adding predict script

LeQua2022/predict.py

@@ -0,0 +1,62 @@
+import argparse
+import quapy as qp
+from data import ResultSubmission, evaluate_submission
+import constants
+import os
+import pickle
+from tqdm import tqdm
+from data import gen_load_samples_T1, load_category_map
+from glob import glob
+import constants
+
+"""
+LeQua2022 prediction script 
+"""
+
+def main(args):
+
+    # check the number of samples
+    nsamples = len(glob(os.path.join(args.samples, '*.txt')))
+    if nsamples not in {constants.DEV_SAMPLES, constants.TEST_SAMPLES}:
+        print(f'Warning: The number of samples does neither coincide with the expected number of '
+              f'dev samples ({constants.DEV_SAMPLES}) nor with the expected number of '
+              f'test samples ({constants.TEST_SAMPLES}).')
+
+    _, categories = load_category_map(args.catmap)
+
+    # load pickled model
+    model = pickle.load(open(args.model, 'rb'))
+
+    # predictions
+    predictions = ResultSubmission(categories=categories)
+    for samplename, sample in tqdm(gen_load_samples_T1(args.samples, args.nf),
+                                   desc='predicting', total=nsamples):
+        predictions.add(samplename, model.quantify(sample))
+
+    # saving
+    basedir = os.path.basename(args.output)
+    if basedir:
+        os.makedirs(basedir, exist_ok=True)
+    predictions.dump(args.output)
+
+
+if __name__=='__main__':
+    parser = argparse.ArgumentParser(description='LeQua2022 prediction script')
+    parser.add_argument('model', metavar='MODEL-PATH', type=str,
+                        help='Path of saved model')
+    parser.add_argument('samples', metavar='SAMPLES-PATH', type=str,
+                        help='Path to the directory containing the samples')
+    parser.add_argument('output', metavar='PREDICTIONS-PATH', type=str,
+                        help='Path where to store the predictions file')
+    parser.add_argument('catmap', metavar='CATEGORY-MAP-PATH', type=str,
+                        help='Path to the category map file')
+    parser.add_argument('nf', metavar='NUM-FEATURES', type=int,
+                        help='Number of features seen during training')
+    args = parser.parse_args()
+
+    if not os.path.exists(args.samples):
+        raise FileNotFoundError(f'path {args.samples} does not exist')
+    if not os.path.isdir(args.samples):
+        raise ValueError(f'path {args.samples} is not a valid directory')
+
+    main(args)