From d5a1a6d7ce511152118f074e62560f6e95af30c2 Mon Sep 17 00:00:00 2001 From: honghanhh Date: Mon, 15 Sep 2025 17:40:49 +0200 Subject: [PATCH 1/3] feat: add lm baseline --- lib/conflicts/baselines/Makefile | 34 +++ lib/conflicts/baselines/README.md | 115 +++++++++ lib/conflicts/baselines/__init__.py | 1 + lib/conflicts/baselines/commons/__init__.py | 1 + lib/conflicts/baselines/commons/config.py | 40 +++ .../baselines/commons/data/__init__.py | 1 + .../data/load_and_preprocess_dataset.py | 199 +++++++++++++++ .../baselines/commons/metrics/__init__.py | 1 + .../commons/metrics/classification_metrics.py | 120 +++++++++ lib/conflicts/baselines/configs/default.yaml | 33 +++ lib/conflicts/baselines/requirements.txt | 18 ++ lib/conflicts/baselines/train.py | 238 ++++++++++++++++++ 12 files changed, 801 insertions(+) create mode 100644 lib/conflicts/baselines/Makefile create mode 100644 lib/conflicts/baselines/README.md create mode 100644 lib/conflicts/baselines/__init__.py create mode 100644 lib/conflicts/baselines/commons/__init__.py create mode 100644 lib/conflicts/baselines/commons/config.py create mode 100644 lib/conflicts/baselines/commons/data/__init__.py create mode 100644 lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py create mode 100644 lib/conflicts/baselines/commons/metrics/__init__.py create mode 100644 lib/conflicts/baselines/commons/metrics/classification_metrics.py create mode 100644 lib/conflicts/baselines/configs/default.yaml create mode 100644 lib/conflicts/baselines/requirements.txt create mode 100644 lib/conflicts/baselines/train.py diff --git a/lib/conflicts/baselines/Makefile b/lib/conflicts/baselines/Makefile new file mode 100644 index 0000000..ebe705e --- /dev/null +++ b/lib/conflicts/baselines/Makefile @@ -0,0 +1,34 @@ +# Conflict Detection Baselines Makefile + +.PHONY: help install test train example clean + +help: ## Show this help message + @echo "Conflict Detection Baselines" + @echo "==========================" + @echo "" + @echo "Available commands:" + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-15s\033[0m %s\n", $$1, $$2}' + +install: ## Install dependencies + pip install -r requirements.txt + +train: ## Train the baseline model + python train.py + +train-debug: ## Train with debug settings + python train.py training.epochs=2 training.eval_steps=10 training.logging_steps=5 + +train-bert: ## Train with BERT base model + python train.py model.model_name_or_path=bert-base-uncased + +train-biobert: ## Train with BioBERT model + python train.py model.model_name_or_path=microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext + +clean: ## Clean output files + rm -rf outputs/ + rm -f example_predictions.json + rm -f *.log + +setup: install ## Complete setup (install dependencies) + +all: setup train ## Complete pipeline (setup + train) diff --git a/lib/conflicts/baselines/README.md b/lib/conflicts/baselines/README.md new file mode 100644 index 0000000..ede110b --- /dev/null +++ b/lib/conflicts/baselines/README.md @@ -0,0 +1,115 @@ +# Conflict Detection and Classification Baselines + +This directory contains baseline implementations for conflict detection and classification in +clinical documents. + +## Overview + +The baseline implements a text classification approach to detect and classify conflicts between +pairs of clinical documents into 6 categories: + +1. **Opposition Conflicts** - Contradictory findings about the same clinical entity +2. **Anatomical Conflicts** - Contradictions regarding body structures and their presence/absence +3. **Value Conflicts** - Contradictory measurements, lab values, or quantitative findings +4. **Contraindication Conflicts** - Conflicts between allergies/contraindications and treatments +5. **Comparison Conflicts** - Temporal contradictions +6. **Descriptive Conflicts** - Statement contradictions + +## Structure + +``` +baselines/ +├── configs/ +│ └── default.yaml # Hydra configuration +├── commons/ +│ ├── data/ +│ │ └── load_and_preprocess_dataset.py +│ └── metrics/ +│ └── classification_metrics.py +├── outputs/ # Generated prediction files +├── train.py # Main training script +└── README.md +``` + +## Usage + +### 1. Install Dependencies + +```bash +pip install transformers datasets scikit-learn wandb hydra-core +``` + +### 2. Run Training + +```bash +python train.py +``` + +### 3. Custom Configuration + +```bash +python train.py model.model_name_or_path=microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext training.epochs=15 +``` + +## Configuration + +The training can be configured via `configs/default.yaml`: + +- **Model**: Uses BiomedBERT by default (can be changed to other models) +- **Data**: 80% train, 10% validation, 10% test split +- **Training**: 10 epochs, batch size 8, learning rate 2e-5 +- **Logging**: Weights & Biases integration + +## Data Format + +The baseline expects a processed JSON file with the following structure: + +- Each entry contains `data` with `doc_1` and `doc_2` text fields +- `annotations` contain conflict type information +- The loader extracts and creates: + - `combined_text`: Concatenated text from both documents + - `conflict_label`: Integer label (0-5) for conflict type + - `conflict_type`: String label for conflict type + - `doc1_id`, `doc2_id`: Document identifiers + - `subject_id`: Patient identifier + +## Output + +The training generates: + +- **Model checkpoints** in `outputs/` +- **Predictions** in `outputs/` (numpy arrays, text files) +- **Metrics** logged to Weights & Biases +- **Classification report** with per-class performance + +## Metrics + +The baseline computes: + +- Accuracy +- Precision, Recall, F1 (macro and weighted) +- Per-class metrics +- Confusion matrix +- Confidence statistics + +## Customization + +To adapt for your data: + +1. **Modify data loading** in `commons/data/load_and_preprocess_dataset.py` +2. **Adjust preprocessing** in the `preprocess_function` +3. **Update conflict types** in the configuration +4. **Change model** via Hydra overrides + +## Example + +```bash +# Train with different model +python train.py model.model_name_or_path=bert-base-uncased + +# Train with different hyperparameters +python train.py training.epochs=20 training.learning_rate=1e-5 + +# Train with different data split +python train.py data.test_ratio=0.15 data.val_ratio=0.15 +``` diff --git a/lib/conflicts/baselines/__init__.py b/lib/conflicts/baselines/__init__.py new file mode 100644 index 0000000..f63b562 --- /dev/null +++ b/lib/conflicts/baselines/__init__.py @@ -0,0 +1 @@ +# Conflict Detection Baselines diff --git a/lib/conflicts/baselines/commons/__init__.py b/lib/conflicts/baselines/commons/__init__.py new file mode 100644 index 0000000..abce62b --- /dev/null +++ b/lib/conflicts/baselines/commons/__init__.py @@ -0,0 +1 @@ +# Commons module for conflict detection baselines diff --git a/lib/conflicts/baselines/commons/config.py b/lib/conflicts/baselines/commons/config.py new file mode 100644 index 0000000..d384ceb --- /dev/null +++ b/lib/conflicts/baselines/commons/config.py @@ -0,0 +1,40 @@ +import os +import sys +from pathlib import Path + +# Conflict type mapping - single source of truth +CONFLICT_TYPES = { + "opposition": 0, + "anatomical": 1, + "value": 2, + "contraindication": 3, + "comparison": 4, + "descriptive": 5, +} + +# Conflict type labels in order +CONFLICT_LABELS = list(CONFLICT_TYPES.keys()) + +# Reverse mapping +ID_TO_LABEL = {idx: label for label, idx in CONFLICT_TYPES.items()} + + +def setup_paths(): + """Setup Python path for imports.""" + parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) + if parent_dir not in sys.path: + sys.path.append(parent_dir) + + +def get_conflicts_data_path(data_path: str = "processed/_12092025.json") -> Path: + """ + Get the full path to the conflicts data file. + + Args: + data_path: Relative path to data file + + Returns: + Full path to data file + """ + conflicts_dir = Path(__file__).parent.parent.parent + return conflicts_dir / data_path diff --git a/lib/conflicts/baselines/commons/data/__init__.py b/lib/conflicts/baselines/commons/data/__init__.py new file mode 100644 index 0000000..1557ce5 --- /dev/null +++ b/lib/conflicts/baselines/commons/data/__init__.py @@ -0,0 +1 @@ +# Data loading utilities diff --git a/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py b/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py new file mode 100644 index 0000000..c3ac7b6 --- /dev/null +++ b/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py @@ -0,0 +1,199 @@ +import json +import logging +from typing import Dict, List + +import pandas as pd +from datasets import Dataset +from sklearn.model_selection import train_test_split + +from ..config import CONFLICT_TYPES, get_conflicts_data_path + +logger = logging.getLogger(__name__) + + +def load_conflict_dataset(data_path: str = "processed/_12092025.json") -> pd.DataFrame: + """ + Load the conflict dataset from processed JSON file. + + Args: + data_path: Path to the processed JSON file + + Returns: + DataFrame with conflict data + """ + full_data_path = get_conflicts_data_path(data_path) + + if not full_data_path.exists(): + raise FileNotFoundError(f"Dataset not found at {full_data_path}") + + logger.info(f"Loading dataset from {full_data_path}") + + # Load JSON data + with open(full_data_path, "r") as f: + data = json.load(f) + + # Convert to DataFrame + df = process_conflict_json_data(data) + + return df + + +def process_conflict_json_data(data: List[Dict]) -> pd.DataFrame: + """ + Process the conflict JSON data into a DataFrame. + + Args: + data: List of conflict data entries from JSON + + Returns: + DataFrame with conflict data + """ + conflict_pairs = [] + + for entry in data: + # Extract data from the entry + doc_data = entry["data"] + annotations = entry.get("annotations", []) + + # Get document texts + doc1_text = doc_data.get("doc_1", "") + doc2_text = doc_data.get("doc_2", "") + + # Create combined text for classification + combined_text = f"Document 1: {doc1_text[:500]} [SEP] Document 2: {doc2_text[:500]}" + + # Extract conflict information from annotations + conflict_type = "opposition" # Default + conflict_label = CONFLICT_TYPES[conflict_type] + + if annotations and len(annotations) > 0: + # Get conflict type from the first annotation + first_annotation = annotations[0] + if "result" in first_annotation and len(first_annotation["result"]) > 0: + result = first_annotation["result"][0] + if "conflict_type" in result: + conflict_type = result["conflict_type"] + conflict_label = CONFLICT_TYPES.get(conflict_type, 0) + + # Create unique IDs for documents + doc1_id = f"doc1_{len(conflict_pairs)}" + doc2_id = f"doc2_{len(conflict_pairs)}" + + conflict_pairs.append( + { + "doc1_id": doc1_id, + "doc2_id": doc2_id, + "doc1_text": doc1_text, + "doc2_text": doc2_text, + "combined_text": combined_text, + "conflict_type": conflict_type, + "conflict_label": conflict_label, + "subject_id": f"subject_{len(conflict_pairs)}", # Generate unique subject ID + "category1": "processed_doc1", + "category2": "processed_doc2", + "timestamp_1": doc_data.get("timestamp_1", ""), + "timestamp_2": doc_data.get("timestamp_2", ""), + "created_at": doc_data.get("created_at", ""), + } + ) + + # Convert to DataFrame + conflict_df = pd.DataFrame(conflict_pairs) + + logger.info(f"Processed {len(conflict_df)} conflict pairs from JSON data") + logger.info(f"Conflict type distribution:\n{conflict_df['conflict_type'].value_counts()}") + + return conflict_df + + +def preprocess_text(text: str, max_length: int = 512) -> str: + """ + Preprocess text for classification. + + Args: + text: Input text + max_length: Maximum length to truncate + + Returns: + Preprocessed text + """ + # Basic text cleaning + text = text.strip() + + # Truncate if too long + if len(text) > max_length: + text = text[:max_length] + + return text + + +def load_and_preprocess_dataset( + data_path: str = "processed/_12092025.json", + test_ratio: float = 0.2, + val_ratio: float = 0.1, + max_length: int = 512, + random_state: int = 42, +): + """ + Load and preprocess the conflict detection dataset. + + Args: + data_path: Path to the processed JSON file + test_ratio: Ratio for test split + val_ratio: Ratio for validation split (from remaining data) + max_length: Maximum text length + random_state: Random state for reproducibility + + Returns: + Tuple of (train_dataset, val_dataset, test_dataset) + """ + # Load the dataset + df = load_conflict_dataset(data_path) + + # Preprocess text + df["combined_text"] = df["combined_text"].apply(lambda x: preprocess_text(x, max_length)) + + # Check class distribution + class_counts = df["conflict_type"].value_counts() + logger.info(f"Class distribution: {class_counts.to_dict()}") + + # Check if we can stratify (each class needs at least 2 samples) + min_class_count = class_counts.min() + can_stratify = min_class_count >= 2 + + if can_stratify: + logger.info("Using stratified splitting") + # Split the data with stratification + train_df, test_df = train_test_split( + df, test_size=test_ratio, random_state=random_state, stratify=df["conflict_type"] + ) + + train_df, val_df = train_test_split( + train_df, + test_size=val_ratio, + random_state=random_state, + stratify=train_df["conflict_type"], + ) + else: + logger.warning( + f"Some classes have only {min_class_count} sample(s). " + f"Using random splitting without stratification." + ) + # Split the data without stratification + train_df, test_df = train_test_split(df, test_size=test_ratio, random_state=random_state) + + train_df, val_df = train_test_split( + train_df, test_size=val_ratio, random_state=random_state + ) + + # Create datasets + train_dataset = Dataset.from_pandas(train_df) + val_dataset = Dataset.from_pandas(val_df) + test_dataset = Dataset.from_pandas(test_df) + + logger.info( + f"Dataset splits - Train: {len(train_dataset)}," + f" Val: {len(val_dataset)}, Test: {len(test_dataset)}" + ) + + return train_dataset, val_dataset, test_dataset diff --git a/lib/conflicts/baselines/commons/metrics/__init__.py b/lib/conflicts/baselines/commons/metrics/__init__.py new file mode 100644 index 0000000..ff67375 --- /dev/null +++ b/lib/conflicts/baselines/commons/metrics/__init__.py @@ -0,0 +1 @@ +# Metrics utilities diff --git a/lib/conflicts/baselines/commons/metrics/classification_metrics.py b/lib/conflicts/baselines/commons/metrics/classification_metrics.py new file mode 100644 index 0000000..4b74327 --- /dev/null +++ b/lib/conflicts/baselines/commons/metrics/classification_metrics.py @@ -0,0 +1,120 @@ +from typing import Any, Dict, List + +import numpy as np +from sklearn.metrics import ( + accuracy_score, + classification_report, + confusion_matrix, + precision_recall_fscore_support, +) + + +def compute_classification_metrics( + y_true: np.ndarray, y_pred: np.ndarray, labels: List[str] = None +) -> Dict[str, Any]: + """ + Compute comprehensive classification metrics. + + Args: + y_true: True labels + y_pred: Predicted labels + labels: Label names for reporting + + Returns: + Dictionary with metrics + """ + # Basic metrics + accuracy = accuracy_score(y_true, y_pred) + + # Precision, recall, F1 (macro and weighted averages) + precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support( + y_true, y_pred, average="macro", zero_division=0 + ) + precision_weighted, recall_weighted, f1_weighted, _ = precision_recall_fscore_support( + y_true, y_pred, average="weighted", zero_division=0 + ) + + # Per-class metrics + precision_per_class, recall_per_class, f1_per_class, support = precision_recall_fscore_support( + y_true, y_pred, average=None, zero_division=0 + ) + + # Confusion matrix + cm = confusion_matrix(y_true, y_pred) + + metrics = { + "accuracy": accuracy, + "precision_macro": precision_macro, + "recall_macro": recall_macro, + "f1_macro": f1_macro, + "precision_weighted": precision_weighted, + "recall_weighted": recall_weighted, + "f1_weighted": f1_weighted, + "confusion_matrix": cm.tolist(), + "support": support.tolist(), + } + + # Add per-class metrics if labels provided + if labels is not None: + per_class_metrics = {} + for i, label in enumerate(labels): + per_class_metrics[f"{label}_precision"] = precision_per_class[i] + per_class_metrics[f"{label}_recall"] = recall_per_class[i] + per_class_metrics[f"{label}_f1"] = f1_per_class[i] + per_class_metrics[f"{label}_support"] = support[i] + + metrics.update(per_class_metrics) + + return metrics + + +def print_classification_report( + y_true: np.ndarray, y_pred: np.ndarray, labels: List[str] = None +) -> str: + """ + Print detailed classification report. + + Args: + y_true: True labels + y_pred: Predicted labels + labels: Label names for reporting + + Returns: + Classification report string + """ + if labels is None: + labels = [f"Class_{i}" for i in range(len(np.unique(y_true)))] + + report = classification_report(y_true, y_pred, target_names=labels, zero_division=0, digits=4) + + return report + + +def compute_confusion_matrix_metrics(cm: np.ndarray) -> Dict[str, float]: + """ + Compute additional metrics from confusion matrix. + + Args: + cm: Confusion matrix + + Returns: + Dictionary with additional metrics + """ + # True positives, false positives, false negatives for each class + tp = np.diag(cm) + fp = cm.sum(axis=0) - tp + fn = cm.sum(axis=1) - tp + tn = cm.sum() - (tp + fp + fn) + + # Additional metrics + specificity = tn / (tn + fp) + sensitivity = tp / (tp + fn) # Same as recall + + return { + "specificity": specificity.tolist(), + "sensitivity": sensitivity.tolist(), + "true_positives": tp.tolist(), + "false_positives": fp.tolist(), + "false_negatives": fn.tolist(), + "true_negatives": tn.tolist(), + } diff --git a/lib/conflicts/baselines/configs/default.yaml b/lib/conflicts/baselines/configs/default.yaml new file mode 100644 index 0000000..4c2c046 --- /dev/null +++ b/lib/conflicts/baselines/configs/default.yaml @@ -0,0 +1,33 @@ +defaults: + - _self_ + - override /hydra/sweeper: basic + - override /hydra/launcher: basic + +data: + test_ratio: 0.2 + val_ratio: 0.1 + max_length: 512 + data_path: "processed/_12092025.json" + +model: + model_name_or_path: "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" + num_labels: 6 + +training: + batch_size: 8 + gradient_accumulation_steps: 4 + epochs: 10 + weight_decay: 0.01 + learning_rate: 2e-5 + push_to_hub: False + eval_steps: 100 + logging_steps: 50 + warmup_steps: 100 + +logging: + output_dir: "outputs" + entity: "clinical-dream-team" + project: "conflict-detection" + tags: ["baseline", "conflict-classification"] + +global_seed: 42 diff --git a/lib/conflicts/baselines/requirements.txt b/lib/conflicts/baselines/requirements.txt new file mode 100644 index 0000000..550fa71 --- /dev/null +++ b/lib/conflicts/baselines/requirements.txt @@ -0,0 +1,18 @@ +# Core dependencies +torch>=1.9.0 +transformers>=4.20.0 +datasets>=2.0.0 +scikit-learn>=1.0.0 +pandas>=1.3.0 +numpy>=1.21.0 + +# Configuration and logging +hydra-core>=1.2.0 +omegaconf>=2.2.0 +wandb>=0.12.0 + +# Data processing +pyarrow>=8.0.0 + +# Optional: for better performance +accelerate>=0.20.0 diff --git a/lib/conflicts/baselines/train.py b/lib/conflicts/baselines/train.py new file mode 100644 index 0000000..2b235f7 --- /dev/null +++ b/lib/conflicts/baselines/train.py @@ -0,0 +1,238 @@ +import logging +import time +from pathlib import Path + +import hydra +import numpy as np +import wandb +from commons.config import CONFLICT_LABELS, ID_TO_LABEL, setup_paths +from commons.data.load_and_preprocess_dataset import load_and_preprocess_dataset +from commons.metrics.classification_metrics import ( + compute_classification_metrics, + print_classification_report, +) +from omegaconf import DictConfig, omegaconf +from sklearn.metrics import accuracy_score, precision_recall_fscore_support +from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + EvalPrediction, + Trainer, + TrainingArguments, +) + +# Setup paths and logging +setup_paths() +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def preprocess_function(examples, tokenizer, max_length=512): + """ + Preprocess the data for training. + + Args: + examples: Batch of examples + tokenizer: Tokenizer object + max_length: Maximum sequence length + + Returns: + Dictionary with tokenized inputs and labels + """ + # Tokenize the combined text + tokenized = tokenizer( + examples["combined_text"], + truncation=True, + padding=True, + max_length=max_length, + return_tensors="pt", + ) + + # Add labels + tokenized["labels"] = examples["conflict_label"] + + return tokenized + + +def compute_metrics(p: EvalPrediction) -> dict: + """ + Compute metrics for evaluation. + + Args: + p: EvalPrediction object + + Returns: + Dictionary with metrics + """ + predictions, labels = p.predictions, p.label_ids + + # Get predicted class labels + predictions = np.argmax(predictions, axis=1) + + # Compute basic metrics + accuracy = accuracy_score(labels, predictions) + precision, recall, f1, _ = precision_recall_fscore_support( + labels, predictions, average="weighted", zero_division=0 + ) + + return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1} + + +@hydra.main(config_name="default", config_path="configs", version_base="1.2") +def main(config: DictConfig): + """ + Main function to train the conflict classification model. + + Args: + config: Hydra configuration object + """ + # Initialize wandb + run = wandb.init( + entity=config.logging.entity, + project=config.logging.project, + tags=config.logging.tags, + settings=wandb.Settings(start_method="thread"), + ) + + # Update wandb config + container = omegaconf.OmegaConf.to_container(config, resolve=True, throw_on_missing=True) + run.config.update(container) + + logger.info("Loading and preprocessing dataset...") + + # Load datasets + dataset_train, dataset_val, dataset_test = load_and_preprocess_dataset( + data_path=config.data.data_path, + test_ratio=config.data.test_ratio, + val_ratio=config.data.val_ratio, + max_length=config.data.max_length, + random_state=config.global_seed, + ) + + # Load tokenizer + logger.info(f"Loading tokenizer: {config.model.model_name_or_path}") + tokenizer = AutoTokenizer.from_pretrained(config.model.model_name_or_path) + + # Preprocess datasets + logger.info("Preprocessing datasets...") + dataset_train = dataset_train.map( + preprocess_function, + batched=True, + fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, + ) + dataset_val = dataset_val.map( + preprocess_function, + batched=True, + fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, + ) + dataset_test = dataset_test.map( + preprocess_function, + batched=True, + fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, + ) + + # Create label mapping + id2label = ID_TO_LABEL + label2id = {label: i for i, label in enumerate(CONFLICT_LABELS)} + + # Load model + logger.info(f"Loading model: {config.model.model_name_or_path}") + model = AutoModelForSequenceClassification.from_pretrained( + config.model.model_name_or_path, + num_labels=config.model.num_labels, + id2label=id2label, + label2id=label2id, + ) + + # Training arguments + training_args = TrainingArguments( + output_dir=config.logging.output_dir, + evaluation_strategy="steps", + save_strategy="steps", + learning_rate=config.training.learning_rate, + per_device_train_batch_size=config.training.batch_size, + per_device_eval_batch_size=config.training.batch_size, + gradient_accumulation_steps=config.training.gradient_accumulation_steps, + num_train_epochs=config.training.epochs, + weight_decay=config.training.weight_decay, + warmup_steps=config.training.warmup_steps, + push_to_hub=config.training.push_to_hub, + eval_steps=config.training.eval_steps, + logging_steps=config.training.logging_steps, + load_best_model_at_end=True, + greater_is_better=True, + metric_for_best_model="f1", + save_total_limit=3, + report_to=["wandb"], + lr_scheduler_type="linear", + seed=config.global_seed, + ) + + # Create trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=dataset_train, + eval_dataset=dataset_val, + tokenizer=tokenizer, + compute_metrics=compute_metrics, + ) + + # Train the model + logger.info("Starting training...") + trainer.train() + + # Evaluate on test set + logger.info("Evaluating on test set...") + test_results = trainer.evaluate(eval_dataset=dataset_test, metric_key_prefix="test") + logger.info(f"Test results: {test_results}") + + # Generate predictions + logger.info("Generating predictions...") + predictions, _, _ = trainer.predict(dataset_test) + + # Save predictions (simplified) + run_name = run.name + run_id = run.id + timestamp = time.strftime("%Y%m%d-%H%M%S") + + # Create output directory + output_dir = Path("outputs") + output_dir.mkdir(exist_ok=True) + + # Save predictions as numpy array + predictions_path = output_dir / f"predictions_{run_name}_{run_id}_{timestamp}.npy" + np.save(predictions_path, predictions) + + # Save predictions as simple text file + predicted_labels = np.argmax(predictions, axis=1) + labels_path = output_dir / f"predicted_labels_{run_name}_{run_id}_{timestamp}.txt" + with open(labels_path, "w") as f: + for label in predicted_labels: + f.write(f"{id2label[label]}\n") + + logger.info(f"Predictions saved to {predictions_path} and {labels_path}") + + # Print detailed classification report + predicted_labels = np.argmax(predictions, axis=1) + true_labels = [example["conflict_label"] for example in dataset_test] + + report = print_classification_report(np.array(true_labels), predicted_labels, CONFLICT_LABELS) + logger.info(f"\nClassification Report:\n{report}") + + # Compute comprehensive metrics + metrics = compute_classification_metrics( + np.array(true_labels), predicted_labels, CONFLICT_LABELS + ) + logger.info(f"Comprehensive metrics: {metrics}") + + # Log metrics to wandb + wandb.log({"test_classification_report": report}) + wandb.log(metrics) + + wandb.finish() + logger.info("Training completed successfully!") + + +if __name__ == "__main__": + main() From 70cfe695fe894753d2529b58e59d806dee87b767 Mon Sep 17 00:00:00 2001 From: honghanhh Date: Wed, 17 Sep 2025 14:52:52 +0200 Subject: [PATCH 2/3] feat: add raw llm baseline --- lib/conflicts/llm_baselines/Makefile | 35 + lib/conflicts/llm_baselines/README.md | 94 + .../llm_baselines/configs/__init__.py | 1 + .../llm_baselines/configs/default.yaml | 35 + .../llm_baselines/configs/few_shot.yaml | 10 + .../llm_baselines/configs/one_shot.yaml | 10 + .../llm_baselines/configs/zero_shot.yaml | 10 + .../llm_baselines/llm_baselines/__init__.py | 28 + .../llm_baselines/llm_baseline.py | 867 ++++++ lib/conflicts/llm_baselines/poetry.lock | 2608 +++++++++++++++++ lib/conflicts/llm_baselines/pyproject.toml | 77 + lib/conflicts/llm_baselines/run_baseline.py | 9 + 12 files changed, 3784 insertions(+) create mode 100644 lib/conflicts/llm_baselines/Makefile create mode 100644 lib/conflicts/llm_baselines/README.md create mode 100644 lib/conflicts/llm_baselines/configs/__init__.py create mode 100644 lib/conflicts/llm_baselines/configs/default.yaml create mode 100644 lib/conflicts/llm_baselines/configs/few_shot.yaml create mode 100644 lib/conflicts/llm_baselines/configs/one_shot.yaml create mode 100644 lib/conflicts/llm_baselines/configs/zero_shot.yaml create mode 100644 lib/conflicts/llm_baselines/llm_baselines/__init__.py create mode 100644 lib/conflicts/llm_baselines/llm_baselines/llm_baseline.py create mode 100644 lib/conflicts/llm_baselines/poetry.lock create mode 100644 lib/conflicts/llm_baselines/pyproject.toml create mode 100644 lib/conflicts/llm_baselines/run_baseline.py diff --git a/lib/conflicts/llm_baselines/Makefile b/lib/conflicts/llm_baselines/Makefile new file mode 100644 index 0000000..8a744c1 --- /dev/null +++ b/lib/conflicts/llm_baselines/Makefile @@ -0,0 +1,35 @@ +.PHONY: help install clean run-zero run-one run-few run-all + +help: ## Show this help message + @echo "Available commands:" + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}' + +install: ## Install the package and dependencies + pip install -e . + +run-zero: ## Run zero-shot baseline + python run_baseline.py --config-name=zero_shot + +run-one: ## Run one-shot baseline + python run_baseline.py --config-name=one_shot + +run-few: ## Run few-shot baseline + python run_baseline.py --config-name=few_shot + +run-qwen: ## Run with Qwen model + python run_baseline.py --config-name=qwen_models + +run-all: run-zero run-one run-few ## Run all baselines + +clean: ## Clean output files + rm -rf outputs/ + rm -rf wandb/ + rm -rf __pycache__/ + rm -rf .pytest_cache/ + find . -name "*.pyc" -delete + find . -name "__pycache__" -type d -exec rm -rf {} + + +setup-env: ## Setup environment variables (requires manual API key) + @echo "Please set your Groq API key:" + @echo "export GROQ_API_KEY='your_api_key_here'" + @echo "Then run: make install" diff --git a/lib/conflicts/llm_baselines/README.md b/lib/conflicts/llm_baselines/README.md new file mode 100644 index 0000000..ee5f398 --- /dev/null +++ b/lib/conflicts/llm_baselines/README.md @@ -0,0 +1,94 @@ +# LLM Baselines for Clinical Text Conflict Classification + +This package provides zero-shot, one-shot, and few-shot prompting baselines for clinical text +conflict classification using various LLM models via Groq API. + +## Installation + +1. Install dependencies: + +```bash +cd lib/conflicts/llm_baselines +pip install -e . +``` + +2. Set up your Groq API key: + +```bash +export GROQ_API_KEY="your_groq_api_key_here" +``` + +## Usage + +### Basic Usage + +Run a zero-shot baseline: + +```bash +python run_baseline.py --config-name=zero_shot +``` + +Run a one-shot baseline: + +```bash +python run_baseline.py --config-name=one_shot +``` + +Run a few-shot baseline: + +```bash +python run_baseline.py --config-name=few_shot +``` + +### Advanced Usage + +Run with a specific model: + +```bash +python run_baseline.py --config-name=qwen_models model.name=qwen/qwen2.5-72b-instruct +``` + +Run with custom parameters: + +```bash +python run_baseline.py \ + --config-name=few_shot \ + experiment.num_examples=10 \ + experiment.batch_size=5 \ + model.name=qwen/qwen2.5-14b-instruct +``` + +### Configuration + +The package uses Hydra for configuration management. Key configuration options: + +- `experiment.shot_type`: "zero", "one", or "few" +- `experiment.num_examples`: Number of examples for few-shot prompting +- `experiment.batch_size`: Batch size for processing +- `model.name`: Model name from Groq API +- `data.data_path`: Path to the conflict dataset + +## Project Structure + +This package uses a simplified single-file design: + +``` +llm_baselines/ +├── llm_baseline.py # All functionality in one consolidated module +├── __init__.py # Package exports +└── configs/ # Configuration files +``` + +All classes and functions are available from the main module: + +- `LLMBaselineRunner` - Main experiment runner +- `GroqLLMClient` - Groq API client +- `LLMEvaluator` - Evaluation metrics +- `ZeroShotPrompt`, `OneShotPrompt`, `FewShotPrompt` - Prompt templates +- `get_prompt_template()` - Factory function for prompts + +## Requirements + +- Python 3.11+ +- Groq API key +- See `pyproject.toml` for full dependency list diff --git a/lib/conflicts/llm_baselines/configs/__init__.py b/lib/conflicts/llm_baselines/configs/__init__.py new file mode 100644 index 0000000..b560e5e --- /dev/null +++ b/lib/conflicts/llm_baselines/configs/__init__.py @@ -0,0 +1 @@ +# Configs module for llm_baselines diff --git a/lib/conflicts/llm_baselines/configs/default.yaml b/lib/conflicts/llm_baselines/configs/default.yaml new file mode 100644 index 0000000..4d5ab41 --- /dev/null +++ b/lib/conflicts/llm_baselines/configs/default.yaml @@ -0,0 +1,35 @@ +defaults: + - _self_ + - override /hydra/sweeper: basic + - override /hydra/launcher: basic + +# Data configuration +data: + test_ratio: 0.2 + val_ratio: 0.1 + max_length: 512 + data_path: "processed/_12092025.json" + +# Model configuration +model: + name: "qwen/qwen3-32b" + +# API configuration +api: + api_key: null # Will use GROQ_API_KEY environment variable + +# Experiment configuration +experiment: + shot_type: "zero" # Options: "zero", "one", "few" + num_examples: 0 # Number of examples for prompting (0 for zero-shot, 1 for one-shot, ignored for few-shot) + batch_size: 10 # Batch size for processing + +# Logging configuration +logging: + output_dir: "outputs" + entity: "clinical-dream-team" + project: "llm-baselines" + tags: ["llm", "conflict-classification", "clinical-text"] + +# Global settings +global_seed: 42 diff --git a/lib/conflicts/llm_baselines/configs/few_shot.yaml b/lib/conflicts/llm_baselines/configs/few_shot.yaml new file mode 100644 index 0000000..6cae41d --- /dev/null +++ b/lib/conflicts/llm_baselines/configs/few_shot.yaml @@ -0,0 +1,10 @@ +defaults: + - default + +experiment: + shot_type: "few" + num_examples: 6 + batch_size: 10 + +logging: + tags: ["llm", "conflict-classification", "clinical-text", "few-shot"] diff --git a/lib/conflicts/llm_baselines/configs/one_shot.yaml b/lib/conflicts/llm_baselines/configs/one_shot.yaml new file mode 100644 index 0000000..32d37ea --- /dev/null +++ b/lib/conflicts/llm_baselines/configs/one_shot.yaml @@ -0,0 +1,10 @@ +defaults: + - default + +experiment: + shot_type: "one" + num_examples: 1 + batch_size: 15 + +logging: + tags: ["llm", "conflict-classification", "clinical-text", "one-shot"] diff --git a/lib/conflicts/llm_baselines/configs/zero_shot.yaml b/lib/conflicts/llm_baselines/configs/zero_shot.yaml new file mode 100644 index 0000000..b1fc943 --- /dev/null +++ b/lib/conflicts/llm_baselines/configs/zero_shot.yaml @@ -0,0 +1,10 @@ +defaults: + - default + +experiment: + shot_type: "zero" + num_examples: 0 + batch_size: 20 + +logging: + tags: ["llm", "conflict-classification", "clinical-text", "zero-shot"] diff --git a/lib/conflicts/llm_baselines/llm_baselines/__init__.py b/lib/conflicts/llm_baselines/llm_baselines/__init__.py new file mode 100644 index 0000000..3614639 --- /dev/null +++ b/lib/conflicts/llm_baselines/llm_baselines/__init__.py @@ -0,0 +1,28 @@ +from .llm_baseline import ( + AVAILABLE_MODELS, + CONFLICT_LABELS, + DEFAULT_MODEL, + FewShotPrompt, + GroqLLMClient, + LLMBaselineRunner, + LLMEvaluator, + OneShotPrompt, + ZeroShotPrompt, + get_prompt_template, + main, +) + +__version__ = "0.1.0" +__all__ = [ + "LLMBaselineRunner", + "GroqLLMClient", + "LLMEvaluator", + "ZeroShotPrompt", + "OneShotPrompt", + "FewShotPrompt", + "get_prompt_template", + "main", + "CONFLICT_LABELS", + "AVAILABLE_MODELS", + "DEFAULT_MODEL", +] diff --git a/lib/conflicts/llm_baselines/llm_baselines/llm_baseline.py b/lib/conflicts/llm_baselines/llm_baselines/llm_baseline.py new file mode 100644 index 0000000..a0e7a86 --- /dev/null +++ b/lib/conflicts/llm_baselines/llm_baselines/llm_baseline.py @@ -0,0 +1,867 @@ +import json +import logging +import os +import sys +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +import hydra +import numpy as np +import wandb +from commons.config import CONFLICT_LABELS, ID_TO_LABEL +from commons.data.load_and_preprocess_dataset import load_and_preprocess_dataset +from datasets import Dataset +from groq import Groq +from omegaconf import DictConfig, omegaconf +from sklearn.metrics import ( + accuracy_score, + classification_report, + confusion_matrix, + precision_recall_fscore_support, +) + +baselines_path = Path(__file__).parent.parent.parent / "baselines" +sys.path.insert(0, str(baselines_path)) + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +AVAILABLE_MODELS = [ + "qwen/qwen3-32b" + # ADD MORE MODELS HERE +] + +DEFAULT_MODEL = "qwen/qwen3-32b" + + +class PromptTemplate: + """Base class for prompt templates.""" + + def __init__(self, conflict_labels: List[str] = None): + self.conflict_labels = conflict_labels or CONFLICT_LABELS + self.labels_str = ", ".join(self.conflict_labels) + self.definitions = """Conflict type definitions: + +1. `opposition` Conflicts: Contradictory findings about the same clinical entity + Examples: + - Normal vs abnormal findings of same body structure: Left breast: Unremarkable\ +<> Left breast demonstrates persistent circumscribed masses + - Negative vs positive statements: No cardiopulmonary disease <> Bibasilar atelectasis + - Lab/vital sign interpretation: Low blood sugar at admission <> Patient\ + admitted with hyperglycemia + - Opposite disorders: Hypernatremia <> Hyponatremia + - Sex information opposites: Female patient <> Testis: Unremarkable + +2. `anatomical` Conflicts: Contradictions regarding body structures and their\ + presence/absence + Examples: + - Absent vs present structures: Cholelithiasis <> The gallbladder is absent + - History of removal vs present structure: Bilat mastectomy (2010) <>\ + Left breast: solid mass + - Imaging vs clinical finding: Procedure: Chest XR <> Brain lesion + - Laterality mismatch: Stable ductal carcinoma of left breast <> Right breast carcinoma + +3. `value` Conflicts: Contradictory measurements, lab values, or quantitative findings + Examples: + - Condition vs measurement: Hypoglycemia <> Blood glucose 145 + - Conflicting lab measurements: 02/11/2022 WBC 8.0 <> 02/11/2022 WBC 5.5 + +4. `contraindication` Conflicts: Conflicts between allergies/contraindications\ + and treatments + Examples: + - Allergy vs prescribed medication: Allergic to acetaminophen <>\ + Home meds include Tylenol + +5. `comparison` Conflicts: Contradictory comparative statements or temporal changes + Examples: + - Increased/decreased vs measurements: Ultrasound shows 3 cm lesion,\ + previously 4 cm, indicating increase + +6. `descriptive` Conflicts: Contradictory descriptive statements about the same condition + Examples: + - Positive vs unlikely statements: Lungs: Pleural effusion unlikely <> Assessment:\ + Pleural effusion + - Conflicting characteristics: Stable small pleural effusion <> Impression:\ + Small pleural effusion + - Multiple vs single statements: Findings: 9 mm lesion right kidney <>\ + Assessment: Right renal lesions""" + + def _get_base_prompt(self) -> str: + """Get the base prompt structure.""" + return f"""You are a medical expert tasked with classifying clinical text conflicts. + +Your task is to analyze pairs of clinical documents and determine what type of \ +conflict exists between them. + +Available conflict types: {self.labels_str} + +{self.definitions} + +IMPORTANT: Respond with ONLY the conflict type name (one of: {self.labels_str}). \ +Do not include any reasoning, explanation, or additional text.""" + + def create_prompt(self, text: str, examples: List[Dict[str, Any]] = None) -> str: + """Create a prompt for the given text and examples.""" + raise NotImplementedError + + +class ZeroShotPrompt(PromptTemplate): + """Zero-shot prompt template.""" + + def create_prompt(self, text: str, examples: List[Dict[str, Any]] = None) -> str: + """Create a zero-shot prompt.""" + return f"""{self._get_base_prompt()} + +Please analyze the following clinical text pair and classify the conflict type: + +{text} + +Conflict type:""" + + +class OneShotPrompt(PromptTemplate): + """One-shot prompt template.""" + + def create_prompt(self, text: str, examples: List[Dict[str, Any]] = None) -> str: + """Create a one-shot prompt.""" + if not examples or len(examples) == 0: + return ZeroShotPrompt(self.conflict_labels).create_prompt(text, examples) + + example = examples[0] + return f"""{self._get_base_prompt()} + +Here's an example: + +Example: +{example['text']} +Conflict type: {example['label']} + +Now analyze the following clinical text pair and respond with ONLY the conflict type: + +{text} +Conflict type:""" + + +class FewShotPrompt(PromptTemplate): + """Few-shot prompt template.""" + + def create_prompt(self, text: str, examples: List[Dict[str, Any]] = None) -> str: + """Create a few-shot prompt.""" + if not examples or len(examples) == 0: + return ZeroShotPrompt(self.conflict_labels).create_prompt(text, examples) + + # Create examples section + examples_text = "" + for i, example in enumerate(examples, 1): + examples_text += ( + f"Example {i}:\n{example['text']}\nConflict type: {example['label']}\n\n" + ) + + return f"""{self._get_base_prompt()} + +Here are {len(examples)} examples, one for each conflict type: + +{examples_text} + +Now analyze the following clinical text pair and respond with ONLY the conflict type: + +{text} +Conflict type:""" + + +def get_prompt_template(shot_type: str) -> PromptTemplate: + """Get the appropriate prompt template based on shot type.""" + if shot_type == "zero": + return ZeroShotPrompt() + elif shot_type == "one": + return OneShotPrompt() + elif shot_type == "few": + return FewShotPrompt() + else: + raise ValueError(f"Unknown shot type: {shot_type}. Must be 'zero', 'one', or 'few'.") + + +class GroqLLMClient: + """Client for interacting with Groq API.""" + + def __init__(self, api_key: Optional[str] = None, model: str = DEFAULT_MODEL): + """Initialize the Groq client.""" + if api_key is None: + api_key = self._get_api_key() + + self.client = Groq(api_key=api_key) + self.model = model + self.conflict_labels = CONFLICT_LABELS + self.id_to_label = ID_TO_LABEL + + def _get_api_key(self) -> str: + """Get API key from environment variable.""" + api_key = os.getenv("GROQ_API_KEY") + if not api_key: + raise ValueError( + "GROQ_API_KEY environment variable not set. " + "Please set it with your Groq API key." + ) + return api_key + + def classify_text( + self, + text: str, + prompt_template: PromptTemplate, + examples: List[Dict[str, Any]] = None, + max_retries: int = 3, + delay: float = 1.0, + ) -> Dict[str, Any]: + """Classify text using the LLM.""" + prompt = prompt_template.create_prompt(text, examples) + + for attempt in range(max_retries): + try: + # Use streaming to get the complete response + completion = self.client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": prompt}], + temperature=0.6, + top_p=0.95, + stream=True, + stop=None, + ) + + # Collect the streaming response + raw_response = "" + for chunk in completion: + if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content: + raw_response += chunk.choices[0].delta.content + + raw_response = raw_response.strip() + + # Check if we got any response + if not raw_response: + raise ValueError("Empty response from LLM") + logger.info(f"LLM raw response: {raw_response}") + predicted_label = self._parse_response(raw_response) + confidence = self._calculate_confidence(raw_response, predicted_label) + logger.info(f"Parsed label: {predicted_label}, Confidence: {confidence}") + + return { + "predicted_label": predicted_label, + "raw_response": raw_response, + "confidence": confidence, + "success": True, + "error": None, + } + + except Exception as e: + logger.warning(f"Attempt {attempt + 1} failed: {str(e)}") + if attempt < max_retries - 1: + time.sleep(delay * (2**attempt)) + else: + return { + "predicted_label": "opposition", + "raw_response": "", + "confidence": 0.0, + "success": False, + "error": str(e), + } + + def _parse_response(self, response: str) -> str: + """Parse the LLM response to extract the predicted label.""" + response = response.lower().strip() + + # First, try to find conflict type after "conflict type:" or at the start + lines = response.split("\n") + for line in lines: + line = line.strip() + if "conflict type:" in line: + # Extract text after "conflict type:" + after_colon = line.split("conflict type:")[-1].strip() + for label in self.conflict_labels: + if label.lower() in after_colon: + return label + elif line and not line.startswith("<") and not line.startswith("think"): + # Check if line starts with a conflict type + for label in self.conflict_labels: + if line.startswith(label.lower()): + return label + + # Fallback: search for any conflict type in the response + for label in self.conflict_labels: + if label.lower() in response: + return label + + # Last resort: check for partial matches + for label in self.conflict_labels: + if any(word in response for word in label.split()): + return label + + logger.warning(f"Could not parse response: {response}") + return "opposition" + + def _calculate_confidence(self, response: str, predicted_label: str) -> float: + """Calculate confidence score based on response characteristics.""" + response = response.lower().strip() + confidence = 0.5 + + if len(response.split()) <= 3: + confidence += 0.2 + + if predicted_label.lower() in response: + confidence += 0.3 + + uncertainty_words = ["maybe", "possibly", "might", "could", "unclear", "uncertain"] + if any(word in response for word in uncertainty_words): + confidence -= 0.2 + + return max(0.0, min(1.0, confidence)) + + def batch_classify( + self, + texts: List[str], + prompt_template: PromptTemplate, + examples: List[Dict[str, Any]] = None, + batch_size: int = 10, + delay_between_batches: float = 1.0, + ) -> List[Dict[str, Any]]: + """Classify multiple texts in batches.""" + results = [] + + for i in range(0, len(texts), batch_size): + batch_texts = texts[i : i + batch_size] + logger.info( + f"Processing batch {i//batch_size + 1}/{(len(texts) + batch_size - 1)//batch_size}" + ) + + batch_results = [] + for text in batch_texts: + result = self.classify_text(text, prompt_template, examples) + batch_results.append(result) + + results.extend(batch_results) + + if i + batch_size < len(texts): + time.sleep(delay_between_batches) + + return results + + +class LLMEvaluator: + """Evaluator for LLM baseline results.""" + + def __init__(self, conflict_labels: List[str] = None): + self.conflict_labels = conflict_labels or CONFLICT_LABELS + self.label_to_id = {label: idx for idx, label in enumerate(self.conflict_labels)} + + def _convert_labels_to_ids(self, labels: List[str]) -> List[int]: + """Convert string labels to integer IDs.""" + return [self.label_to_id[label] for label in labels] + + def _initialize_metrics_arrays(self) -> tuple: + """Initialize arrays for per-class metrics.""" + length = len(self.conflict_labels) + return ( + [0.0] * length, # precision + [0.0] * length, # recall + [0.0] * length, # f1 + [0] * length, # support + ) + + def _populate_per_class_metrics( + self, + unique_labels: List[int], + precision_per_class: np.ndarray, + recall_per_class: np.ndarray, + f1_per_class: np.ndarray, + support_per_class: np.ndarray, + ) -> tuple: + """Populate per-class metrics arrays.""" + all_precision, all_recall, all_f1, all_support = self._initialize_metrics_arrays() + + for i, label_id in enumerate(unique_labels): + all_precision[label_id] = precision_per_class[i] + all_recall[label_id] = recall_per_class[i] + all_f1[label_id] = f1_per_class[i] + all_support[label_id] = support_per_class[i] + + return all_precision, all_recall, all_f1, all_support + + def evaluate_predictions( + self, + true_labels: List[str], + predicted_labels: List[str], + confidence_scores: List[float] = None, + ) -> Dict[str, Any]: + """Evaluate predictions against true labels.""" + true_ids = self._convert_labels_to_ids(true_labels) + predicted_ids = self._convert_labels_to_ids(predicted_labels) + + accuracy = accuracy_score(true_ids, predicted_ids) + precision, recall, f1, support = precision_recall_fscore_support( + true_ids, predicted_ids, average="weighted", zero_division=0 + ) + + unique_labels = sorted(set(true_ids + predicted_ids)) + present_labels = [self.conflict_labels[i] for i in unique_labels] + + ( + precision_per_class, + recall_per_class, + f1_per_class, + support_per_class, + ) = precision_recall_fscore_support( + true_ids, predicted_ids, labels=unique_labels, average=None, zero_division=0 + ) + + class_report = classification_report( + true_ids, + predicted_ids, + labels=unique_labels, + target_names=present_labels, + zero_division=0, + output_dict=True, + ) + + # Convert NumPy types to Python native types in classification report + def convert_numpy_types(obj): + if isinstance(obj, dict): + return {k: convert_numpy_types(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_numpy_types(item) for item in obj] + elif hasattr(obj, "item"): # NumPy scalar + return obj.item() + else: + return obj + + class_report = convert_numpy_types(class_report) + + cm = confusion_matrix(true_ids, predicted_ids) + + confidence_metrics = {} + if confidence_scores: + confidence_metrics = self._calculate_confidence_metrics( + true_labels, predicted_labels, confidence_scores + ) + + all_precision, all_recall, all_f1, all_support = self._populate_per_class_metrics( + unique_labels, precision_per_class, recall_per_class, f1_per_class, support_per_class + ) + + return { + "accuracy": float(accuracy), + "precision": float(precision) if precision is not None else 0.0, + "recall": float(recall) if recall is not None else 0.0, + "f1": float(f1) if f1 is not None else 0.0, + "support": int(support) if support is not None else 0, + "per_class_metrics": { + "precision": [float(x) if x is not None else 0.0 for x in all_precision], + "recall": [float(x) if x is not None else 0.0 for x in all_recall], + "f1": [float(x) if x is not None else 0.0 for x in all_f1], + "support": [int(x) if x is not None else 0 for x in all_support], + }, + "classification_report": class_report, + "confusion_matrix": cm.tolist(), + "confidence_metrics": confidence_metrics, + "unique_labels": [int(x) for x in unique_labels], + "present_labels": present_labels, + } + + def _separate_confidences_by_correctness( + self, confidence_scores: List[float], correct_predictions: List[bool] + ) -> tuple: + """Separate confidence scores by prediction correctness.""" + correct_confidences = [ + c for c, correct in zip(confidence_scores, correct_predictions) if correct + ] + incorrect_confidences = [ + c for c, correct in zip(confidence_scores, correct_predictions) if not correct + ] + return correct_confidences, incorrect_confidences + + def _get_present_labels(self, metrics: Dict[str, Any]) -> List[str]: + """Get present labels from metrics.""" + unique_labels = metrics.get("unique_labels", range(len(self.conflict_labels))) + return [self.conflict_labels[i] for i in sorted(set(unique_labels))] + + def _calculate_confidence_metrics( + self, true_labels: List[str], predicted_labels: List[str], confidence_scores: List[float] + ) -> Dict[str, Any]: + """Calculate confidence-based metrics.""" + correct_predictions = [t == p for t, p in zip(true_labels, predicted_labels)] + correct_confidences, incorrect_confidences = self._separate_confidences_by_correctness( + confidence_scores, correct_predictions + ) + + return { + "avg_confidence_correct": float(np.mean(correct_confidences)) + if correct_confidences + else 0.0, + "avg_confidence_incorrect": float(np.mean(incorrect_confidences)) + if incorrect_confidences + else 0.0, + "confidence_std": float(np.std(confidence_scores)), + "high_confidence_accuracy": self._calculate_high_confidence_accuracy( + correct_predictions, confidence_scores, threshold=0.7 + ), + } + + def _calculate_high_confidence_accuracy( + self, + correct_predictions: List[bool], + confidence_scores: List[float], + threshold: float = 0.7, + ) -> float: + """Calculate accuracy for high-confidence predictions.""" + high_conf_mask = [c >= threshold for c in confidence_scores] + if not any(high_conf_mask): + return 0.0 + + high_conf_correct = [c for c, hc in zip(correct_predictions, high_conf_mask) if hc] + return float(np.mean(high_conf_correct)) + + def print_evaluation_report(self, metrics: Dict[str, Any]) -> str: + """Print a formatted evaluation report.""" + report = [] + report.append("=" * 60) + report.append("LLM BASELINE EVALUATION REPORT") + report.append("=" * 60) + + report.append("\nOverall Metrics:") + report.append(f" Accuracy: {metrics['accuracy']:.4f}") + report.append(f" Precision: {metrics['precision']:.4f}") + report.append(f" Recall: {metrics['recall']:.4f}") + report.append(f" F1-Score: {metrics['f1']:.4f}") + + report.append("\nPer-Class Metrics:") + report.append(f"{'Class':<15} {'Precision':<10} {'Recall':<10} {'F1':<10} {'Support':<10}") + report.append("-" * 60) + + for i, label in enumerate(self.conflict_labels): + precision = metrics["per_class_metrics"]["precision"][i] + recall = metrics["per_class_metrics"]["recall"][i] + f1 = metrics["per_class_metrics"]["f1"][i] + support = metrics["per_class_metrics"]["support"][i] + report.append( + f"{label:<15} {precision:<10.4f} {recall:<10.4f} {f1:<10.4f} {support:<10}" + ) + + if metrics["confidence_metrics"]: + conf_metrics = metrics["confidence_metrics"] + report.append("\nConfidence Metrics:") + report.append( + f" Avg Confidence (Correct): {conf_metrics['avg_confidence_correct']:.4f}" + ) + report.append( + f" Avg Confidence (Incorrect): {conf_metrics['avg_confidence_incorrect']:.4f}" + ) + report.append(f" Confidence Std: {conf_metrics['confidence_std']:.4f}") + report.append( + f" High Confidence Accuracy: {conf_metrics['high_confidence_accuracy']:.4f}" + ) + + report.append("\nConfusion Matrix:") + cm = np.array(metrics["confusion_matrix"]) + present_labels = self._get_present_labels(metrics) + + report.append(f"{'':<15} {'Predicted':<60}") + report.append(f"{'Actual':<15} {' '.join([f'{label[:8]:<8}' for label in present_labels])}") + report.append("-" * 80) + + for i, label in enumerate(present_labels): + row = ( + f"{label[:14]:<15} " + f"{' '.join([f'{cm[i,j]:<8}' for j in range(len(present_labels))])}" + ) + report.append(row) + + report.append("=" * 60) + + return "\n".join(report) + + def save_evaluation_results( + self, metrics: Dict[str, Any], output_path: str, additional_info: Dict[str, Any] = None + ) -> None: + """Save evaluation results to file.""" + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + results = { + "metrics": metrics, + "conflict_labels": self.conflict_labels, + "additional_info": additional_info or {}, + } + + with open(output_path, "w") as f: + json.dump(results, f, indent=2) + + logger.info(f"Evaluation results saved to {output_path}") + + +class LLMBaselineRunner: + """Main runner for LLM baseline experiments.""" + + def __init__(self, config: DictConfig): + """Initialize the runner.""" + self.config = config + self.llm_client = GroqLLMClient(api_key=config.api.get("api_key"), model=config.model.name) + self.evaluator = LLMEvaluator(CONFLICT_LABELS) + + # Extract commonly used config values + self.data_config = config.data + self.experiment_config = config.experiment + self.logging_config = config.logging + + def run_experiment(self) -> Dict[str, Any]: + """Run the complete experiment.""" + logger.info("Starting LLM baseline experiment...") + + run = wandb.init( + entity=self.logging_config.entity, + project=self.logging_config.project, + tags=self.logging_config.tags + [self.experiment_config.shot_type], + settings=wandb.Settings(start_method="thread"), + ) + + container = omegaconf.OmegaConf.to_container( + self.config, resolve=True, throw_on_missing=True + ) + run.config.update(container) + + try: + logger.info("Loading and preprocessing dataset...") + train_dataset, val_dataset, test_dataset = load_and_preprocess_dataset( + data_path=self.data_config.data_path, + test_ratio=self.data_config.test_ratio, + val_ratio=self.data_config.val_ratio, + max_length=self.data_config.max_length, + random_state=self.config.global_seed, + ) + + prompt_template = get_prompt_template(self.experiment_config.shot_type) + examples = self._prepare_examples(train_dataset, self.experiment_config.num_examples) + + logger.info(f"Running {self.experiment_config.shot_type}-shot evaluation...") + results = self._evaluate_on_dataset( + test_dataset, prompt_template, examples, self.experiment_config.batch_size + ) + + self._log_results(results, run) + self._save_results(results, run) + + logger.info("Experiment completed successfully!") + return results + + finally: + wandb.finish() + + def _prepare_examples(self, train_dataset: Dataset, num_examples: int) -> List[Dict[str, Any]]: + """Prepare examples for few-shot prompting.""" + if num_examples == 0: + return [] + + import random + + random.seed(self.config.global_seed) + + examples = [] + + # For few-shot, ensure we have exactly one example from each category + if self.experiment_config.shot_type == "few": + # Group examples by conflict type + examples_by_class = {} + for example in train_dataset: + label = example["conflict_type"] + if label not in examples_by_class: + examples_by_class[label] = [] + examples_by_class[label].append(example) + + # Select one random example from each class + for label in CONFLICT_LABELS: + if label in examples_by_class and len(examples_by_class[label]) > 0: + selected_example = random.choice(examples_by_class[label]) + examples.append({"text": selected_example["combined_text"], "label": label}) + + # Shuffle the examples to avoid bias in order + random.shuffle(examples) + + # For few-shot, we ignore num_examples and use all available categories + logger.info( + f"Prepared {len(examples)} examples (one per conflict type) " + f"for {self.experiment_config.shot_type}-shot prompting" + ) + return examples + + else: + # For zero-shot and one-shot, use the original logic + class_counts = {} + for example in train_dataset: + label = example["conflict_type"] + if class_counts.get(label, 0) < num_examples // len(CONFLICT_LABELS) + 1: + examples.append({"text": example["combined_text"], "label": label}) + class_counts[label] = class_counts.get(label, 0) + 1 + + if len(examples) >= num_examples: + break + + while len(examples) < num_examples and len(examples) < len(train_dataset): + remaining = [ex for ex in train_dataset if ex not in [e["text"] for e in examples]] + if remaining: + example = random.choice(remaining) + examples.append( + {"text": example["combined_text"], "label": example["conflict_type"]} + ) + + logger.info( + f"Prepared {len(examples)} examples " + f"for {self.experiment_config.shot_type}-shot prompting" + ) + return examples[:num_examples] + + def _extract_dataset_data(self, dataset: Dataset) -> tuple: + """Extract texts and labels from dataset.""" + texts = [example["combined_text"] for example in dataset] + true_labels = [example["conflict_type"] for example in dataset] + return texts, true_labels + + def _build_results_dict( + self, + metrics: Dict[str, Any], + predictions: List[Dict[str, Any]], + texts: List[str], + true_labels: List[str], + predicted_labels: List[str], + confidence_scores: List[float], + examples: List[Dict[str, Any]], + ) -> Dict[str, Any]: + """Build results dictionary.""" + return { + "metrics": metrics, + "predictions": predictions, + "true_labels": true_labels, + "predicted_labels": predicted_labels, + "confidence_scores": confidence_scores, + "examples_used": examples, + "config": omegaconf.OmegaConf.to_container(self.config, resolve=True), + } + + def _evaluate_on_dataset( + self, + dataset: Dataset, + prompt_template: PromptTemplate, + examples: List[Dict[str, Any]], + batch_size: int = 10, + ) -> Dict[str, Any]: + """Evaluate on a dataset.""" + texts, true_labels = self._extract_dataset_data(dataset) + + logger.info(f"Classifying {len(texts)} texts...") + predictions = self.llm_client.batch_classify( + texts, prompt_template, examples, batch_size=batch_size + ) + + predicted_labels = [p["predicted_label"] for p in predictions] + confidence_scores = [p["confidence"] for p in predictions] + + metrics = self.evaluator.evaluate_predictions( + true_labels, predicted_labels, confidence_scores + ) + + return self._build_results_dict( + metrics, predictions, texts, true_labels, predicted_labels, confidence_scores, examples + ) + + def _log_results(self, results: Dict[str, Any], run) -> None: + """Log results to wandb.""" + metrics = results["metrics"] + + run.log( + { + "accuracy": metrics["accuracy"], + "precision": metrics["precision"], + "recall": metrics["recall"], + "f1": metrics["f1"], + } + ) + + for i, label in enumerate(CONFLICT_LABELS): + run.log( + { + f"precision_{label}": metrics["per_class_metrics"]["precision"][i], + f"recall_{label}": metrics["per_class_metrics"]["recall"][i], + f"f1_{label}": metrics["per_class_metrics"]["f1"][i], + } + ) + + if metrics["confidence_metrics"]: + conf_metrics = metrics["confidence_metrics"] + run.log( + { + "avg_confidence_correct": conf_metrics["avg_confidence_correct"], + "avg_confidence_incorrect": conf_metrics["avg_confidence_incorrect"], + "high_confidence_accuracy": conf_metrics["high_confidence_accuracy"], + } + ) + + def _create_output_paths(self, run_name: str, timestamp: str) -> tuple: + """Create output directory and file paths.""" + output_dir = Path("outputs") / timestamp + output_dir.mkdir(parents=True, exist_ok=True) + + return ( + output_dir, + output_dir / f"{run_name}_results.json", + output_dir / f"{run_name}_predictions.json", + output_dir / f"{run_name}_report.txt", + ) + + def _save_results(self, results: Dict[str, Any], run) -> None: + """Save results to files.""" + timestamp = time.strftime("%Y%m%d-%H%M%S") + run_name = run.name or f"llm_baseline_{timestamp}" + + output_dir, results_path, predictions_path, report_path = self._create_output_paths( + run_name, timestamp + ) + + self.evaluator.save_evaluation_results( + results["metrics"], + str(results_path), + additional_info={ + "run_name": run_name, + "run_id": run.id, + "timestamp": timestamp, + "config": results["config"], + }, + ) + + with open(predictions_path, "w") as f: + json.dump( + { + "predictions": results["predictions"], + "true_labels": results["true_labels"], + "predicted_labels": results["predicted_labels"], + "confidence_scores": results["confidence_scores"], + }, + f, + indent=2, + ) + + report = self.evaluator.print_evaluation_report(results["metrics"]) + logger.info(f"\n{report}") + + with open(report_path, "w") as f: + f.write(report) + + logger.info(f"Results saved to {output_dir}") + + +@hydra.main(config_name="default", config_path="../configs", version_base="1.2") +def main(config: DictConfig): + """Main function to run LLM baseline experiments.""" + runner = LLMBaselineRunner(config) + results = runner.run_experiment() + return results + + +if __name__ == "__main__": + main() diff --git 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buck-out + | build + | dist +)/ +''' +include = '\.pyi?$' +line-length = 100 +target-version = ['py36', 'py37', 'py38', 'py39'] + +[tool.coverage.report] +exclude_lines = [ + "if __name__ == .__main__.:", + "pass", + "pragma: no cover", + "raise NotImplementedError", + "raise TypeError", + "raise ValueError" +] + +[tool.isort] +known_local_folder = [ + 'llm_baselines', + 'tests' +] +line_length = 100 +profile = 'black' + +[tool.poetry] +authors = ["Clinical Dream Team"] +description = "LLM baselines for clinical text conflict classification using zero-shot, one-shot, and few-shot prompting" +license = "Apache-2.0" +name = "llm-baselines" +readme = "README.md" +repository = "https://github.com/arkhn/open-nlp" +version = "0.1.0" + +[tool.poetry.dependencies] +datasets = "^2.16.1" +groq = "^0.9.0" +httpx = ">=0.23.0,<0.25.0" +hydra-colorlog = "^1.2.0" +hydra-core = "^1.3.2" +hydra-joblib-launcher = "^1.2.0" +numpy = "^1.24.0" +pandas = "^2.2.0" +python = "~3.11" +rich = "^13.5.2" +scikit-learn = "^1.3.0" +tqdm = "^4.66.1" +wandb = "^0.16.3" + +[tool.poetry.dev-dependencies] +pytest = "^7.4.2" +pytest-cov = "^4.1.0" + +[tool.pytest.ini_options] +addopts = "--cov-report term-missing:skip-covered" +markers = [ + "serial", + "slow: marks tests as slow (deselect with '-m \"not slow\"')" +] diff --git a/lib/conflicts/llm_baselines/run_baseline.py b/lib/conflicts/llm_baselines/run_baseline.py new file mode 100644 index 0000000..9f0a3e7 --- /dev/null +++ b/lib/conflicts/llm_baselines/run_baseline.py @@ -0,0 +1,9 @@ +import sys +from pathlib import Path + +from llm_baselines.llm_baseline import main + +sys.path.insert(0, str(Path(__file__).parent)) + +if __name__ == "__main__": + main() From f439b5a2b733a2ef0e67a8662e43d6d08f21e5f3 Mon Sep 17 00:00:00 2001 From: honghanhh Date: Wed, 17 Sep 2025 17:29:18 +0200 Subject: [PATCH 3/3] fix: remove lm benchmarks --- lib/conflicts/baselines/Makefile | 34 --- lib/conflicts/baselines/README.md | 115 --------- lib/conflicts/baselines/__init__.py | 1 - lib/conflicts/baselines/commons/__init__.py | 1 - lib/conflicts/baselines/commons/config.py | 40 --- .../baselines/commons/data/__init__.py | 1 - .../data/load_and_preprocess_dataset.py | 199 --------------- .../baselines/commons/metrics/__init__.py | 1 - .../commons/metrics/classification_metrics.py | 120 --------- lib/conflicts/baselines/configs/default.yaml | 33 --- lib/conflicts/baselines/requirements.txt | 18 -- lib/conflicts/baselines/train.py | 238 ------------------ 12 files changed, 801 deletions(-) delete mode 100644 lib/conflicts/baselines/Makefile delete mode 100644 lib/conflicts/baselines/README.md delete mode 100644 lib/conflicts/baselines/__init__.py delete mode 100644 lib/conflicts/baselines/commons/__init__.py delete mode 100644 lib/conflicts/baselines/commons/config.py delete mode 100644 lib/conflicts/baselines/commons/data/__init__.py delete mode 100644 lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py delete mode 100644 lib/conflicts/baselines/commons/metrics/__init__.py delete mode 100644 lib/conflicts/baselines/commons/metrics/classification_metrics.py delete mode 100644 lib/conflicts/baselines/configs/default.yaml delete mode 100644 lib/conflicts/baselines/requirements.txt delete mode 100644 lib/conflicts/baselines/train.py diff --git a/lib/conflicts/baselines/Makefile b/lib/conflicts/baselines/Makefile deleted file mode 100644 index ebe705e..0000000 --- a/lib/conflicts/baselines/Makefile +++ /dev/null @@ -1,34 +0,0 @@ -# Conflict Detection Baselines Makefile - -.PHONY: help install test train example clean - -help: ## Show this help message - @echo "Conflict Detection Baselines" - @echo "==========================" - @echo "" - @echo "Available commands:" - @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-15s\033[0m %s\n", $$1, $$2}' - -install: ## Install dependencies - pip install -r requirements.txt - -train: ## Train the baseline model - python train.py - -train-debug: ## Train with debug settings - python train.py training.epochs=2 training.eval_steps=10 training.logging_steps=5 - -train-bert: ## Train with BERT base model - python train.py model.model_name_or_path=bert-base-uncased - -train-biobert: ## Train with BioBERT model - python train.py model.model_name_or_path=microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext - -clean: ## Clean output files - rm -rf outputs/ - rm -f example_predictions.json - rm -f *.log - -setup: install ## Complete setup (install dependencies) - -all: setup train ## Complete pipeline (setup + train) diff --git a/lib/conflicts/baselines/README.md b/lib/conflicts/baselines/README.md deleted file mode 100644 index ede110b..0000000 --- a/lib/conflicts/baselines/README.md +++ /dev/null @@ -1,115 +0,0 @@ -# Conflict Detection and Classification Baselines - -This directory contains baseline implementations for conflict detection and classification in -clinical documents. - -## Overview - -The baseline implements a text classification approach to detect and classify conflicts between -pairs of clinical documents into 6 categories: - -1. **Opposition Conflicts** - Contradictory findings about the same clinical entity -2. **Anatomical Conflicts** - Contradictions regarding body structures and their presence/absence -3. **Value Conflicts** - Contradictory measurements, lab values, or quantitative findings -4. **Contraindication Conflicts** - Conflicts between allergies/contraindications and treatments -5. **Comparison Conflicts** - Temporal contradictions -6. **Descriptive Conflicts** - Statement contradictions - -## Structure - -``` -baselines/ -├── configs/ -│ └── default.yaml # Hydra configuration -├── commons/ -│ ├── data/ -│ │ └── load_and_preprocess_dataset.py -│ └── metrics/ -│ └── classification_metrics.py -├── outputs/ # Generated prediction files -├── train.py # Main training script -└── README.md -``` - -## Usage - -### 1. Install Dependencies - -```bash -pip install transformers datasets scikit-learn wandb hydra-core -``` - -### 2. Run Training - -```bash -python train.py -``` - -### 3. Custom Configuration - -```bash -python train.py model.model_name_or_path=microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext training.epochs=15 -``` - -## Configuration - -The training can be configured via `configs/default.yaml`: - -- **Model**: Uses BiomedBERT by default (can be changed to other models) -- **Data**: 80% train, 10% validation, 10% test split -- **Training**: 10 epochs, batch size 8, learning rate 2e-5 -- **Logging**: Weights & Biases integration - -## Data Format - -The baseline expects a processed JSON file with the following structure: - -- Each entry contains `data` with `doc_1` and `doc_2` text fields -- `annotations` contain conflict type information -- The loader extracts and creates: - - `combined_text`: Concatenated text from both documents - - `conflict_label`: Integer label (0-5) for conflict type - - `conflict_type`: String label for conflict type - - `doc1_id`, `doc2_id`: Document identifiers - - `subject_id`: Patient identifier - -## Output - -The training generates: - -- **Model checkpoints** in `outputs/` -- **Predictions** in `outputs/` (numpy arrays, text files) -- **Metrics** logged to Weights & Biases -- **Classification report** with per-class performance - -## Metrics - -The baseline computes: - -- Accuracy -- Precision, Recall, F1 (macro and weighted) -- Per-class metrics -- Confusion matrix -- Confidence statistics - -## Customization - -To adapt for your data: - -1. **Modify data loading** in `commons/data/load_and_preprocess_dataset.py` -2. **Adjust preprocessing** in the `preprocess_function` -3. **Update conflict types** in the configuration -4. **Change model** via Hydra overrides - -## Example - -```bash -# Train with different model -python train.py model.model_name_or_path=bert-base-uncased - -# Train with different hyperparameters -python train.py training.epochs=20 training.learning_rate=1e-5 - -# Train with different data split -python train.py data.test_ratio=0.15 data.val_ratio=0.15 -``` diff --git a/lib/conflicts/baselines/__init__.py b/lib/conflicts/baselines/__init__.py deleted file mode 100644 index f63b562..0000000 --- a/lib/conflicts/baselines/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Conflict Detection Baselines diff --git a/lib/conflicts/baselines/commons/__init__.py b/lib/conflicts/baselines/commons/__init__.py deleted file mode 100644 index abce62b..0000000 --- a/lib/conflicts/baselines/commons/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Commons module for conflict detection baselines diff --git a/lib/conflicts/baselines/commons/config.py b/lib/conflicts/baselines/commons/config.py deleted file mode 100644 index d384ceb..0000000 --- a/lib/conflicts/baselines/commons/config.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -import sys -from pathlib import Path - -# Conflict type mapping - single source of truth -CONFLICT_TYPES = { - "opposition": 0, - "anatomical": 1, - "value": 2, - "contraindication": 3, - "comparison": 4, - "descriptive": 5, -} - -# Conflict type labels in order -CONFLICT_LABELS = list(CONFLICT_TYPES.keys()) - -# Reverse mapping -ID_TO_LABEL = {idx: label for label, idx in CONFLICT_TYPES.items()} - - -def setup_paths(): - """Setup Python path for imports.""" - parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) - if parent_dir not in sys.path: - sys.path.append(parent_dir) - - -def get_conflicts_data_path(data_path: str = "processed/_12092025.json") -> Path: - """ - Get the full path to the conflicts data file. - - Args: - data_path: Relative path to data file - - Returns: - Full path to data file - """ - conflicts_dir = Path(__file__).parent.parent.parent - return conflicts_dir / data_path diff --git a/lib/conflicts/baselines/commons/data/__init__.py b/lib/conflicts/baselines/commons/data/__init__.py deleted file mode 100644 index 1557ce5..0000000 --- a/lib/conflicts/baselines/commons/data/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Data loading utilities diff --git a/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py b/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py deleted file mode 100644 index c3ac7b6..0000000 --- a/lib/conflicts/baselines/commons/data/load_and_preprocess_dataset.py +++ /dev/null @@ -1,199 +0,0 @@ -import json -import logging -from typing import Dict, List - -import pandas as pd -from datasets import Dataset -from sklearn.model_selection import train_test_split - -from ..config import CONFLICT_TYPES, get_conflicts_data_path - -logger = logging.getLogger(__name__) - - -def load_conflict_dataset(data_path: str = "processed/_12092025.json") -> pd.DataFrame: - """ - Load the conflict dataset from processed JSON file. - - Args: - data_path: Path to the processed JSON file - - Returns: - DataFrame with conflict data - """ - full_data_path = get_conflicts_data_path(data_path) - - if not full_data_path.exists(): - raise FileNotFoundError(f"Dataset not found at {full_data_path}") - - logger.info(f"Loading dataset from {full_data_path}") - - # Load JSON data - with open(full_data_path, "r") as f: - data = json.load(f) - - # Convert to DataFrame - df = process_conflict_json_data(data) - - return df - - -def process_conflict_json_data(data: List[Dict]) -> pd.DataFrame: - """ - Process the conflict JSON data into a DataFrame. - - Args: - data: List of conflict data entries from JSON - - Returns: - DataFrame with conflict data - """ - conflict_pairs = [] - - for entry in data: - # Extract data from the entry - doc_data = entry["data"] - annotations = entry.get("annotations", []) - - # Get document texts - doc1_text = doc_data.get("doc_1", "") - doc2_text = doc_data.get("doc_2", "") - - # Create combined text for classification - combined_text = f"Document 1: {doc1_text[:500]} [SEP] Document 2: {doc2_text[:500]}" - - # Extract conflict information from annotations - conflict_type = "opposition" # Default - conflict_label = CONFLICT_TYPES[conflict_type] - - if annotations and len(annotations) > 0: - # Get conflict type from the first annotation - first_annotation = annotations[0] - if "result" in first_annotation and len(first_annotation["result"]) > 0: - result = first_annotation["result"][0] - if "conflict_type" in result: - conflict_type = result["conflict_type"] - conflict_label = CONFLICT_TYPES.get(conflict_type, 0) - - # Create unique IDs for documents - doc1_id = f"doc1_{len(conflict_pairs)}" - doc2_id = f"doc2_{len(conflict_pairs)}" - - conflict_pairs.append( - { - "doc1_id": doc1_id, - "doc2_id": doc2_id, - "doc1_text": doc1_text, - "doc2_text": doc2_text, - "combined_text": combined_text, - "conflict_type": conflict_type, - "conflict_label": conflict_label, - "subject_id": f"subject_{len(conflict_pairs)}", # Generate unique subject ID - "category1": "processed_doc1", - "category2": "processed_doc2", - "timestamp_1": doc_data.get("timestamp_1", ""), - "timestamp_2": doc_data.get("timestamp_2", ""), - "created_at": doc_data.get("created_at", ""), - } - ) - - # Convert to DataFrame - conflict_df = pd.DataFrame(conflict_pairs) - - logger.info(f"Processed {len(conflict_df)} conflict pairs from JSON data") - logger.info(f"Conflict type distribution:\n{conflict_df['conflict_type'].value_counts()}") - - return conflict_df - - -def preprocess_text(text: str, max_length: int = 512) -> str: - """ - Preprocess text for classification. - - Args: - text: Input text - max_length: Maximum length to truncate - - Returns: - Preprocessed text - """ - # Basic text cleaning - text = text.strip() - - # Truncate if too long - if len(text) > max_length: - text = text[:max_length] - - return text - - -def load_and_preprocess_dataset( - data_path: str = "processed/_12092025.json", - test_ratio: float = 0.2, - val_ratio: float = 0.1, - max_length: int = 512, - random_state: int = 42, -): - """ - Load and preprocess the conflict detection dataset. - - Args: - data_path: Path to the processed JSON file - test_ratio: Ratio for test split - val_ratio: Ratio for validation split (from remaining data) - max_length: Maximum text length - random_state: Random state for reproducibility - - Returns: - Tuple of (train_dataset, val_dataset, test_dataset) - """ - # Load the dataset - df = load_conflict_dataset(data_path) - - # Preprocess text - df["combined_text"] = df["combined_text"].apply(lambda x: preprocess_text(x, max_length)) - - # Check class distribution - class_counts = df["conflict_type"].value_counts() - logger.info(f"Class distribution: {class_counts.to_dict()}") - - # Check if we can stratify (each class needs at least 2 samples) - min_class_count = class_counts.min() - can_stratify = min_class_count >= 2 - - if can_stratify: - logger.info("Using stratified splitting") - # Split the data with stratification - train_df, test_df = train_test_split( - df, test_size=test_ratio, random_state=random_state, stratify=df["conflict_type"] - ) - - train_df, val_df = train_test_split( - train_df, - test_size=val_ratio, - random_state=random_state, - stratify=train_df["conflict_type"], - ) - else: - logger.warning( - f"Some classes have only {min_class_count} sample(s). " - f"Using random splitting without stratification." - ) - # Split the data without stratification - train_df, test_df = train_test_split(df, test_size=test_ratio, random_state=random_state) - - train_df, val_df = train_test_split( - train_df, test_size=val_ratio, random_state=random_state - ) - - # Create datasets - train_dataset = Dataset.from_pandas(train_df) - val_dataset = Dataset.from_pandas(val_df) - test_dataset = Dataset.from_pandas(test_df) - - logger.info( - f"Dataset splits - Train: {len(train_dataset)}," - f" Val: {len(val_dataset)}, Test: {len(test_dataset)}" - ) - - return train_dataset, val_dataset, test_dataset diff --git a/lib/conflicts/baselines/commons/metrics/__init__.py b/lib/conflicts/baselines/commons/metrics/__init__.py deleted file mode 100644 index ff67375..0000000 --- a/lib/conflicts/baselines/commons/metrics/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Metrics utilities diff --git a/lib/conflicts/baselines/commons/metrics/classification_metrics.py b/lib/conflicts/baselines/commons/metrics/classification_metrics.py deleted file mode 100644 index 4b74327..0000000 --- a/lib/conflicts/baselines/commons/metrics/classification_metrics.py +++ /dev/null @@ -1,120 +0,0 @@ -from typing import Any, Dict, List - -import numpy as np -from sklearn.metrics import ( - accuracy_score, - classification_report, - confusion_matrix, - precision_recall_fscore_support, -) - - -def compute_classification_metrics( - y_true: np.ndarray, y_pred: np.ndarray, labels: List[str] = None -) -> Dict[str, Any]: - """ - Compute comprehensive classification metrics. - - Args: - y_true: True labels - y_pred: Predicted labels - labels: Label names for reporting - - Returns: - Dictionary with metrics - """ - # Basic metrics - accuracy = accuracy_score(y_true, y_pred) - - # Precision, recall, F1 (macro and weighted averages) - precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support( - y_true, y_pred, average="macro", zero_division=0 - ) - precision_weighted, recall_weighted, f1_weighted, _ = precision_recall_fscore_support( - y_true, y_pred, average="weighted", zero_division=0 - ) - - # Per-class metrics - precision_per_class, recall_per_class, f1_per_class, support = precision_recall_fscore_support( - y_true, y_pred, average=None, zero_division=0 - ) - - # Confusion matrix - cm = confusion_matrix(y_true, y_pred) - - metrics = { - "accuracy": accuracy, - "precision_macro": precision_macro, - "recall_macro": recall_macro, - "f1_macro": f1_macro, - "precision_weighted": precision_weighted, - "recall_weighted": recall_weighted, - "f1_weighted": f1_weighted, - "confusion_matrix": cm.tolist(), - "support": support.tolist(), - } - - # Add per-class metrics if labels provided - if labels is not None: - per_class_metrics = {} - for i, label in enumerate(labels): - per_class_metrics[f"{label}_precision"] = precision_per_class[i] - per_class_metrics[f"{label}_recall"] = recall_per_class[i] - per_class_metrics[f"{label}_f1"] = f1_per_class[i] - per_class_metrics[f"{label}_support"] = support[i] - - metrics.update(per_class_metrics) - - return metrics - - -def print_classification_report( - y_true: np.ndarray, y_pred: np.ndarray, labels: List[str] = None -) -> str: - """ - Print detailed classification report. - - Args: - y_true: True labels - y_pred: Predicted labels - labels: Label names for reporting - - Returns: - Classification report string - """ - if labels is None: - labels = [f"Class_{i}" for i in range(len(np.unique(y_true)))] - - report = classification_report(y_true, y_pred, target_names=labels, zero_division=0, digits=4) - - return report - - -def compute_confusion_matrix_metrics(cm: np.ndarray) -> Dict[str, float]: - """ - Compute additional metrics from confusion matrix. - - Args: - cm: Confusion matrix - - Returns: - Dictionary with additional metrics - """ - # True positives, false positives, false negatives for each class - tp = np.diag(cm) - fp = cm.sum(axis=0) - tp - fn = cm.sum(axis=1) - tp - tn = cm.sum() - (tp + fp + fn) - - # Additional metrics - specificity = tn / (tn + fp) - sensitivity = tp / (tp + fn) # Same as recall - - return { - "specificity": specificity.tolist(), - "sensitivity": sensitivity.tolist(), - "true_positives": tp.tolist(), - "false_positives": fp.tolist(), - "false_negatives": fn.tolist(), - "true_negatives": tn.tolist(), - } diff --git a/lib/conflicts/baselines/configs/default.yaml b/lib/conflicts/baselines/configs/default.yaml deleted file mode 100644 index 4c2c046..0000000 --- a/lib/conflicts/baselines/configs/default.yaml +++ /dev/null @@ -1,33 +0,0 @@ -defaults: - - _self_ - - override /hydra/sweeper: basic - - override /hydra/launcher: basic - -data: - test_ratio: 0.2 - val_ratio: 0.1 - max_length: 512 - data_path: "processed/_12092025.json" - -model: - model_name_or_path: "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" - num_labels: 6 - -training: - batch_size: 8 - gradient_accumulation_steps: 4 - epochs: 10 - weight_decay: 0.01 - learning_rate: 2e-5 - push_to_hub: False - eval_steps: 100 - logging_steps: 50 - warmup_steps: 100 - -logging: - output_dir: "outputs" - entity: "clinical-dream-team" - project: "conflict-detection" - tags: ["baseline", "conflict-classification"] - -global_seed: 42 diff --git a/lib/conflicts/baselines/requirements.txt b/lib/conflicts/baselines/requirements.txt deleted file mode 100644 index 550fa71..0000000 --- a/lib/conflicts/baselines/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -# Core dependencies -torch>=1.9.0 -transformers>=4.20.0 -datasets>=2.0.0 -scikit-learn>=1.0.0 -pandas>=1.3.0 -numpy>=1.21.0 - -# Configuration and logging -hydra-core>=1.2.0 -omegaconf>=2.2.0 -wandb>=0.12.0 - -# Data processing -pyarrow>=8.0.0 - -# Optional: for better performance -accelerate>=0.20.0 diff --git a/lib/conflicts/baselines/train.py b/lib/conflicts/baselines/train.py deleted file mode 100644 index 2b235f7..0000000 --- a/lib/conflicts/baselines/train.py +++ /dev/null @@ -1,238 +0,0 @@ -import logging -import time -from pathlib import Path - -import hydra -import numpy as np -import wandb -from commons.config import CONFLICT_LABELS, ID_TO_LABEL, setup_paths -from commons.data.load_and_preprocess_dataset import load_and_preprocess_dataset -from commons.metrics.classification_metrics import ( - compute_classification_metrics, - print_classification_report, -) -from omegaconf import DictConfig, omegaconf -from sklearn.metrics import accuracy_score, precision_recall_fscore_support -from transformers import ( - AutoModelForSequenceClassification, - AutoTokenizer, - EvalPrediction, - Trainer, - TrainingArguments, -) - -# Setup paths and logging -setup_paths() -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -def preprocess_function(examples, tokenizer, max_length=512): - """ - Preprocess the data for training. - - Args: - examples: Batch of examples - tokenizer: Tokenizer object - max_length: Maximum sequence length - - Returns: - Dictionary with tokenized inputs and labels - """ - # Tokenize the combined text - tokenized = tokenizer( - examples["combined_text"], - truncation=True, - padding=True, - max_length=max_length, - return_tensors="pt", - ) - - # Add labels - tokenized["labels"] = examples["conflict_label"] - - return tokenized - - -def compute_metrics(p: EvalPrediction) -> dict: - """ - Compute metrics for evaluation. - - Args: - p: EvalPrediction object - - Returns: - Dictionary with metrics - """ - predictions, labels = p.predictions, p.label_ids - - # Get predicted class labels - predictions = np.argmax(predictions, axis=1) - - # Compute basic metrics - accuracy = accuracy_score(labels, predictions) - precision, recall, f1, _ = precision_recall_fscore_support( - labels, predictions, average="weighted", zero_division=0 - ) - - return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1} - - -@hydra.main(config_name="default", config_path="configs", version_base="1.2") -def main(config: DictConfig): - """ - Main function to train the conflict classification model. - - Args: - config: Hydra configuration object - """ - # Initialize wandb - run = wandb.init( - entity=config.logging.entity, - project=config.logging.project, - tags=config.logging.tags, - settings=wandb.Settings(start_method="thread"), - ) - - # Update wandb config - container = omegaconf.OmegaConf.to_container(config, resolve=True, throw_on_missing=True) - run.config.update(container) - - logger.info("Loading and preprocessing dataset...") - - # Load datasets - dataset_train, dataset_val, dataset_test = load_and_preprocess_dataset( - data_path=config.data.data_path, - test_ratio=config.data.test_ratio, - val_ratio=config.data.val_ratio, - max_length=config.data.max_length, - random_state=config.global_seed, - ) - - # Load tokenizer - logger.info(f"Loading tokenizer: {config.model.model_name_or_path}") - tokenizer = AutoTokenizer.from_pretrained(config.model.model_name_or_path) - - # Preprocess datasets - logger.info("Preprocessing datasets...") - dataset_train = dataset_train.map( - preprocess_function, - batched=True, - fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, - ) - dataset_val = dataset_val.map( - preprocess_function, - batched=True, - fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, - ) - dataset_test = dataset_test.map( - preprocess_function, - batched=True, - fn_kwargs={"tokenizer": tokenizer, "max_length": config.data.max_length}, - ) - - # Create label mapping - id2label = ID_TO_LABEL - label2id = {label: i for i, label in enumerate(CONFLICT_LABELS)} - - # Load model - logger.info(f"Loading model: {config.model.model_name_or_path}") - model = AutoModelForSequenceClassification.from_pretrained( - config.model.model_name_or_path, - num_labels=config.model.num_labels, - id2label=id2label, - label2id=label2id, - ) - - # Training arguments - training_args = TrainingArguments( - output_dir=config.logging.output_dir, - evaluation_strategy="steps", - save_strategy="steps", - learning_rate=config.training.learning_rate, - per_device_train_batch_size=config.training.batch_size, - per_device_eval_batch_size=config.training.batch_size, - gradient_accumulation_steps=config.training.gradient_accumulation_steps, - num_train_epochs=config.training.epochs, - weight_decay=config.training.weight_decay, - warmup_steps=config.training.warmup_steps, - push_to_hub=config.training.push_to_hub, - eval_steps=config.training.eval_steps, - logging_steps=config.training.logging_steps, - load_best_model_at_end=True, - greater_is_better=True, - metric_for_best_model="f1", - save_total_limit=3, - report_to=["wandb"], - lr_scheduler_type="linear", - seed=config.global_seed, - ) - - # Create trainer - trainer = Trainer( - model=model, - args=training_args, - train_dataset=dataset_train, - eval_dataset=dataset_val, - tokenizer=tokenizer, - compute_metrics=compute_metrics, - ) - - # Train the model - logger.info("Starting training...") - trainer.train() - - # Evaluate on test set - logger.info("Evaluating on test set...") - test_results = trainer.evaluate(eval_dataset=dataset_test, metric_key_prefix="test") - logger.info(f"Test results: {test_results}") - - # Generate predictions - logger.info("Generating predictions...") - predictions, _, _ = trainer.predict(dataset_test) - - # Save predictions (simplified) - run_name = run.name - run_id = run.id - timestamp = time.strftime("%Y%m%d-%H%M%S") - - # Create output directory - output_dir = Path("outputs") - output_dir.mkdir(exist_ok=True) - - # Save predictions as numpy array - predictions_path = output_dir / f"predictions_{run_name}_{run_id}_{timestamp}.npy" - np.save(predictions_path, predictions) - - # Save predictions as simple text file - predicted_labels = np.argmax(predictions, axis=1) - labels_path = output_dir / f"predicted_labels_{run_name}_{run_id}_{timestamp}.txt" - with open(labels_path, "w") as f: - for label in predicted_labels: - f.write(f"{id2label[label]}\n") - - logger.info(f"Predictions saved to {predictions_path} and {labels_path}") - - # Print detailed classification report - predicted_labels = np.argmax(predictions, axis=1) - true_labels = [example["conflict_label"] for example in dataset_test] - - report = print_classification_report(np.array(true_labels), predicted_labels, CONFLICT_LABELS) - logger.info(f"\nClassification Report:\n{report}") - - # Compute comprehensive metrics - metrics = compute_classification_metrics( - np.array(true_labels), predicted_labels, CONFLICT_LABELS - ) - logger.info(f"Comprehensive metrics: {metrics}") - - # Log metrics to wandb - wandb.log({"test_classification_report": report}) - wandb.log(metrics) - - wandb.finish() - logger.info("Training completed successfully!") - - -if __name__ == "__main__": - main()