include experimentSetup class /close #12

.gitignore

@@ -56,3 +56,4 @@ htmlcov/
 .pytest_cache
 /test/data/
 report.html
+/TestExperiment/

run.py

@@ -38,25 +38,75 @@ class run(object):
 
 
 class ExperimentSetup:
+    """
+    params:
+    trainable: Train new model if true, otherwise try to load existing model
+    """
 
-    def __init__(self):
+    def __init__(self, **kwargs):
         self.data_path = None
         self.experiment_path = None
         self.experiment_name = None
-        self.trainable = False
-        self.setup_experiment()
-
-    def setup_experiment(self):
+        self.trainable = None
+        self.fraction_of_train = None
+        self.use_all_stations_on_all_data_sets = None
+        self.network = None
+        self.var_all_dict = None
+        self.all_stations = None
+        self.variables = None
+        self.dimensions = None
+        self.dim = None
+        self.target_dim = None
+        self.target_var = None
+        self.setup_experiment(**kwargs)
+
+    def _set_param(self, param, value, default=None):
+        if default is not None:
+            value = value.get(param, default)
+        setattr(self, param, value)
+        logging.info(f"set experiment attribute: {param}={value}")
+
+    def setup_experiment(self, **kwargs):
 
         # set data path of this experiment
-        self.data_path = helpers.prepare_host()
+        self._set_param("data_path", helpers.prepare_host())
 
         # set experiment name
-        experiment_date = parser.parse_args(["experiment_date"])
-        self.experiment_name, self.experiment_path = helpers.set_experiment_name(experiment_date=experiment_date)
-
-        # set if model shall be trained or not
-        self.trainable = True
+        exp_date = args.experiment_date
+        exp_name, exp_path = helpers.set_experiment_name(experiment_date=exp_date)
+        self._set_param("experiment_name", exp_name)
+        self._set_param("experiment_path", exp_path)
+        helpers.check_path_and_create(self.experiment_path)
+
+        # set if model is trainable
+        self._set_param("trainable", kwargs, default=True)
+
+        # set fraction of train
+        self._set_param("fraction_of_train", kwargs, default=0.8)
+
+        # use all stations on all data sets (train, val, test)
+        self._set_param("use_all_stations_on_all_data_sets", kwargs, default=True)
+        self._set_param("network", kwargs, default="AIRBASE")
+        self._set_param("var_all_dict", kwargs, default={'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'maximum',
+                                                         'u': 'average_values', 'v': 'average_values', 'no': 'dma8eu',
+                                                         'no2': 'dma8eu', 'cloudcover': 'average_values',
+                                                         'pblheight': 'maximum'})
+        self._set_param("all_stations", kwargs, default=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087',
+                                                         'DEBY052', 'DEBY032', 'DEBW022', 'DEBY004', 'DEBY020',
+                                                         'DEBW030', 'DEBW037', 'DEBW031', 'DEBW015', 'DEBW073',
+                                                         'DEBY039', 'DEBW038', 'DEBW081', 'DEBY075', 'DEBW040',
+                                                         'DEBY053', 'DEBW059', 'DEBW027', 'DEBY072', 'DEBW042',
+                                                         'DEBW039', 'DEBY001', 'DEBY113', 'DEBY089', 'DEBW024',
+                                                         'DEBW004', 'DEBY037', 'DEBW056', 'DEBW029', 'DEBY068',
+                                                         'DEBW010', 'DEBW026', 'DEBY002', 'DEBY079', 'DEBW084',
+                                                         'DEBY049', 'DEBY031', 'DEBW019', 'DEBW001', 'DEBY063',
+                                                         'DEBY005', 'DEBW046', 'DEBW103', 'DEBW052', 'DEBW034',
+                                                         'DEBY088', ])
+        self._set_param("variables", kwargs, default=list(self.var_all_dict.keys()))
+        self._set_param("dimensions", kwargs, default={'new_index': ['datetime', 'Stations']})
+        self._set_param("dim", kwargs, default='datetime')
+        self._set_param("target_dim", kwargs, default='variables')
+        self._set_param("target_var", kwargs, default="o3")
 
 
 class PreProcessing(run):
@@ -83,11 +133,12 @@ class PostProcessing(run):
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('experiment date', metavar='exp_date', type=str, nargs=1,  help='set experiment date as string',
-                        default=None)
+    parser.add_argument('--experiment_date', metavar='--exp_date', type=str, nargs=1, default=None,
+                        help="set experiment date as string")
+    args = parser.parse_args()
 
     with run():
-        exp_setup = ExperimentSetup()
+        exp_setup = ExperimentSetup(trainable=True)
 
         PreProcessing(exp_setup)
 

src/helpers.py

@@ -161,7 +161,7 @@ def prepare_host():
 def set_experiment_name(experiment_date=None, experiment_path=None):
 
     if experiment_date is None:
-        experiment_name = ""
+        experiment_name = "TestExperiment"
     else:
         experiment_name = f"{experiment_date}_network/"
     if experiment_path is None: