4_2_X_CAH_text_embed-V2-Task2_Punchlines_Eval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qN9k2Tb5b2NT"
   },
   "source": [
    "\n",
    "Copied from: 4.1-CAH_infer.ipynb\n",
    "\n",
    "Catboost ranking model\n",
    "* https://colab.research.google.com/github/catboost/tutorials/blob/master/ranking/ranking_tutorial.ipynb#scrollTo=6rYwwu5yrOXk\n",
    "\n",
    "Catboost TEXT (inc embeddings):\n",
    "* https://colab.research.google.com/github/catboost/tutorials/blob/master/events/2019_11_30_datastart.ipynb#scrollTo=E0SXaSM8yDyX\n",
    "* https://github.com/catboost/catboost/blob/master/catboost/tutorials/events/2020_11_18_catboost_tutorial/text_embedding_features.ipynb\n",
    "\n",
    "Use train test already made.\n",
    "\n",
    "Can Try cleaning the test for min occ as well.\n",
    "\n",
    "Keep all pairs - will allow model to learn popularity\n",
    "\n",
    "* try 1-class learning, on positives/picked\n",
    "\n",
    "* ranking\n",
    "\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FIjj9BjkY_A9",
    "outputId": "38a320d0-5536-432f-8afe-11fc9faa9454"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bzljhyTQEZds"
   },
   "source": [
    "## Install Sentence Transformer Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AmxRYxNDvn6y",
    "outputId": "f5e07ce0-162c-478c-cdc1-b0d2449853b0"
   },
   "outputs": [],
   "source": [
    "# Install the library using pip\n",
    "# !pip3 install sentence-transformers scikit-learn catboost  -U\n",
    "# !pip3 install nltk -U\n",
    "\n",
    "# import nltk\n",
    "# nltk.download('punkt')\n",
    "\n",
    "\n",
    "# from google.colab import output\n",
    "# output.enable_custom_widget_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "jS9O0eAwlt8U"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# from sentence_transformers import SentenceTransformer, util\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV, cross_val_score, cross_val_predict\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import classification_report, roc_auc_score  \n",
    "\n",
    "# https://stackoverflow.com/questions/53784971/how-to-disable-convergencewarning-using-sklearn\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from catboost import CatBoost,CatBoostClassifier, CatBoostRanker, Pool, MetricVisualizer\n",
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics import classification_report, roc_auc_score, top_k_accuracy_score , ndcg_score\n",
    "from sklearn.model_selection import GroupShuffleSplit, StratifiedGroupKFold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "FcJ95uWKXoJC"
   },
   "outputs": [],
   "source": [
    "# from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\n",
    "# from sentence_transformers import SentenceTransformer, LoggingHandler\n",
    "# from sentence_transformers import models, util, datasets, evaluation, losses, SentencesDataset ## https://www.sbert.net/docs/package_reference/losses.html + SentencesDataset\n",
    "# from sentence_transformers import SentenceTransformer, InputExample, losses ## MultipleNegativesRankingLoss\n",
    "# from torch.utils.data import DataLoader\n",
    "## https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss\n",
    "# from sentence_transformers.readers import InputExample\n",
    "import logging\n",
    "import os\n",
    "import gzip\n",
    "# from torch.utils.data import DataLoader\n",
    "from datetime import datetime\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "1gRQUdycC9aR"
   },
   "outputs": [],
   "source": [
    "# model_name = \"all-MiniLM-L6-v2\"\n",
    "model_name = \"all-MiniLM-L12-v2\"\n",
    "# model_name = \"/content/drive/MyDrive/research/cah/cah_tsdae-model\"\n",
    "min_cooccurences = 1#4 # filter sentences for pairs that occurred at least k times. min 5: 200K. min 1: 1.9M\n",
    "\n",
    "ONE_COL_DATA_FORMAT = False\n",
    "# USE_TEXT_COLS =  [\"text\"]#[\"text\",\t\"white_card_text\"]#[\"black_card_text\",\t\"white_card_text\"]\n",
    "USE_TEXT_COLS = [\"text\",\"white_card_text\"]\n",
    "FASTRUN = False#False#True\n",
    "\n",
    "TRAIN_EMBEDS = True#False # train and save embeddings on texts\n",
    "MASK_EMBEDS = False # filter x train for only those with matches in embeds trained\n",
    "\n",
    "TRAIN_PATH = \"cah_train_games.parquet\"#\"/content/drive/MyDrive/research/cah/cah_train_games.parquet\" # #\n",
    "TEST_PATH = \"cah_test_games.parquet\"#\"/content/drive/MyDrive/research/cah/cah_test_games.parquet\" # #\n",
    "\n",
    "TRAIN_TEST_SEPERATING_GROUP =\"fake_round_id\"#\"white_card_text\"# # # # seperate train/test by this col "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import top_k_accuracy_score\n",
    "def eval_preds(df_test,preds):\n",
    "    df2 = df_test[[\"fake_round_id\",\"won\"]].copy()\n",
    "    df2[\"preds\"] = preds\n",
    "    df2 = df2.sample(frac=1)\n",
    "    df2[\"m_score\"] = df2.groupby(\"fake_round_id\")[\"preds\"].transform(\"max\")\n",
    "    df2[\"correct\"] = ((df2[\"preds\"]==df2[\"m_score\"]) &(df2[\"won\"]>0)).astype(int)#df2.loc[df2[\"preds\"]==df2[\"m_score\"]\n",
    "    # df2.tail(31) ## there are cases wtih multiple values with same score/rank?  - ignore for now\n",
    "    df2.sort_values(\"preds\",ascending=False,inplace=True)\n",
    "    print(classification_report(df2[\"won\"],df2[\"correct\"]))\n",
    "    print(\"rocAUC:\",round(roc_auc_score(df2[\"won\"],df2[\"preds\"]),4))\n",
    "    print(\"top 1 acc by round:\",round(df2.groupby(\"fake_round_id\").head(1)[\"won\"].mean(),4))\n",
    "    print(\"top 2 acc by round:\",round(df2.groupby(\"fake_round_id\").head(2).groupby(\"fake_round_id\")[\"won\"].max().mean(),4))\n",
    "    print(\"top 3 acc by round:\",round(df2.groupby(\"fake_round_id\").head(3).groupby(\"fake_round_id\")[\"won\"].max().mean(),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eIAKz6KVEndZ"
   },
   "source": [
    "## Load the sBERT Model\n",
    "\n",
    "* Default , later try pretrained+ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "5IO_j2Ofv5pq"
   },
   "outputs": [],
   "source": [
    "# # Load the BERT model. Various models trained on Natural Language Inference (NLI) https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/nli-models.md and \n",
    "# ## # https://www.sbert.net/docs/pretrained_models.html\n",
    "# # model = SentenceTransformer(\"paraphrase-MiniLM-L6-v2\") ## \"paraphrase-MiniLM-L12-v2\"\n",
    "\n",
    "# model = SentenceTransformer(model_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I7_Ib3ITEwgO"
   },
   "source": [
    "## Setup a Corpus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K7tGxkF4l-oB",
    "outputId": "10309d22-bb4c-47cb-f5bb-324adba7b1de",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['fake_round_id', 'black_card_text', 'white_card_text', 'won', 'text',\n",
      "       'sum_won'],\n",
      "      dtype='object')\n",
      "1957080 rows\n",
      "text               746559\n",
      "white_card_text      2128\n",
      "fake_round_id      195708\n",
      "won                     2\n",
      "dtype: int64\n",
      "count    746559.000000\n",
      "mean          2.621467\n",
      "std           1.453558\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           3.000000\n",
      "max          15.000000\n",
      "Name: won, dtype: float64\n",
      "1957080\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>won</th>\n",
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       "  </thead>\n",
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       "      <th>9</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Mufasa's death scene</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Shapes and colors</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
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       "      <td>Being fat from noodles</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Letting this loser eat me out</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2989503</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "      <td>Netflix</td>\n",
       "      <td>298951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989502</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "      <td>A bigger, blacker dick</td>\n",
       "      <td>298951</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2989501</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "      <td>All types of girls, even ugly ones</td>\n",
       "      <td>298951</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2989500</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "      <td>Wet butt</td>\n",
       "      <td>298951</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2989504</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "      <td>Slowly easing down onto a cucumber</td>\n",
       "      <td>298951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1957080 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text  \\\n",
       "9        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "5        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "0        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "1        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "2        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "...                                                    ...   \n",
       "2989503  Every Tuesday, I purchase a box of donuts. I s...   \n",
       "2989502  Every Tuesday, I purchase a box of donuts. I s...   \n",
       "2989501  Every Tuesday, I purchase a box of donuts. I s...   \n",
       "2989500  Every Tuesday, I purchase a box of donuts. I s...   \n",
       "2989504  Every Tuesday, I purchase a box of donuts. I s...   \n",
       "\n",
       "                                           white_card_text  fake_round_id  won  \n",
       "9                                     Mufasa's death scene              1    0  \n",
       "5                                        Shapes and colors              1    1  \n",
       "0        Going inside at some point because of the mosq...              1    0  \n",
       "1                                   Being fat from noodles              1    0  \n",
       "2                            Letting this loser eat me out              1    0  \n",
       "...                                                    ...            ...  ...  \n",
       "2989503                                            Netflix         298951    0  \n",
       "2989502                             A bigger, blacker dick         298951    0  \n",
       "2989501                 All types of girls, even ugly ones         298951    0  \n",
       "2989500                                           Wet butt         298951    0  \n",
       "2989504                 Slowly easing down onto a cucumber         298951    1  \n",
       "\n",
       "[1957080 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_parquet(TRAIN_PATH).drop(columns=[\"prior_white\"],errors=\"ignore\") \n",
    "print(df.columns)\n",
    "df = df.filter(USE_TEXT_COLS+[\"fake_round_id\",\"won\"],axis=1)\n",
    "\n",
    "df[\"won\"] = df[\"won\"].astype(int)\n",
    "# df = df.sort_values(by=\"won\",ascending=False) ## get picked pairs first  - ORIG\n",
    "df = df.sample(frac=1).sort_values(\"fake_round_id\") # new\n",
    "\n",
    "print(df.shape[0],\"rows\")\n",
    "print(df.nunique())\n",
    "# df[\"train\"]=True\n",
    "\n",
    "# df[\"pair_count\"] = df.groupby(\"text\")[\"won\"].transform(\"count\") ## can be used to filter sentences occurring less than k times\n",
    "print(df.groupby(\"text\")[\"won\"].count().describe())\n",
    "# print(df[[\"won\",\"pair_count\"]].describe().round(2) )\n",
    "\n",
    "# df = df.drop_duplicates(subset=[\"black_card_text\",\"white_card_text\"],keep=\"first\")#.sample(frac=1)\n",
    "print(df.shape[0])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2128.000000\n",
       "mean      919.680451\n",
       "std       268.305553\n",
       "min        10.000000\n",
       "25%       970.000000\n",
       "50%       996.000000\n",
       "75%      1019.000000\n",
       "max      1973.000000\n",
       "Name: white_card_text, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"white_card_text\"].value_counts().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vyWPiyqQBphJ"
   },
   "source": [
    "Test set\n",
    "* Keeps round level grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 512
    },
    "id": "IxyngBX8ACCc",
    "outputId": "b722ed86-b9d1-4003-8e88-7661c5d0595d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text               367105\n",
      "white_card_text      2127\n",
      "fake_round_id       48928\n",
      "won                     2\n",
      "dtype: int64\n",
      "489280 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "      <th>59</th>\n",
       "      <td>In his newest and most difficult stunt, David ...</td>\n",
       "      <td>Police brutality</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <th>58</th>\n",
       "      <td>In his newest and most difficult stunt, David ...</td>\n",
       "      <td>Tight, red baboon ass</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>In his newest and most difficult stunt, David ...</td>\n",
       "      <td>Authentic Mexican cuisine</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>In his newest and most difficult stunt, David ...</td>\n",
       "      <td>A really cool hat</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>In his newest and most difficult stunt, David ...</td>\n",
       "      <td>Twenty bucks</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989544</th>\n",
       "      <td>Oh my god! Powerful thighs killed Kenny!</td>\n",
       "      <td>Powerful thighs</td>\n",
       "      <td>298955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989543</th>\n",
       "      <td>Oh my god! Holding up the line at Walgreens by...</td>\n",
       "      <td>Holding up the line at Walgreens by trying to ...</td>\n",
       "      <td>298955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989542</th>\n",
       "      <td>Oh my god! A bird that shits human turds kille...</td>\n",
       "      <td>A bird that shits human turds</td>\n",
       "      <td>298955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989540</th>\n",
       "      <td>Oh my god! Brief male nudity killed Kenny!</td>\n",
       "      <td>Brief male nudity</td>\n",
       "      <td>298955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989541</th>\n",
       "      <td>Oh my god! Jeff Bezos killed Kenny!</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>298955</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>489280 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text  \\\n",
       "59       In his newest and most difficult stunt, David ...   \n",
       "58       In his newest and most difficult stunt, David ...   \n",
       "50       In his newest and most difficult stunt, David ...   \n",
       "51       In his newest and most difficult stunt, David ...   \n",
       "52       In his newest and most difficult stunt, David ...   \n",
       "...                                                    ...   \n",
       "2989544           Oh my god! Powerful thighs killed Kenny!   \n",
       "2989543  Oh my god! Holding up the line at Walgreens by...   \n",
       "2989542  Oh my god! A bird that shits human turds kille...   \n",
       "2989540         Oh my god! Brief male nudity killed Kenny!   \n",
       "2989541                Oh my god! Jeff Bezos killed Kenny!   \n",
       "\n",
       "                                           white_card_text  fake_round_id  won  \n",
       "59                                        Police brutality              6    0  \n",
       "58                                   Tight, red baboon ass              6    1  \n",
       "50                               Authentic Mexican cuisine              6    0  \n",
       "51                                       A really cool hat              6    0  \n",
       "52                                            Twenty bucks              6    0  \n",
       "...                                                    ...            ...  ...  \n",
       "2989544                                    Powerful thighs         298955    0  \n",
       "2989543  Holding up the line at Walgreens by trying to ...         298955    0  \n",
       "2989542                      A bird that shits human turds         298955    0  \n",
       "2989540                                  Brief male nudity         298955    0  \n",
       "2989541                                         Jeff Bezos         298955    1  \n",
       "\n",
       "[489280 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_parquet(TEST_PATH).drop(columns=[\"prior_white\"],errors=\"ignore\")\n",
    "# df_test[\"train\"]=False\n",
    "df_test = df_test.filter(USE_TEXT_COLS+[\"fake_round_id\",\"won\"],axis=1)\n",
    "df_test[\"won\"] = df_test[\"won\"].astype(int)\n",
    "\n",
    "df_test = df_test.sample(frac=1).sort_values(\"fake_round_id\")#\n",
    "# df_test = df_test.tail(150000)\n",
    "\n",
    "print(df_test.nunique())\n",
    "print(df_test.shape[0],\"rows\")\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### alt - concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2446360, 4)\n"
     ]
    }
   ],
   "source": [
    "### \n",
    "df = pd.concat([df,df_test]).sort_values(\"fake_round_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kUh_gOK32mZu"
   },
   "source": [
    "### check mean baseline prior\n",
    "* By min cooccurrences of sentence pairs in in raw data\n",
    "1 min occ Prior Acc: 0.2044\n",
    "2 min occ Prior Acc: 0.2032\n",
    "3 min occ Prior Acc: 0.2027\n",
    "4 min occ Prior Acc: 0.2011\n",
    "5 min occ Prior Acc: 0.1922\n",
    "6 min occ Prior Acc: 0.1762\n",
    "7 min occ Prior Acc: 0.1503"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "QRC7aCEE2mBe"
   },
   "outputs": [],
   "source": [
    "df[\"pair_count\"] = df.groupby(\"text\")[\"won\"].transform(\"count\")\n",
    "\n",
    "# for i in range(1,5):\n",
    "#   df_temp = df.loc[df[\"pair_count\"] >=i].copy()\n",
    "#   print(f\"{i} min occ, {df_temp.shape[0]} rows\")\n",
    "#   df_white_prior = df_temp.groupby([\"white_card_text\"], as_index=False)[\"won\"].mean().rename(columns={\"won\":\"white_prior\"}).set_index(\"white_card_text\")\n",
    "#   df_test = df_test.join(df_white_prior,on=\"white_card_text\",how=\"left\")\n",
    "#   prior = df_test[\"white_prior\"].mean()\n",
    "#   df_test[\"white_prior\"] = df_test[\"white_prior\"].fillna(prior)\n",
    "#   print(\"White Prior Acc: %.3f\" %df_test.sort_values(\"white_prior\",ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "  \n",
    "# ## prior for a black-white combination - mean (freq% won), or sum (times won?) , or threshholded max? (over 2 times?)\n",
    "#   df_pair_prior = df_temp.groupby([\"white_card_text\",\"black_card_text\"], as_index=False)[\"won\"].sum().rename(columns={\"won\":\"pair_prior\"}).set_index([\"white_card_text\",\"black_card_text\"])\n",
    "  \n",
    "#   df_test = df_test.join(df_pair_prior,on=[\"white_card_text\",\"black_card_text\"],how=\"left\")\n",
    "#   prior = df_test[\"pair_prior\"].mean()\n",
    "#   df_test[\"pair_prior\"] = df_test[\"pair_prior\"].fillna(prior)\n",
    "#   print(\"Pair Prior (Only)  Acc: %.3f\" %df_test.sort_values(\"pair_prior\",ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "#   print(\"White then Pair Prior Acc: %.3f\" %df_test.sort_values([\"white_prior\",\"pair_prior\"],ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "#   print(\"Pair Prior then White Acc: %.3f\" %df_test.sort_values([\"pair_prior\",\"white_prior\",],ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "  \n",
    "#   df_test.drop(columns=[\"white_prior\",\"pair_prior\"],errors=\"ignore\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-3MaeLPLSdIU"
   },
   "source": [
    "### Get only unique texts - can save compute\n",
    "* Get embeddings/score only for unique combinations/text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "6uCtR7fzSb0H",
    "outputId": "0c22272d-5d7c-4129-dccc-6c8a8907bbf9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784974, 1)\n"
     ]
    },
    {
     "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989503</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989502</th>\n",
       "      <td>Every Tuesday, I purchase a box of donuts. I s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989539</th>\n",
       "      <td>In his farewell address, George Washington fam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989547</th>\n",
       "      <td>Oh my god! Jerking off to a 10-second RealMedi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989545</th>\n",
       "      <td>Oh my god! Breastfeeding a ten-year-old killed...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784974 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text\n",
       "9        Hi MTV! My name is Kendra, I live in Malibu, I...\n",
       "5        Hi MTV! My name is Kendra, I live in Malibu, I...\n",
       "0        Hi MTV! My name is Kendra, I live in Malibu, I...\n",
       "1        Hi MTV! My name is Kendra, I live in Malibu, I...\n",
       "2        Hi MTV! My name is Kendra, I live in Malibu, I...\n",
       "...                                                    ...\n",
       "2989503  Every Tuesday, I purchase a box of donuts. I s...\n",
       "2989502  Every Tuesday, I purchase a box of donuts. I s...\n",
       "2989539  In his farewell address, George Washington fam...\n",
       "2989547  Oh my god! Jerking off to a 10-second RealMedi...\n",
       "2989545  Oh my god! Breastfeeding a ten-year-old killed...\n",
       "\n",
       "[784974 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_texts = pd.concat([df,df_test])[[\"text\"]].drop_duplicates()\n",
    "print(df_texts.shape)\n",
    "df_texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LkJnRnouBFXO"
   },
   "source": [
    "* sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "3nGpc9dNBGvI"
   },
   "outputs": [],
   "source": [
    "if FASTRUN:\n",
    "    df_test = df_test.tail(1000) #df_test.head(8000)\n",
    "    df = df.head(3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OxoLFSfm6kCj"
   },
   "source": [
    "# text preproc \n",
    "* https://colab.research.google.com/github/catboost/tutorials/blob/master/events/2019_11_30_datastart.ipynb#scrollTo=HWrijpMGfFud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "8EHJEZJA_y0B"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.pipeline import Pipeline,  make_pipeline\n",
    "from sklearn.feature_selection import SelectKBest, chi2, SelectFdr\n",
    "\n",
    "vectorizer = CountVectorizer(analyzer='word', ngram_range=(1, 3),min_df=12,strip_accents=\"ascii\",max_features=60123,\n",
    "                            stop_words=\"english\",max_df=0.94) #\n",
    "\n",
    "# pipe = Pipeline([(\"vect\",vectorizer),(\"fs\",SelectKBest(chi2, k=8345))] # ,(\"fs\",SelectFdr(chi2,alpha=0.1)\n",
    "pipe = make_pipeline(vectorizer,SelectKBest(chi2, k=3500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "won    0.101619\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[[\"fake_round_id\",\"won\"]].sample(frac=1).groupby(\"fake_round_id\").head(1).groupby(\"fake_round_id\").max().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "won    0.201173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[[\"fake_round_id\",\"won\"]].sample(frac=1).groupby(\"fake_round_id\").head(2).groupby(\"fake_round_id\").max().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "won    0.292021\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[[\"fake_round_id\",\"won\"]].sample(frac=1).groupby(\"fake_round_id\").head(3).groupby(\"fake_round_id\").max().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jbJLCYQFITUE",
    "outputId": "24aa14b0-4dbd-4a18-ca99-c20db09b1849"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
    "import torch\n",
    "device = \"cuda:0\"# if torch.cuda.is_available() else \"cpu\"\n",
    "print(device)\n",
    "\n",
    "access_token =\"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\" #SECRET!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NMSkR5VjEAfb"
   },
   "source": [
    "##  Extract embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KKsUiAmSD_t_",
    "outputId": "2b2a8aaf-08f0-4955-a534-dcc028ba9950"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all-MiniLM-L6-v2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07d15319a97044dc9a700781c0b4c1fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/24531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 7min 12s\n",
      "Wall time: 4min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "# model_name = \"all-MiniLM-L6-v2\"\n",
    "# # model_name = \"/content/drive/MyDrive/research/cah/cah_tsdae-model\"\n",
    "print(model_name)\n",
    "\n",
    "if TRAIN_EMBEDS:\n",
    "    # big_model = SentenceTransformer(model_name, device='cuda')\n",
    "    big_model = SentenceTransformer(model_name,device=device)\n",
    "\n",
    "    X_embed =  big_model.encode(df_texts[\"text\"].values, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "WkD-f4utFBiy"
   },
   "outputs": [],
   "source": [
    "if TRAIN_EMBEDS:\n",
    "    df_embed = pd.DataFrame(X_embed)\n",
    "    df_embed[\"text\"] = df_texts[\"text\"].values # new\n",
    "    df_embed.columns = df_embed.columns.astype(str)\n",
    "    df_embed.set_index(\"text\",inplace=True)\n",
    "\n",
    "    df_embed.to_parquet(\"cah_embed_L12.parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ryrIkm37E_QD"
   },
   "source": [
    "### CB embed ranker model\n",
    "* maybe not leave in here\n",
    "\n",
    "* __Test??? __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xBTt2G9eLTyp",
    "outputId": "c8431cd0-f4ae-41db-8f18-006b35348f43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784974, 384)\n",
      "0\n",
      "435752 unique texts/index\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',\n",
       "       ...\n",
       "       '374', '375', '376', '377', '378', '379', '380', '381', '382', '383'],\n",
       "      dtype='object', length=384)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embed = pd.read_parquet(\"cah_embed_L12.parquet\") ##\n",
    "print(df_embed.shape)\n",
    "print((df_embed.isna().sum(axis=1) >360).sum())\n",
    "print(df_embed.index.nunique(),\"unique texts/index\") # text col is index\n",
    "df_embed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make my own test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test = df.head(4000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### manually partition by whites\n",
    " * test already presplit by rounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2128 TRAIN_WHITES\n",
      "2127 TEST_WHITES\n"
     ]
    }
   ],
   "source": [
    "TRAIN_WHITES = set(df[\"white_card_text\"])\n",
    "print(len(TRAIN_WHITES),\"TRAIN_WHITES\")\n",
    "\n",
    "\n",
    "TEST_WHITES = df_test[\"white_card_text\"].unique()#set(df_test[\"white_card_text\"])\n",
    "print(len(TEST_WHITES),\"TEST_WHITES\")\n",
    "\n",
    "\n",
    "# df = df.loc[~df[\"white_card_text\"].isin(TEST_WHITES)]\n",
    "# print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>won</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [text, white_card_text, fake_round_id, won]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.loc[~df_test[\"white_card_text\"].isin(TRAIN_WHITES)] # no rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"white_words\"] = df[\"white_card_text\"].str.split().str.len()\n",
    "df[\"white_chars\"] = df[\"white_card_text\"].str.len()\n",
    "\n",
    "df_test[\"white_words\"] = df_test[\"white_card_text\"].str.split().str.len()\n",
    "df_test[\"white_chars\"] = df_test[\"white_card_text\"].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "id": "ljMf0UX7vGCU",
    "outputId": "27bb3292-c1bb-4719-93de-aec56f5fc802"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all nan rows: 0.335088457953858\n"
     ]
    }
   ],
   "source": [
    "if MASK_EMBEDS:\n",
    "    print(df.shape[0],\"# all rows\")\n",
    "    mask = df[\"text\"].isin(df_embed.index) ## filter groups, labels + figure out later why missing ?? \n",
    "    print(mask.sum())\n",
    "    ### exclude optionally cases text lacks sent. embedding\n",
    "    X_train = df.loc[mask][[\"text\"]].merge(df_embed,left_on=\"text\",right_index=True,how=\"left\") ## [\"black_card_text\",\"white_card_text\",\"text\"]\n",
    "\n",
    "    X_train.drop(columns=[\"white_card_text\",\"won\", 'pair_count'],errors=\"ignore\",inplace=True)\n",
    "    print(X_train.shape,\" matched data\")\n",
    "    \n",
    "    \n",
    "    groups_train = df.loc[mask][TRAIN_TEST_SEPERATING_GROUP]\n",
    "else:\n",
    "    X_train = df.merge(df_embed,left_on=\"text\",right_index=True,how=\"left\") ## [\"black_card_text\",\"white_card_text\",\"text\"]\n",
    "    X_train.drop(columns=[\"won\", 'pair_count'],errors=\"ignore\",inplace=True) # \"white_card_text\",\n",
    "    \n",
    "print(\"all nan rows:\",(X_train.isna().sum(axis=1) >380).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assert (X_train.isna().sum(axis=1) >380).mean() < 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.dropna(thresh=30,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>white_words</th>\n",
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       "      <th>380</th>\n",
       "      <th>381</th>\n",
       "      <th>382</th>\n",
       "      <th>383</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Mufasa's death scene</td>\n",
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       "      <td>-0.010823</td>\n",
       "      <td>-0.020046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>-0.012057</td>\n",
       "      <td>-0.021166</td>\n",
       "      <td>0.041128</td>\n",
       "      <td>0.119438</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>0.009197</td>\n",
       "      <td>0.067515</td>\n",
       "      <td>-0.017765</td>\n",
       "      <td>-0.013642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Shapes and colors</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0.032424</td>\n",
       "      <td>-0.101894</td>\n",
       "      <td>0.055120</td>\n",
       "      <td>-0.022391</td>\n",
       "      <td>-0.005835</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046464</td>\n",
       "      <td>-0.017922</td>\n",
       "      <td>-0.042114</td>\n",
       "      <td>0.024329</td>\n",
       "      <td>0.055069</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.048190</td>\n",
       "      <td>-0.004203</td>\n",
       "      <td>-0.090898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Going inside at some point because of the mosq...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>0.011151</td>\n",
       "      <td>-0.133547</td>\n",
       "      <td>0.051276</td>\n",
       "      <td>-0.034160</td>\n",
       "      <td>0.029295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024464</td>\n",
       "      <td>-0.045934</td>\n",
       "      <td>-0.029687</td>\n",
       "      <td>0.075321</td>\n",
       "      <td>0.074868</td>\n",
       "      <td>-0.009658</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>0.051367</td>\n",
       "      <td>-0.055651</td>\n",
       "      <td>-0.058625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Being fat from noodles</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.027754</td>\n",
       "      <td>-0.150084</td>\n",
       "      <td>0.107850</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>-0.038018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032928</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>-0.035775</td>\n",
       "      <td>0.072376</td>\n",
       "      <td>0.074382</td>\n",
       "      <td>0.027448</td>\n",
       "      <td>0.024166</td>\n",
       "      <td>0.039920</td>\n",
       "      <td>-0.057826</td>\n",
       "      <td>-0.084249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Letting this loser eat me out</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0.025971</td>\n",
       "      <td>-0.089726</td>\n",
       "      <td>0.068847</td>\n",
       "      <td>-0.030777</td>\n",
       "      <td>0.027053</td>\n",
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       "      <td>0.015146</td>\n",
       "      <td>-0.035557</td>\n",
       "      <td>-0.043037</td>\n",
       "      <td>0.031479</td>\n",
       "      <td>0.039215</td>\n",
       "      <td>-0.009583</td>\n",
       "      <td>0.034504</td>\n",
       "      <td>0.025060</td>\n",
       "      <td>-0.089068</td>\n",
       "      <td>-0.031565</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2989545</th>\n",
       "      <td>Oh my god! Breastfeeding a ten-year-old killed...</td>\n",
       "      <td>Breastfeeding a ten-year-old</td>\n",
       "      <td>298955</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989544</th>\n",
       "      <td>Oh my god! Powerful thighs killed Kenny!</td>\n",
       "      <td>Powerful thighs</td>\n",
       "      <td>298955</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.032460</td>\n",
       "      <td>0.032564</td>\n",
       "      <td>0.037018</td>\n",
       "      <td>-0.031175</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015945</td>\n",
       "      <td>-0.039174</td>\n",
       "      <td>-0.026949</td>\n",
       "      <td>0.094527</td>\n",
       "      <td>-0.051043</td>\n",
       "      <td>-0.076979</td>\n",
       "      <td>0.128478</td>\n",
       "      <td>-0.101690</td>\n",
       "      <td>-0.064606</td>\n",
       "      <td>-0.095462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989543</th>\n",
       "      <td>Oh my god! Holding up the line at Walgreens by...</td>\n",
       "      <td>Holding up the line at Walgreens by trying to ...</td>\n",
       "      <td>298955</td>\n",
       "      <td>13</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989542</th>\n",
       "      <td>Oh my god! A bird that shits human turds kille...</td>\n",
       "      <td>A bird that shits human turds</td>\n",
       "      <td>298955</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989541</th>\n",
       "      <td>Oh my god! Jeff Bezos killed Kenny!</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>298955</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>0.049788</td>\n",
       "      <td>0.070555</td>\n",
       "      <td>0.021099</td>\n",
       "      <td>-0.012779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024378</td>\n",
       "      <td>-0.019724</td>\n",
       "      <td>0.029430</td>\n",
       "      <td>-0.014492</td>\n",
       "      <td>-0.103844</td>\n",
       "      <td>0.080958</td>\n",
       "      <td>0.049837</td>\n",
       "      <td>-0.036537</td>\n",
       "      <td>-0.019083</td>\n",
       "      <td>0.066007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2446360 rows × 389 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text  \\\n",
       "9        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "5        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "0        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "1        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "2        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "...                                                    ...   \n",
       "2989545  Oh my god! Breastfeeding a ten-year-old killed...   \n",
       "2989544           Oh my god! Powerful thighs killed Kenny!   \n",
       "2989543  Oh my god! Holding up the line at Walgreens by...   \n",
       "2989542  Oh my god! A bird that shits human turds kille...   \n",
       "2989541                Oh my god! Jeff Bezos killed Kenny!   \n",
       "\n",
       "                                           white_card_text  fake_round_id  \\\n",
       "9                                     Mufasa's death scene              1   \n",
       "5                                        Shapes and colors              1   \n",
       "0        Going inside at some point because of the mosq...              1   \n",
       "1                                   Being fat from noodles              1   \n",
       "2                            Letting this loser eat me out              1   \n",
       "...                                                    ...            ...   \n",
       "2989545                       Breastfeeding a ten-year-old         298955   \n",
       "2989544                                    Powerful thighs         298955   \n",
       "2989543  Holding up the line at Walgreens by trying to ...         298955   \n",
       "2989542                      A bird that shits human turds         298955   \n",
       "2989541                                         Jeff Bezos         298955   \n",
       "\n",
       "         white_words  white_chars         0         1         2         3  \\\n",
       "9                  3           20 -0.023548 -0.105384  0.012771 -0.010823   \n",
       "5                  3           17  0.032424 -0.101894  0.055120 -0.022391   \n",
       "0                  9           52  0.011151 -0.133547  0.051276 -0.034160   \n",
       "1                  4           22  0.027754 -0.150084  0.107850 -0.002130   \n",
       "2                  6           29  0.025971 -0.089726  0.068847 -0.030777   \n",
       "...              ...          ...       ...       ...       ...       ...   \n",
       "2989545            3           28       NaN       NaN       NaN       NaN   \n",
       "2989544            2           15 -0.032460  0.032564  0.037018 -0.031175   \n",
       "2989543           13           67       NaN       NaN       NaN       NaN   \n",
       "2989542            6           29       NaN       NaN       NaN       NaN   \n",
       "2989541            2           10 -0.001820  0.049788  0.070555  0.021099   \n",
       "\n",
       "                4  ...       374       375       376       377       378  \\\n",
       "9       -0.020046  ...  0.003600 -0.012057 -0.021166  0.041128  0.119438   \n",
       "5       -0.005835  ...  0.046464 -0.017922 -0.042114  0.024329  0.055069   \n",
       "0        0.029295  ...  0.024464 -0.045934 -0.029687  0.075321  0.074868   \n",
       "1       -0.038018  ...  0.032928  0.001580 -0.035775  0.072376  0.074382   \n",
       "2        0.027053  ...  0.015146 -0.035557 -0.043037  0.031479  0.039215   \n",
       "...           ...  ...       ...       ...       ...       ...       ...   \n",
       "2989545       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989544 -0.001665  ... -0.015945 -0.039174 -0.026949  0.094527 -0.051043   \n",
       "2989543       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989542       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989541 -0.012779  ...  0.024378 -0.019724  0.029430 -0.014492 -0.103844   \n",
       "\n",
       "              379       380       381       382       383  \n",
       "9        0.002443  0.009197  0.067515 -0.017765 -0.013642  \n",
       "5        0.013286  0.059959  0.048190 -0.004203 -0.090898  \n",
       "0       -0.009658  0.128628  0.051367 -0.055651 -0.058625  \n",
       "1        0.027448  0.024166  0.039920 -0.057826 -0.084249  \n",
       "2       -0.009583  0.034504  0.025060 -0.089068 -0.031565  \n",
       "...           ...       ...       ...       ...       ...  \n",
       "2989545       NaN       NaN       NaN       NaN       NaN  \n",
       "2989544 -0.076979  0.128478 -0.101690 -0.064606 -0.095462  \n",
       "2989543       NaN       NaN       NaN       NaN       NaN  \n",
       "2989542       NaN       NaN       NaN       NaN       NaN  \n",
       "2989541  0.080958  0.049837 -0.036537 -0.019083  0.066007  \n",
       "\n",
       "[2446360 rows x 389 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if MASK_EMBEDS:\n",
    "    y_train = list(df.loc[mask][\"won\"])\n",
    "else:\n",
    "    y_train = list(df[\"won\"])\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### opt: drop the text cols\n",
    "# X_train.drop(columns=[\"text\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake_round_id\n",
      "244636\n"
     ]
    }
   ],
   "source": [
    "print(TRAIN_TEST_SEPERATING_GROUP)\n",
    "\n",
    "# groups_train = df.loc[mask][TRAIN_TEST_SEPERATING_GROUP]\n",
    "groups_train = df[TRAIN_TEST_SEPERATING_GROUP]\n",
    "print(len(set(groups_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2446360.0\n",
       "mean           0.1\n",
       "std            0.3\n",
       "min            0.0\n",
       "25%            0.0\n",
       "50%            0.0\n",
       "75%            0.0\n",
       "max            1.0\n",
       "Name: won, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in embed filtered:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1.626613e+06\n",
       "mean     1.000859e-01\n",
       "std      3.001146e-01\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      0.000000e+00\n",
       "max      1.000000e+00\n",
       "Name: won, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df[\"won\"].describe())\n",
    "print(\"in embed filtered:\")\n",
    "display(df.loc[df[\"text\"].isin(df_embed.index)][\"won\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train unique jokes 784974 rows: 2446360\n",
      "Test unique jokes 367105 rows 489280\n"
     ]
    }
   ],
   "source": [
    "print(\"train unique jokes\",df[\"text\"].drop_duplicates().shape[0], \"rows:\",df.shape[0])\n",
    "print(\"Test unique jokes\",df_test[\"text\"].drop_duplicates().shape[0], \"rows\",df_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.000327016465278"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*df_test.shape[0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Zt2kB6OvGJl",
    "outputId": "56a429e3-1894-4697-ce77-9b2a94674dfd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join x_test shape: 323999\n",
      "(489280, 389) Left join shape\n"
     ]
    }
   ],
   "source": [
    "if MASK_EMBEDS:\n",
    "    # X_test = df_test[USE_TEXT_COLS] ## [\"black_card_text\",\"white_card_text\",\"text\"]\n",
    "    mask = df_test[\"text\"].isin(df_embed.index) ## filter groups, labels + figure out later why missing ?? \n",
    "    print(mask.sum())\n",
    "\n",
    "    X_test = df_test.loc[mask][[\"text\"]].join(df_embed,on=\"text\",how=\"left\").drop(columns=[\"text\"]) ## [\"black_card_text\",\"white_card_text\",\"text\"]\n",
    "    # X_test.drop(columns=[\"text\"],inplace=True)\n",
    "    X_test.drop(columns=[\"text\",\"white_card_text\",\"won\", 'pair_count'],errors=\"ignore\",inplace=True)\n",
    "    print(X_test.shape)\n",
    "\n",
    "    y_test = list(df_test.loc[mask][\"won\"])\n",
    "    groups_test = df_test.loc[mask][\"fake_round_id\"]\n",
    "else:\n",
    "    print(\"inner join x_test shape:\",df_test[[\"text\"]].join(df_embed,on=\"text\",how=\"inner\").shape[0])\n",
    "#     X_test = df_test[[\"text\",\"white_card_text\"]].join(df_embed,on=\"text\",how=\"left\") # ORIG\n",
    "#     X_test[\"white_words\"] = X_test[\"white_card_text\"].str.split().str.len()\n",
    "#     X_test[\"white_chars\"] = X_test[\"white_card_text\"].str.len()\n",
    "    \n",
    "    X_test = df_test[[\"text\",\"white_card_text\",\"fake_round_id\",\"white_words\",\"white_chars\"]].join(df_embed,on=\"text\",how=\"left\")\n",
    "    \n",
    "#     X_test.drop(columns=[\"text\",\"white_card_text\",\"won\", 'pair_count'],errors=\"ignore\",inplace=True) ## [\"black_card_text\",\"white_card_text\",\"text\"]\n",
    "    X_test.drop(columns=[\"won\", 'pair_count'],errors=\"ignore\",inplace=True)\n",
    "    \n",
    "    print(X_test.shape, \"Left join shape\")\n",
    "\n",
    "    y_test = list(df_test[\"won\"])\n",
    "    groups_test = df_test[TRAIN_TEST_SEPERATING_GROUP]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "389"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.dropna(thresh=40,axis=1,inplace=True)\n",
    "X_test.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## White/Punchline Level SPLIT\n",
    "* Groupwise split by white cards/punchlines\n",
    "\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "KhgVw4R7wJnd"
   },
   "outputs": [],
   "source": [
    "default_parameters = {\n",
    "#     'iterations': 1000,#1200,\n",
    "#     'custom_metric': [\"AUC\",'AverageGain:top=1','AverageGain:top=2','AverageGain:top=3','PFound:top=1'#,'PFound:top=3',\n",
    "#                       ,\"NDCG\"], #\"Accuracy\",'PrecisionAt:top=1', 'RecallAt:top=1'], #['NDCG', 'PFound', 'AverageGain:top=1',\"AUC\"],\n",
    "    'verbose': 20,\n",
    "    'random_seed': 0,\n",
    "    \"early_stopping_rounds\":5,\n",
    "    'metric_period': 5,\n",
    "     'task_type': 'GPU',\n",
    "#     \"use_best_model\":True,\n",
    "#     \"auto_class_weights\":\"SqrtBalanced\", # Doesn't really affect auto_class_weights; Balanced , \n",
    "}\n",
    "parameters = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_train.copy()\n",
    "y = y_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "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>text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>white_words</th>\n",
       "      <th>white_chars</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>...</th>\n",
       "      <th>374</th>\n",
       "      <th>375</th>\n",
       "      <th>376</th>\n",
       "      <th>377</th>\n",
       "      <th>378</th>\n",
       "      <th>379</th>\n",
       "      <th>380</th>\n",
       "      <th>381</th>\n",
       "      <th>382</th>\n",
       "      <th>383</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Mufasa's death scene</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.023548</td>\n",
       "      <td>-0.105384</td>\n",
       "      <td>0.012771</td>\n",
       "      <td>-0.010823</td>\n",
       "      <td>-0.020046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>-0.012057</td>\n",
       "      <td>-0.021166</td>\n",
       "      <td>0.041128</td>\n",
       "      <td>0.119438</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>0.009197</td>\n",
       "      <td>0.067515</td>\n",
       "      <td>-0.017765</td>\n",
       "      <td>-0.013642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Shapes and colors</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0.032424</td>\n",
       "      <td>-0.101894</td>\n",
       "      <td>0.055120</td>\n",
       "      <td>-0.022391</td>\n",
       "      <td>-0.005835</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046464</td>\n",
       "      <td>-0.017922</td>\n",
       "      <td>-0.042114</td>\n",
       "      <td>0.024329</td>\n",
       "      <td>0.055069</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.048190</td>\n",
       "      <td>-0.004203</td>\n",
       "      <td>-0.090898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Going inside at some point because of the mosq...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>0.011151</td>\n",
       "      <td>-0.133547</td>\n",
       "      <td>0.051276</td>\n",
       "      <td>-0.034160</td>\n",
       "      <td>0.029295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024464</td>\n",
       "      <td>-0.045934</td>\n",
       "      <td>-0.029687</td>\n",
       "      <td>0.075321</td>\n",
       "      <td>0.074868</td>\n",
       "      <td>-0.009658</td>\n",
       "      <td>0.128628</td>\n",
       "      <td>0.051367</td>\n",
       "      <td>-0.055651</td>\n",
       "      <td>-0.058625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Being fat from noodles</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.027754</td>\n",
       "      <td>-0.150084</td>\n",
       "      <td>0.107850</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>-0.038018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032928</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>-0.035775</td>\n",
       "      <td>0.072376</td>\n",
       "      <td>0.074382</td>\n",
       "      <td>0.027448</td>\n",
       "      <td>0.024166</td>\n",
       "      <td>0.039920</td>\n",
       "      <td>-0.057826</td>\n",
       "      <td>-0.084249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I...</td>\n",
       "      <td>Letting this loser eat me out</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0.025971</td>\n",
       "      <td>-0.089726</td>\n",
       "      <td>0.068847</td>\n",
       "      <td>-0.030777</td>\n",
       "      <td>0.027053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015146</td>\n",
       "      <td>-0.035557</td>\n",
       "      <td>-0.043037</td>\n",
       "      <td>0.031479</td>\n",
       "      <td>0.039215</td>\n",
       "      <td>-0.009583</td>\n",
       "      <td>0.034504</td>\n",
       "      <td>0.025060</td>\n",
       "      <td>-0.089068</td>\n",
       "      <td>-0.031565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2989545</th>\n",
       "      <td>Oh my god! Breastfeeding a ten-year-old killed...</td>\n",
       "      <td>Breastfeeding a ten-year-old</td>\n",
       "      <td>298955</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989544</th>\n",
       "      <td>Oh my god! Powerful thighs killed Kenny!</td>\n",
       "      <td>Powerful thighs</td>\n",
       "      <td>298955</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.032460</td>\n",
       "      <td>0.032564</td>\n",
       "      <td>0.037018</td>\n",
       "      <td>-0.031175</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015945</td>\n",
       "      <td>-0.039174</td>\n",
       "      <td>-0.026949</td>\n",
       "      <td>0.094527</td>\n",
       "      <td>-0.051043</td>\n",
       "      <td>-0.076979</td>\n",
       "      <td>0.128478</td>\n",
       "      <td>-0.101690</td>\n",
       "      <td>-0.064606</td>\n",
       "      <td>-0.095462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989543</th>\n",
       "      <td>Oh my god! Holding up the line at Walgreens by...</td>\n",
       "      <td>Holding up the line at Walgreens by trying to ...</td>\n",
       "      <td>298955</td>\n",
       "      <td>13</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989542</th>\n",
       "      <td>Oh my god! A bird that shits human turds kille...</td>\n",
       "      <td>A bird that shits human turds</td>\n",
       "      <td>298955</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989541</th>\n",
       "      <td>Oh my god! Jeff Bezos killed Kenny!</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>298955</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>0.049788</td>\n",
       "      <td>0.070555</td>\n",
       "      <td>0.021099</td>\n",
       "      <td>-0.012779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024378</td>\n",
       "      <td>-0.019724</td>\n",
       "      <td>0.029430</td>\n",
       "      <td>-0.014492</td>\n",
       "      <td>-0.103844</td>\n",
       "      <td>0.080958</td>\n",
       "      <td>0.049837</td>\n",
       "      <td>-0.036537</td>\n",
       "      <td>-0.019083</td>\n",
       "      <td>0.066007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2446360 rows × 389 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      text  \\\n",
       "9        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "5        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "0        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "1        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "2        Hi MTV! My name is Kendra, I live in Malibu, I...   \n",
       "...                                                    ...   \n",
       "2989545  Oh my god! Breastfeeding a ten-year-old killed...   \n",
       "2989544           Oh my god! Powerful thighs killed Kenny!   \n",
       "2989543  Oh my god! Holding up the line at Walgreens by...   \n",
       "2989542  Oh my god! A bird that shits human turds kille...   \n",
       "2989541                Oh my god! Jeff Bezos killed Kenny!   \n",
       "\n",
       "                                           white_card_text  fake_round_id  \\\n",
       "9                                     Mufasa's death scene              1   \n",
       "5                                        Shapes and colors              1   \n",
       "0        Going inside at some point because of the mosq...              1   \n",
       "1                                   Being fat from noodles              1   \n",
       "2                            Letting this loser eat me out              1   \n",
       "...                                                    ...            ...   \n",
       "2989545                       Breastfeeding a ten-year-old         298955   \n",
       "2989544                                    Powerful thighs         298955   \n",
       "2989543  Holding up the line at Walgreens by trying to ...         298955   \n",
       "2989542                      A bird that shits human turds         298955   \n",
       "2989541                                         Jeff Bezos         298955   \n",
       "\n",
       "         white_words  white_chars         0         1         2         3  \\\n",
       "9                  3           20 -0.023548 -0.105384  0.012771 -0.010823   \n",
       "5                  3           17  0.032424 -0.101894  0.055120 -0.022391   \n",
       "0                  9           52  0.011151 -0.133547  0.051276 -0.034160   \n",
       "1                  4           22  0.027754 -0.150084  0.107850 -0.002130   \n",
       "2                  6           29  0.025971 -0.089726  0.068847 -0.030777   \n",
       "...              ...          ...       ...       ...       ...       ...   \n",
       "2989545            3           28       NaN       NaN       NaN       NaN   \n",
       "2989544            2           15 -0.032460  0.032564  0.037018 -0.031175   \n",
       "2989543           13           67       NaN       NaN       NaN       NaN   \n",
       "2989542            6           29       NaN       NaN       NaN       NaN   \n",
       "2989541            2           10 -0.001820  0.049788  0.070555  0.021099   \n",
       "\n",
       "                4  ...       374       375       376       377       378  \\\n",
       "9       -0.020046  ...  0.003600 -0.012057 -0.021166  0.041128  0.119438   \n",
       "5       -0.005835  ...  0.046464 -0.017922 -0.042114  0.024329  0.055069   \n",
       "0        0.029295  ...  0.024464 -0.045934 -0.029687  0.075321  0.074868   \n",
       "1       -0.038018  ...  0.032928  0.001580 -0.035775  0.072376  0.074382   \n",
       "2        0.027053  ...  0.015146 -0.035557 -0.043037  0.031479  0.039215   \n",
       "...           ...  ...       ...       ...       ...       ...       ...   \n",
       "2989545       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989544 -0.001665  ... -0.015945 -0.039174 -0.026949  0.094527 -0.051043   \n",
       "2989543       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989542       NaN  ...       NaN       NaN       NaN       NaN       NaN   \n",
       "2989541 -0.012779  ...  0.024378 -0.019724  0.029430 -0.014492 -0.103844   \n",
       "\n",
       "              379       380       381       382       383  \n",
       "9        0.002443  0.009197  0.067515 -0.017765 -0.013642  \n",
       "5        0.013286  0.059959  0.048190 -0.004203 -0.090898  \n",
       "0       -0.009658  0.128628  0.051367 -0.055651 -0.058625  \n",
       "1        0.027448  0.024166  0.039920 -0.057826 -0.084249  \n",
       "2       -0.009583  0.034504  0.025060 -0.089068 -0.031565  \n",
       "...           ...       ...       ...       ...       ...  \n",
       "2989545       NaN       NaN       NaN       NaN       NaN  \n",
       "2989544 -0.076979  0.128478 -0.101690 -0.064606 -0.095462  \n",
       "2989543       NaN       NaN       NaN       NaN       NaN  \n",
       "2989542       NaN       NaN       NaN       NaN       NaN  \n",
       "2989541  0.080958  0.049837 -0.036537 -0.019083  0.066007  \n",
       "\n",
       "[2446360 rows x 389 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>fake_round_id</th>\n",
       "      <th>won</th>\n",
       "      <th>pred</th>\n",
       "      <th>white_card_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [fake_round_id, won, pred, white_card_text]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_evaluated = pd.DataFrame(columns=[\"fake_round_id\",\"won\",\"pred\",\"white_card_text\"])\n",
    "df_evaluated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300000, 4)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_evaluated_orig = df_evaluated.copy()\n",
    "df_evaluated_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "(600, 7)\n"
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    {
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      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 7)\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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    {
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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    {
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    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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    {
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    {
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    {
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    {
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     "output_type": "stream",
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    {
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    {
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    {
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     "output_type": "stream",
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    {
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    {
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    {
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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    {
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "385\n",
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    },
    {
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    {
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    {
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    {
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    {
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    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    {
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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     "output_type": "stream",
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    {
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     "output_type": "stream",
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    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import GroupShuffleSplit, StratifiedGroupKFold, GroupKFold\n",
    "groups = groups_train\n",
    "cv = GroupShuffleSplit(n_splits=550,test_size=60) ## add a few rounds thyen drop any reoccurring cases before eval\n",
    "\n",
    "# cv = GroupKFold(n_splits=7,test_size=100)\n",
    "# cv = StratifiedGroupKFold(n_splits=5)\n",
    "i = 1\n",
    "for train_idxs, test_idxs in cv.split(X=df, groups=df[\"fake_round_id\"].values):\n",
    "    if i%5 ==0:print(i)\n",
    "\n",
    "    df_train, df_test = df.iloc[train_idxs].copy(), df.iloc[test_idxs].copy()\n",
    "    TEST_WHITES = df_test[\"white_card_text\"].unique()\n",
    "    df_train = df_train.loc[~df_train[\"white_card_text\"].isin(TEST_WHITES)]\n",
    "#     print(df_test.shape)\n",
    "    X_train, X_test = df_train.drop(columns=[\"won\"]), df_test.drop(columns=[\"won\"])\n",
    "    y_train, y_test = df_train[\"won\"],df_test[\"won\"]\n",
    "\n",
    "    train = Pool(\n",
    "    data=X_train,\n",
    "    label=y_train,\n",
    "    group_id= X_train[\"fake_round_id\"], # \"fake_round_id\"#,#groups_train1,\n",
    "    text_features=[\"text\",\"white_card_text\"],)\n",
    "\n",
    "    test = Pool(\n",
    "    data=X_test,\n",
    "    label=y_test,\n",
    "    group_id=X_test[\"fake_round_id\"],\n",
    "    text_features=[\"text\",\"white_card_text\"])\n",
    "    \n",
    "    model = CatBoostClassifier(**default_parameters)\n",
    "    model.fit(train, eval_set=test, plot=False,verbose=False)\n",
    "\n",
    "    preds_pw = model.predict(test,prediction_type=\"Probability\")[:,1]\n",
    "    df_test[\"pred\"] = preds_pw\n",
    "    df_evaluated = pd.concat([df_evaluated,df_test[[\"fake_round_id\",\"won\",\"pred\",\"white_card_text\"]]])\n",
    "    i +=1\n",
    "\n",
    "df_evaluated = df_evaluated.infer_objects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_evaluated.to_csv(\"evaluated_60x500.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>Eating ass</td>\n",
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       "      <td>13144</td>\n",
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       "      <td>0.109397</td>\n",
       "      <td>Grunting for ten minutes and then peeing sand</td>\n",
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       "      <th>131438</th>\n",
       "      <td>13144</td>\n",
       "      <td>1</td>\n",
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       "      <td>13144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105593</td>\n",
       "      <td>Catapults</td>\n",
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       "    <tr>\n",
       "      <th>131434</th>\n",
       "      <td>13144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.103521</td>\n",
       "      <td>A whole different way of talking called Spanish</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>2962463</th>\n",
       "      <td>296247</td>\n",
       "      <td>0</td>\n",
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       "      <td>A groundbreaking new masturbation technique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962460</th>\n",
       "      <td>296247</td>\n",
       "      <td>0</td>\n",
       "      <td>0.129724</td>\n",
       "      <td>Snorting coke off a clown's boner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962461</th>\n",
       "      <td>296247</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090309</td>\n",
       "      <td>Drinking Beyonce's DivaCup and becoming immortal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962464</th>\n",
       "      <td>296247</td>\n",
       "      <td>0</td>\n",
       "      <td>0.099120</td>\n",
       "      <td>A gambling problem</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fake_round_id  won      pred  \\\n",
       "131433           13144    0  0.140067   \n",
       "131430           13144    0  0.109397   \n",
       "131438           13144    1  0.086354   \n",
       "131435           13144    0  0.105593   \n",
       "131434           13144    0  0.103521   \n",
       "...                ...  ...       ...   \n",
       "2962462         296247    1  0.096680   \n",
       "2962463         296247    0  0.107173   \n",
       "2962460         296247    0  0.129724   \n",
       "2962461         296247    0  0.090309   \n",
       "2962464         296247    0  0.099120   \n",
       "\n",
       "                                          white_card_text  \n",
       "131433                                         Eating ass  \n",
       "131430      Grunting for ten minutes and then peeing sand  \n",
       "131438                                   A bird that dies  \n",
       "131435                                          Catapults  \n",
       "131434    A whole different way of talking called Spanish  \n",
       "...                                                   ...  \n",
       "2962462                An overwhelming variety of cheeses  \n",
       "2962463       A groundbreaking new masturbation technique  \n",
       "2962460                 Snorting coke off a clown's boner  \n",
       "2962461  Drinking Beyonce's DivaCup and becoming immortal  \n",
       "2962464                                A gambling problem  \n",
       "\n",
       "[300000 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_evaluated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95    270000\n",
      "           1       1.00      0.14      0.24     30000\n",
      "\n",
      "    accuracy                           0.91    300000\n",
      "   macro avg       0.96      0.57      0.60    300000\n",
      "weighted avg       0.92      0.91      0.88    300000\n",
      "\n",
      "top 1 acc by round: 0.14537335648722402\n",
      "top 2 acc by round: 0.26572633518800726\n",
      "top 3 acc by round: 0.37264769465216\n"
     ]
    }
   ],
   "source": [
    "eval_preds(df_evaluated,df_evaluated[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96    262727\n",
      "           1       1.00      0.18      0.31     29245\n",
      "\n",
      "    accuracy                           0.92    291972\n",
      "   macro avg       0.96      0.59      0.63    291972\n",
      "weighted avg       0.92      0.92      0.89    291972\n",
      "\n",
      "top 1 acc by round: 0.1454796753730021\n",
      "top 2 acc by round: 0.26434418967289225\n",
      "top 3 acc by round: 0.37254137576638197\n"
     ]
    }
   ],
   "source": [
    "eval_preds(df_evaluated.round(2).drop_duplicates(),df_evaluated.round(2).drop_duplicates()[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(282170, 4)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95    253953\n",
      "           1       1.00      0.15      0.26     28217\n",
      "\n",
      "    accuracy                           0.91    282170\n",
      "   macro avg       0.96      0.57      0.61    282170\n",
      "weighted avg       0.92      0.91      0.88    282170\n",
      "\n",
      "top 1 acc by round: 0.14579863203033633\n",
      "top 2 acc by round: 0.26834886770386646\n",
      "top 3 acc by round: 0.3770422085976539\n"
     ]
    }
   ],
   "source": [
    "df_eval2 = df_evaluated.drop_duplicates(subset=[\"fake_round_id\",\"white_card_text\"]).copy()\n",
    "print(df_eval2.shape)\n",
    "eval_preds(df_eval2,df_eval2[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_eval2.to_csv(\"evaluated_2_311k_60x500.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95    280611\n",
      "           1       1.00      0.15      0.26     31179\n",
      "\n",
      "    accuracy                           0.91    311790\n",
      "   macro avg       0.96      0.57      0.61    311790\n",
      "weighted avg       0.92      0.91      0.88    311790\n",
      "\n",
      "rocAUC: 0.5618\n",
      "top 1 acc by round: 0.1464\n",
      "top 2 acc by round: 0.2685\n",
      "top 3 acc by round: 0.3774\n"
     ]
    }
   ],
   "source": [
    "df_eval2 = pd.read_csv(\"evaluated_2_311k_60x500.csv\").drop_duplicates(subset=[\"fake_round_id\",\"white_card_text\"])\n",
    "eval_preds(df_eval2,df_eval2[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fake_round_id       28217\n",
      "won                     2\n",
      "pred               282985\n",
      "white_card_text      2116\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_evaluated.nunique())\n",
    "# print(df_evaluated.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n",
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(311790, 4)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95    280611\n",
      "           1       1.00      0.15      0.26     31179\n",
      "\n",
      "    accuracy                           0.91    311790\n",
      "   macro avg       0.96      0.57      0.61    311790\n",
      "weighted avg       0.92      0.91      0.88    311790\n",
      "\n",
      "top 1 acc by round: 0.14609192084415792\n",
      "top 2 acc by round: 0.2684499182141826\n",
      "top 3 acc by round: 0.3773052374995991\n",
      "CPU times: total: 3h 9min 10s\n",
      "Wall time: 1h 36min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## a few more runs\n",
    "\n",
    "from sklearn.model_selection import GroupShuffleSplit, StratifiedGroupKFold, GroupKFold\n",
    "groups = groups_train\n",
    "cv = GroupShuffleSplit(n_splits=500,test_size=60)\n",
    "\n",
    "cv = GroupShuffleSplit(n_splits=40,test_size=55)\n",
    "\n",
    "# cv = GroupKFold(n_splits=7,test_size=100)\n",
    "# cv = StratifiedGroupKFold(n_splits=5)\n",
    "i = 1\n",
    "for train_idxs, test_idxs in cv.split(X=df, groups=df[\"fake_round_id\"].values):\n",
    "    if i%4 ==0:print(i)\n",
    "\n",
    "    df_train, df_test = df.iloc[train_idxs].copy(), df.iloc[test_idxs].copy()\n",
    "    TEST_WHITES = df_test[\"white_card_text\"].unique()\n",
    "    df_train = df_train.loc[~df_train[\"white_card_text\"].isin(TEST_WHITES)]\n",
    "#     print(df_test.shape)\n",
    "    X_train, X_test = df_train.drop(columns=[\"won\"]), df_test.drop(columns=[\"won\"])\n",
    "    y_train, y_test = df_train[\"won\"],df_test[\"won\"]\n",
    "\n",
    "    train = Pool(\n",
    "    data=X_train,\n",
    "    label=y_train,\n",
    "    group_id= X_train[\"fake_round_id\"], # \"fake_round_id\"#,#groups_train1,\n",
    "    text_features=[\"text\",\"white_card_text\"],)\n",
    "\n",
    "    test = Pool(\n",
    "    data=X_test,\n",
    "    label=y_test,\n",
    "    group_id=X_test[\"fake_round_id\"],\n",
    "    text_features=[\"text\",\"white_card_text\"])\n",
    "    \n",
    "    model = CatBoostClassifier(**default_parameters)\n",
    "    model.fit(train, eval_set=test, plot=False,verbose=False)\n",
    "\n",
    "    preds_pw = model.predict(test,prediction_type=\"Probability\")[:,1]\n",
    "    df_test[\"pred\"] = preds_pw\n",
    "    df_evaluated = pd.concat([df_evaluated,df_test[[\"fake_round_id\",\"won\",\"pred\",\"white_card_text\"]]])\n",
    "    i +=1\n",
    "\n",
    "df_evaluated = df_evaluated.infer_objects()\n",
    "\n",
    "df_eval2 = df_evaluated.drop_duplicates(subset=[\"fake_round_id\",\"white_card_text\",\"won\"]).copy()\n",
    "print(df_eval2.shape)\n",
    "eval_preds(df_eval2,df_eval2[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95    280611\n",
      "           1       1.00      0.15      0.26     31179\n",
      "\n",
      "    accuracy                           0.91    311790\n",
      "   macro avg       0.96      0.57      0.61    311790\n",
      "weighted avg       0.92      0.91      0.88    311790\n",
      "\n",
      "top 1 acc by round: 0.14612399371371756\n",
      "top 2 acc by round: 0.26806504377946694\n",
      "top 3 acc by round: 0.37688829019532377\n"
     ]
    }
   ],
   "source": [
    "eval_preds(df_eval2,df_eval2[\"pred\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    280611\n",
      "           1       1.00      1.00      1.00     31179\n",
      "\n",
      "    accuracy                           1.00    311790\n",
      "   macro avg       1.00      1.00      1.00    311790\n",
      "weighted avg       1.00      1.00      1.00    311790\n",
      "\n",
      "top 1 acc by round: 0.10250489111260784\n",
      "top 2 acc by round: 0.20080823631290293\n",
      "top 3 acc by round: 0.29946438307835405\n"
     ]
    }
   ],
   "source": [
    "eval_preds(df_eval2,[0.1]*df_eval2.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: less than 75% gpu memory available for training. Free: 4321.125 Total: 6143.5625\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2f0eb81e7624dea85a790e558209b42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2890ca1fa90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### classifier \n",
    "model = CatBoostClassifier(**default_parameters)\n",
    "model.fit(train, eval_set=test, plot=True,verbose=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learn': {'Logloss': 0.3149526598676957}, 'validation': {'Logloss': 0.3260277557373047}}\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95       540\n",
      "           1       1.00      0.07      0.12        60\n",
      "\n",
      "    accuracy                           0.91       600\n",
      "   macro avg       0.95      0.53      0.54       600\n",
      "weighted avg       0.92      0.91      0.87       600\n",
      "\n",
      "top 1 acc by round: 0.06666666666666667\n",
      "top 2 acc by round: 0.18333333333333332\n",
      "top 3 acc by round: 0.35\n"
     ]
    }
   ],
   "source": [
    "print(model.get_best_score())\n",
    "print()\n",
    "preds_pw = model.predict(test,prediction_type=\"Probability\")[:,1]\n",
    "# print(\"rocAUC:\",roc_auc_score(y_test1,preds_pw))\n",
    "eval_preds(df_test,preds_pw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # pd.DataFrame({\"white_card_text\":X_train1[\"white_card_text\"],y_train1])\n",
    "# white_prior_train1 = pd.concat([X_train1[\"white_card_text\"],pd.Series(y_train1)],axis=1).groupby([\"white_card_text\"]).mean().fillna(0.1)\n",
    "# # white_prior_train = train.groupby([\"white_card_text\"])[\"\"]\n",
    "\n",
    "# # df_test.merge(white_prior_train1,on=\"white_card_text\")[0]\n",
    "# eval_preds(df_test,df_test.merge(white_prior_train1,on=\"white_card_text\",how=\"left\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       540\n",
      "           1       1.00      1.00      1.00        60\n",
      "\n",
      "    accuracy                           1.00       600\n",
      "   macro avg       1.00      1.00      1.00       600\n",
      "weighted avg       1.00      1.00      1.00       600\n",
      "\n",
      "top 1 acc by round: 0.08333333333333333\n",
      "top 2 acc by round: 0.21666666666666667\n",
      "top 3 acc by round: 0.35\n"
     ]
    }
   ],
   "source": [
    "eval_preds(df_test,[0.5]*len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### ranking models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i-9_fHN7xgYd"
   },
   "outputs": [],
   "source": [
    "def fit_model(loss_function, additional_params=None, train_pool=train, test_pool=test, **kwargs):\n",
    "    parameters = deepcopy(default_parameters)\n",
    "    parameters['loss_function'] = loss_function\n",
    "    parameters['train_dir'] = loss_function\n",
    "    \n",
    "    if additional_params is not None:\n",
    "        parameters.update(additional_params)\n",
    "        \n",
    "#     model = CatBoostRanker(**parameters, **kwargs)\n",
    "    model = CatBoostClassifier(**parameters, **kwargs)\n",
    "    model.fit(train_pool, eval_set=test_pool, plot=True) # plot broken on colab\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OpWNWuvtMJG0",
    "outputId": "bcf42bb1-8489-4c3c-9fb4-83ab4817083e"
   },
   "outputs": [],
   "source": [
    "# # model_pw = fit_model(\"PairLogitPairwise\")\n",
    "# model_pw = fit_model(\"YetiRank\")\n",
    "# preds_pw = model_pw.predict(test)\n",
    "# print(\"rocAUC:\",roc_auc_score(y_test,preds_pw))\n",
    "# eval_preds(df_test,preds_pw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pw = fit_model(\"PairLogitPairwise\") ## pairwise is much slower on cpu\n",
    "preds_pw = model_pw.predict(test)\n",
    "print(\"rocAUC:\",roc_auc_score(y_test1,preds_pw))\n",
    "eval_preds(df_test,preds_pw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "len(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(preds_pw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IpSts6dRMJDZ"
   },
   "outputs": [],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### compare perf to baseline (on masked data)\n",
    "\n",
    "# mask = df[\"text\"].isin(df_embed.index) ## filter groups, labels + figure out later why missing ?? ## all tet are in it\n",
    "# print(mask.sum())\n",
    "\n",
    "# df[\"pair_count\"] = df.groupby(\"text\")[\"won\"].transform(\"count\")\n",
    "\n",
    "# for i in range(1,5):\n",
    "#   df_temp = df.loc[df[\"pair_count\"] >=i].copy()\n",
    "#   print(f\"{i} min occ, {df_temp.shape[0]} rows\")\n",
    "#   df_white_prior = df_temp.groupby([\"white_card_text\"], as_index=False)[\"won\"].mean().rename(columns={\"won\":\"white_prior\"}).set_index(\"white_card_text\")\n",
    "#   df_test = df_test.join(df_white_prior,on=\"white_card_text\",how=\"left\")\n",
    "#   prior = df_test[\"white_prior\"].mean()\n",
    "#   df_test[\"white_prior\"] = df_test[\"white_prior\"].fillna(prior)\n",
    "#   print(\"White Prior Acc: %.3f\" %df_test.sort_values(\"white_prior\",ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "  \n",
    "# ## prior for a black-white combination - mean (freq% won), or sum (times won?) , or threshholded max? (over 2 times?)\n",
    "#   df_pair_prior = df_temp.groupby([\"white_card_text\",\"black_card_text\"], as_index=False)[\"won\"].sum().rename(columns={\"won\":\"pair_prior\"}).set_index([\"white_card_text\",\"black_card_text\"])\n",
    "  \n",
    "#   df_test = df_test.join(df_pair_prior,on=[\"white_card_text\",\"black_card_text\"],how=\"left\")\n",
    "#   prior = df_test[\"pair_prior\"].mean()\n",
    "#   df_test[\"pair_prior\"] = df_test[\"pair_prior\"].fillna(prior)\n",
    "#   print(\"Pair Prior (Only)  Acc: %.3f\" %df_test.sort_values(\"pair_prior\",ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "#   print(\"White then Pair Prior Acc: %.3f\" %df_test.sort_values([\"white_prior\",\"pair_prior\"],ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "#   print(\"Pair Prior then White Acc: %.3f\" %df_test.sort_values([\"pair_prior\",\"white_prior\",],ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean()) ## 17.6% (with min 6) , 17.6% with min 3\n",
    "  \n",
    "#   df_test.drop(columns=[\"white_prior\",\"pair_prior\"],errors=\"ignore\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "g8hYUc07MI7b"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "guiVLzM4JPDw"
   },
   "outputs": [],
   "source": [
    "# # ## https://stackoverflow.com/questions/64342621/how-to-apply-a-sentiment-classifier-to-a-dataframe\n",
    "# # ## got stuck:\n",
    "\n",
    "# try: classifier = pipeline('text-classification', model=model, tokenizer=tokenizer , device=0)# , device=0. # for gpu\n",
    "# except:\n",
    "#     classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)  # CPU\n",
    "\n",
    "\n",
    "# # classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3YN97cadUAWY"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# from transformers.pipelines.base import KeyDataset\n",
    "# import datasets\n",
    "# import tqdm                                                                         \n",
    "# from datasets import load_dataset\n",
    "# https://huggingface.co/docs/datasets/quicktour.html\n",
    "\n",
    "# dataset = load_dataset(\"pandas\",df_test[[\"text\"]]) ### very slow? maybe missing something? \n",
    "\n",
    "# dataset = datasets.load_dataset(\"imdb\", name=\"plain_text\", split=\"unsupervised\")\n",
    "# pipe = pipeline(\"text-classification\", device=0)\n",
    "\n",
    "# pipe = classifier.copy()\n",
    "# for out in pipe(KeyDataset(dataset, \"text\"), batch_size=8, truncation=\"only_first\"):\n",
    "#     print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tnoLZPEXXh4Q"
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# data_tok = tokenizer(list(df_test[\"text\"].values), return_tensors='tf', truncation=True, padding=\"longest\", max_length=384)\n",
    "# print(len(data_tok))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EAA8MNcNPi6q"
   },
   "outputs": [],
   "source": [
    "print(df_test.shape[0])\n",
    "df_test.shape[0]//600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WydpMMlYPd3w"
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # # #### 81 samples: 9.2 Seconds with cpu, 1.3 with gpu\n",
    "# #### CPU:   1 samples: 167 ms (0.1 sec)\n",
    "# df_test.head(df_test.shape[0]//600).assign(sentiment = lambda x: x['text'].apply(lambda s: classifier(s)))\\\n",
    "# .assign(\n",
    "#          label = lambda x: x['sentiment'].apply(lambda s: (s[0]['label'])),\n",
    "#          score = lambda x: x['sentiment'].apply(lambda s: (s[0]['score']))\n",
    "#         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0HK0sRuhLtpD"
   },
   "source": [
    "* The \"score\" is for that class/label! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0KyslYZ3IhhY"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "### SLOW - long time\n",
    "### on cpu: 15 min for 7.4K rows\n",
    "### GPU: 10 min for 71K ; 1.1 min for 8.6K\n",
    "### less effecient this way, done sequentially\n",
    "# https://stackoverflow.com/questions/64342621/how-to-apply-a-sentiment-classifier-to-a-dataframe\n",
    "df_test = (\n",
    "    df_test#.head(10)\n",
    "    .assign(sentiment = lambda x: x['text'].apply(lambda s: classifier(s)))\n",
    "    .assign(\n",
    "         label = lambda x: x['sentiment'].apply(lambda s: (s[0]['label'])),\n",
    "         score = lambda x: x['sentiment'].apply(lambda s: (s[0]['score']))\n",
    "    )\n",
    ")\n",
    "\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T7byA6N2Mbnu"
   },
   "outputs": [],
   "source": [
    "### note that score is for a given label, so we want by label, then by score\n",
    "print(\"label,score Acc:\",df_test.sort_values([\"label\",\"score\",\"fake_round_id\"],ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean())\n",
    "# print(\"label,rev score Acc:\",df_test.sort_values([\"label\",\"score\"],ascending=[False,True]).groupby(\"fake_round_id\").head(1)[\"won\"].mean())\n",
    "\n",
    "# print(\"label Acc:\",df_test.sort_values(\"label\",ascending=False).groupby(\"fake_round_id\").head(1)[\"won\"].mean())\n",
    "# print(\"reverse label Acc:\",df_test.sort_values(\"label\",ascending=True).groupby(\"fake_round_id\").head(1)[\"won\"].mean())"
   ]
  }
 ],
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