SetFit.ipynb

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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m38.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m51.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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      "source": [
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      "execution_count": null,
      "outputs": []
    },
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      "source": [
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        "!unzip data_v0.1.zip"
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "# Read train/test files\n",
        "test_df = pd.read_csv('/content/data/hoc/test.tsv', sep='\\t')\n",
        "train_df = pd.read_csv('/content/data/hoc/train.tsv', sep='\\t')"
      ],
      "metadata": {
        "id": "rJ23hJjWosJ0"
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      "execution_count": null,
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    },
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      "cell_type": "code",
      "source": [
        "test_df.head(32)"
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      "metadata": {
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          "height": 1000
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        "id": "5Dg8c_wzo-1Z",
        "outputId": "e2571975-75ad-4267-fd1d-fbf2bd772b5e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           index                                           sentence  \\\n",
              "0    11872299_s0  PURPOSE We recently reported that overexpressi...   \n",
              "1    11872299_s1  Because cyclin D1 is a downstream sensor of EG...   \n",
              "2    11872299_s2  We further investigated the influence of radia...   \n",
              "3    11872299_s3  METHODS AND MATERIALS Cyclin D1 expression was...   \n",
              "4    11872299_s4  These tumors greatly differed in their radiore...   \n",
              "5    11872299_s5  The expression of cyclin D1 and p27 proteins w...   \n",
              "6    11872299_s6  Cell proliferative activity in tumors was dete...   \n",
              "7    11872299_s7  The effect of irradiation on the expression of...   \n",
              "8    11872299_s8  RESULTS Cyclin D1 expression varied among tumo...   \n",
              "9    11872299_s9  The level of cyclin D1 expression paralleled t...   \n",
              "10  11872299_s10  A 15-Gy dose reduced constitutive expression o...   \n",
              "11  11872299_s11  In contrast , 15 Gy increased the expression o...   \n",
              "12  11872299_s12  Radiation induced no significant apoptosis or ...   \n",
              "13  11872299_s13  CONCLUSION Our findings show a positive correl...   \n",
              "14  11872299_s14  The expression of cyclin D1 and p27 was modifi...   \n",
              "15  11872299_s15  These findings may have important clinical imp...   \n",
              "16   11927016_s0  We have used a combination of vitamin A ( all-...   \n",
              "17   11927016_s1  This chemoradiotherapy is called \" FAR therapy. \"   \n",
              "18   11927016_s2  In this study we examined the effects of all-t...   \n",
              "19   11927016_s3  ATRA at 1 mM ( the order of concentration foun...   \n",
              "20   11927016_s4  This was associated with a decrease in cyclin ...   \n",
              "21   11927016_s5  With YCU-N861 cells , ATRA also caused a decre...   \n",
              "22   11927016_s6  Both ATRA and 5-FU activated c-Jun N-terminal ...   \n",
              "23   11927016_s7  The YCU-H891 cells , in which the epidermal gr...   \n",
              "24   11927016_s8  A dominant negative Stat3 construct strongly e...   \n",
              "25   11927016_s9  In addition there is evidence that activation ...   \n",
              "26  11927016_s10  Therefore , the addition to FAR therapy of age...   \n",
              "27   12011225_s0  BACKGROUND Insulin-like growth factor I ( IGF-...   \n",
              "28   12011225_s1  The major binding protein for IGF-I , insulin-...   \n",
              "29   12011225_s2  In a prospective study of men in Shanghai , Ch...   \n",
              "30   12011225_s3  METHODS From 1986 to 1989 , serum was collecte...   \n",
              "31   12011225_s4  We analyzed IGF-I and IGFBP-3 levels in serum ...   \n",
              "\n",
              "                                               labels  \n",
              "0                                                 NaN  \n",
              "1                                                 NaN  \n",
              "2                                                 NaN  \n",
              "3                                                 NaN  \n",
              "4                                                 NaN  \n",
              "5                                                 NaN  \n",
              "6                                                 NaN  \n",
              "7                                                 NaN  \n",
              "8                  sustaining proliferative signaling  \n",
              "9                  sustaining proliferative signaling  \n",
              "10                 sustaining proliferative signaling  \n",
              "11                         evading growth suppressors  \n",
              "12  resisting cell death,sustaining proliferative ...  \n",
              "13                 sustaining proliferative signaling  \n",
              "14  evading growth suppressors,sustaining prolifer...  \n",
              "15                                                NaN  \n",
              "16                                                NaN  \n",
              "17                                                NaN  \n",
              "18                                                NaN  \n",
              "19                 sustaining proliferative signaling  \n",
              "20  evading growth suppressors,sustaining prolifer...  \n",
              "21                               resisting cell death  \n",
              "22                               resisting cell death  \n",
              "23                                                NaN  \n",
              "24                                                NaN  \n",
              "25                                                NaN  \n",
              "26                                                NaN  \n",
              "27                                                NaN  \n",
              "28                                                NaN  \n",
              "29                                                NaN  \n",
              "30                                                NaN  \n",
              "31                                                NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-caa10f68-24cc-4886-b3ca-b4295045373e\" class=\"colab-df-container\">\n",
              "    <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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>sentence</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11872299_s0</td>\n",
              "      <td>PURPOSE We recently reported that overexpressi...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11872299_s1</td>\n",
              "      <td>Because cyclin D1 is a downstream sensor of EG...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11872299_s2</td>\n",
              "      <td>We further investigated the influence of radia...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11872299_s3</td>\n",
              "      <td>METHODS AND MATERIALS Cyclin D1 expression was...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11872299_s4</td>\n",
              "      <td>These tumors greatly differed in their radiore...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>11872299_s5</td>\n",
              "      <td>The expression of cyclin D1 and p27 proteins w...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>11872299_s6</td>\n",
              "      <td>Cell proliferative activity in tumors was dete...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11872299_s7</td>\n",
              "      <td>The effect of irradiation on the expression of...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11872299_s8</td>\n",
              "      <td>RESULTS Cyclin D1 expression varied among tumo...</td>\n",
              "      <td>sustaining proliferative signaling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>11872299_s9</td>\n",
              "      <td>The level of cyclin D1 expression paralleled t...</td>\n",
              "      <td>sustaining proliferative signaling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11872299_s10</td>\n",
              "      <td>A 15-Gy dose reduced constitutive expression o...</td>\n",
              "      <td>sustaining proliferative signaling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11872299_s11</td>\n",
              "      <td>In contrast , 15 Gy increased the expression o...</td>\n",
              "      <td>evading growth suppressors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11872299_s12</td>\n",
              "      <td>Radiation induced no significant apoptosis or ...</td>\n",
              "      <td>resisting cell death,sustaining proliferative ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>11872299_s13</td>\n",
              "      <td>CONCLUSION Our findings show a positive correl...</td>\n",
              "      <td>sustaining proliferative signaling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>11872299_s14</td>\n",
              "      <td>The expression of cyclin D1 and p27 was modifi...</td>\n",
              "      <td>evading growth suppressors,sustaining prolifer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>11872299_s15</td>\n",
              "      <td>These findings may have important clinical imp...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>11927016_s0</td>\n",
              "      <td>We have used a combination of vitamin A ( all-...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>11927016_s1</td>\n",
              "      <td>This chemoradiotherapy is called \" FAR therapy. \"</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>11927016_s2</td>\n",
              "      <td>In this study we examined the effects of all-t...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>11927016_s3</td>\n",
              "      <td>ATRA at 1 mM ( the order of concentration foun...</td>\n",
              "      <td>sustaining proliferative signaling</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>11927016_s4</td>\n",
              "      <td>This was associated with a decrease in cyclin ...</td>\n",
              "      <td>evading growth suppressors,sustaining prolifer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>11927016_s5</td>\n",
              "      <td>With YCU-N861 cells , ATRA also caused a decre...</td>\n",
              "      <td>resisting cell death</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>11927016_s6</td>\n",
              "      <td>Both ATRA and 5-FU activated c-Jun N-terminal ...</td>\n",
              "      <td>resisting cell death</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>11927016_s7</td>\n",
              "      <td>The YCU-H891 cells , in which the epidermal gr...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>11927016_s8</td>\n",
              "      <td>A dominant negative Stat3 construct strongly e...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>11927016_s9</td>\n",
              "      <td>In addition there is evidence that activation ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>11927016_s10</td>\n",
              "      <td>Therefore , the addition to FAR therapy of age...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>12011225_s0</td>\n",
              "      <td>BACKGROUND Insulin-like growth factor I ( IGF-...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>12011225_s1</td>\n",
              "      <td>The major binding protein for IGF-I , insulin-...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>12011225_s2</td>\n",
              "      <td>In a prospective study of men in Shanghai , Ch...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>12011225_s3</td>\n",
              "      <td>METHODS From 1986 to 1989 , serum was collecte...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>12011225_s4</td>\n",
              "      <td>We analyzed IGF-I and IGFBP-3 levels in serum ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LABELS = ['activating invasion and metastasis', 'avoiding immune destruction',\n",
        "          'cellular energetics', 'enabling replicative immortality', 'evading growth suppressors',\n",
        "          'genomic instability and mutation', 'inducing angiogenesis', 'resisting cell death',\n",
        "          'sustaining proliferative signaling', 'tumor promoting inflammation']"
      ],
      "metadata": {
        "id": "74hS6bGOngPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert labels to hotvec multilabel format (similar to scikit-learn)\n",
        "def hotvec_multilabel(true_df):\n",
        "    data = {}\n",
        "\n",
        "    for i in range(len(true_df)):\n",
        "        true_row = true_df.iloc[i]\n",
        "\n",
        "        key = true_row['index']\n",
        "\n",
        "        data[key] = set()\n",
        "\n",
        "        if not pd.isna(true_row['labels']):\n",
        "            for l in true_row['labels'].split(','):\n",
        "                data[key].add(LABELS.index(l))\n",
        "\n",
        "    y_hotvec = []\n",
        "    for k, (true) in data.items():\n",
        "        t = [0] * len(LABELS)\n",
        "        for i in true:\n",
        "            t[i] = 1\n",
        "\n",
        "        y_hotvec.append(t)\n",
        "\n",
        "    y_hotvec = np.array(y_hotvec)\n",
        "\n",
        "    return y_hotvec"
      ],
      "metadata": {
        "id": "Y4XIktY2ngKR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "import evaluate\n",
        "from setfit import SetFitModel, SetFitTrainer"
      ],
      "metadata": {
        "id": "DLoCBsCwngEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = SetFitModel.from_pretrained(\n",
        "    \"sentence-transformers/paraphrase-mpnet-base-v2\",\n",
        "    multi_target_strategy=\"multi-output\",     # one-vs-rest; multi-output; classifier-chain\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470,
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      },
      "execution_count": null,
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        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multilabel_f1_metric = evaluate.load(\"f1\", \"multilabel\")\n",
        "multilabel_accuracy_metric = evaluate.load(\"accuracy\", \"multilabel\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "Uvu5UEKSnf5h",
        "outputId": "b446a450-a173-4ed5-af64-d6c6fff04e3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c28eef3aafa4cc08fff3c1081d75083"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "211e4f4a55874d15b1c906f5fad41d24"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1/accuracy sentence level\n",
        "def compute_metrics(y_pred, y_test):\n",
        "    return {\n",
        "        \"f1\": multilabel_f1_metric.compute(predictions=y_pred, references=y_test, average=\"micro\")[\"f1\"],\n",
        "        \"accuracy\": multilabel_accuracy_metric.compute(predictions=y_pred, references=y_test)[\"accuracy\"],\n",
        "    }"
      ],
      "metadata": {
        "id": "F7SAz38Bnfze"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_dataset = Dataset.from_dict({\"text\": test_df['sentence'], \"label\": hotvec_multilabel(test_df)})\n",
        "train_dataset = Dataset.from_dict({\"text\": train_df['sentence'], \"label\": hotvec_multilabel(train_df)})"
      ],
      "metadata": {
        "id": "XRyHGGJFnft2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCCzPxdrrmA-",
        "outputId": "9b4101ff-acef-4e0f-8bb6-33f71456d495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['text', 'label'],\n",
            "    num_rows: 10527\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SetFitTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    metric=compute_metrics,\n",
        "    num_iterations=5,\n",
        ")"
      ],
      "metadata": {
        "id": "ysuQjoNhn5tg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()\n",
        "metrics = trainer.evaluate()\n",
        "print(metrics)"
      ],
      "metadata": {
        "id": "qCWyd8-Yn5iD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def divide(x, y):\n",
        "    return np.true_divide(x, y, out=np.zeros_like(x, dtype=np.float64), where=y != 0)"
      ],
      "metadata": {
        "id": "c8eh8D3ToIsA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_p_r_f_arrary(test_predict_label, test_true_label):\n",
        "    num, cat = test_predict_label.shape\n",
        "    acc_list = []\n",
        "    prc_list = []\n",
        "    rec_list = []\n",
        "    f_score_list = []\n",
        "    for i in range(num):\n",
        "        label_pred_set = set()\n",
        "        label_gold_set = set()\n",
        "\n",
        "        for j in range(cat):\n",
        "            if test_predict_label[i, j] == 1:\n",
        "                label_pred_set.add(j)\n",
        "            if test_true_label[i, j] == 1:\n",
        "                label_gold_set.add(j)\n",
        "\n",
        "        uni_set = label_gold_set.union(label_pred_set)\n",
        "        intersec_set = label_gold_set.intersection(label_pred_set)\n",
        "\n",
        "        tt = len(intersec_set)\n",
        "        if len(label_pred_set) == 0:\n",
        "            prc = 0\n",
        "        else:\n",
        "            prc = tt / len(label_pred_set)\n",
        "\n",
        "        acc = tt / len(uni_set)\n",
        "\n",
        "        rec = tt / len(label_gold_set)\n",
        "\n",
        "        if prc == 0 and rec == 0:\n",
        "            f_score = 0\n",
        "        else:\n",
        "            f_score = 2 * prc * rec / (prc + rec)\n",
        "\n",
        "        acc_list.append(acc)\n",
        "        prc_list.append(prc)\n",
        "        rec_list.append(rec)\n",
        "        f_score_list.append(f_score)\n",
        "\n",
        "    mean_prc = np.mean(prc_list)\n",
        "    mean_rec = np.mean(rec_list)\n",
        "    f_score = divide(2 * mean_prc * mean_rec, (mean_prc + mean_rec))\n",
        "    return mean_prc, mean_rec, f_score"
      ],
      "metadata": {
        "id": "N6kY4fE2oIls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_hoc(true_df, pred_df):\n",
        "    data = {}\n",
        "\n",
        "    assert len(true_df) == len(pred_df), \\\n",
        "        f'Gold line no {len(true_df)} vs Prediction line no {len(pred_df)}'\n",
        "\n",
        "    for i in range(len(true_df)):\n",
        "        true_row = true_df.iloc[i]\n",
        "        pred_row = pred_df.iloc[i]\n",
        "        assert true_row['index'] == pred_row['index'], \\\n",
        "            'Index does not match @{}: {} vs {}'.format(i, true_row['index'], pred_row['index'])\n",
        "\n",
        "        key = true_row['index'][:true_row['index'].find('_')]\n",
        "        if key not in data:\n",
        "            data[key] = (set(), set())\n",
        "\n",
        "        if not pd.isna(true_row['labels']):\n",
        "            for l in true_row['labels'].split(','):\n",
        "                data[key][0].add(LABELS.index(l))\n",
        "\n",
        "        if not pd.isna(pred_row['labels']):\n",
        "            for l in pred_row['labels'].split(','):\n",
        "                data[key][1].add(LABELS.index(l))\n",
        "\n",
        "    assert len(data) == 315, 'There are 315 documents in the test set: %d' % len(data)\n",
        "\n",
        "    y_test = []\n",
        "    y_pred = []\n",
        "    for k, (true, pred) in data.items():\n",
        "        t = [0] * len(LABELS)\n",
        "        for i in true:\n",
        "            t[i] = 1\n",
        "\n",
        "        p = [0] * len(LABELS)\n",
        "        for i in pred:\n",
        "            p[i] = 1\n",
        "\n",
        "        y_test.append(t)\n",
        "        y_pred.append(p)\n",
        "\n",
        "    y_test = np.array(y_test)\n",
        "    y_pred = np.array(y_pred)\n",
        "\n",
        "    r, p, f1 = get_p_r_f_arrary(y_pred, y_test)\n",
        "    print('Precision: {:.1f}'.format(p*100))\n",
        "    print('Recall   : {:.1f}'.format(r*100))\n",
        "    print('F1       : {:.1f}'.format(f1*100))"
      ],
      "metadata": {
        "id": "4tvd2PscoIfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_predict_label = trainer.model.predict(test_df['sentence'])"
      ],
      "metadata": {
        "id": "KL55pVQXndNo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert hotvec multilabel to actual labels\n",
        "num, cat = test_predict_label.shape\n",
        "sentence_list = []\n",
        "for i in range(num):\n",
        "    sentence_set = set()\n",
        "    for j in range(cat):\n",
        "        if test_predict_label[i, j] == 1:\n",
        "            sentence_set.add(LABELS[j])\n",
        "    sentence_list.append(','.join(sentence_set))\n",
        "\n",
        "# Reformat for HoC evaluation\n",
        "pred_df = test_df\n",
        "pred_df = pred_df.assign(labels = sentence_list)\n",
        "pred_df['labels'] = pred_df['labels'].replace({'':np.nan})\n",
        "test_df['labels'] = test_df['labels'].replace({'':np.nan})"
      ],
      "metadata": {
        "id": "-G_jOmDkoZJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_hoc(test_df, pred_df)"
      ],
      "metadata": {
        "id": "B_5-DznXonaC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}