robustness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.utils_contributions import *\n",
    "import torch.nn.functional as F\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import json\n",
    "import random\n",
    "import numpy as np\n",
    "random.seed(10)\n",
    "\n",
    "from collections import defaultdict\n",
    "import torch.nn as nn\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jaccard_similarity(list1, list2):\n",
    "    intersection = len(list(set(list1).intersection(list2)))\n",
    "    union = (len(set(list1)) + len(set(list2))) - intersection\n",
    "    return float(intersection) / union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "import math\n",
    "\n",
    "methods_list = ['grad', 'ig_l2', 'ig_abs_mean', 'grad_input_l2', 'grad_input_mean_abs_mean', 'ours']\n",
    "num_seeds = 10\n",
    "results_corr = {}\n",
    "bin = 0.25\n",
    "similarity_method = 'corr' #corr/jaccard\n",
    "\n",
    "for method in methods_list:\n",
    "    np_results_corr = np.zeros((num_seeds,num_seeds))\n",
    "    if method == 'pred_class':\n",
    "        pass\n",
    "    for seed_i in range(0,num_seeds):\n",
    "        for seed_j in range(0,num_seeds):\n",
    "            model1_file = f'./data/multiberts-seed_{seed_i}_sst2_attributions.npy'\n",
    "            model2_file = f'./data/multiberts-seed_{seed_j}_sst2_attributions.npy'\n",
    "            model1_data = np.load(model1_file,allow_pickle=True)[()]\n",
    "            model2_data = np.load(model2_file,allow_pickle=True)[()]\n",
    "            #results_corr[method] = []\n",
    "            results_corr_tmp = []\n",
    "            for i in range(1000):\n",
    "                # Only check if both predictions are the same\n",
    "                if model2_data['pred_class'][i] == model1_data['pred_class'][i]:\n",
    "                    if similarity_method == 'corr':\n",
    "                        # Spearman rank's correlation\n",
    "                        model1_data_rank_ex = get_normalized_rank(np.asarray(model1_data[method][i]))\n",
    "                        model2_data_rank_ex = get_normalized_rank(np.asarray(model2_data[method][i]))\n",
    "                        sp = spearmanr(model1_data_rank_ex,model2_data_rank_ex)\n",
    "                        results_corr_tmp.append(sp[0])\n",
    "                    elif similarity_method == 'jaccard':\n",
    "                        ## Jaccard similarity\n",
    "                        model1_data_rank = list(get_rank(np.asarray(model1_data[method][i])))\n",
    "                        model2_data_rank = list(get_rank(np.asarray(model2_data[method][i])))\n",
    "                        order_index_rank_model1 = sorted(range(len(model1_data_rank)), key=lambda k: model1_data_rank[k],reverse=True)\n",
    "                        order_index_rank_model2 = sorted(range(len(model2_data_rank)), key=lambda k: model2_data_rank[k],reverse=True)\n",
    "                        sentence_len = len(order_index_rank_model1)\n",
    "                        top_k = math.ceil(sentence_len*bin)\n",
    "                        a = order_index_rank_model1[:top_k]\n",
    "                        b = order_index_rank_model2[:top_k]\n",
    "                        sp = jaccard_similarity(a, b)\n",
    "                        results_corr_tmp.append(sp)\n",
    "            print('seed_i', seed_i)\n",
    "            print('seed_j', seed_j)\n",
    "            np_results_corr[seed_i,seed_j] = np.mean(results_corr_tmp)\n",
    "    results_corr[method] = np_results_corr\n",
    "    if similarity_method == 'corr':\n",
    "        # Save correlation values\n",
    "        outfile = f'./data/{similarity_method}.npy'\n",
    "        np.save(outfile,results_corr)\n",
    "    else:\n",
    "        # Save jaccard values\n",
    "        outfile = f'./data/{similarity_method}_{25}.npy'\n",
    "        np.save(outfile,results_corr)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data similarity method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity_file = f'./data/{similarity_method}.npy'\n",
    "results_corr = np.load(similarity_file,allow_pickle=True)[()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_corr_flatten = {}\n",
    "for method in results_corr.keys():\n",
    "    res_method = results_corr[method]\n",
    "    # Extract the diagonal and flatten\n",
    "    flatten_similarity = res_method[~np.eye(res_method.shape[0],dtype=bool)].reshape(res_method.shape[0],-1).flatten()\n",
    "    results_corr_flatten[method] = flatten_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PathPatch6((0.75, 0.713552) ...)\n",
      "PathPatch6((1.75, 0.620015) ...)\n",
      "PathPatch6((2.75, 0.706199) ...)\n",
      "PathPatch6((3.75, 0.651396) ...)\n",
      "PathPatch6((4.75, 0.789026) ...)\n",
      "PathPatch6((5.75, 0.903127) ...)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index= []\n",
    "data = []\n",
    "\n",
    "color_lines = {'ours': '#1f77b4',\n",
    "                'grad': '#ff7f0e',\n",
    "                'ig_l2': '#2ca02c',\n",
    "                'ig_abs_mean': '#d62728',\n",
    "                'grad_input_l2': '#9467bd',\n",
    "                'grad_input_mean_abs_mean': '#8c564b',\n",
    "                'rollout': '#e377c2',\n",
    "                'norm2': '#7f7f7f',\n",
    "                'ours2': '#bcbd22'}\n",
    "\n",
    "method_labels_dict = {'ours': 'ALTI', 'ours2': 'ALTI (+ LN2)', 'ours3': 'ALTI FULL', 'grad':r'Grad$_{\\ell_2}$', 'grad_input_abs': 'Grad input',\n",
    "                         'grad_input_clip': 'Gradxinput (clip)', 'grad_input_l2' : r'G$\\times$I$_{\\ell_2}$',\n",
    "                         'grad_input_mean' : r'G$\\times$I$_{\\mu}$no abs',\n",
    "                         'grad_input_mean_abs_mean': r'G$\\times$I$_{\\mu}$', 'ig_abs': 'IG (abs)',\n",
    "                         'ig_clip': 'IG (clip)', 'ig_l2':r'IG$_{\\ell_2}$', 'ig_mean': 'IG$_{\\mu} no abs$',\n",
    "                         'ig_abs_mean': 'IG$_{\\mu}$', 'norm': 'Norm Rollout', 'norm2': 'Globenc', 'rollout': 'Rollout',\n",
    "                         'blankout': 'blankout'}\n",
    "\n",
    "for i, (key, val) in enumerate(results_corr_flatten.items()):\n",
    "    index.append(method_labels_dict[key])\n",
    "    data.append(val)\n",
    "fig, ax = plt.subplots(figsize =(8, 4),dpi=200)\n",
    "box = ax.boxplot(data, patch_artist=True)\n",
    "plt.setp(box['medians'], color='black')\n",
    "for i, patch in enumerate(box['boxes']):\n",
    "    print(patch)\n",
    "    patch.set_facecolor(color_lines[list(results_corr_flatten.keys())[i]])\n",
    "ax.set_xticklabels(index)\n",
    "ax.set_ylim(0,1)\n",
    "plt.grid(False, linewidth=0.25)# linestyle=(0, (5, 10))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x2600 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_labels_dict = {'ours': 'ALTI', 'ours2': 'ALTI (+ LN2)', 'ours3': 'ALTI FULL', 'grad':r'Grad$_{\\ell_2}$', 'grad_input_abs': 'Grad input',\n",
    "                         'grad_input_clip': 'Gradxinput (clip)', 'grad_input_l2' : r'Grad$\\times$input$_{\\ell_2}$',\n",
    "                         'grad_input_mean' : r'Grad$\\times$input$_{\\mu}$no abs',\n",
    "                         'grad_input_mean_abs_mean': r'Grad$\\times$input$_{\\mu}$', 'ig_abs': 'IG (abs)',\n",
    "                         'ig_clip': 'IG (clip)', 'ig_l2':r'IG$_{\\ell_2}$', 'ig_mean': 'IG$_{\\mu} no abs$',\n",
    "                         'ig_abs_mean': 'IG$_{\\mu}$', 'norm': 'Norm Rollout', 'norm2': 'Globenc', 'rollout': 'Rollout',\n",
    "                         'blankout': 'blankout'}\n",
    "\n",
    "row = 1\n",
    "col = len(methods_list)\n",
    "fig, axs = plt.subplots(row, col,figsize=(20,13), dpi=200,sharey=False)\n",
    "\n",
    "\n",
    "for i, method in enumerate(methods_list):\n",
    "    plt.figure(figsize=(6,6))\n",
    "    df = pd.DataFrame(results_corr[method])\n",
    "    sns.heatmap(df,cmap=\"Blues\",square=True,vmin=0,ax=axs[i])\n",
    "    axs[i].set_title(method_labels_dict[method])\n",
    "\n",
    "fig.tight_layout();"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e704f1cc84ed2b9fe6269600206569b12c45a39f90eb745e8b9b52715147461c"
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}