Nucleotide_transformer__Antibodyipynb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dH_2oBYI5lU5"
      },
      "source": [
        "Goals\n",
        "- Classify antibody sequences as human or non-human "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_cFuaDP3XXB",
        "outputId": "0b2f7ace-0cea-4947-ab8c-671424dc1d9e"
      },
      "outputs": [],
      "source": [
        "!pip install -q biopython transformers datasets huggingface_hub accelerate seaborn ipywidgets scipy\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
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            "3e5ee12f8b3942669d39922b43a80d60",
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          ]
        },
        "id": "GFRCqpPa3uTq",
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      "outputs": [
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              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "weG53zpw4cok"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, TrainingArguments, Trainer, AutoModelForSequenceClassification\n",
        "import torch\n",
        "from sklearn.metrics import matthews_corrcoef, f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from datasets import Dataset\n",
        "from transformers import AutoTokenizer\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import torch\n",
        "from torch.nn.functional import softmax\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zE9-d11z7KjF"
      },
      "source": [
        "### Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8imMGV24_xW5"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/Users/lib_laptop/Downloads/nmep_antibody_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "BjYh01gFAmUh"
      },
      "outputs": [],
      "source": [
        "#List out unique values\n",
        "pd.unique(df['species'])\n",
        "\n",
        "#Map the values\n",
        "di = {'human': 1, 'mouse': 0, 'rat':0,'rhesus':0, 'rabbit':0 }\n",
        "df['species'] = df['species'].replace(di)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {},
      "outputs": [],
      "source": [
        "class_0_df = df[df['species'] == 0]\n",
        "class_1_df = df[df['species'] == 1]\n",
        "\n",
        "num_class_0 = len(class_0_df)\n",
        "\n",
        "class_1_sample = class_1_df.sample(num_class_0)\n",
        "\n",
        "balanced_df = pd.concat([class_0_df, class_1_sample])\n",
        "\n",
        "balanced_df = balanced_df.sample(frac=1).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-pKU9lT7Qew"
      },
      "source": [
        "### Dataset loading & Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "2Gbp4CWv7IyE"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "dataset = Dataset.from_pandas(balanced_df)\n",
        "sequences = dataset['sequence']\n",
        "labels = dataset['species']\n",
        "\n",
        "train_sequences, test_sequences, train_labels, test_labels = train_test_split(sequences, labels, test_size=0.1, random_state=42)\n",
        "train_sequences, val_sequences, train_labels, val_labels = train_test_split(train_sequences, train_labels, test_size=0.05, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PgdDZ-n_4N2"
      },
      "source": [
        "### Tokenising the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {},
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset\n",
        "import torch\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, sequences, labels, tokenizer, max_length=128):\n",
        "        self.sequences = sequences\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.sequences)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        sequence = self.sequences[idx]\n",
        "        label = self.labels[idx]\n",
        "        \n",
        "        tokenized = self.tokenizer(sequence, padding=\"max_length\", truncation=True, max_length=self.max_length)\n",
        "        input_ids = torch.tensor(tokenized['input_ids'], dtype=torch.long)\n",
        "        attention_mask = torch.tensor(tokenized['attention_mask'], dtype=torch.long)\n",
        "        label = torch.tensor(label, dtype=torch.long)\n",
        "\n",
        "        return input_ids, attention_mask, label\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {},
      "outputs": [],
      "source": [
        "tokeniser = AutoTokenizer.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")\n",
        "\n",
        "train_dataset = CustomDataset(train_sequences, train_labels, tokeniser)\n",
        "val_dataset = CustomDataset(val_sequences, val_labels, tokeniser)\n",
        "test_dataset = CustomDataset(test_sequences, test_labels, tokeniser)\n",
        "\n",
        "batch_size = 32  \n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {},
      "outputs": [
        {
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>test_acc</td><td>▁</td></tr><tr><td>test_f1</td><td>▁</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>train_acc</td><td>▁▆▇▇▇▇████</td></tr><tr><td>train_loss</td><td>█▃▂▂▂▂▁▁▁▁</td></tr><tr><td>val_acc</td><td>▂█▅▁▅▅██▁▆</td></tr><tr><td>val_f1</td><td>▁▅▃█▂▂▄▄▃▂</td></tr><tr><td>val_loss</td><td>█▃▂▄▃▃▁▂▃▃</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>test_acc</td><td>0.91704</td></tr><tr><td>test_f1</td><td>0.89314</td></tr><tr><td>test_loss</td><td>0.22296</td></tr><tr><td>train_acc</td><td>0.9148</td></tr><tr><td>train_loss</td><td>0.23127</td></tr><tr><td>val_acc</td><td>0.91637</td></tr><tr><td>val_f1</td><td>0.89229</td></tr><tr><td>val_loss</td><td>0.22373</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">visionary-frog-1</strong> at: <a href='https://wandb.ai/sarrahrose/transformer-attempt-2/runs/zpc83mfb' target=\"_blank\">https://wandb.ai/sarrahrose/transformer-attempt-2/runs/zpc83mfb</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "wandb version 0.16.1 is available!  To upgrade, please run:\n",
              " $ pip install wandb --upgrade"
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              "Tracking run with wandb version 0.16.0"
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              "Run data is saved locally in <code>/Users/lib_laptop/Downloads/wandb/run-20231206_012659-u0wbc85o</code>"
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              "Syncing run <strong><a href='https://wandb.ai/sarrahrose/transformer-attempt-final/runs/u0wbc85o' target=\"_blank\">true-serenity-1</a></strong> to <a href='https://wandb.ai/sarrahrose/transformer-attempt-final' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View run at <a href='https://wandb.ai/sarrahrose/transformer-attempt-final/runs/u0wbc85o' target=\"_blank\">https://wandb.ai/sarrahrose/transformer-attempt-final/runs/u0wbc85o</a>"
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          "execution_count": 116,
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      ],
      "source": [
        "import wandb\n",
        "wandb.init(project=\"transformer-attempt-final\", entity=\"sarrahrose\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "class GoodModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(GoodModel, self).__init__()\n",
        "        self.embedding = nn.Embedding(input_dim, hidden_dim)\n",
        "        self.fc1 = nn.Linear(hidden_dim, hidden_dim)  \n",
        "        self.batch_norm = nn.BatchNorm1d(hidden_dim)  \n",
        "        self.relu = nn.ReLU() \n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim) \n",
        "    def forward(self, x, attention_mask):\n",
        "        embedded = self.embedding(x) \n",
        "        masked_embedded = embedded * attention_mask.unsqueeze(2)  \n",
        "        summed_embedded = torch.sum(masked_embedded, dim=1)  \n",
        "        hidden = self.fc1(summed_embedded)  \n",
        "        hidden = self.batch_norm(hidden) \n",
        "        hidden = self.relu(hidden)  \n",
        "        output = self.fc2(hidden)  \n",
        "        return output   \n",
        "\n",
        "input_dim = len(tokeniser)  \n",
        "hidden_dim = 128\n",
        "output_dim = 2\n",
        "\n",
        "model = GoodModel(input_dim, hidden_dim, output_dim)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {},
      "outputs": [],
      "source": [
        "def train(model, iterator, optimizer, criterion):\n",
        "    model.train()\n",
        "    total_loss = 0.0\n",
        "    correct_predictions = 0\n",
        "    total_predictions = 0\n",
        "    \n",
        "    for batch in iterator:\n",
        "        input_ids, attention_mask, labels = batch\n",
        "        optimizer.zero_grad()\n",
        "        predictions = model(input_ids, attention_mask)\n",
        "        loss = criterion(predictions, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        total_loss += loss.item()\n",
        "        \n",
        "        _, predicted = torch.max(predictions, 1)\n",
        "        correct_predictions += (predicted == labels).sum().item()\n",
        "        total_predictions += labels.size(0)\n",
        "    \n",
        "    train_loss = total_loss / len(iterator)\n",
        "    train_acc = correct_predictions / total_predictions\n",
        "    \n",
        "    return train_loss, train_acc\n",
        "\n",
        "\n",
        "def evaluate(model, iterator, criterion):\n",
        "    model.eval()\n",
        "    total_loss = 0.0\n",
        "    correct_predictions = 0\n",
        "    total_predictions = 0\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for batch in iterator:\n",
        "            input_ids, attention_mask, labels = batch\n",
        "            predictions = model(input_ids, attention_mask)\n",
        "            loss = criterion(predictions, labels)\n",
        "            total_loss += loss.item()\n",
        "            \n",
        "            _, predicted = torch.max(predictions, 1)\n",
        "            correct_predictions += (predicted == labels).sum().item()\n",
        "            total_predictions += labels.size(0)\n",
        "            \n",
        "            all_preds.extend(predicted.tolist())\n",
        "            all_labels.extend(labels.tolist())\n",
        "    \n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')  \n",
        "    return total_loss / len(iterator), correct_predictions / total_predictions, f1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 01\n",
            "\tTraining Loss: 0.083 | Training Acc: 97.34% | Validation Loss: 0.056 | Validation Acc: 98.31% | Validation F1: 0.98\n",
            "Epoch: 02\n",
            "\tTraining Loss: 0.063 | Training Acc: 98.05% | Validation Loss: 0.063 | Validation Acc: 98.05% | Validation F1: 0.98\n",
            "Epoch: 03\n",
            "\tTraining Loss: 0.058 | Training Acc: 98.19% | Validation Loss: 0.052 | Validation Acc: 98.49% | Validation F1: 0.98\n",
            "Epoch: 04\n",
            "\tTraining Loss: 0.056 | Training Acc: 98.26% | Validation Loss: 0.048 | Validation Acc: 98.61% | Validation F1: 0.99\n",
            "Epoch: 05\n",
            "\tTraining Loss: 0.055 | Training Acc: 98.29% | Validation Loss: 0.047 | Validation Acc: 98.61% | Validation F1: 0.99\n",
            "Epoch: 06\n",
            "\tTraining Loss: 0.054 | Training Acc: 98.32% | Validation Loss: 0.045 | Validation Acc: 98.72% | Validation F1: 0.99\n",
            "Epoch: 07\n",
            "\tTraining Loss: 0.053 | Training Acc: 98.35% | Validation Loss: 0.044 | Validation Acc: 98.74% | Validation F1: 0.99\n",
            "Epoch: 08\n",
            "\tTraining Loss: 0.052 | Training Acc: 98.36% | Validation Loss: 0.047 | Validation Acc: 98.64% | Validation F1: 0.99\n",
            "Epoch: 09\n",
            "\tTraining Loss: 0.052 | Training Acc: 98.39% | Validation Loss: 0.058 | Validation Acc: 98.22% | Validation F1: 0.98\n",
            "Epoch: 10\n",
            "\tTraining Loss: 0.051 | Training Acc: 98.41% | Validation Loss: 0.044 | Validation Acc: 98.79% | Validation F1: 0.99\n"
          ]
        }
      ],
      "source": [
        "N_EPOCHS = 10\n",
        "for epoch in range(N_EPOCHS):\n",
        "    train_loss, train_acc = train(model, train_loader, optimizer, criterion)  \n",
        "    val_loss, val_acc, val_f1 = evaluate(model, val_loader, criterion)\n",
        "    \n",
        "    wandb.log({\"train_loss\": train_loss, \"train_acc\": train_acc, \"val_loss\": val_loss, \"val_acc\": val_acc, \"val_f1\": val_f1})\n",
        "    \n",
        "    print(f'Epoch: {epoch + 1:02}')\n",
        "    print(f'\\tTraining Loss: {train_loss:.3f} | Training Acc: {train_acc * 100:.2f}% | Validation Loss: {val_loss:.3f} | Validation Acc: {val_acc * 100:.2f}% | Validation F1: {val_f1:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.042 | Test Acc: 98.79% | Test F1: 0.99\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc, test_f1 = evaluate(model, test_loader, criterion)\n",
        "\n",
        "wandb.log({\"test_loss\": test_loss, \"test_acc\": test_acc, \"test_f1\": test_f1})\n",
        "\n",
        "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc * 100:.2f}% | Test F1: {test_f1:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {},
      "outputs": [],
      "source": [
        "torch.save(model, 'entire_model_esm.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {},
      "outputs": [],
      "source": [
        "newdf = pd.read_csv(\"/Users/lib_laptop/Downloads/inference_dataset - Sheet1.csv\")\n",
        "newdf = newdf.rename(columns={\"Species\": \"species\", \"VH\": \"sequence\"})\n",
        "\n",
        "#Map the values\n",
        "di = {'Human': 1, 'Mouse': 0, 'Chimeric':0,'Humanized':0, 'Humanized/Chimeric':0 }\n",
        "newdf['species'] = newdf['species'].replace(di)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {},
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "inf_dataset = Dataset.from_pandas(newdf)\n",
        "inf_sequences = inf_dataset['sequence']\n",
        "inf_labels = inf_dataset['species']\n",
        "\n",
        "inference_dataset = CustomDataset(inf_sequences, inf_labels, tokeniser)\n",
        "\n",
        "\n",
        "batch_size = 32 \n",
        "inference_loader = DataLoader(inference_dataset, batch_size=batch_size, shuffle = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {},
      "outputs": [],
      "source": [
        "def run_inference(model, loader):\n",
        "    model.eval()  \n",
        "    predictions = []\n",
        "    \n",
        "    with torch.no_grad():  \n",
        "        for batch in loader:\n",
        "            input_ids, attention_mask, _ = batch\n",
        "            outputs = model(input_ids, attention_mask)\n",
        "            probs = softmax(outputs, dim=1)  \n",
        "            predictions.extend(probs.cpu().numpy())  \n",
        "\n",
        "    return predictions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {},
      "outputs": [],
      "source": [
        "predicted_probs = run_inference(model, inference_loader)\n",
        "predicted_probabilities_df = pd.DataFrame(predicted_probs, columns=['Probability_Class_1', 'Probability_Class_2'])\n",
        "inference_results_df = pd.concat([newdf, predicted_probabilities_df], axis=1)\n",
        "inference_results_df.to_csv('inference_results_esm.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Spearman Correlation: -0.376\n",
            "P-Value: 0.000\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import spearmanr\n",
        "\n",
        "inference_results_df = pd.read_csv('inference_results_esm.csv')\n",
        "\n",
        "immunogenicity = inference_results_df['Immunogenicity']\n",
        "predicted_probabilities = inference_results_df['Probability_Class_2']\n",
        "\n",
        "correlation, p_value = spearmanr(predicted_probabilities, immunogenicity)\n",
        "\n",
        "print(f'Spearman Correlation: {correlation:.3f}')\n",
        "print(f'P-Value: {p_value:.3f}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "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>Antibody</th>\n",
              "      <th>Immunogenicity</th>\n",
              "      <th>species</th>\n",
              "      <th>sequence</th>\n",
              "      <th>Probability_Class_1</th>\n",
              "      <th>Probability_Class_2</th>\n",
              "      <th>species_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3F8</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>QVQLKESGPGLVAPSQSLSITCTVSGFSVTNYGVHWVRQPPGKGLE...</td>\n",
              "      <td>0.999876</td>\n",
              "      <td>0.000124</td>\n",
              "      <td>Mouse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abagovomab</td>\n",
              "      <td>68.1</td>\n",
              "      <td>0</td>\n",
              "      <td>QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLD...</td>\n",
              "      <td>0.999422</td>\n",
              "      <td>0.000578</td>\n",
              "      <td>Mouse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abciximab</td>\n",
              "      <td>35.5</td>\n",
              "      <td>0</td>\n",
              "      <td>EVQLQQSGAELVKPGASVKLSCTASGFNIKDTYVHWVKQRPEQGLE...</td>\n",
              "      <td>0.998583</td>\n",
              "      <td>0.001418</td>\n",
              "      <td>Chimeric</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abrilumab</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>QVQLVQSGAEVKKPGASVKVSCKVSGYTLSDLSIHWVRQAPGKGLE...</td>\n",
              "      <td>0.726166</td>\n",
              "      <td>0.273834</td>\n",
              "      <td>Human</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Actoxumab</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>QVQLVESGGGVVQPGRSLRLSCAASGFSFSNYGMHWVRQAPGKGLE...</td>\n",
              "      <td>0.000425</td>\n",
              "      <td>0.999575</td>\n",
              "      <td>Human</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Antibody  Immunogenicity  species  \\\n",
              "0         3F8           100.0        0   \n",
              "1  Abagovomab            68.1        0   \n",
              "2   Abciximab            35.5        0   \n",
              "3   Abrilumab             0.4        1   \n",
              "4   Actoxumab             0.0        1   \n",
              "\n",
              "                                            sequence  Probability_Class_1  \\\n",
              "0  QVQLKESGPGLVAPSQSLSITCTVSGFSVTNYGVHWVRQPPGKGLE...             0.999876   \n",
              "1  QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLD...             0.999422   \n",
              "2  EVQLQQSGAELVKPGASVKLSCTASGFNIKDTYVHWVKQRPEQGLE...             0.998583   \n",
              "3  QVQLVQSGAEVKKPGASVKVSCKVSGYTLSDLSIHWVRQAPGKGLE...             0.726166   \n",
              "4  QVQLVESGGGVVQPGRSLRLSCAASGFSFSNYGMHWVRQAPGKGLE...             0.000425   \n",
              "\n",
              "   Probability_Class_2 species_type  \n",
              "0             0.000124        Mouse  \n",
              "1             0.000578        Mouse  \n",
              "2             0.001418     Chimeric  \n",
              "3             0.273834        Human  \n",
              "4             0.999575        Human  "
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#attach original species\n",
        "spec = pd.read_csv(\"/Users/lib_laptop/Downloads/inference_dataset - Sheet1.csv\")\n",
        "spec = spec.rename(columns={\"Species\": \"species_type\", \"VH\": \"sequence\"})\n",
        "spec.head()\n",
        "inference_results_df['species_type'] = spec['species_type']\n",
        "inference_results_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.scatterplot(data=inference_results_df, x='Probability_Class_2', y='Immunogenicity', hue='species_type')\n",
        "\n",
        "plt.title('Immunogenicity vs Probability of Being Human')\n",
        "plt.xlabel('Probability')\n",
        "plt.ylabel('Immunogenicity')\n",
        "plt.tight_layout()\n",
        "plt.legend(title='Species Type', bbox_to_anchor=(1.00, 1), loc='upper left')\n",
        "plt.show()\n"
      ]
    }
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