parametric_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# パラメトリックテスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "\n",
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## まずは前回のノーマリティテストを行なおう\n",
    "**Question'** なぜ？   \n",
    "\n",
    "**Answer'** パラメトリックテストはデータにガウス分布を仮定しているから"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " CRIM \n",
      "Statistics=0.448, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " ZN \n",
      "Statistics=0.556, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " INDUS \n",
      "Statistics=0.900, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " CHAS \n",
      "Statistics=0.275, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " NOX \n",
      "Statistics=0.936, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " RM \n",
      "Statistics=0.961, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " AGE \n",
      "Statistics=0.892, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " DIS \n",
      "Statistics=0.903, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " RAD \n",
      "Statistics=0.680, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " TAX \n",
      "Statistics=0.815, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " PTRATIO \n",
      "Statistics=0.904, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " B \n",
      "Statistics=0.477, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " LSTAT \n",
      "Statistics=0.937, p=0.000\n",
      "Not look Gaussian\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "for i in range(df.shape[1]):\n",
    "    stat, p = shapiro(df.iloc[:,i])\n",
    "    print(\"\", df.columns[i], \"\")\n",
    "    print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    alpha = 0.05\n",
    "    if p > alpha:\n",
    "        print('Gaussian')\n",
    "    else:\n",
    "        print('Not look Gaussian')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# おっと、ボストンデータは無理っぽい？\n",
    "# 一応プロットもしてみよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12, 9))\n",
    "for i in range(13):\n",
    "    ax = plt.subplot(5,3,i+1)  \n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False)  \n",
    "  \n",
    "    ax.get_xaxis().tick_bottom()  \n",
    "    ax.get_yaxis().tick_left()  \n",
    "\n",
    "    plt.title(df.columns[i]) \n",
    "    plt.hist(df.iloc[:,i], color=\"#3F5D7D\", bins=20)\n",
    "    plt.tight_layout() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMがぽいじゃないか、他の検定でも確認してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " RM \n",
      "Statistics=37.896, p=0.000\n",
      "Not look Gaussian\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import normaltest\n",
    "stat, p = normaltest(df.iloc[:,5])\n",
    "print(\"\", df.columns[5], \"\")\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('looks Gaussian')\n",
    "else:\n",
    "    print('Not look Gaussian')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## だめやん、っちゅーことで前回のiris使おう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "\n",
    "iris_data = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " sepal length (cm) \n",
      "Statistics=0.976, p=0.010\n",
      "Not look Gaussian\n",
      "\n",
      " sepal width (cm) \n",
      "Statistics=0.984, p=0.075\n",
      "Gaussian\n",
      "\n",
      " petal length (cm) \n",
      "Statistics=0.876, p=0.000\n",
      "Not look Gaussian\n",
      "\n",
      " petal width (cm) \n",
      "Statistics=0.903, p=0.000\n",
      "Not look Gaussian\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(iris_data.shape[1]):\n",
    "    stat, p = shapiro(iris_data.iloc[:,i])\n",
    "    print(\"\", iris_data.columns[i], \"\")\n",
    "    print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    alpha = 0.05\n",
    "    if p > alpha:\n",
    "        print('Gaussian')\n",
    "    else:\n",
    "        print('Not look Gaussian')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sepal width (cm) これ使う！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " sepal width (cm) \n",
      "mean:  3.0540000000000007\n",
      "variance  0.18800402684563763\n",
      "sample  150\n"
     ]
    }
   ],
   "source": [
    "print(\"\", iris_data.columns[1], \"\")\n",
    "print('mean: ', np.mean(iris_data.iloc[:,1]))\n",
    "print('variance ', np.var(iris_data.iloc[:,1], ddof=1))\n",
    "# 不偏分散になってる注意\n",
    "print('sample ', 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 標本平均が約3.0cmってことは母平均も3.0cmなんじゃね？\n",
    "## 母平均に対する検定\n",
    "$H_0: \\mu = 3.0$   \n",
    "$H_1: \\mu \\neq 3.0$   \n",
    "\n",
    "母分散が分からへん時は？   \n",
    "# Student t test\n",
    "$$  t = \\frac{\\bar{X} - \\mu}{ \\frac{s^2}{\\sqrt{n}} }  $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5253019083062707"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = (np.mean(iris_data.iloc[:,1]) - 3)/(np.sqrt(np.var(iris_data.iloc[:,1], ddof=1)/150))\n",
    "z # statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12459986648026626"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "p = st.t.pdf(z, df=150-1)\n",
    "p # p-value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ライブラリを使うと"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5253019083062584  0.1293036245318499\n"
     ]
    }
   ],
   "source": [
    "t,p = st.ttest_1samp(iris_data.iloc[:,1], 3)\n",
    "print(t,'',p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## なんかちょっと数値違うけどあっているはず\n",
    "$\\alpha=0.05$とすると   \n",
    "$$ \\alpha < p$$\n",
    "より$H_0$は棄却されない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kiwamizamurai/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2,2,100)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(x, st.t(150-1).pdf(x))\n",
    "plt.scatter(z,0,c='red')\n",
    "plt.grid('True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  結局、統計量tとp値って何？\n",
    "- https://research.miidas.jp/2019/01/ねぇpython、parametric-testって何？（理論編）/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "統計量tは、t検定を使うための数値   \n",
    "p値は、$H_0$の仮定のもとでのその統計量をとる確率   \n",
    "\n",
    "### 上の図に注目\n",
    "今回の統計量は**赤い点**でそれをとる確率(p-value)は0.12だった。   有意水準$(\\alpha)$　＝（ありえないのギリギリ）（仮説の閾値）（許せる範囲）   \n",
    "を$\\alpha = 0.05$とすると\n",
    "$$ 0.05 < 0.12 $$\n",
    "であるが、これは\n",
    "#### めっちゃありえなくない\n",
    "ということ、つまり、あり得る、と考えるので$H_0$を採択。   \n",
    "\n",
    "ちなみにcritical pointとはp値を使うんじゃなくて統計量で判断する時の数値、例えば両側検定をz検定で行う時に$\\alpha = 0.05$とすると   \n",
    "critical point = $\\pm 1.96  \\Longrightarrow  P(|z|>1.96) = 0.05$   になる。つまり、統計量zが$|z| > 1.96$なら$H_0$を棄却する　　　 \n",
    "\n",
    "言葉を変えると統計量がcritical pointより外側にあれば珍しすぎる現象だからありえない、という理由によって$H_0$は棄却される。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}