Copy_of_Interpreting_LM_with_Contrastive_Explanations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5euVgZzMQN9X"
   },
   "source": [
    "# Interpreting Language Models with Contrastive Explanations\n",
    "### ___Kayo Yin and Graham Neubig___\n",
    "This Colab notebook lets you visualize contrastive explanations for language model decisions, based on [Yin and Neubig (2022)](https://arxiv.org/abs/2202.10419).\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "6395f1d9a2624fcbb24c7ad60d7f3561",
      "bd4bba4f22de470eb79133a24247fddc",
      "5a289bf0920a4da1bd60980d45c50361",
      "e5ec3a2aebd143aeb5fea96ce4935977",
      "f64d1a00cd28495c98a8b17a2fa9975f",
      "56d6a7e774c44a918ffc4dda0400f236",
      "e858cb83791c4f46aeccf9b868e23560",
      "aaebc713b5c240f9b56d79bd23314720",
      "946b45e26bac41d3a63983713dd21e0f",
      "a328f21d93ca433ba5f74f58019fcafb",
      "4cd9f45b88b646448e6be64acad0c5c6"
     ]
    },
    "id": "o6u2a6GKR_o2",
    "outputId": "a5940c78-cf1d-4862-bac1-fe42e87a28fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "#@markdown #**Setup Environment**\n",
    "#@markdown Install and import Python dependencies.\n",
    "print('Importing libraries...')\n",
    "from IPython.display import clear_output, Audio\n",
    "import sys\n",
    "sys.path.append('./interpret-lm')\n",
    "from lm_saliency import *\n",
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import random\n",
    "import hashlib\n",
    "import re\n",
    "import sys\n",
    "import tarfile\n",
    "from collections import Counter, defaultdict\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from sklearn.cluster import AgglomerativeClustering, DBSCAN\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
    "# import nltk\n",
    "# from nltk.tokenize import word_tokenize\n",
    "# nltk.download('all')\n",
    "sound_file = 'beep.wav'\n",
    "clear_output()\n",
    "print('Done!')\n",
    "\n",
    "if not torch.cuda.is_available():\n",
    "  print(\"Please restart runtime with GPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@markdown #**Load Language Model**\n",
    "model_name = \"gpt-2\" #@param [\"gpt-2\", \"gpt-neo\"]\n",
    "\n",
    "if model_name == \"gpt-2\":\n",
    "  tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
    "  model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
    "  vocab = list(tokenizer.encoder.keys())\n",
    "elif model_name == \"gpt-neo\":\n",
    "  model = GPTNeoForCausalLM.from_pretrained(\"EleutherAI/gpt-neo-1.3B\")\n",
    "  tokenizer = GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-1.3B\")\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "assert device.type == \"cuda\"\n",
    "model.to(device)\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence_set = \"one_sentence\" #ten_sentences, small_sample, full_set, one_sentence, meronyms, Negative_Polarity\n",
    "BPE = \"normal\"\n",
    "small_vocab = \"split\"\n",
    "vocab = \"Penn\"\n",
    "one_token = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not torch.cuda.is_available():\n",
    "  print(\"Please restart runtime with GPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "y6EkjwnufnJy"
   },
   "outputs": [],
   "source": [
    " #@markdown #**Open pre-made vocab**\n",
    "  #@markdown Open pre-made vocab made from wikitext-103. The vocab is tokenized and tagged using nltk. It is done beforehand because of memory issues.\n",
    "if vocab == \"Wiki\":\n",
    "    with open('/home/scur0635/Baseline_clusters/vocab_tagged.json', 'r') as f:\n",
    "      vocab_tagged = json.load(f)\n",
    "elif vocab == \"Penn\":\n",
    "    vocab_tagged = []\n",
    "    with open(\"postags.txt\", 'r') as file:\n",
    "        for line in file:\n",
    "            # Split the line into a list of words\n",
    "            words = line.split()\n",
    "            # Remove the brackets from the first and last elements in the list\n",
    "            words[1] = words[1].lstrip('(')\n",
    "            words[-1] = words[-1].rstrip(')')\n",
    "\n",
    "            # Append the modified list to data_list\n",
    "            vocab_tagged.append([[words[2], words[1]], words[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "qagMgTGmjWDt"
   },
   "outputs": [],
   "source": [
    "#  #@markdown #**Open pre-made sentence list**\n",
    "#  #@markdown Open pre-made sentence list made from wikitext-103. It is done using regex which is a slow process so done beforehand.\n",
    "# with open('/home/scur0635/Baseline_clusters/sentences_standard.json', 'r') as f:\n",
    "#   sentences_list = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(vocab_tagged[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if sentence_set == \"small_sample\":\n",
    "    sentences = [\"Can you stop the dog from\",\n",
    "                   \"Can you stop the dog\",\n",
    "                   \"Can you stop the\",\n",
    "                   \"Can you stop\",\n",
    "                   \"Can you\"]\n",
    "    \n",
    "elif sentence_set == \"ten_sentences\":\n",
    "    sentences = [   \"Can you stop the dog from\", #barking\n",
    "                    \"Are you sure that he is\",    #going\n",
    "                    \"He went with his dad on a trip, they're going\", #fishing\n",
    "                    \"There is no point in talking to him, he isn't\", #listening\n",
    "                    \"She went to the city with her friends, they're going\", #shopping\n",
    "                    \"Don't go out in the sun, it is\", #scorching\n",
    "                    \"Please turn off the lights before\", #leaving\n",
    "                    \"He couldn't find his keys, so he kept\", #searching\n",
    "                    \"After the rain, the streets were\", #glistening\n",
    "                    \"The children are excited; they're\", #playing\n",
    "                    \"I need to finish my homework before\", #sleeping\n",
    "                    \"She always has a smile on her face, she's always\", #smiling\n",
    "                    \"The movie was so sad that I started\", #crying\n",
    "                    \"The cake is in the oven; it's\", #baking\n",
    "                    \"I forgot to bring my umbrella, and now it's\", #raining\n",
    "                    \"His jokes are hilarious; he always has us\" #laughing\n",
    "                    ]\n",
    "    \n",
    "elif sentence_set == \"meronyms\":\n",
    "    sentences = [   \"This car is a piece of junk, it has a flat\", #tire\n",
    "                    \"Look at the size of that trunk, that must have been an old\", #tree\n",
    "                    \"You can tell by the color of the fins if you can eat this\" #fish\n",
    "                    ]\n",
    "    \n",
    "elif sentence_set == \"Negative_Polarity\":\n",
    "    sentences = [   \"She did not see ghosts\", \n",
    "                    \"I doubt that you saw\", \n",
    "                    \"I really did not expect\" \n",
    "                    ]\n",
    "    \n",
    "elif sentence_set == \"full_set\":\n",
    "    with open('/home/scur0635/Baseline_clusters/sentences_standard.json', 'r') as f:\n",
    "        sentences = json.load(f)\n",
    "        \n",
    "elif sentence_set == \"one_sentence\":\n",
    "    sentences = [\"She did not see ghosts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "IkBIvgKIfeD4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2204\n"
     ]
    }
   ],
   "source": [
    " #@markdown #**Only take f.i. verbs from the vocab**\n",
    "i = 2000\n",
    "if small_vocab == 'split':\n",
    "    \n",
    "    verbs = [item for item in vocab_tagged if item[0][1] in ['VBG']]\n",
    "    sorted_verbs = sorted(verbs, key=lambda x: x[1], reverse=True)    \n",
    "\n",
    "    nouns = [item for item in vocab_tagged if item[0][1] in ['NN']]\n",
    "    sorted_nouns = sorted(nouns, key=lambda x: x[1], reverse=True)    \n",
    "\n",
    "    sorted_words_1 = [x[0] for x in sorted_verbs if not re.findall('[^ a-zA-Z0-9]|unk',x[0][0])]\n",
    "    sorted_words_2 = [x[0] for x in sorted_nouns if not re.findall('[^ a-zA-Z0-9]|unk',x[0][0])]\n",
    "    \n",
    "    sorted_double = sorted_words_1[:i]+sorted_words_2[:i]\n",
    "    \n",
    "elif small_vocab == '1_pos':\n",
    "    \n",
    "    verbs = [item for item in vocab_tagged if item[0][1] in ['VBG']]\n",
    "    sorted_verbs = sorted(verbs, key=lambda x: x[1], reverse=True)    \n",
    "    sorted_double = [x[0] for x in sorted_verbs if not re.findall('[^ a-zA-Z0-9]|unk',x[0][0])]\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "else:\n",
    "    vocabbaby = [[tagged[0][0], tagged[1]] for tagged in vocab_tagged]\n",
    "\n",
    "    word_freq_dict = {}\n",
    "\n",
    "    # Iterate through each item in the vocab list\n",
    "    for item in vocabbaby:\n",
    "        word = item[0]\n",
    "        freq = item[1]\n",
    "\n",
    "        # If the word is already in the dictionary, add the frequency\n",
    "        if word in word_freq_dict:\n",
    "            word_freq_dict[word] += freq\n",
    "        else:\n",
    "            # If the word is not in the dictionary, add it with the current frequency\n",
    "            word_freq_dict[word] = freq\n",
    "\n",
    "    # Convert the dictionary back to a list of lists\n",
    "    result_vocab = [[word, freq] for word, freq in word_freq_dict.items()]\n",
    "\n",
    "    sorted_words = sorted(result_vocab, key=lambda x: x[1], reverse=True)\n",
    "    sorted_double = [x for x in sorted_words if not re.findall('[^ a-zA-Z0-9]|unk',x[0][0])]\n",
    "# Print the sorted verbs\n",
    "\n",
    "top_words = sorted_double[:2*i]\n",
    "top_words = [x[0] for x in top_words]\n",
    "\n",
    "if one_token == True:\n",
    "    for word in top_words:\n",
    "        if len(tokenizer(word)['input_ids']) > 1:\n",
    "            top_words.remove(word)\n",
    "print(len(top_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "w8a4SS87bJne"
   },
   "outputs": [],
   "source": [
    "if sentence_set == \"ten_sentences\":\n",
    "    targets = [\"barking\", \"going\", \"fishing\", \"listening\", \"shopping\", \"scorching\", \"leaving\", \"searching\", \"glistening\", \"playing\", \"sleeping\", \"smiling\", \"crying\", \"baking\", \"raining\", \"laughing\"]\n",
    "elif sentence_set == \"small_sample\":\n",
    "    targets = [\"barking\", \"from\", \"dog\", \"the\", \"stop\"]\n",
    "elif sentence_set == \"one_sentence\":\n",
    "    targets = [\"anywhere.\"]\n",
    "elif sentence_set == \"meronyms\":\n",
    "    targets = [\"tire\", \"tree\", \"fish\"]\n",
    "elif sentence_set == \"Negative_Polarity\":\n",
    "    targets = [\"anywhere\", \"anybody\", \"anyone\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Tq9Lx5CapgGW"
   },
   "outputs": [],
   "source": [
    "#  #@markdown #**Sample set X**\n",
    "\n",
    "if sentence_set == \"full_set\":\n",
    "    y_t = \"go\"\n",
    "    sample_size = 100 #@param [100, 200, 300, 400, 500]\n",
    "    target_list = [x for x in sentences_list for trgt in [\" \"+y_t+\" \", \" \"+y_t+\",\",\" \"+y_t+\".\",\" \"+y_t+\"?\",\" \"+y_t+\"!\"] if trgt in x]\n",
    "    samples = random.sample(target_list, sample_size)\n",
    "    samples = [x.split(y_t)[0] for x in samples if re.search(r'.+ .+ '+y_t, x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iDWv2P_rUd3w"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "DcdnhWc3nVM4"
   },
   "outputs": [],
   "source": [
    "#@markdown #**Retrieve contrastive explanation function**\n",
    "if BPE == \"BPE_on\":\n",
    "    def expl_vec(target, foil, input, explanation):\n",
    "        \n",
    "        correct_tokens = tokenizer(\" \" + target)\n",
    "        foil_tokens = tokenizer(\" \" + foil)\n",
    "        input = input.strip() + \" \"\n",
    "        input_ids = tokenizer(input)['input_ids']\n",
    "        attention_masks = tokenizer(input)['attention_mask']\n",
    "        \n",
    "        extra_ids = []\n",
    "        extra_masks = []\n",
    "        prediction_list = []\n",
    "        \n",
    "        for i, j in zip(foil_tokens['input_ids'],foil_tokens['attention_mask']):\n",
    "            full_input_ids = input_ids\n",
    "            full_attention_masks = attention_masks\n",
    "            for a, b in zip(extra_ids, extra_masks):\n",
    "                full_input_ids.append(a)\n",
    "                full_attention_masks.append(b)\n",
    "            extra_ids.append(i)\n",
    "            extra_masks.append(j)\n",
    "            \n",
    "        if explanation == \"erasure\":\n",
    "          contra_explanation = erasure_scores(model, input_tokens, attention_ids, correct=CORRECT_ID, foil=FOIL_ID, normalize=True)\n",
    "        else:\n",
    "          saliency_matrix, embd_matrix = saliency(model, input_tokens, attention_ids, foil=FOIL_ID)\n",
    "          if explanation == \"input x gradient\":\n",
    "            contra_explanation = input_x_gradient(saliency_matrix, embd_matrix, normalize=True)\n",
    "          elif explanation == \"gradient norm\":\n",
    "            contra_explanation = l1_grad_norm(saliency_matrix, normalize=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "elif BPE == \"normal\":\n",
    "    def expl_vec(target, foil, input, explanation):\n",
    "\n",
    "      input = input.strip() + \" \"\n",
    "      input_tokens = tokenizer(input)['input_ids']\n",
    "      attention_ids = tokenizer(input)['attention_mask']\n",
    "      CORRECT_ID = tokenizer(\" \"+ target)['input_ids'][0]\n",
    "      FOIL_ID = tokenizer(\" \"+ foil)['input_ids'][0]\n",
    "\n",
    "\n",
    "      if explanation == \"erasure\":\n",
    "        contra_explanation = erasure_scores(model, input_tokens, attention_ids, correct=CORRECT_ID, foil=FOIL_ID, normalize=True)\n",
    "      else:\n",
    "        saliency_matrix, embd_matrix = saliency(model, input_tokens, attention_ids, foil=FOIL_ID, correct = CORRECT_ID)\n",
    "        if explanation == \"input x gradient\":\n",
    "          contra_explanation = input_x_gradient(saliency_matrix, embd_matrix, normalize=True)\n",
    "        elif explanation == \"gradient norm\":\n",
    "          contra_explanation = l1_grad_norm(saliency_matrix, normalize=True)\n",
    "      return contra_explanation\n",
    "\n",
    "elif BPE == \"discard\":\n",
    "    def expl_vec(target, foil, input, explanation):\n",
    "\n",
    "      input = input.strip() + \" \"\n",
    "      input_tokens = tokenizer(input)['input_ids']\n",
    "      attention_ids = tokenizer(input)['attention_mask']\n",
    "      CORRECT_ID = tokenizer(\" \"+ target)['input_ids'][0]\n",
    "      FOIL_ID = tokenizer(\" \"+ foil)['input_ids'][0]\n",
    "\n",
    "\n",
    "      if explanation == \"erasure\":\n",
    "        contra_explanation = erasure_scores(model, input_tokens, attention_ids, correct=CORRECT_ID, foil=FOIL_ID, normalize=True)\n",
    "      else:\n",
    "        saliency_matrix, embd_matrix = saliency(model, input_tokens, attention_ids, foil=FOIL_ID)\n",
    "        if explanation == \"input x gradient\":\n",
    "          contra_explanation = input_x_gradient(saliency_matrix, embd_matrix, normalize=True)\n",
    "        elif explanation == \"gradient norm\":\n",
    "          contra_explanation = l1_grad_norm(saliency_matrix, normalize=True)\n",
    "      return contra_explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one_sentence\n"
     ]
    }
   ],
   "source": [
    "if sentence_set == \"meronyms\": \n",
    "    top_words = [\"iron\", \"man\", \"steak\"]\n",
    "elif sentence_set == \"Negative_Polarity\":\n",
    "    top_words = [\"somewhere\", \"somebody\", \"someone\" ]\n",
    "\n",
    "\n",
    "if sentence_set in [\"meronyms\",\"Negative_Polarity\"]:\n",
    "    results = []\n",
    "    for foil, target, sentence in zip(top_words,targets, sentences):\n",
    "        e_y_t = expl_vec(target, foil, sentence, \"erasure\")\n",
    "        results.append(e_y_t)\n",
    "\n",
    "\n",
    "elif sentence_set in [\"small_sample\", \"one_sentence\"]:\n",
    "    results = []\n",
    "    for i, target in enumerate(targets):\n",
    "        temp_list = []\n",
    "        for foil in top_words:\n",
    "            e_y_t = expl_vec(target, foil, sentences[i], \"input x gradient\")\n",
    "            temp_list.append(e_y_t)\n",
    "        results.append(temp_list)\n",
    "\n",
    "elif sentence_set == \"full_set\":\n",
    "    results = []\n",
    "    for target in targets:\n",
    "      for foil in top_words:\n",
    "        vec_list = []\n",
    "        for input in sentences:\n",
    "          e_x_y_t = expl_vec(target, foil, input, \"input x gradient\")\n",
    "          vec_list.append(e_x_y_t)\n",
    "        e_y_t = np.concatenate(vec_list)\n",
    "        results.append(e_y_t)\n",
    "\n",
    "elif sentence_set in [\"ten_sentences\"]:\n",
    "    results = []\n",
    "    for foil in top_words:\n",
    "        vec_list = []\n",
    "        for input,target in zip(sentences, targets):\n",
    "          e_x_y_t = expl_vec(target, foil, input, \"input x gradient\")\n",
    "          vec_list.append(e_x_y_t)\n",
    "        e_y_t = np.concatenate(vec_list)\n",
    "        results.append(e_y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "l1_6pzztwDFG"
   },
   "outputs": [],
   "source": [
    "def fancy_dendrogram(*args, **kwargs):\n",
    "    max_d = kwargs.pop('max_d', None)\n",
    "    if max_d and 'color_threshold' not in kwargs:\n",
    "        kwargs['color_threshold'] = max_d\n",
    "    annotate_above = kwargs.pop('annotate_above', 0)\n",
    "\n",
    "    ddata = dendrogram(*args, **kwargs)\n",
    "\n",
    "    if not kwargs.get('no_plot', False):\n",
    "        plt.title('Hierarchical Clustering Dendrogram (truncated)')\n",
    "        plt.xlabel('sample index or (cluster size)')\n",
    "        plt.ylabel('distance')\n",
    "        for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):\n",
    "            x = 0.5 * sum(i[1:3])\n",
    "            y = d[1]\n",
    "            if y > annotate_above:\n",
    "                plt.plot(x, y, 'o', c=c)\n",
    "                plt.annotate(\"%.3g\" % y, (x, y), xytext=(0, -5),\n",
    "                             textcoords='offset points',\n",
    "                             va='top', ha='center')\n",
    "        if max_d:\n",
    "            plt.axhline(y=max_d, c='k')\n",
    "    return ddata\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[array([ 0.16952182,  0.22510691,  0.22264835, -0.20444669,  0.17827626],\n",
      "      dtype=float32)]]\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "4h78MfecwHWG",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.16952182  0.22510691  0.22264835 -0.20444669  0.17827626]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The number of observations cannot be determined on an empty distance matrix.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/scratch-local/scur0635.4709776/ipykernel_3701426/2089533956.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mlinkage_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ward'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mall_linkage_matrices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/scipy/cluster/hierarchy.py\u001b[0m in \u001b[0;36mlinkage\u001b[0;34m(y, method, metric, optimal_ordering)\u001b[0m\n\u001b[1;32m   1065\u001b[0m                          \"finite values.\")\n\u001b[1;32m   1066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1067\u001b[0;31m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_obs_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1068\u001b[0m     \u001b[0mmethod_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_LINKAGE_METHODS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36mnum_obs_y\u001b[0;34m(Y)\u001b[0m\n\u001b[1;32m   2628\u001b[0m     \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2629\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2630\u001b[0;31m         raise ValueError(\"The number of observations cannot be determined on \"\n\u001b[0m\u001b[1;32m   2631\u001b[0m                          \"an empty distance matrix.\")\n\u001b[1;32m   2632\u001b[0m     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The number of observations cannot be determined on an empty distance matrix."
     ]
    }
   ],
   "source": [
    "all_linkage_matrices = []\n",
    "# results = [results]\\\n",
    "for i, result in enumerate(results):\n",
    "    X = np.array(result)\n",
    "    print(X)\n",
    "    linkage_matrix = linkage(X, 'ward')\n",
    "    all_linkage_matrices.append(linkage_matrix)\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    fancy_dendrogram(\n",
    "        linkage_matrix,\n",
    "        truncate_mode='lastp',\n",
    "        p=8,\n",
    "        leaf_rotation=90.,\n",
    "        leaf_font_size=8.,\n",
    "        show_contracted=True,\n",
    "        annotate_above=1,  # useful in small plots so annotations don't overlap\n",
    "    )\n",
    "    plt.title(f'Dendrogram Plot for Result {1+i}')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'Dendrogram_Result_Normalized+fix_{1+i}.png', dpi = 300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_words_to_clusters(words, indices):\n",
    "    word_clusters = {}\n",
    "\n",
    "    for word, cluster_index in zip(words, indices):\n",
    "        if cluster_index not in word_clusters:\n",
    "            word_clusters[cluster_index] = [word]\n",
    "        else:\n",
    "            word_clusters[cluster_index].append(word)\n",
    "\n",
    "    return word_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def take_mean_vec(top_words, results, cluster_index,clusters_words):\n",
    "    listie = []\n",
    "    for i in zip(top_words, results):\n",
    "        if i[0] in clusters_words[cluster_index]:\n",
    "            listie.append(np.array(i[1]))\n",
    "    mean_vectors = np.mean(listie, axis=0)\n",
    "    return mean_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'linkage_matrix' 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/scur0635.4709776/ipykernel_3701426/2951205185.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;31m#---------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mvisualize_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentences\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinkage_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.5\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'linkage_matrix' is not defined"
     ]
    }
   ],
   "source": [
    "if sentence_set == \"ten_sentences\":\n",
    "    clusters = fcluster(linkage_matrix, t = 15, criterion= 'distance')\n",
    "    clusters_words = dict(sorted(map_words_to_clusters(top_words, clusters).items()))\n",
    "    for cluster_index, cluster_words in clusters_words.items():\n",
    "            print(f\"Cluster {cluster_index}: Cluster length {len(cluster_words)} :{cluster_words[:10]}......{cluster_words[-10:]}\")\n",
    "    cluster_means = [take_mean_vec(top_words, results[0], x,clusters_words) for x in clusters_words.keys()]\n",
    "    num_clusters = len(cluster_means)\n",
    "    subvector_lengths = [len(tokenizer(x)['input_ids']) for x in sentences]\n",
    "    split_cluster_means = [\n",
    "        [\n",
    "            item[sum(subvector_lengths[:i]): sum(subvector_lengths[:i+1])]\n",
    "            for i in range(len(subvector_lengths))\n",
    "        ]\n",
    "        for item in cluster_means\n",
    "    ]\n",
    "    for clustermean in split_cluster_means:    \n",
    "        for i,sentencey in enumerate(sentences):\n",
    "                visualize(clustermean[i], tokenizer,[tokenizer(sentencey)['input_ids']], print_text=True, title=f\"Mean vector for Sentence {i+1}\")\n",
    "    \n",
    "\n",
    "else:\n",
    "    def visualize_clusters(top_words, sample,  result, linkage_matrix, theta, criterion_var = 'distance'):\n",
    "\n",
    "        clusters = fcluster(linkage_matrix, t = theta, criterion= criterion_var)\n",
    "    #     clusters = DBSCAN(eps=.25, min_samples=150).fit(linkage_matrix).labels_\n",
    "\n",
    "        clusters_words = dict(sorted(map_words_to_clusters(top_words, clusters).items()))\n",
    "\n",
    "        for cluster_index, cluster_words in clusters_words.items():\n",
    "            print(f\"Cluster {cluster_index}: Cluster length {len(cluster_words)} :{cluster_words[:10]}.....{cluster_words[-10:]}\\n\")\n",
    "\n",
    "        cluster_means = [take_mean_vec(top_words, result, x,clusters_words) for x in clusters_words.keys()]\n",
    "        num_clusters = len(cluster_means)\n",
    "\n",
    "        for i in range(num_clusters):\n",
    "#         for i in [2,0,1]:\n",
    "            visualize(cluster_means[i], tokenizer,[tokenizer(sample)['input_ids']], print_text=True, title=f\"Mean vector for Cluster {i+1}\")\n",
    "    \n",
    "#---------------------------------------------------------------------------------------    \n",
    "    i = 0\n",
    "    visualize_clusters(top_words, sentences[i],results[i],linkage_matrix, 2.5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Audio(sound_file, autoplay=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eHRER+fkFcdVVf4tevY7bZlxVVVX8/vefi1mznoqIiGbNWsWPfvR2FBa23t1bAyARVxTf3dAtALAfqti8OQb8+7/H/HffjYiIrq1bxxvXXRclhYXbjC1buzYG/cd/xJLy8oiIOLF//5jw/e/XSx8T33wzGuXnx+AePbLPa02cGJfcckumXnDDDdGzXbuc1527fHn0u+aaTP25o46Kx666qs4Hb5WtXRtH/ud/RumHZ9I6tWoVpb/5TSbYEABqy7vwwqnV1dVDar/vLwcAe+zeF1/MhGtERPzqX/6lzkCJ1kVF8auLLsrUE+fMiYmzZ9dLHycefngMP+ywrM3avra0rCz+XOPA19nDhgnXAAAAdsu906dnDnxFRPzqrLO2e+ArIqJ18+bxq89/PlNPnD8/Jr799h738MjFF8dXhg7d6YGviIhh3bvHN084IVO/u3ZtvLBgwR73EBGRl5cXPz/jjKz3nps7t17WBgAADi6p77UmjBqVU7hGRMTnP/ax+NKgQZl6WmlpLKjxuwEAANhTU6bcmwnXiIg499xfbTdcIyKisLB1nHPOrzL1vHkTY+7cifXSx9e//kgcd9xXdhquERHRs+ewOPnkb2bqNWvejbfffqHO8StXLogpU+7J1Mcff9l2wzUiIvLz8+OCC26JgoJDIiJiw4b3Y8KE3+Z6GwAAwEHm3kmTMuEaERG/+ud/3m64RsSH57X++Z8z9cQ334yJb75ZL32c2L9/DO/Xr97Pa02YMyer/q8vfKHOcI2Irff4jc98JlMve//9eLvG7wcAciVgA4A99uDLL2dedy4pibOHDdvh+HOGDYtOJR99SFZz/oHuzgkTYktVVaa+9JRTGrAbAADgQPbg669nXndu2TLO3skBqXOOOCI6tWy53fn7ylkDB2bVc2qEMe6pHq1bR7saAYbLPvig3tYGAAAOHvZatdaudf9zfAkRAACoR9OnP5h5XVzcOY488uwdjh88+JwoLu603fn70qBBZ2XVy5fPqWNkxGuvPRzV1dWZ+qST/m2Ha7dq1SUGDfpCpm6oewQAAPZ/D77ySuZ155KSOHvIkB2OP2fo0OjUqtVH8ydP3lut1Yt3a30HcFD37judM6hbt6x65Zo19doTAAcHARsA7JENFRXx7IwZmfr0wYOjUcGOU94bFRTE6UcemanHTp261/rb126fMCHzulf79nFqjk8IAwAAqGlDZWU8O3dupj69f//c9lqHHZapx86evdf6q0ubWun4H2zcWK/rb96yJfO6ZdOm9bo2AACQPnut7azdvPleWxsAADi4VVRsiDfffDZTH3746VFQ0GiHcwoKGsWAAadn6jfeGLvX+tuRwsI2WfXGjXUHv9fssW3b3tGx44Cdrn/EEWdmXi9dOjNWrlywG10CAAAp21BREc/OmpWpTx80KLfPtQYNytRjp03ba/3VhxbNmmXVGysrdzqn9piSWp+jAUAuBGwAsEdml5bGphqbk0/U+ILhjtQct3jlyihbu7bee9vXJs6eHXOXLcvUl5x8cuTl5TVgRwAAwIFq9vLlsWnz5kz9iZ49c5r3iV69Mq8Xl5dH2fr19d3aDi0sK8uq2xcV1dvary1ZEuUbNmTqXH8nAAAA/2CvtZ21y8v32toAAMDBbfny2bF586ZM3bv3J3Ka16fPR+PKyxfHunVlOxi9d6xatTCrLipqX+fYJUtey7zO9R5rjystnZ5zbwAAwMFh9pIl2ee1+vXLaV7NcYtXrdqvz2sd16dPVj1+zpydzhlXIwy/Q3Fx9OvYsd77AiB9AjYA2COzS0uz6r45bkxqj6u9zoFo9Lhxmdf5eXlx8UknNWA3AADAgWz2ihVZdd927XKa17dt2+x1li+vt55y8dAbb2TVw+spBGPtpk1xxcMPZ+r+7dvHmYcfXi9rAwAABw97re2s/frrmdeNCwpiSLdu9bY2AABwcFu2bHZW3b5935zmtWuXPa72OvvC9OkPZdW9ew/f7rjy8ndi48Y1mTrXe2zTpmfk5zfK1A1xjwAAwP5t9pIlWXXfDh1ymrfNea1a6+xPhvbpE8cdemim/s5998WqNWvqHD953ry4pcbZre+ceWbk5zsiDcCu89cDgD2y8L33surutb5gWJcetb6wWHudA80H69fHQ5MnZ+rPHHlkdMvxdwEAAFBb7ScId2/VKqd5PUpKdrjO3rSwrCzunjYtUw/p1i365/ih3vZsqKyMOStWxG//9rc44pe/jJcWLYqIiA4tWsTDX/lKNCoo2OOeAQCAg4u9VraXFy2K5+bNy9SfHzgwWjZtWi9rAwAAlJUtzKpLSrrnNK916x47XGdvW7VqYbz66t2Zunv3IdGxY/86x9aU6z3m5xdESUnXTL2v7xEAANj/7fZ5rVrj9vfzWn+8/PLo+eEZs7eWLYujvv/9uPm552LBu+9G5ebNsaGiIl5ftCi+98ADcfL//E9srKyMiIjLP/nJ+NZppzVk6wAcwBrtfAgA1O2DDRuy6laFhTnNK27ePKteU2udA819kybF+k2bMvWlp5zSgN0AAAAHug82bsyqWzVrltO84lrj1tTYp+xNW6qq4uL77osNH354FRHx8zPO2KU1/ve55+I/nnqqzp83adQoLjjqqLjujDOiQ4sWu90rAABw8DoY91p1WbtpU1x8771RXV0dERGN8vPjp5/9bL2sDQAAEBGxYcMHWXXz5q1ymtesWXFWvXFj3U8vrm9VVVtizJiLo7Lyo+8zfuELP69z/MaN2ffYrFmrnK/VtOlH97kv7xEAADgwbHNeq9Y5rLps87lWrc/H9jd9O3aMV370o7jsttti7LRp8c6qVXH57bfXOX5A585xzRlnxFdPOmkfdglAavIbugEADmxra220mjVunNO82uMO9ICN0ePGZV63bdEiPj9kSAN2AwAAHOjW1jqs1eyQQ3KaV3vcvvpw7NrHH48Jb7+dqS/7+Mfj1L596239/Ly8+PLgwXH58OHCNQAAgN1mr7VVdXV1XHzfffFWjSeW/eT00+Ow9u33eG0AAIB/2LRpbVZ9yCG5hRzWHrdp074Ln3j00Wtj7twJmfr44y+Lww47tc7xte+xcePc7rH2WAEbAABAbdt8rrW757X284CNiIh2LVvGvaNGxS8vuGCH99m5pCSu+NSn4kvDhu3D7gBIUaOGbgCAA1vl5s1ZdaP83LKbGhUUZK+zZUu99bSvzXrnnXhl3rxM/S8nnhiNG/kTCwAA7L7ae6Sc91q1xlVWVdVbT3X5zQsvxA0TJ2bqwZ07x6/POmuX1+ndpk2cdthhEbH1sNeaTZtifllZrFizJqqqq+POKVPizilT4ouDBsWtI0Zsk7QPAACwMwfjXmt7rh47Nh5+441M/dn+/eM7p9Z9YAwAAGB3VFVVZtX5+bl9p66gIHvcli2VdYysX+PG/Saef/6GTN216+D44hd/vcM5tXvL9R5rj639uwIAADiYzmvd99JL8e277orlq1dn3uvVrl10b9MmtlRVxYL33osl5eWxtLw8vvHHP8YPH3kkbrn00jhn6NAG7BqAA5nTvwDskcKmTbPqjZWV0bxJk53O21hRkb1ODnP2V6PHjcuqLz3llAbqBAAASEXtPdLGzZujeQ4J9Bsrs798V5hjav3uuvPVV+PKxx7L1H3bto2nvva1aJrjU6BrGjF4cIwYPHib92cuWxY/Hzcu7po6NSIiHnrjjXjn/fdjwqhR0US4IQAAsAsOxr1WbT/+61+zgjuO69EjHrjoosjLy9vjtQEAAGpq3Lgwq968eWM0btx8p/MqK7Ofrlx7nb3h5ZfvjIcfvjJTt2/fN6644qk45JCmdU+KbXur3fuO1By7L+4RAAA4sGzzuVai57X+d+zY+I8HHsjUXxo2LP5nxIjo27Fj1rjpCxfG1ffcE+Nmz45Va9fGF3/zm7hr5Mi4YPjwfd0yAAnwDXQA9khRrYCN9Zs25bRhW19rw9biAH3ycOXmzTGm5pcQ+/aNgd26NWBHAABACopqHdZaX1GR06Gv9bUOfbXYix+O3TttWnz1/vujuro6IiJ6tW4dz11+eXRq2bJer/OxTp1izAUXxHHdu8e/PfpoRERMXrw4/ve55+K/TzutXq8FAACk7WDfa/3i+efjv//yl0w9pFu3+PPXvhZF+/kXKwEAgIa3ZMkb8eij/77TcWef/Yvo0mVQREQ0aVKU9bOKivU5BWxUVKzPqps2bbELne66KVPujbvu+mpmH9amTa/45jefi+LiTjudu717zFXNsU2a7N17BAAADjzbnNeqqNi981pNdxwc2JD+9tZb8Z8PPpipR3360/Hbr3xlu2OP6tkznv3ud+MLN9wQj0+fHtXV1XHZbbfFyQMGROeSkn3VMgCJyG/oBgA4sLVrkf3BzrL3389p3rLy8qy6bYsD8wOisVOnxso1azL1paec0oDdAAAAqWhXlP1lvGU19h07suyDD7LqtoV752lXD73+elx0771R9eEXDXuUlMS4kSOj2178oGrU8cfHp/r2zdS/mzQp80VHAACAXBzMe60bJkyI7zz5ZKY+umvX+OvXvx7FB2gIPgAAsG+tW1cWc+b8Zaf/1q0ry8wpKmqXtcbq1ctyutYHH2SPKyxsu+c3UIdp0x6KO++8KKqrqyIionXrHvGtb42LkpLcHrJV+x5r974jNccWFe29ewQAAA5M7WqFr+d8XqvWuP35vNbPxo7NfAewXcuW8Ysvf3mH4/Pz8+OmSy6JQwoKIiJi7caNcfPzz+/1PgFIj4ANAPZI/y5dsupF772X07xFK1fucJ0DxegaG7HCJk3ivOHDG7AbAAAgFf3bt8+qF9UKKaxL7XG116kPj86YEeffdVdsrtr6RcMeJSUx/oorokfr1vV+rdrOGzw48/q9tWtjbq29JQAAwI4crHutG194Ia4aOzZTH9WlSzxz2WVR0nznT44GAADYXR069M+qy8oW5TSv9riOHfvXMXLPvPbao3H77edHVdXmiNgarnHlleO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      "text/plain": [
       "<Figure size 4320x4320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "only length-1 arrays can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/scratch-local/scur0635.4709776/ipykernel_3701426/2080826350.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"Input x Gradient Meronym {i+1}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mvisualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"Input x Gradient Meronym {i+1}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/gpfs/home3/scur0635/Baseline_clusters/lm_saliency.py\u001b[0m in \u001b[0;36mvisualize\u001b[0;34m(attention, tokenizer, input_ids, gold, normalize, print_text, save_file, title, figsize, fontsize)\u001b[0m\n\u001b[1;32m    153\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0mattention\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mattention\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m     \u001b[0mattention\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "visualize(results[0][0], tokenizer,[tokenizer(sentences[0])['input_ids']], print_text=True, title=f\"Input x Gradient Meronym {i+1}\")\n",
    "for i in range(len(results)):\n",
    "    visualize(results[i], tokenizer,[tokenizer(sentences[i])['input_ids']], print_text=True, title=f\"Input x Gradient Meronym {i+1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in ['stating', 'filming', 'noting', 'forming', 'arriving', 'consisting', 'citing', 'forcing', 'defeating', 'completing', 'believing', 'earning', 'establishing', 'indicating', 'destroying', 'praising', 'extending', 'preparing', 'hoping', 'commenting', 'advancing', 'flooding', 'owing', 'ranking', 'capturing', 'qualifying', 'occurring', 'depicting', 'releasing', 'measuring']:\n",
    "#     print(tokenizer(\" \"+i)[\"input_ids\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "clusters = fcluster(linkage_matrix, t = 26.6, criterion= 'distance')\n",
    "clusters_words = dict(sorted(map_words_to_clusters(top_words, clusters).items()))\n",
    "for cluster_index, cluster_words in clusters_words.items():\n",
    "        print(f\"Cluster {cluster_index}: Cluster length {len(cluster_words)} :{cluster_words[:10]}\")\n",
    "cluster_means = [take_mean_vec(top_words, results, x,clusters_words) for x in clusters_words.keys()]\n",
    "num_clusters = len(cluster_means)\n",
    "subvector_lengths = [len(tokenizer(x)['input_ids']) for x in sentences]\n",
    "split_cluster_means = [\n",
    "    [\n",
    "        item[sum(subvector_lengths[:i]): sum(subvector_lengths[:i + 1])]\n",
    "        for i in range(len(subvector_lengths))\n",
    "    ]\n",
    "    for item in cluster_means\n",
    "]\n",
    "for clustermean in split_cluster_means:\n",
    "    for i,sentencey in enumerate(sentences):\n",
    "            visualize(clustermean[i], tokenizer,[tokenizer(sentencey)['input_ids']], print_text=True, title=f\"Mean vector for Cluster {i+1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Small_sample' 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/scur0635.4698110/ipykernel_4099728/4161494607.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mvisualize_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_words\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSmall_sample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m73\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Small_sample' is not defined"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "visualize_clusters(top_words,Small_sample[i], result_list[i], result_list[i], 73 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "neigh = NearestNeighbors(n_neighbors = 2)\n",
    "nbrs = neigh.fit(results)\n",
    "distances, indices = nbrs.kneighbors(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = np.sort(distances, axis=0)[:,1]\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(distances)\n",
    "plt.title('K-distance graph')\n",
    "plt.xlabel('Data points sorted by distance')\n",
    "plt.ylabel('Epsilon')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenizer([\"a\",\"a.\",\"alexander\", \"alex\", \"arizona\", \"aria\", \"a.v\", \"aaron\"])['input_ids']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# a.m.\n",
    "# aa\n",
    "# aires\n",
    "# ai\n",
    "# alexandria\n",
    "# auburn\n",
    "# aif\n",
    "# alexandra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_duplicate_array_indices(arr_list):\n",
    "    # Convert each array to a tuple for hashability\n",
    "    arr_tuples = [tuple(arr.flatten()) for arr in arr_list]\n",
    "\n",
    "    # Use defaultdict to store indices of each tuple\n",
    "    index_dict = defaultdict(list)\n",
    "    for idx, arr_tuple in enumerate(arr_tuples):\n",
    "        index_dict[arr_tuple].append(idx)\n",
    "\n",
    "    # Filter tuples with more than one occurrence\n",
    "    duplicate_dict = {k: v for k, v in index_dict.items() if len(v) > 1}\n",
    "\n",
    "    return duplicate_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keys_with_more_than_x_values(input_dict, x):\n",
    "    result_keys = [(key, values) for key, values in input_dict.items() if len(values) > x]\n",
    "    return result_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "a.\n",
      "alexander\n",
      "alex\n",
      "arizona\n",
      "aria\n",
      "a.v\n",
      "aaron\n",
      "a.m.\n",
      "aa\n",
      "aires\n",
      "ai\n",
      "alexandria\n",
      "auburn\n",
      "aif\n",
      "alexandra\n"
     ]
    }
   ],
   "source": [
    "# print(keys_with_more_than_x_values(find_duplicate_array_indices(result_list[0]),10))\n",
    "for i in [5, 1571, 1780, 3272, 3865, 5616, 5675, 6192, 6492, 7116, 7420, 7528, 7565, 8446, 9078, 9173]:\n",
    "    print(top_words[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'distances' 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/scur0635.4624074/ipykernel_1086558/3181797608.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdifferences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mslopes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdifferences\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Find the index of the maximum slope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'distances' is not defined"
     ]
    }
   ],
   "source": [
    "differences = np.diff(distances)\n",
    "\n",
    "slopes = differences / np.diff(np.arange(len(distances)))\n",
    "\n",
    "# Find the index of the maximum slope\n",
    "max_slope_index = np.argmax(slopes)\n",
    "\n",
    "# The optimal epsilon is the distance at the point with the maximum slope\n",
    "optimal_epsilon = distances[max_slope_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6632238290182775\n"
     ]
    }
   ],
   "source": [
    "print(optimal_epsilon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Bar width for better visualization\n",
    "num_bars = len(cluster_means[0])\n",
    "bar_width = 0.8 / num_bars  # Adjusted bar width for better visualization\n",
    "\n",
    "# Set up colors for each cluster\n",
    "colors = plt.cm.tab10(np.arange(num_bars))\n",
    "\n",
    "# Create individual subplots for each cluster\n",
    "for i in range(num_clusters):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.bar(np.arange(num_bars), cluster_means[i], width=bar_width, color=colors)\n",
    "    \n",
    "    # Add labels and title\n",
    "    ax.set_xlabel(f'Dimension')\n",
    "    ax.set_ylabel(f'Mean Value')\n",
    "    ax.set_title(f'Mean Values for Cluster {i+1}')\n",
    "    ax.set_xticks(range(num_bars))\n",
    "    ax.set_xticklabels([f'Dim {k+1}' for k in range(num_bars)])\n",
    "\n",
    "    # Add a horizontal line at y=0 for better visualization\n",
    "    ax.axhline(y=0, color='black', linewidth=1, linestyle='--', alpha=0.5)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "VFm3YdQRyQeN"
   },
   "outputs": [],
   "source": [
    "# #@markdown #**Function for finding indexes of a certain cluster**\n",
    "# def ClusterIndicesNumpy(clustNum, labels_array): #numpy\n",
    "#     return np.where(labels_array == clustNum)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "yeZ05f2EBt05"
   },
   "outputs": [],
   "source": [
    "# #@markdown #**Function for retrieving words from indexes**\n",
    "# def retrieve_words(cluster, words, kmeans):\n",
    "#   idx = ClusterIndicesNumpy(cluster, kmeans.labels_)\n",
    "#   results = []\n",
    "#   for word in words:\n",
    "#     if words.index(word) in idx:\n",
    "#       results.append(word)\n",
    "#   return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "B1nD6-ZX_3ap",
    "outputId": "ec6de776-0ed4-4a88-b007-d3af7d861a52"
   },
   "outputs": [],
   "source": [
    "# Foil_cluster = retrieve_words(7, top_words, kmeans)\n",
    "# print(Foil_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "GpRLjJXtAdP0"
   },
   "outputs": [],
   "source": [
    "# for i in range(8):\n",
    "#   cluster = retrieve_words(i, top_words, kmeans)\n",
    "#   print('Cluster',i)\n",
    "#   print('------------------------------')\n",
    "#   print(cluster[:10])\n",
    "#   print('------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LVw3BNFQ4LBm"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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