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ritvikmath
GitHub
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348639903f
commit
c7918ac389
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from random import choice"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Restaurant:\n",
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" def __init__(self, mu, dev):\n",
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" self.mu = mu\n",
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" self.dev = dev\n",
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" def sample(self):\n",
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" return np.random.normal(self.mu, self.dev)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 91,
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"metadata": {},
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"outputs": [],
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"source": [
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"def explore_only(candidates, num_days):\n",
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" scores = []\n",
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" for _ in range(num_days):\n",
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" scores.append(choice(candidates).sample())\n",
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" return sum(scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 92,
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"metadata": {},
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"outputs": [],
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"source": [
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"def exploit_only(candidates, num_days):\n",
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" scores = [c.sample() for c in candidates]\n",
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" chosen = candidates[np.argmax(scores)]\n",
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" for _ in range(num_days - len(candidates)):\n",
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" scores.append(chosen.sample())\n",
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" return sum(scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 93,
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"metadata": {},
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"outputs": [],
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"source": [
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"def epsilon_greedy(candidates, num_days, epsilon=0.05):\n",
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" scores = []\n",
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" history = {idx: [c.sample()] for idx,c in enumerate(candidates)}\n",
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" for _ in range(num_days - len(candidates)):\n",
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" p = np.random.random()\n",
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" #explore\n",
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" if p < epsilon:\n",
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" chosen = choice(candidates)\n",
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" #exploit\n",
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" else:\n",
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" chosen = candidates[sorted(history.items(), key=lambda pair: np.mean(pair[1]))[-1][0]]\n",
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" score = chosen.sample()\n",
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" scores.append(score)\n",
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" history[candidates.index(chosen)].append(score)\n",
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" return sum(scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 94,
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"metadata": {},
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"outputs": [],
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"source": [
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"def ucb1(candidates, num_days):\n",
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" scores = []\n",
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" history = {idx: [c.sample()] for idx,c in enumerate(candidates)}\n",
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" for t in range(len(candidates), num_days):\n",
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" mu_plus_ucb = [np.mean(history[idx]) + np.sqrt(2*np.log(t) / len(history[idx])) for idx in range(len(candidates))]\n",
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" chosen = candidates[np.argmax(mu_plus_ucb)]\n",
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" \n",
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" score = chosen.sample()\n",
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" scores.append(score)\n",
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" history[candidates.index(chosen)].append(score)\n",
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" return sum(scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 171,
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"metadata": {},
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"outputs": [],
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"source": [
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"dev_factor = 0.5\n",
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"num_restaurants = 3\n",
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"\n",
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"mu_vals = [3*i for i in range(1,num_restaurants+1)]\n",
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"dev_vals = [mu*dev_factor for mu in mu_vals]\n",
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"mu_dev_pairs = zip(mu_vals, dev_vals)\n",
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"\n",
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"candidates = [Restaurant(mu,dev) for mu,dev in mu_dev_pairs]\n",
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"\n",
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"num_days = 300\n",
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"\n",
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"optimal_average = max(mu_vals)*num_days"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 172,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Explore Only Mean Regret: 0.33400345242040025\n"
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]
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}
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],
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"source": [
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"explore_only_vals = []\n",
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"for _ in range(1000):\n",
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" val = explore_only(candidates, num_days)\n",
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" explore_only_vals.append(val)\n",
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"print('Explore Only Mean Regret: %s'%((optimal_average - np.mean(explore_only_vals)) / optimal_average))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 173,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Exploit Only Mean Regret: 0.10974979914722435\n"
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]
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}
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],
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"source": [
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"exploit_only_vals = []\n",
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"for _ in range(1000):\n",
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" val = exploit_only(candidates, num_days)\n",
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" exploit_only_vals.append(val)\n",
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"print('Exploit Only Mean Regret: %s'%((optimal_average - np.mean(exploit_only_vals)) / optimal_average))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 174,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epsilon Greedy Mean Regret (10%): 0.061901290618584424\n"
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]
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}
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],
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"source": [
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"epsilon_greedy_vals = []\n",
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"for _ in range(1000):\n",
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" val = epsilon_greedy(candidates, num_days, 0.1)\n",
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" epsilon_greedy_vals.append(val)\n",
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"print('Epsilon Greedy Mean Regret (10%%): %s'%((optimal_average - np.mean(epsilon_greedy_vals)) / optimal_average))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 175,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"UCB1 Mean Regret: 0.05807450789812113\n"
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]
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}
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],
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"source": [
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"ucb1_vals = []\n",
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"for _ in range(1000):\n",
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" val = ucb1(candidates, num_days)\n",
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" ucb1_vals.append(val)\n",
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"print('UCB1 Mean Regret: %s'%((optimal_average - np.mean(ucb1_vals)) / optimal_average))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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