sample_NB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hebrew Study Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nb_code import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon  6 Jan 2020 12:33:27 GMT\n"
     ]
    }
   ],
   "source": [
    "! date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary = 'sample_vocab/test.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing TF...\n",
      "   |     0.00s No structure info in otext, the structure part of the T-API cannot be used\n"
     ]
    },
    {
     "data": {
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       "    font-size: large;\n",
       "    color: #000044;\n",
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       "}\n",
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       "    direction: ltr;\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hebrew ready for study.\n",
      "this is session 1:\n",
      "score 0: 100 terms\n",
      "total: 100\n"
     ]
    }
   ],
   "source": [
    "voc = loadStudy(vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Controls:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following scores were changed \n",
      "0->6 \t\t 402\n",
      "0->2 \t\t 73\n",
      "0->5 \t\t 832\n",
      "0->3 \t\t 1847\n",
      "0->4 \t\t 687\n",
      "0->1 \t\t 43\n",
      "\n",
      "duration:  0:00:10.546899\n",
      "\n",
      "seconds per term: 0.11\n"
     ]
    }
   ],
   "source": [
    "voc.learn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of Known Term Set (Scores 3-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-962630bf1fb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvoc\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~/github/mahir/nb_code.py\u001b[0m in \u001b[0;36mplot_progress\u001b[0;34m(heb)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0mlast_transition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt_dates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcycle_bounds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# get date of last cycle switch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'n-learned since last cycle:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplt_learned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdate2nlearned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlast_transition\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     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "plot_progress(voc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unlearned Terms by Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "2    16\n",
       "1  1373"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_freqs(voc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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