adding CLI utilities

score.py

@@ -0,0 +1,67 @@
+
+import time
+
+from ifcb import DataDirectory
+
+from dataloader import IFCB_ASPECT_RATIO
+
+from classifier import load_extract_parallel, load_model, score_distributions
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Score anomalies in point cloud data')
+    parser.add_argument('data_dir', help='Directory containing point cloud data')
+    parser.add_argument('--id-file', default=None, help='File containing list of IDs to load')
+    parser.add_argument('--n-jobs', type=int, default=-1, help='Number of parallel jobs for load/extraction phase')
+    parser.add_argument('--aspect-ratio', type=float, default=IFCB_ASPECT_RATIO, help='Camera frame aspect ratio (width/height)')
+    parser.add_argument('--chunk-size', type=int, default=100, help='Number of PIDs to process in each chunk')
+    parser.add_argument('--model', default='classifier.pkl', help='Model load path')
+    parser.add_argument('--output', default='scores.csv', help='Output CSV file path')
+
+    args = parser.parse_args()
+
+    beginning = time.time()
+
+    print(f'Loading model from {args.model}')
+
+    classifier = load_model(args.model)
+
+    then = time.time()
+
+    print(f'Extracting features from point clouds in {args.data_dir}')
+
+    if args.id_file is not None:
+        with open(args.id_file, 'r') as f:
+            pids = [line.strip() for line in f]
+    else:
+        pids = []
+        for bin in DataDirectory(args.data_dir):
+            pids.append(bin.lid)
+
+    feature_results = load_extract_parallel(pids, args.data_dir, aspect_ratio=args.aspect_ratio, n_jobs=args.n_jobs, chunk_size=args.chunk_size)
+
+    elapsed = time.time() - then
+
+    print(f'Extracted features for {len(feature_results)} point clouds in {elapsed:.2f} seconds')
+
+    then = time.time()
+
+    print(f'Scoring point clouds using classifier')
+
+    results = score_distributions(classifier, feature_results)
+
+    print(results)
+
+    elapsed = time.time() - then
+
+    print(f'Scored {len(results)} point clouds in {elapsed:.2f} seconds')
+
+    print('Saving results ...')
+
+    with open(args.output, 'w') as csv_file:
+        csv_file.write('pid,anomaly_score\n')
+        for scoredict in results:
+            csv_file.write(f"{scoredict['pid']},{scoredict['anomaly_score']:.4f}\n")
+

train.py

@@ -0,0 +1,62 @@
+import time 
+
+from ifcb import DataDirectory
+
+from classifier import load_extract_parallel, save_model, train_classifier
+from dataloader import IFCB_ASPECT_RATIO
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Train a classifier on point cloud data')
+    parser.add_argument('data_dir', help='Directory containing point cloud data')
+    parser.add_argument('--id-file', default=None, help='File containing list of IDs to load')
+    parser.add_argument('--n-jobs', type=int, default=-1, help='Number of parallel jobs')
+    parser.add_argument('--contamination', type=float, default=0.1, help='Expected fraction of anomalous distributions')
+    parser.add_argument('--aspect-ratio', type=float, default=IFCB_ASPECT_RATIO, help='Camera frame aspect ratio (width/height)')
+    parser.add_argument('--chunk-size', type=int, default=100, help='Number of PIDs to process in each chunk')
+    parser.add_argument('--model', default='classifier.pkl', help='Model save/load path')
+
+    args = parser.parse_args()
+
+    beginning = time.time()
+
+    if args.id_file is not None:
+        with open(args.id_file, 'r') as f:
+            pids = [line.strip() for line in f]
+    else:
+        pids = []
+        for bin in DataDirectory(args.data_dir):
+            pids.append(bin.lid)
+
+    then = time.time()
+
+    print(f'Loading and performing feature extraction on {len(pids)} point clouds')
+
+    # Extract features from point clouds
+    feature_results = load_extract_parallel(pids, args.data_dir, aspect_ratio=args.aspect_ratio, n_jobs=args.n_jobs, chunk_size=args.chunk_size)
+
+    elapsed = time.time() - then
+
+    print(f'Extracted features for {len(feature_results)} point clouds in {elapsed:.2f} seconds')
+
+    then = time.time()
+
+    # Train the classifier
+
+    print(f'Training classifier')
+
+    classifier = train_classifier(feature_results, contamination=args.contamination, n_jobs=args.n_jobs)
+
+    elapsed = time.time() - then
+
+    print(f'Trained classifier in {elapsed:.2f} seconds')
+
+    # save the classifier
+    save_model(classifier, args.model)
+
+    elapsed = time.time() - beginning
+
+    print(f'Total load/extract/train time: {elapsed:.2f} seconds')
+