plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-18 21:24:10.912674: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-18 21:24:11.035527: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-02-18 21:24:11.035585: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-02-18 21:24:11.053496: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-02-18 21:24:11.090985: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-18 21:24:11.867854: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras.backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, Lambda\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7794\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 4, 100)            779400    \n",
      "                                                                 \n",
      " lambda (Lambda)             (None, 100)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 7794)              787194    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1566594 (5.98 MB)\n",
      "Trainable params: 1566594 (5.98 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-18 21:24:12.765771: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.791306: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.791484: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.793375: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.793560: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.793761: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.840113: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.840249: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.840333: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-02-18 21:24:12.840395: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2427 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with open('vocab.txt',\"r\") as f:\n",
    "    unique_words=f.readlines()\n",
    "    \n",
    "vocab=[]\n",
    "\n",
    "for word in unique_words:\n",
    "    vocab.append(word.strip())\n",
    "\n",
    "vocab_size=len(vocab)+1\n",
    "print(vocab_size)\n",
    "embed_size = 100\n",
    "window_size=2\n",
    "\n",
    "\n",
    "cbow = Sequential()\n",
    "cbow.add(Embedding(input_dim=vocab_size, output_dim=embed_size, input_length=window_size*2))\n",
    "cbow.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(embed_size,)))\n",
    "cbow.add(Dense(vocab_size, activation='softmax'))\n",
    "cbow.compile(loss='categorical_crossentropy', optimizer='rmsprop')\n",
    "\n",
    "cbow.load_weights('model_weights.h5')\n",
    "\n",
    "print(cbow.summary())\n",
    "\n",
    "weights = cbow.get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean(vec1,vec2):\n",
    "    return np.linalg.norm(vec1 - vec2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'of', 'and', 'in', 'to', 'a', 'one', 'is', 'that', 'nine', 'as', 'zero', 's', 'by', 'two', 'or', 'for', 'eight', 'his', 'lincoln', 'was', 'on', 'with', 'six', 'he', 'three', 'an', 'it', 'from', 'five', 'be', 'not', 'four', 'are', 'at', 'seven', 'this', 'autism', 'which', 'have', 'aristotle', 'also', 'who', 'some', 'rand', 'they', 'other', 'war', 'her', 'anarchism', 'achilles', 'many', 'had', 'all', 'but', 'such', 'more', 'were', 'people', 'their', 'autistic', 'most', 'states', 'would', 'has', 'can', 'first', 'anarchists', 'she', 'may', 'philosophy', 'anarchist', 'see', 'time', 'state', 'him', 'abraham', 'these', 'about', 'when', 'i', 'best', 'there', 'been', 'often', 'its', 'united', 'after', 'what', 'social', 'others', 'if', 'union', 'altruism', 'than', 'only', 'however', 'into', 'new', 'while', 'own', 'american', 'alabama', 'during', 'general', 'do', 'life', 'slavery', 'will', 'no', 'them', 'academy', 'used', 'so', 'because', 'early', 'although', 'movement', 'both', 'those', 'film', 'abu', 'history', 'man', 'form', 'dhabi', 'president', 'being', 'national', 'society', 'world', 'use', 'person', 'ayn', 'illinois', 'including', 'any', 'through', 'even', 'de', 'works', 'children', 'against', 'political', 'called', 'over', 'example', 'government', 'present', 'out', 'name', 'term', 'work', 'human', 'moral', 'years', 'between', 'anarcho', 'we', 'up', 'greek', 'anti', 'ideas', 'thought', 'another', 'well', 'then', 'part', 'e', 'individual', 'later', 'party', 'like', 'left', 'non', 'awards', 'free', 'known', 'language', 'different', 'made', 'age', 'albedo', 'civil', 'where', 'did', 'much', 'became', 'plato', 'award', 'individuals', 'since', 'good', 'disorder', 'letter', 'without', 'believe', 'th', 'groups', 'within', 'must', 'self', 'could', 'system', 'due', 'high', 'g', 'second', 'act', 'major', 'right', 'd', 'politics', 'republican', 'how', 'before', 'law', 'found', 'modern', 'property', 'international', 'ethics', 'disorders', 'syndrome', 'help', 'ethical', 'u', 'should', 'according', 'century', 'considered', 'book', 'set', 'very', 'robert', 'now', 'though', 'certain', 'particular', 'causes', 'under', 'behavior', 'spectrum', 'great', 'c', 'theory', 'post', 'several', 'cause', 'each', 'body', 'university', 'english', 'still', 'movements', 'until', 'natural', 'said', 'institute', 'took', 'chinese', 'thus', 'writings', 'following', 'science', 'child', 'asperger', 'year', 'public', 'slaves', 'way', 'based', 'capitalism', 'alexander', 'support', 'common', 'back', 'culture', 'living', 'things', 'lee', 'bc', 'logic', 'house', 'sometimes', 'held', 'published', 'times', 'wrote', 'believed', 'important', 'further', 'action', 'army', 'popular', 'just', 'views', 'earth', 'john', 'among', 'effect', 'son', 'shall', 'branden', 'means', 'economic', 'proudhon', 'rights', 'number', 'take', 'long', 'lack', 'links', 'you', 'city', 'list', 'developmental', 'autistics', 'chance', 'confederate', 'troy', 'death', 'grant', 'objectivist', 'rather', 'every', 'point', 'famous', 'capital', 'co', 'never', 'black', 'land', 'day', 'books', 'interests', 'george', 'january', 'play', 'center', 'march', 'father', 'obligation', 'authoritarian', 'anarchy', 'does', 'might', 't', 'reason', 'interest', 'doctrine', 'edited', 'words', 'include', 'saw', 'same', 'members', 'today', 'votes', 'development', 'led', 'election', 'areas', 'make', 'terms', 'give', 'generally', 'knowledge', 'constitution', 'area', 'america', 'values', 'al', 'republicans', 'revolution', 'word', 'related', 'upon', 'william', 'opposed', 'workers', 'around', 'acts', 'began', 'school', 'usually', 'm', 'article', 'view', 'communication', 'fact', 'universal', 'little', 'know', 'become', 'effects', 'increase', 'functioning', 'save', 'hector', 'washington', 'press', 'metaphysics', 'films', 'class', 'french', 'place', 'argue', 'communism', 'described', 'supported', 'my', 'few', 'need', 'cnt', 'violence', 'christian', 'months', 'god', 'information', 'specific', 'references', 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'evolutionary', 'comte', 'morality', 'nietzsche', 'selfish', 'game', 'promote', 'anthem', 'atlas', 'shrugged', 'describe', 'positive', 'label', 'relations', 'economics', 'origins', 'predecessors', 'division', 'godwin', 'rothbard', 'find', 'am', 'indian', 'mainly', 'elements', 'pierre', 'goods', 'equality', 'notes', 'together', 'operative', 'followers', 'france', 'discussion', 'here', 'stirner', 'notion', 'assert', 'failed', 'internal', 'meeting', 'strongly', 'followed', 'genuine', 'marx', 'figure', 'leaders', 'split', 'socialism', 'satisfaction', 'needs', 'journal', 'emma', 'syndicalist', 'viewed', 'cgt', 'developing', 'remained', 'inspired', 'don', 'politicians', 'contemporary', 'claims', 'russian', 'bolsheviks', 'turned', 'russia', 'policy', 'collective', 'eastern', 'fascists', 'represents', 'leadership', 'oppose', 'christianity', 'declared', 'criticised', 'fundamental', 'problem', 'green', 'proper', 'believes', 'women', 'nozick', 'historians', 'jean', 'examples', 'numbers', 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      "belief impression 10.420958\n",
      "attention awareness 6.4312973\n",
      "beauty age 13.628328\n",
      "book theme 12.499334\n",
      "friend mother 7.2961464\n",
      "vitamin iron 4.679369\n",
      "chapter choice 7.7424216\n",
      "winner presence 6.135264\n",
      "winner goal 8.130176\n",
      "condition boy 10.8630705\n",
      "cent size 5.593934\n",
      "liquor century 11.494745\n",
      "night chapter 7.0188227\n",
      "cent wife 5.0978775\n",
      "course stomach 5.668783\n",
      "pupil president 17.143147\n",
      "bath wife 4.8020496\n",
      "people cent 19.294558\n",
      "formula log 5.3449955\n",
      "camera president 16.896324\n",
      "liquor band 8.163967\n",
      "worker camera 3.5198836\n",
      "people gun 18.266932\n",
      "princess island 13.047556\n",
      "people balloon 18.957067\n",
      "dollar people 18.688185\n",
      "bath balloon 4.009163\n",
      "log bath 4.9043703\n",
      "go come 12.48813\n",
      "take steal 12.806903\n",
      "occur happen 4.5770664\n",
      "protect defend 7.522856\n",
      "kill destroy 5.6636505\n",
      "create make 8.43075\n",
      "accept reject 7.393043\n",
      "ignore avoid 6.3185306\n",
      "carry bring 6.7833896\n",
      "leave enter 2.9498503\n",
      "choose elect 5.3987775\n",
      "lose fail 3.8789425\n",
      "achieve accomplish 5.2210064\n",
      "make construct 9.402987\n",
      "receive give 10.282077\n",
      "think decide 7.1353683\n",
      "agree argue 8.602241\n",
      "destroy make 8.328357\n",
      "save protect 12.505558\n",
      "understand know 10.851277\n",
      "take receive 12.146895\n",
      "decide choose 4.9365983\n",
      "accept believe 12.809148\n",
      "spend save 11.174777\n",
      "leave go 5.9351473\n",
      "come attend 11.618527\n",
      "know believe 12.022486\n",
      "forget ignore 3.3820932\n",
      "arrive leave 3.801111\n",
      "accept deny 6.85773\n",
      "arrive come 11.9606495\n",
      "agree differ 4.7024527\n",
      "send receive 4.3434176\n",
      "win dominate 5.551631\n",
      "participate join 9.051864\n",
      "leave remain 3.7965949\n",
      "go enter 6.6678147\n",
      "take carry 12.326596\n",
      "forget learn 4.6666927\n",
      "appoint elect 5.3582625\n",
      "engage marry 5.6469817\n",
      "go send 5.9301357\n",
      "speak hear 8.484164\n",
      "lose keep 5.2728744\n",
      "go allow 8.082553\n",
      "destroy construct 5.8995905\n",
      "steal buy 3.3922482\n",
      "forget know 11.073197\n",
      "create imagine 5.52612\n",
      "do happen 16.451834\n",
      "win accomplish 5.351464\n",
      "give deny 10.802018\n",
      "get put 6.4666977\n",
      "appear attend 9.5087385\n",
      "pretend imagine 4.1398373\n",
      "cherish keep 4.9564004\n",
      "argue differ 8.764144\n",
      "behave obey 3.1905837\n",
      "cooperate participate 4.5732446\n",
      "achieve try 5.455012\n",
      "say participate 15.727108\n",
      "come bring 12.011587\n",
      "take leave 12.74084\n",
      "conclude decide 3.7193046\n",
      "argue prove 7.959218\n",
      "argue justify 8.1655445\n",
      "make become 11.735751\n",
      "give put 10.159845\n",
      "expand grow 4.495062\n",
      "keep protect 6.146363\n",
      "explain prove 5.1011887\n",
      "get remain 6.4786153\n",
      "reject avoid 6.2198358\n",
      "argue persuade 9.291274\n",
      "pursue persuade 4.030507\n",
      "discuss explain 5.552973\n",
      "withdraw reject 4.399755\n",
      "keep borrow 5.9707975\n",
      "give borrow 10.503752\n",
      "kill defend 5.9526615\n",
      "become seem 10.86508\n",
      "pretend seem 6.223141\n",
      "accomplish become 10.760155\n",
      "determine predict 4.4412594\n",
      "remain retain 3.7982774\n",
      "forget retain 2.8374405\n",
      "want choose 5.392463\n",
      "lose get 5.586895\n",
      "try think 6.7160463\n",
      "become appear 10.896579\n",
      "leave ignore 3.402694\n",
      "keep give 9.755208\n",
      "give allow 8.681915\n",
      "bring send 6.344904\n",
      "absorb learn 4.739876\n",
      "leave appear 9.280835\n",
      "create destroy 5.1718793\n",
      "begin go 6.5427504\n",
      "get buy 5.4958854\n",
      "join marry 7.991864\n",
      "communicate pray 9.40545\n",
      "give steal 11.275296\n",
      "give know 10.399579\n",
      "cooperate join 8.5115595\n",
      "borrow want 5.034717\n",
      "send attend 3.93228\n",
      "absorb withdraw 3.5100312\n",
      "attend arrive 3.982388\n",
      "367 632\n"
     ]
    }
   ],
   "source": [
    "filename = 'SimLex-999/SimLex-999.txt'\n",
    "\n",
    "\n",
    "print(vocab)\n",
    "\n",
    "with open(filename,\"r\") as f:\n",
    "    content = f.readlines()\n",
    "content = content[1:]\n",
    "found=0\n",
    "notfound=0\n",
    "for line in content:\n",
    "    word1 = line.split()[0]\n",
    "    word2 = line.split()[1]\n",
    "    \n",
    "    if word1 in vocab and word2 in vocab:\n",
    "        found+=1\n",
    "        # print(\"Word found !\",end=\" \")\n",
    "        print(word1,word2,euclidean(weights[vocab.index(word1)],weights[vocab.index(word2)]))\n",
    "    else:\n",
    "        notfound+=1\n",
    "print(found,notfound)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have already defined `content`, `vocab`, `weights`, and `euclidean` function\n",
    "\n",
    "distances = []\n",
    "\n",
    "for line in content:\n",
    "    words = line.split()\n",
    "    word1, word2 = words[:2]\n",
    "    similarity_score = float(words[3])  # Assuming the similarity score is a float value\n",
    "    \n",
    "    if word1 in vocab and word2 in vocab:\n",
    "        distance = euclidean(weights[vocab.index(word1)], weights[vocab.index(word2)])\n",
    "        distances.append((word1, word2, distance, similarity_score))\n",
    "\n",
    "# Sort distances by the third element (euclidean distance)\n",
    "sorted_distances = sorted(distances, key=lambda x: x[2])\n",
    "\n",
    "# Plot top 5 highest euclidean distances\n",
    "top_5_highest = sorted_distances[-5:]\n",
    "top_5_highest_labels = [f'{pair[0]} - {pair[1]} ({pair[3]})' for pair in top_5_highest]\n",
    "top_5_highest_distances = [pair[2] for pair in top_5_highest]\n",
    "\n",
    "plt.barh(top_5_highest_labels, top_5_highest_distances, color='skyblue')\n",
    "plt.xlabel('Euclidean Distance')\n",
    "plt.title('Top 5 Highest Euclidean Distances')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n",
    "\n",
    "# Plot top 5 smallest euclidean distances\n",
    "top_5_smallest = sorted_distances[:5]\n",
    "top_5_smallest_labels = [f'{pair[0]} - {pair[1]} ({pair[3]})' for pair in top_5_smallest]\n",
    "top_5_smallest_distances = [pair[2] for pair in top_5_smallest]\n",
    "\n",
    "plt.barh(top_5_smallest_labels, top_5_smallest_distances, color='lightgreen')\n",
    "plt.xlabel('Euclidean Distance')\n",
    "plt.title('Top 5 Smallest Euclidean Distances')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}