tests.ipynb

{
 "cells": [
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   "execution_count": 1,
   "id": "b18927c0",
   "metadata": {
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     "start_time": "2022-04-13T14:00:03.813419Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b669ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-13T14:08:27.980120Z",
     "start_time": "2022-04-13T14:08:27.963867Z"
    }
   },
   "outputs": [],
   "source": [
    "tf.config.list_physical_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from src import DataLoader"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb90aac3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-13T14:01:56.266859Z",
     "start_time": "2022-04-13T14:00:47.197810Z"
    }
   },
   "outputs": [],
   "source": [
    "data_path = os.path.join('data')\n",
    "file_name = 'beatles.txt'\n",
    "window_size = 10\n",
    "stride = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "dl = DataLoader(file_name, window_size, stride)\n",
    "dl.load()\n",
    "dl.sanitize()\n",
    "dl.tokenize()\n",
    "x, y = dl.create_x_y(debug=False)\n",
    "d = dl.one_hot_dict"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;31mTypeError\u001B[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Input \u001B[0;32mIn [13]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m one_hot_func \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mvectorize(\u001B[38;5;28;01mlambda\u001B[39;00m x: d[x])\n\u001B[0;32m----> 2\u001B[0m \u001B[43mone_hot_func\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mshape\n",
      "File \u001B[0;32m~/anaconda3/envs/cosc525_project4/lib/python3.9/site-packages/numpy/lib/function_base.py:2163\u001B[0m, in \u001B[0;36mvectorize.__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m   2160\u001B[0m     vargs \u001B[38;5;241m=\u001B[39m [args[_i] \u001B[38;5;28;01mfor\u001B[39;00m _i \u001B[38;5;129;01min\u001B[39;00m inds]\n\u001B[1;32m   2161\u001B[0m     vargs\u001B[38;5;241m.\u001B[39mextend([kwargs[_n] \u001B[38;5;28;01mfor\u001B[39;00m _n \u001B[38;5;129;01min\u001B[39;00m names])\n\u001B[0;32m-> 2163\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_vectorize_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/anaconda3/envs/cosc525_project4/lib/python3.9/site-packages/numpy/lib/function_base.py:2249\u001B[0m, in \u001B[0;36mvectorize._vectorize_call\u001B[0;34m(self, func, args)\u001B[0m\n\u001B[1;32m   2246\u001B[0m outputs \u001B[38;5;241m=\u001B[39m ufunc(\u001B[38;5;241m*\u001B[39minputs)\n\u001B[1;32m   2248\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ufunc\u001B[38;5;241m.\u001B[39mnout \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m-> 2249\u001B[0m     res \u001B[38;5;241m=\u001B[39m \u001B[43masanyarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43moutputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43motypes\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   2250\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   2251\u001B[0m     res \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mtuple\u001B[39m([asanyarray(x, dtype\u001B[38;5;241m=\u001B[39mt)\n\u001B[1;32m   2252\u001B[0m                  \u001B[38;5;28;01mfor\u001B[39;00m x, t \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(outputs, otypes)])\n",
      "\u001B[0;31mValueError\u001B[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "one_hot_func = np.vectorize(lambda x: d[x])\n",
    "one_hot_func(x).shape"
   ],
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    "pycharm": {
     "name": "#%%\n"
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   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31941, 10, 37)\n"
     ]
    }
   ],
   "source": [
    "res = np.array([list(map(d.__getitem__, row)) for row in x])\n",
    "print(res.shape)\n"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    {
     "data": {
      "text/plain": "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n       0., 0., 0.])"
     },
     "execution_count": 24,
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    "res[0][0]"
   ],
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   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [],
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    "collapsed": false,
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     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
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     "name": "#%%\n"
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