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tok41/05_dice_test_chi2.ipynb

Created July 3, 2022 14:17
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05_dice_test_chi2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "7eb88410-fcf0-4ca0-ada2-df424b2bdad9",
"metadata": {},
"source": [
"# About\n",
"\n",
"サイコロ出目の差異を検定する"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b95fa87b-7c5d-45d4-a841-4626bd3268cd",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import collections\n",
"import scipy\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set(font_scale=1.5)\n",
"c_list = sns.color_palette().as_hex()\n",
"color_num = len(c_list)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3b2bc2dd-fc56-4901-94c9-c811f32195f7",
"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>player</th>\n",
" <th>remainings</th>\n",
" <th>dice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ガキ</td>\n",
" <td>28.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ガキ</td>\n",
" <td>28.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ようきひ</td>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ようきひ</td>\n",
" <td>28.0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>さくま</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" player remainings dice\n",
"0 ガキ 28.0 3\n",
"1 ガキ 28.0 3\n",
"2 ようきひ 28.0 4\n",
"3 ようきひ 28.0 5\n",
"4 さくま 28.0 1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_path = \"../data/game_stat1_melt.csv\"\n",
"df = pd.read_csv(data_path)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3b5d8e75-38d4-463c-8d0c-5b726e2cfa9d",
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"6\" halign=\"left\">remainings</th>\n",
" <th>total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>dice</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th>player</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>さくま</th>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" <td>15</td>\n",
" <td>12</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ようきひ</th>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ガキ</th>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" remainings total\n",
"dice 1 2 3 4 5 6 \n",
"player \n",
"さくま 13 11 13 12 15 12 76\n",
"ようきひ 8 7 6 15 14 11 61\n",
"ガキ 11 13 12 7 5 8 56"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp = df.groupby([\"player\", \"dice\"]).count().unstack()\n",
"df_summary = tmp.assign(total=tmp.sum(axis=1))\n",
"df_summary"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d5e8e1f1-ab77-4cec-b8c0-a9c834f42c58",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 4))\n",
"ax = fig.subplots(1, 1)\n",
"df_summary[\"remainings\"].plot.bar(ax=ax);\n",
"plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left');\n",
"ax.xaxis.set_ticklabels([]);"
]
},
{
"cell_type": "markdown",
"id": "d857cf29-1305-400e-9a75-f9a499c33923",
"metadata": {},
"source": [
"サイコロ出目の大小で２分する"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "02a343da-b7a9-427e-ad5e-381d3fb7ffa6",
"metadata": {},
"outputs": [],
"source": [
"df2 = df.assign(is_large=df.dice>3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "32bf1083-12bf-4dad-8f0e-ec9fdba82953",
"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>is_large</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" </tr>\n",
" <tr>\n",
" <th>player</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>さくま</th>\n",
" <td>37</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ようきひ</th>\n",
" <td>21</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ガキ</th>\n",
" <td>36</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"is_large False True\n",
"player \n",
"さくま 37 39\n",
"ようきひ 21 40\n",
"ガキ 36 20"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_summary = df2.groupby([\"player\", \"is_large\"]).count()[\"dice\"].unstack()\n",
"df_summary"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c57ea1d0-a94a-4824-85d8-28bdb42a238c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 4))\n",
"ax = fig.subplots(1, 1)\n",
"df_summary.plot.bar(ax=ax);\n",
"plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left');\n",
"ax.xaxis.set_ticklabels([]);\n",
"ax.set_title(\"is large\");"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a82e0c5a-5d3d-4c78-aaf1-7ce3ff3aef60",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([37, 39])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([38., 38.])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=0.05263157894736842, pvalue=0.8185458083820435)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# さくま\n",
"dice_cnt = df_summary.loc[\"さくま\"].values\n",
"exp = (np.ones(2) / 2) * sum(dice_cnt)\n",
"\n",
"display(dice_cnt)\n",
"display(exp)\n",
"\n",
"scipy.stats.chisquare(dice_cnt, f_exp=exp)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "281c8658-8b64-4018-9521-dcbf7019741a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([21, 40])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([30.5, 30.5])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=5.918032786885246, pvalue=0.014986682498962593)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ようきひ\n",
"dice_cnt = df_summary.loc[\"ようきひ\"].values\n",
"exp = (np.ones(2) / 2) * sum(dice_cnt)\n",
"\n",
"display(dice_cnt)\n",
"display(exp)\n",
"\n",
"scipy.stats.chisquare(dice_cnt, f_exp=exp)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "97b1b5b9-8fb6-4263-91e4-fa7420048d26",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([36, 20])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([28., 28.])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=4.571428571428571, pvalue=0.03250944464571958)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ガキ\n",
"dice_cnt = df_summary.loc[\"ガキ\"].values\n",
"exp = (np.ones(2) / 2) * sum(dice_cnt)\n",
"\n",
"display(dice_cnt)\n",
"display(exp)\n",
"\n",
"scipy.stats.chisquare(dice_cnt, f_exp=exp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b29be62-4f9c-420d-8321-89687f97f2e8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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