ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/anhphantq/ensemble/submission_588_21_1.csv',\n",
       " '/home/anhphantq/ensemble/submission_second_588.csv',\n",
       " '/home/anhphantq/ensemble/submission_589_20_1.csv',\n",
       " '/home/anhphantq/ensemble/submission_first_588.csv',\n",
       " '/home/anhphantq/ensemble/public_submission.csv',\n",
       " '/home/anhphantq/ensemble/submission_588_latest.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glob import glob\n",
    "DIR = '/home/anhphantq/ensemble/*.csv'\n",
    "paths = glob(DIR, recursive= True) \n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def read_sub(path, shift=0): # higher shift more important\n",
    "    '''a helper function for loading and preprocessing submissions'''\n",
    "    return  (\n",
    "        pl.read_csv(path)\n",
    "            .with_column(pl.col('labels').str.split(by=' '))\n",
    "            .with_column(pl.col(\"labels\").arr.lengths().alias(\"num\"))\n",
    "            .rename({'labels': 'aid'})\n",
    "            .with_column(pl.col('num').apply(lambda x: [i - shift for i  in range(x)]).alias('vote'))\n",
    "            .explode(['aid', 'vote'])\n",
    "            \n",
    "    )[['session_type','aid', 'vote']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "<small>shape: (5, 3)</small>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>\n",
       "session_type\n",
       "</th>\n",
       "<th>\n",
       "aid\n",
       "</th>\n",
       "<th>\n",
       "vote\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
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       "str\n",
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       "3.8\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌─────────────────┬─────────┬──────┐\n",
       "│ session_type    ┆ aid     ┆ vote │\n",
       "│ ---             ┆ ---     ┆ ---  │\n",
       "│ str             ┆ str     ┆ f64  │\n",
       "╞═════════════════╪═════════╪══════╡\n",
       "│ 12899779_clicks ┆ 59625   ┆ -0.2 │\n",
       "│ 12899779_clicks ┆ 941596  ┆ 0.8  │\n",
       "│ 12899779_clicks ┆ 737445  ┆ 1.8  │\n",
       "│ 12899779_clicks ┆ 1253524 ┆ 2.8  │\n",
       "│ 12899779_clicks ┆ 894169  ┆ 3.8  │\n",
       "└─────────────────┴─────────┴──────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shifts = [0.2,0.2,0.9,0.2, -18 ,0.1]\n",
    "assert len(paths) == len(shifts)\n",
    "subs = [read_sub(path, weight) for path, weight in zip(paths, shifts)]\n",
    "subs[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbd73aa64eac4a418d3a67a0d9d99842",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "rs = subs[0]\n",
    "\n",
    "for i in tqdm(range(1, len(subs))):\n",
    "    rs= rs.join(subs[i], how = 'outer', on = ['session_type', 'aid'], suffix= f'_{i}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rs.select([pl.col('^vote.*$')])\n",
    "subs = (rs\n",
    "    \n",
    "    .fill_null(30)\n",
    "    \n",
    "    .with_column(pl.min(pl.all().exclude([\"aid\", 'session_type', 'num_votes'])).alias(\"rank_sum\"))\n",
    "    .sort([pl.col('rank_sum')], reverse= [False])\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<small>shape: (154665484, 9)</small>\n",
       "<thead>\n",
       "<tr>\n",
       "<th>\n",
       "session_type\n",
       "</th>\n",
       "<th>\n",
       "aid\n",
       "</th>\n",
       "<th>\n",
       "vote\n",
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       "vote_1\n",
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       "<tr>\n",
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       "&quot;13183948_carts...\n",
       "</td>\n",
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       "30.0\n",
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       "36\n",
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       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "&quot;13130567_order...\n",
       "</td>\n",
       "<td>\n",
       "&quot;504182&quot;\n",
       "</td>\n",
       "<td>\n",
       "30.0\n",
       "</td>\n",
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       "30.0\n",
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       "<td>\n",
       "30.0\n",
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       "<td>\n",
       "30.0\n",
       "</td>\n",
       "<td>\n",
       "32\n",
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       "<td>\n",
       "30.0\n",
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       "<td>\n",
       "30.0\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "&quot;14224278_click...\n",
       "</td>\n",
       "<td>\n",
       "&quot;1436238&quot;\n",
       "</td>\n",
       "<td>\n",
       "30.0\n",
       "</td>\n",
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       "30.0\n",
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       "30.0\n",
       "</td>\n",
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       "30.0\n",
       "</td>\n",
       "<td>\n",
       "37\n",
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       "30.0\n",
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       "30.0\n",
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       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "&quot;14246898_click...\n",
       "</td>\n",
       "<td>\n",
       "&quot;423558&quot;\n",
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       "30.0\n",
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       "30.0\n",
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       "<td>\n",
       "30.0\n",
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       "30.0\n",
       "</td>\n",
       "<td>\n",
       "37\n",
       "</td>\n",
       "<td>\n",
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       "shape: (154665484, 9)\n",
       "┌─────────────────┬─────────┬──────┬────────┬─────┬────────┬────────┬────────┬──────────┐\n",
       "│ session_type    ┆ aid     ┆ vote ┆ vote_1 ┆ ... ┆ vote_3 ┆ vote_4 ┆ vote_5 ┆ rank_sum │\n",
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       "╞═════════════════╪═════════╪══════╪════════╪═════╪════════╪════════╪════════╪══════════╡\n",
       "│ 12899779_clicks ┆ 59625   ┆ -0.2 ┆ -0.2   ┆ ... ┆ -0.2   ┆ 33     ┆ -0.1   ┆ -0.9     │\n",
       "│ 12899780_clicks ┆ 1142000 ┆ -0.2 ┆ -0.2   ┆ ... ┆ -0.2   ┆ 34     ┆ -0.1   ┆ -0.9     │\n",
       "│ 12899781_clicks ┆ 199008  ┆ -0.2 ┆ -0.2   ┆ ... ┆ -0.2   ┆ 35     ┆ -0.1   ┆ -0.9     │\n",
       "│ 12899782_clicks ┆ 1033148 ┆ 17.8 ┆ 0.8    ┆ ... ┆ 2.8    ┆ 23     ┆ 5.9    ┆ -0.9     │\n",
       "│ ...             ┆ ...     ┆ ...  ┆ ...    ┆ ... ┆ ...    ┆ ...    ┆ ...    ┆ ...      │\n",
       "│ 13672965_orders ┆ 720731  ┆ 30.0 ┆ 30.0   ┆ ... ┆ 30.0   ┆ 35     ┆ 30.0   ┆ 30.0     │\n",
       "│ 13396558_carts  ┆ 203378  ┆ 30.0 ┆ 30.0   ┆ ... ┆ 30.0   ┆ 36     ┆ 30.0   ┆ 30.0     │\n",
       "│ 14215691_carts  ┆ 500706  ┆ 30.0 ┆ 30.0   ┆ ... ┆ 30.0   ┆ 35     ┆ 30.0   ┆ 30.0     │\n",
       "│ 13939491_carts  ┆ 812524  ┆ 30.0 ┆ 30.0   ┆ ... ┆ 30.0   ┆ 33     ┆ 30.0   ┆ 30.0     │\n",
       "└─────────────────┴─────────┴──────┴────────┴─────┴────────┴────────┴────────┴──────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 52s, sys: 16.6 s, total: 5min 8s\n",
      "Wall time: 3min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds = subs.groupby('session_type').agg([\n",
    "    pl.col('aid').head(20).alias('labels')\n",
    "])\n",
    "\n",
    "preds = preds.with_column(pl.col('labels').apply(lambda lst: ' '.join([str(aid) for aid in lst])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "len = preds['labels'].apply(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "shape: (5015409, 2)\n",
       "┌─────────────────┬─────────────────────────────────────┐\n",
       "│ session_type    ┆ labels                              │\n",
       "│ ---             ┆ ---                                 │\n",
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       "└─────────────────┴─────────────────────────────────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_NAME = 'ultimate_ensemble_submission.csv'\n",
    "preds.write_csv(FILE_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████| 783M/783M [00:33<00:00, 24.4MB/s]\n",
      "Successfully submitted to OTTO – Multi-Objective Recommender System"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c otto-recommender-system -f {FILE_NAME} -m \"hehe\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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