{
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp transforms.session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session\n",
    "> Create sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from nbdev.showdoc import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def construct_session_sequences(df, sessionID, itemID):\n",
    "    \"\"\"\n",
    "    Given a dataset in pandas df format, construct a list of lists where each sublist\n",
    "    represents the interactions relevant to a specific session, for each sessionID. \n",
    "    These sublists are composed of a series of itemIDs (str) and are the core training \n",
    "    data used in the Word2Vec algorithm. \n",
    "    This is performed by first grouping over the SessionID column, then casting to list\n",
    "    each group's series of values in the ItemID column. \n",
    "    INPUTS\n",
    "    ------------\n",
    "    df:                 pandas dataframe\n",
    "    sessionID: str      column name in the df that represents invididual sessions\n",
    "    itemID: str         column name in the df that represents the items within a session\n",
    "    \"\"\"\n",
    "    grp_by_session = df.groupby([sessionID])\n",
    "\n",
    "    session_sequences = []\n",
    "    for name, group in grp_by_session:\n",
    "        session_sequences.append(list(group[itemID].values))\n",
    "\n",
    "    return session_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   SessionID  ItemID\n",
       "0          1     111\n",
       "1          1     123\n",
       "2          1     345\n",
       "3          2      45\n",
       "4          2     334\n",
       "5          2     342\n",
       "6          2    8970\n",
       "7          2     345"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame.from_dict({\n",
    "    'SessionID':[1,1,1,2,2,2,2,2],\n",
    "    'ItemID':[111,123,345,45,334,342,8970,345]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[111, 123, 345], [45, 334, 342, 8970, 345]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "construct_session_sequences(df, sessionID='SessionID', itemID='ItemID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Sparsh A.\n",
      "\n",
      "Last updated: 2021-12-26 08:50:09\n",
      "\n",
      "recohut: 0.0.7\n",
      "\n",
      "Compiler    : GCC 7.5.0\n",
      "OS          : Linux\n",
      "Release     : 5.4.144+\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 2\n",
      "Architecture: 64bit\n",
      "\n",
      "IPython: 5.5.0\n",
      "pandas : 1.1.5\n",
      "numpy  : 1.19.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "%reload_ext watermark\n",
    "%watermark -a \"Sparsh A.\" -m -iv -u -t -d -p recohut"
   ]
  }
 ],
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