merging remote into local

fedbiomed/common/__init__.py

@@ -1,17 +0,0 @@
-from . import constants, exceptions, message, logger, utils
-from .metrics import Metrics, MetricTypes
-from .messaging import Messaging
-from .singleton import SingletonMeta
-from .tasks_queue import TasksQueue
-from .repository import Repository
-
-__all__ = ["constants",
-           "exceptions",
-           "logger",
-           "message",
-           "utils",
-           "Messaging",
-           "Metrics",
-           "Repository",
-           "SingletonMeta",
-           "TasksQueue"]

fedbiomed/common/json.py

@@ -67,10 +67,6 @@ def serialize_msg(msg: dict) -> str:
     return json.dumps(msg)
 
 
-def _deserialize_training_args(msg):
-    """TrainingArgs is a class and must be specifically deserialized"""
-    pass
-
 
 def _serialize_training_args(msg):
     """TrainingArgs is a class and must be specifically serialized"""

fedbiomed/common/training_args.py

@@ -10,8 +10,8 @@
 from fedbiomed.common.exceptions import FedbiomedUserInputError
 from fedbiomed.common.logger import logger
 from fedbiomed.common.metrics import MetricTypes
-from fedbiomed.common.validator import SchemeValidator, ValidateError, \
-    RuleError, validator_decorator
+from fedbiomed.common.validator import SchemeValidator, ValidatorError, \
+    ValidateError, RuleError, validator_decorator
 
 
 class TrainingArgs():
@@ -32,13 +32,17 @@ def __init__(self, ta: Dict = None, extra_scheme: Dict = None, only_required: bo
                     update the scheme of the default training args.
                     Warning: this is a dangerous featuret, provided to
                     developpers, to ease the test of future Fed-Biomed features
+            only_required: if True, the object is initialized only with required
+                    values defined in the default_scheme (+ extra_scheme).
+                    If False, then all default values will also be returned
+                    (not only the required key/value pairs).
 
         Raises:
             FedbiomedUserInputError: in case of bad value or bad extra_scheme
         """
         self._scheme = TrainingArgs.default_scheme()
 
-        if extra_scheme is None:
+        if not isinstance(extra_scheme, dict):
             extra_scheme = {}
 
         for k in extra_scheme:
@@ -60,7 +64,7 @@ def __init__(self, ta: Dict = None, extra_scheme: Dict = None, only_required: bo
         try:
             self._ta = self._sc.populate_with_defaults( ta,
                                                         only_required = only_required)
-        except RuleError as e:
+        except ValidatorError as e:
             # scheme has required keys without defined default value
             msg = ErrorNumbers.FB410.value + f": {e}"
             logger.critical(msg)
@@ -110,7 +114,7 @@ def _test_ratio_hook( v: Any) -> bool:
 
     @staticmethod
     @validator_decorator
-    def _loss_rate_hook( v: Any) -> bool:
+    def _lr_hook( v: Any):
         """
         Test if lr is greater than 0.
         """
@@ -125,9 +129,9 @@ def default_scheme(cls) -> Dict:
         Returns the default (base) scheme for TrainingArgs.
         """
         return  {
-            # loss rate
+            # lr
             "lr": {
-                "rules": [ float, cls._loss_rate_hook ],
+                "rules": [ float, cls._lr_hook ],
                 "required": False,
 #                "default": 0.01
             },
@@ -341,17 +345,15 @@ def default_value(self, key: str) -> Any:
             raise FedbiomedUserInputError(msg)
 
 
-    def dict(self) -> Dict:
-        """Returns the training_args as a dictionnary."""
+    def dict(self):
+        """Returns a copy of the training_args as a dictionary."""
 
-        if 'test_metric' in self._ta and \
-           isinstance(self._ta['test_metric'], MetricTypes):
+        ta = deepcopy(self._ta)
+        if 'test_metric' in ta and \
+           isinstance(ta['test_metric'], MetricTypes):
             # replace MetricType value by a string
-            ta = deepcopy(self._ta)
-            ta['test_metric'] = self._ta['test_metric'].name
-            return ta
-
-        return self._ta
+            ta['test_metric'] = ta['test_metric'].name
+        return ta
 
 
     def get(self, key: str, default: Any = None) -> Any:

fedbiomed/common/validator.py

@@ -83,6 +83,7 @@ def my_validation_funct( value ):
 import inspect
 import sys
 
+from copy import deepcopy
 from enum import Enum
 from typing import Any, Callable, Dict, Union
 
@@ -243,7 +244,7 @@ def validate(self, value: Dict) -> bool:
         Validate a value against the scheme passed at creation time.
 
         Args:
-             value (dict):  value (json) to validate against the scheme passed
+             value (dict):  value (dict) to validate against the scheme passed
                      at __init__
         Returns:
             True if value is valid
@@ -296,13 +297,18 @@ def populate_with_defaults(self, value: Dict, only_required: bool = True) -> Dic
 
         Raises:
             RuleError: if scheme provided at init contains a required rules without default value
-
+            ValidatorError: if input value was not a dict
         """
         if not self.is_valid():  # pragma: no cover
             return {}
 
         # check the value against the scheme
-        result = value
+        if isinstance(value, dict):
+            result = deepcopy(value)
+        else:
+            raise ValidatorError("input value is not a dict")
+
+
         for k, v in self._scheme.items():
             if 'required' in v and v['required'] is True:
 

tests/test_experiment.py

@@ -754,9 +754,8 @@ def test_experiment_13_set_training_arguments(self):
         # test update of testing_args with argument `reset` set to False
         ma_expected_2 = {'lr': 0.2}
         train_args_2 = self.test_exp.set_training_args(ma_expected_2, reset=False).dict()
-        ma_expected_2.update(ma_expected)
+        ma_expected_2.update(ma_expected_2)
         self.assertSubDictInDict(ma_expected_2, train_args_2)
-        self.assertSubDictInDict(ma_expected, train_args_2)
 
         # test update of testing_args with argument `reset` set to True
         ma_expected_3 = {'lr': 1e-4}

tests/test_training_args.py

@@ -37,7 +37,7 @@ def test_training_args_01_init(self):
 
         # lr has no default value
         with self.assertRaises(FedbiomedUserInputError):
-            self.assertEqual( t['lr'], t.default_value('lr'))
+            t.default_value('lr')
 
         # and test_ratio has
         self.assertEqual( t['test_ratio'], t.default_value('test_ratio'))

tests/test_validator.py

@@ -181,7 +181,9 @@ def test_validator_03_registration(self):
 
 
     def test_validator_04_another_one(self):
-
+        """
+        provide my own hook
+        """
         v = Validator()
         self.assertTrue(v.register( 'probability', self.hook_probability_check))
 
@@ -205,6 +207,9 @@ def test_validator_04_another_one(self):
 
 
     def test_validator_05_without_decorator(self):
+        """
+        provide a hook without using the @validator_decorator
+        """
         v = Validator()
 
         rule_name = 'rule_02'
@@ -222,7 +227,9 @@ def test_validator_05_without_decorator(self):
 
 
     def test_validator_06_strict_or_not(self):
-
+        """
+        test the script flag
+        """
         v = Validator()
 
         rule_name = 'this_rule_is_unknown'