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 "cells": [
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   "source": [
    "# Quantus + NLP\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/understandable-machine-intelligence-lab/Quantus/main?labpath=tutorials%2FTutorial_NLP_Demonstration.ipynb)\n",
    "\n",
    "\n",
    "This tutorial demonstrates how to use the library for robustness evaluation\n",
    "explanation of text classification models.\n",
    "For this purpose, we use a pre-trained `Distilbert` model from [Huggingface](https://huggingface.co/models) and `GLUE/SST2` dataset [here](https://huggingface.co/datasets/sst2).\n",
    "\n",
    "This is not a working example yet, and is meant only for demonstration purposes \n",
    "so far. For this demo, we use a (yet) unreleased version of Quantus.\n",
    "\n",
    "Author: Artem Sereda\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eWK9ebfMUVRG4mrOAQvXdJ452SMLfffv?usp=sharing)"
   ],
   "metadata": {
    "collapsed": false,
    "id": "1sXtIxKhnXp9"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from __future__ import annotations"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Use an unreleased version of Quantus.\n",
    "!pip install 'quantus @ git+https://github.com/aaarrti/Quantus.git@nlp-domain' --no-deps\n",
    "!pip install transformers datasets nlpaug tf_explain tensorflow_probability"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "import tensorflow as tf\n",
    "from functools import partial\n",
    "import logging\n",
    "from typing import NamedTuple, List, Any\n",
    "from transformers import AutoTokenizer, TFDistilBertForSequenceClassification, TFPreTrainedModel, PreTrainedTokenizerFast\n",
    "import quantus.nlp as qn\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "# Suppress debug logs.\n",
    "logging.getLogger('absl').setLevel(logging.WARNING)\n",
    "tf.config.list_physical_devices()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1) Preliminaries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.1 Load pre-trained model and tokenizer from [huggingface](https://huggingface.co/models) hub"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: AMD Radeon Pro 560\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFDistilBertForSequenceClassification.\n",
      "\n",
      "All the layers of TFDistilBertForSequenceClassification were initialized from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "model = TFDistilBertForSequenceClassification.from_pretrained(MODEL_NAME)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.2 Load test split of [GLUE/SST2](https://huggingface.co/datasets/sst2) dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Found cached dataset sst2 (/Users/artemsereda/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5)\n"
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   "source": [
    "BATCH_SIZE = 8\n",
    "dataset = load_dataset(\"sst2\")['test']\n",
    "x_batch = dataset['sentence'][:BATCH_SIZE]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Run an example inference, and demonstrate models predictions."
   ],
   "metadata": {
    "collapsed": false
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   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                   0         1\n0             uneasy mishmash of styles and genres .  negative\n1  this film 's relationship to actual tension is...  negative\n2  by the end of no such thing the audience , lik...  positive\n3  director rob marshall went out gunning to make...  positive\n4  lathan and diggs have considerable personal ch...  positive\n5  a well-made and often lovely depiction of the ...  positive\n6  none of this violates the letter of behan 's b...  negative\n7  although it bangs a very cliched drum at times...  positive",
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     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "CLASS_NAMES = ['negative', 'positive']\n",
    "\n",
    "def decode_labels(y_batch: np.ndarray, class_names: List[str]) -> List[str]:\n",
    "    \"\"\"A helper function to map integer labels to human-readable class names.\"\"\"\n",
    "    return [class_names[i] for i in y_batch]\n",
    "\n",
    "# Run tokenizer.\n",
    "tokens = tokenizer(x_batch, padding='longest', return_tensors='tf')\n",
    "logits = model(**tokens).logits\n",
    "y_batch = tf.argmax(tf.nn.softmax(logits), axis=1).numpy()\n",
    "\n",
    "# Show the x, y data.\n",
    "pd.DataFrame([x_batch, decode_labels(y_batch, CLASS_NAMES)]).T"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.3 Helper functions: visualise explanations\n",
    "\n",
    "There are not many XAI libraries for NLP out there, so here we fully relly on our own implementations of explanation methods. This section write functions to visualise our explanations."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "def plot_textual_heatmap(explanations: List[qn.TokenSalience]):\n",
    "\n",
    "    \"\"\"\n",
    "    Plots attributions over a batch of text sequence explanations.\n",
    "\n",
    "    References:\n",
    "        - https://stackoverflow.com/questions/74046734/plot-text-saliency-map-in-jupyter-notebook\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    explanations: List of Named tuples (tokens, salience) containing batch of explanations.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    plot: matplotplib.pyplot object, which will be automatically rendered by jupyter.\n",
    "    \"\"\"\n",
    "\n",
    "    h_len = len(explanations)\n",
    "    v_len = len(explanations[0].tokens)\n",
    "\n",
    "    tokens = np.asarray([i.tokens for i in explanations]).reshape(-1)\n",
    "    colors = np.asarray([i.salience for i in explanations]).reshape(-1)\n",
    "\n",
    "    fig, axes = plt.subplots(h_len, v_len, figsize=(v_len, h_len*0.5), gridspec_kw=dict(left=0., right=1.))\n",
    "    for i, ax in enumerate(axes.ravel()):\n",
    "        rect = plt.Rectangle((0, 0), 1, 1, color=(1., 1 - colors[i],  1- colors[i]))\n",
    "        ax.add_patch(rect)\n",
    "        ax.text(0.5, 0.5, tokens[i], ha='center', va='center')\n",
    "        ax.set_xlim(0, 1)\n",
    "        ax.set_ylim(0, 1)\n",
    "        ax.axis('off')\n",
    "\n",
    "        ax = fig.add_axes([0, 0.05, 1 , 0.9], fc=[0, 0, 0, 0])\n",
    "    for axis in ['left', 'right']:\n",
    "        ax.spines[axis].set_visible(False)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    return plt"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.4 Helper functions: generate explanations\n",
    "\n",
    "Write out functions to generate explanations using baseline methods: Gradient Norm and Integrated Gradients"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "@tf.function(jit_compile=True)\n",
    "def normalize(x: tf.Tensor) -> tf.Tensor:\n",
    "    \"\"\"\n",
    "    Normalize attribution values to comply with RGB standards.\n",
    "        - Take absolute values.\n",
    "        - Scale attribution scores, so that maximum value is 1.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: 1D tensor containing attribution scores.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    x: 1D tensor containing normalized attribution scores.\n",
    "    \"\"\"\n",
    "    abs = tf.abs(x)\n",
    "    max = tf.reduce_max(abs)\n",
    "    return abs / max\n",
    "\n",
    "\n",
    "def explain_gradient_norm(\n",
    "        model: TFPreTrainedModel,\n",
    "        token_ids: tf.Tensor,\n",
    "        attention_mask: tf.Tensor,\n",
    "        target: int,\n",
    "        tokenizer: PreTrainedTokenizerFast\n",
    ") -> qn.TokenSalience:\n",
    "    \"\"\"\n",
    "    Computes token attribution score using the Gradient Norm method for a single point.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model:\n",
    "        Huggingface model, which is subject to explanation.\n",
    "    token_ids:\n",
    "        1D Array of token ids.\n",
    "    attention_mask:\n",
    "        1D array of attention mask.\n",
    "    target:\n",
    "        Predicted label.\n",
    "    tokenizer:\n",
    "        Huggingface tokenizer used to convert input_ids back to plain text tokens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    a: quantus.nlp.TokenSalience\n",
    "        Named tuple (tokens, salience), with tokens and their respective attribution scores.\n",
    "    \"\"\"\n",
    "    # Convert tokens to embeddings.\n",
    "    embeddings = model.distilbert.get_input_embeddings()(input_ids=token_ids)\n",
    "    with tf.GradientTape() as tape:\n",
    "        tape.watch(embeddings)\n",
    "        logits = model(None,\n",
    "                       inputs_embeds=embeddings,\n",
    "                       attention_mask=attention_mask\n",
    "                       ).logits\n",
    "        logits_for_label = tf.gather(logits, axis=1, indices=target)\n",
    "\n",
    "    # Compute gradients of logits with respect to embeddings.\n",
    "    grads = tape.gradient(logits_for_label, embeddings)\n",
    "    # Compute L2 norm of gradients.\n",
    "    grad_norm = tf.linalg.norm(grads, axis=-1)\n",
    "    with tf.device('cpu'):\n",
    "        scores = normalize(grad_norm[0]).numpy()\n",
    "    return qn.TokenSalience(tokenizer.convert_ids_to_tokens(token_ids), scores)\n",
    "\n",
    "\n",
    "def explain_gradient_norm_batch(\n",
    "        model: TFPreTrainedModel,\n",
    "        inputs: List[str],\n",
    "        targets: np.ndarray,\n",
    "        tokenizer: PreTrainedTokenizerFast\n",
    ") -> List[qn.TokenSalience]:\n",
    "    \"\"\"\n",
    "    Computes token attribution score using the Gradient Norm method for batch.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model:\n",
    "        Huggingface model, which is subject to explanation.\n",
    "    inputs:\n",
    "        List of plain text inputs.\n",
    "    targets:\n",
    "        1D array of predicted labels.\n",
    "    tokenizer:\n",
    "        Huggingface tokenizer used to convert input_ids back to plain text tokens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    a_batch: List of quantus.nlp.TokenSalience.\n",
    "        List of named tuples (tokens, salience), with tokens and their respective attribution scores.\n",
    "    \"\"\"\n",
    "    \"\"\"A wrapper around explain_gradient_norm which allows calling it on batch\"\"\"\n",
    "    tokens = tokenizer(inputs, return_tensors='tf', padding='longest')\n",
    "    batch_size = len(targets)\n",
    "    return [\n",
    "        explain_gradient_norm(model, tokens['input_ids'][i], tokens['attention_mask'][i], targets[i], tokenizer)\n",
    "        for i in range(batch_size)\n",
    "    ]\n",
    "\n",
    "\n",
    "@tf.function(jit_compile=True)\n",
    "def get_interpolated_inputs(\n",
    "        baseline: tf.Tensor,\n",
    "        target: tf.Tensor,\n",
    "        num_steps: int\n",
    ") -> tf.Tensor:\n",
    "    \"\"\"\n",
    "    Gets num_step linearly interpolated inputs from baseline to target.\n",
    "    Reference: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py#L238\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    interpolated_inputs: <float32>[num_steps, num_tokens, emb_size]\n",
    "    \"\"\"\n",
    "    baseline = tf.cast(baseline, dtype=tf.float64)\n",
    "    target = tf.cast(target, dtype=tf.float64)\n",
    "    delta = target - baseline  # <float32>[num_tokens, emb_size]\n",
    "    # Creates scale values array of shape [num_steps, num_tokens, emb_dim],\n",
    "    # where the values in scales[i] are the ith step from np.linspace. <float32>[num_steps, 1, 1]\n",
    "    scales = tf.linspace(0, 1, num_steps + 1)[:, tf.newaxis, tf.newaxis]\n",
    "    shape = (num_steps + 1,) + delta.shape\n",
    "    # <float32>[num_steps, num_tokens, emb_size]\n",
    "    deltas = scales * tf.broadcast_to(delta, shape)\n",
    "    interpolated_inputs = baseline + deltas\n",
    "    return interpolated_inputs\n",
    "\n",
    "\n",
    "\n",
    "def explain_int_grad(\n",
    "        model: TFPreTrainedModel,\n",
    "        token_ids: tf.Tensor,\n",
    "        attention_mask: tf.Tensor,\n",
    "        target: int,\n",
    "        tokenizer: PreTrainedTokenizerFast,\n",
    "        num_steps: int\n",
    ") -> qn.TokenSalience:\n",
    "    \"\"\"\n",
    "    Computes token attribution score using the Integrated Gradients method for a single point.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model:\n",
    "        Huggingface model, which is subject to explanation.\n",
    "    token_ids:\n",
    "        1D Array of token ids.\n",
    "    attention_mask:\n",
    "        1D array of attention mask.\n",
    "    target:\n",
    "        Predicted label.\n",
    "    tokenizer:\n",
    "        Huggingface tokenizer used to convert input_ids back to plain text tokens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    a: quantus.nlp.TokenSalience\n",
    "        Named tuple (tokens, salience), with tokens and their respective attribution scores.\n",
    "    \"\"\"\n",
    "    # Convert tokens to embeddings.\n",
    "    embeddings = model.distilbert.get_input_embeddings()(input_ids=token_ids)[0]\n",
    "    baseline = tf.zeros_like(embeddings)\n",
    "    # Generate interpolation from 0 to embeddings.\n",
    "    with tf.device('cpu'):\n",
    "        interpolated_embeddings = get_interpolated_inputs(baseline, embeddings, num_steps)\n",
    "    interpolated_embeddings = tf.cast(interpolated_embeddings, tf.float32)\n",
    "    interpolated_attention_mask = tf.stack([attention_mask for i in range(num_steps + 1)])\n",
    "    with tf.GradientTape() as tape:\n",
    "        tape.watch(interpolated_embeddings)\n",
    "        logits = model(None,\n",
    "                       inputs_embeds=interpolated_embeddings,\n",
    "                       attention_mask=interpolated_attention_mask,\n",
    "                       ).logits\n",
    "        logits_for_label = tf.gather(logits, axis=1, indices=target)\n",
    "\n",
    "    # Compute gradients of logits with respect to interpolations.\n",
    "    grads = tape.gradient(logits_for_label, interpolated_embeddings)\n",
    "    # Integrate gradients.\n",
    "    int_grad = tfp.math.trapz(tfp.math.trapz(grads, axis=0))\n",
    "    with tf.device('cpu'):\n",
    "        scores = normalize(int_grad).numpy()\n",
    "    return qn.TokenSalience(tokenizer.convert_ids_to_tokens(token_ids), scores)\n",
    "\n",
    "\n",
    "def explain_int_grad_batch(\n",
    "        model: TFPreTrainedModel,\n",
    "        inputs: List[str],\n",
    "        targets: np.ndarray,\n",
    "        tokenizer: PreTrainedTokenizerFast,\n",
    "        num_steps: int = 10\n",
    ") -> List[qn.TokenSalience]:\n",
    "    \"\"\"\n",
    "    Computes token attribution score using the Integrated Gradients method for batch.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model:\n",
    "        Huggingface model, which is subject to explanation.\n",
    "    inputs:\n",
    "        List of plain text inputs.\n",
    "    targets:\n",
    "        1D array of predicted labels.\n",
    "    tokenizer:\n",
    "        Huggingface tokenizer used to convert input_ids back to plain text tokens.\n",
    "\n",
    "    num_steps: int.\n",
    "        Number of interpolations steps, default=10.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    a_batch: List of quantus.nlp.TokenSalience.\n",
    "        List of named tuples (tokens, salience), with tokens and their respective attribution scores.\n",
    "    \"\"\"\n",
    "    tokens = tokenizer(inputs, return_tensors='tf', padding='longest')\n",
    "    batch_size = len(targets)\n",
    "    return [\n",
    "        explain_int_grad(model, tokens['input_ids'][i], tokens['attention_mask'][i], targets[i], tokenizer, num_steps)\n",
    "        for i in range(batch_size)\n",
    "    ]\n",
    "\n",
    "\n",
    "\n",
    "# Create functions which match the signature required by Quantus.\n",
    "explain_gradient_norm_func = partial(explain_gradient_norm_batch, tokenizer=tokenizer)\n",
    "explain_int_grad_func = partial(explain_int_grad_batch, tokenizer=tokenizer)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.5 Visualise the explanations."
   ],
   "metadata": {
    "id": "Bo1yUcCh_zBD"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Visualise GradNorm.\n",
    "a_batch_grad_norm = explain_gradient_norm_func(model, x_batch[2:5], y_batch[2:5])\n",
    "plot_textual_heatmap(a_batch_grad_norm)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "jNogPZU8ShAr",
    "outputId": "25608dfc-5f87-4571-b8a8-909ab6de5a3f"
   },
   "execution_count": 11,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vv/f22t8y7d1l96ynv9mzgy0j5w0000gn/T/ipykernel_28733/2299850630.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_axes([0, 0.05, 1 , 0.9], fc=[0, 0, 0, 0])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/Users/artemsereda/anaconda3/envs/quantus/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2400x150 with 73 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vv/f22t8y7d1l96ynv9mzgy0j5w0000gn/T/ipykernel_28733/2299850630.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_axes([0, 0.05, 1 , 0.9], fc=[0, 0, 0, 0])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<module 'matplotlib.pyplot' from '/Users/artemsereda/anaconda3/envs/quantus/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2400x150 with 73 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise Integrated Gradients explanations.\n",
    "a_batch_int_grad = explain_int_grad_func(model, x_batch[2:5], y_batch[2:5])\n",
    "plot_textual_heatmap(a_batch_int_grad)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "MqR9AJsPnXqJ",
    "outputId": "2a1d268c-da94-4840-eec8-040184db7f82"
   }
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   "cell_type": "markdown",
   "source": [
    "## 2) Quantitative analysis using Quantus\n",
    "For this example, we compute [Sensitivity](https://arxiv.org/abs/1901.09392) metric"
   ],
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   "source": [
    "# This is only a workaround to account for hardcoded attribute access in lib.\n",
    "class ModelTuple(NamedTuple):\n",
    "    model: Any\n",
    "    tokenizer: Any\n",
    "\n",
    "# This is also only a workaround to account for hardcoded attribute access in lib.\n",
    "model_stub = ModelTuple(model, tokenizer)\n",
    "model_stub.model.bert = model.distilbert\n",
    "model_stub.model.bert.embeddings.word_embeddings = model.distilbert.embeddings.weight"
   ],
   "metadata": {
    "id": "-l0yasspnXqJ"
   }
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   "source": [
    "Average Sensitivity captures the average change in explanations under slight perturbation"
   ],
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    "collapsed": false,
    "id": "_4bfX_ITnXqK"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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   "source": [
    "# Instantiate metric.\n",
    "avg_sensitivity = qn.AvgSensitivity()\n",
    "\n",
    "# Evaluate avg sensitivity for Gradient Norm.\n",
    "avg_sensitivity_grad_norm = avg_sensitivity(\n",
    "    model=model_stub,\n",
    "    x_batch=x_batch,\n",
    "    y_batch=y_batch,\n",
    "    perturb_func=qn.change_spelling,\n",
    "    explain_func=explain_gradient_norm_func,\n",
    ").mean()\n",
    "\n",
    "# Evaluate avg sensitivity for Integrated Gradients.\n",
    "avg_sensitivity_int_grad = avg_sensitivity(\n",
    "    model=model_stub,\n",
    "    x_batch=x_batch,\n",
    "    y_batch=y_batch,\n",
    "    perturb_func=qn.change_spelling,\n",
    "    explain_func=explain_int_grad_func\n",
    ").mean()"
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    "# Instantiate metric.\n",
    "max_sensitivity = qn.MaxSensitivity()\n",
    "\n",
    "# Evaluate max sensitivity metric for Gradient Norm.\n",
    "max_sensitivity_grad_norm = max_sensitivity(\n",
    "    model=model_stub,\n",
    "    x_batch=x_batch,\n",
    "    y_batch=y_batch,\n",
    "    perturb_func=qn.change_spelling,\n",
    "    explain_func=explain_gradient_norm_func,\n",
    ").mean()\n",
    "\n",
    "# Evaluate max sensitivity metric for Integrated Gradients.\n",
    "max_sensitivity_int_grad = max_sensitivity(\n",
    "    model=model_stub,\n",
    "    x_batch=x_batch,\n",
    "    y_batch=y_batch,\n",
    "    perturb_func=qn.change_spelling,\n",
    "    explain_func=explain_int_grad_func\n",
    ").mean()"
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    "Maximum Sensitivity captures the maximal change in explanations under slight perturbation"
   ],
   "metadata": {
    "collapsed": false
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  },
  {
   "cell_type": "markdown",
   "source": [
    "Display results in tabular form"
   ],
   "metadata": {
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    "id": "4_I1JIQgnXqL"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "                     Gradient Norm  Integrated Gradients\nAverage Sensitivity       0.126966              0.236630\nMax Sensitivity           0.259777              0.190411",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Gradient Norm</th>\n      <th>Integrated Gradients</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Average Sensitivity</th>\n      <td>0.126966</td>\n      <td>0.236630</td>\n    </tr>\n    <tr>\n      <th>Max Sensitivity</th>\n      <td>0.259777</td>\n      <td>0.190411</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
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   "source": [
    "# Reformat the results.\n",
    "all_results = np.asarray([\n",
    "    [\n",
    "        avg_sensitivity_grad_norm,\n",
    "        avg_sensitivity_int_grad\n",
    "    ],\n",
    "    [\n",
    "        max_sensitivity_grad_norm,\n",
    "        max_sensitivity_int_grad\n",
    "    ]\n",
    "])\n",
    "\n",
    "# Print out the evaluation outcome!\n",
    "pd.DataFrame(\n",
    "    all_results,\n",
    "    columns=['Gradient Norm', 'Integrated Gradients'],\n",
    "    index=['Average Sensitivity', 'Max Sensitivity']\n",
    ")"
   ],
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