Main loop

supervisor.py

@@ -19,6 +19,7 @@ import os
 import datetime
 from sklearn import metrics
 from pathlib import Path
+from operator import itemgetter
 
 from sklearn.ensemble import RandomForestClassifier
 
@@ -37,6 +38,23 @@ def setup_yaml():
   yaml.add_representer(OrderedDict, represent_dict_order)    
 setup_yaml()
 
+test_dir = Path('./Tests')
+
+def update_queue():
+    tmp_queue = list()
+    for child in test_dir.iterdir():
+        if child.is_file() and child.suffix == '.yml':
+            tmp_queue.append({'expe_file': child, 'priority': get_priority(child)})
+            
+    queue = sorted(tmp_queue, key=itemgetter('priority'))
+    return queue
+
+
+def get_priority(yml_file):
+    with open(yml_file) as f:
+        expe = OrderedDict(yaml.safe_load(f)['expe'])
+    return expe['priority']
+    
 
 def run(expe_file):
     with open(expe_file) as f:
@@ -47,7 +65,6 @@ def run(expe_file):
 
     ### Keep track of time 
     kronos = Kronos()
-    start_time = time.time()
     
     ### Compute descriptors
     descriptors = compute_descriptors(expe)
@@ -61,18 +78,23 @@ def run(expe_file):
     metrics = run_metrics(expe, classification)
     kronos.time('metrics')
     
-    ### Create report WIP WIP WIP WIP WIP WIP WIP
-expe_report = OrderedDict()
+    ### Create report
+    expe_report = create_report(kronos)
     
-expe_report['supervisor'] = os.uname()[1]
+    ### Name and write prediction
+    oname = '{}_{}'.format(Path(expe_file).stem, expe_hashes['global'][:6])
+    oname_tif = oname + '.tif'
+    triskele.write(oname_tif, classification)
 
-for timev, datek in zip((start_time, end_time), ('start_date', 'end_date')):
-    expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S')
-
-ressources = kronos.get_times()
-ressources['ram'] = None
-
-expe_report['ressources'] = ressources
+    ### Write report and results
+    oname_yml = oname + '.yml'
+    with open(oname_yml, 'w') as of:
+        yaml.dump(OrderedDict({'expe': expe, 
+                   'expe_hashes': expe_hashes, 
+                   'expe_report': expe_report,
+                   'expe_classification': oname_tif,
+                   'expe_results': metrics}),
+                  of, default_flow_style=False, encoding=None, allow_unicode=True)
         
 
 def compute_hashes(expe):
@@ -121,7 +143,7 @@ def compute_classification(expe, descriptors):
     return prediction
 
 
-def compute_metrics(ground_truth, classication):
+def compute_metrics(ground_truth, classification):
     """Return dict of metrics for ground_truth and classification prediction in parameters"""
     f = np.nonzero(classification)
     pred = classification[f].ravel()
@@ -142,11 +164,26 @@ def run_metrics(expe, classification):
     return compute_metrics(gt, classification)
 
 
+def create_report(kronos):
+    expe_report = OrderedDict()
+
+    expe_report['supervisor'] = os.uname()[1]
+
+    for timev, datek in zip((kronos.get_start_date(), kronos.get_end_date()), ('start_date', 'end_date')):
+        expe_report[datek] = datetime.datetime.fromtimestamp(timev).strftime('Le %d/%m/%Y à %H:%M:%S')
+
+    ressources = kronos.get_times()
+    ressources['ram'] = None
+
+    expe_report['ressources'] = ressources
+    return expe_report
+
 
 class Kronos(object):
     def __init__(self):
         self._pt = time.process_time()
         self._times = OrderedDict()
+        self._stime = time.time()
         
     def time(self, name):
         self._times[name + '_process_time'] = time.process_time() - self._pt
@@ -154,3 +191,25 @@ class Kronos(object):
         
     def get_times(self):
         return self._times
+    
+    def get_start_date(self):
+        return self._stime
+    
+    def get_end_date(self):
+        return time.time()
+
+    
+def watch_folder():
+    time.sleep(10)
+    
+    
+def main():
+    while(True):
+        queue = update_queue()
+        if not queue:
+            watch_folder()
+            continue
+        run(queue.pop()['expe_file'])
+    
+if __name__ == '__main__':
+    main()