n_gram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:27.218925Z",
     "start_time": "2024-04-04T12:10:24.782662Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import n_gram\n",
    "import torch\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "data = n_gram.load_data(test_size=0.2, train_size=0.8)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:27.234979Z",
     "start_time": "2024-04-04T12:10:27.220878Z"
    }
   },
   "id": "9aaa33bddec8f196",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "data[\"dev\"], data[\"test\"] = train_test_split(data[\"test\"], test_size=0.5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:27.250514Z",
     "start_time": "2024-04-04T12:10:27.236045Z"
    }
   },
   "id": "e1c5803214df4c6d",
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Bigram model"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ec745e67117b4dc8"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "num_grams = 2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:27.266532Z",
     "start_time": "2024-04-04T12:10:27.254612Z"
    }
   },
   "id": "39ab79e77b20266d",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Counting"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "628736892f951f3a"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev loss: \t2.4607\n",
      "Test loss: \t2.4652\n"
     ]
    }
   ],
   "source": [
    "counts = n_gram.create_count_matrix(data[\"train\"], data[\"stoi\"], num_grams=num_grams, smoothing_factor=1)\n",
    "\n",
    "print(f\"\"\"Dev loss: \\t{n_gram.calculate_counts_loss(data[\"dev\"], counts, data[\"stoi\"]):.4f}\"\"\")\n",
    "print(f\"\"\"Test loss: \\t{n_gram.calculate_counts_loss(data[\"test\"], counts, data[\"stoi\"]):.4f}\"\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:29.689675Z",
     "start_time": "2024-04-04T12:10:27.267515Z"
    }
   },
   "id": "89081da01aadbe5c",
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "# NN"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6b0e9fba09b1011c"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev loss: \t2.5250\n",
      "Test loss: \t2.5289\n"
     ]
    }
   ],
   "source": [
    "nn_data = dict()\n",
    "nn_data[\"train\"] = n_gram.prepare_for_nn(data[\"train\"], data[\"stoi\"], num_grams)\n",
    "nn_data[\"dev\"] = n_gram.prepare_for_nn(data[\"dev\"], data[\"stoi\"], num_grams)\n",
    "nn_data[\"test\"] = n_gram.prepare_for_nn(data[\"test\"], data[\"stoi\"], num_grams)\n",
    "\n",
    "model = n_gram.init_nn_model(len(data[\"stoi\"]), num_grams)\n",
    "\n",
    "loss_history = n_gram.train_nn(nn_data[\"train\"][\"X\"], nn_data[\"train\"][\"y\"], model, verbose=False)\n",
    "\n",
    "print(f\"\"\"Dev loss: \\t{n_gram.calculate_nn_loss(nn_data[\"dev\"][\"X\"], nn_data[\"dev\"][\"y\"], model):.4f}\"\"\")\n",
    "print(f\"\"\"Test loss: \\t{n_gram.calculate_nn_loss(nn_data[\"test\"][\"X\"], nn_data[\"test\"][\"y\"], model):.4f}\"\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:35.994062Z",
     "start_time": "2024-04-04T12:10:29.694647Z"
    }
   },
   "id": "303e866da12a1a5b",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Loss')"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(torch.arange(loss_history.size(0)), loss_history)\n",
    "plt.title(\"Train loss history\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:36.121609Z",
     "start_time": "2024-04-04T12:10:35.995062Z"
    }
   },
   "id": "ae721852fb30e690",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Trigram model"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "23fc8236b20ee294"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "num_grams = 3"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:36.136623Z",
     "start_time": "2024-04-04T12:10:36.122644Z"
    }
   },
   "id": "e55c76884b7faddf",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Counting"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f9d37f0edfcce611"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev loss: \t2.1155\n",
      "Test loss: \t2.1378\n"
     ]
    }
   ],
   "source": [
    "counts = n_gram.create_count_matrix(data[\"train\"], data[\"stoi\"], num_grams=num_grams, smoothing_factor=1)\n",
    "\n",
    "print(f\"\"\"Dev loss: \\t{n_gram.calculate_counts_loss(data[\"dev\"], counts, data[\"stoi\"]):.4f}\"\"\")\n",
    "print(f\"\"\"Test loss: \\t{n_gram.calculate_counts_loss(data[\"test\"], counts, data[\"stoi\"]):.4f}\"\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:38.514574Z",
     "start_time": "2024-04-04T12:10:36.137607Z"
    }
   },
   "id": "2b6d18e1bfd70bac",
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "## NN"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c0a101166ecccba0"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev loss: \t2.4680\n",
      "Test loss: \t2.4804\n"
     ]
    }
   ],
   "source": [
    "nn_data = dict()\n",
    "nn_data[\"train\"] = n_gram.prepare_for_nn(data[\"train\"], data[\"stoi\"], num_grams)\n",
    "nn_data[\"dev\"] = n_gram.prepare_for_nn(data[\"dev\"], data[\"stoi\"], num_grams)\n",
    "nn_data[\"test\"] = n_gram.prepare_for_nn(data[\"test\"], data[\"stoi\"], num_grams)\n",
    "\n",
    "model = n_gram.init_nn_model(len(data[\"stoi\"]), num_grams)\n",
    "\n",
    "loss_history = n_gram.train_nn(nn_data[\"train\"][\"X\"], nn_data[\"train\"][\"y\"], model, verbose=False)\n",
    "\n",
    "print(f\"\"\"Dev loss: \\t{n_gram.calculate_nn_loss(nn_data[\"dev\"][\"X\"], nn_data[\"dev\"][\"y\"], model):.4f}\"\"\")\n",
    "print(f\"\"\"Test loss: \\t{n_gram.calculate_nn_loss(nn_data[\"test\"][\"X\"], nn_data[\"test\"][\"y\"], model):.4f}\"\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:46.359759Z",
     "start_time": "2024-04-04T12:10:38.516579Z"
    }
   },
   "id": "abfb35a11066f29c",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Loss')"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(torch.arange(loss_history.size(0)), loss_history)\n",
    "plt.title(\"Train loss history\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-04T12:10:46.454994Z",
     "start_time": "2024-04-04T12:10:46.360743Z"
    }
   },
   "id": "42a33480bd42637c",
   "execution_count": 11
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}