doc/source/examples/overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0c87744",
   "metadata": {},
   "source": [
    "# Alibi Overview Example\n",
    "\n",
    "This notebook aims to demonstrate each of the explainers Alibi provides on the same model and dataset. Unfortunately, this isn't possible as white-box neural network methods exclude tree-based white-box methods. Hence we will train both a neural network(TensorFlow) and a random forest model on the same dataset and apply the full range of explainers to see what insights we can obtain.\n",
    "\n",
    "The results and code from this notebook are used in the [documentation overview](../overview/high_level.md).\n",
    "\n",
    "This notebook requires the seaborn package for visualization which can be installed via pip:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13814dfa-27d8-424e-8ee4-8687b99fd2d5",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enusre all dependencies are met for this example, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[all]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ed8c1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b42fe771-fbcd-45e0-a613-6d93a0d7e80d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from io import BytesIO, StringIO\n",
    "from io import BytesIO\n",
    "from zipfile import ZipFile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import joblib\n",
    "import os.path\n",
    "\n",
    "import tensorflow as tf\n",
    "np.random.seed(0)\n",
    "tf.random.set_seed(0)\n",
    "\n",
    "FROM_SCRATCH = False\n",
    "TF_MODEL_FNAME = 'tf-clf-wine'\n",
    "RFC_FNAME = 'rfc-wine'\n",
    "ENC_FNAME = 'wine_encoder'\n",
    "DEC_FNAME = 'wine_decoder'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b561fff-7745-4219-bb84-1787cb7a3501",
   "metadata": {},
   "source": [
    "## Preparing the data.\n",
    "\n",
    "We're using the [wine-quality](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/) dataset, a numeric tabular dataset containing features that refer to the chemical composition of wines and quality ratings. To make this a simple classification task, we bucket all wines with ratings greater than five as good, and the rest we label bad. We also normalize all the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07175f7a-df18-435c-8508-2c37b0242322",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_wine_ds():\n",
    "    url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
    "    resp = requests.get(url, timeout=2)\n",
    "    resp.raise_for_status()\n",
    "    string_io = StringIO(resp.content.decode('utf-8'))\n",
    "    return pd.read_csv(string_io, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4927000-30b8-4fe4-84eb-a5d778fa072a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fetch_wine_ds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae34b435-ed6c-4380-ba8b-1005cf7868ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'] = 'bad'\n",
    "df.loc[(df['quality'] > 5), 'class'] = 'good'\n",
    "\n",
    "features = [\n",
    "    'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
    "    'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
    "    'pH', 'sulphates', 'alcohol'\n",
    "]\n",
    "\n",
    "df['good'] = 0\n",
    "df['bad'] = 0\n",
    "df.loc[df['class'] == 'good', 'good'] = 1\n",
    "df.loc[df['class'] == 'bad', 'bad'] = 1\n",
    "\n",
    "data = df[features].to_numpy()\n",
    "labels = df[['class','good', 'bad']].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, labels, random_state=0)\n",
    "X_train, X_test = X_train.astype('float32'), X_test.astype('float32')\n",
    "y_train_lab, y_test_lab = y_train[:, 0], y_test[:, 0]\n",
    "y_train, y_test = y_train[:, 1:].astype('float32'), y_test[:, 1:].astype('float32')\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a294d4-82f2-4729-ad66-872fd2925f4c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Select good wine instance \n",
    "\n",
    "We partition the dataset into good and bad portions and select an instance of interest. I've chosen it to be a good quality wine. \n",
    "\n",
    "**Note** that bad wines are class 1 and correspond to the second model output being high, whereas good wines are class 0 and correspond to the first model output being high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "926bbabd-946b-4ef3-9aec-6e27df923504",
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_wines = np.array([a for a, b in zip(X_train, y_train) if b[1] == 1])\n",
    "good_wines = np.array([a for a, b in zip(X_train, y_train) if b[1] == 0])\n",
    "x = np.array([[9.2, 0.36, 0.34, 1.6, 0.062, 5., 12., 0.99667, 3.2, 0.67, 10.5]]) # prechosen instance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd1a33a-5a4e-4c6c-a3c8-56776d656fc5",
   "metadata": {},
   "source": [
    "## Training models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3ff7b6-50c7-4e8c-9436-188cefba608d",
   "metadata": {},
   "source": [
    "### Creating an Autoencoder\n",
    "\n",
    "For some of the explainers, we need an autoencoder to check whether example instances are close to the training data distribution or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af5d71d3-729a-4fee-9f5a-b60bbe8dadc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow import keras\n",
    "\n",
    "\n",
    "ENCODING_DIM = 7\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 100\n",
    "\n",
    "\n",
    "class AE(keras.Model):\n",
    "    def __init__(self, encoder: keras.Model, decoder: keras.Model, **kwargs) -> None:\n",
    "        super().__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "\n",
    "    def call(self, x: tf.Tensor, **kwargs):\n",
    "        z = self.encoder(x)\n",
    "        x_hat = self.decoder(z)\n",
    "        return x_hat\n",
    "    \n",
    "def make_ae():\n",
    "    len_input_output = X_train.shape[-1]\n",
    "\n",
    "    encoder = keras.Sequential()\n",
    "    encoder.add(Dense(units=ENCODING_DIM*2, activation=\"relu\", input_shape=(len_input_output, )))\n",
    "    encoder.add(Dense(units=ENCODING_DIM, activation=\"relu\"))\n",
    "\n",
    "    decoder = keras.Sequential()\n",
    "    decoder.add(Dense(units=ENCODING_DIM*2, activation=\"relu\", input_shape=(ENCODING_DIM, )))\n",
    "    decoder.add(Dense(units=len_input_output, activation=\"linear\"))\n",
    "\n",
    "    ae = AE(encoder=encoder, decoder=decoder)\n",
    "\n",
    "    ae.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    history = ae.fit(\n",
    "        scaler.transform(X_train), \n",
    "        scaler.transform(X_train), \n",
    "        batch_size=BATCH_SIZE, \n",
    "        epochs=EPOCHS, \n",
    "        verbose=False,)\n",
    "\n",
    "    # loss = history.history['loss']\n",
    "    # plt.plot(loss)\n",
    "    # plt.xlabel('Epoch')\n",
    "    # plt.ylabel('MSE-Loss')\n",
    "\n",
    "    ae.encoder.save(f'{ENC_FNAME}.h5')\n",
    "    ae.decoder.save(f'{DEC_FNAME}.h5')\n",
    "    return ae\n",
    "\n",
    "def load_ae_model():\n",
    "    encoder = load_model(f'{ENC_FNAME}.h5')\n",
    "    decoder = load_model(f'{DEC_FNAME}.h5')\n",
    "    return AE(encoder=encoder, decoder=decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91035836-4c13-489b-a865-bed0817aaeb0",
   "metadata": {},
   "source": [
    "### Random Forest Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1371f3be-fafd-45c6-978b-d2c65924d21e",
   "metadata": {},
   "source": [
    "We need a tree-based model to get results for the tree SHAP explainer. Hence we train a random forest on the wine-quality dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07246f62-3040-475e-843e-b0366c3d94fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "\n",
    "def make_rfc():\n",
    "    rfc = RandomForestClassifier(n_estimators=50)\n",
    "    rfc.fit(scaler.transform(X_train), y_train_lab)\n",
    "    y_pred = rfc.predict(scaler.transform(X_test))\n",
    "\n",
    "    print('accuracy_score:', accuracy_score(y_pred, y_test_lab))\n",
    "    print('f1_score:', f1_score(y_test_lab, y_pred, average=None))\n",
    "\n",
    "    joblib.dump(rfc, f\"{RFC_FNAME}.joblib\")\n",
    "    return rfc\n",
    "\n",
    "\n",
    "def load_rfc_model():\n",
    "    return joblib.load(f\"{RFC_FNAME}.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbdd201e-2783-42d3-a345-cf742361be67",
   "metadata": {},
   "source": [
    "### Tensorflow Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c29f129-3e75-4fc7-8893-18ab09019ae9",
   "metadata": {},
   "source": [
    "Finally, we also train a TensorFlow model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a28547f-1e0a-4bf4-9f18-5451127ddb77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers \n",
    "\n",
    "def make_tf_model():\n",
    "    inputs = keras.Input(shape=X_train.shape[1])\n",
    "    x = layers.Dense(6, activation=\"relu\")(inputs)\n",
    "    outputs = layers.Dense(2, activation=\"softmax\")(x)\n",
    "    model = keras.Model(inputs, outputs)\n",
    "\n",
    "    model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=['accuracy'])\n",
    "    history = model.fit(\n",
    "        scaler.transform(X_train), \n",
    "        y_train,\n",
    "        epochs=30, \n",
    "        verbose=False, \n",
    "        validation_data=(scaler.transform(X_test), y_test),\n",
    "    )\n",
    "\n",
    "    y_pred = model(scaler.transform(X_test)).numpy().argmax(axis=1)\n",
    "    print('accuracy_score:', accuracy_score(y_pred, y_test.argmax(axis=1)))\n",
    "    print('f1_score:', f1_score(y_pred, y_test.argmax(axis=1), average=None))\n",
    "\n",
    "    model.save(f'{TF_MODEL_FNAME}.h5')\n",
    "    return model\n",
    "\n",
    "def load_tf_model():\n",
    "    return load_model(f'{TF_MODEL_FNAME}.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7e779e-656d-48be-91b2-14ae32e2838c",
   "metadata": {},
   "source": [
    "### Load/Make models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6faa7413-c589-45ed-9c64-96b8ddbac469",
   "metadata": {},
   "source": [
    "We save and load the same models each time to ensure stable results. If they don't exist we create new ones. If you want to generate new models on each notebook run, then set `FROM_SCRATCH=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4dc61ede-5732-4641-b004-0be40fbf9e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score: 0.74\n",
      "f1_score: [0.75471698 0.72340426]\n",
      "accuracy_score: 0.815\n",
      "f1_score: [0.8        0.82790698]\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "if FROM_SCRATCH or not os.path.isfile(f'{TF_MODEL_FNAME}.h5'):\n",
    "    model = make_tf_model()\n",
    "    rfc = make_rfc()\n",
    "    ae = make_ae()\n",
    "else:\n",
    "    rfc = load_rfc_model()\n",
    "    model = load_tf_model() \n",
    "    ae = load_ae_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a89f7e-7a76-4c9f-b313-ce963227f548",
   "metadata": {},
   "source": [
    "## Util functions\n",
    "\n",
    "These are utility functions for exploring results. The first shows two instances of the data side by side and compares the difference. We'll use this to see how the counterfactuals differ from their original instances. The second function plots the importance of each feature. This will be useful for visualizing the attribution methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "231a5665-6dfc-475c-b456-0f25598c319b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_instances(x, cf):\n",
    "    \"\"\"\n",
    "    Show the difference in values between two instances.\n",
    "    \"\"\"\n",
    "    x = x.astype('float64')\n",
    "    cf = cf.astype('float64')\n",
    "    for f, v1, v2 in zip(features, x[0], cf[0]):\n",
    "        print(f'{f:<25} instance: {round(v1, 3):^10} counter factual: {round(v2, 3):^10} difference: {round(v1 - v2, 7):^5}')\n",
    "\n",
    "def plot_importance(feat_imp, feat_names, class_idx, **kwargs):\n",
    "    \"\"\"\n",
    "    Create a horizontal barchart of feature effects, sorted by their magnitude.\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.DataFrame(data=feat_imp, columns=feat_names).sort_values(by=0, axis='columns')\n",
    "    feat_imp, feat_names = df.values[0], df.columns\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    y_pos = np.arange(len(feat_imp))\n",
    "    ax.barh(y_pos, feat_imp)\n",
    "    ax.set_yticks(y_pos)\n",
    "    ax.set_yticklabels(feat_names, fontsize=15)\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_xlabel(f'Feature effects for class {class_idx}', fontsize=15)\n",
    "    return ax, fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a723129-19e5-4f87-b1c2-bfec2a4c69b1",
   "metadata": {},
   "source": [
    "## Local Feature Attribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559872af-99b6-4314-b418-0be491f3f405",
   "metadata": {},
   "source": [
    "### Integrated Gradients\n",
    "\n",
    "The integrated gradients (IG) method computes the attribution of each feature by integrating the model partial derivatives along a path from a baseline point to the instance. This accumulates the changes in the prediction that occur due to the changing feature values. These accumulated values represent how each feature contributes to the prediction for the instance of interest.\n",
    "\n",
    "We illustrate the application of IG to the instance of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8fb53ef-d965-4bd5-b70c-09badbfbfc38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/.local/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class \"good\"'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import IntegratedGradients\n",
    "\n",
    "ig = IntegratedGradients(model,\n",
    "                         layer=None,\n",
    "                         method=\"gausslegendre\",\n",
    "                         n_steps=50,\n",
    "                         internal_batch_size=100)\n",
    "\n",
    "result = ig.explain(scaler.transform(x), target=0)\n",
    "\n",
    "plot_importance(result.data['attributions'][0], features, '\"good\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91775149-0ff9-46d7-a71d-d32c910c2757",
   "metadata": {},
   "source": [
    "### Kernel SHAP\n",
    "\n",
    "Kernel SHAP is a method for computing the Shapley values of a model around an instance. Shapley values are a game-theoretic method of assigning payout to players depending on their contribution to an overall goal. In our case, the features are the players, and the payouts are the attributions.\n",
    "\n",
    "Here we give an example of Kernel SHAP method applied to the Tensorflow model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "442a2603-1aed-4cd2-a3dc-f4965f5efaec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.55it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class 0'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: model(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result = explainer.explain(x)\n",
    "\n",
    "plot_importance(result.shap_values[0], features, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a6161f-13b6-4156-b378-bd689e68d00f",
   "metadata": {},
   "source": [
    "Here we apply Kernel SHAP to the Tree-based model to compare to the tree-based methods we run later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77deef77-42c1-4450-b381-fde4b1079e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.21it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class \"Good\"'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: rfc.predict_proba(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result = explainer.explain(x)\n",
    "\n",
    "plot_importance(result.shap_values[1], features, '\"Good\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f0d0aa-f805-4ca7-9d26-4280548dd66b",
   "metadata": {},
   "source": [
    "### Interventional treeSHAP\n",
    "\n",
    "Interventional tree SHAP computes the same Shapley values as the kernel SHAP method above. The difference is that it's much faster for tree-based models. Here it is applied to the random forest we trained. Comparison with the kernel SHAP results above show very similar outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d3b1ce0-8d52-4a6f-b30c-40779b9b2a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class \"Good\"'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import TreeShap\n",
    "\n",
    "tree_explainer_interventional = TreeShap(rfc, model_output='raw', task='classification')\n",
    "tree_explainer_interventional.fit(scaler.transform(X_train[0:100]))\n",
    "result = tree_explainer_interventional.explain(scaler.transform(x), check_additivity=False)\n",
    "\n",
    "plot_importance(result.shap_values[1], features, '\"Good\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c05d4f-13b8-4266-bced-510664ba7342",
   "metadata": {},
   "source": [
    "### Path Dependent treeSHAP\n",
    "\n",
    "Path Dependent tree SHAP gives the same results as the Kernel SHAP method, only faster. Here it is applied to a random forest model. Again very similar results to kernel SHAP and Interventional tree SHAP as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aeef4572-2c4e-41ea-8298-80c95476be96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class \"Good\"'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_dependent_explainer = TreeShap(rfc, model_output='raw', task='classification')\n",
    "path_dependent_explainer.fit()\n",
    "result = path_dependent_explainer.explain(scaler.transform(x))\n",
    "\n",
    "plot_importance(result.shap_values[1], features, '\"Good\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ccd4c7-b4b9-4b3b-bf5e-75aeb8653bc7",
   "metadata": {},
   "source": [
    "**Note**: There is some difference between the kernel SHAP and integrated gradient applied to the TensorFlow model and the SHAP methods applied to the random forest. This is expected due to the combination of different methods and models. They are reasonably similar overall. Notably, the ordering is nearly the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecdab93-3226-4b76-a0d0-e01b740b865e",
   "metadata": {},
   "source": [
    "## Local Necessary Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c32759e-e66f-4af9-91af-e5d1deb01019",
   "metadata": {},
   "source": [
    "### Anchors\n",
    "\n",
    "Anchors tell us what features need to stay the same for a specific instance for the model to give the same classification. In the case of a trained image classification model, an anchor for a given instance would be a minimal subset of the image that the model uses to make its decision.\n",
    "\n",
    "Here we apply Anchors to the tensor flow model trained on the wine-quality dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e36d1c9-49e1-4da8-9bc8-da312e2b31b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from alibi.explainers import AnchorTabular\n",
    "\n",
    "predict_fn = lambda x: model.predict(scaler.transform(x))\n",
    "explainer = AnchorTabular(predict_fn, features)\n",
    "explainer.fit(X_train, disc_perc=(25, 50, 75))\n",
    "result = explainer.explain(x, threshold=0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1600dade-52d0-40b8-a29c-d8279bbb1a94",
   "metadata": {},
   "source": [
    "The result is a set of predicates that tell you whether a point in the data set is in the anchor or not. If it is in the anchor, it is very likely to have the same classification as the instance `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b3722d3-f837-4343-a2de-c7f4d4849223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anchor = ['alcohol > 10.10', 'volatile acidity <= 0.39']\n",
      "Precision =  0.9513641755634639\n",
      "Coverage =  0.16263552960800667\n"
     ]
    }
   ],
   "source": [
    "print('Anchor =', result.data['anchor'])\n",
    "print('Precision = ', result.data['precision'])\n",
    "print('Coverage = ', result.data['coverage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a91fed-7171-4dae-abd4-9e8709bb5aa8",
   "metadata": {},
   "source": [
    "## Global Feature Attribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a604116-6772-469d-b251-05fb920f6fb7",
   "metadata": {},
   "source": [
    "### ALE \n",
    "\n",
    "ALE plots show the dependency of model output on a subset of the input features. They provide global insight describing the model's behaviour over the input space. Here we use ALE to directly visualize the relationship between the TensorFlow model's predictions and the alcohol content of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3596db3d-40bb-42e4-9b59-1ae18f6be64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='alcohol', ylabel='ALE'>]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import ALE\n",
    "from alibi.explainers.ale import plot_ale\n",
    "\n",
    "predict_fn = lambda x: model(scaler.transform(x)).numpy()[:, 0]\n",
    "ale = ALE(predict_fn, feature_names=features)\n",
    "exp = ale.explain(X_train)\n",
    "plot_ale(exp, features=['alcohol'], line_kw={'label': 'Probability of \"good\" class'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43934de4-cdb7-497c-9eb4-d83bb1a9a8c6",
   "metadata": {},
   "source": [
    "## Counterfactuals\n",
    "\n",
    "Next, we apply each of the \"counterfactuals with reinforcement learning\", \"counterfactual instances\", \"contrastive explanation method\", and the \"counterfactuals with prototypes\" methods. We also plot the kernel SHAP values to show how the counterfactual methods change the attribution of each feature leading to the change in prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aacbe189-a135-4ef9-bcf4-35df9019d5bb",
   "metadata": {},
   "source": [
    "### Counter Factuals with Reinforcement Learning\n",
    "\n",
    "CFRL trains a new model when fitting the explainer called an actor that takes instances and produces counterfactuals. It does this using reinforcement learning. In reinforcement learning, an actor model takes some state as input and generates actions; in our case, the actor takes an instance with a target classification and attempts to produce a member of the target class. Outcomes of actions are assigned rewards dependent on a reward function designed to encourage specific behaviors. In our case, we reward correctly classified counterfactuals generated by the actor. As well as this, we reward counterfactuals that are close to the data distribution as modeled by an autoencoder. Finally, we require that they are sparse perturbations of the original instance. The reinforcement training step pushes the actor to take high reward actions. CFRL is a black-box method as the process by which we update the actor to maximize the reward only requires estimating the reward via sampling the counterfactuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7fbd60a-64af-43f9-80d3-9b78cfde8079",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:50<00:00, 90.18it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 167.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance class prediction: 0\n",
      "Counterfactual class prediction: 1\n"
     ]
    }
   ],
   "source": [
    "from alibi.explainers import CounterfactualRL \n",
    "\n",
    "predict_fn = lambda x: model(x)\n",
    "\n",
    "cfrl_explainer = CounterfactualRL(\n",
    "    predictor=predict_fn,               # The model to explain\n",
    "    encoder=ae.encoder,                 # The encoder\n",
    "    decoder=ae.decoder,                 # The decoder\n",
    "    latent_dim=7,                       # The dimension of the autoencoder latent space\n",
    "    coeff_sparsity=0.5,                 # The coefficient of sparsity\n",
    "    coeff_consistency=0.5,              # The coefficient of consistency\n",
    "    train_steps=10000,                  # The number of training steps\n",
    "    batch_size=100,                     # The batch size\n",
    ")\n",
    "\n",
    "cfrl_explainer.fit(X=scaler.transform(X_train))\n",
    "\n",
    "result_cfrl = cfrl_explainer.explain(X=scaler.transform(x), Y_t=np.array([1]))\n",
    "print(\"Instance class prediction:\", model.predict(scaler.transform(x))[0].argmax())\n",
    "print(\"Counterfactual class prediction:\", model.predict(result_cfrl.data['cf']['X'])[0].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6559f2e3-72db-4ca3-b949-8e3d7371a10a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity             instance:    9.2     counter factual:   8.965    difference: 0.2350657\n",
      "volatile acidity          instance:    0.36    counter factual:   0.349    difference: 0.0108247\n",
      "citric acid               instance:    0.34    counter factual:   0.242    difference: 0.0977357\n",
      "residual sugar            instance:    1.6     counter factual:   2.194    difference: -0.5943643\n",
      "chlorides                 instance:   0.062    counter factual:   0.059    difference: 0.0031443\n",
      "free sulfur dioxide       instance:    5.0     counter factual:   6.331    difference: -1.3312454\n",
      "total sulfur dioxide      instance:    12.0    counter factual:   14.989   difference: -2.9894428\n",
      "density                   instance:   0.997    counter factual:   0.997    difference: -0.0003435\n",
      "pH                        instance:    3.2     counter factual:   3.188    difference: 0.0118126\n",
      "sulphates                 instance:    0.67    counter factual:   0.598    difference: 0.0718592\n",
      "alcohol                   instance:    10.5    counter factual:   9.829    difference: 0.6712008\n"
     ]
    }
   ],
   "source": [
    "cfrl = scaler.inverse_transform(result_cfrl.data['cf']['X'])\n",
    "compare_instances(x, cfrl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce1c7e49-c6dc-41c5-9214-d33f42569231",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  2.66it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00,  2.55it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class 0'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: model(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result_x = explainer.explain(x)\n",
    "result_cfrl = explainer.explain(cfrl)\n",
    "\n",
    "plot_importance(result_x.shap_values[0], features, 0)\n",
    "plot_importance(result_cfrl.shap_values[0], features, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9223ff2e-66c7-40a0-ba8a-7655ef7650ca",
   "metadata": {},
   "source": [
    "### Counterfactual Instances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d142f8a-c97f-4965-8b76-840b0aed5164",
   "metadata": {},
   "source": [
    "First we need to revert to using tfv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4fc13edf-3e05-4a7f-b5d6-f822923df98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/alex/miniconda3/envs/alibi-explain/lib/python3.8/site-packages/tensorflow/python/compat/v2_compat.py:111: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38105a7b-b86e-4720-9015-e7838ed82e0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n",
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model, load_model\n",
    "model = load_tf_model() \n",
    "ae = load_ae_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d48f384-3c88-4735-84c0-af9b0d76b7bd",
   "metadata": {},
   "source": [
    "The counterfactual instance method in alibi generates counterfactuals by defining a loss that prefers interpretable instances close to the target class. It then uses gradient descent to move within the feature space until it obtains a counterfactual of sufficient quality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33cf22f1-0adb-4cfb-b258-aadcab34c46e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/alex/Development/alibi-explain/alibi/explainers/counterfactual.py:170: The name tf.keras.backend.get_session is deprecated. Please use tf.compat.v1.keras.backend.get_session instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance class prediction: 0\n",
      "Counterfactual class prediction: 1\n"
     ]
    }
   ],
   "source": [
    "from alibi.explainers import Counterfactual\n",
    "\n",
    "explainer = Counterfactual(\n",
    "    model,                              # The model to explain\n",
    "    shape=(1,) + X_train.shape[1:],     # The shape of the model input\n",
    "    target_proba=0.51,                  # The target class probability\n",
    "    tol=0.01,                           # The tolerance for the loss\n",
    "    target_class='other',               # The target class to obtain  \n",
    ")\n",
    "\n",
    "result_cf = explainer.explain(scaler.transform(x))\n",
    "print(\"Instance class prediction:\", model.predict(scaler.transform(x))[0].argmax())\n",
    "print(\"Counterfactual class prediction:\", model.predict(result_cf.data['cf']['X'])[0].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56a4399b-659c-4d60-bd2c-e3c6ca259f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity             instance:    9.2     counter factual:    9.23    difference: -0.030319\n",
      "volatile acidity          instance:    0.36    counter factual:    0.36    difference: 0.0004017\n",
      "citric acid               instance:    0.34    counter factual:   0.334    difference: 0.0064294\n",
      "residual sugar            instance:    1.6     counter factual:   1.582    difference: 0.0179322\n",
      "chlorides                 instance:   0.062    counter factual:   0.061    difference: 0.0011683\n",
      "free sulfur dioxide       instance:    5.0     counter factual:   4.955    difference: 0.0449123\n",
      "total sulfur dioxide      instance:    12.0    counter factual:   11.324   difference: 0.6759205\n",
      "density                   instance:   0.997    counter factual:   0.997    difference: -5.08e-05\n",
      "pH                        instance:    3.2     counter factual:   3.199    difference: 0.0012383\n",
      "sulphates                 instance:    0.67    counter factual:    0.64    difference: 0.0297857\n",
      "alcohol                   instance:    10.5    counter factual:    9.88    difference: 0.6195097\n"
     ]
    }
   ],
   "source": [
    "cf = scaler.inverse_transform(result_cf.data['cf']['X'])\n",
    "compare_instances(x, cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0d7becb3-3006-468a-9b98-1ebd02dc7288",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.23s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class 0'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: model.predict(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result_x = explainer.explain(x)\n",
    "result_cf = explainer.explain(cf)\n",
    "\n",
    "plot_importance(result_x.shap_values[0], features, 0)\n",
    "plot_importance(result_cf.shap_values[0], features, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ad53ba-d63e-4edc-b9d0-bc4ba305a593",
   "metadata": {},
   "source": [
    "### Contrastive Explanations Method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5db134-dd36-44c1-9b19-b04dddd66a2b",
   "metadata": {},
   "source": [
    "The CEM method generates counterfactuals by defining a loss that prefers interpretable instances close to the target class. It also adds an autoencoder reconstruction loss to ensure the counterfactual stays within the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7aa6351b-01fd-4f6b-8f05-92f187b2737a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance class prediction: 0\n",
      "Counterfactual class prediction: 1\n"
     ]
    }
   ],
   "source": [
    "from alibi.explainers import CEM\n",
    "\n",
    "cem = CEM(model,                            # model to explain\n",
    "          shape=(1,) + X_train.shape[1:],   # shape of the model input\n",
    "          mode='PN',                        # pertinant negative mode\n",
    "          kappa=0.2,                        # Confidence parameter for the attack loss term\n",
    "          beta=0.1,                         # Regularization constant for L1 loss term\n",
    "          ae_model=ae                       # autoencoder model\n",
    ")\n",
    "\n",
    "cem.fit(\n",
    "    scaler.transform(X_train), # scaled training data\n",
    "    no_info_type='median'      # non-informative value for each feature\n",
    ")\n",
    "result_cem = cem.explain(scaler.transform(x), verbose=False)\n",
    "cem_cf = result_cem.data['PN']\n",
    "\n",
    "print(\"Instance class prediction:\", model.predict(scaler.transform(x))[0].argmax())\n",
    "print(\"Counterfactual class prediction:\", model.predict(cem_cf)[0].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "96abe72c-2e01-402d-b2ed-9b61f208a95b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity             instance:    9.2     counter factual:    9.2     difference: 2e-07\n",
      "volatile acidity          instance:    0.36    counter factual:    0.36    difference: -0.0 \n",
      "citric acid               instance:    0.34    counter factual:    0.34    difference: -0.0 \n",
      "residual sugar            instance:    1.6     counter factual:   1.479    difference: 0.1211611\n",
      "chlorides                 instance:   0.062    counter factual:   0.057    difference: 0.0045941\n",
      "free sulfur dioxide       instance:    5.0     counter factual:   2.707    difference: 2.2929246\n",
      "total sulfur dioxide      instance:    12.0    counter factual:    12.0    difference: 1.9e-06\n",
      "density                   instance:   0.997    counter factual:   0.997    difference: -0.0004602\n",
      "pH                        instance:    3.2     counter factual:    3.2     difference: -0.0 \n",
      "sulphates                 instance:    0.67    counter factual:   0.549    difference: 0.121454\n",
      "alcohol                   instance:    10.5    counter factual:   9.652    difference: 0.8478804\n"
     ]
    }
   ],
   "source": [
    "cem_cf = result_cem.data['PN']\n",
    "cem_cf = scaler.inverse_transform(cem_cf)\n",
    "compare_instances(x, cem_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "298651d6-de8b-405e-a174-a207df37ffe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.22s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.21s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Feature effects for class 0'>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: model.predict(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result_x = explainer.explain(x)\n",
    "result_cem_cf = explainer.explain(cem_cf)\n",
    "\n",
    "plot_importance(result_x.shap_values[0], features, 0)\n",
    "plot_importance(result_cem_cf.shap_values[0], features, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f58f8b-944a-4a5d-8d74-fc2fce4b0f6d",
   "metadata": {},
   "source": [
    "### Counterfactual With Prototypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8777b84e-7bc6-47ca-be2b-10943084103f",
   "metadata": {},
   "source": [
    "Like the previous two methods, \"counterfactuals with prototypes\" defines a loss that guides the counterfactual towards the target class while also using an autoencoder to ensure it stays within the data distribution. As well as this, it uses prototype instances of the target class to ensure that the generated counterfactual is interpretable as a member of the target class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7a63b6f-58a1-4fc0-9aa6-528507158eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance class prediction: 0\n",
      "Counterfactual class prediction: 1\n"
     ]
    }
   ],
   "source": [
    "from alibi.explainers import CounterfactualProto\n",
    "\n",
    "explainer = CounterfactualProto(\n",
    "    model,                           # The model to explain\n",
    "    shape=(1,) + X_train.shape[1:],  # shape of the model input\n",
    "    ae_model=ae,                     # The autoencoder\n",
    "    enc_model=ae.encoder             # The encoder\n",
    ")\n",
    "\n",
    "explainer.fit(scaler.transform(X_train)) # Fit the explainer with scaled data\n",
    "\n",
    "result_proto = explainer.explain(scaler.transform(x), verbose=False)\n",
    "\n",
    "proto_cf = result_proto.data['cf']['X']\n",
    "print(\"Instance class prediction:\", model.predict(scaler.transform(x))[0].argmax())\n",
    "print(\"Counterfactual class prediction:\", model.predict(proto_cf)[0].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e6116d6-b739-436c-8179-1cbaddf218c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity             instance:    9.2     counter factual:    9.2     difference: 2e-07\n",
      "volatile acidity          instance:    0.36    counter factual:    0.36    difference: -0.0 \n",
      "citric acid               instance:    0.34    counter factual:    0.34    difference: -0.0 \n",
      "residual sugar            instance:    1.6     counter factual:    1.6     difference: 1e-07\n",
      "chlorides                 instance:   0.062    counter factual:   0.062    difference:  0.0 \n",
      "free sulfur dioxide       instance:    5.0     counter factual:    5.0     difference:  0.0 \n",
      "total sulfur dioxide      instance:    12.0    counter factual:    12.0    difference: 1.9e-06\n",
      "density                   instance:   0.997    counter factual:   0.997    difference: -0.0 \n",
      "pH                        instance:    3.2     counter factual:    3.2     difference: -0.0 \n",
      "sulphates                 instance:    0.67    counter factual:   0.623    difference: 0.0470144\n",
      "alcohol                   instance:    10.5    counter factual:   9.942    difference: 0.558073\n"
     ]
    }
   ],
   "source": [
    "proto_cf = scaler.inverse_transform(proto_cf)\n",
    "compare_instances(x, proto_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1d1e5206-cca6-4897-a486-8a4bf070e93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16304971136152735\n",
      "-0.20360063157975683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alibi.explainers import KernelShap\n",
    "\n",
    "predict_fn = lambda x: model.predict(scaler.transform(x))\n",
    "\n",
    "explainer = KernelShap(predict_fn, task='classification')\n",
    "\n",
    "explainer.fit(X_train[0:100])\n",
    "\n",
    "result_x = explainer.explain(x)\n",
    "result_proto_cf = explainer.explain(cem_cf)\n",
    "\n",
    "plot_importance(result_x.shap_values[0], features, 0)\n",
    "print(result_x.shap_values[0].sum())\n",
    "plot_importance(result_proto_cf.shap_values[0], features, 0)\n",
    "print(result_proto_cf.shap_values[0].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f4ccc1-d2ef-4623-a8b8-dae2518c3d26",
   "metadata": {},
   "source": [
    "Looking at the ALE plots below, we can see how the counterfactual methods change the features to flip the prediction. Note that the ALE plots potentially miss details local to individual instances as they are global insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "20c990ef-808f-4502-9806-6d03c6635a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='sulphates', ylabel='ALE'>,\n",
       "        <AxesSubplot:xlabel='alcohol', ylabel='ALE'>,\n",
       "        <AxesSubplot:xlabel='residual sugar', ylabel='ALE'>],\n",
       "       [<AxesSubplot:xlabel='chlorides', ylabel='ALE'>,\n",
       "        <AxesSubplot:xlabel='free sulfur dioxide', ylabel='ALE'>,\n",
       "        <AxesSubplot:xlabel='total sulfur dioxide', ylabel='ALE'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ale(exp, features=['sulphates', 'alcohol', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide'], line_kw={'label': 'Probability of \"good\" class'})"
   ]
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}