{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n", "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n", "Requirement already satisfied: tqdm in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (4.67.1)\n" ] } ], "source": [ "!pip install -q transformers datasets accelerate bitsandbytes pysam pandas pyarrow fastparquet\n", "!apt-get install -q samtools\n", "!pip install tqdm\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n" ] } ], "source": [ "print(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting evaluate\n", " Downloading 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kiwisolver-1.4.9 matplotlib-3.10.8 pillow-12.1.0 pyparsing-3.3.1\n" ] } ], "source": [ "!pip install evaluate\n", "!pip install scikit-learn\n", "!pip install matplotlib\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "NUM_PROCS: 8 MAX_LEN: 1000\n", "Loaded: 299999 train | 14073 test\n", "Feature means/std computed.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Setting TOKENIZERS_PARALLELISM=false for forked processes.\n", "Tokenizing train (num_proc=8): 100%|██████████| 299999/299999 [01:30<00:00, 3321.33 examples/s]\n", "Setting TOKENIZERS_PARALLELISM=false for forked processes.\n", "Packing/normalizing features train (num_proc=8): 100%|██████████| 299999/299999 [00:30<00:00, 9687.11 examples/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[train] sample keys: ['labels', 'input_ids', 'attention_mask', 'features']\n", "[train] shapes/dtypes: input_ids=torch.Size([1000])/torch.int64, features=9/torch.float32, labels=torch.int64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Setting TOKENIZERS_PARALLELISM=false for forked processes.\n", "Tokenizing test (num_proc=8): 100%|██████████| 14073/14073 [00:05<00:00, 2406.31 examples/s]\n", "Setting TOKENIZERS_PARALLELISM=false for forked processes.\n", "Packing/normalizing features test (num_proc=8): 100%|██████████| 14073/14073 [00:02<00:00, 4779.46 examples/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[test] sample keys: ['labels', 'input_ids', 'attention_mask', 'features']\n", "[test] shapes/dtypes: input_ids=torch.Size([1000])/torch.int64, features=9/torch.float32, labels=torch.int64\n", "✅ Cell 3 complete. Train size: 299999 Test size: 14073\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# ==== CELL 3: Re-tokenize + normalize extra features + build torch-format HF Datasets ====\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "from datasets import Dataset\n", "from transformers import AutoTokenizer\n", "\n", "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n", "NUM_PROCS = min(8, max(1, (os.cpu_count() or 8) - 2))\n", "MAX_LEN = 1000\n", "MODEL_NAME = \"InstaDeepAI/nucleotide-transformer-500m-human-ref\"\n", "\n", "# --------------- feature list (same order everywhere) ---------------\n", "feature_cols = [\n", " \"gnomad_af\",\n", " \"GERP++_RS_rankscore\",\n", " \"GERP_91_mammals_rankscore\",\n", " \"phyloP100way_vertebrate_rankscore\",\n", " \"phyloP470way_mammalian_rankscore\",\n", " \"phyloP17way_primate_rankscore\",\n", " \"phastCons100way_vertebrate_rankscore\",\n", " \"phastCons470way_mammalian_rankscore\",\n", " \"phastCons17way_primate_rankscore\",\n", "]\n", "\n", "print(\"NUM_PROCS:\", NUM_PROCS, \"MAX_LEN:\", MAX_LEN)\n", "\n", "# 1) load\n", "train_df = pd.read_parquet(\"Balanced_300k_SEQUENCES.parquet\")\n", "test_df = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\")\n", "print(\"Loaded:\", len(train_df), \"train |\", len(test_df), \"test\")\n", "\n", "# basic check\n", "for c in feature_cols:\n", " if c not in train_df.columns:\n", " raise RuntimeError(f\"Missing column in train dataframe: {c}\")\n", "\n", "# 2) compute train mean/std for normalization (stable)\n", "train_feat = train_df[feature_cols].astype(float).fillna(0.0)\n", "feat_means = train_feat.mean(axis=0).astype(np.float32).values\n", "feat_stds = train_feat.std(axis=0).replace(0, 1).astype(np.float32).values\n", "\n", "print(\"Feature means/std computed.\")\n", "\n", "# 3) tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n", "\n", "# 4) helper to build dataset\n", "from datasets import Dataset\n", "\n", "def build_and_tokenize(df, split_name=\"train\"):\n", " # keep only required columns\n", " sub = df[[\"raw_sequence\", \"clean_label\"] + feature_cols].copy()\n", " sub[\"labels\"] = (sub[\"clean_label\"] == \"Pathogenic\").astype(np.int64)\n", " sub = sub.drop(columns=[\"clean_label\"])\n", " ds = Dataset.from_pandas(sub, preserve_index=False)\n", "\n", " # tokenization\n", " def tok_fn(batch):\n", " enc = tokenizer(\n", " batch[\"raw_sequence\"],\n", " truncation=True,\n", " padding=\"max_length\",\n", " max_length=MAX_LEN\n", " )\n", " return enc\n", "\n", " ds = ds.map(\n", " tok_fn,\n", " batched=True,\n", " batch_size=128,\n", " num_proc=NUM_PROCS,\n", " remove_columns=[\"raw_sequence\"],\n", " desc=f\"Tokenizing {split_name}\"\n", " )\n", "\n", " # pack & normalize features\n", " def pack_norm_features(batch):\n", " # stack columns in correct order and normalize by train stats\n", " arrs = [np.array(batch[c], dtype=np.float32) for c in feature_cols]\n", " stacked = np.stack(arrs, axis=1) # (batch, n_features)\n", " # normalize\n", " stacked = (stacked - feat_means) / feat_stds\n", " return {\"features\": stacked.tolist()}\n", "\n", " ds = ds.map(\n", " pack_norm_features,\n", " batched=True,\n", " batch_size=512,\n", " num_proc=NUM_PROCS,\n", " remove_columns=feature_cols,\n", " desc=f\"Packing/normalizing features {split_name}\"\n", " )\n", "\n", " # set torch format (ensures Trainer receives torch tensors)\n", " ds.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"features\", \"labels\"])\n", "\n", " # sample sanity\n", " s = ds[0]\n", " print(f\"[{split_name}] sample keys: {list(s.keys())}\")\n", " print(f\"[{split_name}] shapes/dtypes: input_ids={s['input_ids'].shape}/{s['input_ids'].dtype}, features={len(s['features'])}/{s['features'].dtype}, labels={s['labels'].dtype}\")\n", " return ds\n", "\n", "train_fast = build_and_tokenize(train_df, \"train\")\n", "test_fast = build_and_tokenize(test_df, \"test\")\n", "\n", "print(\"✅ Cell 3 complete. Train size:\", len(train_fast), \"Test size:\", len(test_fast))\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔍 RUNNING PRE-FLIGHT CHECKS...\n", "✅ GPU DETECTED: NVIDIA H200\n", " VRAM: 150.12 GB\n", " 🚀 GOD MODE HARDWARE CONFIRMED.\n", "✅ Train Set: 299999 samples\n", "✅ Test Set: 14073 samples\n", " Structure: OK (Torch tensors present)\n", "✅ Detected modern Transformers: Using 'eval_strategy'\n", "\n", "System Ready. Using strategy key: 'eval_strategy'\n" ] } ], "source": [ "# ===== CELL 3.5 — SYSTEM & VARIABLE VERIFICATION =====\n", "import torch\n", "import transformers\n", "from transformers import TrainingArguments\n", "import psutil\n", "\n", "print(\"🔍 RUNNING PRE-FLIGHT CHECKS...\")\n", "\n", "# 1. HARDWARE VERIFICATION (Confirm H200)\n", "if torch.cuda.is_available():\n", " gpu_name = torch.cuda.get_device_name(0)\n", " vram_gb = torch.cuda.get_device_properties(0).total_memory / 1e9\n", " print(f\"✅ GPU DETECTED: {gpu_name}\")\n", " print(f\" VRAM: {vram_gb:.2f} GB\")\n", " \n", " if \"H200\" in gpu_name or \"H100\" in gpu_name:\n", " print(\" 🚀 GOD MODE HARDWARE CONFIRMED.\")\n", " else:\n", " print(\" ⚠️ WARNING: You are not on H100/H200. Adjust batch sizes!\")\n", "else:\n", " raise RuntimeError(\"❌ NO GPU DETECTED! Training will fail.\")\n", "\n", "# 2. DATASET VERIFICATION\n", "try:\n", " print(f\"✅ Train Set: {len(train_fast)} samples\")\n", " print(f\"✅ Test Set: {len(test_fast)} samples\")\n", " \n", " # Check Column Names (Must match model expectations)\n", " sample = train_fast[0]\n", " required_keys = [\"input_ids\", \"attention_mask\", \"features\", \"labels\"]\n", " missing = [k for k in required_keys if k not in sample.keys()]\n", " \n", " if missing:\n", " raise ValueError(f\"❌ DATASET MISSING KEYS: {missing}\")\n", " print(\" Structure: OK (Torch tensors present)\")\n", " \n", "except NameError:\n", " raise RuntimeError(\"❌ Datasets 'train_fast' or 'test_fast' not found. Did Cell 3 run?\")\n", "\n", "# 3. HUGGING FACE ARGUMENT CHECK (The \"eval_strategy\" Bug Fix)\n", "# Newer transformers use 'eval_strategy', older use 'evaluation_strategy'\n", "import inspect\n", "args_sig = inspect.signature(TrainingArguments.__init__)\n", "valid_params = args_sig.parameters.keys()\n", "\n", "if \"eval_strategy\" in valid_params:\n", " EVAL_STRATEGY_KEY = \"eval_strategy\"\n", " print(\"✅ Detected modern Transformers: Using 'eval_strategy'\")\n", "else:\n", " EVAL_STRATEGY_KEY = \"evaluation_strategy\"\n", " print(\"⚠️ Detected older Transformers: Using 'evaluation_strategy'\")\n", "\n", "print(f\"\\nSystem Ready. Using strategy key: '{EVAL_STRATEGY_KEY}'\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of EsmModel were not initialized from the model checkpoint at InstaDeepAI/nucleotide-transformer-500m-human-ref and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "🔥 Encoder gradient checkpointing enabled\n", "🚀 NT-500M biological fine-tuning started (A100, fresh run)\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "💾 SAVING FINAL H200 MODEL...\n", "✅ H200 MODEL SAVED → ./PathoPreter_Final_A100\n", " (Config updated with 'custom_feature_dim' for easier inference)\n", "🚀 A100 RUN COMPLETE.\n" ] } ], "source": [ "# ===== CELL 4 — SAFE BIOLOGICAL FINE-TUNE (NT-500M, A100) — CORRECTED =====\n", "import os\n", "import torch\n", "import torch.nn as nn\n", "import numpy as np\n", "from transformers import AutoModel, TrainingArguments, Trainer, EarlyStoppingCallback\n", "from transformers.modeling_outputs import SequenceClassifierOutput\n", "import evaluate\n", "from safetensors.torch import save_file\n", "\n", "# 1. Optimizations for A100 (Ampere)\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n", "torch.backends.cuda.matmul.allow_tf32 = True\n", "torch.backends.cudnn.allow_tf32 = True\n", "\n", "# -----------------------------\n", "# Metrics\n", "# -----------------------------\n", "auc_metric = evaluate.load(\"roc_auc\")\n", "\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " # Softmax implementation for stability\n", " exp = np.exp(logits - np.max(logits, axis=1, keepdims=True))\n", " probs = exp[:, 1] / exp.sum(axis=1)\n", " return auc_metric.compute(prediction_scores=probs, references=labels)\n", "\n", "# -----------------------------\n", "# Load NT-500M Encoder\n", "# -----------------------------\n", "MODEL_NAME = \"InstaDeepAI/nucleotide-transformer-500m-human-ref\"\n", "FEATURE_DIM = len(feature_cols) # Ensure feature_cols is defined in previous cells\n", "NUM_LABELS = 2\n", "\n", "encoder = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\n", "encoder.gradient_checkpointing_enable()\n", "print(\"🔥 Encoder gradient checkpointing enabled\")\n", "\n", "hidden_size = encoder.config.hidden_size\n", "\n", "# -----------------------------\n", "# Hybrid Classifier (Corrected Pooling)\n", "# -----------------------------\n", "class SequenceClassificationWithFeatures(nn.Module):\n", " def __init__(self, encoder, hidden_size, feature_dim, num_labels):\n", " super().__init__()\n", " self.encoder = encoder\n", " self.feature_dim = feature_dim\n", " self.classifier = nn.Sequential(\n", " nn.Linear(hidden_size + feature_dim, hidden_size),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(hidden_size, num_labels)\n", " )\n", "\n", " def forward(self, input_ids=None, attention_mask=None, features=None, labels=None):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)\n", "\n", " # Robust pooling strategy\n", " if hasattr(out, \"pooler_output\") and out.pooler_output is not None:\n", " pooled = out.pooler_output\n", " else:\n", " # Mean pooling if pooler_output is missing\n", " last = out.last_hidden_state\n", " mask = attention_mask.unsqueeze(-1)\n", " pooled = (last * mask).sum(1) / mask.sum(1).clamp(min=1e-9)\n", "\n", " # Ensure features match device and dtype\n", " features = features.to(pooled.device).to(pooled.dtype)\n", " \n", " # Concatenate and Classify\n", " x = torch.cat([pooled, features], dim=1)\n", " logits = self.classifier(x)\n", "\n", " loss = None\n", " if labels is not None:\n", " loss = nn.CrossEntropyLoss()(logits, labels)\n", "\n", " return SequenceClassifierOutput(loss=loss, logits=logits)\n", "\n", "model = SequenceClassificationWithFeatures(\n", " encoder=encoder,\n", " hidden_size=hidden_size,\n", " feature_dim=FEATURE_DIM,\n", " num_labels=NUM_LABELS\n", ")\n", "\n", "# -----------------------------\n", "# Data collator\n", "# -----------------------------\n", "def data_collator(batch):\n", " return {\n", " \"input_ids\": torch.stack([b[\"input_ids\"] for b in batch]),\n", " \"attention_mask\": torch.stack([b[\"attention_mask\"] for b in batch]),\n", " # Improved tensor creation to handle varying input types safely\n", " \"features\": torch.stack([torch.as_tensor(b[\"features\"], dtype=torch.float32) for b in batch]),\n", " \"labels\": torch.tensor([b[\"labels\"] for b in batch], dtype=torch.long),\n", " }\n", "\n", "# -----------------------------\n", "# Training config\n", "# -----------------------------\n", "# Effective Batch Size = 64 * 2 = 128 (Ideal for NT-500M stability)\n", "training_args = TrainingArguments(\n", " output_dir=\"./PathoPreter_v1_A100\",\n", "\n", " per_device_train_batch_size=64,\n", " gradient_accumulation_steps=2,\n", " per_device_eval_batch_size=32,\n", "\n", " learning_rate=2e-5,\n", " num_train_epochs=2,\n", " weight_decay=0.02,\n", " lr_scheduler_type=\"cosine\",\n", " warmup_ratio=0.05,\n", "\n", " bf16=True, # Essential for A100\n", " fp16=False,\n", "\n", " optim=\"adamw_torch_fused\",\n", " max_grad_norm=0.5,\n", "\n", " dataloader_num_workers=8,\n", " dataloader_pin_memory=True,\n", "\n", " eval_strategy=\"steps\",\n", " eval_steps=500,\n", " save_strategy=\"steps\",\n", " save_steps=500,\n", " logging_steps=200,\n", "\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"roc_auc\",\n", " greater_is_better=True,\n", " save_total_limit=2,\n", "\n", " report_to=\"none\"\n", ")\n", "\n", "torch.cuda.empty_cache()\n", "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_fast,\n", " eval_dataset=test_fast,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", " callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]\n", ")\n", "\n", "print(\"🚀 NT-500M biological fine-tuning started (A100, fresh run)\")\n", "trainer.train()\n", "\n", "# ========================================================\n", "# 💾 TITANIUM SAVE — A100 MODEL\n", "# ========================================================\n", "\n", "print(\"\\n💾 SAVING FINAL A100 MODEL...\")\n", "\n", "save_path = \"./PathoPreter_Final_A100\"\n", "os.makedirs(save_path, exist_ok=True)\n", "\n", "# 1. Get the base encoder config\n", "raw_model = model.encoder\n", "if hasattr(raw_model, \"_orig_mod\"):\n", " raw_model = raw_model._orig_mod\n", "\n", "# 2. INJECT CUSTOM DIMENSIONS INTO CONFIG\n", "# This is crucial for easy inference loading later!\n", "raw_model.config.custom_feature_dim = FEATURE_DIM\n", "raw_model.config.custom_num_labels = NUM_LABELS\n", "raw_model.config.architectures = [\"SequenceClassificationWithFeatures\"]\n", "\n", "# 3. Save Config and Tokenizer\n", "raw_model.config.save_pretrained(save_path)\n", "tokenizer.save_pretrained(save_path)\n", "\n", "# 4. Save Weights (Cleaning prefixes)\n", "raw_state = model.state_dict()\n", "clean_state = {}\n", "\n", "for k, v in raw_state.items():\n", " k = k.replace(\"_orig_mod.\", \"\")\n", " # Remove 'encoder.' prefix to match HF format, but KEEP 'classifier' keys\n", " if k.startswith(\"encoder.\"):\n", " clean_state[k.replace(\"encoder.\", \"\", 1)] = v\n", " else:\n", " clean_state[k] = v\n", "\n", "save_file(clean_state, os.path.join(save_path, \"model.safetensors\"))\n", "\n", "# 5. Save Scaling Factors\n", "np.save(os.path.join(save_path, \"feat_means.npy\"), feat_means)\n", "np.save(os.path.join(save_path, \"feat_stds.npy\"), feat_stds)\n", "\n", "print(f\"✅ A100 MODEL SAVED → {save_path}\")\n", "print(\" (Config updated with 'custom_feature_dim' for easier inference)\")\n", "print(\"🚀 A100 RUN COMPLETE.\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🚀 Running final evaluation to get probabilities...\n" ] }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Extracted 14073 predictions.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 129516 (\\N{DNA DOUBLE HELIX}) missing from font(s) DejaVu Sans.\n", " fig.canvas.print_figure(bytes_io, **kw)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "📊 Brier Score: 0.1297\n", "✅ GOOD: Reliable enough for research.\n" ] } ], "source": [ "# ============================================================\n", "# CELL: Model Calibration (Fixed Version)\n", "# ============================================================\n", "\n", "from sklearn.calibration import calibration_curve\n", "from sklearn.metrics import brier_score_loss\n", "import matplotlib.pyplot as plt\n", "import torch\n", "import numpy as np\n", "\n", "# 1. Generate Predictions first! (This fixes the NameError)\n", "print(\"🚀 Running final evaluation to get probabilities...\")\n", "predictions_output = trainer.predict(test_fast)\n", "\n", "# Extract Labels (y_true) and Probabilities (y_probs)\n", "y_true = predictions_output.label_ids\n", "logits = predictions_output.predictions\n", "\n", "# Convert Logits -> Probabilities (Softmax)\n", "y_probs = torch.softmax(torch.tensor(logits), dim=-1)[:, 1].numpy()\n", "\n", "print(f\"✅ Extracted {len(y_probs)} predictions.\")\n", "\n", "# 2. Calibration Plot Function\n", "def plot_calibration_report(y_true, y_probs, model_name=\"PathoPreter\"):\n", " # Calculate Brier Score (Lower is better)\n", " brier = brier_score_loss(y_true, y_probs)\n", " \n", " # Generate Calibration Curve\n", " prob_true, prob_pred = calibration_curve(y_true, y_probs, n_bins=10)\n", " \n", " # Plotting\n", " plt.figure(figsize=(10, 7))\n", " plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly Calibrated')\n", " plt.plot(prob_pred, prob_true, marker='s', label=f'{model_name} (Brier: {brier:.4f})', color='darkorange')\n", " \n", " plt.xlabel('Mean Predicted Probability', fontsize=12)\n", " plt.ylabel('Fraction of Positives (Actual)', fontsize=12)\n", " plt.title(f'🧬 Clinical Reliability Diagram: {model_name}', fontsize=14)\n", " plt.legend(loc='lower right')\n", " plt.grid(alpha=0.3)\n", " \n", " plt.text(0.05, 0.9, \"Above line = Under-confident\\nBelow line = Over-confident\", \n", " fontsize=10, bbox=dict(facecolor='white', alpha=0.5))\n", " \n", " plt.show()\n", " \n", " print(f\"📊 Brier Score: {brier:.4f}\")\n", " if brier < 0.1:\n", " print(\"✅ EXCELLENT: Model is highly reliable for clinical use.\")\n", " elif brier < 0.15:\n", " print(\"✅ GOOD: Reliable enough for research.\")\n", " elif brier < 0.2:\n", " print(\"⚠️ MODERATE: Probabilities slightly skewed.\")\n", " else:\n", " print(\"❌ POOR: Use rankings only.\")\n", "\n", "# 3. Run it\n", "plot_calibration_report(y_true, y_probs)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Aligned evaluation rows: 14221\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "======================================================================\n", "🏆 PATHOGENICITY SOTA LEADERBOARD — ClinVar-Unseen Test\n", "======================================================================\n", "Model | ROC-AUC | PR-AUC \n", "----------------------------------------------------------------------\n", "PathoPreter | 0.9186 | 0.9284\n", "BayesDel | 0.5949 | 0.7097\n", "CADD | 0.5921 | 0.7079\n", "ClinPred | 0.5886 | 0.6095\n", "AlphaMissense | 0.5879 | 0.6050\n", "REVEL | 0.5847 | 0.6026\n", "ESM1b | 0.5763 | 0.5938\n", "PrimateAI | 0.5661 | 0.5850\n", "MPC | 0.5596 | 0.5821\n", "EVE | 0.5223 | 0.5504\n", "======================================================================\n", "\n", "🔥 PathoPreter gain over CADD: +0.3265 ROC-AUC\n", "🔥 PathoPreter gain over REVEL: +0.3339 ROC-AUC\n", "🔥 PathoPreter gain over AlphaMissense: +0.3307 ROC-AUC\n" ] } ], "source": [ "# ============================================================\n", "# CELL FINAL — PathoPreter vs DBNSFP SOTA Leaderboard\n", "# ============================================================\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "from sklearn.metrics import roc_auc_score, average_precision_score, confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "# ---------------------------------------------\n", "# 1. Run model on HF test dataset\n", "# ---------------------------------------------\n", "preds = trainer.predict(test_fast) # use test_fast if you used cell-3 pipeline\n", "logits = preds.predictions\n", "labels = preds.label_ids\n", "\n", "# Convert logits -> probability of Pathogenic\n", "probs = torch.softmax(torch.tensor(logits), dim=-1)[:,1].cpu().numpy()\n", "\n", "# ---------------------------------------------\n", "# 2. Recover original variant IDs\n", "# ---------------------------------------------\n", "# test_fast was created from test_df in cell-3, in same order\n", "test_df = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\").reset_index(drop=True)\n", "\n", "assert len(test_df) == len(probs), \"❌ Row mismatch between model and dataframe\"\n", "\n", "pred_df = pd.DataFrame({\n", " \"variant_id\": test_df[\"variant_id\"].values,\n", " \"y_true\": labels,\n", " \"PathoPreter\": probs\n", "})\n", "\n", "# ---------------------------------------------\n", "# 3. Collect DBNSFP benchmark tools\n", "# ---------------------------------------------\n", "bench_cols = [\n", " \"CADD_raw_rankscore\",\n", " \"REVEL_rankscore\",\n", " \"AlphaMissense_rankscore\",\n", " \"PrimateAI_rankscore\",\n", " \"MPC_rankscore\",\n", " \"ClinPred_rankscore\",\n", " \"BayesDel_addAF_rankscore\",\n", " \"EVE_rankscore\",\n", " \"ESM1b_rankscore\"\n", "]\n", "\n", "bench_df = test_df[[\"variant_id\"] + bench_cols].copy()\n", "\n", "# ---------------------------------------------\n", "# 4. Merge safely (alignment lock)\n", "# ---------------------------------------------\n", "merged = pred_df.merge(bench_df, on=\"variant_id\", how=\"inner\")\n", "\n", "print(\"Aligned evaluation rows:\", len(merged))\n", "\n", "y = merged[\"y_true\"].values\n", "\n", "# ---------------------------------------------\n", "# 5. Confusion Matrix (PathoPreter)\n", "# ---------------------------------------------\n", "y_pred = (merged[\"PathoPreter\"] > 0.5).astype(int)\n", "\n", "cm = confusion_matrix(y, y_pred)\n", "disp = ConfusionMatrixDisplay(cm, display_labels=[\"Benign\", \"Pathogenic\"])\n", "disp.plot(cmap=\"Blues\")\n", "plt.title(\"PathoPreter Confusion Matrix (Unseen Test)\")\n", "plt.show()\n", "\n", "# ---------------------------------------------\n", "# 6. Full SOTA Leaderboard\n", "# ---------------------------------------------\n", "models = {\n", " \"PathoPreter\": merged[\"PathoPreter\"],\n", " \"CADD\": merged[\"CADD_raw_rankscore\"],\n", " \"REVEL\": merged[\"REVEL_rankscore\"],\n", " \"AlphaMissense\": merged[\"AlphaMissense_rankscore\"],\n", " \"PrimateAI\": merged[\"PrimateAI_rankscore\"],\n", " \"MPC\": merged[\"MPC_rankscore\"],\n", " \"ClinPred\": merged[\"ClinPred_rankscore\"],\n", " \"BayesDel\": merged[\"BayesDel_addAF_rankscore\"],\n", " \"EVE\": merged[\"EVE_rankscore\"],\n", " \"ESM1b\": merged[\"ESM1b_rankscore\"],\n", "}\n", "\n", "results = []\n", "for name, scores in models.items():\n", " auc = roc_auc_score(y, scores)\n", " pr = average_precision_score(y, scores)\n", " results.append((name, auc, pr))\n", "\n", "results.sort(key=lambda x: x[1], reverse=True)\n", "\n", "print(\"\\n\" + \"=\"*70)\n", "print(\"🏆 PATHOGENICITY SOTA LEADERBOARD — ClinVar-Unseen Test\")\n", "print(\"=\"*70)\n", "print(f\"{'Model':20s} | {'ROC-AUC':8s} | {'PR-AUC':8s}\")\n", "print(\"-\"*70)\n", "\n", "for name, auc, pr in results:\n", " print(f\"{name:20s} | {auc:8.4f} | {pr:8.4f}\")\n", "\n", "print(\"=\"*70)\n", "\n", "# ---------------------------------------------\n", "# 7. Gains vs CADD (industry baseline)\n", "# ---------------------------------------------\n", "scores = dict((n,a) for n,a,_ in results)\n", "\n", "print(f\"\\n🔥 PathoPreter gain over CADD: {scores['PathoPreter'] - scores['CADD']:+.4f} ROC-AUC\")\n", "print(f\"🔥 PathoPreter gain over REVEL: {scores['PathoPreter'] - scores['REVEL']:+.4f} ROC-AUC\")\n", "print(f\"🔥 PathoPreter gain over AlphaMissense: {scores['PathoPreter'] - scores['AlphaMissense']:+.4f} ROC-AUC\")\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting seaborn\n", " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from seaborn) (2.2.6)\n", "Requirement already satisfied: pandas>=1.2 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from seaborn) (2.3.3)\n", "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from seaborn) (3.10.8)\n", "Requirement already satisfied: contourpy>=1.0.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.2)\n", "Requirement already satisfied: cycler>=0.10 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.61.1)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from 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(from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n", "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", "Installing collected packages: seaborn\n", "Successfully installed seaborn-0.13.2\n" ] } ], "source": [ "!pip install seaborn" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_10337/1626609002.py:31: UserWarning: Glyph 127942 (\\N{TROPHY}) missing from font(s) DejaVu Sans.\n", " plt.tight_layout()\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "# 1. Create the dataframe from the 'results' list you already have\n", "# (This fixes the NameError)\n", "leaderboard_df = pd.DataFrame(results, columns=[\"Model\", \"ROC-AUC\", \"PR-AUC\"])\n", "\n", "# 2. Define the plotting function\n", "def plot_sota_gains(df):\n", " # Sort for plotting\n", " df_plot = df.sort_values(\"ROC-AUC\", ascending=True)\n", " \n", " plt.figure(figsize=(10, 6))\n", " \n", " # Highlight PathoPreter in Red/Orange, others in Blue\n", " colors = ['#A1C9F4' if x != 'PathoPreter' else '#FF9F9B' for x in df_plot['Model']]\n", " \n", " bars = plt.barh(df_plot['Model'], df_plot['ROC-AUC'], color=colors, edgecolor='black')\n", " \n", " # Add values on the bars\n", " for bar in bars:\n", " width = bar.get_width()\n", " plt.text(width + 0.01, bar.get_y() + bar.get_height()/2, \n", " f'{width:.4f}', va='center', fontweight='bold')\n", "\n", " plt.axvline(x=0.5, color='red', linestyle='--', label='Random Guess (0.5)')\n", " plt.title(\"🏆 PathoPreter v1 vs. Global Benchmarks (ROC-AUC)\", fontsize=14)\n", " plt.xlabel(\"ROC-AUC Score\", fontsize=12)\n", " plt.xlim(0.5, 1.0) # Zoom in to show the contrast clearly\n", " plt.grid(axis='x', linestyle=':', alpha=0.7)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# 3. Plot it\n", "plot_sota_gains(leaderboard_df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " error: subprocess-exited-with-error\n", " \n", " × Getting requirements to build wheel did not run successfully.\n", " │ exit code: 1\n", " ╰─> [34 lines of output]\n", " # pysam: Cython 3.2.4 is available - using cythonize if necessary\n", " # pysam: htslib mode is shared\n", " # pysam: HTSLIB_CONFIGURE_OPTIONS=None\n", " '.' is not recognized as an internal or external command,\n", " operable program or batch file.\n", " '.' is not recognized as an internal or external command,\n", " operable program or batch file.\n", " # pysam: htslib configure options: None\n", " Traceback (most recent call last):\n", " File \"c:\\Users\\yrohi\\Documents\\CODING\\PROJECTS\\Pathopreter_xgboost\\.venv\\lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 389, in \n", " main()\n", " File \"c:\\Users\\yrohi\\Documents\\CODING\\PROJECTS\\Pathopreter_xgboost\\.venv\\lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 373, in main\n", " json_out[\"return_val\"] = hook(**hook_input[\"kwargs\"])\n", " File \"c:\\Users\\yrohi\\Documents\\CODING\\PROJECTS\\Pathopreter_xgboost\\.venv\\lib\\site-packages\\pip\\_vendor\\pyproject_hooks\\_in_process\\_in_process.py\", line 143, in get_requires_for_build_wheel\n", " return hook(config_settings)\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Temp\\pip-build-env-9qb6yr4x\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 331, in get_requires_for_build_wheel\n", " return self._get_build_requires(config_settings, requirements=[])\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Temp\\pip-build-env-9qb6yr4x\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 301, in _get_build_requires\n", " self.run_setup()\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Temp\\pip-build-env-9qb6yr4x\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 512, in run_setup\n", " super().run_setup(setup_script=setup_script)\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Temp\\pip-build-env-9qb6yr4x\\overlay\\Lib\\site-packages\\setuptools\\build_meta.py\", line 317, in run_setup\n", " exec(code, locals())\n", " File \"\", line 468, in \n", " File \"\", line 81, in run_make_print_config\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Programs\\Python\\Python310\\lib\\subprocess.py\", line 421, in check_output\n", " return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Programs\\Python\\Python310\\lib\\subprocess.py\", line 503, in run\n", " with Popen(*popenargs, **kwargs) as process:\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Programs\\Python\\Python310\\lib\\subprocess.py\", line 971, in __init__\n", " self._execute_child(args, executable, preexec_fn, close_fds,\n", " File \"C:\\Users\\yrohi\\AppData\\Local\\Programs\\Python\\Python310\\lib\\subprocess.py\", line 1456, in _execute_child\n", " hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n", " FileNotFoundError: [WinError 2] The system cannot find the file specified\n", " [end of output]\n", " \n", " note: This error originates from a subprocess, and is likely not a problem with pip.\n", "ERROR: Failed to build 'pysam' when getting requirements to build wheel\n", "'apt-get' is not recognized as an internal or external command,\n", "operable program or batch file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tqdm in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (4.67.1)\n", "Requirement already satisfied: colorama in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from tqdm) (0.4.6)\n" ] } ], "source": [ "!pip install -q transformers datasets accelerate bitsandbytes pysam pandas pyarrow fastparquet\n", "!apt-get install -q samtools\n", "!pip install tqdm\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting seaborn\n", " Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from seaborn) (1.26.4)\n", "Requirement already satisfied: pandas>=1.2 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from seaborn) (2.1.4)\n", "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from seaborn) (3.10.8)\n", "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.2)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.61.1)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\n", "Requirement already satisfied: pillow>=8 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\n", "Requirement already satisfied: pyparsing>=3 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.3.1)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.3)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\yrohi\\documents\\coding\\projects\\pathopreter_xgboost\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n", "Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n", "Installing collected packages: seaborn\n", "Successfully installed seaborn-0.13.2\n" ] } ], "source": [ "!pip install seaborn\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded test rows: 14073\n", "Final eval rows: 14073\n", " CADD_raw_rankscore: Acc=0.5280 F1=0.6790 AP=0.7048 thresh=0.2114\n", " REVEL_rankscore: Acc=0.5326 F1=0.6799 AP=0.6004 thresh=0.5393\n", " AlphaMissense_rankscore: Acc=0.5302 F1=0.6792 AP=0.6035 thresh=0.2655\n", " PrimateAI_rankscore: Acc=0.5205 F1=0.6749 AP=0.5813 thresh=0.2336\n", " MPC_rankscore: Acc=0.5136 F1=0.6707 AP=0.5786 thresh=0.2331\n", " ClinPred_rankscore: Acc=0.5393 F1=0.6837 AP=0.6066 thresh=0.2707\n", " BayesDel_addAF_rankscore: Acc=0.5383 F1=0.6838 AP=0.7063 thresh=0.2284\n", " EVE_rankscore: Acc=0.5039 F1=0.6689 AP=0.5466 thresh=0.0639\n", " ESM1b_rankscore: Acc=0.5241 F1=0.6742 AP=0.5911 thresh=0.4397\n", "\n", "============================================================\n", "🏆 BENCHMARK LEADERBOARD (thresholds optimized for F1)\n", "============================================================\n", " Model Accuracy Best_Threshold F1_Score AP\n", " ClinPred 53.93% 0.2707 0.6837 0.6066\n", "BayesDel_addAF 53.83% 0.2284 0.6838 0.7063\n", " REVEL 53.26% 0.5393 0.6799 0.6004\n", " AlphaMissense 53.02% 0.2655 0.6792 0.6035\n", " CADD 52.80% 0.2114 0.6790 0.7048\n", " ESM1b 52.41% 0.4397 0.6742 0.5911\n", " PrimateAI 52.05% 0.2336 0.6749 0.5813\n", " MPC 51.36% 0.2331 0.6707 0.5786\n", " EVE 50.39% 0.0639 0.6689 0.5466\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ============================================================\n", "# CELL: BENCHMARK ACCURACY & CONFUSION MATRIX (robust)\n", "# ============================================================\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import (\n", " confusion_matrix,\n", " accuracy_score,\n", " precision_recall_curve,\n", " f1_score,\n", " average_precision_score,\n", ")\n", "\n", "# -------------- config --------------\n", "TEST_FILE = \"test_enriched_SEQUENCES.parquet\"\n", "\n", "benchmarks = [\n", " \"CADD_raw_rankscore\", \"REVEL_rankscore\", \"AlphaMissense_rankscore\",\n", " \"PrimateAI_rankscore\", \"MPC_rankscore\", \"ClinPred_rankscore\",\n", " \"BayesDel_addAF_rankscore\", \"EVE_rankscore\", \"ESM1b_rankscore\"\n", "]\n", "\n", "# -------------- load --------------\n", "df = pd.read_parquet(TEST_FILE)\n", "print(\"Loaded test rows:\", len(df))\n", "\n", "# -------------- clean & map labels --------------\n", "# find label column\n", "label_col = None\n", "for c in (\"clean_label\", \"label\", \"labels\"):\n", " if c in df.columns:\n", " label_col = c\n", " break\n", "if label_col is None:\n", " raise KeyError(\"Could not find 'label', 'clean_label', or 'labels' column in test file.\")\n", "\n", "def map_label_val(x):\n", " if pd.isna(x):\n", " return np.nan\n", " s = str(x).strip().lower()\n", " if s in (\"pathogenic\", \"path\", \"p\", \"1\", \"true\", \"t\", \"yes\", \"y\"):\n", " return 1\n", " if s in (\"benign\", \"benign \", \"b\", \"0\", \"false\", \"f\", \"no\", \"n\"):\n", " return 0\n", " # try numeric\n", " try:\n", " nv = float(s)\n", " if nv == 1.0: return 1\n", " if nv == 0.0: return 0\n", " except:\n", " pass\n", " return np.nan\n", "\n", "raw_labels = df[label_col]\n", "y_true = raw_labels.map(map_label_val)\n", "\n", "# drop unknowns\n", "if y_true.isnull().any():\n", " drop_count = int(y_true.isnull().sum())\n", " print(f\"⚠️ Dropping {drop_count} rows with unmappable labels.\")\n", " valid_mask = ~y_true.isnull()\n", " df = df.loc[valid_mask].reset_index(drop=True)\n", " y_true = y_true.loc[valid_mask].reset_index(drop=True)\n", "else:\n", " y_true = y_true.reset_index(drop=True)\n", "\n", "y_true = y_true.astype(int)\n", "print(\"Final eval rows:\", len(y_true))\n", "if len(np.unique(y_true)) < 2:\n", " raise RuntimeError(\"Need at least two label classes in test set for evaluation.\")\n", "\n", "# -------------- evaluate each benchmark --------------\n", "results = []\n", "confusion_matrices = {}\n", "\n", "for tool in benchmarks:\n", " if tool not in df.columns:\n", " print(f\"⚠️ Skipping {tool} (not present in parquet).\")\n", " continue\n", "\n", " scores = df[tool].astype(float).copy()\n", " # fill NaN with median (stable)\n", " if scores.isna().any():\n", " median_val = float(scores.median())\n", " scores = scores.fillna(median_val)\n", "\n", " # handle constant scores (no thresholds)\n", " unique_vals = np.unique(scores)\n", " if len(unique_vals) == 1:\n", " # fallback threshold = median (or 0.5) and compute metrics\n", " best_thresh = unique_vals[0]\n", " y_pred = (scores >= best_thresh).astype(int)\n", " acc = accuracy_score(y_true, y_pred)\n", " f1_best = f1_score(y_true, y_pred, zero_division=0)\n", " results.append({\n", " \"Model\": tool.replace(\"_rankscore\", \"\").replace(\"_raw\", \"\"),\n", " \"Accuracy\": acc,\n", " \"Best_Threshold\": float(best_thresh),\n", " \"F1_Score\": float(f1_best),\n", " \"AP\": float(average_precision_score(y_true, scores))\n", " })\n", " confusion_matrices[tool.replace(\"_rankscore\", \"\").replace(\"_raw\", \"\")] = confusion_matrix(y_true, y_pred)\n", " print(f\" {tool}: constant scores — using threshold {best_thresh:.4f}\")\n", " continue\n", "\n", " # compute PR curve then choose threshold maximizing F1 (properly index thresholds)\n", " precision, recall, thresholds = precision_recall_curve(y_true, scores)\n", " # precision,recall length = len(thresholds)+1\n", " f1_all = (2 * precision * recall) / (precision + recall + 1e-12)\n", " # only consider indices that have associated thresholds => 1..end\n", " if len(thresholds) > 0:\n", " idx_rel = np.argmax(f1_all[1:]) # best among entries that map to thresholds\n", " best_idx = idx_rel + 1 # absolute index into precision/recall arrays\n", " best_thresh = float(thresholds[best_idx - 1])\n", " else:\n", " # very rare: fallback\n", " best_thresh = float(np.median(scores))\n", "\n", " y_pred = (scores >= best_thresh).astype(int)\n", " acc = accuracy_score(y_true, y_pred)\n", " f1_best = f1_score(y_true, y_pred, zero_division=0)\n", " ap = average_precision_score(y_true, scores)\n", "\n", " results.append({\n", " \"Model\": tool.replace(\"_rankscore\", \"\").replace(\"_raw\", \"\"),\n", " \"Accuracy\": acc,\n", " \"Best_Threshold\": best_thresh,\n", " \"F1_Score\": float(f1_best),\n", " \"AP\": float(ap)\n", " })\n", " confusion_matrices[tool.replace(\"_rankscore\", \"\").replace(\"_raw\", \"\")] = confusion_matrix(y_true, y_pred)\n", "\n", " print(f\" {tool}: Acc={acc:.4f} F1={f1_best:.4f} AP={ap:.4f} thresh={best_thresh:.4f}\")\n", "\n", "# -------------- report --------------\n", "if not results:\n", " print(\"❌ No benchmarks found.\")\n", "else:\n", " res_df = pd.DataFrame(results).sort_values(\"Accuracy\", ascending=False).reset_index(drop=True)\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"🏆 BENCHMARK LEADERBOARD (thresholds optimized for F1)\")\n", " print(\"=\" * 60)\n", " display_cols = [\"Model\", \"Accuracy\", \"Best_Threshold\", \"F1_Score\", \"AP\"]\n", " print(res_df[display_cols].to_string(index=False, formatters={\n", " 'Accuracy': '{:.2%}'.format,\n", " 'Best_Threshold': '{:.4f}'.format,\n", " 'F1_Score': '{:.4f}'.format,\n", " 'AP': '{:.4f}'.format\n", " }))\n", "\n", " # plot confusion matrices in grid\n", " n_tools = len(res_df)\n", " cols = 3\n", " rows = (n_tools + cols - 1) // cols\n", " fig, axes = plt.subplots(rows, cols, figsize=(5 * cols, 4 * rows))\n", " axes = axes.flatten()\n", "\n", " for i, row in res_df.iterrows():\n", " name = row[\"Model\"]\n", " cm = confusion_matrices.get(name)\n", " ax = axes[i]\n", " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False, ax=ax,\n", " xticklabels=[\"Pred Benign\", \"Pred Patho\"], yticklabels=[\"Act Benign\", \"Act Patho\"])\n", " ax.set_title(f\"{name}\\nAcc: {row['Accuracy']:.1%} F1: {row['F1_Score']:.3f}\")\n", "\n", " # hide unused axes\n", " for j in range(i + 1, len(axes)):\n", " axes[j].axis(\"off\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧠 INITIALIZING SHAP ANALYSIS...\n", "📉 Extracting embeddings for 150 samples...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Encoding Data: 1%| | 5/440 [00:03<04:30, 1.61it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Data Prepared. Background shape: torch.Size([100, 1289])\n", "⚡ Calculating SHAP values (GradientExplainer)...\n", "✅ SHAP Calculation Complete.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_10337/4129238603.py:111: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", " shap.summary_plot(\n", "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/shap/plots/_beeswarm.py:723: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", " summary_legacy(\n", "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/shap/plots/_beeswarm.py:743: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", " summary_legacy(\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_10337/4129238603.py:131: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "🏆 FINAL VERDICT:\n", "✅ SUPERHUMAN: The model is primarily looking at the DNA (64.9%)!\n" ] } ], "source": [ "# ==========================================\n", "# 📊 CELL 5: SHAP EXPLAINABILITY (DNA vs Tabular) — FIXED\n", "# ==========================================\n", "!pip install -q shap matplotlib seaborn\n", "\n", "import shap\n", "import torch\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from tqdm import tqdm\n", "\n", "print(\"🧠 INITIALIZING SHAP ANALYSIS...\")\n", "\n", "# 1. CONFIGURATION\n", "# SHAP is expensive. We use a representative subset.\n", "N_BACKGROUND = 100\n", "N_TEST = 50\n", "TOTAL_SAMPLES = N_BACKGROUND + N_TEST\n", "\n", "# 2. DEFINE SHAP WRAPPER (Focus on Classifier Head)\n", "class HeadWrapper(torch.nn.Module):\n", " def __init__(self, classifier):\n", " super().__init__()\n", " self.classifier = classifier\n", "\n", " def forward(self, combined_input):\n", " return self.classifier(combined_input)\n", "\n", "# Handle compiled models\n", "raw_model = model if not hasattr(model, \"_orig_mod\") else model._orig_mod\n", "shap_model = HeadWrapper(raw_model.classifier).to(\"cpu\").eval()\n", "\n", "# 3. GENERATE LATENT EMBEDDINGS\n", "print(f\"📉 Extracting embeddings for {TOTAL_SAMPLES} samples...\")\n", "\n", "dataloader = torch.utils.data.DataLoader(\n", " test_fast, \n", " batch_size=32, \n", " shuffle=True, \n", " collate_fn=data_collator\n", ")\n", "\n", "latent_vectors = []\n", "collected = 0\n", "\n", "model.eval()\n", "\n", "# --- FIX IS HERE: Get device from the first parameter ---\n", "device = next(model.parameters()).device \n", "# --------------------------------------------------------\n", "\n", "for batch in tqdm(dataloader, desc=\"Encoding Data\"):\n", " if collected >= TOTAL_SAMPLES: break\n", " \n", " with torch.no_grad():\n", " input_ids = batch[\"input_ids\"].to(device)\n", " mask = batch[\"attention_mask\"].to(device)\n", " # Ensure features are float32 before moving to device\n", " feats = batch[\"features\"].to(dtype=torch.float32).to(device)\n", " \n", " # A. Get DNA Embedding\n", " enc_module = raw_model.encoder\n", " out = enc_module(input_ids=input_ids, attention_mask=mask, return_dict=True)\n", " \n", " # Mean Pooling\n", " if hasattr(out, \"pooler_output\") and out.pooler_output is not None:\n", " pooled = out.pooler_output\n", " else:\n", " hidden = out.last_hidden_state\n", " mask_exp = mask.unsqueeze(-1)\n", " pooled = (hidden * mask_exp).sum(1) / mask_exp.sum(1).clamp(min=1e-9)\n", " \n", " # B. Concatenate\n", " combined = torch.cat([pooled, feats], dim=1).cpu()\n", " latent_vectors.append(combined)\n", " collected += len(batch[\"input_ids\"])\n", "\n", "# Prepare Tensors\n", "full_data = torch.cat(latent_vectors, dim=0)\n", "background_data = full_data[:N_BACKGROUND]\n", "test_data = full_data[N_BACKGROUND : N_BACKGROUND+N_TEST]\n", "\n", "print(f\"✅ Data Prepared. Background shape: {background_data.shape}\")\n", "\n", "# 4. RUN SHAP (GradientExplainer)\n", "print(\"⚡ Calculating SHAP values (GradientExplainer)...\")\n", "explainer = shap.GradientExplainer(shap_model, background_data)\n", "shap_values = explainer.shap_values(test_data)\n", "\n", "# Handle list output\n", "if isinstance(shap_values, list):\n", " vals = shap_values[1]\n", "else:\n", " vals = shap_values\n", "\n", "print(\"✅ SHAP Calculation Complete.\")\n", "\n", "# ==========================================\n", "# 📈 PLOTTING\n", "# ==========================================\n", "\n", "# A. TABULAR FEATURE IMPORTANCE\n", "# We look only at the last columns corresponding to features\n", "tabular_shap = vals[:, -len(feature_cols):]\n", "tabular_data = test_data[:, -len(feature_cols):].numpy()\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.title(\"Impact of Clinical Features on Prediction\")\n", "shap.summary_plot(\n", " tabular_shap, \n", " tabular_data, \n", " feature_names=feature_cols, \n", " show=False,\n", " plot_type=\"dot\"\n", ")\n", "plt.tight_layout()\n", "plt.savefig(\"shap_tabular_beeswarm.png\", dpi=300)\n", "plt.show()\n", "\n", "# B. DNA vs TABULAR BATTLE\n", "dna_impact = np.abs(vals[:, :-len(feature_cols)]).sum(axis=1).mean()\n", "tab_impact = np.abs(vals[:, -len(feature_cols):]).sum(axis=1).mean()\n", "\n", "total = dna_impact + tab_impact\n", "dna_pct = (dna_impact / total) * 100\n", "tab_pct = (tab_impact / total) * 100\n", "\n", "plt.figure(figsize=(8, 5))\n", "sns.barplot(\n", " x=[\"DNA Sequence\", \"Clinical Features\"], \n", " y=[dna_impact, tab_impact], \n", " palette=[\"#2ecc71\", \"#3498db\"]\n", ")\n", "plt.title(f\"Model Intelligence Source\\n(DNA: {dna_pct:.1f}% vs Features: {tab_pct:.1f}%)\")\n", "plt.ylabel(\"Mean Absolute SHAP Value\")\n", "plt.savefig(\"shap_modality_comparison.png\", dpi=300)\n", "plt.show()\n", "\n", "print(\"\\n🏆 FINAL VERDICT:\")\n", "if dna_pct > 50:\n", " print(f\"✅ SUPERHUMAN: The model is primarily looking at the DNA ({dna_pct:.1f}%)!\")\n", "else:\n", " print(f\"⚖️ HYBRID: The model relies heavily on conservation scores ({tab_pct:.1f}%)!\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# ======================================================\n", "# 🔌 Bridge: DataFrame → HF-style Torch batch for Ablation\n", "# ======================================================\n", "\n", "def prepare_batch(df_batch):\n", " # 1) Tokenize DNA\n", " enc = tokenizer(\n", " df_batch[\"raw_sequence\"].tolist(),\n", " truncation=True,\n", " padding=\"max_length\",\n", " max_length=MAX_LEN,\n", " return_tensors=\"pt\"\n", " )\n", "\n", " # 2) Pack features in correct order\n", " feats = []\n", " for c in feature_cols:\n", " feats.append(df_batch[c].astype(np.float32).values)\n", "\n", " feats = np.stack(feats, axis=1) # (batch, n_features)\n", "\n", " # 3) Labels\n", " labels = (df_batch[\"clean_label\"] == \"Pathogenic\").astype(np.int64).values\n", "\n", " return {\n", " \"input_ids\": enc[\"input_ids\"],\n", " \"attention_mask\": enc[\"attention_mask\"],\n", " \"features\": torch.tensor(feats, dtype=torch.float32),\n", " \"labels\": torch.tensor(labels, dtype=torch.long),\n", " }\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🧪 STARTING FULL ABLATION SUITE (Tensor-Native)...\n", "🔥 Model locked to: cuda\n", "📊 Establishing Baseline Performance...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Testing: Baseline Model: 1%| | 1/110 [00:01<02:15, 1.25s/it]" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Baseline AUC established: 0.9182\n", "\n", "🔥 RUNNING TENSOR ABLATION TESTS...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "🏆 ABLATION IMPACT RANKING (What matters most?):\n", " name auc drop\n", " No DNA + No Conservation 0.517988 0.400191\n", " No DNA + No gnomAD 0.557964 0.360215\n", " No DNA (Sequence Blind) 0.558319 0.359860\n", " No PhastCons 0.901000 0.017178\n", " No Tabular (Pure DNA) 0.908187 0.009992\n", " No gnomAD + No Conservation 0.908187 0.009992\n", "No Conservation (All Scores) 0.911717 0.006462\n", " No gnomAD (Freq Blind) 0.915300 0.002879\n", " No PhyloP 0.917488 0.000691\n", " No GERP (Evo Blind) 0.922640 -0.004461\n" ] } ], "source": [ "# ==========================================\n", "# 📉 CELL 2: FULL ABLATION STUDY (Tensor-Native) — FIXED\n", "# ==========================================\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import torch\n", "from tqdm import tqdm\n", "from sklearn.metrics import roc_curve, auc\n", "from torch.utils.data import DataLoader\n", "\n", "print(\"🧪 STARTING FULL ABLATION SUITE (Tensor-Native)...\")\n", "\n", "# 1. SETUP DEVICE & MODEL\n", "# Explicitly force GPU to fix \"Device Mismatch\" error\n", "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model.to(DEVICE)\n", "print(f\"🔥 Model locked to: {DEVICE}\")\n", "\n", "# 2. DEFINE ABLATION FUNCTION\n", "def evaluate_tensor_ablation(test_config, baseline_auc=None):\n", " removals = test_config.get(\"remove\", [])\n", " name = test_config[\"name\"]\n", " \n", " all_preds = []\n", " all_labels = []\n", " BATCH_SIZE = 128\n", " \n", " # Create DataLoader from 'test_fast'\n", " loader = DataLoader(test_fast, batch_size=BATCH_SIZE, shuffle=False, collate_fn=data_collator)\n", " \n", " model.eval()\n", " \n", " for batch in tqdm(loader, desc=f\"Testing: {name}\", leave=False):\n", " # Move inputs to GPU\n", " input_ids = batch[\"input_ids\"].to(DEVICE)\n", " mask = batch[\"attention_mask\"].to(DEVICE)\n", " features = batch[\"features\"].to(DEVICE)\n", " labels = batch[\"labels\"].to(\"cpu\") # Keep labels on CPU for sklearn metrics\n", " \n", " # --- DESTRUCTION LOGIC ---\n", " \n", " # 1. Kill DNA? (Shuffle input_ids within batch)\n", " if \"DNA\" in removals:\n", " perm = torch.randperm(input_ids.size(0))\n", " input_ids = input_ids[perm]\n", " mask = mask[perm]\n", " \n", " # 2. Kill Tabular Features? (Zero out columns)\n", " if \"gnomAD\" in removals:\n", " indices = [i for i, c in enumerate(feature_cols) if \"gnomad\" in c.lower()]\n", " for idx in indices:\n", " features[:, idx] = 0.0\n", " \n", " if \"GERP\" in removals:\n", " indices = [i for i, c in enumerate(feature_cols) if \"gerp\" in c.lower()]\n", " for idx in indices:\n", " features[:, idx] = 0.0\n", "\n", " if \"PhyloP\" in removals:\n", " indices = [i for i, c in enumerate(feature_cols) if \"phylop\" in c.lower()]\n", " for idx in indices:\n", " features[:, idx] = 0.0\n", " \n", " if \"PhastCons\" in removals:\n", " indices = [i for i, c in enumerate(feature_cols) if \"phastcons\" in c.lower()]\n", " for idx in indices:\n", " features[:, idx] = 0.0\n", " \n", " if \"All_Conservation\" in removals:\n", " gnomad_idxs = [i for i, c in enumerate(feature_cols) if \"gnomad\" in c.lower()]\n", " # Kill everything that is NOT gnomAD\n", " for i in range(features.size(1)):\n", " if i not in gnomad_idxs:\n", " features[:, i] = 0.0\n", " \n", " if \"All_Tabular\" in removals:\n", " features.fill_(0.0)\n", "\n", " # --- INFERENCE ---\n", " with torch.no_grad():\n", " # Ensure float32 for features\n", " features = features.to(dtype=torch.float32)\n", " \n", " out = model(input_ids=input_ids, attention_mask=mask, features=features)\n", " logits = out.logits\n", " probs = torch.softmax(logits, dim=1)[:, 1]\n", " \n", " all_preds.extend(probs.cpu().numpy())\n", " all_labels.extend(labels.numpy())\n", " \n", " # Metrics\n", " fpr, tpr, _ = roc_curve(all_labels, all_preds)\n", " roc_auc = auc(fpr, tpr)\n", " \n", " drop = 0.0\n", " if baseline_auc is not None:\n", " drop = baseline_auc - roc_auc\n", " \n", " return {\"name\": name, \"auc\": roc_auc, \"drop\": drop, \"fpr\": fpr, \"tpr\": tpr}\n", "\n", "# 3. CALCULATE BASELINE\n", "print(\"📊 Establishing Baseline Performance...\")\n", "baseline_res = evaluate_tensor_ablation({\"name\": \"Baseline Model\", \"remove\": []})\n", "baseline_auc = baseline_res[\"auc\"]\n", "print(f\"✅ Baseline AUC established: {baseline_auc:.4f}\")\n", "\n", "# 4. DEFINE ABLATION GROUPS\n", "ablation_tests = [\n", " {\"name\": \"No DNA (Sequence Blind)\", \"remove\": [\"DNA\"]},\n", " {\"name\": \"No gnomAD (Freq Blind)\", \"remove\": [\"gnomAD\"]},\n", " {\"name\": \"No GERP (Evo Blind)\", \"remove\": [\"GERP\"]},\n", " {\"name\": \"No PhyloP\", \"remove\": [\"PhyloP\"]},\n", " {\"name\": \"No PhastCons\", \"remove\": [\"PhastCons\"]},\n", " {\"name\": \"No Conservation (All Scores)\", \"remove\": [\"All_Conservation\"]},\n", " {\"name\": \"No DNA + No gnomAD\", \"remove\": [\"DNA\", \"gnomAD\"]},\n", " {\"name\": \"No DNA + No Conservation\", \"remove\": [\"DNA\", \"All_Conservation\"]},\n", " {\"name\": \"No gnomAD + No Conservation\", \"remove\": [\"gnomAD\", \"All_Conservation\"]},\n", " {\"name\": \"No Tabular (Pure DNA)\", \"remove\": [\"All_Tabular\"]}\n", "]\n", "\n", "# 5. RUN ALL TESTS\n", "results = [baseline_res] \n", "plt.figure(figsize=(12, 8))\n", "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", "\n", "# Plot Baseline\n", "plt.plot(baseline_res[\"fpr\"], baseline_res[\"tpr\"], lw=3, color='black', \n", " label=f'Baseline (AUC={baseline_auc:.3f})')\n", "\n", "print(\"\\n🔥 RUNNING TENSOR ABLATION TESTS...\")\n", "for test in ablation_tests:\n", " res = evaluate_tensor_ablation(test, baseline_auc=baseline_auc)\n", " results.append(res)\n", " \n", " plt.plot(res[\"fpr\"], res[\"tpr\"], lw=1.5, alpha=0.7, label=f'{res[\"name\"]} (AUC={res[\"auc\"]:.3f})')\n", "\n", "# 6. FINALIZE PLOT\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate')\n", "plt.ylabel('True Positive Rate')\n", "plt.title('PathoPreter Ablation: Feature Importance Analysis')\n", "plt.legend(loc=\"lower right\", fontsize='small')\n", "plt.grid(True, alpha=0.3)\n", "plt.savefig(\"ablation_study_10_tests.png\", dpi=300)\n", "plt.show()\n", "\n", "# 7. PRINT LEADERBOARD\n", "print(\"\\n🏆 ABLATION IMPACT RANKING (What matters most?):\")\n", "res_df = pd.DataFrame([r for r in results if r[\"name\"] != \"Baseline Model\"])\n", "res_df = res_df[[\"name\", \"auc\", \"drop\"]]\n", "res_df = res_df.sort_values(\"drop\", ascending=False)\n", "print(res_df.to_string(index=False))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "📦 PACKAGING MODEL FROM: ./PathoPreter_Final_A100\n", "✅ Saved model_metadata.json (feature columns preserved)\n", "✅ Generated modeling_pathopreter.py (Auto-load script)\n", "🚀 Zipping to PathoPreter_A100_Complete.zip...\n", " - Storing (Fast): model.safetensors\n", "\n", "✅ SUCCESS! Download 'PathoPreter_A100_Complete.zip' now.\n", " It contains the Model, Tokenizer, Scalers, and Python Class Code.\n" ] } ], "source": [ "# ==========================================\n", "# 📦 CELL 6: THE \"TIME CAPSULE\" PACKAGER\n", "# ==========================================\n", "import json\n", "import zipfile\n", "import os\n", "from tqdm import tqdm\n", "\n", "# Configuration\n", "SOURCE_DIR = \"./PathoPreter_Final_A100\"\n", "OUTPUT_ZIP = \"PathoPreter_A100_Complete.zip\"\n", "\n", "print(f\"📦 PACKAGING MODEL FROM: {SOURCE_DIR}\")\n", "\n", "# 1. SAVE CRITICAL METADATA (Column Names)\n", "# This is the #1 thing people forget 2 months later!\n", "metadata = {\n", " \"feature_cols\": feature_cols,\n", " \"num_labels\": NUM_LABELS,\n", " \"feature_dim\": FEATURE_DIM,\n", " \"model_type\": \"nt-500m-hybrid\",\n", " \"base_model\": \"InstaDeepAI/nucleotide-transformer-500m-human-ref\"\n", "}\n", "\n", "with open(os.path.join(SOURCE_DIR, \"model_metadata.json\"), \"w\") as f:\n", " json.dump(metadata, f, indent=4)\n", "print(\"✅ Saved model_metadata.json (feature columns preserved)\")\n", "\n", "# 2. GENERATE PYTHON MODEL CLASS FILE\n", "# We write the class definition to a .py file so you can import it easily later\n", "model_code = \"\"\"\n", "import torch\n", "import torch.nn as nn\n", "from transformers import AutoModel\n", "from transformers.modeling_outputs import SequenceClassifierOutput\n", "\n", "class SequenceClassificationWithFeatures(nn.Module):\n", " def __init__(self, encoder, hidden_size, feature_dim, num_labels):\n", " super().__init__()\n", " self.encoder = encoder\n", " self.feature_dim = feature_dim\n", " self.classifier = nn.Sequential(\n", " nn.Linear(hidden_size + feature_dim, hidden_size),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(hidden_size, num_labels)\n", " )\n", "\n", " @classmethod\n", " def from_pretrained(cls, path, device=\"cpu\"):\n", " import json\n", " import os\n", " from safetensors.torch import load_file\n", " \n", " # Load Config\n", " with open(os.path.join(path, \"model_metadata.json\"), \"r\") as f:\n", " meta = json.load(f)\n", " \n", " # Load Encoder\n", " encoder = AutoModel.from_pretrained(path, trust_remote_code=True)\n", " \n", " # Init Model\n", " model = cls(\n", " encoder=encoder, \n", " hidden_size=encoder.config.hidden_size,\n", " feature_dim=meta[\"feature_dim\"],\n", " num_labels=meta[\"num_labels\"]\n", " )\n", " \n", " # Load Weights\n", " state_dict = load_file(os.path.join(path, \"model.safetensors\"))\n", " model.load_state_dict(state_dict)\n", " \n", " return model.to(device)\n", "\n", " def forward(self, input_ids=None, attention_mask=None, features=None, labels=None):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)\n", "\n", " if hasattr(out, \"pooler_output\") and out.pooler_output is not None:\n", " pooled = out.pooler_output\n", " else:\n", " last = out.last_hidden_state\n", " mask = attention_mask.unsqueeze(-1)\n", " pooled = (last * mask).sum(1) / mask.sum(1).clamp(min=1e-9)\n", "\n", " # Cast features to match model dtype/device\n", " features = features.to(pooled.device).to(pooled.dtype)\n", " x = torch.cat([pooled, features], dim=1)\n", " logits = self.classifier(x)\n", "\n", " loss = None\n", " if labels is not None:\n", " loss = nn.CrossEntropyLoss()(logits, labels)\n", "\n", " return SequenceClassifierOutput(loss=loss, logits=logits)\n", "\"\"\"\n", "\n", "with open(os.path.join(SOURCE_DIR, \"modeling_pathopreter.py\"), \"w\") as f:\n", " f.write(model_code)\n", "print(\"✅ Generated modeling_pathopreter.py (Auto-load script)\")\n", "\n", "# 3. CREATE README INSTRUCTIONS\n", "readme_text = \"\"\"\n", "PATHO-PRETER v1 (A100 Fine-Tune)\n", "================================\n", "Trained by: Rohit\n", "Base Model: Nucleotide Transformer 500M\n", "Architecture: Hybrid (DNA Sequence + 9 Conservation Scores)\n", "\n", "HOW TO LOAD:\n", "1. Unzip this folder.\n", "2. Place 'modeling_pathopreter.py' in your python path.\n", "3. Run:\n", " \n", " from modeling_pathopreter import SequenceClassificationWithFeatures\n", " import numpy as np\n", "\n", " # Load Model\n", " model = SequenceClassificationWithFeatures.from_pretrained(\"./PathoPreter_Final_A100\")\n", " \n", " # Load Scalers (Crucial for inference!)\n", " means = np.load(\"./PathoPreter_Final_A100/feat_means.npy\")\n", " stds = np.load(\"./PathoPreter_Final_A100/feat_stds.npy\")\n", "\n", " # Preprocessing\n", " # (features - means) / stds\n", "\"\"\"\n", "with open(os.path.join(SOURCE_DIR, \"README.txt\"), \"w\") as f:\n", " f.write(readme_text)\n", "\n", "# 4. SMART ZIP (No Compression for Weights)\n", "print(f\"🚀 Zipping to {OUTPUT_ZIP}...\")\n", "\n", "with zipfile.ZipFile(OUTPUT_ZIP, 'w') as zf:\n", " for root, dirs, files in os.walk(SOURCE_DIR):\n", " for file in files:\n", " file_path = os.path.join(root, file)\n", " arcname = os.path.relpath(file_path, start=SOURCE_DIR)\n", " \n", " # DECISION: Compress text, Store binary weights (for speed)\n", " if file.endswith(\".safetensors\") or file.endswith(\".bin\"):\n", " compression = zipfile.ZIP_STORED\n", " print(f\" - Storing (Fast): {arcname}\")\n", " else:\n", " compression = zipfile.ZIP_DEFLATED\n", " \n", " zf.write(file_path, arcname, compress_type=compression)\n", "\n", "print(f\"\\n✅ SUCCESS! Download '{OUTPUT_ZIP}' now.\")\n", "print(\" It contains the Model, Tokenizer, Scalers, and Python Class Code.\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Samples: 14073 14073\n", "Real AUC: 0.9182\n", "Permutation AUC:0.5044\n", "✅ No leakage or memorization detected\n" ] } ], "source": [ "from sklearn.metrics import roc_auc_score\n", "import numpy as np\n", "\n", "# Use the same objects you created in calibration cell\n", "# These came from: preds = trainer.predict(test_fast)\n", "\n", "y_test = preds.label_ids # shape = (N_test,)\n", "logits = preds.predictions # shape = (N_test, 2)\n", "probs = torch.softmax(torch.tensor(logits), dim=-1)[:,1].numpy()\n", "\n", "print(\"Samples:\", len(y_test), len(probs))\n", "\n", "# --- real AUC ---\n", "real_auc = roc_auc_score(y_test, probs)\n", "\n", "# --- permutation ---\n", "perm = np.random.permutation(y_test)\n", "perm_auc = roc_auc_score(perm, probs)\n", "\n", "print(f\"Real AUC: {real_auc:.4f}\")\n", "print(f\"Permutation AUC:{perm_auc:.4f}\")\n", "\n", "if perm_auc < 0.55:\n", " print(\"✅ No leakage or memorization detected\")\n", "else:\n", " print(\"❌ Possible leakage — investigate splits\")\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "🧬 PathoPreter Performance by Allele Frequency\n", "============================================================\n", "Ultra-rare (<1e-6) | N= 8027 | Pathogenic%= 67.1% | AUC=0.9238\n", "Rare (1e-6–1e-4) | N= 4665 | Pathogenic%= 34.7% | AUC=0.9069\n", "Low-freq (1e-4–1e-2) | N= 967 | Pathogenic%= 5.5% | AUC=0.8219\n", "Common (>1e-2) | N= 414 | Pathogenic%= 0.5% | AUC=0.9472\n", "\n", "📊 Summary\n" ] }, { "data": { "text/html": [ "
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AF binNPathogenic %ROC-AUC
0Ultra-rare (<1e-6)80270.6713590.923828
1Rare (1e-6–1e-4)46650.3466240.906936
2Low-freq (1e-4–1e-2)9670.0548090.821890
3Common (>1e-2)4140.0048310.947209
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" ], "text/plain": [ " AF bin N Pathogenic % ROC-AUC\n", "0 Ultra-rare (<1e-6) 8027 0.671359 0.923828\n", "1 Rare (1e-6–1e-4) 4665 0.346624 0.906936\n", "2 Low-freq (1e-4–1e-2) 967 0.054809 0.821890\n", "3 Common (>1e-2) 414 0.004831 0.947209" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ======================================================\n", "# 🧬 CELL: Performance by Allele Frequency (gnomAD bins)\n", "# ======================================================\n", "\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.metrics import roc_auc_score\n", "\n", "# Reload test dataframe (same order as test_fast)\n", "df = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\").reset_index(drop=True)\n", "\n", "# Safety check\n", "assert len(df) == len(probs), \"❌ Test set mismatch — check alignment\"\n", "\n", "# Attach predictions\n", "df[\"PathoPreter\"] = probs\n", "df[\"y_true\"] = y_test\n", "\n", "# Define frequency bins used in clinical genomics\n", "bins = [0, 1e-6, 1e-4, 1e-2, 1]\n", "labels = [\"Ultra-rare (<1e-6)\", \"Rare (1e-6–1e-4)\", \"Low-freq (1e-4–1e-2)\", \"Common (>1e-2)\"]\n", "\n", "df[\"AF_bin\"] = pd.cut(df[\"gnomad_af\"], bins=bins, labels=labels, include_lowest=True)\n", "\n", "print(\"\\n🧬 PathoPreter Performance by Allele Frequency\")\n", "print(\"=\" * 60)\n", "\n", "rows = []\n", "for bin in labels:\n", " sub = df[df[\"AF_bin\"] == bin]\n", " if len(sub) < 100:\n", " print(f\"{bin:18s} | too few samples ({len(sub)})\")\n", " continue\n", " \n", " if len(np.unique(sub[\"y_true\"])) < 2:\n", " print(f\"{bin:18s} | only one class present\")\n", " continue\n", " \n", " auc = roc_auc_score(sub[\"y_true\"], sub[\"PathoPreter\"])\n", " pos_rate = sub[\"y_true\"].mean()\n", " \n", " rows.append((bin, len(sub), pos_rate, auc))\n", " print(f\"{bin:18s} | N={len(sub):6d} | Pathogenic%={pos_rate*100:5.1f}% | AUC={auc:.4f}\")\n", "\n", "# Summary table\n", "af_df = pd.DataFrame(rows, columns=[\"AF bin\", \"N\", \"Pathogenic %\", \"ROC-AUC\"])\n", "\n", "print(\"\\n📊 Summary\")\n", "display(af_df)\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🏥 CASE 1: Testing potential pathogenic variant...\n", "\n", "========================================\n", "🧬 VARIANT DIAGNOSIS REPORT\n", "========================================\n", "Input Sequence: AAAAAAAAAA...AAAAAAAAAA\n", "Prediction: 🟢 BENIGN\n", "Probability: 0.0124\n", "Confidence: 98.8%\n", "========================================\n", "\n", "😊 CASE 2: Testing common benign variant...\n", "\n", "========================================\n", "🧬 VARIANT DIAGNOSIS REPORT\n", "========================================\n", "Input Sequence: TTTTTTTTTT...TTTTTTTTTT\n", "Prediction: 🟢 BENIGN\n", "Probability: 0.0012\n", "Confidence: 99.9%\n", "========================================\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_10337/1800919678.py:47: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:253.)\n", " features_tensor = torch.tensor([feats], dtype=torch.float32).to(DEVICE)\n" ] }, { "data": { "text/plain": [ "0.001151399570517242" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ==========================================\n", "# 🚀 CELL 7: THE \"SINGLE VARIANT PREDICTOR\" (The App)\n", "# ==========================================\n", "import torch\n", "import numpy as np\n", "\n", "def predict_single_variant(dna_sequence, gnomad_af=0.0, conservation_scores=None):\n", " \"\"\"\n", " Simulates a real-world clinical query.\n", " dna_sequence: 60bp string (center is the mutation)\n", " gnomad_af: Allele Frequency (0.0 to 1.0)\n", " conservation_scores: list of [GERP, PhyloP, etc...] (Defaults to averages if None)\n", " \"\"\"\n", " \n", " # 1. Validation\n", " dna_sequence = dna_sequence.upper().strip()\n", " if len(dna_sequence) < 10:\n", " return \"❌ Error: Sequence too short. Needs ~60bp context.\"\n", " \n", " # 2. Tokenize DNA (Using the k-mer tokenizer from training)\n", " # Note: We manually simulate tokenization for this demo or use the loaded tokenizer\n", " inputs = tokenizer(dna_sequence, return_tensors=\"pt\", padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True)\n", " \n", " # 3. Prepare Tabular Features\n", " # If user doesn't provide detailed scores, we use the DATASET MEANS (Imputation)\n", " # This is exactly what web-servers like CADD do for missing data.\n", " if conservation_scores is None:\n", " # Create a vector of zeros (representing mean values after normalization)\n", " # We assume 0.0 because we standardized features (mean=0, std=1) during training\n", " feats = np.zeros(len(feature_cols)) \n", " else:\n", " feats = np.array(conservation_scores)\n", "\n", " # 4. Inject Specific inputs (like gnomAD) if known\n", " # Find gnomAD column index\n", " try:\n", " g_idx = [i for i, c in enumerate(feature_cols) if \"gnomad\" in c.lower()][0]\n", " # Normalize gnomAD (Assuming approx mean=0.005, std=0.05 from training data stats)\n", " # For the demo, we just set it raw, but in prod you load the scaler.\n", " feats[g_idx] = (gnomad_af - feat_means[g_idx]) / feat_stds[g_idx]\n", " except:\n", " pass\n", "\n", " # 5. Convert to Tensor\n", " input_ids = inputs[\"input_ids\"].to(DEVICE)\n", " mask = inputs[\"attention_mask\"].to(DEVICE)\n", " features_tensor = torch.tensor([feats], dtype=torch.float32).to(DEVICE)\n", " \n", " # 6. Predict\n", " model.eval()\n", " with torch.no_grad():\n", " out = model(input_ids, mask, features_tensor)\n", " prob = torch.softmax(out.logits, dim=1)[0, 1].item()\n", " \n", " # 7. Format Output\n", " risk_level = \"🔴 PATHOGENIC\" if prob > 0.5 else \"🟢 BENIGN\"\n", " confidence = prob if prob > 0.5 else 1 - prob\n", " \n", " print(\"\\n\" + \"=\"*40)\n", " print(f\"🧬 VARIANT DIAGNOSIS REPORT\")\n", " print(\"=\"*40)\n", " print(f\"Input Sequence: {dna_sequence[:10]}...{dna_sequence[-10:]}\")\n", " print(f\"Prediction: {risk_level}\")\n", " print(f\"Probability: {prob:.4f}\")\n", " print(f\"Confidence: {confidence:.1%}\")\n", " print(\"=\"*40 + \"\\n\")\n", " \n", " return prob\n", "\n", "# --- TEST CASE 1: A Real Pathogenic Mutation (CFTR DeltaF508 equivalent context) ---\n", "# High conservation (implicit), low frequency\n", "seq_patho = \"A\" * 60 # Placeholder sequence for demo\n", "print(\"🏥 CASE 1: Testing potential pathogenic variant...\")\n", "predict_single_variant(seq_patho, gnomad_af=0.00001)\n", "\n", "# --- TEST CASE 2: A Common Benign Variant ---\n", "# High frequency (50%)\n", "seq_benign = \"T\" * 60 # Placeholder\n", "print(\"😊 CASE 2: Testing common benign variant...\")\n", "predict_single_variant(seq_benign, gnomad_af=0.50)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "🧬 PATHOPRETER — VARIANT REPORT\n", "==================================================\n", "Sequence (snippet): AAAAAAAAAAAA...AAAAAAAAAAAA\n", "gnomAD AF: 4.500e-01\n", "Prediction: 🟢 BENIGN\n", "Pathogenic prob: 0.0000\n", "==================================================\n", "\n", "\n", "==================================================\n", "🧬 PATHOPRETER — VARIANT REPORT\n", "==================================================\n", "Sequence (snippet): TTGCTGACCTGC...CTGAGGCTGACT\n", "gnomAD AF: 1.000e-05\n", "Prediction: 🟢 BENIGN\n", "Pathogenic prob: 0.0018\n", "==================================================\n", "\n" ] }, { "data": { "text/plain": [ "0.0018386653391644359" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import numpy as np\n", "\n", "# Make sure model is on correct device\n", "DEVICE = next(model.parameters()).device\n", "\n", "def predict_single_variant(\n", " dna_sequence,\n", " gnomad_af=0.0,\n", " conservation_scores=None,\n", "):\n", " \"\"\"\n", " Clinical-style single variant inference.\n", "\n", " dna_sequence: ~60–1000bp centered on variant\n", " gnomad_af: allele frequency (0–1)\n", " conservation_scores: list in same order as feature_cols\n", " [GERP++, GERP91, phyloP100, phyloP470, phyloP17,\n", " phastCons100, phastCons470, phastCons17]\n", " (optional)\n", " \"\"\"\n", "\n", " # --------------------------\n", " # 1. Validate DNA\n", " # --------------------------\n", " dna_sequence = dna_sequence.upper().strip()\n", " if len(dna_sequence) < 30:\n", " raise ValueError(\"DNA sequence too short — need ≥30bp context\")\n", "\n", " # --------------------------\n", " # 2. Tokenize DNA\n", " # --------------------------\n", " inputs = tokenizer(\n", " dna_sequence,\n", " return_tensors=\"pt\",\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=tokenizer.model_max_length,\n", " )\n", "\n", " input_ids = inputs[\"input_ids\"].to(DEVICE)\n", " mask = inputs[\"attention_mask\"].to(DEVICE)\n", "\n", " # --------------------------\n", " # 3. Prepare tabular features\n", " # --------------------------\n", " # If user does not provide scores → assume dataset mean (0 after normalization)\n", " if conservation_scores is None:\n", " feats = np.zeros(len(feature_cols), dtype=np.float32)\n", " else:\n", " feats = np.array(conservation_scores, dtype=np.float32)\n", "\n", " # Inject gnomAD if available\n", " for i, c in enumerate(feature_cols):\n", " if \"gnomad\" in c.lower():\n", " feats[i] = (gnomad_af - feat_means[i]) / feat_stds[i]\n", "\n", " # Normalize all other features\n", " feats = (feats - feat_means) / feat_stds\n", "\n", " # Convert safely to tensor\n", " features_tensor = torch.from_numpy(feats.astype(np.float32)).unsqueeze(0).to(DEVICE)\n", "\n", " # --------------------------\n", " # 4. Run model\n", " # --------------------------\n", " model.eval()\n", " with torch.no_grad():\n", " out = model(\n", " input_ids=input_ids,\n", " attention_mask=mask,\n", " features=features_tensor,\n", " )\n", " prob = torch.softmax(out.logits, dim=1)[0, 1].item()\n", "\n", " # --------------------------\n", " # 5. Format clinical output\n", " # --------------------------\n", " if prob >= 0.9:\n", " risk = \"🔴 HIGHLY PATHOGENIC\"\n", " elif prob >= 0.7:\n", " risk = \"🟠 LIKELY PATHOGENIC\"\n", " elif prob >= 0.3:\n", " risk = \"🟡 UNCERTAIN\"\n", " else:\n", " risk = \"🟢 BENIGN\"\n", "\n", " print(\"\\n\" + \"=\"*50)\n", " print(\"🧬 PATHOPRETER — VARIANT REPORT\")\n", " print(\"=\"*50)\n", " print(f\"Sequence (snippet): {dna_sequence[:12]}...{dna_sequence[-12:]}\")\n", " print(f\"gnomAD AF: {gnomad_af:.3e}\")\n", " print(f\"Prediction: {risk}\")\n", " print(f\"Pathogenic prob: {prob:.4f}\")\n", " print(\"=\"*50 + \"\\n\")\n", "\n", " return prob\n", "\n", "\n", "# ============================\n", "# 🧪 Example tests\n", "# ============================\n", "\n", "# Fake benign DNA (nonsense)\n", "seq_benign = \"A\" * 80\n", "predict_single_variant(seq_benign, gnomad_af=0.45)\n", "\n", "# Realistic coding DNA-like context (toy example)\n", "seq_realistic = \"TTGCTGACCTGCTGCTGAGGCTGACTTCAGGTGCTGATGGCTGAGGCTGACT\"\n", "predict_single_variant(seq_realistic, gnomad_af=0.00001)\n", "\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "⚠️ 'gene' column missing. Extracting from variant_id...\n", "\n", "================================================================================\n", "From 14000 balanced (1:1 path:benign) test samples\n", "================================================================================\n", "\n", "================================================================================\n", "💀 WORST FALSE NEGATIVES (Dangerous Misses): 260 cases\n", "The patient is SICK, but model said SAFE with high confidence.\n", "================================================================================\n", " gene variant_id PathoPreter_Prob gnomad_af\n", "Unknown 19_3532086_C_A 0.001082 0.0\n", "Unknown 6_116120454_A_T 0.001389 0.0\n", "Unknown 2_47800542_G_T 0.001410 0.0\n", "Unknown 19_3532086_C_G 0.001598 0.0\n", "Unknown 17_80110949_C_G 0.002051 0.0\n", "\n", "================================================================================\n", "🚨 WORST FALSE POSITIVES (False Alarms): 769 cases\n", "The patient is HEALTHY, but model said SICK with high confidence.\n", "================================================================================\n", " gene variant_id PathoPreter_Prob gnomad_af\n", "Unknown 10_110780974_A_T 0.999030 0.000000\n", "Unknown 18_45038505_A_G 0.998675 0.000010\n", "Unknown 18_2923825_G_A 0.997155 0.000003\n", "Unknown 10_106706548_G_A 0.997110 0.000205\n", "Unknown 5_88728563_G_A 0.996972 0.000000\n", "\n", "💡 INSIGHT:\n", "- If FN are mostly in 'Low-Freq' range, it's likely Recessive Carrier issues.\n", "- If FP have high gnomAD, check if they are risk factors rather than direct causes.\n" ] } ], "source": [ "# ==========================================\n", "# 🕵️‍♂️ CELL 8: THE AUTOPSY (Error Analysis) — FIXED\n", "# ==========================================\n", "import pandas as pd\n", "\n", "# 1. Reload Data & Predictions\n", "df = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\").reset_index(drop=True)\n", "df[\"PathoPreter_Prob\"] = probs # From previous prediction cell\n", "df[\"y_true\"] = y_test # From previous prediction cell\n", "\n", "# 2. Smart Column Detection\n", "# If 'gene' is missing, try to find a similar column or extract from variant_id\n", "available_cols = df.columns.tolist()\n", "gene_col = \"gene\"\n", "\n", "if \"gene\" not in available_cols:\n", " # Try common alternatives\n", " for alt in [\"gene_symbol\", \"symbol\", \"gene_name\", \"genename\"]:\n", " if alt in available_cols:\n", " gene_col = alt\n", " break\n", " else:\n", " # Fallback: Extract gene from variant_id if it looks like \"NM_...(GENE):...\"\n", " print(\"⚠️ 'gene' column missing. Extracting from variant_id...\")\n", " df[\"gene\"] = df[\"variant_id\"].apply(lambda x: x.split(\"(\")[1].split(\")\")[0] if \"(\" in x and \")\" in x else \"Unknown\")\n", " gene_col = \"gene\"\n", "\n", "# 3. Define Columns to Display (Only use what actually exists)\n", "cols_to_show = [gene_col, \"variant_id\", \"PathoPreter_Prob\"]\n", "if \"gnomad_af\" in available_cols:\n", " cols_to_show.append(\"gnomad_af\")\n", "\n", "# 4. Identify Failures\n", "# False Negatives: Actual=1, Model said 0 (Prob < 0.2 for \"Confident Miss\")\n", "fn_mask = (df[\"y_true\"] == 1) & (df[\"PathoPreter_Prob\"] < 0.2)\n", "\n", "# False Positives: Actual=0, Model said 1 (Prob > 0.8 for \"Confident Alarm\")\n", "fp_mask = (df[\"y_true\"] == 0) & (df[\"PathoPreter_Prob\"] > 0.8)\n", "\n", "print(\"\\n\" + \"=\"*80)\n", "print(f\"From 14000 balanced (1:1 path:benign) test samples\")\n", "print(\"=\"*80)\n", "# 5. Display WORST False Negatives (Dangerous Misses)\n", "print(\"\\n\" + \"=\"*80)\n", "print(f\"💀 WORST FALSE NEGATIVES (Dangerous Misses): {fn_mask.sum()} cases\")\n", "print(\"The patient is SICK, but model said SAFE with high confidence.\")\n", "print(\"=\"*80)\n", "\n", "if fn_mask.sum() > 0:\n", " # Sort by lowest probability (most confident wrong answer)\n", " worst_fn = df[fn_mask].sort_values(\"PathoPreter_Prob\").head(5)\n", " print(worst_fn[cols_to_show].to_string(index=False))\n", "else:\n", " print(\"✅ Amazing! No confident False Negatives found.\")\n", "\n", "# 6. Display WORST False Positives (False Alarms)\n", "print(\"\\n\" + \"=\"*80)\n", "print(f\"🚨 WORST FALSE POSITIVES (False Alarms): {fp_mask.sum()} cases\")\n", "print(\"The patient is HEALTHY, but model said SICK with high confidence.\")\n", "print(\"=\"*80)\n", "\n", "if fp_mask.sum() > 0:\n", " # Sort by highest probability (most confident wrong answer)\n", " worst_fp = df[fp_mask].sort_values(\"PathoPreter_Prob\", ascending=False).head(5)\n", " print(worst_fp[cols_to_show].to_string(index=False))\n", "else:\n", " print(\"✅ Amazing! No confident False Positives found.\")\n", "\n", "print(\"\\n💡 INSIGHT:\")\n", "print(\"- If FN are mostly in 'Low-Freq' range, it's likely Recessive Carrier issues.\")\n", "print(\"- If FP have high gnomAD, check if they are risk factors rather than direct causes.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 2 }