{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56c5bf21-53d3-4403-89b3-4cd0a5b0777b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/ebay/notebooks/haorzhang/examples/pusl_github\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, concatenate_datasets\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6948f5d-bf4b-4704-8249-0bfe965bcccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_dir = \"data\"\n",
    "\n",
    "def load_data_by_name(data_name, split):\n",
    "    postfix = \"csv\"\n",
    "    assert data_name in [\"Eurlex-4.3K\", \"AmazonCat-13K\"]\n",
    "    data_path = os.path.join(data_dir, data_name)\n",
    "    num_data_files = {}\n",
    "    num_path_list = []\n",
    "\n",
    "    for file_name in os.listdir(data_path):\n",
    "        if file_name.startswith(\"num_\") and (split+\".\") in file_name:\n",
    "            file_path = os.path.join(data_path, file_name)\n",
    "            num_data_files[file_name] = file_path\n",
    "            num_path_list.append(file_path)\n",
    "    print(\"data list\", num_path_list)\n",
    "    \n",
    "    num_dataset_list = [\n",
    "        load_dataset(postfix, data_files=num_data, split=\"train\")\n",
    "        for num_data in num_path_list\n",
    "    ]\n",
    "    concat_dataset = concatenate_datasets(num_dataset_list)\n",
    "    return concat_dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f268925-b786-42bf-9192-5148fda9d75a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec8e19a0-8acd-4a8a-8d0e-ae53fad37443",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "eurlex_train = load_data_by_name(\"Eurlex-4.3K\", split=\"train\")\n",
    "amazoncat_train = load_data_by_name(\"AmazonCat-13K\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "310494b0-bdd6-40be-8ff5-30cffa4442ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# amazoncat_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "761906b8-6c1d-4d48-adcf-20589d9a0385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data list ['data/AmazonCat-13K/num_1_test.csv', 'data/AmazonCat-13K/num_15_test.csv', 'data/AmazonCat-13K/num_3_test.csv', 'data/AmazonCat-13K/num_5_test.csv', 'data/AmazonCat-13K/num_7_test.csv', 'data/AmazonCat-13K/num_8_test.csv', 'data/AmazonCat-13K/num_14_test.csv', 'data/AmazonCat-13K/num_17_test.csv', 'data/AmazonCat-13K/num_18_test.csv', 'data/AmazonCat-13K/num_16_test.csv', 'data/AmazonCat-13K/num_4_test.csv', 'data/AmazonCat-13K/num_11_test.csv', 'data/AmazonCat-13K/num_19_test.csv', 'data/AmazonCat-13K/num_6_test.csv', 'data/AmazonCat-13K/num_9_test.csv', 'data/AmazonCat-13K/num_10_test.csv', 'data/AmazonCat-13K/num_13_test.csv', 'data/AmazonCat-13K/num_2_test.csv', 'data/AmazonCat-13K/num_12_test.csv']\n"
     ]
    },
    {
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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    {
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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    {
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       "model_id": "be4d2ec7d3054d75bf80cee8d477f480",
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       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "a87c1ab290264b459eb1840b49e671c9",
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "Generating train split: 0 examples [00:00, ? examples/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a25a2da1c30496db4679c422d7c05b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83193442191a48f4bd5811863e4cfb7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2427fbeb4bc4fd48deb461459d7aaec",
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       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amazoncat_test = load_data_by_name(\"AmazonCat-13K\", split=\"test\")\n",
    "df_amazoncat_test = amazoncat_test.to_pandas()\n",
    "df_amazoncat_test_narrow = df_amazoncat_test[df_amazoncat_test[\"num_keyphrases\"]  <= 2*5]\n",
    "df_amazoncat_test_diverse = df_amazoncat_test[df_amazoncat_test[\"num_keyphrases\"] > 2*5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "49f925bf-8bb3-492e-8fed-dd7cad5649f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data list ['data/Eurlex-4.3K/num_1_test.csv', 'data/Eurlex-4.3K/num_15_test.csv', 'data/Eurlex-4.3K/num_21_test.csv', 'data/Eurlex-4.3K/num_23_test.csv', 'data/Eurlex-4.3K/num_28_test.csv', 'data/Eurlex-4.3K/num_30_test.csv', 'data/Eurlex-4.3K/num_35_test.csv', 'data/Eurlex-4.3K/num_3_test.csv', 'data/Eurlex-4.3K/num_5_test.csv', 'data/Eurlex-4.3K/num_7_test.csv', 'data/Eurlex-4.3K/num_8_test.csv', 'data/Eurlex-4.3K/num_14_test.csv', 'data/Eurlex-4.3K/num_17_test.csv', 'data/Eurlex-4.3K/num_18_test.csv', 'data/Eurlex-4.3K/num_20_test.csv', 'data/Eurlex-4.3K/num_26_test.csv', 'data/Eurlex-4.3K/num_16_test.csv', 'data/Eurlex-4.3K/num_37_test.csv', 'data/Eurlex-4.3K/num_40_test.csv', 'data/Eurlex-4.3K/num_42_test.csv', 'data/Eurlex-4.3K/num_4_test.csv', 'data/Eurlex-4.3K/num_11_test.csv', 'data/Eurlex-4.3K/num_19_test.csv', 'data/Eurlex-4.3K/num_22_test.csv', 'data/Eurlex-4.3K/num_27_test.csv', 'data/Eurlex-4.3K/num_34_test.csv', 'data/Eurlex-4.3K/num_39_test.csv', 'data/Eurlex-4.3K/num_6_test.csv', 'data/Eurlex-4.3K/num_9_test.csv', 'data/Eurlex-4.3K/num_10_test.csv', 'data/Eurlex-4.3K/num_13_test.csv', 'data/Eurlex-4.3K/num_32_test.csv', 'data/Eurlex-4.3K/num_33_test.csv', 'data/Eurlex-4.3K/num_41_test.csv', 'data/Eurlex-4.3K/num_2_test.csv', 'data/Eurlex-4.3K/num_12_test.csv', 'data/Eurlex-4.3K/num_24_test.csv', 'data/Eurlex-4.3K/num_25_test.csv', 'data/Eurlex-4.3K/num_29_test.csv', 'data/Eurlex-4.3K/num_31_test.csv', 'data/Eurlex-4.3K/num_36_test.csv', 'data/Eurlex-4.3K/num_38_test.csv']\n"
     ]
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      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
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    },
    {
     "data": {
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       "model_id": "e23b74600bf3483da0ee0739f2eb4243",
       "version_major": 2,
       "version_minor": 0
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       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
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   ],
   "source": [
    "eurlex_test = load_data_by_name(\"Eurlex-4.3K\", split=\"test\")\n",
    "df_eurlex_test = eurlex_test.to_pandas()\n",
    "df_eurlex_test_narrow = df_eurlex_test[df_eurlex_test[\"num_keyphrases\"]  <= 2*15]\n",
    "df_eurlex_test_diverse = df_eurlex_test[df_eurlex_test[\"num_keyphrases\"] > 2*15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16165803-a38b-434b-baf4-ac4b7d5a0eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def return_eval(pred2score, target2score, mean):\n",
    "    mean2 = 2 * mean\n",
    "    pred  = [p.lower() for p in pred2score]\n",
    "    target = [p.lower() for p in target2score]\n",
    "    o = len(set(target))\n",
    "\n",
    "    intersect = len(set(pred[:o]).intersection(set(target)))\n",
    "    budgetaccone = len(set(pred[:mean]).intersection(set(target)))/mean\n",
    "    budgetacctwo = len(set(pred[:mean2]).intersection(set(target)))/mean2\n",
    "    prec = intersect/len(set(pred[:o])) if len(pred) > 0 else 0.0\n",
    "    rec = intersect/len(target)\n",
    "\n",
    "    \n",
    "    kmean = len(set(pred[:mean]))\n",
    "    k2mean = len(set(pred[:mean2]))\n",
    "\n",
    "    if prec==0 and rec==0:\n",
    "        f1=0\n",
    "    else:\n",
    "        f1 = 2*prec*rec/(prec+rec)\n",
    "    \n",
    "    return {\"P@O\":100*prec, \"R@O\": 100*rec, \"F1@O\":100*f1, \"B@mean\": budgetaccone, \"B@2mean\": budgetacctwo, \"#k@mean\": kmean, \"#k@2mean\": k2mean}\n",
    "\n",
    "def final_metric_results(preds_keyphrases, labels_keyphrases, mean):\n",
    "    avg_scores = defaultdict(list)\n",
    "    for pred, target in zip(preds_keyphrases, labels_keyphrases):\n",
    "\n",
    "            all_exact_results = return_eval(pred, target, mean)\n",
    "            \n",
    "            for m_name, value in all_exact_results.items():\n",
    "                    avg_scores[m_name].append(value)\n",
    "\n",
    "            avg_scores[\"pred_kpnum\"].append(len(set(pred)))\n",
    "            avg_scores[\"gt_kpnum\"].append(len(set(target)))\n",
    "            \n",
    "    avg_scores = {m_name: round(np.mean(values),2) for m_name, values in avg_scores.items()}\n",
    "\n",
    "    return avg_scores\n",
    "    \n",
    "def generate_results(df, mean):\n",
    "    \n",
    "    labels_keyphrases = [p.lower().split(\";\") for p in df[\"target\"]]\n",
    "    preds_keyphrases = []\n",
    "    for i in range(len(df)):\n",
    "        # preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"])[:k])\n",
    "        preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"]))\n",
    "        \n",
    "    print(\"@\",mean) \n",
    "    return final_metric_results(preds_keyphrases, labels_keyphrases, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a99636a-3950-495a-bf1b-68344e994088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4313"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ac7d00a-da2c-4e23-a384-c197c99b11bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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