add impl

Author: codelion

examples/multilabel_usage.py

@@ -0,0 +1,302 @@
+#!/usr/bin/env python3
+"""
+Multi-Label Adaptive Classifier Example
+
+This example demonstrates how to use the MultiLabelAdaptiveClassifier
+for text classification tasks where each text can belong to multiple categories.
+
+Key features demonstrated:
+1. Training with multi-label data
+2. Making multi-label predictions
+3. Adaptive threshold handling for many labels
+4. Label-specific threshold customization
+5. Saving and loading multi-label models
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
+
+from adaptive_classifier import MultiLabelAdaptiveClassifier
+import torch
+
+
+def create_sample_data():
+    """Create sample multi-label training data."""
+
+    # Sample texts with multiple labels each
+    training_data = [
+        {
+            "text": "Scientists discover new species of butterfly in Amazon rainforest with unique wing patterns",
+            "labels": ["science", "nature", "discovery", "biology"]
+        },
+        {
+            "text": "Tech startup raises $50M in Series A funding to develop AI-powered healthcare solutions",
+            "labels": ["technology", "business", "healthcare", "funding"]
+        },
+        {
+            "text": "Climate change impacts ocean temperature causing coral bleaching in Great Barrier Reef",
+            "labels": ["environment", "climate", "nature", "science"]
+        },
+        {
+            "text": "NBA playoffs feature exciting games with record-breaking performances by star players",
+            "labels": ["sports", "entertainment", "basketball"]
+        },
+        {
+            "text": "New renewable energy technology could reduce costs by 40% according to MIT research",
+            "labels": ["technology", "science", "environment", "energy"]
+        },
+        {
+            "text": "Archaeological team uncovers 2000-year-old Roman artifacts in excavation site",
+            "labels": ["history", "science", "discovery", "archaeology"]
+        },
+        {
+            "text": "Stock market reaches new highs as investors show confidence in economic recovery",
+            "labels": ["business", "finance", "economy"]
+        },
+        {
+            "text": "Machine learning breakthrough helps doctors diagnose rare diseases more accurately",
+            "labels": ["technology", "healthcare", "science", "ai"]
+        },
+        {
+            "text": "Wildlife conservation efforts show success in protecting endangered tiger populations",
+            "labels": ["nature", "environment", "conservation", "wildlife"]
+        },
+        {
+            "text": "Olympic athletes prepare for upcoming games with intensive training programs",
+            "labels": ["sports", "olympics", "training", "fitness"]
+        },
+        {
+            "text": "Quantum computing research makes progress toward solving complex optimization problems",
+            "labels": ["technology", "science", "computing", "research"]
+        },
+        {
+            "text": "Sustainable agriculture practices help farmers reduce environmental impact while increasing yield",
+            "labels": ["environment", "agriculture", "sustainability", "farming"]
+        },
+        {
+            "text": "Music festival features artists from diverse genres attracting thousands of fans",
+            "labels": ["entertainment", "music", "culture", "events"]
+        },
+        {
+            "text": "Space agency announces plans for Mars mission with new rocket technology",
+            "labels": ["science", "space", "technology", "exploration"]
+        },
+        {
+            "text": "Educational technology helps students learn programming through interactive online courses",
+            "labels": ["education", "technology", "programming", "learning"]
+        }
+    ]
+
+    # Extract texts and labels
+    texts = [item["text"] for item in training_data]
+    labels = [item["labels"] for item in training_data]
+
+    return texts, labels
+
+
+def demonstrate_basic_usage():
+    """Demonstrate basic multi-label classification."""
+
+    print("=" * 60)
+    print("MULTI-LABEL ADAPTIVE CLASSIFIER - BASIC USAGE")
+    print("=" * 60)
+
+    # Create classifier
+    classifier = MultiLabelAdaptiveClassifier(
+        model_name="distilbert/distilbert-base-cased",
+        default_threshold=0.5,
+        min_predictions=1,  # Ensure at least 1 prediction
+        max_predictions=5   # Limit to top 5 predictions
+    )
+
+    # Load training data
+    texts, labels = create_sample_data()
+
+    print(f"Training with {len(texts)} examples")
+    print(f"Example text: {texts[0][:60]}...")
+    print(f"Example labels: {labels[0]}")
+
+    # Train the classifier
+    classifier.add_examples(texts, labels)
+
+    # Get statistics
+    stats = classifier.get_label_statistics()
+    print(f"\nTraining completed:")
+    print(f"- Total labels: {stats['num_classes']}")
+    print(f"- Total examples: {stats['total_examples']}")
+    print(f"- Adaptive threshold: {stats['adaptive_threshold']:.3f}")
+
+    return classifier
+
+
+def demonstrate_predictions(classifier):
+    """Demonstrate making predictions."""
+
+    print("\n" + "=" * 60)
+    print("MAKING PREDICTIONS")
+    print("=" * 60)
+
+    # Test texts
+    test_texts = [
+        "Researchers develop new AI algorithm for medical diagnosis",
+        "Football team wins championship in exciting final match",
+        "Solar panel efficiency increases with new manufacturing technique",
+        "Ancient civilization discovered through satellite imagery analysis"
+    ]
+
+    for text in test_texts:
+        print(f"\nText: {text}")
+
+        # Make multi-label prediction
+        predictions = classifier.predict_multilabel(text)
+
+        print("Predictions:")
+        if predictions:
+            for label, confidence in predictions:
+                print(f"  {label}: {confidence:.4f}")
+        else:
+            print("  No predictions above threshold")
+
+    return test_texts
+
+
+def demonstrate_threshold_adjustment(classifier):
+    """Demonstrate threshold adjustment for different scenarios."""
+
+    print("\n" + "=" * 60)
+    print("THRESHOLD ADJUSTMENT")
+    print("=" * 60)
+
+    test_text = "AI researchers publish breakthrough study on climate modeling using machine learning"
+
+    print(f"Test text: {test_text}")
+
+    # Try different thresholds
+    thresholds = [0.1, 0.3, 0.5, 0.7, 0.9]
+
+    print(f"\n{'Threshold':<10} {'Predictions':<12} {'Labels'}")
+    print("-" * 50)
+
+    for threshold in thresholds:
+        predictions = classifier.predict_multilabel(test_text, threshold=threshold)
+        labels_str = ", ".join([label for label, _ in predictions[:3]])
+
+        print(f"{threshold:<10.1f} {len(predictions):<12} {labels_str}")
+
+
+def demonstrate_saving_loading(classifier):
+    """Demonstrate saving and loading the model."""
+
+    print("\n" + "=" * 60)
+    print("SAVING AND LOADING")
+    print("=" * 60)
+
+    # Save the model
+    save_path = "./multilabel_classifier"
+    print(f"Saving classifier to {save_path}")
+    classifier.save(save_path)
+
+    # Load the model
+    print("Loading classifier...")
+    loaded_classifier = MultiLabelAdaptiveClassifier.load(save_path)
+
+    # Verify it works
+    test_text = "New medical technology helps treat cancer patients"
+
+    print(f"\nTesting loaded classifier:")
+    print(f"Text: {test_text}")
+
+    predictions = loaded_classifier.predict_multilabel(test_text)
+    print("Predictions:")
+    for label, confidence in predictions:
+        print(f"  {label}: {confidence:.4f}")
+
+    return loaded_classifier
+
+
+def demonstrate_incremental_learning(classifier):
+    """Demonstrate adding new labels incrementally."""
+
+    print("\n" + "=" * 60)
+    print("INCREMENTAL LEARNING - ADDING NEW LABELS")
+    print("=" * 60)
+
+    # Add new examples with new labels
+    new_texts = [
+        "Chef creates innovative fusion cuisine combining Asian and European flavors",
+        "Food delivery service expands to new cities with sustainable packaging",
+        "Restaurant industry adapts to new dining trends post-pandemic",
+        "Cooking show features celebrity chefs competing in culinary challenges"
+    ]
+
+    new_labels = [
+        ["food", "cuisine", "cooking", "culture"],
+        ["business", "food", "sustainability"],
+        ["business", "food", "trends"],
+        ["entertainment", "food", "cooking", "tv"]
+    ]
+
+    print("Adding new examples with 'food' and 'cooking' labels...")
+    classifier.add_examples(new_texts, new_labels)
+
+    # Test with food-related text
+    food_text = "Nutritionist recommends healthy meal planning for busy professionals"
+
+    print(f"\nTesting with food-related text:")
+    print(f"Text: {food_text}")
+
+    predictions = classifier.predict_multilabel(food_text)
+    print("Predictions:")
+    for label, confidence in predictions:
+        print(f"  {label}: {confidence:.4f}")
+
+    # Show updated statistics
+    stats = classifier.get_label_statistics()
+    print(f"\nUpdated statistics:")
+    print(f"- Total labels: {stats['num_classes']}")
+    print(f"- Total examples: {stats['total_examples']}")
+
+
+def main():
+    """Main function to run all demonstrations."""
+
+    print("Multi-Label Adaptive Classifier Example")
+    print("Fixing the 'No labels met the threshold criteria' issue\n")
+
+    try:
+        # Basic usage
+        classifier = demonstrate_basic_usage()
+
+        # Making predictions
+        demonstrate_predictions(classifier)
+
+        # Threshold adjustment
+        demonstrate_threshold_adjustment(classifier)
+
+        # Saving and loading
+        loaded_classifier = demonstrate_saving_loading(classifier)
+
+        # Incremental learning
+        demonstrate_incremental_learning(loaded_classifier)
+
+        print("\n" + "=" * 60)
+        print("EXAMPLE COMPLETED SUCCESSFULLY")
+        print("=" * 60)
+
+        # Final statistics
+        final_stats = loaded_classifier.get_label_statistics()
+        print(f"\nFinal Model Statistics:")
+        print(f"- Labels: {final_stats['num_classes']}")
+        print(f"- Examples: {final_stats['total_examples']}")
+        print(f"- Default threshold: {final_stats['default_threshold']}")
+        print(f"- Adaptive threshold: {final_stats['adaptive_threshold']:.3f}")
+
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

src/adaptive_classifier/__init__.py

@@ -1,12 +1,15 @@
 from .classifier import AdaptiveClassifier
 from .models import Example, AdaptiveHead, ModelConfig
 from .memory import PrototypeMemory
+from .multilabel import MultiLabelAdaptiveClassifier, MultiLabelAdaptiveHead
 from huggingface_hub import ModelHubMixin
 
 __version__ = "0.0.17"
 
 __all__ = [
     "AdaptiveClassifier",
+    "MultiLabelAdaptiveClassifier",
+    "MultiLabelAdaptiveHead",
     "Example",
     "AdaptiveHead",
     "ModelConfig",