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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/wakita/d01456f5a71f2554ce2d26fbfc04fef2/analyze.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "dW5-k-vFnBeH"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"source": [
"import re\n",
"\n",
"SHARED_URLS = ['https://docs.google.com/spreadsheets/d/1-XoYQ9Sg5ig7HjTjD6etH1mS8itWGLE6/edit?usp=sharing&ouid=109324711459999350423&rtpof=true&sd=true',\n",
" 'https://docs.google.com/spreadsheets/d/1yEw1mb_oEYOGrXxRPdNeMxLXOBbeaMZS/edit?usp=sharing&ouid=109324711459999350423&rtpof=true&sd=true']\n",
"\n",
"Optimal, Score = [ pd.read_excel(re.compile('/edit?.').split(url)[0] + '/export?format=xlsx') for url in SHARED_URLS]\n",
"display(Optimal, Score)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 830
},
"id": "LYg3EI7enCpr",
"outputId": "61da461f-010d-47db-9b6c-fc8eddf2dd21"
},
"execution_count": 24,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" Greedy ε-Greedy (ε=0.01) ε-Greedy (ε=0.1) Random\n",
"0 0.108 0.108 0.096 0.092\n",
"1 0.148 0.158 0.126 0.112\n",
"2 0.184 0.196 0.154 0.086\n",
"3 0.212 0.228 0.170 0.106\n",
"4 0.226 0.244 0.202 0.092\n",
".. ... ... ... ...\n",
"995 0.360 0.620 0.814 0.076\n",
"996 0.360 0.618 0.804 0.126\n",
"997 0.360 0.618 0.802 0.100\n",
"998 0.360 0.614 0.816 0.112\n",
"999 0.360 0.610 0.800 0.068\n",
"\n",
"[1000 rows x 4 columns]"
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Greedy</th>\n",
" <th>ε-Greedy (ε=0.01)</th>\n",
" <th>ε-Greedy (ε=0.1)</th>\n",
" <th>Random</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.108</td>\n",
" <td>0.108</td>\n",
" <td>0.096</td>\n",
" <td>0.092</td>\n",
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" <tr>\n",
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" <td>0.184</td>\n",
" <td>0.196</td>\n",
" <td>0.154</td>\n",
" <td>0.086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.212</td>\n",
" <td>0.228</td>\n",
" <td>0.170</td>\n",
" <td>0.106</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>0.226</td>\n",
" <td>0.244</td>\n",
" <td>0.202</td>\n",
" <td>0.092</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>995</th>\n",
" <td>0.360</td>\n",
" <td>0.620</td>\n",
" <td>0.814</td>\n",
" <td>0.076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>0.360</td>\n",
" <td>0.618</td>\n",
" <td>0.804</td>\n",
" <td>0.126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.360</td>\n",
" <td>0.618</td>\n",
" <td>0.802</td>\n",
" <td>0.100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>0.360</td>\n",
" <td>0.614</td>\n",
" <td>0.816</td>\n",
" <td>0.112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>0.360</td>\n",
" <td>0.610</td>\n",
" <td>0.800</td>\n",
" <td>0.068</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 4 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-920f4151-f709-4553-a18e-7638f842b478')\"\n",
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"\n",
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" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
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" gap: 12px;\n",
" }\n",
"\n",
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"\n",
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"\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-920f4151-f709-4553-a18e-7638f842b478');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"\n",
"\n",
"<div id=\"df-25d454aa-98de-40f2-a178-8382bf1390ff\">\n",
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"\n",
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"\n",
" [theme=dark] .colab-df-quickchart {\n",
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"\n",
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" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
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"\n",
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" 40% {\n",
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"</style>\n",
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" async function quickchart(key) {\n",
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" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
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"</div>\n",
"\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "Optimal",
"summary": "{\n \"name\": \"Optimal\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"Greedy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.016307330530439965,\n \"min\": 0.108,\n \"max\": 0.36,\n \"num_unique_values\": 34,\n \"samples\": [\n 0.324,\n 0.332,\n 0.348\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\u03b5-Greedy (\\u03b5=0.01)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07373276528570379,\n \"min\": 0.108,\n \"max\": 0.622,\n \"num_unique_values\": 145,\n \"samples\": [\n 0.474,\n 0.616,\n 0.386\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\u03b5-Greedy (\\u03b5=0.1)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.11740849974261568,\n \"min\": 0.096,\n \"max\": 0.832,\n \"num_unique_values\": 211,\n \"samples\": [\n 0.372,\n 0.766,\n 0.684\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Random\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.013921006803763507,\n \"min\": 0.056,\n \"max\": 0.144,\n \"num_unique_values\": 43,\n \"samples\": [\n 0.06,\n 0.08,\n 0.124\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
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},
{
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"data": {
"text/plain": [
" Greedy ε-Greedy (ε=0.01) ε-Greedy (ε=0.1) Random\n",
"0 0.021231 0.067631 -0.045738 0.060837\n",
"1 0.250444 0.323660 0.230938 0.076302\n",
"2 0.467238 0.466118 0.519590 0.006162\n",
"3 0.519858 0.527568 0.505870 0.046655\n",
"4 0.636244 0.595820 0.519846 -0.011775\n",
".. ... ... ... ...\n",
"995 0.979005 1.329103 1.301382 -0.037785\n",
"996 0.988799 1.321482 1.341733 0.102610\n",
"997 1.067629 1.318242 1.393244 0.044101\n",
"998 1.015660 1.278874 1.406476 -0.059169\n",
"999 1.023465 1.372427 1.369495 -0.079574\n",
"\n",
"[1000 rows x 4 columns]"
],
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" <th>0</th>\n",
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" <td>0.060837</td>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <tr>\n",
" <th>995</th>\n",
" <td>0.979005</td>\n",
" <td>1.329103</td>\n",
" <td>1.301382</td>\n",
" <td>-0.037785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>0.988799</td>\n",
" <td>1.321482</td>\n",
" <td>1.341733</td>\n",
" <td>0.102610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>1.067629</td>\n",
" <td>1.318242</td>\n",
" <td>1.393244</td>\n",
" <td>0.044101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>1.015660</td>\n",
" <td>1.278874</td>\n",
" <td>1.406476</td>\n",
" <td>-0.059169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>1.023465</td>\n",
" <td>1.372427</td>\n",
" <td>1.369495</td>\n",
" <td>-0.079574</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 4 columns</p>\n",
"</div>\n",
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"\n",
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" padding: 0 0 0 0;\n",
" width: 32px;\n",
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" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-286ddb27-4785-425c-a550-6827b9c9c6b8\">\n",
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"\n",
" .colab-df-quickchart {\n",
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" border: none;\n",
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" display: none;\n",
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" padding: 0;\n",
" width: 32px;\n",
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"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
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"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
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" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
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"</style>\n",
"\n",
" <script>\n",
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" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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" } catch (error) {\n",
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" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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"</div>\n",
"\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "Score",
"summary": "{\n \"name\": \"Score\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"Greedy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06807006866196508,\n \"min\": 0.0212305136017403,\n \"max\": 1.15579571008189,\n \"num_unique_values\": 1000,\n \"samples\": [\n 1.06975494580924,\n 1.08650327111251,\n 0.964748249871887\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\u03b5-Greedy (\\u03b5=0.01)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.11987737444989936,\n \"min\": 0.0676308873776483,\n \"max\": 1.42798628846677,\n \"num_unique_values\": 1000,\n \"samples\": [\n 1.21239096536414,\n 1.27270281889939,\n 1.2929040857902\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"\\u03b5-Greedy (\\u03b5=0.1)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12672994391245015,\n \"min\": -0.0457383279485781,\n \"max\": 1.49702769308564,\n \"num_unique_values\": 1000,\n \"samples\": [\n 1.30588228144363,\n 1.31476371915389,\n 1.42747755532021\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Random\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.062303591664066814,\n \"min\": -0.166921614144505,\n \"max\": 0.204812142666449,\n \"num_unique_values\": 1000,\n \"samples\": [\n 0.0778737181661815,\n 0.0154042725267808,\n -0.0532084488138831\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {}
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]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "43aExswlnBeK",
"outputId": "b4085b0c-1cc2-46ce-bf77-98dc916071d4"
},
"outputs": [
{
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],
"source": [
"fig = px.line(Optimal, x=Optimal.index, y=Optimal.columns, title='最適アクションの選択率の変化')\n",
"fig.update_layout(xaxis_title='試行数', yaxis_title='選択率')"
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},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
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"height": 542
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"id": "22PbdyJjnBeK",
"outputId": "a6057faa-7193-4812-a2b3-cc7d3bab03e7"
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"outputs": [
{
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"source": [
"fig = px.line(Score, x=Score.index, y=Score.columns, title='スコアの変化')\n",
"fig.update_layout(xaxis_title='試行数', yaxis_title='スコア')"
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},
{
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Raw
Optimal.csv
Greedy ε-Greedy (ε=0.01) ε-Greedy (ε=0.1) Random
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Raw
Score.csv
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