5.0     0.068944\n",
       "1.0     0.039826\n",
       "6.0     0.025775\n",
       "7.0     0.009157\n",
       "8.0     0.005087\n",
       "9.0     0.002422\n",
       "11.0    0.002374\n",
       "10.0    0.000678\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14264320b00>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x142643e4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20640"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)+len(test_set)\n",
    "\n",
    "len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.0\n",
       "1    5.0\n",
       "2    5.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    3.0\n",
       "6    3.0\n",
       "7    3.0\n",
       "8    2.0\n",
       "9    3.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " housing[\"income_cat\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}