nonpara_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonparametric test\n",
    "- http://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_nonparametric/index.html\n",
    "- http://hs-www.hyogo-dai.ac.jp/~kawano/HStat/?plugin=cssj&page=2009%2F13th%2FSign_Test\n",
    "- https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今回はノンパラメトリックテストを行う。   \n",
    "既存のライブラリと模範解答としながら自分でも手を動かす。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mann-Whitney U Test\n",
    "データ数は20程度まで"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  B\n",
       "0   7  3\n",
       "1   5  6\n",
       "2   6  4\n",
       "3   4  2\n",
       "4  12  1"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [7, 5, 6, 4, 12],\n",
    "    'B': [3, 6, 4, 2, 1]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 注意が必要\n",
    "- https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html\n",
    "\n",
    "デフォルトではp値は両側検定で得た値の1/2でreturnされる、つまり2倍する必要がある"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=3.000, p=0.029\n",
      "Same distribution (fail to reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "stat, p = mannwhitneyu(df[\"A\"], df[\"B\"])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "alpha = 0.05\n",
    "if 2*p > alpha:\n",
    "    print('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "    print('Different distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 統計量が3、今、n_1=n_2=5なのでcritical valueを見てみよう\n",
    "ただし有意水準0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display_png\n",
    "display_png(Image(\"https://accendoreliability.com/wp-content/uploads/2014/04/critical-values-of-u-5-percent.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 棄却されなかったplotしてみよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "ig, ax = plt.subplots(figsize=(12,8))\n",
    "df.plot.kde(ax=ax, legend=False, title='Histogram: A vs. B')\n",
    "df.plot.hist(density=True, ax=ax)\n",
    "ax.set_ylabel('Probability')\n",
    "ax.grid(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# んーーー謎い、ハイ次"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sign Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B  diff\n",
      "0  85  75    10\n",
      "1  70  50    20\n",
      "2  40  50   -10\n",
      "3  65  40    25\n",
      "4  80  20    60\n",
      "5  75  65    10\n",
      "6  55  40    15\n",
      "7  20  25    -5\n",
      "number of +:  6\n",
      "number of -:  2\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'A': [85, 70, 40, 65, 80, 75, 55, 20],\n",
    "    'B': [75, 50, 50, 40, 20, 65, 40, 25]\n",
    "})\n",
    "df['diff'] = df[\"A\"] - df[\"B\"]\n",
    "print(df)\n",
    "print(\"number of +: \", len(df)-sum(df['diff']<0))\n",
    "print(\"number of -: \", sum(df['diff']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value\n",
      "one-tail-Probability: 0.14453125000000006\n",
      "two-tail-Probability: 0.2890625000000001\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "print(\"p-value\")\n",
    "print(\"one-tail-Probability:\" , sum(binom.pmf([0,1,2], 8, p=0.5)))\n",
    "print(\"two-tail-Probability:\" , 2*sum(binom.pmf([0,1,2], 8, p=0.5)))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 有意水準0.05とするとどちらの場合もp値>$\\alpha$より棄却しない"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wilcoxon Signed Rank Test\n",
    "こいつを書くのがめんどかった、、、、、"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=4.000, p=0.049\n",
      "Different distribution (reject H0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Jin/anaconda/envs/py35/lib/python3.5/site-packages/scipy/stats/morestats.py:2388: UserWarning: Warning: sample size too small for normal approximation.\n",
      "  warnings.warn(\"Warning: sample size too small for normal approximation.\")\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import wilcoxon\n",
    "\n",
    "stat, p = wilcoxon(df[\"A\"], df[\"B\"])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "    print('Different distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データ数が小さいので中心極限定理が使えないというwarningがでてる、なぜだ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "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>index</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   A   B  diff\n",
       "0      7  20  25    -5\n",
       "1      0  85  75    10\n",
       "2      2  40  50   -10\n",
       "3      5  75  65    10\n",
       "4      6  55  40    15\n",
       "5      1  70  50    20\n",
       "6      3  65  40    25\n",
       "7      4  80  20    60"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s = df.reindex(df[\"diff\"].abs().sort_values().index)\n",
    "df_s = df_s.reset_index()\n",
    "df_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "絶対値でかぶりを確認する、なぜならrankをassignするときに平均値になるから"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5     1\n",
      "10    3\n",
      "15    1\n",
      "20    1\n",
      "25    1\n",
      "60    1\n",
      "Name: diff, dtype: int64\n",
      "[1 4 5 6 7 8]\n"
     ]
    }
   ],
   "source": [
    "vs = abs(df_s[\"diff\"]).value_counts()\n",
    "vs = vs.sort_index()\n",
    "print(vs)\n",
    "print(np.cumsum([vs]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rankのcolumnを作ろう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "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>index</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>diff</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>-10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   A   B  diff  rank\n",
       "0      7  20  25    -5     1\n",
       "1      0  85  75    10     3\n",
       "2      2  40  50   -10     3\n",
       "3      5  75  65    10     3\n",
       "4      6  55  40    15     5\n",
       "5      1  70  50    20     6\n",
       "6      3  65  40    25     7\n",
       "7      4  80  20    60     8"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s[\"rank\"] = 0\n",
    "num = 0\n",
    "for i in range(len(vs)):\n",
    "    if vs.iloc[i] == 1:\n",
    "        df_s[\"rank\"][num] = np.cumsum(np.array(vs)[:i+1])[i]\n",
    "        num += 1\n",
    "    else:\n",
    "        for j in range(vs.iloc[i]):\n",
    "            df_s[\"rank\"][num] = sum(range(np.cumsum(np.array(vs)[:i+1])[i-1]+1, np.cumsum(np.array(vs)[:i+1])[i]+1))/vs.iloc[i]\n",
    "            num += 1\n",
    "df_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "統計量 W はプラマイの小さい方をとる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " -:  4 \n",
      " +:  32\n"
     ]
    }
   ],
   "source": [
    "W_minus = 0\n",
    "W_plus = 0\n",
    "for i in range(len(df_s)):\n",
    "    if df_s[\"diff\"][i] < 0:\n",
    "        W_minus += df_s[\"rank\"][i]\n",
    "    else:\n",
    "        W_plus += df_s[\"rank\"][i]\n",
    "print(\" -: \", W_minus, \"\\n\", \"+: \", W_plus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 下のpipは無視していい、pdfをwandというやつでplotしようと思ったけど無理だった"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: pip in /Users/Jin/.pyenv/versions/anaconda3-4.1.0/lib/python3.6/site-packages (18.1)\n",
      "Requirement already satisfied: Wand in /Users/Jin/.pyenv/versions/anaconda3-4.1.0/lib/python3.6/site-packages (0.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip \n",
    "!pip install Wand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"800\"\n",
       "            src=\"https://math.ucalgary.ca/files/math/wilcoxon_signed_rank_table.pdf\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1a251234a8>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(\"https://math.ucalgary.ca/files/math/wilcoxon_signed_rank_table.pdf\", width=600, height=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\alpha=0.05$、片側検定で$4\\leq 6$より棄却"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 手で書くと少ししんどかった。wandってやつも使えんかった"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py35]",
   "language": "python",
   "name": "conda-env-py35-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}