har_trees: Add a quicklabel tool

Author: jonnor

examples/har_trees/har_quicklabel.py

@@ -0,0 +1,82 @@
+
+"""
+Take the activity markers in har_record.py as labels
+"""
+
+import os
+
+import pandas
+
+from har_data2labelstudio import load_har_record
+
+def parse():
+    import argparse
+    parser = argparse.ArgumentParser(description='')
+
+    parser.add_argument('--dataset', type=str, default='uci_har',
+                        help='Which dataset to use')
+    parser.add_argument('--config', type=str, default='data/configurations/uci_har.yaml',
+                        help='Which dataset/training config to use')
+
+    parser.add_argument('--data-dir', metavar='DIRECTORY', type=str, default='./data/raw/uci_har',
+                        help='Where the input data is stored')
+    parser.add_argument('--out-dir', metavar='DIRECTORY', type=str, default='./data/processed',
+                        help='Where to store results')
+
+    parser.add_argument('--features', type=str, default='timebased',
+                        help='Which feature-set to use')
+    parser.add_argument('--window-length', type=int, default=128,
+                        help='Length of each window to classify (in samples)')
+    parser.add_argument('--window-hop', type=int, default=64,
+                        help='How far to hop for next window to classify (in samples)')
+
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    args = parse()
+
+    dataset = args.dataset
+    out_path = os.path.join(args.out_dir, f'{dataset}.parquet')
+    data_path = os.path.join(args.data_dir)
+
+    # Lookup data
+    recordings = load_har_record(data_path)
+
+    print(recordings.columns)
+
+    print(recordings.head(5))
+
+    # Create packed dataframe
+    dfs = []
+    for filename, row in recordings.iterrows():
+        classname = row.classname
+        filename = filename.rstrip('.npy')
+        #print(filename, classname)    
+
+        d = row.data
+        d = d.reset_index()
+        d['subject'] = 'unknown'
+        d['file'] = filename
+        d['activity'] = classname
+        dfs.append(d)
+
+    #p = 'data/processed/pamap2.parquet'
+    
+    out = pandas.concat(dfs, ignore_index=True)
+    print(out)
+    out.to_parquet(out_path)
+
+    #return
+    # Sanity check
+    df = pandas.read_parquet(out_path)
+    print(df.columns)
+    print(df.activity.value_counts())
+    print(df.file.value_counts())
+    print(df.head())
+
+
+if __name__ == '__main__':
+    main()