real-world/demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## A Brief Demo using Open Bandit Dataset and Pipeline\n",
    "\n",
    "1. Data Loading and Preprocessing (Random Bucket of Open Bandit Dataset)\n",
    "2. Off-Policy Learning (IPWLearner and NNPolicyLearner)\n",
    "3. Off-Policy Evaluation (IPWLearner vs NNPolicyLearner)\n",
    "\n",
    "### \"What is the best new policy for the ZOZOTOWN recommendation interface?\""
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import obp\n",
    "from obp.dataset import OpenBanditDataset\n",
    "from obp.policy import IPWLearner, NNPolicyLearner\n",
    "from obp.ope import (\n",
    "    RegressionModel,\n",
    "    OffPolicyEvaluation,\n",
    "    SelfNormalizedInverseProbabilityWeighting as SNIPS,\n",
    "    DoublyRobust as DR,\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "print(obp.__version__)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.5.1\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## (1) Data Loading and Preprocessing\n",
    "\n",
    "Here we use a random bucket of the Open Bandit Pipeline. We can download this by using `obp.dataset.OpenBanditDataset`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# define OpenBanditDataset class to handle the real bandit data\n",
    "dataset = OpenBanditDataset(\n",
    "    behavior_policy=\"random\", campaign=\"all\", data_path=\"./open_bandit_dataset\",\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/usaito/.pyenv/versions/3.9.5/envs/zr-obp/lib/python3.9/site-packages/obp-0.5.1-py3.9.egg/obp/dataset/real.py:203: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  item_feature_cat = self.item_context.drop(\"item_feature_0\", 1).apply(\n",
      "/Users/usaito/.pyenv/versions/3.9.5/envs/zr-obp/lib/python3.9/site-packages/obp-0.5.1-py3.9.egg/obp/dataset/real.py:206: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  self.action_context = pd.concat([item_feature_cat, item_feature_0], 1).values\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# logged bandit data collected by the uniform random policy\n",
    "training_bandit_data, test_bandit_data = dataset.obtain_batch_bandit_feedback(\n",
    "    test_size=0.3, is_timeseries_split=True,\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# ignore the position effect for a demo purpose\n",
    "training_bandit_data[\"position\"] = None \n",
    "test_bandit_data[\"position\"] = None"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# number of actions\n",
    "dataset.n_actions"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# sample size\n",
    "dataset.n_rounds"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1374327"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## (2) Off-Policy Learning (OPL)\n",
    "\n",
    "Train two new policies: `obp.policy.IPWLearner` and `obp.policy.NNPolicyLearner`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### IPWLearner"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "ipw_learner = IPWLearner(\n",
    "    n_actions=dataset.n_actions,\n",
    "    base_classifier=RandomForestClassifier(\n",
    "        n_estimators=100, max_depth=5, min_samples_leaf=10, random_state=12345\n",
    "    ),\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# fit\n",
    "ipw_learner.fit(\n",
    "    context=training_bandit_data[\"context\"], # context; x\n",
    "    action=training_bandit_data[\"action\"], # action; a\n",
    "    reward=training_bandit_data[\"reward\"], # reward; r\n",
    "    pscore=training_bandit_data[\"pscore\"], # propensity score; pi_b(a|x)\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# predict (make new decisions)\n",
    "action_dist_ipw = ipw_learner.predict(\n",
    "    context=test_bandit_data[\"context\"]\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### NNPolicyLearner"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "nn_learner = NNPolicyLearner(\n",
    "    n_actions=dataset.n_actions,\n",
    "    dim_context=dataset.dim_context,\n",
    "    solver=\"adam\",\n",
    "    off_policy_objective=\"ipw\", # = ips\n",
    "    batch_size=32, \n",
    "    random_state=12345,\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# fit\n",
    "nn_learner.fit(\n",
    "    context=training_bandit_data[\"context\"], # context; x\n",
    "    action=training_bandit_data[\"action\"], # action; a\n",
    "    reward=training_bandit_data[\"reward\"], # reward; r\n",
    "    pscore=training_bandit_data[\"pscore\"], # propensity score; pi_b(a|x)\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "policy learning: 100%|████████████████████████████| 200/200 [00:12<00:00, 16.51it/s]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# predict (make new decisions)\n",
    "action_dist_nn = nn_learner.predict(\n",
    "    context=test_bandit_data[\"context\"]\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## (3) Off-Policy Evaluation\n",
    "\n",
    "Estimating the performance of IPWLearner and NNPolicyLearner via OPE."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### (3-1) obtain a reward estimator\n",
    "`obp.ope.RegressionModel` simplifies the process of reward modeling\n",
    "\n",
    "$r(x,a) = \\mathbb{E} [r \\mid x, a] \\approx \\hat{r}(x,a)$"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "regression_model = RegressionModel(\n",
    "    n_actions=dataset.n_actions, \n",
    "    base_model=LogisticRegression(C=100, max_iter=500, random_state=12345),\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "estimated_rewards = regression_model.fit_predict(\n",
    "    context=test_bandit_data[\"context\"], \n",
    "    action=test_bandit_data[\"action\"], \n",
    "    reward=test_bandit_data[\"reward\"], \n",
    "    random_state=12345,\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### (4-2) OPE\n",
    "`obp.ope.OffPolicyEvaluation` simplifies the OPE process\n",
    "\n",
    "$V(\\pi_e) \\approx \\hat{V} (\\pi_e; \\mathcal{D}_0, \\theta)$ using DM, IPS, and DR"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "ope = OffPolicyEvaluation(\n",
    "    bandit_feedback=test_bandit_data,\n",
    "    ope_estimators=[\n",
    "        SNIPS(estimator_name=\"SNIPS\"),\n",
    "        DR(estimator_name=\"DR\"),\n",
    "    ]\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### (4-3) Visualize the OPE results\n",
    "\n",
    "Output the relative performances of the trained policies compared to the logging policy (uniform random)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "ope.visualize_off_policy_estimates_of_multiple_policies(\n",
    "    policy_name_list=[\"IPWLearner\", \"NNPolicyLearner\"],\n",
    "    action_dist_list=[action_dist_ipw, action_dist_nn],\n",
    "    estimated_rewards_by_reg_model=estimated_rewards,\n",
    "    n_bootstrap_samples=100,\n",
    "    is_relative=True,\n",
    "    random_state=12345,\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x892.8 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Both policy learner outperforms the random baseline. In particular, NNPolicyLearner seems to be the best, improving the random baseline by about 70\\%. It also outperforms IPWLearner in both SNIPS and DR."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "64b446a4e17784c2dc3dbe74bf0708929a003fb4822b30671d57ebdef413b716"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit ('zr-obp': pyenv)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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