Efficiency_718.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "611de0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "import math\n",
    "\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
    "\n",
    "def seed_everything(seed):\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "seed_everything(0)\n",
    "MAX_LEN = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b5061ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoConfig, AutoTokenizer, AutoModelForMaskedLM, AutoModel\n",
    "\n",
    "\n",
    "class VecModel(nn.Module):\n",
    "    def __init__(self, model_name, size, has_top=True):\n",
    "        super(VecModel, self).__init__()\n",
    "        conf = AutoConfig.from_pretrained(model_name)\n",
    "        self.backbone = AutoModel.from_config(conf)\n",
    "        \n",
    "        self.has_top = has_top\n",
    "        \n",
    "        if self.has_top:\n",
    "            self.bn = nn.BatchNorm1d(size)\n",
    "            self.top = nn.Linear(size, size)\n",
    "\n",
    "    def forward(self, ids, mask):\n",
    "        out = self.backbone(ids, mask)[0]\n",
    "        out = (out[:, 1:MAX_LEN//2, :]*mask[:, 1:MAX_LEN//2, None]).mean(axis=1)\n",
    "        \n",
    "        if self.has_top:\n",
    "            out = self.top(self.bn(out))\n",
    "        \n",
    "        return F.normalize(out)\n",
    "\n",
    "    def save(self, path):\n",
    "        torch.save(self.state_dict(), path)\n",
    "\n",
    "    def load(self, path):\n",
    "        self.load_state_dict(torch.load(path, map_location='cpu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d65f903",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_id</th>\n",
       "      <th>content_ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t_00004da3a1b2</td>\n",
       "      <td>c_1108dd0c7a5d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t_00004da3a1b2</td>\n",
       "      <td>c_376c5a8eb028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t_00004da3a1b2</td>\n",
       "      <td>c_5bc0e1e2cba0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t_00004da3a1b2</td>\n",
       "      <td>c_76231f9d0b5e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t_00068291e9a4</td>\n",
       "      <td>c_639ea2ef9c95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279914</th>\n",
       "      <td>t_fff9e5407d13</td>\n",
       "      <td>c_d64037a72376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279915</th>\n",
       "      <td>t_fffbe1d5d43c</td>\n",
       "      <td>c_46f852a49c08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279916</th>\n",
       "      <td>t_fffbe1d5d43c</td>\n",
       "      <td>c_6659207b25d5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279917</th>\n",
       "      <td>t_fffe14f1be1e</td>\n",
       "      <td>c_cece166bad6a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279918</th>\n",
       "      <td>t_fffe811a6da9</td>\n",
       "      <td>c_92b8fad372ee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279919 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              topic_id     content_ids\n",
       "0       t_00004da3a1b2  c_1108dd0c7a5d\n",
       "1       t_00004da3a1b2  c_376c5a8eb028\n",
       "2       t_00004da3a1b2  c_5bc0e1e2cba0\n",
       "3       t_00004da3a1b2  c_76231f9d0b5e\n",
       "4       t_00068291e9a4  c_639ea2ef9c95\n",
       "...                ...             ...\n",
       "279914  t_fff9e5407d13  c_d64037a72376\n",
       "279915  t_fffbe1d5d43c  c_46f852a49c08\n",
       "279916  t_fffbe1d5d43c  c_6659207b25d5\n",
       "279917  t_fffe14f1be1e  c_cece166bad6a\n",
       "279918  t_fffe811a6da9  c_92b8fad372ee\n",
       "\n",
       "[279919 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df = pd.read_csv(\"data/correlations.csv\")\n",
    "\n",
    "corr_df[\"content_ids\"] = corr_df[\"content_ids\"].apply(lambda x: x.split())\n",
    "\n",
    "corr_df = corr_df.explode('content_ids').reset_index(drop=True)\n",
    "corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70e52483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61517, 9)\n",
      "(61517, 10)\n",
      "(61517, 12)\n",
      "(61517, 14)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>channel</th>\n",
       "      <th>category</th>\n",
       "      <th>level</th>\n",
       "      <th>language</th>\n",
       "      <th>parent</th>\n",
       "      <th>has_content</th>\n",
       "      <th>parent_title</th>\n",
       "      <th>grandpa</th>\n",
       "      <th>grandpa_title</th>\n",
       "      <th>ggrandpa</th>\n",
       "      <th>ggrandpa_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t_00004da3a1b2</td>\n",
       "      <td>Откриването на резисторите</td>\n",
       "      <td>Изследване на материали, които предизвикват на...</td>\n",
       "      <td>000cf7</td>\n",
       "      <td>source</td>\n",
       "      <td>4</td>\n",
       "      <td>bg</td>\n",
       "      <td>t_16e29365b50d</td>\n",
       "      <td>True</td>\n",
       "      <td>Открития и проекти</td>\n",
       "      <td>t_c85886762db7</td>\n",
       "      <td>Физика</td>\n",
       "      <td>t_27a0a3c7ee9a</td>\n",
       "      <td>Наука</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t_00068291e9a4</td>\n",
       "      <td>Entradas e saídas de uma função</td>\n",
       "      <td>Entenda um pouco mais sobre funções.</td>\n",
       "      <td>8e286a</td>\n",
       "      <td>source</td>\n",
       "      <td>4</td>\n",
       "      <td>pt</td>\n",
       "      <td>t_d14b6c2a2b70</td>\n",
       "      <td>True</td>\n",
       "      <td>Álgebra: funções</td>\n",
       "      <td>t_461c9a8dd863</td>\n",
       "      <td>9º Ano</td>\n",
       "      <td>t_a6420bcff569</td>\n",
       "      <td>Matemática por ano (Alinhada à BNCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t_00069b63a70a</td>\n",
       "      <td>Transcripts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6e3ba4</td>\n",
       "      <td>source</td>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>t_4054df11a74e</td>\n",
       "      <td>True</td>\n",
       "      <td>Flow Charts: Logical Thinking?</td>\n",
       "      <td>t_acbbd893e6af</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>t_9c5f09334d83</td>\n",
       "      <td>MIT Blossoms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t_0006d41a73a8</td>\n",
       "      <td>Графики на експоненциални функции (Алгебра 2 н...</td>\n",
       "      <td>Научи повече за графиките на сложните показате...</td>\n",
       "      <td>000cf7</td>\n",
       "      <td>source</td>\n",
       "      <td>4</td>\n",
       "      <td>bg</td>\n",
       "      <td>t_e2452e21d252</td>\n",
       "      <td>True</td>\n",
       "      <td>Показателни и логаритмични функции</td>\n",
       "      <td>t_5f4cc8e02423</td>\n",
       "      <td>Алгебра (цялото съдържание)</td>\n",
       "      <td>t_afd8db5e4f44</td>\n",
       "      <td>Математика</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t_0008768bdee6</td>\n",
       "      <td>100 સુધીનો સરવાળો</td>\n",
       "      <td>37 અને 49 જેવી બે-અંકની સંખ્યાઓ ઉમેરતા શીખો.</td>\n",
       "      <td>5223e0</td>\n",
       "      <td>supplemental</td>\n",
       "      <td>4</td>\n",
       "      <td>gu</td>\n",
       "      <td>t_0da7a331d666</td>\n",
       "      <td>True</td>\n",
       "      <td>100 સુધીના સરવાળા અને બાદબાકી</td>\n",
       "      <td>t_d20fc01fdf38</td>\n",
       "      <td>પ્રારંભિક ગણિત</td>\n",
       "      <td>t_38bca5075c51</td>\n",
       "      <td>ધોરણ ૩</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id                                              title  \\\n",
       "0  t_00004da3a1b2                         Откриването на резисторите   \n",
       "1  t_00068291e9a4                    Entradas e saídas de uma função   \n",
       "2  t_00069b63a70a                                        Transcripts   \n",
       "3  t_0006d41a73a8  Графики на експоненциални функции (Алгебра 2 н...   \n",
       "4  t_0008768bdee6                                  100 સુધીનો સરવાળો   \n",
       "\n",
       "                                         description channel      category  \\\n",
       "0  Изследване на материали, които предизвикват на...  000cf7        source   \n",
       "1               Entenda um pouco mais sobre funções.  8e286a        source   \n",
       "2                                                NaN  6e3ba4        source   \n",
       "3  Научи повече за графиките на сложните показате...  000cf7        source   \n",
       "4       37 અને 49 જેવી બે-અંકની સંખ્યાઓ ઉમેરતા શીખો.  5223e0  supplemental   \n",
       "\n",
       "   level language          parent  has_content  \\\n",
       "0      4       bg  t_16e29365b50d         True   \n",
       "1      4       pt  t_d14b6c2a2b70         True   \n",
       "2      3       en  t_4054df11a74e         True   \n",
       "3      4       bg  t_e2452e21d252         True   \n",
       "4      4       gu  t_0da7a331d666         True   \n",
       "\n",
       "                         parent_title         grandpa  \\\n",
       "0                  Открития и проекти  t_c85886762db7   \n",
       "1                    Álgebra: funções  t_461c9a8dd863   \n",
       "2      Flow Charts: Logical Thinking?  t_acbbd893e6af   \n",
       "3  Показателни и логаритмични функции  t_5f4cc8e02423   \n",
       "4       100 સુધીના સરવાળા અને બાદબાકી  t_d20fc01fdf38   \n",
       "\n",
       "                 grandpa_title        ggrandpa  \\\n",
       "0                       Физика  t_27a0a3c7ee9a   \n",
       "1                       9º Ano  t_a6420bcff569   \n",
       "2                  Engineering  t_9c5f09334d83   \n",
       "3  Алгебра (цялото съдържание)  t_afd8db5e4f44   \n",
       "4               પ્રારંભિક ગણિત  t_38bca5075c51   \n",
       "\n",
       "                         ggrandpa_title  \n",
       "0                                 Наука  \n",
       "1  Matemática por ano (Alinhada à BNCC)  \n",
       "2                          MIT Blossoms  \n",
       "3                            Математика  \n",
       "4                                ધોરણ ૩  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_df = pd.read_csv(\"data/topics.csv\")\n",
    "topic_df[\"title\"] = topic_df[\"title\"].fillna(\"\")\n",
    "\n",
    "title_map = topic_df.set_index(\"id\")[\"title\"].to_dict()\n",
    "parent_map = topic_df.set_index(\"id\")[\"parent\"].to_dict()\n",
    "\n",
    "topic_df = topic_df[topic_df[\"has_content\"]].reset_index(drop=True)\n",
    "print(topic_df.shape)\n",
    "\n",
    "topic_df[\"parent_title\"] = topic_df[\"parent\"].apply(lambda x: title_map.get(x, \"\"))\n",
    "print(topic_df.shape)\n",
    "\n",
    "topic_df[\"grandpa\"] = topic_df[\"parent\"].apply(lambda x: parent_map.get(x))\n",
    "topic_df[\"grandpa_title\"] = topic_df[\"grandpa\"].apply(lambda x: title_map.get(x, \"\"))\n",
    "print(topic_df.shape)\n",
    "\n",
    "topic_df[\"ggrandpa\"] = topic_df[\"grandpa\"].apply(lambda x: parent_map.get(x))\n",
    "topic_df[\"ggrandpa_title\"] = topic_df[\"ggrandpa\"].apply(lambda x: title_map.get(x, \"\"))\n",
    "print(topic_df.shape)\n",
    "\n",
    "topic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d560d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20506\n",
       "1    20506\n",
       "2    20505\n",
       "Name: fold, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "N_FOLDS = 3\n",
    "\n",
    "splits = KFold(N_FOLDS, shuffle=True, random_state=0).split(topic_df, topic_df[\"has_content\"])\n",
    "\n",
    "topic_df[\"fold\"] = 0\n",
    "\n",
    "for f, (train_ind, val_ind) in enumerate(splits):\n",
    "    topic_df.loc[val_ind, \"fold\"] = f\n",
    "\n",
    "topic_df[\"fold\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f2a048f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    53269\n",
       "0     8248\n",
       "Name: fold, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_df.loc[(topic_df[\"category\"] == \"source\") | (topic_df[\"fold\"] > 0), \"fold\"] = 1\n",
    "topic_df[\"fold\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "175a86b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41024"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_topic = LabelEncoder()\n",
    "topic_df[\"t\"] = topic_df[\"title\"].fillna(\"\") + \" | \" + topic_df[\"description\"].fillna(\"\")\n",
    "topic_df[\"label\"] = le_topic.fit_transform(topic_df[\"t\"])\n",
    "\n",
    "len(le_topic.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fd28ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(154047, 9)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>kind</th>\n",
       "      <th>text</th>\n",
       "      <th>language</th>\n",
       "      <th>copyright_holder</th>\n",
       "      <th>license</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c_00002381196d</td>\n",
       "      <td>Sumar números de varios dígitos: 48,029+233,930</td>\n",
       "      <td>Suma 48,029+233,930 mediante el algoritmo está...</td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>es</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sumar números de varios dígitos: 48,029+233,93...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_000087304a9e</td>\n",
       "      <td>Trovare i fattori di un numero</td>\n",
       "      <td>Sal trova i fattori di 120.\\n\\n</td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>it</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trovare i fattori di un numero | video | Sal t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c_0000ad142ddb</td>\n",
       "      <td>Sumar curvas de demanda</td>\n",
       "      <td>Cómo añadir curvas de demanda\\n\\n</td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>es</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sumar curvas de demanda | video | Cómo añadir ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c_0000c03adc8d</td>\n",
       "      <td>Nado de aproximação</td>\n",
       "      <td>Neste vídeo você vai aprender o nado de aproxi...</td>\n",
       "      <td>document</td>\n",
       "      <td>\\nNado de aproximação\\nSaber nadar nas ondas ...</td>\n",
       "      <td>pt</td>\n",
       "      <td>Sikana Education</td>\n",
       "      <td>CC BY-NC-ND</td>\n",
       "      <td>Nado de aproximação | document | Neste vídeo v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c_00016694ea2a</td>\n",
       "      <td>geometry-m3-topic-a-overview.pdf</td>\n",
       "      <td>Estándares Comunes del Estado de Nueva York\\n\\...</td>\n",
       "      <td>document</td>\n",
       "      <td>Estándares Comunes del Estado de Nueva York\\n\\...</td>\n",
       "      <td>es</td>\n",
       "      <td>Engage NY</td>\n",
       "      <td>CC BY-NC-SA</td>\n",
       "      <td>geometry-m3-topic-a-overview.pdf | document | ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               id                                             title  \\\n",
       "0  c_00002381196d  Sumar números de varios dígitos: 48,029+233,930    \n",
       "1  c_000087304a9e                    Trovare i fattori di un numero   \n",
       "2  c_0000ad142ddb                           Sumar curvas de demanda   \n",
       "3  c_0000c03adc8d                               Nado de aproximação   \n",
       "4  c_00016694ea2a                  geometry-m3-topic-a-overview.pdf   \n",
       "\n",
       "                                         description      kind  \\\n",
       "0  Suma 48,029+233,930 mediante el algoritmo está...     video   \n",
       "1                    Sal trova i fattori di 120.\\n\\n     video   \n",
       "2                  Cómo añadir curvas de demanda\\n\\n     video   \n",
       "3  Neste vídeo você vai aprender o nado de aproxi...  document   \n",
       "4  Estándares Comunes del Estado de Nueva York\\n\\...  document   \n",
       "\n",
       "                                                text language  \\\n",
       "0                                                NaN       es   \n",
       "1                                                NaN       it   \n",
       "2                                                NaN       es   \n",
       "3  \\nNado de aproximação\\nSaber nadar nas ondas ...       pt   \n",
       "4  Estándares Comunes del Estado de Nueva York\\n\\...       es   \n",
       "\n",
       "   copyright_holder      license  \\\n",
       "0               NaN          NaN   \n",
       "1               NaN          NaN   \n",
       "2               NaN          NaN   \n",
       "3  Sikana Education  CC BY-NC-ND   \n",
       "4         Engage NY  CC BY-NC-SA   \n",
       "\n",
       "                                                   t  \n",
       "0  Sumar números de varios dígitos: 48,029+233,93...  \n",
       "1  Trovare i fattori di un numero | video | Sal t...  \n",
       "2  Sumar curvas de demanda | video | Cómo añadir ...  \n",
       "3  Nado de aproximação | document | Neste vídeo v...  \n",
       "4  geometry-m3-topic-a-overview.pdf | document | ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_df = pd.read_csv(\"data/content.csv\")\n",
    "content_df.loc[content_df[\"title\"] == content_df[\"description\"], \"description\"] = None\n",
    "content_df[\"description\"].fillna(content_df[\"text\"].fillna(\"\").apply(lambda x: x[:256]), inplace=True)\n",
    "content_df[\"t\"] = content_df[\"title\"].fillna(\"\") + \" | \" + content_df[\"kind\"].fillna(\"\") + \" | \" + content_df[\"description\"].fillna(\"\")\n",
    "\n",
    "print(content_df.shape)\n",
    "content_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dddd282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9733569582391859, 0.9165319675164073)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_number(x, is_subtopic=False):\n",
    "    chapter, rest = x.split(\":\", 1)\n",
    "    \n",
    "    if is_subtopic:\n",
    "        chapter = chapter.split(\".\", 1)[0]\n",
    "        x = f\"{chapter}:{rest}\"\n",
    "    chapter = int(chapter)\n",
    "\n",
    "    return chapter, x\n",
    "\n",
    "\n",
    "def extract_chapters(df, is_subtopic):\n",
    "    titles = df[\"title\"].fillna(\"\").values\n",
    "    \n",
    "    chapters = np.zeros(len(titles))\n",
    "    new_titles = np.array(titles)\n",
    "    \n",
    "    for i, title in enumerate(titles):\n",
    "        try:\n",
    "            chapters[i], new_titles[i] = extract_number(title, is_subtopic)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "    df[\"chapter\"], df[\"title\"] = chapters, new_titles\n",
    "    df[\"chapter\"] = df[\"chapter\"].astype(int)\n",
    "    \n",
    "    df.loc[df[\"chapter\"] == 0, \"chapter\"] = None\n",
    "    return df\n",
    "    \n",
    "topic_df = extract_chapters(topic_df, False)\n",
    "content_df = extract_chapters(content_df, True)\n",
    "\n",
    "topic_df[\"chapter\"].isnull().mean(), content_df[\"chapter\"].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08a09a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((53269, 18), (8248, 18))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEST_FOLD = 0\n",
    "\n",
    "topic_df[\"t\"] = topic_df[\"title\"] + \" @ \" + topic_df[\"parent_title\"] + \" @ \" + topic_df[\"grandpa_title\"] + \" @ \" + topic_df[\"ggrandpa_title\"] + \" | \" + topic_df[\"description\"].fillna(\"\")\n",
    "\n",
    "topic_df_test = topic_df[topic_df[\"fold\"] == TEST_FOLD].reset_index(drop=True)\n",
    "topic_df_train = topic_df[topic_df[\"fold\"] != TEST_FOLD].reset_index(drop=True)\n",
    "\n",
    "topic_df_train.shape, topic_df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fe9791a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141107, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_contents = corr_df.merge(topic_df_train[[\"id\"]], left_on=\"topic_id\", right_on=\"id\")[[\"content_ids\"]].drop_duplicates()\n",
    "train_contents.rename(columns={\"content_ids\": \"id\"}, inplace=True)\n",
    "train_contents[\"is_train\"] = True\n",
    "train_contents.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e14aa955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9159996624406836"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_df = content_df.merge(train_contents, on=\"id\", how=\"left\")\n",
    "content_df[\"is_train\"].fillna(False, inplace=True)\n",
    "content_df[\"is_train\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87570f15",
   "metadata": {},
   "outputs": [
    {
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       "                    id                  label\n",
       "0       c_00002381196d         [27817, 27750]\n",
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       "2       c_0000ad142ddb                [11785]\n",
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       "4       c_00016694ea2a         [21301, 32510]\n",
       "...                ...                    ...\n",
       "154042  c_fffcbdd4de8b                 [2312]\n",
       "154043  c_fffe15a2d069  [27466, 27467, 16294]\n",
       "154044  c_fffed7b0d13a  [22032, 22033, 22034]\n",
       "154045  c_ffff04ba7ac7                 [3455]\n",
       "154046  c_ffffe5254266                [10100]\n",
       "\n",
       "[154047 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#corr_df[\"label\"] = le_topic.transform(corr_df[\"topic_id\"])\n",
    "corr_df = corr_df.merge(topic_df[[\"id\", \"label\"]].rename(columns={\"id\": \"topic_id\"}), on=\"topic_id\")\n",
    "\n",
    "\n",
    "content_labels = corr_df.groupby(\"content_ids\").agg({\"label\": list}).reset_index()\n",
    "content_labels = content_labels.rename(columns={\"content_ids\": \"id\"})\n",
    "content_labels[\"label\"] = content_labels[\"label\"].apply(lambda x: list(set(x)))\n",
    "\n",
    "topic_df_train[\"label\"] = topic_df_train[\"label\"].apply(lambda x: [x])\n",
    "topic_df_test[\"label\"] = topic_df_test[\"label\"].apply(lambda x: [x])\n",
    "\n",
    "content_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52c928a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(154047, 11)\n"
     ]
    },
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       "    <tr>\n",
       "      <th>154045</th>\n",
       "      <td>c_ffff04ba7ac7</td>\n",
       "      <td>SA of a Cone</td>\n",
       "      <td></td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA of a Cone | video |</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[3455]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154046</th>\n",
       "      <td>c_ffffe5254266</td>\n",
       "      <td>The Jats</td>\n",
       "      <td></td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Jats | video |</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[10100]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154047 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id                                             title  \\\n",
       "0       c_00002381196d  Sumar números de varios dígitos: 48,029+233,930    \n",
       "1       c_000087304a9e                    Trovare i fattori di un numero   \n",
       "2       c_0000ad142ddb                           Sumar curvas de demanda   \n",
       "3       c_0000c03adc8d                               Nado de aproximação   \n",
       "4       c_00016694ea2a                  geometry-m3-topic-a-overview.pdf   \n",
       "...                ...                                               ...   \n",
       "154042  c_fffcbdd4de8b                                      2: Diffusion   \n",
       "154043  c_fffe15a2d069                      Sommare facendo gruppi da 10   \n",
       "154044  c_fffed7b0d13a                            Introdução à subtração   \n",
       "154045  c_ffff04ba7ac7                                      SA of a Cone   \n",
       "154046  c_ffffe5254266                                          The Jats   \n",
       "\n",
       "                                              description      kind  \\\n",
       "0       Suma 48,029+233,930 mediante el algoritmo está...     video   \n",
       "1                         Sal trova i fattori di 120.\\n\\n     video   \n",
       "2                       Cómo añadir curvas de demanda\\n\\n     video   \n",
       "3       Neste vídeo você vai aprender o nado de aproxi...  document   \n",
       "4       Estándares Comunes del Estado de Nueva York\\n\\...  document   \n",
       "...                                                   ...       ...   \n",
       "154042  What will eventually happen to these dyes?\\n\\n...     html5   \n",
       "154043  Sal somma 5+68 spezzando il 5 in un 2 e un 3.\\n\\n     video   \n",
       "154044  Sal fala sobre o que significa subtrair. Os ex...     video   \n",
       "154045                                                        video   \n",
       "154046                                                        video   \n",
       "\n",
       "                                                     text language  \\\n",
       "0                                                     NaN       es   \n",
       "1                                                     NaN       it   \n",
       "2                                                     NaN       es   \n",
       "3       \\nNado de aproximação\\nSaber nadar nas ondas ...       pt   \n",
       "4       Estándares Comunes del Estado de Nueva York\\n\\...       es   \n",
       "...                                                   ...      ...   \n",
       "154042  What will eventually happen to these dyes?\\n\\n...       en   \n",
       "154043                                                NaN       it   \n",
       "154044                                                NaN       pt   \n",
       "154045                                                NaN       en   \n",
       "154046                                                NaN       en   \n",
       "\n",
       "        copyright_holder      license  \\\n",
       "0                    NaN          NaN   \n",
       "1                    NaN          NaN   \n",
       "2                    NaN          NaN   \n",
       "3       Sikana Education  CC BY-NC-ND   \n",
       "4              Engage NY  CC BY-NC-SA   \n",
       "...                  ...          ...   \n",
       "154042    CSU and Merlot  CC BY-NC-SA   \n",
       "154043               NaN          NaN   \n",
       "154044               NaN          NaN   \n",
       "154045               NaN          NaN   \n",
       "154046               NaN          NaN   \n",
       "\n",
       "                                                        t  chapter  is_train  \\\n",
       "0       Sumar números de varios dígitos: 48,029+233,93...      NaN      True   \n",
       "1       Trovare i fattori di un numero | video | Sal t...      NaN      True   \n",
       "2       Sumar curvas de demanda | video | Cómo añadir ...      NaN      True   \n",
       "3       Nado de aproximação | document | Neste vídeo v...      NaN      True   \n",
       "4       geometry-m3-topic-a-overview.pdf | document | ...      NaN      True   \n",
       "...                                                   ...      ...       ...   \n",
       "154042  2. 12: Diffusion | html5 | What will eventuall...      2.0      True   \n",
       "154043  Sommare facendo gruppi da 10 | video | Sal som...      NaN      True   \n",
       "154044  Introdução à subtração | video | Sal fala sobr...      NaN      True   \n",
       "154045                            SA of a Cone | video |       NaN      True   \n",
       "154046                                The Jats | video |       NaN      True   \n",
       "\n",
       "                        label  \n",
       "0              [27817, 27750]  \n",
       "1              [13283, 13284]  \n",
       "2                     [11785]  \n",
       "3                     [23664]  \n",
       "4              [21301, 32510]  \n",
       "...                       ...  \n",
       "154042                 [2312]  \n",
       "154043  [27466, 27467, 16294]  \n",
       "154044  [22032, 22033, 22034]  \n",
       "154045                 [3455]  \n",
       "154046                [10100]  \n",
       "\n",
       "[154047 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(content_df.shape)\n",
    "\n",
    "content_df = content_df.merge(content_labels, on=\"id\") #, how=\"left\")\n",
    "content_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4908271c",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = \"xlm-roberta-large\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f84911e",
   "metadata": {},
   "outputs": [],
   "source": [
    "NW = 8\n",
    "BS = 64\n",
    "\n",
    "class LECRDataset(Dataset):\n",
    "    def __init__(self, df, tokenizer, max_len=512, aug=False):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_len = max_len\n",
    "        self.mask_token = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)\n",
    "        self.aug = aug\n",
    "        self.num_classes = len(le_topic.classes_)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.iloc[index]\n",
    "        encoding = self.tokenizer(str(row.t), padding='max_length', \n",
    "                                  truncation=True, max_length=self.max_len)\n",
    "\n",
    "        encoding = {key: torch.as_tensor(val) for key, val in encoding.items()}\n",
    "\n",
    "        if self.aug:\n",
    "            ix = torch.rand(size=(self.max_len,)) < 0.15\n",
    "            encoding[\"input_ids\"][ix] = self.mask_token\n",
    "\n",
    "        return encoding\n",
    "    \n",
    "model_w = [1.0]\n",
    "\n",
    "models = [VecModel(\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\", 384, has_top=False)]\n",
    "for model in models:\n",
    "    model.cuda()\n",
    "    model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e59400c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "models[0].load(\"models/vec_model_v36_mini.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e0c701b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "\n",
    "def to_vec(models, ds):\n",
    "    val_loader = DataLoader(ds, batch_size=BS, shuffle=False, num_workers=NW,\n",
    "                             pin_memory=False, drop_last=False)\n",
    "\n",
    "    tbar = tqdm(val_loader, file=sys.stdout)\n",
    "\n",
    "    vectors = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for idx, data in enumerate(tbar):\n",
    "            ids, mask = data[\"input_ids\"].cuda(), data[\"attention_mask\"].cuda()\n",
    "\n",
    "            vec = torch.cat([model_w[i]*models[i](ids, mask) for i in range(len(models))], axis=1)\n",
    "            vectors.append(vec.detach().cpu().numpy())\n",
    "\n",
    "    V = np.concatenate(vectors)\n",
    "    \n",
    "    return V\n",
    "\n",
    "\n",
    "\n",
    "def get_matches(V_topic, V_content, topic_ids, content_ids, n_neighbors=5):\n",
    "    \n",
    "    neighbors_model = NearestNeighbors(n_neighbors=n_neighbors, metric='cosine', n_jobs=-1)\n",
    "    neighbors_model.fit(V_content)\n",
    "    dists, indices = neighbors_model.kneighbors(V_topic)\n",
    "    \n",
    "    res_df = pd.DataFrame({\"topic_id\": np.repeat(topic_ids, n_neighbors),\n",
    "                           'content_id': content_ids[indices.ravel()],\n",
    "                           'vec_dist': dists.ravel()\n",
    "                          })\n",
    "    return res_df\n",
    "\n",
    "\n",
    "def f2(recall, precision):\n",
    "    return 5*precision*recall/(4*precision + recall)\n",
    "\n",
    "def metric(pred_df):\n",
    "    scores = np.zeros(pred_df.shape[0])\n",
    "\n",
    "    for i, (gt, pred) in enumerate(zip(pred_df[\"content_ids\"].values, pred_df[\"pred\"].values)):\n",
    "        hits = 0\n",
    "\n",
    "        gt, pred = gt.split(), pred.split()\n",
    "\n",
    "        for l in gt:\n",
    "            if l in pred:\n",
    "                hits += 1\n",
    "\n",
    "        if hits > 0:           \n",
    "            recall = hits / len(gt)\n",
    "            precision = hits / len(pred)\n",
    "\n",
    "            scores[i] = f2(recall, precision)\n",
    "\n",
    "    return scores.mean()\n",
    "    \n",
    "\n",
    "def evaluate(topic_df, content_df):    \n",
    "    topic_ds = LECRDataset(topic_df, tokenizer, MAX_LEN, aug=False)\n",
    "    content_ds = LECRDataset(content_df, tokenizer, MAX_LEN, aug=False)\n",
    "\n",
    "\n",
    "    V_topic = to_vec(model, topic_ds)\n",
    "    V_content = to_vec(model, content_ds)\n",
    "\n",
    "    res_df = get_matches(V_topic, V_content, topic_df[\"id\"].values, content_df[\"id\"].values)\n",
    "\n",
    "    res_df[\"rank\"] = res_df.groupby(\"topic_id\")[\"vec_dist\"].rank(method=\"first\", ascending=True)\n",
    "\n",
    "    pred_df = res_df.copy() #res_df[(res_df[\"rank\"] == 1) | (res_df[\"vec_dist\"] < 0.1)]\n",
    "    pred_df = pred_df.groupby(\"topic_id\")[\"content_id\"].apply(lambda x: \" \".join(list(x)))\n",
    "    pred_df = pred_df.reset_index().rename(columns={\"content_id\": 'pred'})\n",
    "    pred_df = pred_df.merge(pd.read_csv(\"data/correlations.csv\"), on=\"topic_id\")\n",
    "\n",
    "    return metric(pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba1a1ffc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 2407/2407 [00:39<00:00, 60.33it/s]\n"
     ]
    }
   ],
   "source": [
    "content_ds = LECRDataset(content_df, tokenizer, MAX_LEN, aug=False)\n",
    "\n",
    "VC = to_vec(models, content_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d45b4d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152135"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_idx_map = {k: i for i, k in enumerate(content_df[\"t\"].values)}\n",
    "len(vec_idx_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7945120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130736, 12)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_df = content_df[content_df[\"language\"].isin(topic_df_test[\"language\"].unique())].reset_index(drop=True)\n",
    "content_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aabef77e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 833/833 [00:13<00:00, 60.24it/s]\n"
     ]
    }
   ],
   "source": [
    "topic_ds = LECRDataset(topic_df_train, tokenizer, MAX_LEN, aug=False)\n",
    "\n",
    "V_topic_train = to_vec(models, topic_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "81d0c424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for model in models:\n",
    "    model.to(\"cpu\")\n",
    "\n",
    "\n",
    "def inference_one(text):\n",
    "    encoding = tokenizer(text, truncation=True, max_length=MAX_LEN)\n",
    "    encoding = {key: torch.as_tensor(val).unsqueeze(0) for key, val in encoding.items()}\n",
    "    \n",
    "    \n",
    "    with torch.no_grad():\n",
    "        ids, mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
    "        vec = torch.cat([model_w[i]*models[i](ids, mask) for i in range(len(models))], axis=1)\n",
    "        \n",
    "        \n",
    "    return vec.detach().cpu().numpy()[0]\n",
    "\n",
    "\n",
    "len(inference_one(\"Hey there\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13632477",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████| 8248/8248 [01:09<00:00, 119.22it/s]\n"
     ]
    }
   ],
   "source": [
    "def inference(df):\n",
    "    V = np.zeros((df.shape[0], 384))\n",
    "    \n",
    "    for i, text in tqdm(enumerate(df[\"t\"].values), total=df.shape[0]):\n",
    "        V[i] = inference_one(text)\n",
    "        \n",
    "    return V\n",
    "\n",
    "\n",
    "V_topic = inference(topic_df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "499d3372",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████| 130736/130736 [00:00<00:00, 850665.33it/s]\n"
     ]
    }
   ],
   "source": [
    "def inference_content(df, vec_idx_map, VC):\n",
    "    V = np.zeros((df.shape[0], 384))\n",
    "    \n",
    "    for i, text in tqdm(enumerate(df[\"t\"].values), total=df.shape[0]):\n",
    "        idx = vec_idx_map.get(text)\n",
    "        \n",
    "        if idx is None:\n",
    "            V[i] = inference_one(text)\n",
    "        else:\n",
    "            V[i] = VC[idx]\n",
    "        \n",
    "    return V\n",
    "\n",
    "V_content = inference_content(content_df, vec_idx_map, VC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e421798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164960, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_dfs = []\n",
    "\n",
    "for lang in topic_df_test[\"language\"].unique():\n",
    "    topic_ix = np.where(topic_df_test[\"language\"] == lang)[0]\n",
    "    content_ix = np.where(content_df[\"language\"] == lang)[0]\n",
    "    \n",
    "    res_dfs.append(get_matches(V_topic[topic_ix], V_content[content_ix], \n",
    "                               topic_df_test[\"id\"].values[topic_ix], content_df[\"id\"].values[content_ix],\n",
    "                    n_neighbors=20))\n",
    "res_df = pd.concat(res_dfs)\n",
    "res_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "181bf8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_max_train(df, V):\n",
    "    max_train_scores = np.zeros(df.shape[0])\n",
    "    matches = np.zeros(df.shape[0])\n",
    "\n",
    "    for lang in df[\"language\"].unique():\n",
    "        topic_ix = np.where(topic_df_train[\"language\"] == lang)[0]\n",
    "        ix = np.where(df[\"language\"] == lang)[0]\n",
    "\n",
    "        neighbors_model = NearestNeighbors(n_neighbors=1, metric='cosine', n_jobs=4)\n",
    "        neighbors_model.fit(V_topic_train[topic_ix])\n",
    "        dists, idx = neighbors_model.kneighbors(V[ix])\n",
    "\n",
    "        max_train_scores[ix] = dists.ravel()\n",
    "        matches[ix] = topic_ix[idx.ravel()]  \n",
    "        \n",
    "    return max_train_scores, matches\n",
    "\n",
    "\n",
    "content_max_train_scores, _ = get_max_train(content_df, V_content)\n",
    "    \n",
    "content_df[\"max_train_score\"] = content_max_train_scores\n",
    "content_df[\"max_train_score\"].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6f7b184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_max_train_scores, topic_matches = get_max_train(topic_df_test, V_topic)\n",
    "    \n",
    "topic_df_test[\"max_train_score\"] = topic_max_train_scores\n",
    "topic_df_test[\"matched_topic\"] = topic_df_train[\"id\"].values[topic_matches.astype(int)]\n",
    "\n",
    "topic_df_test[\"max_train_score\"].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16e991d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36102, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_degree_match_df = topic_df_test.merge(corr_df, left_on=\"matched_topic\", right_on=\"topic_id\")[[\"id\", \"content_ids\"]]\n",
    "second_degree_match_df[\"second_degree\"] = True\n",
    "second_degree_match_df.rename(columns={\"id\": \"topic_id\", \"content_ids\": \"content_id\"}, inplace=True)\n",
    "second_degree_match_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b869bb41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "en\n",
      "136622 28053 65939\n",
      "----\n",
      "es\n",
      "81953 11769 30844\n",
      "----\n",
      "gu\n",
      "30561 1809 3677\n",
      "----\n",
      "hi\n",
      "37631 1373 4042\n",
      "----\n",
      "bn\n",
      "23102 1731 2513\n",
      "----\n",
      "fr\n",
      "33965 3034 10682\n",
      "----\n",
      "pt\n",
      "37485 3425 10435\n",
      "----\n",
      "fil\n",
      "4291 224 516\n",
      "----\n",
      "as\n",
      "9677 126 641\n",
      "----\n",
      "sw\n",
      "21455 2082 1447\n",
      "----\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(164960, 3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sparse_dot_topn import awesome_cossim_topn\n",
    "\n",
    "\n",
    "def get_matches(topic_df, content_df):\n",
    "    tfidf = TfidfVectorizer(analyzer=\"char\", ngram_range=(4, 4), min_df=2)\n",
    "\n",
    "    tfidf.fit(pd.concat([content_df[\"t\"], topic_df[\"t\"]]))\n",
    "    print(len(tfidf.vocabulary_), topic_df.shape[0], content_df.shape[0])\n",
    "    \n",
    "    topic_df = topic_df[topic_df[\"fold\"] == TEST_FOLD].reset_index(drop=True)\n",
    "    \n",
    "    V_topic = tfidf.transform(topic_df[\"title\"] + \" \" + topic_df[\"t\"])\n",
    "    V_content = tfidf.transform(content_df[\"title\"] + \" \" + content_df[\"t\"])\n",
    "\n",
    "    d = awesome_cossim_topn(V_topic, V_content.T, 20, 0.01, use_threads=True, n_jobs=4)\n",
    "\n",
    "    non_zeros = d.nonzero()\n",
    "\n",
    "    name_indices = non_zeros[0]\n",
    "    gt_indices = non_zeros[1]\n",
    "\n",
    "    content_ids = content_df[\"id\"].values\n",
    "    topic_ids = topic_df[\"id\"].values\n",
    "\n",
    "    left_side = np.empty(gt_indices.size, dtype=object)\n",
    "    right_side = np.empty(gt_indices.size, dtype=object)\n",
    "    match_score = np.zeros(gt_indices.size)\n",
    "\n",
    "    for index in range(gt_indices.size):\n",
    "        left_side[index] = topic_ids[name_indices[index]]\n",
    "        right_side[index] = content_ids[gt_indices[index]]\n",
    "        match_score[index] = d.data[index]\n",
    "\n",
    "    res_df = pd.DataFrame({\"topic_id\": left_side,\n",
    "                           'content_id': right_side,\n",
    "                           'match_score': match_score\n",
    "                          })\n",
    "    return res_df\n",
    "\n",
    "\n",
    "res_dfs = []\n",
    "\n",
    "for lang in topic_df[topic_df[\"fold\"] == TEST_FOLD][\"language\"].unique():\n",
    "    print(lang)\n",
    "    content_df_lang = content_df[(content_df[\"language\"] == lang)].reset_index(drop=True)\n",
    "    topic_df_lang = topic_df[(topic_df[\"language\"] == lang)].reset_index(drop=True)\n",
    "    \n",
    "    if content_df_lang.shape[0] > 0:\n",
    "        res_dfs.append(get_matches(topic_df_lang, content_df_lang))\n",
    "        \n",
    "    print(\"----\")\n",
    "    \n",
    "res_df2 = pd.concat(res_dfs)\n",
    "res_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bcdacf37",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_df[\"target\"] = 1\n",
    "corr_df.rename(columns={\"content_ids\": \"content_id\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b92a024",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_df.columns = [f\"topic_{col}\" for col in topic_df.columns]\n",
    "content_df.columns = [f\"content_{col}\" for col in content_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a8e4a481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_id</th>\n",
       "      <th>content_id</th>\n",
       "      <th>dup_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_061d9f90bb06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_242ddc729eec</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_61b851222e17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_b4fa764f5218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_c25053d6fafd</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385495</th>\n",
       "      <td>t_ffa56a59eb42</td>\n",
       "      <td>c_6d9c9523ad59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385496</th>\n",
       "      <td>t_ffba5459a977</td>\n",
       "      <td>c_04a421dba8aa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385497</th>\n",
       "      <td>t_ffba5459a977</td>\n",
       "      <td>c_787a7a2e7217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385498</th>\n",
       "      <td>t_ffba5459a977</td>\n",
       "      <td>c_a46e0ec1377b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385499</th>\n",
       "      <td>t_ffba5459a977</td>\n",
       "      <td>c_a6c82a21c03a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>385500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              topic_id      content_id  dup_count\n",
       "0       t_0016d30772f3  c_061d9f90bb06          2\n",
       "1       t_0016d30772f3  c_242ddc729eec          2\n",
       "2       t_0016d30772f3  c_61b851222e17          3\n",
       "3       t_0016d30772f3  c_b4fa764f5218          1\n",
       "4       t_0016d30772f3  c_c25053d6fafd          3\n",
       "...                ...             ...        ...\n",
       "385495  t_ffa56a59eb42  c_6d9c9523ad59          1\n",
       "385496  t_ffba5459a977  c_04a421dba8aa          1\n",
       "385497  t_ffba5459a977  c_787a7a2e7217          1\n",
       "385498  t_ffba5459a977  c_a46e0ec1377b          1\n",
       "385499  t_ffba5459a977  c_a6c82a21c03a          1\n",
       "\n",
       "[385500 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_df = []\n",
    "\n",
    "topic_df[\"key\"] = topic_df[\"topic_title\"].fillna(\"\") + \" | \" + topic_df[\"topic_description\"].fillna(\"\")\n",
    "\n",
    "\n",
    "train_df, val_df = topic_df[topic_df[\"topic_fold\"] != TEST_FOLD].reset_index(drop=True), topic_df[topic_df[\"topic_fold\"] == TEST_FOLD].reset_index(drop=True)\n",
    "\n",
    "\n",
    "lookup = train_df.merge(corr_df, on=\"topic_id\").groupby([\"key\", \"content_id\"])[\"topic_channel\"].count().reset_index()\n",
    "lookup.rename(columns={\"topic_channel\": \"dup_count\"}, inplace=True)\n",
    "\n",
    "\n",
    "dup_df.append(val_df[[\"topic_id\", \"key\"]].merge(lookup, on=[\"key\"])[[\"topic_id\", 'content_id', \"dup_count\"]])\n",
    "    \n",
    "dup_df = pd.concat(dup_df).reset_index(drop=True)\n",
    "dup_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7a203de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(663554, 6)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = res_df.merge(res_df2, how=\"outer\", on=[\"topic_id\", \"content_id\"])\n",
    "res_df = res_df.merge(dup_df, how=\"outer\", on=[\"topic_id\", \"content_id\"])\n",
    "res_df = res_df.merge(second_degree_match_df, how=\"outer\", on=[\"topic_id\", \"content_id\"])\n",
    "\n",
    "res_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "094ff50b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736146, 7)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_df = []\n",
    "\n",
    "topic_df[\"key\"] = topic_df[\"topic_title\"].fillna(\"\")\n",
    "\n",
    "\n",
    "train_df, val_df = topic_df[topic_df[\"topic_fold\"] != TEST_FOLD].reset_index(drop=True), topic_df[topic_df[\"topic_fold\"] == TEST_FOLD].reset_index(drop=True)\n",
    "\n",
    "\n",
    "lookup = train_df.merge(corr_df, on=\"topic_id\").groupby([\"key\", \"content_id\"])[\"topic_channel\"].count().reset_index()\n",
    "lookup.rename(columns={\"topic_channel\": \"tdup_count\"}, inplace=True)\n",
    "\n",
    "\n",
    "dup_df.append(val_df[[\"topic_id\", \"key\"]].merge(lookup, on=[\"key\"])[[\"topic_id\", 'content_id', \"tdup_count\"]])\n",
    "    \n",
    "dup_df = pd.concat(dup_df).reset_index(drop=True)\n",
    "\n",
    "\n",
    "res_df = res_df.merge(dup_df, how=\"outer\", on=[\"topic_id\", \"content_id\"])\n",
    "res_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ba8e166c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844718, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_df = []\n",
    "\n",
    "topic_df[\"key\"] = topic_df[\"topic_parent\"].fillna(topic_df[\"topic_id\"])\n",
    "\n",
    "train_df, val_df = topic_df[topic_df[\"topic_fold\"] != TEST_FOLD].reset_index(drop=True), topic_df[topic_df[\"topic_fold\"] == TEST_FOLD].reset_index(drop=True)\n",
    "\n",
    "\n",
    "lookup = train_df.merge(corr_df, on=\"topic_id\").groupby([\"key\", \"content_id\"])[\"topic_channel\"].count().reset_index()\n",
    "lookup.rename(columns={\"topic_channel\": \"pdup_count\"}, inplace=True)\n",
    "\n",
    "\n",
    "dup_df.append(val_df[[\"topic_id\", \"key\"]].merge(lookup, on=[\"key\"])[[\"topic_id\", 'content_id', \"pdup_count\"]])\n",
    "\n",
    "dup_df = pd.concat(dup_df).reset_index(drop=True)\n",
    "\n",
    "\n",
    "res_df = res_df.merge(dup_df, how=\"outer\", on=[\"topic_id\", \"content_id\"])\n",
    "res_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e27a5aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8600442309186461"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def best_case_metric(pred_df):\n",
    "    scores = np.zeros(pred_df.shape[0])\n",
    "\n",
    "    for i, (gt, pred) in enumerate(zip(pred_df[\"content_ids\"].values, pred_df[\"pred\"].values)):\n",
    "        hits = 0\n",
    "\n",
    "        gt, pred = gt.split(), pred.split()\n",
    "\n",
    "        for l in gt:\n",
    "            if l in pred:\n",
    "                hits += 1\n",
    "\n",
    "        if hits > 0:           \n",
    "            recall = hits / len(gt)\n",
    "            precision = 1 #hits / len(pred)\n",
    "\n",
    "            scores[i] = f2(recall, precision)\n",
    "\n",
    "    return scores.mean()\n",
    "\n",
    "\n",
    "pred_df = res_df.groupby(\"topic_id\")[\"content_id\"].apply(lambda x: \" \".join(list(x)))\n",
    "pred_df = pred_df.reset_index().rename(columns={\"content_id\": 'pred'})\n",
    "pred_df = pred_df.merge(pd.read_csv(\"data/correlations.csv\"), on=\"topic_id\")\n",
    "\n",
    "best_case_metric(pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10fbf59a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844718, 38)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = res_df.merge(topic_df, on=\"topic_id\", how=\"left\")\n",
    "res_df = res_df.merge(content_df, on=\"content_id\", how=\"left\")\n",
    "\n",
    "res_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0720ca6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.029357726483867988, 24799.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = res_df.merge(corr_df[[\"topic_id\", \"content_id\", \"target\"]], on=[\"topic_id\", \"content_id\"], how=\"left\")\n",
    "res_df[\"target\"].fillna(0, inplace=True)\n",
    "\n",
    "res_df[\"target\"].mean(), res_df[\"target\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1dce824f",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df[\"topic_language\"] = res_df[\"topic_language\"].astype(\"category\")\n",
    "res_df[\"topic_category\"] = res_df[\"topic_category\"].astype(\"category\")\n",
    "res_df[\"content_kind\"] = res_df[\"content_kind\"].astype(\"category\")\n",
    "res_df[\"topic_channel\"] = res_df[\"topic_channel\"].astype(\"category\")\n",
    "\n",
    "res_df[\"len_topic_title\"] = res_df[\"topic_title\"].fillna(\"\").apply(len)\n",
    "res_df[\"len_topic_description\"] = res_df[\"topic_description\"].fillna(\"\").apply(len)\n",
    "res_df[\"len_content_title\"] = res_df[\"content_title\"].fillna(\"\").apply(len)\n",
    "res_df[\"len_content_description\"] = res_df[\"content_description\"].fillna(\"\").apply(len)\n",
    "res_df[\"len_content_text\"] = res_df[\"content_text\"].fillna(\"\").apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e7306e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df[\"match_score_max\"] = res_df.groupby(\"topic_id\")[\"match_score\"].transform(\"max\")\n",
    "res_df[\"match_score_min\"] = res_df.groupby(\"topic_id\")[\"match_score\"].transform(\"min\")\n",
    "\n",
    "res_df[\"vec_dist_max\"] = res_df.groupby(\"topic_id\")[\"vec_dist\"].transform(\"max\")\n",
    "res_df[\"vec_dist_min\"] = res_df.groupby(\"topic_id\")[\"vec_dist\"].transform(\"min\")\n",
    "\n",
    "res_df[\"dup_count\"] = res_df[\"dup_count\"].fillna(0)\n",
    "res_df[\"total_count\"] = res_df.groupby(\"topic_id\")[\"content_id\"].transform(\"count\")\n",
    "res_df[\"dup_count_mean\"] = res_df.groupby(\"topic_id\")[\"dup_count\"].transform(\"mean\")\n",
    "\n",
    "res_df[\"tdup_count\"] = res_df[\"tdup_count\"].fillna(0)\n",
    "res_df[\"tdup_count_mean\"] = res_df.groupby(\"topic_id\")[\"tdup_count\"].transform(\"mean\")\n",
    "\n",
    "res_df[\"pdup_count\"] = res_df[\"pdup_count\"].fillna(0)\n",
    "res_df[\"pdup_count_mean\"] = res_df.groupby(\"topic_id\")[\"pdup_count\"].transform(\"mean\")\n",
    "\n",
    "res_df[\"same_chapter\"] = res_df[\"topic_chapter\"] == res_df[\"content_chapter\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8f425693",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df[\"starts_same\"] = res_df[\"topic_title\"].apply(lambda x: x.split(\" \", 1)[0]) == res_df[\"content_title\"].apply(lambda x: str(x).split(\" \", 1)[0])\n",
    "\n",
    "res_df[\"content_is_train\"] = res_df[\"content_is_train\"].astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "99c7da5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df.loc[~res_df[\"content_is_train\"], \"content_max_train_score\"] = None\n",
    "\n",
    "res_df[\"second_degree\"].fillna(False, inplace=True)\n",
    "res_df[\"topic_max_train_score\"] = res_df[\"topic_id\"].map(topic_df_test.set_index(\"id\")[\"max_train_score\"].to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b215c127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 18839, number of negative: 614699\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010080 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4230\n",
      "[LightGBM] [Info] Number of data points in the train set: 633538, number of used features: 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.029736 -> initscore=-3.485204\n",
      "[LightGBM] [Info] Start training from score -3.485204\n",
      "[200]\tvalid_0's auc: 0.990573\n",
      "[400]\tvalid_0's auc: 0.99097\n",
      "[600]\tvalid_0's auc: 0.991058\n",
      "[800]\tvalid_0's auc: 0.991145\n",
      "\n",
      "Fold 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 18570, number of negative: 614968\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009597 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4233\n",
      "[LightGBM] [Info] Number of data points in the train set: 633538, number of used features: 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.029312 -> initscore=-3.500023\n",
      "[LightGBM] [Info] Start training from score -3.500023\n",
      "[200]\tvalid_0's auc: 0.990516\n",
      "[400]\tvalid_0's auc: 0.991023\n",
      "[600]\tvalid_0's auc: 0.991189\n",
      "[800]\tvalid_0's auc: 0.991201\n",
      "\n",
      "Fold 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 18438, number of negative: 615102\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010034 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4228\n",
      "[LightGBM] [Info] Number of data points in the train set: 633540, number of used features: 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.029103 -> initscore=-3.507374\n",
      "[LightGBM] [Info] Start training from score -3.507374\n",
      "[200]\tvalid_0's auc: 0.989535\n",
      "[400]\tvalid_0's auc: 0.989957\n",
      "[600]\tvalid_0's auc: 0.990165\n",
      "[800]\tvalid_0's auc: 0.99013\n",
      "\n",
      "Fold 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 18550, number of negative: 614988\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012423 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4229\n",
      "[LightGBM] [Info] Number of data points in the train set: 633538, number of used features: 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.\n",
      "  _log_warning('Overriding the parameters from Reference Dataset.')\n",
      "/home/aerdem/venv/lib/python3.8/site-packages/lightgbm/basic.py:1513: UserWarning: categorical_column in param dict is overridden.\n",
      "  _log_warning(f'{cat_alias} in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.029280 -> initscore=-3.501133\n",
      "[LightGBM] [Info] Start training from score -3.501133\n",
      "[200]\tvalid_0's auc: 0.989113\n",
      "[400]\tvalid_0's auc: 0.989518\n",
      "[600]\tvalid_0's auc: 0.989591\n",
      "[800]\tvalid_0's auc: 0.989444\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import lightgbm as lgb\n",
    "\n",
    "N_FOLDS = 4\n",
    "gkf = GroupKFold(N_FOLDS)\n",
    "\n",
    "\n",
    "lgb_param = {\"objective\": \"binary\",\n",
    "             \"metric\": 'auc',\n",
    "             \"boosting\": \"gbdt\",\n",
    "             \"learning_rate\": 0.05,\n",
    "             \"is_unbalance\": True,\n",
    "            \"min_data_in_leaf\": 1024,\n",
    "            \"num_leaves\": 31,\n",
    "            \"feature_fraction\": 0.8,\n",
    "             \"subsample\": 0.5,\n",
    "             \"subsample_freq\": 1,\n",
    "                    }\n",
    "\n",
    "y_oof = np.zeros(res_df.shape[0])\n",
    "\n",
    "features = [\"match_score\", \"match_score_max\", \"match_score_min\",\n",
    "            \"vec_dist\", \"vec_dist_max\", \"vec_dist_min\", \"len_content_text\",\n",
    "            \"len_topic_title\", \"len_topic_description\", \"len_content_title\", \"len_content_description\",\n",
    "             \"dup_count\", \"total_count\", \"dup_count_mean\",\n",
    "            \"tdup_count\", \"tdup_count_mean\", \"pdup_count\", \"pdup_count_mean\",\n",
    "            \"topic_language\", \"topic_category\", \"content_kind\", \"topic_level\",\n",
    "            \"same_chapter\", \"starts_same\", \"content_is_train\", \"topic_channel\",\n",
    "            \"content_max_train_score\", \"topic_max_train_score\", \"second_degree\"\n",
    "           ]\n",
    "\n",
    "scores = []\n",
    "\n",
    "\n",
    "for f, (train_ind, val_ind) in enumerate(gkf.split(res_df, res_df[\"target\"], groups=res_df[\"topic_id\"])):\n",
    "    print(\"Fold\", f)\n",
    "    train_df, val_df = res_df.iloc[train_ind].reset_index(drop=True), res_df.iloc[val_ind].reset_index(drop=True)\n",
    "        \n",
    "    lgb_train = lgb.Dataset(train_df[features], train_df[\"target\"])\n",
    "    lgb_val = lgb.Dataset(val_df[features], val_df[\"target\"])\n",
    "\n",
    "    model = lgb.train(lgb_param, lgb_train, num_boost_round=800, #early_stopping_rounds=20, \n",
    "                      valid_sets=[lgb_val],\n",
    "                      verbose_eval=200)\n",
    "    model.save_model(f'models/lgb_{f}.txt')\n",
    "    \n",
    "    y_oof[val_ind] += model.predict(val_df[features])\n",
    "    scores.append(roc_auc_score(val_df[\"target\"], y_oof[val_ind]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bf74fb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904797969350063"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ba11a622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_importance(importance_type=\"gain\", booster=model, figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9a54c183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904586480722126"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df[\"pred\"] = y_oof\n",
    "\n",
    "roc_auc_score(res_df[\"target\"], res_df[\"pred\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92947283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_id</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t_000d1fb3f2f5</td>\n",
       "      <td>c_1ec97b588bce c_b8d730238789 c_7868f655c31e c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t_0016d30772f3</td>\n",
       "      <td>c_061d9f90bb06 c_ea312de91d4f c_242ddc729eec c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t_001bcbb22694</td>\n",
       "      <td>c_454736da21be</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t_0020bde404c1</td>\n",
       "      <td>c_aa8f7f0cbeb3 c_2b349c258301 c_b1154d3cc25a c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t_00260f878951</td>\n",
       "      <td>c_86f126d7f1f8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8243</th>\n",
       "      <td>t_ffb0a70e5e48</td>\n",
       "      <td>c_f3b75faf5480 c_81f88e566297 c_f7c218334f49 c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8244</th>\n",
       "      <td>t_ffb1d959761e</td>\n",
       "      <td>c_529e1d59a7d2 c_9b993553de45 c_38fc31dfa9ac c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8245</th>\n",
       "      <td>t_ffba5459a977</td>\n",
       "      <td>c_a46e0ec1377b c_04a421dba8aa c_787a7a2e7217 c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8246</th>\n",
       "      <td>t_ffdc013937fc</td>\n",
       "      <td>c_6b761d3bf85f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8247</th>\n",
       "      <td>t_ffe8df837f62</td>\n",
       "      <td>c_ea0a2006e2af c_1251a6f1c5c1 c_72f1b23b472e c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8248 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            topic_id                                               pred\n",
       "0     t_000d1fb3f2f5  c_1ec97b588bce c_b8d730238789 c_7868f655c31e c...\n",
       "1     t_0016d30772f3  c_061d9f90bb06 c_ea312de91d4f c_242ddc729eec c...\n",
       "2     t_001bcbb22694                                     c_454736da21be\n",
       "3     t_0020bde404c1  c_aa8f7f0cbeb3 c_2b349c258301 c_b1154d3cc25a c...\n",
       "4     t_00260f878951                                     c_86f126d7f1f8\n",
       "...              ...                                                ...\n",
       "8243  t_ffb0a70e5e48  c_f3b75faf5480 c_81f88e566297 c_f7c218334f49 c...\n",
       "8244  t_ffb1d959761e  c_529e1d59a7d2 c_9b993553de45 c_38fc31dfa9ac c...\n",
       "8245  t_ffba5459a977  c_a46e0ec1377b c_04a421dba8aa c_787a7a2e7217 c...\n",
       "8246  t_ffdc013937fc                                     c_6b761d3bf85f\n",
       "8247  t_ffe8df837f62  c_ea0a2006e2af c_1251a6f1c5c1 c_72f1b23b472e c...\n",
       "\n",
       "[8248 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df[\"rank\"] = res_df.groupby(\"topic_id\")[\"pred\"].rank(method=\"first\", ascending=False)\n",
    "\n",
    "res_df[\"gap\"] = res_df.groupby(\"topic_id\")[\"pred\"].transform(\"max\") - res_df[\"pred\"]\n",
    "\n",
    "res_df[\"content_dist\"] = res_df[\"gap\"] + res_df.groupby(\"content_id\")[\"pred\"].transform(\"max\") - res_df[\"pred\"]\n",
    "\n",
    "res_df[\"content_rank\"] = res_df.groupby(\"content_id\")[\"content_dist\"].rank(method=\"first\")\n",
    "\n",
    "pred_df = res_df[(res_df[\"rank\"] == 1) | \n",
    "                 ((res_df[\"gap\"] < 0.25) & res_df[\"content_is_train\"] & (res_df[\"pred\"] > 0.05)) | \n",
    "                 (~res_df[\"content_is_train\"] & (res_df[\"content_rank\"] == 1) & (res_df[\"content_dist\"] < 0.65)) | \n",
    "                 (~res_df[\"content_is_train\"] & (res_df[\"gap\"] < 0.05))]\n",
    "\n",
    "\n",
    "pred_df = pred_df.groupby(\"topic_id\")[\"content_id\"].apply(lambda x: \" \".join(list(x)))\n",
    "pred_df = pred_df.reset_index().rename(columns={\"content_id\": 'pred'})\n",
    "\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "057ab1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8248, 3)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = pred_df.merge(pd.read_csv(\"data/correlations.csv\"), on=\"topic_id\")\n",
    "pred_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "df6faec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7177253285207978"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric(pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0089d22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98e10585",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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