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session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2025-10-17T06:45:24.598845Z","iopub.status.busy":"2025-10-17T06:45:24.597914Z","iopub.status.idle":"2025-10-17T06:45:26.518194Z","shell.execute_reply":"2025-10-17T06:45:26.517293Z"},"papermill":{"duration":1.926831,"end_time":"2025-10-17T06:45:26.519579","exception":false,"start_time":"2025-10-17T06:45:24.592748","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"39f4da25","cell_type":"markdown","source":"# Packages","metadata":{"papermill":{"duration":0.003091,"end_time":"2025-10-17T06:45:26.526346","exception":false,"start_time":"2025-10-17T06:45:26.523255","status":"completed"},"tags":[]}},{"id":"5777f2ef","cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.execute_input":"2025-10-17T06:45:26.534037Z","iopub.status.busy":"2025-10-17T06:45:26.533217Z","iopub.status.idle":"2025-10-17T06:45:26.537062Z","shell.execute_reply":"2025-10-17T06:45:26.536422Z"},"papermill":{"duration":0.008815,"end_time":"2025-10-17T06:45:26.538249","exception":false,"start_time":"2025-10-17T06:45:26.529434","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"9fcc7dba","cell_type":"code","source":"!pip install biopython obonet --quiet\n!pip install torch torchvision torchaudio --quiet\n!pip install transformers biopython --quiet","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2025-10-17T06:45:26.545215Z","iopub.status.busy":"2025-10-17T06:45:26.545005Z","iopub.status.idle":"2025-10-17T06:46:58.115125Z","shell.execute_reply":"2025-10-17T06:46:58.114056Z"},"papermill":{"duration":91.575305,"end_time":"2025-10-17T06:46:58.116704","exception":false,"start_time":"2025-10-17T06:45:26.541399","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"a2fcda78","cell_type":"markdown","source":"# 🧩 CAFA6 Data Loading & Parsing (Step 1)","metadata":{"papermill":{"duration":0.019886,"end_time":"2025-10-17T06:46:58.157129","exception":false,"start_time":"2025-10-17T06:46:58.137243","status":"completed"},"tags":[]}},{"id":"e00ea370","cell_type":"code","source":"%%time\n# =========================================================\n# CAFA6 - Step 1: Load & Explore Data\n# =========================================================\nimport pandas as pd\n\nfrom Bio import SeqIO\nimport obonet\n\n# ---------------------------------------------------------\n# File paths (update if needed)\n# ---------------------------------------------------------\nBASE_PATH = \"/kaggle/input/cafa-6-protein-function-prediction\"\nTRAIN_PATH = f\"{BASE_PATH}/Train\"\nTEST_PATH = f\"{BASE_PATH}/Test\"\n\n# ---------------------------------------------------------\n# 1. Load TSV files\n# ---------------------------------------------------------\ntrain_terms = pd.read_csv(\n    f\"{TRAIN_PATH}/train_terms.tsv\",\n    sep=\"\\t\",\n    names=[\"protein_id\", \"go_term\", \"ontology\"]\n)\n\ntrain_taxonomy = pd.read_csv(\n    f\"{TRAIN_PATH}/train_taxonomy.tsv\",\n    sep=\"\\t\",\n    names=[\"protein_id\", \"taxon_id\"]\n)\n\nia = pd.read_csv(\n    f\"{BASE_PATH}/IA.tsv\",\n    sep=\"\\t\",\n    names=[\"go_term\", \"weight\"]\n)\n\n# ---------------------------------------------------------\n# 2. Load ontology graph (go-basic.obo)\n# ---------------------------------------------------------\ngo_graph = obonet.read_obo(f\"{TRAIN_PATH}/go-basic.obo\")\nprint(f\"Ontology terms loaded: {len(go_graph)}\")\n\n# Example: extract parent relationships\nroot_nodes = {\n    'BP': 'GO:0008150',\n    'CC': 'GO:0005575',\n    'MF': 'GO:0003674'\n}\n\n# ---------------------------------------------------------\n# 3. Load FASTA sequences (train & test superset)\n# ---------------------------------------------------------\ndef load_fasta_sequences(file_path):\n    \"\"\"Return dict {protein_id: sequence}\"\"\"\n    sequences = {}\n    for record in SeqIO.parse(file_path, \"fasta\"):\n        prot_id = record.id.split(\"|\")[1] if \"|\" in record.id else record.id\n        sequences[prot_id] = str(record.seq)\n    return sequences\n\ntrain_sequences = load_fasta_sequences(f\"{TRAIN_PATH}/train_sequences.fasta\")\ntest_sequences = load_fasta_sequences(f\"{TEST_PATH}/testsuperset.fasta\")\n\nprint(f\"Train sequences: {len(train_sequences)}\")\nprint(f\"Test superset sequences: {len(test_sequences)}\")\n\n# ---------------------------------------------------------\n# 4. Merge annotations and taxonomy\n# ---------------------------------------------------------\ntrain_df = train_terms.merge(train_taxonomy, on=\"protein_id\", how=\"left\")\nprint(f\"Train dataframe: {train_df.shape}\")\n\n# ---------------------------------------------------------\n# 5. Quick data summary\n# ---------------------------------------------------------\nprint(\"\\nData Summary:\")\nprint(f\"- train_terms: {train_terms.shape}\")\nprint(f\"- train_taxonomy: {train_taxonomy.shape}\")\nprint(f\"- ia: {ia.shape}\")\nprint(f\"- train_sequences: {len(train_sequences)}\")\nprint(f\"- test_sequences: {len(test_sequences)}\")\nprint(f\"- Ontology terms (GO): {len(go_graph)}\")\n\n# Preview few examples\nprint(\"\\nSample train_terms:\")\ndisplay(train_terms.head())\n\nprint(\"\\nSample train_taxonomy:\")\ndisplay(train_taxonomy.head())\n\nprint(\"\\nSample IA:\")\ndisplay(ia.head())\n\n# Example check of GO hierarchy\nexample_term = list(go_graph.nodes())[0]\nprint(f\"\\nExample GO term: {example_term}\")\nprint(\"Parents:\", list(go_graph.predecessors(example_term)))\nprint(\"Children:\", list(go_graph.successors(example_term)))\n","metadata":{"execution":{"iopub.execute_input":"2025-10-17T06:46:58.199086Z","iopub.status.busy":"2025-10-17T06:46:58.198314Z","iopub.status.idle":"2025-10-17T06:47:09.142495Z","shell.execute_reply":"2025-10-17T06:47:09.141550Z"},"papermill":{"duration":10.966257,"end_time":"2025-10-17T06:47:09.143725","exception":false,"start_time":"2025-10-17T06:46:58.177468","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"4c08351b","cell_type":"markdown","source":"# 🧭 Step 2: Exploratory Data Analysis (EDA)\n\nBelow is a well-structured EDA section to add right after dataset loading.\n\nIt covers:\n\nOverview of GO ontologies\n\nDistribution of GO terms & taxonomies\n\nSequence length statistics\n\nRelationship between ontology, taxonomy, and terms\n\nVisualization of embeddings (optional)","metadata":{"papermill":{"duration":0.020344,"end_time":"2025-10-17T06:47:09.184792","exception":false,"start_time":"2025-10-17T06:47:09.164448","status":"completed"},"tags":[]}},{"id":"d8f08e28","cell_type":"code","source":"%%time\n# =========================================================\n# Step 2: Exploratory Data Analysis (EDA)\n# =========================================================\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n\n# Style setup\nplt.style.use(\"seaborn-v0_8-muted\")\nsns.set_palette(\"coolwarm\")\n\n# ---------------------------------------------------------\n# 1. Ontology distribution\n# ---------------------------------------------------------\nplt.figure(figsize=(6,4))\nsns.countplot(x=\"ontology\", data=train_terms)\nplt.title(\"Distribution of Ontology Types\")\nplt.xlabel(\"Ontology (Aspect)\")\nplt.ylabel(\"Count\")\nplt.tight_layout()\nplt.show()\n\n# ---------------------------------------------------------\n# 2. GO term frequency\n# ---------------------------------------------------------\ngo_counts = train_terms[\"go_term\"].value_counts().head(20)\nplt.figure(figsize=(8,4))\nsns.barplot(y=go_counts.index, x=go_counts.values)\nplt.title(\"Top 20 Most Frequent GO Terms\")\nplt.xlabel(\"Count\")\nplt.ylabel(\"GO Term\")\nplt.tight_layout()\nplt.show()\n\n# ---------------------------------------------------------\n# 3. Taxonomy overview\n# ---------------------------------------------------------\ntax_counts = train_taxonomy[\"taxon_id\"].value_counts().head(10)\nplt.figure(figsize=(8,4))\nsns.barplot(x=tax_counts.index.astype(str), y=tax_counts.values)\nplt.title(\"Top 10 Taxon IDs (Species)\")\nplt.xlabel(\"Taxon ID\")\nplt.ylabel(\"Protein Count\")\nplt.tight_layout()\nplt.show()\n\n# ---------------------------------------------------------\n# 4. Sequence length distribution\n# ---------------------------------------------------------\nseq_lengths = [len(seq) for seq in train_sequences.values()]\nplt.figure(figsize=(8,4))\nsns.histplot(seq_lengths, bins=50, kde=True)\nplt.title(\"Distribution of Protein Sequence Lengths\")\nplt.xlabel(\"Sequence Length (aa)\")\nplt.ylabel(\"Count\")\nplt.tight_layout()\nplt.show()\n\nprint(f\"🧬 Average sequence length: {np.mean(seq_lengths):.1f} aa\")\nprint(f\"Longest sequence: {np.max(seq_lengths)} aa\")\nprint(f\"Shortest sequence: {np.min(seq_lengths)} aa\")\n\n# ---------------------------------------------------------\n# 5. Ontology × Taxonomy heatmap (co-occurrence)\n# ---------------------------------------------------------\nmerged = train_terms.merge(train_taxonomy, on=\"protein_id\", how=\"left\")\nont_tax_counts = (\n    merged.groupby([\"ontology\", \"taxon_id\"])\n    .size()\n    .reset_index(name=\"count\")\n    .pivot(index=\"ontology\", columns=\"taxon_id\", values=\"count\")\n    .fillna(0)\n)\nplt.figure(figsize=(10,4))\nsns.heatmap(np.log1p(ont_tax_counts), cmap=\"viridis\")\nplt.title(\"Ontology × Taxonomy Co-occurrence (log scale)\")\nplt.xlabel(\"Taxon ID\")\nplt.ylabel(\"Ontology\")\nplt.tight_layout()\nplt.show()\n\n# ---------------------------------------------------------\n# 6. IA weight distribution (Information Content)\n# ---------------------------------------------------------\nplt.figure(figsize=(8,4))\nsns.histplot(ia[\"weight\"], bins=50, kde=True)\nplt.title(\"GO Term Information Content (IA Weights)\")\nplt.xlabel(\"Weight\")\nplt.ylabel(\"Frequency\")\nplt.tight_layout()\nplt.show()\n\nprint(f\"🧠 IA weights: mean={ia['weight'].mean():.3f}, max={ia['weight'].max():.3f}\")","metadata":{"execution":{"iopub.execute_input":"2025-10-17T06:47:09.226845Z","iopub.status.busy":"2025-10-17T06:47:09.226416Z","iopub.status.idle":"2025-10-17T06:47:14.307939Z","shell.execute_reply":"2025-10-17T06:47:14.306971Z"},"papermill":{"duration":5.10432,"end_time":"2025-10-17T06:47:14.309133","exception":false,"start_time":"2025-10-17T06:47:09.204813","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"b6e538f2","cell_type":"markdown","source":"# 🧬 Step 2 – Protein Embedding Extraction (ESM2 baseline)","metadata":{"papermill":{"duration":0.025319,"end_time":"2025-10-17T06:47:14.361676","exception":false,"start_time":"2025-10-17T06:47:14.336357","status":"completed"},"tags":[]}},{"id":"08511f40","cell_type":"code","source":"import torch\nfrom transformers import AutoTokenizer, AutoModel\nfrom Bio import SeqIO\nimport numpy as np\nimport pandas as pd\nfrom tqdm.notebook import tqdm\n\n# ---------------------------------------------------------\n# Load model + tokenizer\n# ---------------------------------------------------------\nMODEL_NAME = \"facebook/esm2_t6_8M_UR50D\"  # small and fast baseline\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\nmodel = AutoModel.from_pretrained(MODEL_NAME)\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel = model.to(device)\nmodel.eval()\n\nprint(f\"✅ Loaded {MODEL_NAME} on {device}\")\n\n# ---------------------------------------------------------\n# Function to compute mean-pooled embeddings\n# ---------------------------------------------------------\ndef get_protein_embedding(sequence: str):\n    \"\"\"Return mean pooled embedding for one protein sequence.\"\"\"\n    inputs = tokenizer(sequence, return_tensors=\"pt\", truncation=True, max_length=1022)\n    with torch.no_grad():\n        outputs = model(**{k: v.to(device) for k, v in inputs.items()})\n    # Mean-pool across tokens (excluding [CLS], [EOS])\n    emb = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()\n    return emb\n\n# ---------------------------------------------------------\n# Compute embeddings for all test sequences\n# ---------------------------------------------------------\ntest_fasta_path = '/kaggle/input/cafa-6-protein-function-prediction/Test/testsuperset.fasta'  # Path to the test FASTA file\ntest_sequences = {record.id: str(record.seq) for record in SeqIO.parse(test_fasta_path, \"fasta\")}\n\n# You can limit to first N for quick testing\nN = 1000  # change to None or len(test_sequences) for full run\n\nseq_items = list(test_sequences.items())[:N]\ntest_embeddings = []\ntest_protein_ids = []\n\nfor prot_id, seq in tqdm(seq_items, desc=\"Embedding test proteins\"):\n    try:\n        emb = get_protein_embedding(seq)\n        test_embeddings.append(emb)\n        test_protein_ids.append(prot_id)\n    except Exception as e:\n        print(f\"⚠️ Skipped {prot_id}: {e}\")\n\n# ---------------------------------------------------------\n# Save test embeddings\n# ---------------------------------------------------------\ntest_emb_df = pd.DataFrame(test_embeddings)\ntest_emb_df.insert(0, \"protein_id\", test_protein_ids)\ntest_emb_df.to_parquet(\"test_esm2_embeddings.parquet\", index=False)\n\nprint(f\"\\n✅ Saved {len(test_emb_df)} test protein embeddings with shape {test_emb_df.shape}\")\ndisplay(test_emb_df.head())","metadata":{},"outputs":[],"execution_count":null},{"id":"fa35407d","cell_type":"code","source":"%%time\n# =========================================================\n# Step 2: Extract Protein Embeddings using ESM2 (facebook/esm2_t6_8M_UR50D)\n# =========================================================\n\n\nimport torch\nfrom transformers import AutoTokenizer, AutoModel\nfrom Bio import SeqIO\nimport numpy as np\nimport pandas as pd\nfrom tqdm.notebook import tqdm\n\n# ---------------------------------------------------------\n# Load model + tokenizer\n# ---------------------------------------------------------\nMODEL_NAME = \"facebook/esm2_t6_8M_UR50D\"  # small and fast baseline\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\nmodel = AutoModel.from_pretrained(MODEL_NAME)\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel = model.to(device)\nmodel.eval()\n\nprint(f\"✅ Loaded {MODEL_NAME} on {device}\")\n\n# ---------------------------------------------------------\n# Function to compute mean-pooled embeddings\n# ---------------------------------------------------------\ndef get_protein_embedding(sequence: str):\n    \"\"\"Return mean pooled embedding for one protein sequence.\"\"\"\n    inputs = tokenizer(sequence, return_tensors=\"pt\", truncation=True, max_length=1022)\n    with torch.no_grad():\n        outputs = model(**{k: v.to(device) for k, v in inputs.items()})\n    # Mean-pool across tokens (excluding [CLS], [EOS])\n    emb = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()\n    return emb\n\n# ---------------------------------------------------------\n# Compute embeddings for all training sequences\n# ---------------------------------------------------------\n# You can limit to first N for quick testing\n\nseq_items = list(train_sequences.items())\nembeddings = []\nprotein_ids = []\n\nfor prot_id, seq in tqdm(seq_items, desc=\"Embedding proteins\"):\n    try:\n        emb = get_protein_embedding(seq)\n        embeddings.append(emb)\n        protein_ids.append(prot_id)\n    except Exception as e:\n        print(f\"⚠️ Skipped {prot_id}: {e}\")\n\n# ---------------------------------------------------------\n# Save embeddings\n# ---------------------------------------------------------\nemb_df = pd.DataFrame(embeddings)\nemb_df.insert(0, \"protein_id\", protein_ids)\nemb_df.to_parquet(\"train_esm2_embeddings.parquet\", index=False)\n\nprint(f\"\\n✅ Saved {len(emb_df)} protein embeddings with shape {emb_df.shape}\")\ndisplay(emb_df.head())\n","metadata":{"execution":{"iopub.execute_input":"2025-10-17T06:47:14.415646Z","iopub.status.busy":"2025-10-17T06:47:14.415128Z","iopub.status.idle":"2025-10-17T06:48:14.911568Z","shell.execute_reply":"2025-10-17T06:48:14.910712Z"},"papermill":{"duration":60.524514,"end_time":"2025-10-17T06:48:14.912805","exception":false,"start_time":"2025-10-17T06:47:14.388291","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}