{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Offline learning with a loan pricing example\n", "\n", "Rarely do we start using a multi-armed bandit in a situation where we have absolutely zero historical data. While we can set our prior via a heuristic, we could also directly use that data with a variety of off-policy learning strategies to build an agent with a historically-justified prior. \"Off-policy learning\" means that we have historical actions and rewards that were generated independently of our agent. This policy might have been a random policy, a suboptimal policy or sometimes even an unknown one.\n", "\n", "While learning from off-line data can be more challenging, it can be a good way to give your contextual bandit a warm start.\n", "\n", "## Loan pricing example\n", "\n", "Your task is to learn the best interest rate to offer to a customer.\n", "There are three main components to your profits\n", "\n", "+ Conversion rate: the probability that a customer will convert to a loan\n", "+ Cost of risk (CoR): the cost of the risk of defaulting on the loan\n", "+ Revenue: total income generated from the customer. This includes the amount of the loan, fees, etc.\n", "\n", "This is a simplification, but we can write the expected profit as\n", "\n", "$$ p(conversion) * ( revenue - CoR ) $$\n", "\n", "In principle, the interest rate affects all three components, but in this example we'll only consider its effect on the conversion rate and cost of risk. In the `oracle` class below, you can see that each arm (interest rate) has a different effect on the conversion rate and a different cost of risk.\n", "\n", "The context are the features of the loan and the customer. The task of the contextual bandit is to learn the best interest rate to offer to each context.\n", "\n", "+ *Context*: In this example, we use a single feature: the credit score of the customer. The credit score goes from -5 to 5\n", "+ *Actions / arms*: We have two actions (interest rates) to choose from: 10% and 15%\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from matplotlib import style\n", "from numpy.typing import NDArray\n", "from scipy.special import expit\n", "\n", "style.use(\"fivethirtyeight\")\n", "\n", "rng = np.random.default_rng(111)\n", "\n", "\n", "class LoanOracle:\n", " def __init__(self, rng: np.random.Generator = rng):\n", " self.context = None\n", " self.rewards = []\n", " self.optimal_rewards = []\n", " self.expected_rewards = [] # p of the article chosen for the given context\n", " self.rng = rng\n", "\n", " self.true_coefs = np.array([[1, 0.2], [1, 0.15]])\n", " self.cost_of_risk = np.array([0.5, 0.3])\n", "\n", " # Action 1 (interest rate 10%) doesn't have any effect on conversion rate\n", " # Action 2 (interest rate 15%) reduces conversion rate by 0.5\n", " self.conv_effect_of_interest_rate = np.array([0, -0.5])\n", "\n", " def set_context(self, context: NDArray[np.float64]):\n", " self.context = context\n", "\n", " def generate_reward(\n", " self,\n", " _coef: NDArray[np.float64],\n", " _cost_of_risk: float,\n", " _conv_effect_of_interest_rate: float,\n", " ) -> int:\n", " assert self.context is not None\n", "\n", " # Probability of conversion\n", " p_conversion = expit(\n", " np.dot(self.context, _coef) + _conv_effect_of_interest_rate\n", " )\n", " conversion = rng.binomial(1, p_conversion)\n", "\n", " reward = conversion * (np.dot(self.context, _coef) - _cost_of_risk)\n", " expected_reward = p_conversion * (np.dot(self.context, _coef) - _cost_of_risk)\n", " self.rewards.append(reward)\n", " self.expected_rewards.append(expected_reward)\n", " return reward\n", "\n", " def best_expected_reward(self):\n", " assert self.context is not None\n", "\n", " p_conversions = expit(\n", " np.dot(self.context, self.true_coefs.T) + self.conv_effect_of_interest_rate\n", " )\n", " expected_rewards = p_conversions * (\n", " np.dot(self.context, self.true_coefs.T) - self.cost_of_risk\n", " )\n", " self.optimal_rewards.append(np.max(expected_rewards))\n", "\n", " def interest_rate_10(self):\n", " \"\"\"\n", " 10% interest rate\n", " Works well for contexts where credit score is below ~1.3\n", " \"\"\"\n", " return self.generate_reward(\n", " self.true_coefs[0],\n", " self.cost_of_risk[0],\n", " self.conv_effect_of_interest_rate[0],\n", " )\n", "\n", " def interest_rate_15(self):\n", " \"\"\"\n", " 15% interest rate\n", " Works well for contexts where credit score is above ~2\n", " \"\"\"\n", " return self.generate_reward(\n", " self.true_coefs[1],\n", " self.cost_of_risk[1],\n", " self.conv_effect_of_interest_rate[1],\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing the context and optimal action / arm\n", "\n", "We'll construct the expected rewards for both actions over the context space (credit score of the customer from -5 to 5). We'll plot from -10 to 10, but in the simulation we'll use values between -5 and 5." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expected_rewards_for_action_1 = []\n", "expected_rewards_for_action_2 = []\n", "credit_scores = np.linspace(-10, 10, 100)\n", "oracle = LoanOracle()\n", "\n", "for credit_score in credit_scores:\n", " context = np.array([1.0, credit_score])\n", "\n", " p_conversion_1 = expit(\n", " np.dot(context, oracle.true_coefs[0]) + oracle.conv_effect_of_interest_rate[0]\n", " )\n", " expected_reward_1 = p_conversion_1 * (\n", " np.dot(context, oracle.true_coefs[0]) - oracle.cost_of_risk[0]\n", " )\n", " expected_rewards_for_action_1.append(expected_reward_1)\n", "\n", " p_conversion_2 = expit(\n", " np.dot(context, oracle.true_coefs[1]) + oracle.conv_effect_of_interest_rate[1]\n", " )\n", " expected_reward_2 = p_conversion_2 * (\n", " np.dot(context, oracle.true_coefs[1]) - oracle.cost_of_risk[1]\n", " )\n", " expected_rewards_for_action_2.append(expected_reward_2)\n", "\n", "# Find intersection point where action 1 becomes better than action 2\n", "diff = np.array(expected_rewards_for_action_1) - np.array(expected_rewards_for_action_2)\n", "intersection_idx = np.where(np.diff(np.signbit(diff)))[0][0]\n", "\n", "action_intersect = float(credit_scores[intersection_idx])\n", "\n", "plt.plot(credit_scores, expected_rewards_for_action_1, label=\"action 1\")\n", "plt.plot(credit_scores, expected_rewards_for_action_2, label=\"action 2\")\n", "plt.xlabel(\"Credit score\")\n", "plt.ylabel(\"Expected reward\")\n", "plt.axvline(\n", " x=action_intersect,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=f\"Intersection at x = {action_intersect:.2f}\",\n", ")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from enum import Enum\n", "\n", "\n", "class InterestRate(Enum):\n", " interest_rate_10 = 1\n", " interest_rate_15 = 2\n", "\n", " def take_action(self, oracle: LoanOracle):\n", " if self == InterestRate.interest_rate_10:\n", " return oracle.interest_rate_10()\n", " elif self == InterestRate.interest_rate_15:\n", " return oracle.interest_rate_15()\n", " else:\n", " raise ValueError(\"invalid param\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using online data\n", "\n", "First we'll use online data (i.e., we will start with a weak prior and learn / update while we are taking actions).\n", "\n", "We set up a `ContextualAgent` with a `UpperConfidenceBound` policy and a `NormalInverseGammaRegressor` learner and two arms.\n", "\n", "\n", "In the simulation, besides the regret, we'll also record the context and the actions taken to plot them later.\n", "\n", "Where does `true_reward_mean = 0.45`come from? It's better if the reward update is centered in 0 because of how `NormalInverseGammaRegressor()` builds its prior. In real life, we wouldn't know what this true reward mean is in advance, but we can simulate and get a number that is at least within the right scale.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from bayesianbandits import (\n", " Arm,\n", " ContextualAgent,\n", " NormalInverseGammaRegressor,\n", " UpperConfidenceBound,\n", ")\n", "\n", "arms = [\n", " Arm(InterestRate.interest_rate_10, learner=NormalInverseGammaRegressor()),\n", " Arm(InterestRate.interest_rate_15, learner=NormalInverseGammaRegressor()),\n", "]\n", "\n", "agent = ContextualAgent(\n", " arms=arms,\n", " policy=UpperConfidenceBound(0.96),\n", " random_seed=rng,\n", ")\n", "\n", "# Simulation starts\n", "####################\n", "\n", "oracle = LoanOracle()\n", "\n", "true_reward_mean = 0.45\n", "\n", "user_credit_scores = []\n", "actions = []\n", "\n", "for _ in range(3000):\n", " # user_credit_score is a float between -5 and 5\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " agent.update(\n", " np.atleast_2d(context), np.atleast_1d(oracle.rewards[-1] - true_reward_mean)\n", " )\n", "\n", " user_credit_scores.append(user_credit_score)\n", " actions.append(action.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cumulative regret is sublinear on most simulation runs. Bear in mind that we are approximating a highly non-linear function (the true data generating process of the expected reward) with a linear one (the linear model that LinUCB uses through our `NormalInverseGammaRegressor` learners).\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "linucb_regret = np.cumsum(\n", " np.array(oracle.optimal_rewards) - np.array(oracle.expected_rewards)\n", ")\n", "linucb_regret_at_1000 = linucb_regret[1000]\n", "plt.plot(linucb_regret, label=\"LinUCB\")\n", "plt.xlabel(\"Round\")\n", "plt.ylabel(\"Cumulative Regret\")\n", "\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above, we found an intersection where the interest rate 10% becomes better than the 15% interest rate. If the bandit learnt correctly, we should see this interest rate more often after the intersection.\n", "\n", "The plot below shows this is the case. This is particularly interesting since we are using a linear model to approximate a highly non-linear data generating process.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "action_binary = np.array([1 if a == \"interest_rate_15\" else 0 for a in actions])\n", "\n", "bin_edges = np.linspace(-5, 5, 25)\n", "bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", "\n", "# Calculate proportion of 1s for each bin\n", "proportions = []\n", "for i in range(len(bin_edges) - 1):\n", " mask = (user_credit_scores >= bin_edges[i]) & (\n", " user_credit_scores < bin_edges[i + 1]\n", " )\n", " bin_values = action_binary[mask]\n", " proportion = np.mean(bin_values) if len(bin_values) > 0 else 0\n", " proportions.append(proportion)\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.bar(bin_centers, proportions, width=np.diff(bin_edges)[0])\n", "plt.axvline(\n", " x=action_intersect,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=f\"Threshold at {action_intersect:.2f}\",\n", ")\n", "plt.xlabel(\"Credit score\")\n", "plt.ylabel(\"Proportion of 15% interest rate\")\n", "plt.title(\"Interest Rate Selection by Credit Score\")\n", "plt.legend()\n", "plt.grid(True, alpha=0.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we are using the actions from t = 0. If you plot the actions from, say, t = 1000 onwards, you'd see that the histogram would have a sharp cut at the threshold. In other words, the agent has learnt the best action to take for each context and barely makes mistakes (or explores) after that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Offline learning with a random uniform policy\n", "\n", "We'll first simulate offline data. We'll use a random uniform policy, which shouldn't result in an agent with too much behavioral bias, as it's essentially getting a simple random sample of measurements of each arm. For a different policy, if you want an unbiased estimate, you should use some technique that corrects it, such as inverse propensity scoring, doubly robust estimation, etc.\n", "\n", "We will save the actions and contexts. We can get the rewards later from the oracle. There won't be any agent updates, since we're not doing online learning.\n", "\n", "\n", "We'll write a `simulate_offline_data` function that takes a `policy` as an object to make it clear that this \"off-line\" data is always created by some sort of policy, even if it's one unrelated to the contextual bandit we'll use online. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def simulate_offline_data(oracle, time_periods, policy):\n", " contexts = []\n", " actions = []\n", "\n", " for _ in range(time_periods):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " action = policy()\n", " action.take_action(oracle)\n", "\n", " contexts.append(context)\n", " actions.append(action)\n", "\n", " return contexts, actions\n", "\n", "\n", "def plot_cumulative_regret(regret, title, comparison_regret=linucb_regret_at_1000):\n", " plt.plot(regret)\n", " plt.xlabel(\"Round\")\n", " plt.ylabel(\"Cumulative Regret\")\n", " plt.title(title)\n", " plt.axhline(\n", " y=comparison_regret,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=\"LinUCB at 1000 rounds\",\n", " )\n", " plt.legend()\n", "\n", "\n", "oracle = LoanOracle()\n", "\n", "\n", "def random_uniform_policy():\n", " return InterestRate(rng.integers(1, 3))\n", "\n", "\n", "contexts, actions = simulate_offline_data(oracle, 3000, random_uniform_policy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the rewards, contexts and actions, we can update our agent offline. We'll use a decay rate of 0.95 to ensure that the learners don't converge too quickly and keep learning when the online data starts.\n", "\n", "We'll write a `create_agent` function just to save a few lines of code later." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def create_agent(alpha=0.96):\n", " arms = [\n", " Arm(InterestRate.interest_rate_10, learner=NormalInverseGammaRegressor()),\n", " Arm(InterestRate.interest_rate_15, learner=NormalInverseGammaRegressor()),\n", " ]\n", "\n", " return ContextualAgent(\n", " arms=arms, policy=UpperConfidenceBound(alpha), random_seed=rng\n", " )\n", "\n", "\n", "offline_agent = create_agent()\n", "\n", "# Learn from offline data\n", "for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", "\n", " offline_agent.decay(context, decay_rate=0.95)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally let's run the agent with new data and see how it performs. Will the offline learning help it converge faster?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "online_rounds = 1000\n", "for _ in range(online_rounds):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context), np.atleast_1d(oracle.rewards[-1] - true_reward_mean)\n", " )\n", "\n", "\n", "offline_plus_online_regret = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", ")\n", "\n", "plot_cumulative_regret(\n", " offline_plus_online_regret,\n", " title=\"Offline learning + online learning. Only showing online regret\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cumulative regret of the offline learning + online learning is much better than the regret of the online learning alone after 1000 rounds. It is entirely possible, however, that a offline learning strategy could make the agent worse, if it biases the agent towards a suboptimal policy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Offline learning with a biased policy\n", "\n", "Now let's see if we generate data with a policy that chooses action 1 (interest rate 10%) with 80% probability and action 2 (interest rate 15%) with 20% probability. It's worth remembering that not only is using historical data an example of offline learning with a biased policy - so is adding an arm to the agent (since it's equivalent to all of the historical data being generated by a policy that never chose the new arm). Having a plan to compensate for this bias is crucial.\n", "\n", "We'll run the same steps as before:\n", "\n", "1. Simulate offline data\n", "2. Learn from offline data\n", "3. Run online learning\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1 Simulate offline data\n", "oracle = LoanOracle()\n", "\n", "\n", "def action_1_more_likely_policy():\n", " return InterestRate(1) if rng.uniform() < 0.8 else InterestRate(2)\n", "\n", "\n", "contexts, actions = simulate_offline_data(oracle, 3000, action_1_more_likely_policy)\n", "\n", "# 2. Learn from offline data\n", "offline_agent = create_agent()\n", "\n", "for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", "\n", " offline_agent.decay(context, decay_rate=0.95)\n", "\n", "# 3 Run online learning\n", "online_rounds = 1000\n", "for _ in range(online_rounds):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context), np.atleast_1d(oracle.rewards[-1] - true_reward_mean)\n", " )\n", "\n", "\n", "offline_plus_online_regret = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", ")\n", "\n", "plot_cumulative_regret(\n", " offline_plus_online_regret,\n", " title=\"Offline learning with biased policy + online learning \\n Only showing online regret\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The offline learning with a biased policy did worse than the LinUCB agent without any warm start from offline data.\n", "Can we still use the offline data but somehow correct for the bias?\n", "\n", "One way to do this is to use inverse propensity scoring (IPS). Other technique is [doubly robust estimation](https://arxiv.org/pdf/1503.02834.pdf).\n", "\n", "We won't be doing IPS here, but we'll use its intuition to use a different decay rate depending on the action taken. Because action one was used 4 times more than action two, we will use a higher decay rate for action 1 and a lower one for action 2. In this case, we'll use a decay rate of 0.97 for action 1 and 0.99 for action 2 (remember that a value of 0.9 gives a HIGHER decay than 0.95, in the sense that we're letting the learning decay faster)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1. Simulate offline data\n", "oracle = LoanOracle()\n", "\n", "contexts, actions = simulate_offline_data(oracle, 3000, action_1_more_likely_policy)\n", "\n", "# 2. Learn from offline data\n", "offline_agent = create_agent()\n", "decay_for_action_1 = 0.97\n", "decay_for_action_2 = 0.99\n", "\n", "for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", " if token == InterestRate.interest_rate_10:\n", " offline_agent.decay(context, decay_rate=decay_for_action_1)\n", " else:\n", " offline_agent.decay(context, decay_rate=decay_for_action_2)\n", "\n", "# 3 Run online learning\n", "online_rounds = 1000\n", "for _ in range(online_rounds):\n", " user_age = rng.uniform(-5, 5)\n", " context = np.array([1.0, user_age])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context), np.atleast_1d(oracle.rewards[-1] - true_reward_mean)\n", " )\n", "\n", "\n", "offline_plus_online_regret = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", ")\n", "\n", "plot_cumulative_regret(\n", " offline_plus_online_regret,\n", " title=\"Offline learning with biased policy and correction \\n Only showing online regret\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This higher decay for the more likely historical action seems to have encouraged exploration, but does not necessarily lead to better performance. The cumulative regret at the end of the study period is still higher than the LinUCB agent without any warm start from offline data.\n", "\n", "Do note that these runs have a high variance, so the results might not be representative. We'll run the experiment multiple times to get a better idea.\n", "\n", "## Conclusion: run the experiment multiple times\n", "\n", "To finish off, we'll run the experiment multiple times and see the median performance. You don't need to run the rest of this code (it might a few minutes), but we'll leave it here for reference. I'll write four functions to run the experiment with different policies:\n", "\n", "1. LinUCB just online learning: `run_linucb_online_learning`\n", "2. Offline learning with uniform policy: `run_offline_learning_with_uniform_policy`\n", "3. Offline learning with biased policy and no correction: `run_offline_learning_with_biased_policy_no_correction`\n", "4. Offline learning with biased policy and correction: `run_offline_learning_with_biased_policy_and_correction`\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "REPETITIONS = 20\n", "\n", "\n", "# 1. LinUCB just online learning\n", "def run_linucb_online_learning(repetitions, time_periods=3000):\n", " regrets = np.zeros((repetitions, time_periods), dtype=np.float64)\n", "\n", " for rep in range(repetitions):\n", " oracle = LoanOracle()\n", " agent = create_agent()\n", "\n", " for _ in range(time_periods):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " agent.update(\n", " np.atleast_2d(context),\n", " np.atleast_1d(oracle.rewards[-1] - true_reward_mean),\n", " )\n", "\n", " regrets[rep, :] = np.cumsum(\n", " np.array(oracle.optimal_rewards) - np.array(oracle.expected_rewards)\n", " )\n", "\n", " return regrets\n", "\n", "\n", "# 2. Offline learning with uniform policy\n", "def run_offline_learning_with_uniform_policy(\n", " repetitions, offline_time_periods=3000, decay_rate=0.95, online_time_periods=1000\n", "):\n", " online_regrets = np.zeros((repetitions, online_time_periods), dtype=np.float64)\n", "\n", " def uniform_policy():\n", " return InterestRate(1) if rng.uniform() < 0.5 else InterestRate(2)\n", "\n", " for rep in range(repetitions):\n", " # 1 Simulate offline data\n", "\n", " oracle = LoanOracle()\n", " contexts, actions = simulate_offline_data(\n", " oracle, offline_time_periods, uniform_policy\n", " )\n", "\n", " # 2. Learn from offline data\n", " offline_agent = create_agent()\n", "\n", " for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", "\n", " offline_agent.decay(context, decay_rate=decay_rate)\n", "\n", " # 3 Run online learning\n", " for _ in range(online_time_periods):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context),\n", " np.atleast_1d(oracle.rewards[-1] - true_reward_mean),\n", " )\n", "\n", " online_regrets[rep, :] = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", " )\n", "\n", " return online_regrets\n", "\n", "\n", "# 3. Offline learning with biased policy and no correction (same decay rate for both actions)\n", "def run_offline_learning_with_biased_policy_no_correction(\n", " repetitions,\n", " offline_time_periods=3000,\n", " decay_rate=0.95,\n", " online_time_periods=1000,\n", " prob_of_action_1=0.8,\n", "):\n", " online_regrets = np.zeros((repetitions, online_time_periods), dtype=np.float64)\n", "\n", " def biased_policy():\n", " return InterestRate(1) if rng.uniform() < prob_of_action_1 else InterestRate(2)\n", "\n", " for rep in range(repetitions):\n", " # 1 Simulate offline data\n", "\n", " oracle = LoanOracle()\n", " contexts, actions = simulate_offline_data(\n", " oracle, offline_time_periods, biased_policy\n", " )\n", "\n", " # 2. Learn from offline data\n", " offline_agent = create_agent()\n", "\n", " for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", "\n", " offline_agent.decay(context, decay_rate=decay_rate)\n", "\n", " # 3 Run online learning\n", " for _ in range(online_time_periods):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context),\n", " np.atleast_1d(oracle.rewards[-1] - true_reward_mean),\n", " )\n", "\n", " online_regrets[rep, :] = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", " )\n", "\n", " return online_regrets\n", "\n", "\n", "# 4. Offline learning with biased policy and correction (different decay rates for both actions)\n", "def run_offline_learning_with_biased_policy_and_correction(\n", " repetitions,\n", " offline_time_periods=3000,\n", " decay_rates=[0.97, 0.995],\n", " online_time_periods=1000,\n", " prob_of_action_1=0.8,\n", "):\n", " online_regrets = np.zeros((repetitions, online_time_periods), dtype=np.float64)\n", "\n", " def biased_policy():\n", " return InterestRate(1) if rng.uniform() < prob_of_action_1 else InterestRate(2)\n", "\n", " for rep in range(repetitions):\n", " # 1 Simulate offline data\n", "\n", " oracle = LoanOracle()\n", " contexts, actions = simulate_offline_data(\n", " oracle, offline_time_periods, biased_policy\n", " )\n", "\n", " # 2. Learn from offline data\n", " offline_agent = create_agent()\n", "\n", " for token, reward, context in zip(actions, oracle.rewards, contexts):\n", " offline_agent.select_for_update(token).update(\n", " np.atleast_2d(context), np.atleast_1d(reward - true_reward_mean)\n", " )\n", " if token == InterestRate.interest_rate_10:\n", " offline_agent.decay(context, decay_rate=decay_rates[0])\n", " else:\n", " offline_agent.decay(context, decay_rate=decay_rates[1])\n", "\n", " # 3 Run online learning\n", " for _ in range(online_time_periods):\n", " user_credit_score = rng.uniform(-5, 5)\n", "\n", " context = np.array([1.0, user_credit_score])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = offline_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " offline_agent.update(\n", " np.atleast_2d(context),\n", " np.atleast_1d(oracle.rewards[-1] - true_reward_mean),\n", " )\n", "\n", " online_regrets[rep, :] = np.cumsum(\n", " np.array(oracle.optimal_rewards[-online_rounds:])\n", " - np.array(oracle.expected_rewards[-online_rounds:])\n", " )\n", "\n", " return online_regrets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we run these four functions and plot the results. We'll use the median values (over the repetitions) of the cumulative regret per time period.\n", "\n", "Generally, we observe that having a warm start from offline data can help the agent learn faster, even if the offline data is biased. However, taking steps to correct for the bias can lead to even better performance. Indeed, the best median performance is achieved by the offline learning with biased policy and correction.\n", "\n", "*Note: We suppress warnings about divide by zero and invalid values that arise in a few simulation runs just to keep the output clean*" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import warnings\n", "\n", "\n", "# Supress divide by zero and invalid value warnings\n", "def custom_warning_filter(message, category, filename, lineno, file=None, line=None):\n", " if category is RuntimeWarning:\n", " if \"divide by zero\" in str(message) or \"invalid value\" in str(message):\n", " return None\n", " return True\n", "\n", "\n", "warnings.showwarning = custom_warning_filter\n", "\n", "\n", "linucb_regrets = run_linucb_online_learning(REPETITIONS)\n", "offline_uniform_policy_regrets = run_offline_learning_with_uniform_policy(REPETITIONS)\n", "\n", "biased_policy_no_correction_regrets = (\n", " run_offline_learning_with_biased_policy_no_correction(\n", " REPETITIONS, prob_of_action_1=0.9\n", " )\n", ")\n", "biased_policy_with_correction_regrets = (\n", " run_offline_learning_with_biased_policy_and_correction(\n", " REPETITIONS, prob_of_action_1=0.9, decay_rates=[0.97, 0.995]\n", " )\n", ")\n", "\n", "# Plot the 4 median regrets in one figure\n", "# ####################################3\n", "fig, axs = plt.subplots(2, 2, figsize=(8, 8))\n", "\n", "# LinUCB\n", "linucb_median_regret = np.median(linucb_regrets, axis=0)\n", "linucb_mean_regret_at_1000 = linucb_median_regret[1000]\n", "axs[0, 0].plot(linucb_median_regret, label=\"LinUCB\")\n", "axs[0, 0].set_title(\"LinUCB\")\n", "axs[0, 0].set_xlabel(\"Round\")\n", "axs[0, 0].set_ylabel(\"Cumulative Regret\")\n", "\n", "# Offline learning with uniform policy\n", "offline_uniform_policy_median_regrets = np.median(\n", " offline_uniform_policy_regrets, axis=0\n", ")\n", "axs[0, 1].plot(offline_uniform_policy_median_regrets)\n", "axs[0, 1].set_title(\"Offline learning w uniform policy\")\n", "axs[0, 1].axhline(\n", " y=linucb_mean_regret_at_1000,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=\"LinUCB at 1000 rounds\",\n", ")\n", "axs[0, 1].set_xlabel(\"Round\")\n", "axs[0, 1].set_ylabel(\"Cumulative Regret\")\n", "\n", "\n", "# Offline learning with biased policy no correction\n", "biased_policy_no_correction_median_regrets = np.median(\n", " biased_policy_no_correction_regrets, axis=0\n", ")\n", "axs[1, 0].plot(biased_policy_no_correction_median_regrets)\n", "axs[1, 0].set_title(\"Offline learning \\n biased policy w/o correction\")\n", "axs[1, 0].axhline(\n", " y=linucb_mean_regret_at_1000,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=\"LinUCB at 1000 rounds\",\n", ")\n", "axs[1, 0].set_xlabel(\"Round\")\n", "axs[1, 0].set_ylabel(\"Cumulative Regret\")\n", "\n", "# Offline learning with biased policy with correction\n", "biased_policy_with_correction_median_regrets = np.median(\n", " biased_policy_with_correction_regrets, axis=0\n", ")\n", "axs[1, 1].plot(biased_policy_with_correction_median_regrets)\n", "axs[1, 1].set_title(\"Offline learning \\n biased policy w correction\")\n", "axs[1, 1].axhline(\n", " y=linucb_mean_regret_at_1000,\n", " linestyle=\"--\",\n", " color=\"#e5ae38\",\n", " label=\"LinUCB at 1000 rounds\",\n", ")\n", "axs[1, 1].set_xlabel(\"Round\")\n", "axs[1, 1].set_ylabel(\"Cumulative Regret\")\n", "\n", "for ax in [axs[0, 1], axs[1, 0], axs[1, 1]]:\n", " ax.legend()\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus: Optimize the decay rates with optuna\n", "\n", "We'll leave this code commented out, but you can run it to optimize the decay rates." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# import optuna\n", "# optuna.logging.set_verbosity(optuna.logging.WARNING)\n", "\n", "# def objective(trial: optuna.Trial):\n", "# #np.random.seed(3)\n", "# decay_rate_action_1 = trial.suggest_float(\"decay_rate_action_1\", 0.80, 0.97)\n", "# decay_rate_action_2 = trial.suggest_float(\"decay_rate_action_2\", 0.94, 1.0)\n", "# REPETITIONS = 30\n", "# biased_policy_with_correction_regrets = run_offline_learning_with_biased_policy_and_correction(REPETITIONS,\n", "# prob_of_action_1=0.9,\n", "# decay_rates=[decay_rate_action_1, decay_rate_action_2])\n", "# regret_ten_last_periods_avg = biased_policy_with_correction_regrets.mean(axis=0)[-10::].mean()\n", "# return regret_ten_last_periods_avg\n", "\n", "\n", "# study = optuna.create_study(direction=\"minimize\")\n", "# study.optimize(objective, n_trials=30)" ] } ], "metadata": { "kernelspec": { "display_name": "bayesianbandits", "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": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 2 }