SAGA solver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 노트북의 코드에 대한 설명은 [New SAGA solver](https://tensorflow.blog/2017/12/15/new-saga-solver/) 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 0.20.0\n",
      "numpy 1.15.2\n",
      "scipy 1.1.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p sklearn,numpy,scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer, load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression, Ridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, stratify=cancer.target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트 점수: 0.927\n",
      "테스트 세트 점수: 0.930\n"
     ]
    }
   ],
   "source": [
    "logreg_sag = LogisticRegression(solver='sag', max_iter=10000).fit(X_train, y_train)\n",
    "print(\"훈련 세트 점수: {:.3f}\".format(logreg_sag.score(X_train, y_train)))\n",
    "print(\"테스트 세트 점수: {:.3f}\".format(logreg_sag.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트 점수: 0.920\n",
      "테스트 세트 점수: 0.937\n"
     ]
    }
   ],
   "source": [
    "logreg_saga = LogisticRegression(solver='saga', max_iter=10000).fit(X_train, y_train)\n",
    "print(\"훈련 세트 점수: {:.3f}\".format(logreg_saga.score(X_train, y_train)))\n",
    "print(\"테스트 세트 점수: {:.3f}\".format(logreg_saga.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for california housing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
       "          1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
       "          3.78800000e+01, -1.22230000e+02],\n",
       "        [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
       "          9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
       "          3.78600000e+01, -1.22220000e+02],\n",
       "        [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
       "          1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
       "          3.78500000e+01, -1.22240000e+02],\n",
       "        [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
       "          1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
       "          3.78500000e+01, -1.22250000e+02],\n",
       "        [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
       "          1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
       "          3.78500000e+01, -1.22250000e+02]]),\n",
       " array([4.526, 3.585, 3.521, 3.413, 3.422]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.data[:5], housing.target[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedInc',\n",
       " 'HouseAge',\n",
       " 'AveRooms',\n",
       " 'AveBedrms',\n",
       " 'Population',\n",
       " 'AveOccup',\n",
       " 'Latitude',\n",
       " 'Longitude']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block\n",
      "        - HouseAge      median house age in block\n",
      "        - AveRooms      average number of rooms\n",
      "        - AveBedrms     average number of bedrooms\n",
      "        - Population    block population\n",
      "        - AveOccup      average house occupancy\n",
      "        - Latitude      house block latitude\n",
      "        - Longitude     house block longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "http://lib.stat.cmu.edu/datasets/\n",
      "\n",
      "The target variable is the median house value for California districts.\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(housing.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트 점수: 0.061\n",
      "테스트 세트 점수: 0.062\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(solver='sag').fit(X_train, y_train)\n",
    "print(\"훈련 세트 점수: {:.3f}\".format(ridge.score(X_train, y_train)))\n",
    "print(\"테스트 세트 점수: {:.3f}\".format(ridge.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 세트 점수: 0.035\n",
      "테스트 세트 점수: 0.036\n"
     ]
    }
   ],
   "source": [
    "ridge_saga = Ridge(solver='saga').fit(X_train, y_train)\n",
    "print(\"훈련 세트 점수: {:.3f}\".format(ridge_saga.score(X_train, y_train)))\n",
    "print(\"테스트 세트 점수: {:.3f}\".format(ridge_saga.score(X_test, y_test)))"
   ]
  }
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