{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 7章 線形モデル上のバンディット問題の実装\n", "\n", "Author: hagino3000 \n", "Date: 2016-11-30\n", "\n", "「バンディット問題の理論とアルゴリズム」[1] の7章のアルゴリズムを実装して動かしてみる． \n", "報酬が正規分布のパターン \n", "\n", "- LinUCB\n", "- 線形モデル上のトンプソン抽出\n", "\n", "\n", "[1] 「バンディット問題の理論とアルゴリズム」本多淳也／中村篤祥・著 http://www.kspub.co.jp/book/detail/1529175.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 実験データ\n", "\n", "報酬はネット広告のCPMとする \n", "デザインとフォントは時刻tのコンテキスト(オーディエンス属性)に依存する，コンテキスト付きバンディット問題" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 真の報酬モデル\n", "campaigns = {\n", " 'campaign_a': 10,\n", " 'campaign_b': 13,\n", " 'campaign_c': 19,\n", " 'campaign_d': 16,\n", " 'campaign_e': 18,\n", " 'campaign_f': 18\n", "}\n", "attribute = {\n", " 'men_20': 1,\n", " 'men_30': 50,\n", " 'female_20': 8,\n", " 'female_30': 20 \n", "}\n", "# デザインとfontはユーザー属性に依存して報酬が決まる\n", "contextual_attrs = {\n", " 'men_20': {\n", " 'design': {\n", " 'a': 20,\n", " 'b': -1,\n", " 'c': 16,\n", " 'd': 17\n", " },\n", " 'font': {'a': 10, 'b': 5}\n", " },\n", " 'men_30': {\n", " 'design': {\n", " 'a': 5,\n", " 'b': -1,\n", " 'c': 3,\n", " 'd': 4\n", " },\n", " 'font': {'a': 10, 'b': -1}\n", " },\n", " 'female_20': {\n", " 'design': {\n", " 'a': -20,\n", " 'b': 40, \n", " 'c': 30,\n", " 'd': 35\n", " },\n", " 'font': {'a': 5,'b': -10}\n", " }, \n", " 'female_30': {\n", " 'design': {\n", " 'a': -10,\n", " 'b': 20, \n", " 'c': 15,\n", " 'd': 17\n", " },\n", " 'font': {'a': -5, 'b': 3}\n", " }\n", "}\n", "\n", "contextual_columns = []\n", "contextual_values = []\n", "for attr, attr_v in contextual_attrs.items():\n", " for el, el_v in attr_v.items():\n", " for type_name, value in el_v.items():\n", " contextual_columns.append(attr + '_' + el + '_' + type_name)\n", " contextual_values.append(value)\n", "\n", "actions = []\n", "for a in [campaigns.keys(), attribute.keys(), contextual_columns]:\n", " actions.extend(a)\n", "theta = []\n", "for a in [campaigns.values(), attribute.values(), contextual_values]:\n", " theta.extend(a)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['campaign_b', 'campaign_e', 'campaign_a', 'campaign_c', 'campaign_d', 'campaign_f', 'men_30', 'men_20', 'female_20', 'female_30', 'men_30_design_a', 'men_30_design_b', 'men_30_design_c', 'men_30_design_d', 'men_30_font_a', 'men_30_font_b', 'men_20_design_a', 'men_20_design_b', 'men_20_design_c', 'men_20_design_d', 'men_20_font_a', 'men_20_font_b', 'female_20_design_a', 'female_20_design_b', 'female_20_design_c', 'female_20_design_d', 'female_20_font_a', 'female_20_font_b', 'female_30_design_a', 'female_30_design_b', 'female_30_design_c', 'female_30_design_d', 'female_30_font_a', 'female_30_font_b']\n" ] } ], "source": [ "# actionの要素\n", "print(actions)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[13, 18, 10, 19, 16, 18, 50, 1, 8, 20, 5, -1, 3, 4, 10, -1, 20, -1, 16, 17, 10, 5, -20, 40, 30, 35, 5, -10, -10, 20, 15, 17, -5, 3]\n" ] } ], "source": [ "# 真の\\theta\n", "print(theta)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 真の分散\n", "var = [\n", " 64, 81, 64, 32, 32, 32, # campaign (a,b,c,d)\n", " 16, 16, 16, 16, # ユーザー属性\n", " 128, 128, 64, 10, 9, 9, # men_20\n", " 64, 64, 64, 10, 9, 9, # men_30\n", " 128, 128, 64, 10, 16, 16, # female_20\n", " 64, 64, 64, 10, 16, 16 # female_30\n", "]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "empty_row = {}\n", "for c in actions:\n", " empty_row[c] = 0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'campaign_b': 0, 'men_20': 0, 'female_20_design_c': 0, 'men_20_font_a': 0, 'female_20_design_d': 0, 'men_20_design_a': 0, 'men_20_design_d': 0, 'men_30_font_b': 0, 'men_30_design_b': 0, 'men_20_design_c': 0, 'female_20_design_a': 0, 'men_30': 0, 'men_30_design_d': 0, 'female_20_design_b': 0, 'female_30_font_b': 0, 'campaign_d': 0, 'female_30_design_d': 0, 'men_30_design_c': 0, 'men_20_font_b': 0, 'men_30_design_a': 0, 'campaign_e': 0, 'female_30_font_a': 0, 'female_30_design_a': 0, 'female_20_font_b': 0, 'campaign_f': 0, 'female_30_design_c': 0, 'men_30_font_a': 0, 'campaign_a': 0, 'female_20_font_a': 0, 'men_20_design_b': 0, 'campaign_c': 0, 'female_30': 0, 'female_30_design_b': 0, 'female_20': 0}\n" ] } ], "source": [ "print(empty_row)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ret = []\n", "for k, v in campaigns.items():\n", " cp = k\n", " cp_value = v\n", " for k, v in attribute.items():\n", " attr = k\n", " attr_value = v\n", " for k, v in contextual_attrs[attr]['design'].items():\n", " design = k\n", " design_value = v\n", " for k, v in contextual_attrs[attr]['font'].items():\n", " font = k\n", " font_value = v\n", " row = empty_row.copy()\n", " row[cp] = 1\n", " row[attr] = 1\n", " row[attr + '_design_' + design] = 1\n", " row[attr + '_font_' + font] = 1\n", " ret.append(row)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 選択可能なactionの組み合わせ" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sum(np.random.multivariate_normal(action.T*theta, cov)*action)\n", "\n", "# コンテキストの生成\n", "contexts = [k for k in attribute.keys()]\n", "def get_context(t):\n", " return contexts[t % len(contexts)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LinUCB\n", "\n", "118頁，アルゴリズム7.1\n", "\n", "入力: 誤差項の分散 $\\sigma^2$ \n", "パラメータ: $\\sigma_0^2, \\alpha > 0$\n", "\n", "$\n", "A^{-1} \\leftarrow \\frac{\\sigma^2_0}{\\sigma^2}I_d. \\ //\\ |a|*|a|の行列\\\\\n", "b \\leftarrow 0. \\ //\\ サイズ |a| のベクトル \\\\\n", "\\mathrm{for}\\ t = 1,2,....,T\\ \\mathrm{do} \\\\\n", "\\hspace{10pt}\\hat{\\theta} \\leftarrow A^{-1}b. \\\\\n", "\\hspace{10pt}//\\ 各行動iについてUCBスコアの計算，ただし \\alpha_t = \\alpha\\sqrt{logt} \\\\\n", "\\hspace{10pt}\\overline{\\mu_i} = a_{i,t}^{\\mathrm{T}}\\hat{\\theta} + \\alpha_t\\sigma\\sqrt{a_{i,t}^{\\mathrm{T}}A^{-1}a_{i,t}}\\\\\n", "\\hspace{10pt}スコア最大の行動\\ i\\ \\leftarrow\\ argmax_i\\overline{\\mu_i}_(t) を選択して報酬 X(t) を観測 \\\\\n", "\\hspace{10pt}A^{-1} \\leftarrow A^{-1} - \\frac{A^{-1}a_{i,t}^{\\mathrm{T}}A^{-1}}{1 + a_{i,t}^{\\mathrm{T}}A^{-1}a_{i,t}}. \\\\\n", "\\hspace{10pt}b \\leftarrow b + a_{i,t}X(t). \\\\\n", "\\mathrm{end\\ for}\n", "$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calc_new_a_inv(A_inv, a_i_t):\n", " return A_inv - (A_inv*(np.matrix(a_i_t).T)*(a_i_t)*A_inv)/(1 + a_i_t.dot(A_inv).dot(a_i_t))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def linucb(T=1000, input_sigma2=600, param_sigma02=300, param_alpha=10):\n", " A_inv = np.matrix((param_sigma02/input_sigma2)*np.identity(len(actions)))\n", " b = np.zeros(len(theta)) \n", "\n", " # results\n", " rewards = []\n", " for t in range(1, T):\n", " alpha_t = param_alpha*np.sqrt(np.log(t))\n", " theta_hat = A_inv.A.dot(b)\n", " context = get_context(t)\n", " ucb_scores = []\n", " selectable_actions = []\n", " for i in df_actions[df_actions[context] != 0].index.values:\n", " a_i = df_actions.iloc[i].values\n", " ucb_score = a_i.dot(theta_hat) + alpha_t*np.sqrt(input_sigma2)*np.sqrt(a_i.dot(A_inv).dot(a_i))\n", " selectable_actions.append(a_i)\n", " ucb_scores.append(ucb_score)\n", " # 時刻tにおける行動 a_{i,t}\n", " a_i_t = selectable_actions[np.array(ucb_scores).argmax()]\n", " # 時刻tに観測した報酬\n", " reward = get_reward(a_i_t)\n", " b += a_i_t*reward\n", " A_inv = calc_new_a_inv(A_inv, a_i_t)\n", " rewards.append(reward)\n", " return theta_hat, np.array(rewards), b" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "theta_hat, rewards, b = linucb(T=100)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 14.53061365, 9.78462332, 9.71007363, 14.58204499,\n", " 17.01065977, 14.69544093, 30.59600655, 13.95948132,\n", " 17.49680942, 18.26115901, 11.4884724 , 6.90732137,\n", " 5.22941458, 6.97079821, 19.30198941, 11.29401714,\n", " 10.79295043, -7.61631206, 6.48850423, 4.29433873,\n", " 9.76500159, 4.19447973, -24.58929766, 19.26854197,\n", " 10.35454461, 12.4630205 , 13.28393645, 4.21287297,\n", " -6.30122581, 8.31559702, 11.21558378, 5.03120402,\n", " 3.8246519 , 14.4365071 ])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta_hat" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 856.4513572 , 722.34134241, 736.97551954, 892.26361629,\n", " 1043.59357427, 870.56997045, 1667.47811359, 1006.72586595,\n", " 1243.82953542, 1204.1618652 , 447.58529452, 410.93608622,\n", " 397.5128319 , 411.44390094, 889.79486272, 777.68325087,\n", " 341.39186993, 119.95002905, 305.08046894, 240.30349803,\n", " 603.05227993, 403.67358602, 13.27846259, 514.6666897 ,\n", " 320.62109798, 395.26328514, 717.9124203 , 525.91711512,\n", " 212.74721487, 311.18866351, 395.3124673 , 284.91351952,\n", " 506.58255834, 697.57930686])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_linucb_test(T, trial, param_sigma02=300, param_alpha=10, plot=True):\n", " rewards_result = []\n", " loss_result = []\n", " for _ in range(trial):\n", " theta_hat, rewards, b = linucb(T=T, param_sigma02=param_sigma02, param_alpha=param_alpha)\n", " loss = sum(theta - theta_hat)\n", " rewards_result.append(rewards.cumsum())\n", " loss_result.append(loss)\n", " \n", " reward_gained_mean = np.array(rewards_result).mean(axis=0)\n", " if plot:\n", " plt.figure(figsize=(14, 2))\n", " plt.xlabel('time')\n", " plt.ylabel('Average Rewards')\n", " plt.plot(reward_gained_mean)\n", " print('Theta - ThetaHat:{}'.format(np.mean(loss_result)))\n", " return reward_gained_mean" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:63.725991464525215\n" ] }, { "data": { "image/png": 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c3MyGDRumvaEiIiJvxA4PQk+Xs3BRqHPy2AU9nRDphewsKC2HsgpMabkzJPfi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\nTbNDgV3oAmMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rewards = run_linucb_test(T=100, trial=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## パラメータチューニング" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:257.03402780757153\n", "Theta - ThetaHat:90.46384226519248\n", "Theta - ThetaHat:56.88788795419678\n" ] } ], "source": [ "rewards_1 = run_linucb_test(T=200, trial=10, param_alpha=0.5, plot=False)\n", "rewards_2 = run_linucb_test(T=200, trial=10, param_alpha=1, plot=False)\n", "rewards_3 = run_linucb_test(T=200, trial=10, param_alpha=10, plot=False)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ePUwEBZUyb14+7dqV3/aUyNshAexdqlWrViQmJlJUVITRaGT+/PlUVFRUO/2m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ADwt23btk07duxQRUWFkpKSJEnJycl69NFHTZ4MAIDriwILAICPmD17tjZs2GD2GAAAmIZTiAEA\nAAAAPoECCwCAj/D39xcnTgEAejMKLAAAPiImJkbl5eWSJI/HY/I0AABcf1wDCwCAjyguLtbq1atl\nt9s1cOBAzZs3z+yRAAC4riiwAAAAAACfwCnEAAAAAACfQIEFAAAAAPgECiwAAAAAwCdQYAEAAAAA\nPoECCwAAAADwCRRYAAAAAIBPoMACAAAAAHwCBRYAAAAA4BP+D8hbtP1D1JLHAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.plot(rewards_1, color='b', label='alpha=0.5')\n", "plt.plot(rewards_2, color='g', label='alpha=1')\n", "plt.plot(rewards_3, color='r', label='alpha=10')\n", "plt.ylabel('AVG Reward')\n", "plt.xlabel('t')\n", "plt.legend(loc='best')\n", "plt.title(u'LinUCB 獲得報酬の比較 T=100')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 正規分布モデルのThompson抽出\n", "\n", "121頁, アルゴリズム7.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "入力: 誤差項の分散 $\\sigma^2$ \n", "パラメータ: $\\sigma_0^2 > 0$\n", "\n", "$\n", "A^{-1} \\leftarrow \\frac{\\sigma^2_0}{\\sigma^2}I_d. \\ //\\ |a|*|a|の行列\\\\\n", "b \\leftarrow 0. \\ //\\ サイズ |a| のベクトル \\\\\n", "\\mathrm{for}\\ t = 1,2,....,T\\ \\mathrm{do} \\\\\n", "\\hspace{10pt}\\tilde{\\theta}\\ を多変量正規分布\\ N(A^{-1}b, \\sigma^2A^{-1})\\ から生成. \\\\\n", "\\hspace{10pt}i \\leftarrow argmax_i\\tilde{\\theta}^{\\mathrm{T}}_a{i,t} を選択して報酬 X(t) を観測. \\\\\n", "\\hspace{10pt}A^{-1} \\leftarrow A^{-1} - \\frac{A^{-1}a_{i,t}^{\\mathrm{T}}A^{-1}}{1 + a_{i,t}^{\\mathrm{T}}A^{-1}a_{i,t}}. \\\\\n", "\\hspace{10pt}b \\leftarrow b + a_{i,t}X(t). \\\\\n", "\\mathrm{end\\ for}\n", "$" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def thompson_sampling(T=1000, input_sigma2=600, param_sigma02=300):\n", " A_inv = np.matrix((param_sigma02/input_sigma2)*np.identity(len(actions)))\n", " b = np.zeros(len(theta))\n", "\n", " # results\n", " rewards = []\n", " for t in range(1, T):\n", " theta_hat = np.random.multivariate_normal(A_inv.A.dot(b), input_sigma2*A_inv)\n", " context = get_context(t)\n", " estimated_rewards = []\n", " selectable_actions = []\n", " for i in df_actions[df_actions[context] != 0].index.values:\n", " a_i = df_actions.iloc[i].values\n", " estimated_reward = a_i.T.dot(theta_hat)\n", " selectable_actions.append(a_i)\n", " estimated_rewards.append(estimated_reward)\n", " # 時刻tにおける行動 a_{i,t}\n", " a_i_t = selectable_actions[np.array(estimated_rewards).argmax()]\n", " # 時刻tに観測した報酬\n", " reward = get_reward(a_i_t)\n", " b += a_i_t*reward\n", " A_inv = calc_new_a_inv(A_inv, a_i_t)\n", " rewards.append(reward)\n", " return theta_hat, np.array(rewards), b" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_thompson_test(T, trial, param_sigma02=300, plot=True):\n", " rewards_result = []\n", " loss_result = []\n", " for _ in range(trial):\n", " theta_hat, rewards, b = thompson_sampling(T=T, param_sigma02=param_sigma02)\n", " loss = sum(theta - theta_hat)\n", " rewards_result.append(rewards.cumsum())\n", " loss_result.append(loss)\n", " \n", " reward_gained_mean = np.array(rewards_result).mean(axis=0)\n", " if plot:\n", " plt.figure(figsize=(14, 2))\n", " plt.xlabel('time')\n", " plt.ylabel('Average Rewards')\n", " plt.plot(reward_gained_mean)\n", " print('Theta - ThetaHat:{}'.format(np.mean(loss_result)))\n", " return reward_gained_mean" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# T=50でテスト\n", "theta_hat, rewards, b = thompson_sampling(50)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 177.10981489, 767.60378005, 78.26496963, 64.03909114,\n", " 670.36470162, 852.19817323, 793.22636584, 532.44309037,\n", " 683.78695961, 600.12411474, 124.37953194, 534.01684212,\n", " 70.74918421, 64.08080757, 337.00157908, 456.22478676,\n", " 276.75009065, 15.20556368, 50.06212916, 190.42530687,\n", " 263.89670732, 268.54638305, -6.97339226, 366.26848043,\n", " 210.22934029, 114.26253115, 637.75802219, 46.02893742,\n", " 120.3080005 , 188.64823895, 50.09302538, 241.07484991,\n", " 229.95235362, 370.17176112])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 全ての項から報酬が発生している (選択した) 事を確認\n", "b" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 24.07216274, 22.98629102, -18.31993822, 51.58168936,\n", " 6.51475214, 10.50800333, -2.89270074, 12.34578023,\n", " 21.90048015, 39.11116141, 19.96005306, 15.09704087,\n", " 17.13343085, -3.196844 , 26.59533384, 32.54684625,\n", " 20.84462828, 2.34040877, 14.01844919, 18.38577395,\n", " 17.68934943, 1.08709252, -6.65323407, 3.84993341,\n", " 37.35664516, 39.66359091, 3.49748522, -15.02561303,\n", " 12.23708412, -1.56656816, 2.2747278 , 13.31612231,\n", " -9.98406846, 3.80514864])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# t=50時点での\\theta_hat\n", "theta_hat" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:207.6028828218782\n" ] }, { "data": { "image/png": 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zwgsvsHDhwlhGO1zwhBG28nnxxRezefNmVqxYAcCyZcvi+rqBhC3O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\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rewards_result = run_thompson_test(T=10, trial=20)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:81.85155468519741\n" ] }, { "data": { "image/png": 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q+ituo45+4kZurNtTlunGItiJZ6I92I5W6G6H2blQOQdTWQOVNZiKGqiYg8nJ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\nFRERERERkbSioCoiIiIiIiJp5f8HTmW6gkk2XrkAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rewards_result = run_thompson_test(T=100, trial=20)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:51.01937271706911\n" ] }, { "data": { "image/png": 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hJ4fsimlkT5lOdmk5WVfOIctXRlZpgCxfGVbuxPP4RHKx2LJlC319fQBUVVVRXV2d8Xsz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\n4gj/P2SM6reH6/L8AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rewards_result = run_thompson_test(T=500, trial=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## パラメータチューニング" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:328.6442545756642\n", "Theta - ThetaHat:229.50188291634032\n", "Theta - ThetaHat:94.99269819170101\n", "Theta - ThetaHat:36.96957365265881\n", "Theta - ThetaHat:7.9246099354507695\n" ] } ], "source": [ "rewards_1 = run_thompson_test(T=200, trial=10, param_sigma02=1, plot=False)\n", "rewards_2 = run_thompson_test(T=200, trial=10, param_sigma02=10, plot=False)\n", "rewards_3 = run_thompson_test(T=200, trial=10, param_sigma02=100, plot=False)\n", "rewards_4 = run_thompson_test(T=200, trial=10, param_sigma02=700, plot=False)\n", "rewards_5 = run_thompson_test(T=200, trial=10, param_sigma02=1500, plot=False)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AbLKNxZyzaM8Ry37sMzXgSokVHSxtGGJry1e2tnhYWf1pxZQAVgghhBBCCCGe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\nVZWR06cNmEyQlmYlN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.plot(rewards_1, color='b', label='param_sigma02=1')\n", "plt.plot(rewards_2, color='g', label='param_sigma02=10')\n", "plt.plot(rewards_3, color='r', label='param_sigma02=100')\n", "plt.plot(rewards_4, color='y', label='param_sigma02=700')\n", "plt.plot(rewards_5, color='c', label='param_sigma02=1500')\n", "plt.ylabel('AVG Reward')\n", "plt.xlabel('t')\n", "plt.legend(loc='best')\n", "plt.title(u'Thonpson抽出 獲得報酬の比較 T=200')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 比較用のepsilon greedy実装" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def epsilon_greedy(T=1000, epsilon=0.1):\n", " # results\n", " rewards = []\n", " selected_counts = np.zeros(len(df_actions))\n", " gained_rewards = np.zeros(len(df_actions))\n", " for t in range(1, T):\n", " context = get_context(t)\n", " estimated_rewards = []\n", " selectable_actions = []\n", " selectable_actions_idx = []\n", " for i in df_actions[df_actions[context] != 0].index.values:\n", " a_i = df_actions.iloc[i].values\n", " estimated_reward = gained_rewards[i]/selected_counts[i]\n", " selectable_actions.append(a_i)\n", " selectable_actions_idx.append(i)\n", " estimated_rewards.append(estimated_reward)\n", " # 時刻tにおける行動 a_{i,t}\n", " if np.random.rand() > epsilon:\n", " _idx = np.array(estimated_rewards).argmax()\n", " else:\n", " # 探索\n", " _idx = np.random.randint(len(selectable_actions))\n", " a_i_t = selectable_actions[_idx]\n", " action_idx = selectable_actions_idx[_idx]\n", " # 時刻tに観測した報酬\n", " reward = get_reward(a_i_t)\n", " rewards.append(reward)\n", " selected_counts[action_idx] += 1\n", " gained_rewards[action_idx] += reward\n", "\n", " return np.array(rewards), selected_counts" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_epsilon_greedy_test(T, trial, plot=True):\n", " rewards_result = []\n", " selected_counts_result = []\n", " for _ in range(trial):\n", " rewards, selected_counts = epsilon_greedy(T=T)\n", " rewards_result.append(rewards.cumsum())\n", " selected_counts_result.append(selected_counts)\n", " \n", " reward_gained_mean = np.array(rewards_result).mean(axis=0)\n", " selected_count_mean = np.array(selected_counts_result).mean(axis=0)\n", " if plot:\n", " plt.figure(figsize=(14, 2))\n", " plt.xlabel('time')\n", " plt.ylabel('Average Rewards')\n", " plt.plot(reward_gained_mean)\n", " return reward_gained_mean, selected_count_mean" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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t23bKc7/fj9fr5fvf/36PX1teXk5RURFTp04FAgsYvfDCCzz00EOkpKSwePFi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\nAwcOHGDjxo0YY7jiiiv44Q9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, selected_count = run_epsilon_greedy_test(10, trial=20, plot=True)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, selected_count = run_epsilon_greedy_test(100, trial=20, plot=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.05, 1. , 1.05, 1.05, 1.1 , 1.05, 1. , 1.05,\n", " 1. , 1.05, 1. , 1. , 1. , 1. , 1. , 1.05, 1. ,\n", " 1. , 1. , 1.1 , 1. , 1. , 1.05, 1. , 1.05, 1.1 ,\n", " 1. , 1.05, 1. , 1.05, 1. , 1. , 1.05, 1.05, 1. ,\n", " 1. , 1. , 1. , 1. , 1.05, 1.1 , 1.05, 1. , 1.05,\n", " 1. , 1.05, 1. , 1.05, 1. , 1.05, 1. , 1.05, 1.05,\n", " 1.05, 1. , 1. , 1.05, 1. , 1. , 1.05, 1. , 1.05,\n", " 1. , 1. , 1. , 1.05, 0.95, 0.95, 0.7 , 0.6 , 0.15,\n", " 1.05, 1. , 1.05, 1. , 0.95, 0.85, 0.6 , 0.5 , 1.05,\n", " 1. , 1.05, 1. , 1. , 0.95, 0.7 , 0.65, 1. , 1. ,\n", " 0.95, 0.95, 0.95, 0.85, 0.75, 0.55, 0. , 0.05, 0.05,\n", " 0. , 0.05, 0.05, 0.05, 0.05, 0.15, 0.05, 0.05, 0.1 ,\n", " 0.15, 0. , 0.15, 0.05, 0. , 0. , 0. , 0. , 0. ,\n", " 0.2 , 0.15, 0.05, 0.25, 0.05, 0. , 0.1 , 0.05, 0.1 ,\n", " 0. , 0.05, 0. , 0.1 , 0. , 0.1 , 0.05, 0.1 , 0.1 ,\n", " 0. , 0.05, 0. , 0.05, 0. , 0. , 0.1 , 0.1 , 0.05,\n", " 0. , 0. , 0. , 0.05, 0.1 , 0.05, 0. , 0.1 , 0.05,\n", " 0.05, 0.05, 0.05, 0.05, 0.1 , 0.1 , 0.1 , 0.05, 0.05,\n", " 0.1 , 0.1 , 0. , 0.05, 0.05, 0.05, 0. , 0.1 , 0. ,\n", " 0.05, 0.15, 0.05, 0.1 , 0.1 , 0.1 , 0.05, 0.05, 0.05,\n", " 0.05, 0.05, 0. , 0.1 , 0.05, 0.05, 0.15, 0. , 0.05,\n", " 0. , 0.05, 0.1 ])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selected_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 比較" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:244.02451579349977\n", "Theta - ThetaHat:6.4405172936127455\n" ] } ], "source": [ "T=100\n", "trial=10\n", "result_ep, selected_ep = run_epsilon_greedy_test(T=T, trial=trial, plot=False)\n", "result_ucb = run_linucb_test(T=T, trial=trial, param_alpha=1, plot=False)\n", "result_thompson = run_thompson_test(T=T, trial=trial, param_sigma02=1500, plot=False)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HVZdWqSSoCiGEEEIIIURVUlWMzp373+hp48bkjBpF7ssvo1pYVHV1VUKCqhBC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JWLL0zPPvssJ06c4LnnnqO4uFg1K0EtMjKSxMRERo0ahd1uZ9asWVgsFtWtBLXGxkbe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\nIkHl/wPIq5KHNuWp4QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.plot(result_ep, color='b', label='Epsilon-Greedy')\n", "plt.plot(result_ucb, color='g', label='LinUCB')\n", "plt.plot(result_thompson, color='r', label='Thompson Sampling')\n", "plt.ylabel('AVG Reward')\n", "plt.xlabel('t')\n", "plt.legend(loc='best')\n", "plt.title(u'獲得報酬の比較 T={}'.format(T))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theta - ThetaHat:66.48742196571689\n", "Theta - ThetaHat:127.27522657399402\n" ] } ], "source": [ "T=500\n", "trial=10\n", "result_ep, selected_ep = run_epsilon_greedy_test(T=T, trial=trial, plot=False)\n", "result_ucb = run_linucb_test(T=T, trial=trial, param_alpha=1, plot=False)\n", "result_thompson = run_thompson_test(T=T, trial=trial, param_sigma02=1500, plot=False)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fMWIEAC+88IJxzqmDgwMDBw7k+++/Z+rUqbetZrxq1SrjIkxz5swhPj6en376qULP8Z0M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sfj4Y9//KMyKyNab28vzc3NrFy5kvnz51NYWHhDv9feFlOIGxoaKC0txe/34/P56Orq0oOfZURJ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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.plot(result_ep, color='b', label='Epsilon-Greedy')\n", "plt.plot(result_ucb, color='g', label='LinUCB')\n", "plt.plot(result_thompson, color='r', label='Thompson Sampling')\n", "plt.ylabel('AVG Reward')\n", "plt.xlabel('t')\n", "plt.legend(loc='best')\n", "plt.title(u'獲得報酬の比較 T={}'.format(T))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "T=100あたりで差が付くが，T=500あたりでLinUCBがThompson抽出に追いつく" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }