{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKDyraqEuvff"
   },
   "source": [
    "# Hands-on: Explainable Reinforcement Learning with SHAP & LIME\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hjlv-L5cvLEn"
   },
   "source": [
    "## 1. Introduction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rsm194-qkw4I"
   },
   "source": [
    "In this notebook we will use SHAP and LIME to understand which features influence the behaviour of a Reinforcement Learning agent, and show how these tecniques can return unreliable results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W5533eUSvfNY"
   },
   "source": [
    "## 2. Agent\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d0XZEM2QIc7W"
   },
   "source": [
    "Let's start by training a simple Cart Pole agent from Gymnasium (https://gymnasium.farama.org/environments/classic_control/cart_pole/). The aim of the agent is to stay balanced.\n",
    "\n",
    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    "\n",
    "Observation Space:\n",
    "- Cart Position\n",
    "- Cart Velocity\n",
    "- Pole Angle\n",
    "- Pole Angular Velocity\n",
    "\n",
    "Actions:\n",
    "- 0: Push cart to the left\n",
    "- 1: Push cart to the right\n",
    "\n",
    "Since the goal is to keep the pole upright for as long as possible, by default, a reward of +1 is given for every step taken, including the termination step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rMxRSytIaE3"
   },
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22407,
     "status": "ok",
     "timestamp": 1778751563393,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "1_B0YLSUvsTP",
    "outputId": "dc1dffd4-f565-4994-90c8-d92201d39643"
   },
   "outputs": [],
   "source": [
    "!pip install stable-baselines3\n",
    "import gymnasium as gym\n",
    "from stable_baselines3 import PPO\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1HvmPsfXo37d"
   },
   "source": [
    "### 2.1 Train the agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uSl_FSRWpvcq"
   },
   "source": [
    "Train the agent using [PPO](https://arxiv.org/pdf/1707.06347) (or your favourite model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "qkEYvS8sujTP"
   },
   "outputs": [],
   "source": [
    "env = gym.make(\"CartPole-v1\", render_mode=\"rgb_array\")\n",
    "model = PPO(\"MlpPolicy\", env, verbose=0).learn(total_timesteps=10_000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q-v0lhqpJeEk"
   },
   "source": [
    "### 2.2 Evaluate the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1352,
     "status": "ok",
     "timestamp": 1778751590648,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "v9uxH2AHJgf8",
    "outputId": "0fe68c46-95cb-4fe9-c99a-d5f28de071c0"
   },
   "outputs": [],
   "source": [
    "eval_rewards = []\n",
    "n_episodes = 10\n",
    "max_steps = 500\n",
    "for ep in range(n_episodes):\n",
    "    obs, _ = env.reset(seed=ep)\n",
    "    episode_reward = 0\n",
    "    done = False\n",
    "    n_steps = 0\n",
    "    while not done and n_steps < max_steps:\n",
    "        action, _ = model.predict(obs, deterministic=False)\n",
    "        obs, reward, done, done2, info = env.step(action)\n",
    "        episode_reward += reward\n",
    "        n_steps +=1\n",
    "    eval_rewards.append(episode_reward)\n",
    "\n",
    "print(f\"Mean: {np.mean(eval_rewards)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dp3HmKTwvy8-"
   },
   "source": [
    "## 3. SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Umm41GAajolB"
   },
   "source": [
    "[SHAP](https://dl.acm.org/doi/10.5555/3295222.3295230) is an XAI attribution method based on game-theoretical Shapley values for explaining machine learning predictions.\n",
    "\n",
    "**Intuition**: with SHAP, we aim to explain the prediction *y* (i.e. the action) of an instance *x* (i.e. a state) by computing the contribution of each feature of *x* (e.g. PolAngle) to *y*.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9-l0dSewsbt_"
   },
   "source": [
    "\n",
    "\n",
    "In this section, will explore the use of Shapely values to explain which state features have a positive, negative or negligible influence on the action of the agent, at a local and global level. We suppose the agent is blackbox and we can only observe its chosen actions; we assume we have no information about Q values, V values or policy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p8oewTj2kZlR"
   },
   "source": [
    "\n",
    "Here you have a good guide to get a theoretical and practical understanding of SHAP: https://mbrenndoerfer.com/writing/shap-shapley-additive-explanations-complete-guide-model-interpretability-feature-attribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j-V5CoQln8MX"
   },
   "source": [
    "### 3.1 Using (and fooling) SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7J23WY9BIqoz"
   },
   "source": [
    "What happens if we augment the state space with a random variable that is ignored by the policy? We expect SHAP to assign no importance to this feature, since it does not influence the agent's decision. Let's test it!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XMPI03buvB7u"
   },
   "source": [
    " Wrap the environment to add a \"noise\" feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1778751652940,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "xvcOxmzS-2rV",
    "outputId": "afc34b86-340a-4ef9-866a-2df003415fb8"
   },
   "outputs": [],
   "source": [
    "# Environment wrapper\n",
    "class NoiseWrapper(gym.ObservationWrapper):\n",
    "    def __init__(self, env):\n",
    "        super().__init__(env)\n",
    "        # Add 1 extra dimension for the noise feature\n",
    "        low = np.append(self.observation_space.low, -1.0)\n",
    "        high = np.append(self.observation_space.high, 1.0)\n",
    "        self.observation_space = gym.spaces.Box(low, high, dtype=np.float32)\n",
    "\n",
    "    def observation(self, obs):\n",
    "        # Append a random value to the observation\n",
    "        noise = np.array([np.random.uniform(-1, 1)], dtype=np.float32)\n",
    "        return np.concatenate([obs, noise])\n",
    "\n",
    "# Black-box policy wrapper\n",
    "def predict_shap(observation):\n",
    "    # The model only trained on 4 features -> we exclude the 5th (the noise)\n",
    "    clean_obs = observation[:, :4]\n",
    "    action, _ = model.predict(clean_obs, deterministic=False) #If True, no importance is attributed to NOISE\n",
    "    return action\n",
    "\n",
    "shap_env = NoiseWrapper(env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WLOyn9Jej51k"
   },
   "source": [
    "\n",
    "Exact Shapely values are generally intectable to compute; therefore, we will use an commonly-used approximation tecnique called [KernelSHAP](https://shap.readthedocs.io/en/latest/generated/shap.KernelExplainer.html).\n",
    "Documentation available at: https://shap.readthedocs.io/en/latest/generated/shap.KernelExplainer.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4280,
     "status": "ok",
     "timestamp": 1778751659653,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "x4vJXaOPwjsD",
    "outputId": "a2420269-493f-4fbe-967d-341458c7380d"
   },
   "outputs": [],
   "source": [
    "import shap\n",
    "# Generate observation data (states) to understand distribution of values\n",
    "n_states = 500\n",
    "observation_data = np.array([shap_env.observation_space.sample() for _ in range(n_states)])\n",
    "explainer = shap.KernelExplainer(predict_shap, observation_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rPU605VsyEw6"
   },
   "source": [
    "### 3.2 Local Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVif6_o4QcNp"
   },
   "source": [
    "Let's start by sampling a state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1778751671709,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "O41ex3BlQmsg",
    "outputId": "32b6d711-a5f3-4032-a2d6-2c306e55a989"
   },
   "outputs": [],
   "source": [
    "feature_names=[\"CartPos\", \"CartVel\", \"PoleAngle\", \"PoleAngVel\", \"NOISE\"]\n",
    "test_state = shap_env.observation_space.sample().reshape(1, -1)\n",
    "for name, value in zip(feature_names, test_state[0]):\n",
    "    print(f\"{name}: {value}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iZGomY_1MCVR"
   },
   "source": [
    "Render the state to visualize the cartpole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 784
    },
    "executionInfo": {
     "elapsed": 763,
     "status": "ok",
     "timestamp": 1778751677089,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "8TGMJ-ibHVWP",
    "outputId": "99ab23c5-50e7-4153-dad7-d30ede5b67fe"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "render_state = test_state[0][:4].copy()\n",
    "render_state[0] = 0.0\n",
    "next_action = \"Push Cart to Left\" if predict_shap(test_state)[0] else \"Push Cart to Right\"\n",
    "#Teleport the cart to the center for better visualization\n",
    "shap_env.unwrapped.state = render_state\n",
    "frame = shap_env.render()\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(frame)\n",
    "plt.title(f\"Next Action: {next_action}\")\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "print(f\"Cartpole visualized at the center (actual position was: {test_state[0][0]:.2f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oCcUkIzey_Xn"
   },
   "source": [
    "**Which features contributed locally to the selection of action *a* in this state *s*?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 444,
     "referenced_widgets": [
      "5a9d374c8af0481bb3268aa91e55b386"
     ]
    },
    "executionInfo": {
     "elapsed": 297,
     "status": "ok",
     "timestamp": 1778751681106,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "FR13ROb-xYHh",
    "outputId": "bf5e9d92-4a5a-4600-c860-af9ae8254bbd"
   },
   "outputs": [],
   "source": [
    "shap_values = explainer.shap_values(test_state)\n",
    "shap.summary_plot(shap_values, test_state, plot_type=\"bar\", feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LGQm3yx4zq8R"
   },
   "source": [
    "--> Some attributed importance to NOISE feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qM2QNhjDzMGt"
   },
   "source": [
    "### 3.3 Global Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tx7bcJkKQZWZ"
   },
   "source": [
    "**Which features contributed to chosen actions in general?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444,
     "referenced_widgets": [
      "3f3d7123631c49499f80f7c456c866b6",
      "63d7a964af36487ab67cef4ed9892f4e",
      "4d5219455e5b447a847e2c4845179ecb",
      "9cdc061995874ce9b87953fd77494d4d",
      "705e019e88b2497eb256f12638d3b696",
      "4214208546c4499ab34bb2262d73806b",
      "d1a559b1e99349c9b7e9b552e3d79db9",
      "3621443727fb4a69813a82fef9272f44",
      "155603507a0a4b7ca05a3d1900554340",
      "acf41a2febab49fe9ceeb5e9ccc63099",
      "7980c703de6242d0987c3a169f01f2a7"
     ]
    },
    "executionInfo": {
     "elapsed": 13399,
     "status": "ok",
     "timestamp": 1778751701090,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "rytC5ASxPvjq",
    "outputId": "e4c3aa01-cb39-4b38-9378-a54dc217d0de"
   },
   "outputs": [],
   "source": [
    "# Mean absolute SHAP values across all observations\n",
    "shap_values = explainer.shap_values(observation_data)\n",
    "shap.summary_plot(shap_values, observation_data, plot_type=\"bar\", feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4qoQ-3_YzteM"
   },
   "source": [
    "--> Some attributed importance to NOISE feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ui7mQIgLR7qF"
   },
   "source": [
    "* Feature dependency: approximated shapley values assume feature *independence*. In methods such as KernelSHAP, when evaluating a coalition of features, features excluded from the coalition are assigned random values sampled from a background distribution. If correlations between features exist, these sampled values may be unrealistic with the remaining ones, and can produce unrealistic predicions. This might lead to irrelevant features being assigned non-zero importance, and important features being assigned near-zero importance.\n",
    "\n",
    "*   Correlation, not causation: the imporance scores show how features correlate with predictions of the model, but say nothing about the causal mechanisms behind\n",
    "\n",
    "* Interpretability: not easily interpretable by layusers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8WZbHIvv1Gxx"
   },
   "source": [
    "## 4. LIME\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XtWUuVxN7YGL"
   },
   "source": [
    "[LIME](https://dl.acm.org/doi/10.1145/2939672.2939778) (Local Interpretable Model-Agnostic Explanations) is a XAI method to extract feature importance in the prediction of an instance. It does so by learning a local interpretable model around the instance to be explained.\n",
    "\n",
    "**Intuition** of how the method works:\n",
    "1. Create perturbed samples around the instance to be explained by retaining and replacing feature values\n",
    "2. Train an interpretable model (e.g. linear models) to approximate the black-box model locally. Give more weight to new samples that are closer to original sample (in SHAP we give more weight to small or large coalitions of features)\n",
    "3. Use the model's weights to represent feature importance\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bSmke65VkG7y"
   },
   "source": [
    "Here you have a good guide to have a theoretical and practical understanding of LIME: https://mbrenndoerfer.com/writing/lime-local-interpretable-model-agnostic-explanations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VmiCNb_m2Hf_"
   },
   "source": [
    "### 4.1 Using (and fooling) LIME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_xnTSfzM1LgZ"
   },
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 8762,
     "status": "ok",
     "timestamp": 1778751721088,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "QQ7fQ9ibu1NW",
    "outputId": "aab7f1a6-8bf1-4b7b-ce4a-f9b38e56ea01"
   },
   "outputs": [],
   "source": [
    "!pip install lime\n",
    "import lime\n",
    "from lime import lime_tabular\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a2p-P7mV3266"
   },
   "source": [
    "Since LIME expects action probability distribution, we will relax the black-box model assumption, and use the action probability distribution instead of just the observed action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "7nFRbMf8Buad"
   },
   "outputs": [],
   "source": [
    "def predict_lime(observation):\n",
    "    action, _ = model.predict(observation, deterministic=False)\n",
    "    obs_tensor, _ = model.policy.obs_to_tensor(observation)\n",
    "    distribution = model.policy.get_distribution(obs_tensor)\n",
    "    probs = distribution.distribution.probs\n",
    "    return probs.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzyPMeJaBzcO"
   },
   "source": [
    "Instantiate explainer. Documentation available at: https://lime-ml.readthedocs.io/en/latest/lime.html#module-lime.lime_tabular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 74,
     "status": "ok",
     "timestamp": 1778754916563,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "0aDIOXcurNYJ",
    "outputId": "4d02cf1a-46fe-47a3-f5fb-fc035c12bf5e"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Generate observation data (states) to understand distribution of values\n",
    "n_states = 500\n",
    "observation_data = np.array([env.observation_space.sample() for _ in range(n_states)])\n",
    "\n",
    "feature_names=[\"CartPos\", \"CartVel\", \"PoleAngle\", \"PoleAngVel\"]\n",
    "action_names = ['PushLeft', 'PushRight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1778755093529,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "2uR5VCr1D9-Z",
    "outputId": "845ca4e6-ddc4-4e04-97bb-6797cd292bce"
   },
   "outputs": [],
   "source": [
    "lime_explainer = lime_tabular.LimeTabularExplainer(\n",
    "    training_data = observation_data,\n",
    "    feature_names=feature_names,\n",
    "    class_names=action_names,\n",
    "    discretize_continuous= True,\n",
    "    mode='classification'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kolzkXM45RRj"
   },
   "source": [
    "### 4.2 Local Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SBLWuekxCIlf"
   },
   "source": [
    "Let's choose a state for which we aim to explain the predicted action. One option is using the same state generated for SHAP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 68,
     "status": "ok",
     "timestamp": 1778755067118,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "gsK-rYG-CBdm",
    "outputId": "b7331127-afcf-4a0a-955e-374091cfbff4"
   },
   "outputs": [],
   "source": [
    "test = test_state[:,:4]\n",
    "\n",
    "explanation = lime_explainer.explain_instance(\n",
    "    test.flatten(),\n",
    "    predict_lime,\n",
    "    num_features=len(feature_names),  # explain using all features\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1XO0CIgClO0"
   },
   "source": [
    "**Which features contributed locally to the selection of action *a* in this state *s*?**\n",
    "Are they similar to SHAP's?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "executionInfo": {
     "elapsed": 343,
     "status": "ok",
     "timestamp": 1778755072171,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "StTeCnyI-axb",
    "outputId": "3849c4c7-80f6-4628-bee5-3ec84ee4ba6f"
   },
   "outputs": [],
   "source": [
    "explanation.show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jDAC8tws51L_"
   },
   "source": [
    "Positive weights indicate that the feature positively contributes towards the predicted actoon, while negative weights indicate that the feature contributes against the predicted action (and in favour of the other action)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dxuSjB1YASTg"
   },
   "source": [
    "In the above example, the countinous features were discretized (discretize_continouos = True) before perturbation. What would happen if we do not discretize? Let's try it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 47,
     "status": "ok",
     "timestamp": 1778755196879,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "IjY4tsnyAv1n",
    "outputId": "10af89b8-742a-4b96-d150-18ea3df029cf"
   },
   "outputs": [],
   "source": [
    "lime_explainer_con = lime_tabular.LimeTabularExplainer(\n",
    "    training_data = observation_data,\n",
    "    feature_names=feature_names,\n",
    "    class_names=action_names,\n",
    "    discretize_continuous= False,\n",
    "    mode='classification'\n",
    ")\n",
    "\n",
    "explanation_con = lime_explainer_cat.explain_instance(\n",
    "    test.flatten(),\n",
    "    predict_lime,\n",
    "    num_features=len(feature_names),\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "executionInfo": {
     "elapsed": 792,
     "status": "ok",
     "timestamp": 1778755201547,
     "user": {
      "displayName": "Sara Montese",
      "userId": "02138181393559819022"
     },
     "user_tz": -120
    },
    "id": "pn_ODN8yBM4n",
    "outputId": "28533c60-0a5f-4773-c321-25e3393386e0"
   },
   "outputs": [],
   "source": [
    "explanation_con.show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eh7a5W5LA9hd"
   },
   "source": [
    "You may have noticed that the choice of discretization for continuous features before perturbation could influence results.\n",
    "\n",
    "LIME has several other parameters (e.g. discretizer, kernel_width, kernel, distance_metric). Check the [documentation](https://lime-ml.readthedocs.io/en/latest/lime.html#module-lime.lime_tabular) and try different values.\n",
    "**Are feature importance results for the same instance ... stable?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-EX2MDMS6mdW"
   },
   "source": [
    "Global explanations cannot be extracted using LIME."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ros9IT_qAA3I"
   },
   "source": [
    "### 4.3 Limitations\n",
    "* Non-linear dependencies: assumes that the local decision boundary can be linearly approximated\n",
    "* Instability: the method has a lot of hyperparameters which may affect the resulting explanation (e.g. distance metric, perturbation strategy, discretization strategy).\n",
    "* Local explanations only\n",
    "* Interpretability: not easily interpretable by layusers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JNZ2YkjZujuu"
   },
   "source": [
    "### 5. Additional Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HxbPvpPFupT_"
   },
   "source": [
    "* Why Is the Current XAI Not Meeting the Expectations? https://cacm.acm.org/opinion/why-is-the-current-xai-not-meeting-the-expectations/\n",
    "* Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods: https://dl.acm.org/doi/abs/10.1145/3375627.3375830\n",
    "* Impossibility theorems for feature attribution: https://www.pnas.org/doi/10.1073/pnas.2304406120\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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