main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vkhamesi/proteins/blob/main/main.ipynb)"
      ],
      "metadata": {
        "id": "MtXbLUvKsz9B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3suAwdhaNCfa",
        "outputId": "83bb8a4c-e2e0-46bb-c147-56b357ee7c6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading cafa-5-protein-function-prediction.zip to /content\n",
            " 98% 113M/115M [00:00<00:00, 120MB/s] \n",
            "100% 115M/115M [00:00<00:00, 125MB/s]\n",
            "Archive:  cafa-5-protein-function-prediction.zip\n",
            "  inflating: IA.txt                  \n",
            "  inflating: Test (Targets)/testsuperset-taxon-list.tsv  \n",
            "  inflating: Test (Targets)/testsuperset.fasta  \n",
            "  inflating: Train/go-basic.obo      \n",
            "  inflating: Train/train_sequences.fasta  \n",
            "  inflating: Train/train_taxonomy.tsv  \n",
            "  inflating: Train/train_terms.tsv   \n",
            "  inflating: sample_submission.tsv   \n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# Download the data\n",
        "os.environ[\"KAGGLE_USERNAME\"] = \"YOUR_KAGGLE_USERNAME\"\n",
        "os.environ[\"KAGGLE_KEY\"] = \"YOUR_KAGGLE_KEY\"\n",
        "\n",
        "! pip install transformers obonet biopython peft --quiet\n",
        "! kaggle competitions download -c \"cafa-5-protein-function-prediction\"\n",
        "! unzip cafa-5-protein-function-prediction.zip\n",
        "\n",
        "import obonet\n",
        "from Bio import SeqIO\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import trange, tqdm\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.metrics import RocCurveDisplay, recall_score, precision_score, f1_score\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import AutoTokenizer, AutoModel, logging\n",
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "logging.set_verbosity_error()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iqL6oaXgvHMk"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)\n",
        "mlb = MultiLabelBinarizer()\n",
        "\n",
        "# Mapping between proteins and gene ontology functions\n",
        "train_terms = pd.read_csv(\"./Train/train_terms.tsv\", sep=\"\\t\")\n",
        "functions = train_terms.groupby(\"EntryID\")[\"term\"].apply(list)\n",
        "functions_df = pd.DataFrame(mlb.fit_transform(functions), columns=mlb.classes_, index=functions.index)\n",
        "functions_df = functions_df[functions_df.sum(axis=0).sort_values(ascending=False)[:1000].index]\n",
        "functions_df = functions_df.loc[functions_df.sum(axis=1).sort_values(ascending=False)[:10000].index]\n",
        "functions_df = functions_df[np.random.permutation(functions_df.columns)].sample(frac=1)\n",
        "functions = functions_df.apply(lambda row: row.index[row == 1].tolist(), axis=1).to_dict()\n",
        "\n",
        "# Description of each gene ontology function\n",
        "go_graph = obonet.read_obo(\"./Train/go-basic.obo\")\n",
        "go_to_desc = {id_: (data.get(\"namespace\") +\n",
        "                    data.get(\"def\")).replace(\"_\", \" \").replace('\"', \" \")\n",
        "                    for id_, data in go_graph.nodes(data=True)}\n",
        "go_to_desc = {k: go_to_desc[k] for k in functions_df.columns}\n",
        "\n",
        "# Amino-acid sequence of each protein\n",
        "protein_to_seq = SeqIO.to_dict(SeqIO.parse(open(\"./Train/train_sequences.fasta\"), \"fasta\"))\n",
        "protein_to_seq = {k: protein_to_seq[k] for k in functions_df.index}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "uPE1mzArNh4i"
      },
      "outputs": [],
      "source": [
        "class ProteinsDataset(Dataset):\n",
        "\n",
        "    def __init__(self, protein_to_seq, go_to_desc, functions):\n",
        "\n",
        "        self.protein_list = list(protein_to_seq.keys())\n",
        "        self.protein_to_seq = protein_to_seq\n",
        "        self.go_list = list(go_to_desc.keys())\n",
        "        self.go_to_desc = go_to_desc\n",
        "        self.functions = functions\n",
        "\n",
        "        self.protein_tokenizer = AutoTokenizer.from_pretrained(\"yarongef/DistilProtBert\")\n",
        "        self.go_tokenizer = AutoTokenizer.from_pretrained(\"nlpie/distil-biobert\")\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.protein_to_seq) * len(self.go_to_desc)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "\n",
        "        # Retrieve the protein sequence\n",
        "        protein_idx = idx // len(self.go_to_desc)\n",
        "        protein_name = self.protein_list[protein_idx]\n",
        "        protein_sequence = \" \".join(str(self.protein_to_seq[protein_name].seq))\n",
        "\n",
        "        # Retrieve the gene ontology description\n",
        "        go_idx = idx - protein_idx * len(self.go_to_desc)\n",
        "        go_name = self.go_list[go_idx]\n",
        "        go_description = self.go_to_desc[go_name]\n",
        "\n",
        "        # Retrieve the label\n",
        "        label = float(go_name in self.functions[protein_name])\n",
        "\n",
        "        # Tokenize sequences\n",
        "        protein_inputs = self.protein_tokenizer.encode_plus(\n",
        "            protein_sequence, padding=\"max_length\", max_length=1024,\n",
        "            truncation=True, return_tensors=\"pt\"\n",
        "        )\n",
        "        go_inputs = self.go_tokenizer.encode_plus(\n",
        "            go_description, padding=\"max_length\", max_length=256,\n",
        "            truncation=True, return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        protein_input_ids = protein_inputs[\"input_ids\"].squeeze()\n",
        "        protein_attention_mask = protein_inputs[\"attention_mask\"].squeeze()\n",
        "        go_input_ids = go_inputs[\"input_ids\"].squeeze()\n",
        "        go_attention_mask = go_inputs[\"attention_mask\"].squeeze()\n",
        "\n",
        "        return (protein_input_ids, protein_attention_mask,\n",
        "                go_input_ids, go_attention_mask, label)"
      ]
    },
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      ],
      "source": [
        "batch_size = 32\n",
        "\n",
        "n_prot = 100\n",
        "n_func = 100\n",
        "\n",
        "train_dataset = ProteinsDataset(\n",
        "    {k: protein_to_seq[k] for k in list(protein_to_seq)[:n_prot]},\n",
        "    {k: go_to_desc[k] for k in list(go_to_desc)[:n_func]},\n",
        "    functions\n",
        ")\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "n_prot_val = 20\n",
        "n_func_val = 20\n",
        "\n",
        "val_dataset = ProteinsDataset(\n",
        "    {k: protein_to_seq[k] for k in list(protein_to_seq)[n_prot:n_prot+n_prot_val]},\n",
        "    {k: go_to_desc[k] for k in list(go_to_desc)[n_func:n_func+n_func_val]},\n",
        "    functions\n",
        ")\n",
        "\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "CXGmd_B7NlXr"
      },
      "outputs": [],
      "source": [
        "class ProteinPredictor(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(ProteinPredictor, self).__init__()\n",
        "\n",
        "        # Load the pre-trained models\n",
        "        self.protein_model = AutoModel.from_pretrained(\"yarongef/DistilProtBert\")\n",
        "        self.go_model = AutoModel.from_pretrained(\"nlpie/distil-biobert\")\n",
        "\n",
        "        # Define linear layers for mapping embeddings to hidden dimensions\n",
        "        self.protein_embedding_dim = self.protein_model.config.hidden_size\n",
        "        self.go_embedding_dim = self.go_model.config.hidden_size\n",
        "\n",
        "        # Reshape\n",
        "        self.protein_reshape = nn.Linear(self.protein_embedding_dim, 256)\n",
        "        self.go_reshape = nn.Linear(self.go_embedding_dim, 256)\n",
        "\n",
        "        # Final MLP prediction\n",
        "        self.mlp = nn.Sequential(\n",
        "            nn.Linear(3 * 256, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.1),\n",
        "            nn.Linear(256, 1)\n",
        "        )\n",
        "\n",
        "    def forward(self, protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask):\n",
        "\n",
        "        # Generate protein embeddings\n",
        "        protein_outputs = self.protein_model(input_ids=protein_input_ids,\n",
        "                                             attention_mask=protein_attention_mask)\n",
        "        # Average pooling\n",
        "        protein_embeddings = protein_outputs.last_hidden_state.mean(dim=1)\n",
        "\n",
        "        # Generate gene ontology embeddings\n",
        "        go_outputs = self.go_model(input_ids=go_input_ids,\n",
        "                                   attention_mask=go_attention_mask)\n",
        "        # Average pooling\n",
        "        go_embeddings = go_outputs.last_hidden_state.mean(dim=1)\n",
        "\n",
        "        # Reshape\n",
        "        protein_embeddings_reshaped = torch.tanh(self.protein_reshape(protein_embeddings))\n",
        "        go_embeddings_reshaped = torch.tanh(self.go_reshape(go_embeddings))\n",
        "\n",
        "        # Concatenate embeddings\n",
        "        combined_embeddings = torch.cat((protein_embeddings_reshaped,\n",
        "                                         go_embeddings_reshaped,\n",
        "                                         torch.absolute(protein_embeddings_reshaped - go_embeddings_reshaped)), dim=1)\n",
        "\n",
        "        # Last MLP pass for prediction\n",
        "        output = self.mlp(combined_embeddings)\n",
        "\n",
        "        return output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "cvsZNrLsXBKa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
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          ]
        },
        "outputId": "197a14bb-9a64-439b-cd08-0f115579a6c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/924M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4847cc9111304f36b857ad8b90829d87"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebdc4533d41b44adb8bad1e81ac7a260"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/263M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69b45ad190704c21bcf44320fc483603"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1,486,336 || all params: 298,257,409 || trainable%: 0.49834000938431006\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_dropout=0.1,\n",
        "    target_modules=[\n",
        "        \"go_model.encoder.layer.0.output.dense\",\n",
        "        \"go_model.encoder.layer.3.output.dense\",\n",
        "        \"go_model.encoder.layer.5.output.dense\",\n",
        "        \"protein_model.encoder.layer.13.output.dense\",\n",
        "        \"protein_model.encoder.layer.14.output.dense\"],\n",
        "    modules_to_save=[\n",
        "        \"protein_reshape\",\n",
        "        \"go_reshape\",\n",
        "        \"mlp.0\",\n",
        "        \"mlp.2\"]\n",
        ")\n",
        "\n",
        "model = ProteinPredictor()\n",
        "peft_model = get_peft_model(model, config)\n",
        "peft_model.print_trainable_parameters()\n",
        "model = peft_model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PsCuB-RNsSeA"
      },
      "outputs": [],
      "source": [
        "weight = functions_df.iloc[:n_prot][functions_df.columns[:n_func]].mean().mean()\n",
        "pos_weight = (1 - weight) / weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "QZf3ZpVfQtbs"
      },
      "outputs": [],
      "source": [
        "optimizer = torch.optim.AdamW(model.parameters(), lr=0.001, amsgrad=True)\n",
        "criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(pos_weight).to(device))\n",
        "num_epochs = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4f548cd6fc1842bba2d5523b4f8d1d3a",
            "5cd02e0e7f0d4ab0b566d8bd8480ef9c",
            "b223fa1bc55145e4b8fd8cf6f8e376ae",
            "896406bc570f4ff0a5423a5dd660d60a",
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            "48e67628608a4dff88e2905423f31a28",
            "9820ff22721b4e669dbe12214c841aac"
          ]
        },
        "id": "qPYt32rpmaYG",
        "outputId": "a5775157-5969-4ec2-e422-de9e671cdfe2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f548cd6fc1842bba2d5523b4f8d1d3a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "scaler = torch.cuda.amp.GradScaler(enabled=True)\n",
        "bar = trange(num_epochs)\n",
        "\n",
        "loss_history = []\n",
        "val_loss_history = []\n",
        "\n",
        "for epoch in bar:\n",
        "\n",
        "    # Training step\n",
        "    model.train()\n",
        "    for protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, labels in train_dataloader:\n",
        "\n",
        "        with torch.autocast(device_type=device.type, dtype=torch.float16, enabled=True):\n",
        "\n",
        "            protein_input_ids = protein_input_ids.to(device)\n",
        "            protein_attention_mask = protein_attention_mask.to(device)\n",
        "            go_input_ids = go_input_ids.to(device)\n",
        "            go_attention_mask = go_attention_mask.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "            loss = criterion(outputs, labels.unsqueeze(1).float())\n",
        "\n",
        "        scaler.scale(loss).backward()\n",
        "        scaler.step(optimizer)\n",
        "        scaler.update()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        loss_history.append(loss.item())\n",
        "        bar.set_postfix(loss=f\"{np.mean(loss_history[-train_dataset.__len__()//batch_size:]):,.3f}\")\n",
        "\n",
        "    # Evaluation step\n",
        "    model.eval()\n",
        "    for protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, labels in val_dataloader:\n",
        "\n",
        "        val_loss = []\n",
        "\n",
        "        with torch.no_grad():\n",
        "\n",
        "            protein_input_ids = protein_input_ids.to(device)\n",
        "            protein_attention_mask = protein_attention_mask.to(device)\n",
        "            go_input_ids = go_input_ids.to(device)\n",
        "            go_attention_mask = go_attention_mask.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "            loss = criterion(outputs, labels.unsqueeze(1).float())\n",
        "\n",
        "            val_loss.append(loss.item())\n",
        "\n",
        "    val_loss_history.append(np.mean(val_loss))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "7b8afec235eb445daea84f035ae18dfd",
            "c60d02553852433499f8d036023011d1",
            "0908d3e81007460282e09ecc474fa3dc",
            "2b6005f7923a41f0a7f9e8024f20a2d8"
          ]
        },
        "id": "iCB-7EMpfA0v",
        "outputId": "02e5419a-aae5-48a1-97aa-b7d74d2c26dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "403d917dc983470ea253b13dd29e11b1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Training data\n",
        "\n",
        "model.eval()\n",
        "y_pred = []\n",
        "y_true = []\n",
        "\n",
        "for i in tqdm(range(train_dataset.__len__() // 10)):\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, label = train_dataset.__getitem__(i)\n",
        "\n",
        "        protein_input_ids = torch.reshape(protein_input_ids, (1, 1024)).to(device)\n",
        "        protein_attention_mask = torch.reshape(protein_attention_mask, (1, 1024)).to(device)\n",
        "        go_input_ids = torch.reshape(go_input_ids, (1, 256)).to(device)\n",
        "        go_attention_mask = torch.reshape(go_attention_mask, (1, 256)).to(device)\n",
        "\n",
        "        output = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "        y_true.append(label)\n",
        "        y_pred.append(torch.sigmoid(output).item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "s-6KGB7bguVb",
        "outputId": "0f5529bd-9e10-4515-e25f-6ad5d7a8d769"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.613\n",
            "Precision: 0.414\n",
            "F1-Score: 0.494\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "RocCurveDisplay.from_predictions(y_true, y_pred)\n",
        "print(f\"Recall: {recall_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"Precision: {precision_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"F1-Score: {f1_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "SlST3inV9hg7"
      },
      "outputs": [],
      "source": [
        "# Seen proteins, unseen functions\n",
        "\n",
        "test_dataset = ProteinsDataset({k: protein_to_seq[k] for k in list(protein_to_seq)[:100]},\n",
        "                               {k: go_to_desc[k] for k in list(go_to_desc)[100:200]},\n",
        "                               functions)\n",
        "\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "224758c8cc22488e8569852adad25a5d",
            "e771c9c6463f43b2a37becd9139fa754",
            "c61155269877436d8f2568d7eac67252",
            "55d2c2de7665482e9a3a7291f0721a05",
            "10021fb8cab54e759941e198535a7032",
            "15ccfa2f6c614c1698e699039fc0a7bf",
            "f6ffbff590844e21b56bf94d781115a0",
            "f34ace685ac648ff99cbde206b679a03",
            "5483f097ce9e4f8899b3a1a7c583e27f",
            "a262904a32f84e42ad4f533db8dd2710",
            "23691265825a4f8bb477676573739c77"
          ]
        },
        "id": "JIr--qsQ9ypw",
        "outputId": "4fcf1521-ad11-4937-8a4e-fb61896c486d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "224758c8cc22488e8569852adad25a5d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.eval()\n",
        "y_pred = []\n",
        "y_true = []\n",
        "\n",
        "for i in tqdm(range(test_dataset.__len__() // 10)):\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, label = test_dataset.__getitem__(i)\n",
        "\n",
        "        protein_input_ids = torch.reshape(protein_input_ids, (1, 1024)).to(device)\n",
        "        protein_attention_mask = torch.reshape(protein_attention_mask, (1, 1024)).to(device)\n",
        "        go_input_ids = torch.reshape(go_input_ids, (1, 256)).to(device)\n",
        "        go_attention_mask = torch.reshape(go_attention_mask, (1, 256)).to(device)\n",
        "\n",
        "        output = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "        y_true.append(label)\n",
        "        y_pred.append(torch.sigmoid(output).item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Wd4Tvi8d-Fex",
        "outputId": "60eb5970-b46d-4e55-afba-62171489d4f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.207\n",
            "Precision: 0.205\n",
            "F1-Score: 0.206\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "RocCurveDisplay.from_predictions(y_true, y_pred)\n",
        "print(f\"Recall: {recall_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"Precision: {precision_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"F1-Score: {f1_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ScyrhkUtBWVU"
      },
      "outputs": [],
      "source": [
        "# Unseen proteins, seen functions\n",
        "\n",
        "test_dataset = ProteinsDataset({k: protein_to_seq[k] for k in list(protein_to_seq)[100:200]},\n",
        "                               {k: go_to_desc[k] for k in list(go_to_desc)[:100]},\n",
        "                               functions)\n",
        "\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d0a7a978d411405eafaacfa6316b84fe",
            "3e67696975074fc5a8d9426ff50f179c",
            "ac1b952f3d2e4c38b8bd833e5f7655c0",
            "f895e5867f444b4a80d61e2cf6798d14",
            "9d5cad6e53f9440e93be2bb92d8cd835",
            "d8d4ee5d3a5840cdbefcf1302a5820ad",
            "201e0b52204643769401f91ad425ded7",
            "2add409cf742445c8766fae4eda09bcc",
            "f50470b4f3d04412aec5ee85c18ae226",
            "3633947cd1674dbcba39097054c13279",
            "4b8aba1f2f054b3e8edf7378ee739133"
          ]
        },
        "id": "oE7qZ9s8I5TC",
        "outputId": "e8242f6d-9dcd-4d73-f6ed-74faf31dc73e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0a7a978d411405eafaacfa6316b84fe"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.eval()\n",
        "y_pred = []\n",
        "y_true = []\n",
        "\n",
        "for i in tqdm(range(test_dataset.__len__() // 10)):\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, label = test_dataset.__getitem__(i)\n",
        "\n",
        "        protein_input_ids = torch.reshape(protein_input_ids, (1, 1024)).to(device)\n",
        "        protein_attention_mask = torch.reshape(protein_attention_mask, (1, 1024)).to(device)\n",
        "        go_input_ids = torch.reshape(go_input_ids, (1, 256)).to(device)\n",
        "        go_attention_mask = torch.reshape(go_attention_mask, (1, 256)).to(device)\n",
        "\n",
        "        output = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "        y_true.append(label)\n",
        "        y_pred.append(torch.sigmoid(output).item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "yWBdBezOI9Iy",
        "outputId": "4f888cfc-3969-4cd2-9014-55cc4007d6aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.530\n",
            "Precision: 0.353\n",
            "F1-Score: 0.424\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "RocCurveDisplay.from_predictions(y_true, y_pred)\n",
        "print(f\"Recall: {recall_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"Precision: {precision_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"F1-Score: {f1_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "v49AIL8SKwFQ"
      },
      "outputs": [],
      "source": [
        "# Unseen proteins, unseen functions\n",
        "\n",
        "test_dataset = ProteinsDataset({k: protein_to_seq[k] for k in list(protein_to_seq)[100:200]},\n",
        "                               {k: go_to_desc[k] for k in list(go_to_desc)[100:200]},\n",
        "                               functions)\n",
        "\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "577b38a801c14b65a7b58575811fa734",
            "457d230bd684459b835009baa5e093d4",
            "889e2c56ebe44e03a5b2dba507b0f67a",
            "ac51d894d9d0474fb379b1e99e4cf696",
            "7b905723bf984eea9e25422197cbf808",
            "36bd87be0ad24711918273ef668a64cd",
            "01b4991b46fc4c1991a05a91d9ceb06d",
            "4e18c4c7cf3346ac928bc7c8549312cf",
            "f4d5c318b0754138b593adc2733c02c2",
            "6d5c11fc1fd045c794802f9db390f289",
            "0620c6dd9602423bb23543fcc2a1f923"
          ]
        },
        "id": "X1jT-Ug9LQWQ",
        "outputId": "ded54c73-cc84-434d-e11c-b1518965a3e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "577b38a801c14b65a7b58575811fa734"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.eval()\n",
        "y_pred = []\n",
        "y_true = []\n",
        "\n",
        "for i in tqdm(range(test_dataset.__len__() // 10)):\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask, label = test_dataset.__getitem__(i)\n",
        "\n",
        "        protein_input_ids = torch.reshape(protein_input_ids, (1, 1024)).to(device)\n",
        "        protein_attention_mask = torch.reshape(protein_attention_mask, (1, 1024)).to(device)\n",
        "        go_input_ids = torch.reshape(go_input_ids, (1, 256)).to(device)\n",
        "        go_attention_mask = torch.reshape(go_attention_mask, (1, 256)).to(device)\n",
        "\n",
        "        output = model(protein_input_ids, protein_attention_mask, go_input_ids, go_attention_mask)\n",
        "        y_true.append(label)\n",
        "        y_pred.append(torch.sigmoid(output).item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "hQQ2wDoNLSdZ",
        "outputId": "d08b0811-7382-4610-d4d3-1a81c5c219c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.214\n",
            "Precision: 0.195\n",
            "F1-Score: 0.204\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "RocCurveDisplay.from_predictions(y_true, y_pred)\n",
        "print(f\"Recall: {recall_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"Precision: {precision_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")\n",
        "print(f\"F1-Score: {f1_score(y_true, (np.asarray(y_pred) > 0.5).astype(int)):.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hv2mXFI2iHf7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "d21e86c10ec14c46ab07f8a1fa99bae3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_fcf058cc97d541aa924fcfbe9eb2ccd8",
              "IPY_MODEL_26effa465a5d4754b592f0dc0184275d",
              "IPY_MODEL_300bffefd3694e3ea54499a977c97afd"
            ],
            "layout": "IPY_MODEL_0a1f91033f2041ee95f33d956b02e2ee"
          }
        },
        "fcf058cc97d541aa924fcfbe9eb2ccd8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_78de46dcc6a141c493189b2c6c13c202",
            "placeholder": "​",
            "style": "IPY_MODEL_528430da0a3a48d08c1794a19cf5096b",
            "value": "Downloading (…)okenizer_config.json: 100%"
          }
        },
        "26effa465a5d4754b592f0dc0184275d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
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