Data_research_contrastive (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YZMuYmPleIbr"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o6u2a6GKR_o2",
    "outputId": "56f13245-118d-43cd-9693-bdf8a9642abd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "#@title #**Setup:** Imports and dependencies\n",
    "#@markdown Install and import Python dependencies.\n",
    "print('Installing dependencies...')\n",
    "# !pip install inseq\n",
    "!pip install jsonlines\n",
    "print('Importing libraries...')\n",
    "from transformers import GPT2Tokenizer\n",
    "import argparse\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import torch\n",
    "import pandas as pd\n",
    "import inseq\n",
    "import gc\n",
    "import jsonlines\n",
    "from IPython.display import clear_output\n",
    "import numpy as np\n",
    "clear_output()\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import GPT2Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JkZAqsOKhCsT",
    "outputId": "8188e646-d00c-41e0-e54a-c8bec531c7a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "#@title #**Setup:** load Drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "form",
    "id": "oF5XzM7oYNMP"
   },
   "outputs": [],
   "source": [
    "#@title #**Setup:** Set seed\n",
    "\n",
    "import random\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "form",
    "id": "CQa4aaAA27B1"
   },
   "outputs": [],
   "source": [
    "#@title #**Utils:** reciprocal_rank, average, tokenizer\n",
    "\n",
    "def average(lst):\n",
    "  return sum(lst)/len(lst)\n",
    "\n",
    "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
    "\n",
    "def reciprocal_rank(predictions, targets):\n",
    "    # Combine predictions and targets into pairs\n",
    "    pairs = list(zip(predictions, targets))\n",
    "\n",
    "    # Sort the pairs based on the prediction values (in descending order)\n",
    "    sorted_pairs = sorted(pairs, key=lambda x: x[0], reverse=True)\n",
    "\n",
    "    # Find the rank of the first correct prediction\n",
    "    rank = next((i + 1 for i, (pred, target) in enumerate(sorted_pairs) if target), 0)\n",
    "\n",
    "    # Calculate reciprocal rank\n",
    "    reciprocal_rank = 1 / rank if rank > 0 else 0\n",
    "\n",
    "    return reciprocal_rank\n",
    "\n",
    "def get_target(sentence):\n",
    "    b = sentence.split(' ')\n",
    "    prefix_l = 0\n",
    "    postix_l = 0\n",
    "    target_l = 0\n",
    "    check = False\n",
    "    for i,word in enumerate(b):\n",
    "        if i != 0:\n",
    "            word = \" \"+word\n",
    "            \n",
    "        if check == True:\n",
    "            postix_l += len(tokenizer(word)['input_ids'])\n",
    "        if \"n't\" in word:\n",
    "            target_l = len(tokenizer(word)['input_ids'])\n",
    "            check = True\n",
    "        if check == False:\n",
    "            prefix_l += len(tokenizer(word)['input_ids'])\n",
    "    return([False]*prefix_l+[True]*target_l+[False]*postix_l)\n",
    "\n",
    "import random\n",
    "\n",
    "def generate_one_hot_list(length):\n",
    "    # Check if the length is valid\n",
    "    if length <= 0:\n",
    "        raise ValueError(\"Length must be greater than 0\")\n",
    "\n",
    "    # Choose a random index for the \"hot\" element\n",
    "    hot_index = random.randint(0, length - 1)\n",
    "\n",
    "    # Create the one-hot list\n",
    "    one_hot_list = [0] * length\n",
    "    one_hot_list[hot_index] = 1\n",
    "\n",
    "    return one_hot_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bv3rgH4LeNW2"
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "form",
    "id": "pbPeYdoQdsbP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We don't want to raise 3.671572208404541\n"
     ]
    }
   ],
   "source": [
    "#@title #**Data:** load in NPI from BLIMP\n",
    "# filename = \"npi_present_1\" #@param [\"npi_present_1\", 'npi_present_2', \"only_npi_scope\", \"distractor_agreement_relational_noun\", \"determiner_noun_agreement_1\"]\n",
    "# with jsonlines.open( filename + \".jsonl\", 'r') as f:\n",
    "#     dataframe = pd.DataFrame(f)\n",
    "sentences = []\n",
    "values = []\n",
    "with open(\"prefix+value.tsv\", 'r', encoding='utf-8') as ifh:\n",
    "    for line in ifh:\n",
    "        sentence, value = line.strip().split('\\t')\n",
    "        sentences.append(sentence)\n",
    "        values.append(value)\n",
    "print(sentence,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "oI64ayv-3HqJ",
    "outputId": "2589fab3-a32b-4772-9e85-f4e5e4825205"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataframe' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/scratch-local/scur0635.5272673/ipykernel_624945/1455300811.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#@title #**Data:** Show data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'dataframe' is not defined"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('full_baselines.json', 'r') as ifh:\n",
    "    full_baselines = json.load(ifh)\n",
    "    \n",
    "with open('full_contrastive.json', 'r') as ofh:\n",
    "    full_contrastive = json.load(ofh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aV2ebBTWeT73"
   },
   "source": [
    "# Run Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AonoYT2A0DhV",
    "outputId": "5854bc0a-41a6-44ec-fbc5-e1f670e719e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradient_shap 3844\n",
      "{'Method': ['input_x_gradient', 'input_x_gradient', 'integrated_gradients', 'integrated_gradients', 'deeplift', 'deeplift', 'gradient_shap', 'gradient_shap'], 'class': ['baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive'], 'MRR': [0.45954726223666537, 0.47106032872957454, 0.5119757858051222, 0.5762466346233245, 0.4913494381674213, 0.537680026456681, 0.5516195887074394, 0.5500471846967836], 'Dist': [6.986992715920915, 6.986992715920915, 6.986992715920915, 6.986992715920915, 6.986992715920915, 6.986992715920915, 6.986992715920915, 6.986992715920915]}\n"
     ]
    }
   ],
   "source": [
    "#@title #**Loop through blimp NPI and give MRR**\n",
    "import torch\n",
    "explanations = [\"input_x_gradient\",\"integrated_gradients\",\"deeplift\", \"gradient_shap\"] #\"input_x_gradient\", \"integrated_gradients\", \"lime\", \"deeplift\", \"gradient_shap\",\"discretized_integrated_gradients\"\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "rev = 'False'\n",
    "Results = {'Method': [], 'class':[], 'MRR': [], 'Dist': []}\n",
    "\n",
    "full_baselines = {\"input_x_gradient\":[], \"integrated_gradients\":[], \"lime\":[], \"deeplift\":[], \"gradient_shap\":[],\"discretized_integrated_gradients\":[]}\n",
    "full_contrastive= {\"input_x_gradient\":[], \"integrated_gradients\":[], \"lime\":[], \"deeplift\":[], \"gradient_shap\":[],\"discretized_integrated_gradients\":[]}\n",
    "\n",
    "full_baselines_org = {\"input_x_gradient\":[], \"integrated_gradients\":[], \"lime\":[], \"deeplift\":[], \"gradient_shap\":[],\"discretized_integrated_gradients\":[]}\n",
    "full_contrastive_org= {\"input_x_gradient\":[], \"integrated_gradients\":[], \"lime\":[], \"deeplift\":[], \"gradient_shap\":[],\"discretized_integrated_gradients\":[]}\n",
    "\n",
    "\n",
    "\n",
    "for explanation in explanations:\n",
    "\n",
    "  gc.collect()\n",
    "  model_inseq = inseq.load_model(\"gpt2\", explanation)\n",
    "\n",
    "\n",
    "  baseline_ranks = []\n",
    "  contrastive_ranks = []\n",
    "  dist = []\n",
    "  i = 0\n",
    "  for sentence in sentences:\n",
    "    i += 1\n",
    "    target = \"any\"\n",
    "    foil = \"some\"\n",
    "\n",
    "    baseline = model_inseq.attribute(\n",
    "        sentence,\n",
    "        sentence + \" \" + target,\n",
    "        attributed_fn=attributed_fn,\n",
    "    )\n",
    "\n",
    "    contrast = model_inseq.attribute(\n",
    "        sentence,\n",
    "        sentence + \" \" + foil,\n",
    "        attributed_fn=attributed_fn,\n",
    "    )\n",
    "\n",
    "    clear_output()\n",
    "    print(f'{explanation} {i}')\n",
    "#     sent_len = len(tokenizer(sentence)['input_ids'])\n",
    "\n",
    "    base_tensor = baseline[0].target_attributions\n",
    "    contrastive_tensor = contrast[0].target_attributions\n",
    "\n",
    "\n",
    "    baseline_att = base_tensor.sum(axis = 2)\n",
    "    contrast_att = contrastive_tensor.sum(axis = 2)\n",
    "    baseline_att = torch.flatten(baseline_att[~torch.any(baseline_att.isnan(),dim=1)]).numpy()\n",
    "    contrast_att = torch.flatten(contrast_att[~torch.any(contrast_att.isnan(),dim=1)]).numpy()\n",
    "\n",
    "\n",
    "#     if filename == 'determiner_noun_agreement_1':\n",
    "#       targets = [False] * (sent_len - 1)+[True]\n",
    "#     else:\n",
    "#       targets = [True] + [False] * (sent_len - 1)\n",
    "\n",
    "#     baseline_att = baseline_att[:len(targets)]\n",
    "#     contrast_att = contrast_att[:len(targets)]\n",
    "    targets = get_target(sentence)\n",
    "    \n",
    "    if rev == 'True':\n",
    "      contrastive_attributions = contrast_att- baseline_att\n",
    "    if rev == 'False':\n",
    "      contrastive_attributions = baseline_att-contrast_att\n",
    "\n",
    "    rank_baseline = reciprocal_rank(baseline_att, targets)\n",
    "    rank_contrastive = reciprocal_rank(contrastive_attributions, targets)\n",
    "\n",
    "    assert len(targets) == len(baseline_att), f'Target/Baseline mismatch \\n targets = {targets},\\n baseline_att = {baseline_att}'\n",
    "    assert len(targets) == len(contrastive_attributions), f'Target/Contrast mismatch {targets}, {contrast_att}'\n",
    "    assert len(contrastive_attributions) == len(baseline_att), f'Baseline/Contrast mismatch {baseline_att}, {contrast_att}'\n",
    "\n",
    "    baseline_ranks.append(rank_baseline)\n",
    "    contrastive_ranks.append(rank_contrastive)\n",
    "    dist.append(len(targets))\n",
    "\n",
    "  if rev == 'True':\n",
    "      full_baselines[explanation] = baseline_ranks\n",
    "      full_contrastive[explanation] = contrastive_ranks\n",
    "  if rev == 'False':\n",
    "      full_baselines_org[explanation] = baseline_ranks\n",
    "      full_contrastive_org[explanation] = contrastive_ranks\n",
    "    \n",
    "  AVG_dist = average(dist)\n",
    "  MRR_baseline = average(baseline_ranks)\n",
    "  MRR_contrastive = average(contrastive_ranks)\n",
    "\n",
    "\n",
    "  Results['Method'].append(explanation)\n",
    "  Results['Method'].append(explanation)\n",
    "\n",
    "  Results['class'].append('baseline')\n",
    "  Results['class'].append('contrastive')\n",
    "\n",
    "  Results['MRR'].append(MRR_baseline)\n",
    "  Results['MRR'].append(MRR_contrastive)\n",
    "\n",
    "  Results['Dist'].append(AVG_dist)\n",
    "  Results['Dist'].append(AVG_dist)\n",
    "\n",
    "  print(Results)\n",
    "dataframe_results = pd.DataFrame(data=Results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('full_baselines_org.json', 'w') as ifh:\n",
    "    json.dump(full_baselines_org, ifh)\n",
    "    \n",
    "with open('full_contrastive_org.json', 'w') as ofh:\n",
    "    json.dump(full_contrastive_org, ofh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('full_baselines.json', 'r') as ifh:\n",
    "    full_baselines = json.load(ifh)\n",
    "    \n",
    "with open('full_contrastive.json', 'r') as ofh:\n",
    "    full_contrastive = json.load(ofh)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_baselines_org['input_x_gradient']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zip and sort based on x_values\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import json\n",
    "\n",
    "method = 'integrated_gradients'\n",
    "\n",
    "values = [float(x) for x in values]\n",
    "sorted_data = sorted(zip(values, random_ranks, full_baselines_org[method],full_contrastive_org[method]), key=lambda x: x[0])\n",
    "x_sorted, random_baselines,y1_sorted, y2_sorted = zip(*sorted_data)\n",
    "\n",
    "\n",
    "# Plotting with smaller dots\n",
    "window_size = 50\n",
    "y1_averages = np.convolve(y1_sorted, np.ones(window_size)/window_size, mode='valid')\n",
    "y2_averages = np.convolve(y2_sorted, np.ones(window_size)/window_size, mode='valid')\n",
    "y3_averages = np.convolve(random_baselines, np.ones(window_size)/window_size, mode='valid')\n",
    "x_averages = np.convolve(x_sorted, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "# Fit polynomial trendlines\n",
    "degree = 1  # You can adjust the degree of the polynomial\n",
    "z1 = np.polyfit(x_averages, y1_averages, degree)\n",
    "z2 = np.polyfit(x_averages, y2_averages, degree)\n",
    "z3 = np.polyfit(x_averages, y3_averages, degree)\n",
    "\n",
    "# Create polynomial functions\n",
    "p1 = np.poly1d(z1)\n",
    "p2 = np.poly1d(z2)\n",
    "p3 = np.poly1d(z3)\n",
    "\n",
    "\n",
    "# Plotting\n",
    "plt.scatter(x_averages[:len(y1_averages)], y1_averages, label='Baseline MRR per 50 points', s=5)\n",
    "plt.scatter(x_averages[:len(y2_averages)], y2_averages, label='Contrastive MRR per 50 points', s=5)\n",
    "\n",
    "\n",
    "# Plot trendlines\n",
    "plt.plot(x_averages, p1(x_averages), label='Trendline Baseline', color='purple')\n",
    "plt.plot(x_averages, p2(x_averages), label='Trendline Contrastive', color='red')\n",
    "plt.plot(x_averages, p3(x_averages), label='Trendline Random Baseline', color='yellow')\n",
    "\n",
    "# Adding labels and legend\n",
    "plt.xlabel('Perplexity difference between \"some\" and \"any\". Higher is better.')\n",
    "plt.ylabel('RR')\n",
    "plt.legend(fontsize = 7)\n",
    "plt.title(f'{method} Baseline vs Contrastive on \"ANY\" dataset.')\n",
    "# Set a higher resolution (dpi) for the plot\n",
    "plt.savefig('scatter_plot_averages.png', dpi=400)  # Adjust the filename and dpi as needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5338012703278967\n"
     ]
    }
   ],
   "source": [
    "#Random Baseline\n",
    "\n",
    "#@title #**Loop through blimp NPI and give MRR**\n",
    "import torch\n",
    "explanation = 'random'\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "rev = 'True'\n",
    "Results = {'Method': [], 'class':[], 'MRR': [], 'Dist': []}\n",
    "random_ranks = []\n",
    "for sentence in sentences:\n",
    "\n",
    "  targets = get_target(sentence)\n",
    "  baseline_att =  generate_one_hot_list(len(targets))\n",
    "\n",
    "  rank_random = reciprocal_rank(baseline_att, targets)\n",
    "\n",
    "  random_ranks.append(rank_random)\n",
    "  \n",
    "\n",
    "MRR_baseline = average(random_ranks)\n",
    "\n",
    "\n",
    "print(MRR_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lime 3844\n",
      "{'Method': ['lime', 'lime'], 'class': ['baseline', 'contrastive'], 'MRR': [0.6280301592382264, 0.35882940597445095], 'Dist': [6.986992715920915, 6.986992715920915]}\n"
     ]
    }
   ],
   "source": [
    "#LIME ONLY\n",
    "\n",
    "import torch\n",
    "explanations = [\"lime\"] #\"input_x_gradient\", \"integrated_gradients\", \"lime\", \"deeplift\", \"gradient_shap\",\"discretized_integrated_gradients\"\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "Results = {'Method': [], 'class':[], 'MRR': [], 'Dist': []}\n",
    "for explanation in explanations:\n",
    "\n",
    "  gc.collect()\n",
    "  model_inseq = inseq.load_model(\"gpt2\", explanation)\n",
    "\n",
    "\n",
    "  baseline_ranks = []\n",
    "  contrastive_ranks = []\n",
    "  dist = []\n",
    "  i = 0\n",
    "  for sentence in sentences:\n",
    "    i += 1\n",
    "    target = \"any\"\n",
    "    foil = \"some\"\n",
    "\n",
    "    baseline = model_inseq.attribute(\n",
    "        sentence,\n",
    "        sentence + \" \" + target,\n",
    "        attributed_fn=attributed_fn,\n",
    "    )\n",
    "\n",
    "    contrast = model_inseq.attribute(\n",
    "        sentence,\n",
    "        sentence + \" \" + target,\n",
    "        attributed_fn='contrast_logits_diff',\n",
    "        contrast_targets=sentence + \" \"+foil,\n",
    "    )\n",
    "\n",
    "    clear_output()\n",
    "    print(f'{explanation} {i}')\n",
    "#     sent_len = len(tokenizer(sentence)['input_ids'])\n",
    "\n",
    "    base_tensor = baseline[0].target_attributions\n",
    "    contrastive_tensor = contrast[0].target_attributions\n",
    "\n",
    "\n",
    "    baseline_att = base_tensor.sum(axis = 2)\n",
    "    contrast_att = contrastive_tensor.sum(axis = 2)\n",
    "    baseline_att = torch.flatten(baseline_att[~torch.any(baseline_att.isnan(),dim=1)]).numpy()\n",
    "    contrast_att = torch.flatten(contrast_att[~torch.any(contrast_att.isnan(),dim=1)]).numpy()\n",
    "\n",
    "\n",
    "#     if filename == 'determiner_noun_agreement_1':\n",
    "#       targets = [False] * (sent_len - 1)+[True]\n",
    "#     else:\n",
    "#       targets = [True] + [False] * (sent_len - 1)\n",
    "\n",
    "#     baseline_att = baseline_att[:len(targets)]\n",
    "#     contrast_att = contrast_att[:len(targets)]\n",
    "    targets = get_target(sentence)\n",
    "\n",
    "    rank_baseline = reciprocal_rank(baseline_att, targets)\n",
    "    rank_contrastive = reciprocal_rank(contrast_att, targets)\n",
    "\n",
    "\n",
    "    baseline_ranks.append(rank_baseline)\n",
    "    contrastive_ranks.append(rank_contrastive)\n",
    "    dist.append(len(targets))\n",
    "\n",
    "\n",
    "  AVG_dist = average(dist)\n",
    "  MRR_baseline = average(baseline_ranks)\n",
    "  MRR_contrastive = average(contrastive_ranks)\n",
    "\n",
    "\n",
    "  Results['Method'].append(explanation)\n",
    "  Results['Method'].append(explanation)\n",
    "\n",
    "  Results['class'].append('baseline')\n",
    "  Results['class'].append('contrastive')\n",
    "\n",
    "  Results['MRR'].append(MRR_baseline)\n",
    "  Results['MRR'].append(MRR_contrastive)\n",
    "\n",
    "  Results['Dist'].append(AVG_dist)\n",
    "  Results['Dist'].append(AVG_dist)\n",
    "\n",
    "  print(Results)\n",
    "dataframe_results = pd.DataFrame(data=Results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('full_baselines_org.json', 'r') as ifh:\n",
    "    full_baselines_org = json.load(ifh)\n",
    "    \n",
    "with open('full_contrastive_org.json', 'r') as ofh:\n",
    "    full_contrastive_org = json.load(ofh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_baselines_org['lime'] = baseline_ranks\n",
    "full_contrastive_org['lime'] = contrastive_ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('full_baselines_org.json', 'w') as ifh:\n",
    "    json.dump(full_baselines_org, ifh)\n",
    "    \n",
    "with open('full_contrastive_org.json', 'w') as ofh:\n",
    "    json.dump(full_contrastive_org, ofh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       " 0.3333333333333333,\n",
       " 1.0,\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_baselines_org['lime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tmqCozwreZrJ"
   },
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "cEKpbSlwoqE9",
    "outputId": "cbcaf7a2-6961-4dd4-f131-a56a3ac267d5"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** npi_present_1 reversal results.\n",
    "results_npi_1 = {'Method': ['input_x_grad','input_x_grad', 'input_x_grad','input_x_grad', 'input_x_grad'],'class': ['baseline','baseline_Paper','tgt-foil','foil-tgt','tgt-foil_Paper'], 'MRR':[0.4699,0.510,0.4948,0.738683333,0.787]}\n",
    "df_res = pd.DataFrame(data=results_npi_1)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "WUdcG5r36q8K",
    "outputId": "1718f838-415f-45c9-c39b-2e54d68c8ee2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2193x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** npi_present_1 results.\n",
    "\n",
    "results_npi_1 = {'Method': ['input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients','input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients'],'class': ['baseline','baseline','baseline','baseline','baseline','baseline', 'contrastive','contrastive','contrastive','contrastive','contrastive','contrastive'], 'MRR':[0.4699,0.5841333333, 0.6716833333,0.45684999999 ,0.6275928, 0.54976666666 ,0.738683333,0.69993571, 0.5014499999999973,0.748433333,0.5938833,  0.6834761904]}\n",
    "df_res = pd.DataFrame(data=results_npi_1)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "agR9MZ_360OF",
    "outputId": "9b7ca324-65ea-4da1-e5a5-61ae90677901"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DAKrHx4m1H570fWiHHXYorrnmmkpl5557btGrV6+iKIrikksuKbbaaqtSnCiKorj88stXeQJ58803r3XMu+66a/GrX/2q9P2/E9yK4oPY9d3vfrdS2b333lvUqlWreOedd4pZs2YVSYrp06eXtj/zzDNFknVKXL/99tuLOnXqlBLli6Io7rzzzlXedB83btxa+/vyl79c/O///m9RFB+ccNerV6/405/+VNr+6quvFg0bNlxt4vq9995bNG3atHj33Xcr9bvDDjsUl156aVEUH8TnRo0aFf/5z39K20855ZSiZ8+epe8f/e/hQ4cffngxaNCgtc4BAD6utSWu//jHPy59f+CBB4okpZsURVEUf/zjH4sGDRqUvn/pS18qRo8eXWkfv/vd74o2bdpUzQQAYBOytnPO+fPnF7Vr1y5eeumlSu2+9KUvFSNHjiyKYu3n3EXxQUz/6INlRVEU/fv3L91EL4rVJ65/qFmzZsWVV15Z+u66MwDVbV3u7X7a7+teeOGFRceOHYsVK1ascvva7jOsyn8fizPPPLOoXbt28eKLL5bKbr/99qJWrVqVrskDUNm63GP86PXXNf2b/uG91DvvvHOV+1rVvd31iU3/nQC9qnuURVEUS5YsKbbffvtKMXJt13/vuOOOok6dOpXOa2+//fZPnLi+vvF0Xe7f9uzZsxgyZEil7XvvvfdK8b9Vq1bF8uXL1zjuhx9+uEhSvPnmm5XG/dFjvS5/o5EjRxa77LJLpe2nnnrqOieu9+/fv/jyl79cqeyYY45ZKXG9bt26xcsvv7zGvl555ZUiSfHkk08WRVEUl1566Up5B5dccskaE9fX9RrG+uZJvPrqq0WSUsI/rEqdDbl6O1B9unTpUvrfZWVlad26dek1Jx/q1atX6X/XqVMnPXr0yDPPPFOl46pXr17+8Ic/pEuXLmnfvn0uuuiidWr3/PPP57333suee+5ZKmvWrFnptS4f1aNHj5XKJk2alF/+8peZO3du3nrrrbz//vtp2rRpafszzzyTE044oVKbXr165W9/+9sqxzNnzpy8/fbbOfDAAyuVr1ixIrvttlulso/+Ldq0aZMkefnll7PtttuubroAbALWJdZ+1LJlyzJ37twcd9xxGTx4cKn8/fffT7NmzZIks2bNSpcuXUqv+EpSKfZ91H/Hu7feeitnnXVWbrvttixcuDDvv/9+3nnnnSxYsGCN83j88cfzxBNP5A9/+EOprCiKVFRU5IUXXsjs2bNTp06ddO/evbR95513rvSKsjWZNWtW2rVrl9atW6/3nMrLyzN69Oj86U9/yksvvZQVK1Zk+fLladSoUZJk7ty5WbFiRXr27Flqs+WWW67y98FH5/vWW29lq622qlT+zjvvVHp9WocOHbLZZpuVvrdp02aNf98kOfHEE/PVr341jz76aA466KD069cvvXv3XmMbANiQPvr7pFWrVkmSzp07Vyp7991385///CdNmzbN448/nn/84x8577zzSnXKy8vz7rvv5u233y7FXAD4LFrbOeeTTz6Z8vLydOzYsVK75cuXl84513bO3alTpySVr9V/+P3DV9IDwKZoXe/tfprv637ta1/LuHHjsv322+fggw/OoYcemsMPPzx16vxfWs7a7jOs7Vgkybbbbpu2bduWvvfq1SsVFRWZNWtWpevyAPyf9b3HuKZ/02fOnJnatWtnv/32W+M+Pxrz1ic2rYv33nsvX/3qV9O+ffv84he/KJWv7frvM888k3bt2mXrrbcubf/v89OPY33j6brcv501a1ZOOumkStv33HPP3H333ZXKOnfunHr16lUqmzFjRs4666w8/vjjef3111NRUZEkWbBgQXbZZZdVjmld/kbPPPNMpf+GkvU7frNmzcpRRx210pz+8pe/VCpr3759WrRoUansueeey6hRo/LQQw9lyZIlleb0+c9/Ps8888xKeQdrG9u6XsNY3zyJLbfcMscee2z69u2bAw88MH369MnRRx9d+m8DkkTiOnxK1K1bt9L3srKyUpBaF7Vq1UpRFJXK3nvvvQ0ytvvvvz9J8tprr+W1115L48aNN0i/H/rv/h544IEcc8wxOfvss9O3b980a9Ys1157bS688MKPvY+33norSXLbbbdVuhCQJPXr16/0/aN/i7KysiRZr78FADXT+sbaD2PH5ZdfvtIJbO3atdd7//8d7374wx/mzjvvzAUXXJAdd9wxDRs2zP/8z/9kxYoVa+znrbfeyve+972cfPLJK23bdtttM3v27PUe28f133M6//zz84tf/CLjxo1L586d07hx4/zgBz9Y65zW5K233kqbNm0ybdq0lbZ9NBn/4/yWOuSQQzJ//vxMnjw5d955Z770pS9lyJAhueCCCz72eAFgfazq/HNN56RvvfVWzj777HzlK19Zqa+PXtAGAFb21ltvpXbt2pkxY8ZK5/VNmjQp1VnTOTcAfNZ9mu/rtmvXLrNmzcpdd92VO++8MyeddFLOP//83HPPPaX9rOk6dFUcCwA+njX9m96wYcN16uOjMW99YtO6OPHEE/Ovf/0r06dPr/SAVHVd/13feLqu92/XxX//tli2bFn69u2bvn375g9/+ENatGiRBQsWpG/fvmu857yh/0afxKry6g4//PC0b98+l19+ebbeeutUVFTk85///Ce+j74u1zA+zn30K6+8MieffHKmTJmSSZMm5cc//nHuvPPO7LXXXh97vHy6SFyHz5AHH3ww++67b5IPVnudMWNGhg4dmiRp0aJF3nzzzSxbtqwUAGfOnFmpfb169VJeXr5e+5w7d26GDx+eyy+/PJMmTcrAgQNz1113pVatWmtst/3226du3bp5+OGHS8HwjTfeyOzZs0tzWJ37778/7du3z+mnn14qmz9/fqU6nTp1ykMPPZQBAwaUyh588MHV9rnLLrukfv36WbBgwVqfmlyTj3MMAaj5PnyK+6P/xrdq1Spbb711nn/++RxzzDGrbLfTTjvl97//fZYvX1464X344YfXaZ//+Mc/cuyxx5aeyn7rrbcyb968lcb133Fn9913zz//+c/suOOOq+x35513Lv1O2GOPPZJ88PT30qVL12lcO+20U/71r39l8eLFpZVf12dORx55ZL71rW8l+eCCxuzZs0tPvu+www6pW7duHnroodLvg9dffz2zZ89ebXzefffds2jRotSpUycdOnRYp3GsyupieIsWLTJw4MAMHDgwX/jCF3LKKadIXAegxtp9990za9as1f4OAIDPsrWdc+62224pLy/Pyy+/nC984Qur7GNt59wf+u9r0Q8++GBpJbOPw3VnAKrbx723+2m7r9uwYcMcfvjhOfzwwzNkyJDsvPPOefLJJ7P77ruvte26HIvkg5VV//3vf5dWy33wwQdTq1atNb6ZFOCz7uPcY1zdv+mdO3dORUVF7rnnnvTp02ed9r8usWlV95tXZezYsfnTn/6U+++/f6UVy9d2/bdTp07517/+lYULF5ZWv15TTN0QVne/em33b3faaac8/PDDleL/utxzfvbZZ/Pqq6/mpz/9adq1a5ckeeSRR1YaU1L5WK/L36hTp0659dZbK5Wtz/H7cE4ftS5zevXVVzNr1qxcfvnlpesR991330pj+93vfpd333239JDC2sa2rtcw1mRN/93utttu2W233TJy5Mj06tUr11xzjcR1SiSuw2fI+PHj87nPfS6dOnXKRRddlNdffz3f+c53kiQ9e/ZMo0aN8qMf/Sgnn3xyHnrooVx11VWV2nfo0CEvvPBCZs6cmW222SabbbbZGp8qKy8vz7e+9a307ds3gwYNysEHH5zOnTvnwgsvzCmnnLLGsW622WYZOHBgTjnllGy55ZZp2bJlzjzzzNSqVav0dN7qfO5zn8uCBQty7bXXZo899shtt92Wm266qVKdYcOG5dhjj02PHj2y99575w9/+EOefvrpbL/99qsdzw9/+MMMHz48FRUV2WefffLGG2/kH//4R5o2bZqBAweucUwfWt9jCMCmoWXLlmnYsGGmTJmSbbbZJg0aNEizZs1y9tln5+STT06zZs1y8MEHZ/ny5XnkkUfy+uuvZ8SIEfnmN7+Z008/Pd/97ndz2mmnZcGCBaWk53WJdzfeeGMOP/zwlJWV5YwzzljpyeYOHTrk73//e77+9a+nfv36ad68eU499dTstddeGTp0aI4//vg0btw4//znP3PnnXfm4osvzk477ZSDDz443/ve93LJJZekTp06+cEPfrDOqwcceOCB2WGHHTJw4MD8/Oc/z5tvvpkf//jH6zyn66+/Pvfff3+22GKLjB07NosXLy4lrjdp0iTHHXdcTjnllGy11VZp2bJlTj/99DU+ENenT5/06tUr/fr1y89//vN07Ngx//73v3PbbbflqKOOWuWraVelQ4cOeeihhzJv3rw0adIkW265Zc4666x07949u+66a5YvX56//OUvnyjRAACq2qhRo3LYYYdl2223zf/8z/+kVq1aefzxx/PUU0/lJz/5SXUPDwCq1drOOTt27JhjjjkmAwYMyIUXXpjddtstr7zySqZOnZouXbrky1/+8lrPuT/0j3/8Iz//+c/Tr1+/3Hnnnbnuuuty2223feyxu+4MQHX7uPd2P033da+66qqUl5eX7rv//ve/T8OGDdO+fft12t+6HIvkgxVzBw4cmAsuuCD/+c9/cvLJJ+foo49O69at12k/AJ9F63uPcU3/pm+11VYZOHBgvvOd7+SXv/xlunbtmvnz5+fll1/O0Ucfvcr+1iU2tW/fPmVlZfnLX/6SQw89NA0bNiy93etDd911V/7f//t/GT9+fJo3b55FixYl+SDJvlmzZmu9/tunT5907NgxAwcOzPnnn5///Oc/lR6Yqgqriqfrcv/2+9//fgYPHpwePXqkd+/emTRpUp544onVxv8PbbvttqlXr15+9atf5YQTTshTTz2Vc889t1KdVR3rdfkbnXDCCaWct+OPPz4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      "text/plain": [
       "<Figure size 3000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** npi_present_2 results.\n",
    "\n",
    "results_npi_2 = {'Method': ['input_x_gradient', 'input_x_gradient', 'integrated_gradients', 'integrated_gradients', 'lime', 'lime', 'deeplift', 'deeplift', 'gradient_shap', 'gradient_shap', 'discretized_integrated_gradients', 'discretized_integrated_gradients'], 'class': ['baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive', 'baseline', 'contrastive'], 'MRR': [0.5579499999999994, 0.6310166666666669, 0.6647666666666667, 0.6632000000000005, 0.7978833333333338, 0.6919000000000005, 0.5639999999999991, 0.6976000000000007, 0.6956000000000004, 0.678716666666667,0.6370833333333334, 0.6774000000000001]}\n",
    "df_res = pd.DataFrame(data=results_npi_2)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "HE9-tHvP68wJ",
    "outputId": "e69dc526-d2df-4fa2-b02b-584efd8db2eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2193x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** determiner_noun_agreement_1 results.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "results_determiner_noun = {'Method': ['input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients', 'discretized_integrated_gradients'],'class': ['baseline','baseline','baseline','baseline','baseline', 'contrastive','contrastive','contrastive','contrastive','contrastive', 'baseline', 'contrastive'], 'MRR':[0.9082428571428581,0.8196607142857156,0.31504126984126846,0.8419928571428584,0.5638011904761897,0.5770000000000003,0.30320277777777643,0.8205642857142863,0.5633964285714284,0.5110353174603166,0.6711214285714291,0.3070515873015864]}\n",
    "df_res = pd.DataFrame(data=results_determiner_noun)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2193x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** determiner_noun_agreement_1 results.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "results_npi_1 = {'Method': ['input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients','input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients','input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients'],'class': ['baseline','baseline','baseline','baseline','baseline','baseline','tgt-foil','tgt-foil','tgt-foil','tgt-foil','tgt-foil','tgt-foil', 'foil-tgt','foil-tgt','foil-tgt','foil-tgt','foil-tgt','foil-tgt'], 'MRR':[0.4699,0.5841333333, 0.6716833333,0.45684999999 ,0.6275928, 0.54976666666 ,0.4948, 0.49498333,0.6947166666666674,0.498061904, 0.597342857,0.50434999,0.738683333,0.69993571, 0.5014499999999973,0.748433333,0.5938833,  0.6834761904]}\n",
    "df_res = pd.DataFrame(data=results_npi_1)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 753x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "results_npi_1 = {'Method': ['lime_true', 'lime_sub', 'lime_true','lime_sub', 'lime_true','lime_sub'],'class': ['baseline','baseline','tgt-foil','tgt-foil', 'foil-tgt','foil-tgt'], 'MRR':[0.672,0.672,0.672,0.695,0.579,0.501]}\n",
    "df_res = pd.DataFrame(data=results_npi_1)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2193x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title #**Results:** determiner_noun_agreement_1 results.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "results_npi_1 = {'Method': ['input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients','input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients','input_x_grad', 'integrated_grads', 'lime', 'deeplift', 'gradient_shap', 'discretized_integrated_gradients'],'class': ['baseline','baseline','baseline','baseline','baseline','baseline','tgt-foil','tgt-foil','tgt-foil','tgt-foil','tgt-foil','tgt-foil', 'foil-tgt','foil-tgt','foil-tgt','foil-tgt','foil-tgt','foil-tgt'], 'MRR':[0.4699,0.5841333333, 0.6716833333,0.45684999999 ,0.6275928, 0.54976666666 ,0.4948, 0.49498333,0.67236904,0.498061904, 0.597342857,0.50434999,0.738683333,0.69993571, 0.5787190476,0.748433333,0.5938833,  0.6834761904]}\n",
    "df_res = pd.DataFrame(data=results_npi_1)\n",
    "# Assuming 'class' is the column containing class information\n",
    "g = sns.catplot(kind='bar', data=df_res, x='class', y='MRR', col='Method', hue='class')\n",
    "\n",
    "# Customize the plot as needed\n",
    "g.set_axis_labels('Class', 'MRR')\n",
    "g.set_titles(col_template='{col_name}')\n",
    "g.despine(left=True)\n",
    "\n",
    "# Add values on top of the bars\n",
    "for ax in g.axes.flat:\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width() / 2., p.get_height(), f'{p.get_height():.3f}', ha='center', va='bottom')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gThIn-lPdll0"
   },
   "source": [
    "# Old Code from here down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "lZ_CHAtCo_ou"
   },
   "outputs": [],
   "source": [
    "#@title #**Loop through blimp NPI and give MRR**\n",
    "import torch\n",
    "explanation = \"deeplift\" #@param [\"input_x_gradient\", \"integrated_gradients\", \"occlusion\", \"lime\", \"deeplift\", \"gradient_shap\", \"discretized_integrated_gradients\"]\n",
    "aggregator = \"sum\" #@param [\"sum\", \"mean\", \"vnorm\", \"max\", \"min\", \"prod\", \"absmax\", \"default\"]\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "rev = \"False\" #@param ['True', 'False']\n",
    "gc.collect()\n",
    "model_inseq = inseq.load_model(\"gpt2\", explanation)\n",
    "\n",
    "baseline_ranks = []\n",
    "contrastive_ranks = []\n",
    "dist = []\n",
    "\n",
    "for index, row in dataframe.iterrows():\n",
    "  sentence = row[\"one_prefix_prefix\"]\n",
    "  target = row[\"one_prefix_word_good\"]\n",
    "  foil = row[\"one_prefix_word_bad\"]\n",
    "\n",
    "\n",
    "  # encoded_target = tokenizer(' '+ target)['input_ids'][0]\n",
    "  # target = tokenizer.decode(encoded_target)\n",
    "\n",
    "  # encoded_foil = tokenizer(' '+ foil)['input_ids'][0]\n",
    "  # foil = tokenizer.decode(encoded_foil)\n",
    "\n",
    "\n",
    "  baseline = model_inseq.attribute(\n",
    "      sentence,\n",
    "      sentence + \" \" + target,\n",
    "      attributed_fn=attributed_fn,\n",
    "  )\n",
    "\n",
    "\n",
    "  contrast = model_inseq.attribute(\n",
    "      sentence,\n",
    "      sentence + \" \" + foil,\n",
    "      attributed_fn=attributed_fn,\n",
    "  )\n",
    "\n",
    "  clear_output()\n",
    "  print(f'target = {target}, foil = {foil}, sentence = {sentence}')\n",
    "  sent_len = len(tokenizer(sentence)['input_ids'])\n",
    "\n",
    "\n",
    "  base_tensor = baseline[0].target_attributions\n",
    "  contrastive_tensor = contrast[0].target_attributions\n",
    "\n",
    "  print(base_tensor.size())\n",
    "  print(contrastive_tensor.size())\n",
    "\n",
    "\n",
    "  # contrastive_feature = base_tensor-contrastive_tensor\n",
    "  # contrastive = copy.deepcopy(baseline)\n",
    "  # contrastive[0].target_attributions = contrastive_feature\n",
    "  baseline_att = base_tensor.sum(axis = 2)\n",
    "  contrast_att = contrastive_tensor.sum(axis = 2)\n",
    "  baseline_att = torch.flatten(baseline_att[~torch.any(baseline_att.isnan(),dim=1)]).numpy()\n",
    "  contrast_att = torch.flatten(contrast_att[~torch.any(contrast_att.isnan(),dim=1)]).numpy()\n",
    "\n",
    "\n",
    "  if filename == 'determiner_noun_agreement_1':\n",
    "    targets = [False] * (sent_len - 1)+[True]\n",
    "  else:\n",
    "    targets = [True] + [False] * (sent_len - 1)\n",
    "  baseline_att = baseline_att[:len(targets)]\n",
    "  contrast_att = contrast_att[:len(targets)]\n",
    "\n",
    "  if rev == 'True':\n",
    "    contrastive_attributions = contrast_att- baseline_att\n",
    "  if rev == 'False':\n",
    "    contrastive_attributions = baseline_att-contrast_att\n",
    "\n",
    "  rank_baseline = reciprocal_rank(baseline_att, targets)\n",
    "  rank_contrastive = reciprocal_rank(contrastive_attributions, targets)\n",
    "\n",
    "  assert len(targets) == len(baseline_att), f'Target/Baseline mismatch \\n targets = {targets},\\n baseline_att = {baseline_att}'\n",
    "  assert len(targets) == len(contrastive_attributions), f'Target/Contrast mismatch {targets}, {contrast_att}'\n",
    "  assert len(contrastive_attributions) == len(baseline_att), f'Baseline/Contrast mismatch {baseline_att}, {contrast_att}'\n",
    "\n",
    "  baseline_ranks.append(rank_baseline)\n",
    "  contrastive_ranks.append(rank_contrastive)\n",
    "  dist.append(sent_len)\n",
    "\n",
    "\n",
    "AVG_dist = average(dist)\n",
    "MRR_baseline = average(baseline_ranks)\n",
    "MRR_contrastive = average(contrastive_ranks)\n",
    "\n",
    "print(f'Average Distance is   {AVG_dist}')\n",
    "print(f'Baseline MRR is       {MRR_baseline}')\n",
    "print(f'Contrastive MRR is    {MRR_contrastive}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aGKxnyQv-nSh"
   },
   "source": [
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "npi_present_1:\n",
    "\n",
    "Average Distance is   3.15\n",
    "\n",
    "Baseline MRR is       0.470\n",
    "\n",
    "Contrastive MRR is    0.495\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "npi_present_2:\n",
    "\n",
    "Average Distance is   2.489\n",
    "\n",
    "Baseline MRR is       0.558\n",
    "\n",
    "Contrastive MRR is    0.769\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "............![image.png](data:image/png;base64,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)\n",
    "\n",
    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)\n",
    "\n",
    "determiner_noun_agreement_1\n",
    "\n",
    "Average Distance is   1 (always)\n",
    "\n",
    "Baseline MRR is       0.908\n",
    "\n",
    "Contrastive MRR is    0.394\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "npi_present_1: REVERSED\n",
    "\n",
    "Average Distance is   3.15\n",
    "\n",
    "Baseline MRR is       0.470\n",
    "\n",
    "Contrastive MRR is    0.739\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "determiner_noun_agreement_1: REVERSED\n",
    "\n",
    "Average Distance is   1 (always)\n",
    "\n",
    "Baseline MRR is       0.908\n",
    "\n",
    "Contrastive MRR is    0.577\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "TmE-J7zqJD5o"
   },
   "outputs": [],
   "source": [
    "#@title #**Plots** ?\n",
    "\n",
    "# Data for MRR-baseline\n",
    "labels_baseline = ['npi-1', 'npi-1*','npi-1-rev', 'det', 'det*',  'det-rev']\n",
    "MRR_baseline = [0.470, 0.463, 0.470,0.908, 0.983,  0.908]\n",
    "\n",
    "# Data for MRR-contrastive\n",
    "labels_contrastive = ['npi-1', 'npi-1*','npi-1-rev', 'det', 'det*',  'det-rev']\n",
    "MRR_contrastive = [0.495, 0.759, 0.739, 0.394, 0.603, 0.577]\n",
    "\n",
    "# Color palette\n",
    "colors = sns.color_palette('Dark2')\n",
    "\n",
    "# Plotting MRR-baseline and MRR-contrastive horizontally\n",
    "fig, (ax_baseline, ax_contrastive) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# Bar width\n",
    "bar_width = 0.4\n",
    "bar_positions = np.arange(len(labels_baseline))\n",
    "\n",
    "# Bar plots for MRR-baseline\n",
    "ax_baseline.bar(bar_positions, MRR_baseline, color=colors[0], width=bar_width, edgecolor='grey', label='MRR-baseline')\n",
    "\n",
    "# Adding labels and legend for MRR-baseline\n",
    "ax_baseline.set_xlabel('Labels', fontweight='bold', fontsize=15)\n",
    "ax_baseline.set_xticks(bar_positions)\n",
    "ax_baseline.set_xticklabels(labels_baseline)\n",
    "ax_baseline.legend()\n",
    "\n",
    "# Bar plots for MRR-contrastive\n",
    "ax_contrastive.bar(bar_positions, MRR_contrastive, color=colors[1], width=bar_width, edgecolor='grey', label='MRR-contrastive')\n",
    "\n",
    "# Adding labels and legend for MRR-contrastive\n",
    "ax_contrastive.set_xlabel('Labels', fontweight='bold', fontsize=15)\n",
    "ax_contrastive.set_xticks(bar_positions)\n",
    "ax_contrastive.set_xticklabels(labels_contrastive)\n",
    "ax_contrastive.legend()\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plots horizontally\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "BWZuQvPWv5fA"
   },
   "outputs": [],
   "source": [
    "#@title #**Inseq:** explanations with paper hyperparams\n",
    "sentence = \"Can you stop the dog from\" #@param {type:\"string\"}\n",
    "target = \"barking\" #@param {type:\"string\"}\n",
    "foil = \"walking\" #@param {type:\"string\"}\n",
    "explanation = \"input_x_gradient\" #@param [\"input_x_gradient\", \"integrated_gradients\", \"occlusion\", \"lime\", \"deeplift\", \"gradient_shap\", \"discretized_integrated_gradients\"]\n",
    "aggregator = \"sum\" #@param [\"sum\", \"mean\", \"vnorm\", \"max\", \"min\", \"prod\", \"absmax\", \"default\"]\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "contrast_attributed_fn = \"contrast_logits_diff\" #@param [\"contrast_logits\", \"contrast_prob\", \"contrast_logits_diff\", \"contrast_prob_diff\"]\n",
    "\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()\n",
    "model_inseq = inseq.load_model(\"gpt2\", explanation)\n",
    "\n",
    "\n",
    "baseline = model_inseq.attribute(\n",
    "    sentence,\n",
    "    sentence + \" \" + target,\n",
    "    internal_batch_size=50,\n",
    "    attributed_fn=attributed_fn,\n",
    "    n_steps=500\n",
    ")\n",
    "\n",
    "\n",
    "contrast = model_inseq.attribute(\n",
    "    sentence,\n",
    "    sentence + \" \" + foil,\n",
    "    internal_batch_size=50,\n",
    "    attributed_fn=attributed_fn,\n",
    "    n_steps=500\n",
    ")\n",
    "\n",
    "\n",
    "contrastive_feature = baseline[0].target_attributions - contrast[0].target_attributions\n",
    "contrastive = copy.deepcopy(baseline)\n",
    "contrastive[0].target_attributions = contrastive_feature\n",
    "\n",
    "\n",
    "if explanation in [\"input_x_gradient\",\"occlusion\", \"lime\", \"discretized_integrated_gradients\"]:\n",
    "  out = model_inseq.attribute(\n",
    "      sentence,\n",
    "      sentence + \" \" + target,\n",
    "      attributed_fn= contrast_attributed_fn,\n",
    "      contrast_targets= sentence + \" \" + foil,\n",
    "      # We also visualize the corresponding step score\n",
    "      step_scores=[contrast_attributed_fn]\n",
    "  )\n",
    "\n",
    "\n",
    "clear_output()\n",
    "\n",
    "\n",
    "print(\"Baseline saliency with forced target\")\n",
    "baseline.show(aggregator = aggregator)\n",
    "print(\"Baseline saliency with forced foil\")\n",
    "contrast.show(aggregator = aggregator)\n",
    "print(\"Contrastive saliency by subtracting feature matrices\")\n",
    "contrastive.show(aggregator = aggregator)\n",
    "if explanation in [\"input_x_gradient\",\"occlusion\", \"lime\", \"discretized_integrated_gradients\"]:\n",
    "  print(\"Contrastive saliency by inseq library\")\n",
    "  out.show(aggregator = aggregator)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#@title #**Loop through blimp NPI and give MRR** Use for disc_int_grads\n",
    "#@markdown I hate colab so much pls kill me.\n",
    "import torch\n",
    "explanations = [\"input_x_gradient\", \"integrated_gradients\", \"lime\", \"deeplift\", \"gradient_shap\"] #\"discretized_integrated_gradients\"\n",
    "attributed_fn = \"logit\" #@param [\"logit\", \"probability\", \"entropy\", \"crossentropy\", \"perplexity\", \"pcxmi\", \"kl_divergence\", \"in_context_pvi\", \"mc_dropout_prob_avg\", \"top_p_size\"]\n",
    "revs = ['True', 'False']\n",
    "Results = []\n",
    "\n",
    "\n",
    "for explanation in explanations:\n",
    "\n",
    "  gc.collect()\n",
    "  model_inseq = inseq.load_model(\"gpt2\", explanation)\n",
    "\n",
    "  for rev in revs:\n",
    "\n",
    "    baseline_ranks = []\n",
    "    contrastive_ranks = []\n",
    "    dist = []\n",
    "\n",
    "    for index, row in dataframe.iterrows():\n",
    "      sentence = row[\"one_prefix_prefix\"]\n",
    "      target = row[\"one_prefix_word_good\"]\n",
    "      foil = row[\"one_prefix_word_bad\"]\n",
    "\n",
    "      baseline = model_inseq.attribute(\n",
    "          sentence,\n",
    "          sentence + \" \" + target,\n",
    "          attributed_fn=attributed_fn,\n",
    "      )\n",
    "\n",
    "      contrast = model_inseq.attribute(\n",
    "          sentence,\n",
    "          sentence + \" \" + foil,\n",
    "          attributed_fn=attributed_fn,\n",
    "      )\n",
    "\n",
    "      clear_output()\n",
    "      print(f'target = {target}, foil = {foil}, sentence = {sentence}')\n",
    "      sent_len = len(tokenizer(sentence)['input_ids'])\n",
    "\n",
    "\n",
    "      base_tensor = baseline[0].target_attributions\n",
    "      contrastive_tensor = contrast[0].target_attributions\n",
    "\n",
    "      print(base_tensor.size())\n",
    "      print(contrastive_tensor.size())\n",
    "\n",
    "\n",
    "      baseline_att = base_tensor.sum(axis = 2)\n",
    "      contrast_att = contrastive_tensor.sum(axis = 2)\n",
    "      baseline_att = torch.flatten(baseline_att[~torch.any(baseline_att.isnan(),dim=1)]).numpy()\n",
    "      contrast_att = torch.flatten(contrast_att[~torch.any(contrast_att.isnan(),dim=1)]).numpy()\n",
    "\n",
    "\n",
    "      if filename == 'determiner_noun_agreement_1':\n",
    "        targets = [False] * (sent_len - 1)+[True]\n",
    "      else:\n",
    "        targets = [True] + [False] * (sent_len - 1)\n",
    "\n",
    "      baseline_att = baseline_att[:len(targets)]\n",
    "      contrast_att = contrast_att[:len(targets)]\n",
    "\n",
    "      if rev == 'True':\n",
    "        contrastive_attributions = contrast_att- baseline_att\n",
    "      if rev == 'False':\n",
    "        contrastive_attributions = baseline_att-contrast_att\n",
    "\n",
    "      rank_baseline = reciprocal_rank(baseline_att, targets)\n",
    "      rank_contrastive = reciprocal_rank(contrastive_attributions, targets)\n",
    "\n",
    "      assert len(targets) == len(baseline_att), f'Target/Baseline mismatch \\n targets = {targets},\\n baseline_att = {baseline_att}'\n",
    "      assert len(targets) == len(contrastive_attributions), f'Target/Contrast mismatch {targets}, {contrast_att}'\n",
    "      assert len(contrastive_attributions) == len(baseline_att), f'Baseline/Contrast mismatch {baseline_att}, {contrast_att}'\n",
    "\n",
    "      baseline_ranks.append(rank_baseline)\n",
    "      contrastive_ranks.append(rank_contrastive)\n",
    "      dist.append(sent_len)\n",
    "\n",
    "\n",
    "    AVG_dist = average(dist)\n",
    "    MRR_baseline = average(baseline_ranks)\n",
    "    MRR_contrastive = average(contrastive_ranks)\n",
    "\n",
    "    Results.append((f'Method: {explanation}', [AVG_dist,MRR_baseline,MRR_contrastive]))\n",
    "    print(Results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7QAk2_n5Mz2"
   },
   "source": [
    "hallo ik ben jan"
   ]
  }
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