Skip to content

Test

test

Test CLI for Antibody Classification Pipeline

Professional command-line interface for testing trained antibody classifiers: 1. Load trained models from pickle files 2. Evaluate on test datasets with performance metrics 3. Generate confusion matrices and comprehensive logging

Usage

antibody-test --model experiments/checkpoints/antibody_classifier.pkl --data sample_data.csv antibody-test --config test_config.yaml antibody-test --model m1.pkl m2.pkl --data d1.csv d2.csv

Classes

Functions

main()

Main entry point for antibody-test CLI

Source code in src/antibody_training_esm/cli/test.py 
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
def main() -> int:
    """Main entry point for antibody-test CLI"""
    parser = argparse.ArgumentParser(
        description="Testing for antibody classification models",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
    # Test single model on single dataset (auto-detects threshold from dataset name)
    antibody-test --model experiments/checkpoints/antibody_classifier.pkl --data sample_data.csv

    # Test on PSR dataset with auto-detected threshold (0.5495 for Harvey/Shehata)
    antibody-test --model model.pkl --data data/test/harvey/fragments/VHH_only_harvey.csv

    # Test multiple models on multiple datasets
    antibody-test --model experiments/checkpoints/model1.pkl experiments/checkpoints/model2.pkl --data dataset1.csv dataset2.csv

    # Use configuration file
    antibody-test --config test_config.yaml

    # Override device, batch size, and threshold
    antibody-test --config test_config.yaml --device cuda --batch-size 64 --threshold 0.6

    # Create sample configuration
    antibody-test --create-config
        """,
    )

    parser.add_argument(
        "--model", nargs="+", help="Path(s) to trained model pickle files"
    )
    parser.add_argument("--data", nargs="+", help="Path(s) to test dataset CSV files")
    parser.add_argument("--config", help="Path to test configuration YAML file")
    parser.add_argument(
        "--output-dir",
        default="./experiments/benchmarks",
        help="Output directory for results",
    )
    parser.add_argument(
        "--device",
        choices=["cpu", "cuda", "mps", "auto"],
        help="Device to use for inference: auto (CUDA > MPS > CPU), or explicit (overrides config)",
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        help="Batch size for embedding extraction (overrides config)",
    )
    parser.add_argument(
        "--threshold",
        type=float,
        help="Manual decision threshold override (default: auto-detect from dataset name). "
        "Use 0.5 for ELISA datasets (Boughter, Jain) or 0.5495 for PSR datasets (Harvey, Shehata).",
    )
    parser.add_argument(
        "--sequence-column",
        type=str,
        help="Column name for sequences in dataset (default: 'sequence', overrides config)",
    )
    parser.add_argument(
        "--label-column",
        type=str,
        help="Column name for labels in dataset (default: 'label', overrides config)",
    )
    parser.add_argument(
        "--create-config", action="store_true", help="Create sample configuration file"
    )

    args = parser.parse_args()

    # Create sample config if requested
    if args.create_config:
        create_sample_test_config()
        return 0

    # Load configuration
    if args.config:
        config = load_config_file(args.config)
    else:
        if not args.model or not args.data:
            parser.error("Either --config or both --model and --data must be specified")

        config = TestConfig(
            model_paths=args.model, data_paths=args.data, output_dir=args.output_dir
        )

    # Override config with command line arguments
    if args.device:
        config.device = args.device
    if args.batch_size:
        config.batch_size = args.batch_size
    if args.threshold:
        config.threshold = args.threshold
    if args.sequence_column:
        config.sequence_column = args.sequence_column
    if args.label_column:
        config.label_column = args.label_column

    # Run testing
    try:
        tester = ModelTester(config)
        results = tester.run_comprehensive_test()

        print(f"\n{'=' * 60}")
        print("TESTING COMPLETED SUCCESSFULLY!")
        print(f"{'=' * 60}")
        print(f"Results saved to: {config.output_dir}")

        # Print summary
        for dataset_name, dataset_results in results.items():
            print(f"\nDataset: {dataset_name}")
            print("-" * 40)
            for model_name, model_results in dataset_results.items():
                print(f"Model: {model_name}")
                if "test_scores" in model_results:
                    for metric, value in model_results["test_scores"].items():
                        print(f"  {metric}: {value:.4f}")

        return 0

    except KeyboardInterrupt:
        print("Error during testing: Interrupted by user", file=sys.stderr)
        return 1
    except Exception as e:
        print(f"Error during testing: {e}", file=sys.stderr)
        return 1