2. Unsupervised Sampling.ipynb

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    "# =========================================================================================\n",
    "# Libraries\n",
    "# =========================================================================================\n",
    "import gc\n",
    "import time\n",
    "import math\n",
    "import random\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# Custom libraries\n",
    "from utils.unsupervised_utils import read_data, get_neighbors, build_training_set\n",
    "from utils.utils import read_config\n",
    "from utils.metrics import get_pos_score, get_f2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06acb700",
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     "text": [
      "env: TOKENIZERS_PARALLELISM=false\n",
      "env: TRANSFORMERS_NO_ADVISORY_WARNINGS=true\n"
     ]
    }
   ],
   "source": [
    "%env TOKENIZERS_PARALLELISM=false # tokens并行\n",
    "%env TRANSFORMERS_NO_ADVISORY_WARNINGS=true # 关闭性能警告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a09466c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = read_config() # 读取配置文件\n",
    "DATA_PATH = \"../raw_data/\""
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "4e98d8ee",
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     "text": [
      "FOR MODE - test\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 171/171 [00:07<00:00, 23.05it/s]\n"
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     "text": [
      " \n",
      "--------------------------------------------------\n",
      "topics.shape: (6251, 6)\n",
      "content.shape: (154047, 4)\n",
      "correlations.shape: (61517, 2)\n"
     ]
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     "text": [
      " \n",
      "Training KNN model...\n"
     ]
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      "100%|█████████████████████████████████████| 6251/6251 [00:03<00:00, 2073.95it/s]\n"
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     "text": [
      "Max Recall @100: 0.93963\n",
      "F2-Score @100: 0.1395\n"
     ]
    },
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      "100%|█████████████████████████████████████| 6251/6251 [00:03<00:00, 1677.49it/s]\n"
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      "Our test set has 625100 rows\n",
      "FOR MODE - train\n"
     ]
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      "100%|█████████████████████████████████████████| 171/171 [00:07<00:00, 22.93it/s]\n"
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      " \n",
      "--------------------------------------------------\n",
      "topics.shape: (55266, 6)\n",
      "content.shape: (154047, 4)\n",
      "correlations.shape: (61517, 2)\n"
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      " \n",
      "Training KNN model...\n"
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      "100%|███████████████████████████████████| 55266/55266 [00:27<00:00, 2038.81it/s]\n"
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      "Max Recall @100: 0.99931\n",
      "F2-Score @100: 0.1697\n"
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      "100%|███████████████████████████████████| 55266/55266 [00:32<00:00, 1696.79it/s]\n"
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     "text": [
      "Our train set has 5526600 rows\n"
     ]
    }
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   "source": [
    "# 对于 \"test\" 和 \"train\" 两种模式进行循环操作\n",
    "for mode in [\n",
    "    \"test\",\n",
    "    \"train\"\n",
    "]:\n",
    "   \n",
    "    print(f\"FOR MODE - {mode}\")\n",
    "    \n",
    "    # 读取数据\n",
    "    topics, content, correlations, _ = read_data(data_path=DATA_PATH, config_obj=config, read_mode=mode)\n",
    "    \n",
    "    # 最近邻算法\n",
    "    topics, content = get_neighbors(topic_df=topics, content_df=content, config_obj=config)\n",
    "    \n",
    "    # # 将 target 与 topic 进行合并，并计算 max positive score\n",
    "    topics = topics.merge(correlations, how = 'inner', left_on = ['id'], right_on = ['topic_id'])\n",
    "    pos_score = get_pos_score(topics['content_ids'], topics['predictions'])\n",
    "    f2_score = get_f2_score(topics['content_ids'], topics['predictions'])\n",
    "    print(f'Max Recall @{config[\"unsupervised_model\"][\"top_n\"]}: {pos_score}')\n",
    "    print(f'F2-Score @{config[\"unsupervised_model\"][\"top_n\"]}: {f2_score}')\n",
    "    \n",
    "    # 设置 content 的索引为 id\n",
    "    content.set_index('id', inplace = True)\n",
    "    \n",
    "    # 构建监督训练的训练集，在下一步中使用\n",
    "    df = build_training_set(topic_df=topics, content_df=content)\n",
    "    print(f'Our {mode} set has {len(df)} rows')\n",
    "    \n",
    "    df.to_parquet(f'./generated_files/unsupervised_{mode}.parquet')\n",
    "    df.head()\n",
    "    \n",
    "    del df, topics, content, correlations\n",
    "    gc.collect()"
   ]
  }
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