ssl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "MfO30nSoUlsg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\yanis\\anaconda3\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from tensorflow.keras.datasets import imdb\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "import time\n",
    "\n",
    "from src.dataset import Dataset\n",
    "from src.lstm import LSTM\n",
    "from src.ssm import SSM\n",
    "from src.particle_gibbs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Device: GPU if available, CPU else\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eNTWEQ_MUlsi"
   },
   "source": [
    "# Import and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "37zg2hZNUlsi",
    "outputId": "7bf71b0f-cd6b-413c-a07d-d917099f4b66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<START> although i had seen <UNK> in a theater way back in <UNK> i couldn't remember anything of the plot except for vague images of kurt thomas running and fighting against a backdrop of stone walls and disappointment regarding the ending br br after reading some of the other reviews i picked up a copy of the newly released dvd to once again enter the world of <UNK> br br it turns out this is one of those films produced during the <UNK> that would go directly to video today the film stars <UNK> <UNK> kurt thomas as jonathan <UNK> <UNK> out of the blue to <UNK> the nation of <UNK> to enter and hopefully win the game a <UNK> <UNK> <UNK> by the khan who <UNK> his people by yelling what sounds like <UNK> power the goal of the mission involves the star wars defense system jonathan is trained in the martial arts by princess <UNK> who never speaks or leaves the house once trained tries to blend in with the <UNK> by wearing a bright red <UNK> with <UNK> of blue and white needless to say <UNK> finds himself running and fighting for his life along the stone streets of <UNK> on his way to a date with destiny and the game br br star kurt thomas was ill served by director robert <UNK> who it looks like was never on the set the so called script is just this side of incompetent see other reviews for the many <UNK> throughout the town of <UNK> has a few good moments but is ultimately ruined by bad editing the ending <UNK> still there's the <UNK> of a good action adventure here a hong kong version with more <UNK> action and faster pace might even be pretty good\n",
      "X shape: (200, 170)\n",
      "len(X[0]): 170\n",
      "len(X[1]): 170\n",
      "X[0]: [   1   14   22   16   43  530  973 1622 1385   65  458 4468   66 3941\n",
      "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
      "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
      "  172 4536 1111   17  546   38   13  447    4  192   50   16    6  147\n",
      " 2025   19   14   22    4 1920 4613  469    4   22   71   87   12   16\n",
      "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
      "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
      "    2   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
      "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
      "   52    5   14  407   16   82    2    8    4  107  117    2   15  256\n",
      "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
      "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
      " 2071   56]\n"
     ]
    }
   ],
   "source": [
    "NUM_WORDS = 5000\n",
    "INDEX_FROM = 3\n",
    "TOTAL_LENGTH = 170\n",
    "\n",
    "# --- Import the IMDB data and only consider the ``top_words``` most used words\n",
    "np.load.__defaults__=(None, True, True, 'ASCII')\n",
    "(X, y), (X_test, y_test) = imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)\n",
    "np.load.__defaults__=(None, False, True, 'ASCII')\n",
    "\n",
    "word_to_id = imdb.get_word_index()\n",
    "word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}\n",
    "word_to_id[\"<PAD>\"] = 0\n",
    "word_to_id[\"<START>\"] = 1\n",
    "word_to_id[\"<UNK>\"] = 2\n",
    "\n",
    "id_to_word = {value:key for key,value in word_to_id.items()}\n",
    "print(' '.join(id_to_word[id] for id in X[1000] ))\n",
    "\n",
    "# --- filter sequences with at least 170 words\n",
    "X = [lst for lst in X if len(lst) >= TOTAL_LENGTH]\n",
    "# --- truncate input sequences\n",
    "X = sequence.pad_sequences(X, maxlen=TOTAL_LENGTH, truncating='post')\n",
    "# --- Keep only 200 sequences\n",
    "N = 200\n",
    "X = X[:N]\n",
    "\n",
    "print(\"X shape:\", X.shape)\n",
    "print(\"len(X[0]):\", len(X[0]))\n",
    "print(\"len(X[1]):\", len(X[1]))\n",
    "print(\"X[0]:\", X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_elements, counts = np.unique(X, return_counts=True)\n",
    "occurrences_dict = dict(zip(unique_elements, counts))\n",
    "\n",
    "keys = list(occurrences_dict.keys())[:500]\n",
    "values = list(occurrences_dict.values())[:500]\n",
    "\n",
    "plt.bar(keys, values)\n",
    "plt.title(\"Word distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D4Y9Tr9dIClo",
    "outputId": "4450cdaf-6cfe-459a-f97b-133e96ece774"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (200, 100)\n",
      "X_test shape: (200, 70)\n"
     ]
    }
   ],
   "source": [
    "# Train - test split\n",
    "TRAIN_SEQUENCE_LENGTH = 100\n",
    "X_train = X[:, :TRAIN_SEQUENCE_LENGTH]\n",
    "X_test = X[:, TRAIN_SEQUENCE_LENGTH:]\n",
    "\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZVJyJMZTUlsl"
   },
   "source": [
    "## Train and test SSL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "X0AKORMeUlsl"
   },
   "outputs": [],
   "source": [
    "HIDDEN_SIZE = 64\n",
    "TEST_SEQUENCE_LENGTH = 70\n",
    "MODEL_LENGTH = 10\n",
    "NUM_TOPICS = 50\n",
    "NUM_WORDS = 5000\n",
    "N_EPOCHS = 5\n",
    "NUM_PARTICULES = 10\n",
    "N = 200\n",
    "\n",
    "lstm_model=LSTM(input_size=NUM_TOPICS, hidden_size=HIDDEN_SIZE, output_size=NUM_TOPICS, model_length=MODEL_LENGTH, batch_size=1, device=device)\n",
    "lstm_model = lstm_model.to(device)\n",
    "criterion = nn.CrossEntropyLoss(reduction='sum')\n",
    "optimizer = optim.Adam(lstm_model.parameters())\n",
    "ssm_model = SSM(num_words=NUM_WORDS, num_topics=NUM_TOPICS, sequence_length=TRAIN_SEQUENCE_LENGTH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "08ig8h2-Uwbe",
    "outputId": "a198bd0d-7866-4648-db49-c2e4138050b2"
   },
   "outputs": [],
   "source": [
    "X_train_torch = torch.tensor(X_train, dtype = torch.long)\n",
    "X_test_torch = torch.tensor(X_test, dtype = torch.long)\n",
    "previous_z_1_T_star = torch.randint(1, NUM_TOPICS + 1, (X_train_torch.shape[0], TRAIN_SEQUENCE_LENGTH))\n",
    "\n",
    "list_perplexity = []\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    start_time = time.time()\n",
    "    print(\"epoch: {}\".format(epoch+1))\n",
    "    # Sample topics z*_1,...,z*_T on train sequences, by Particle Gibbs, in order to train LSTM and SSM parts\n",
    "    z_star_train = []\n",
    "    for i in range(N):\n",
    "        X_train_torch_i = X_train_torch[i]\n",
    "        z_star_train_i = particle_gibbs(X_train_torch_i, previous_z_1_T_star[i], NUM_PARTICULES, NUM_TOPICS, NUM_WORDS, TRAIN_SEQUENCE_LENGTH, lstm_model, ssm_model)\n",
    "        z_star_train.append(z_star_train_i)\n",
    "        z_star_one_hot_train_i = F.one_hot(z_star_train_i-1, num_classes=NUM_TOPICS).float()\n",
    "        dataset_i=Dataset(topics=z_star_one_hot_train_i, model_length=MODEL_LENGTH)\n",
    "        dataloader_i = DataLoader(dataset_i, batch_size=1)\n",
    "        lstm_model.train_model(dataloader_i, optimizer, criterion)\n",
    "        ssm_model.compute_MLE_SSM(X_train_torch[:i+1,:], torch.stack(z_star_train)[:i+1,:])\n",
    "    \n",
    "    z_star_train = torch.stack(z_star_train)\n",
    "    previous_z_1_T_star = z_star_train\n",
    "    # Sample topics from LSTM, on test sequences\n",
    "    z_pred = []\n",
    "    for i in range(N):\n",
    "        input_sequence = z_star_train[i]\n",
    "        input_seq_one_hot = F.one_hot(input_sequence-1, num_classes=NUM_TOPICS).float()\n",
    "        for t in range(TEST_SEQUENCE_LENGTH):\n",
    "            z_next = torch.tensor([lstm_model.sample_next_z(input_seq_one_hot)])\n",
    "            input_sequence = torch.cat([input_sequence, z_next])\n",
    "            input_seq_one_hot = F.one_hot(input_sequence-1, num_classes=NUM_TOPICS).float()\n",
    "        z_pred_i = input_sequence[-TEST_SEQUENCE_LENGTH:]\n",
    "        z_pred_i = z_pred_i.cpu()\n",
    "        z_pred.append(z_pred_i)\n",
    "    z_pred = torch.stack(z_pred)\n",
    "    # Compute p(x_t | z*_t) and perplexity on test sequences\n",
    "    test_loss = 0\n",
    "    for i in range(N):\n",
    "        loss_i = 0\n",
    "        z_pred_i = z_pred[i]\n",
    "        proba_x_pred_i = []\n",
    "        for t in range(TEST_SEQUENCE_LENGTH):\n",
    "            z_pred_i_t = z_pred_i[t].item()\n",
    "            proba_x_pred_i_t = ssm_model.predict_proba(z_pred_i_t)\n",
    "            cross_entropy = F.cross_entropy(proba_x_pred_i_t, F.one_hot(X_test_torch[i][t]-1, num_classes=NUM_WORDS).float(), reduction = \"none\").item()\n",
    "            loss_i+=cross_entropy\n",
    "        test_loss += loss_i / TEST_SEQUENCE_LENGTH\n",
    "    mean_loss = test_loss/N\n",
    "    perplexity = np.exp(mean_loss)\n",
    "    print(\"Perplexity: {}\".format(perplexity))\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    print(\"Elapsed Time: {:.2f} seconds\".format(elapsed_time))\n",
    "    list_perplexity.append(perplexity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(range(1, N_EPOCHS + 1), list_perplexity, 'o-', color='red', label='Perplexity')\n",
    "plt.title('Topics K = 50')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Perplexity')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YWrJkiW6//XbLJSL79u2TdL74UJKcnBzLZX8lueaaa6yeFxUsLr7cxMvLS2azWTk5OWrQoIEOHTokJycnq7uDSZKvr6/q1q1r2SMrLCxMAwcO1NSpU/Xqq6/qpptuUmRkpO67775K2Uw5MTFRp0+f1ueff25zx799+/bJMAy1bt262GMvtaF6TEyM4uPjdejQIbVo0UJLlizRuXPn9MADD5R6nLOzs0JDQ7Vu3TpJ5wtTPXv2VI8ePVRYWKiNGzfKx8dHJ0+etCpMHTp0SCEhITbjtW3b1vK6v7+/pf3iu0AWfSbFzfeGG25QZmam5fnw4cP14Ycf6vbbb1fTpk3Vu3dvDRo0SH369Cl1bkWKO8f111+vDz/8UJL0008/yTAMPfvss3r22WeLHePYsWNWxa2L51NeRfO/4YYbbF5r06aN1q9fb9Xm5OSka6+91mYO0vm9tIpz/Phx5ebmWn0OZXX8+HGrS89q166t2rVrl3uc0ubZtm1bffHFFzabx1/8ebVq1UpOTk4lzlOS9u/fr4EDB5Yay1NPPaXVq1erS5cuuu6669S7d2/dd999lstTL+Wpp57SqlWr9Mgjj6hu3bq65557ynScpDKf43I9/vjjWrBggVavXq2uXbtKkmrVqmWzx54ky35dFxaKAQAoDwpTAIBK1aRJE/Xs2VMffvihJk6cqI0bN+rw4cOWvX4kWVZDTZ8+XYGBgcWOU5ZfaktagVJSu1HMxs+lMZlMWrp0qTZu3KhPP/1UX3zxhR566CHNnDlTGzduvKxfvMsjIiJCqampeuWVV3TTTTdZrZQxm82WzcWLm++lYrv33ns1duxYffDBB5o4caLef/99de7cudiCwMV69OihF198UX/99ZfWrVunp59+WnXr1pW/v7/WrVtn2YfpwsJUef2TX4K9vb21bds2ffHFF/r888/1+eef6+2331ZMTIwWLlx42eMWKfr+feKJJxQREVFsn4uLntX9l/rg4GCrmx5Mnjy53DcBsBd7rexq27at9uzZo5UrVyo1NVXLli3T66+/rkmTJmnq1KmXPL527dr6/PPP1atXL91///3y9PS0WvVYmosLfaWd43L+P1RUvD958qSlrXHjxjYr/aTzq9ik8/9vBwDgclCYAgBUuujoaA0fPlx79uxRSkqK3N3dddddd1leb9WqlSTJ09NT4eHhlR5fixYtZDabtW/fPsuKHen8pTenTp1SixYtrPp37dpVXbt21YsvvqhFixbp/vvv1+LFi/Xwww9X6OVNXbt21aOPPqo777xTUVFRWr58uWrUOP9Pe6tWrWQYhlq2bHlZlwrVr19fffv21QcffKD7779f3377rZKSksp0bM+ePVVQUKD//Oc/OnLkiKUA1atXL0th6vrrr7cUqKTz7/mePXtsxtq9e7fl9dIUvV602u5CxY3r4uKiu+66S3fddZfMZrOGDx+uN954Q88++6xN0ehixZ1j7969lk23i1Yi1axZ0+7fvyV9PxXNf8+ePbrlllusXtuzZ4/N+2c2m/Xzzz9bfW/s3btXkkq8A2SjRo3k6elZ7B0IL+WDDz6wutTr4tVaZXXhPC+2e/duNWzY0Gq1lHT+87pwRdpPP/0ks9lc6p0uW7VqVaZ5enh4KDo6WtHR0SooKNCAAQP04osvasKECVaF4pI0aNBAX375pbp3764BAwYoLS1NoaGhlzzu4kJfSS63APjzzz9LkuWuopIUGBioNWvWWG5sUOS7776zvA4AwOVgjykAQKUbOHCgnJ2d9Z///EdLlizRnXfeafXLZFBQkFq1aqUZM2YUe/vy48ePV2h8d9xxhyTZFGJmzZolSZa73v3xxx82q6yKfjkruuSl6I5Yp06dqpBYw8PDtXjxYqWmpuqBBx6wrNYZMGCAnJ2dNXXqVJsYDcPQ77//fsmxH3jgAe3cuVPjxo2Ts7Oz7r333jLFFBISopo1a+rll19W/fr11b59e0nnC1YbN27U119/bbNa6o477tCmTZuUkZFhacvLy9P8+fPl5+dns0/UxRo3bqzAwEAtXLhQOTk5lva0tDSb/akunruTk5PlTonFXap0sRUrVlitHNm0aZO+++473X777ZLOr8i66aab9MYbb1hWk1zon3z/Fv2cXPz91LlzZ3l7eys5OdlqDp9//rl27dpl+Z690Ny5cy1fG4ahuXPnqmbNmrr11luLPbeTk5MiIyP16aefavPmzTavX/x9dqHu3bsrPDzc8rjcwtSFn/OF78H27dv15ZdfWn52LzRv3jyr53PmzJEky+dVnIEDB+qHH36w3IHzQkXzvPj7yMXFRe3atZNhGOXac65p06ZKS0uTh4eH+vbtq//+97+XPMZee0zl5ubafM8bhqEXXnhBkqxW/N1zzz0qLCzU/PnzLW35+fl6++23FRISwh35AACXjRVTAIBK5+3trZtvvlmzZs3Sn3/+qejoaKvXnZyc9Oabb+r2229X+/btFRcXp6ZNm+rIkSNas2aNPD099emnn1ZYfAEBAYqNjdX8+fN16tQphYWFadOmTVq4cKEiIyN18803S5IWLlyo119/XXfffbdatWqlP//8UwsWLJCnp6flF+RatWqpXbt2SklJ0fXXX6/69evL39//svbpKUlkZKTlUjRPT0+98cYbatWqlV544QVNmDBBBw8eVGRkpOrUqaMDBw5o+fLlGjZsmJ544olSx+3bt68aNGhg2QPM29u7TPG4u7srKChIGzdu1F133WVZ5dOrVy/l5eUpLy/PpjA1fvx4/ec//9Htt9+uUaNGqX79+lq4cKEOHDigZcuWycnp0n9LS0xMVN++fdWjRw899NBDOnnypObMmaP27dtbFTgffvhhnTx5UrfccouaNWumQ4cOac6cOQoMDLRaIVeS6667Tj169NBjjz2m/Px8JSUlqUGDBnryySctfebNm6cePXqoQ4cOGjp0qK699lplZ2crIyNDv/76q3744YcyvZcXa9WqlerWravk5GTVqVNHHh4eCgkJUcuWLfXyyy8rLi5OYWFhGjx4sLKzszV79mz5+flp7NixVuO4ubkpNTVVsbGxCgkJ0eeff67PPvtMEydOtFolc7GXXnpJX375pcLCwjRs2DC1bdtWv/32m5YsWaL169dbbghQkaZPn67bb79doaGhGjJkiM6ePas5c+bIy8ur2NVBBw4cUL9+/dSnTx9lZGTo/fff13333aeAgIASzzFu3DgtXbpUUVFReuihhxQUFKSTJ0/qk08+UXJysgICAtS7d2/5+vqqe/fu8vHx0a5duzR37lz17dtXderUKdecWrdurS+++EI33XSTIiIitH79+lKLd/baYyozM1ODBw/W4MGDdd111+ns2bNavny5vv32Ww0bNkydOnWy9A0JCVFUVJQmTJigY8eO6brrrtPChQt18OBB/fvf/7ZLPACAq5QD7gQIAKgmim51X9yt4w3jf7dFv/jW9oZhGAsWLDAkGXXq1LG6vf2Ftm7dagwYMMBo0KCB4erqarRo0cIYNGiQkZ6eXmpca9asMSQZS5YsKVO8RbeVP378uKXt3LlzxtSpU42WLVsaNWvWNJo3b25MmDDB+Ouvvyx9MjMzjcGDBxvXXHON4erqanh7ext33nmnsXnzZqvxN2zYYAQFBRkuLi6XvIV7Uexr1qyxtMXGxhotWrSwPC96X6dPn2517Ouvv25IMp544glL27Jly4wePXoYHh4ehoeHh9GmTRtjxIgRxp49e0oc/0LDhw83JBmLFi0qMebijBs3zpBkvPzyy1bt1113nSHJ2L9/v80x+/fvN+655x6jbt26hpubm9GlSxdj5cqVVn1K+mwvnG/btm0NV1dXo127dsZHH31kM7+lS5cavXv3Nry9vQ0XFxfjmmuuMR555BHjt99+K3VOF77vM2fONJo3b264uroaPXv2NH744Ydi5xMTE2P4+voaNWvWNJo2bWrceeedxtKlSy19LvUzVJyPP/7YaNeunVGjRg2bn6+UlBTjxhtvNFxdXY369esb999/v/Hrr79aHR8bG2t4eHgY+/fvN3r37m24u7sbPj4+xuTJk43CwkKrvsV9vx46dMiIiYkxGjVqZLi6uhrXXnutMWLECCM/P7/Mcyir48ePFxvD6tWrje7duxu1atUyPD09jbvuusvYuXOnVZ+in+udO3ca99xzj1GnTh2jXr16Rnx8vM3/c1q0aGHExsZatf3+++9GfHy80bRpU8PFxcVo1qyZERsba5w4ccIwDMN44403jF69eln+/9SqVStj3LhxRk5OTqlzKunn1zAMY926dUatWrWMli1bGkeOHCnju3T5fv75ZyMqKsrw8/Mz3NzcDHd3dyMoKMhITk42zGazTf+zZ88aTzzxhOHr62u4uroawcHBRmpqaoXHCQCo3kyGUcq6awAAcFUbO3as/v3vfysrK8tyWeLV6uDBg2rZsqWmT59+ydVmVdmDDz6opUuXFnuZbHUyZcoUTZ06VcePH1fDhg0dHQ4AACgBe0wBAIBi/fXXX3r//fc1cODAq74oBQAAgIrBHlMAAMDKsWPHtHr1ai1dulS///67Ro8e7eiQAAAAUE1RmAIAAFZ27typ+++/X97e3nrttde4DTwAAAAqDHtMAQAAAAAAwCHYYwoAAAAAAAAOQWEKAAAAAAAADkFhCgAAAAAAAA5BYQoAAAAAAAAOQWEKAAAAAAAADkFhCgAAAAAAAA5BYQoAAAAAAAAOQWEKAAAAAAAADkFhCgAAAAAAAA5BYQr4/9auXSuTyaSlS5dW+Lmys7N1zz33qEGDBjKZTEpKSrKcf+3atRV+/urinXfekclk0sGDBy1tN910k2666SbLc3t/rhePX91U9/lJkp+fnx588EFHhwEAVxxypSsPuZL9Vff5SeRKqHwUplCtmUymMj0qO8EZO3asvvjiC02YMEHvvfee+vTpU6nnr6qOHj2qKVOmaNu2bY4OBZXozJkzmjJlyhX5i8aXX36pIUOGyN/fX87OzvLz83N0SABQLuRKVxZypavT1ZIrmc1mvfLKK2rZsqXc3NzUsWNH/ec//6m8YOEwNRwdAFCR3nvvPavn7777rtLS0mza27Ztq127dlVaXF999ZX69++vJ554wtJ2/fXX6+zZs3Jxcam0OKqao0ePaurUqfLz81NgYOBljfHll1/aNyhUuDNnzmjq1KmSVCl/gdyzZ4+cnOzzd5lFixYpJSVFnTp1UpMmTewyJgBUJnKlKwu50tXpasmVnn76aU2bNk1Dhw5VcHCwPv74Y913330ymUy699577RIPqiYKU6jW/u///s/q+caNG5WWlmbTLqlSk61jx46pbt26Vm1OTk5yc3OrtBiqq6s5WS2LvLw8eXh4ODoMh3J1dbXbWC+99JIWLFigmjVr6s4779T27dvtNjYAVAZypasPuVLpyJUckysdOXJEM2fO1IgRIzR37lxJ0sMPP6ywsDCNGzdOUVFRcnZ2tltcqFq4lA+4iNls1osvvqhmzZrJzc1Nt956q3766Sebft9995369OkjLy8vubu7KywsTN9++22pYxdd528YhubNm2dZHi/JZt+E+Ph41a5dW2fOnLEZZ/DgwfL19VVhYaGl7fPPP1fPnj3l4eGhOnXqqG/fvtqxY8cl51sU0/r16zVq1Cg1atRIdevW1SOPPKKCggKdOnVKMTExqlevnurVq6cnn3xShmFYjZGXl6fHH39czZs3l6urq2644QbNmDHDpl9aWpp69OihunXrqnbt2rrhhhs0ceJEy/yDg4MlSXFxcZb35p133rnkHC5Uluv+8/Pzdeedd8rLy0sbNmyQdP5zT0pKUvv27eXm5iYfHx898sgj+uOPP0oc5/Tp0/Lw8NDo0aNtXvv111/l7OysxMTEEo/v1KmTBgwYYNXWoUMHmUwm/fjjj5a2lJQUmUwmq18Itm7dqttvv12enp6qXbu2br31Vm3cuNFqrKLP9uuvv9bw4cPl7e2tZs2aWV6fP3++WrVqpVq1aqlLly5at25dsXHOmTNH7du3l7u7u+rVq6fOnTtr0aJFJc6rPA4ePKhGjRpJkqZOnWr53KdMmWLp89VXX1m+t+vWrav+/fvb/HI0ZcoUmUwm7d69W4MGDZKnp6caNGig0aNH66+//rLqW9y+CadOndLYsWPl5+cnV1dXNWvWTDExMTpx4kSp8Tdp0kQ1a9a8/DcAAK5A5ErkSuRK1siVSlbWXOnjjz/WuXPnNHz4cEubyWTSY489pl9//VUZGRmXHANXLgpTwEWmTZum5cuX64knntCECRO0ceNG3X///VZ9vvrqK/Xq1Uu5ubmaPHmyXnrpJZ06dUq33HKLNm3aVOLYvXr1siyNv+222/Tee+/ZLJUvEh0drby8PH322WdW7WfOnNGnn36qe+65x/JXg/fee099+/ZV7dq19fLLL+vZZ5/Vzp071aNHD6vNLkszcuRI7du3T1OnTlW/fv00f/58Pfvss7rrrrtUWFiol156ST169ND06dOtYjYMQ/369dOrr76qPn36aNasWbrhhhs0btw4JSQkWPrt2LFDd955p/Lz8/Xcc89p5syZ6tevnyVBbdu2rZ577jlJ0rBhwyzvTa9evcoUf1mdPXtWd911lzZs2KDVq1erW7dukqRHHnlE48aNU/fu3TV79mzFxcXpgw8+UEREhM6dO1fsWLVr19bdd9+tlJQUq8RXkv7zn//IMAyb750L9ezZU+vXr7c8P3nypHbs2CEnJyerxGfdunVq1KiR2rZtK+n8e9mzZ0/98MMPevLJJ/Xss8/qwIEDuummm/Tdd9/ZnGf48OHauXOnJk2apPHjx0uS/v3vf+uRRx6Rr6+vXnnlFXXv3l39+vXTL7/8YnXsggULNGrUKLVr105JSUmaOnWqAgMDiz3P5WjUqJH+9a9/SZLuvvtuy+delISuXr1aEREROnbsmKZMmaKEhARt2LBB3bt3L/Z7e9CgQfrrr7+UmJioO+64Q6+99pqGDRtWagynT59Wz549NWfOHPXu3VuzZ8/Wo48+qt27d+vXX3+1yzwBoDohVyJXIlf6H3Il++RKW7dulYeHh+UzLNKlSxfL66jGDOAqMmLECKOkb/s1a9YYkoy2bdsa+fn5lvbZs2cbkoz//ve/hmEYhtlsNlq3bm1EREQYZrPZ0u/MmTNGy5Ytjdtuu+2ScUgyRowYUez516xZYzlP06ZNjYEDB1r1+/DDDw1JxjfffGMYhmH8+eefRt26dY2hQ4da9cvKyjK8vLxs2i/29ttvG5Js5hMaGmqYTCbj0UcftbT9/fffRrNmzYywsDBL24oVKwxJxgsvvGA17j333GOYTCbjp59+MgzDMF599VVDknH8+PESY/n+++8NScbbb79daswXx37gwAFLW1hYmFV8Re/rkiVLjD///NMICwszGjZsaGzdutXSZ926dYYk44MPPrAaPzU11ab94vG/+OILQ5Lx+eefWx3bsWNHq37FWbJkiSHJ2Llzp2EYhvHJJ58Yrq6uRr9+/Yzo6Girse6++27L88jISMPFxcXYv3+/pe3o0aNGnTp1jF69etm8Pz169DD+/vtvS3tBQYHh7e1tBAYGWn2vz58/35BkFXf//v2N9u3blzqPf+r48eOGJGPy5Mk2rwUGBhre3t7G77//bmn74YcfDCcnJyMmJsbSNnnyZEOS0a9fP6vjhw8fbkgyfvjhB0tbixYtjNjYWMvzSZMmGZKMjz76yOb8F/5MXErfvn2NFi1alLk/AFRF5Eq2yJXIlYqQK1VcrtS3b1/j2muvtWnPy8szJBnjx48v83lw5WHFFHCRuLg4q2vve/bsKUn6+eefJUnbtm3Tvn37dN999+n333/XiRMndOLECeXl5enWW2/VN998I7PZ/I/jMJlMioqK0qpVq3T69GlLe0pKipo2baoePXpIOr/k+9SpUxo8eLAllhMnTsjZ2VkhISFas2ZNmc43ZMgQy1J5SQoJCZFhGBoyZIilzdnZWZ07d7a8F5K0atUqOTs7a9SoUVbjPf744zIMQ59//rkkWfaJ+Pjjj+3y/pRXTk6Oevfurd27d2vt2rVWG4YuWbJEXl5euu2226zew6CgINWuXbvU9zA8PFxNmjTRBx98YGnbvn27fvzxx2L357hQ0ffWN998I+n8X/uCg4N12223Wf4KeOrUKW3fvt3St7CwUF9++aUiIyN17bXXWsZq3Lix7rvvPq1fv165ublW5xk6dKjVNfmbN2/WsWPH9Oijj1p9rz/44IPy8vKyOrZu3br69ddf9f3335c6l4rw22+/adu2bXrwwQdVv359S3vHjh112223adWqVTbHjBgxwur5yJEjJanYvkWWLVumgIAA3X333TavXfgzAQA4j1zpPHIlciWJXMleudLZs2eL3duqaF+5s2fP2uU8qJooTAEXueaaa6ye16tXT5Is18/v27dPkhQbG6tGjRpZPd58803l5+crJyfHLrFER0fr7Nmz+uSTTySdX0a7atUqRUVFWf4RKIrnlltusYnnyy+/1LFjx8p0rovnXfSPbvPmzW3aL9xL4NChQ2rSpInq1Klj1a9oGe6hQ4csc+nevbsefvhh+fj46N5779WHH35YaYnXmDFj9P3332v16tVq37691Wv79u1TTk6OvL29bd7D06dPl/oeOjk56f7779eKFSsse1x88MEHcnNzU1RUVKkx+fj4qHXr1pbEat26derZs6d69eqlo0eP6ueff9a3334rs9lsSbaOHz+uM2fO6IYbbrAZr23btjKbzTZLzFu2bGn1vOgzad26tVV7zZo1rRI4SXrqqadUu3ZtdenSRa1bt9aIESMuuT+IJGVlZVk9LieZKIqzpLkW/ZJzoYvn1KpVKzk5OZV6mcb+/fvl7+9f7vgA4GpFrnQeuRK5kkSuZC+1atVSfn6+TXvR/le1atWq0PPDsbgrH3CRku72YPz/zSmLkoPp06eXeJve2rVr2yWWrl27ys/PTx9++KHuu+8+ffrppzp79qyio6MtfYriee+99+Tr62szRo0aZfsxL2nexbUbF23UWRa1atXSN998ozVr1uizzz5TamqqUlJSdMstt+jLL7+s8Lts9O/fX4sXL9a0adP07rvvWt0C12w2y9vb2+oveRcq2nCyJDExMZo+fbpWrFihwYMHa9GiRZYNQy+lR48eSk9P19mzZ7VlyxZNmjRJ/v7+qlu3rtatW6ddu3apdu3auvHGG8s34Qv8k3/I27Ztqz179mjlypVKTU3VsmXL9Prrr2vSpEmW2xYXp3HjxlbP3377bZtNNCsDK54AwP7IlS7dTq5kjVzJFrmStcaNG2vNmjUyDMMqpt9++03S+U3UUX1RmALKqVWrVpIkT09PhYeHV/j5Bg0apNmzZys3N1cpKSny8/NT165dbeLx9vaulHgu1qJFC61evVp//vmn1V8Cd+/ebXm9iJOTk2699VbdeuutmjVrll566SU9/fTTWrNmjcLDwyv0H8bIyEj17t1bDz74oOrUqWPZRFI6/x6uXr1a3bt3v6zExN/fXzfeeKM++OADNWvWTIcPH9acOXPKdGzPnj319ttva/HixSosLFS3bt3k5OSkHj16WJKtbt26WZLRRo0ayd3dXXv27LEZa/fu3XJycrL5y+3Fij6Tffv26ZZbbrG0nzt3TgcOHFBAQIBVfw8PD0VHRys6OloFBQUaMGCAXnzxRU2YMKHE23anpaVZPb/4L68XKulzL4qzpLk2bNjQ5nbO+/bts/qr508//SSz2Sw/P78Sz9+qVasSb10MACg/ciVr5ErkSsUhV7IWGBioN998U7t27VK7du0s7UWbyJdU5Eb1wKV8QDkFBQWpVatWmjFjhtV+BkWOHz9u1/NFR0crPz9fCxcuVGpqqgYNGmT1ekREhDw9PfXSSy8Ve0cUe8dzsTvuuEOFhYWaO3euVfurr74qk8mk22+/XdL5u6hcrOgfmKJlu0X/cJ46dapCYo2JidF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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_proba_by_topic, top_proba_word_index = ssm_model.phi.topk(5, dim=0, largest=True)\n",
    "\n",
    "num_subplots = (2, 2)\n",
    "\n",
    "fig, axs = plt.subplots(num_subplots[0], num_subplots[1], figsize=(12, 10))\n",
    "axs = axs.flatten()\n",
    "\n",
    "for j in [0, 9, 24, 49]:\n",
    "    categories = top_proba_word_index[:, 2].tolist()\n",
    "    categories = [str(v) for v in categories]\n",
    "    values = top_proba_by_topic[:, j].tolist()\n",
    "    values = [round(v, 2) for v in values]\n",
    "    if j==9:\n",
    "        ax = axs[1]\n",
    "    elif j==24:\n",
    "        ax = axs[2]\n",
    "    elif j==49:\n",
    "        ax = axs[3]\n",
    "    else:\n",
    "        ax = axs[0]\n",
    "\n",
    "    bars = ax.bar(categories, values, color=plt.cm.viridis(np.linspace(0, 1, len(categories))))\n",
    "\n",
    "    for i in range(3):\n",
    "        bars[i].set_linewidth(2)\n",
    "\n",
    "    for bar, value in zip(bars, values):\n",
    "        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), str(value),\n",
    "                ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    ax.set_xlabel('Word Index')\n",
    "    ax.set_ylabel('Conditional Likelihood')\n",
    "    ax.set_title('The five most likely words - topic {}'.format(j + 1))\n",
    "\n",
    "plt.suptitle(\"Five most likely words per topic - Topics K = 50\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}