Adjust config check. Modify the exception type to TypeError in function check_model.

mindarmour/privacy/evaluation/_check_config.py

@@ -24,45 +24,39 @@ TAG = "check_config"
 
 
 def _is_positive_int(item):
-    """
-    Verify that the value is a positive integer.
-    """
-    if not isinstance(item, int) or item <= 0:
+    """Verify that the value is a positive integer."""
+    if not isinstance(item, int):
         return False
-    return True
-
+    return item > 0
 
 def _is_non_negative_int(item):
-    """
-    Verify that the value is a non-negative integer.
-    """
-    if not isinstance(item, int) or item < 0:
+    """Verify that the value is a non-negative integer."""
+    if not isinstance(item, int):
         return False
-    return True
+    return item >= 0
 
 
 def _is_positive_float(item):
-    """
-    Verify that value is a positive number.
-    """
-    if not isinstance(item, (int, float)) or item <= 0:
+    """Verify that value is a positive number."""
+    if not isinstance(item, (int, float)):
         return False
-    return True
+    return item > 0
 
 
 def _is_non_negative_float(item):
-    """
-    Verify that value is a non-negative number.
-    """
-    if not isinstance(item, (int, float)) or item < 0:
+    """Verify that value is a non-negative number."""
+    if not isinstance(item, (int, float)):
         return False
-    return True
+    return item >= 0
+
+def _is_range_0_1_float(item):
+    if not isinstance(item, (int, float)):
+        return False
+    return 0 <= item < 1
 
 
 def _is_positive_int_tuple(item):
-    """
-    Verify that the input parameter is a positive integer tuple.
-    """
+    """Verify that the input parameter is a positive integer tuple."""
     if not isinstance(item, tuple):
         return False
     for i in item:
@@ -72,21 +66,29 @@ def _is_positive_int_tuple(item):
 
 
 def _is_dict(item):
-    """
-    Check whether the type is dict.
-    """
+    """Check whether the type is dict."""
     return isinstance(item, dict)
 
 
+def _is_list(item):
+    """Check whether the type is list"""
+    return isinstance(item, list)
+
+
+def _is_str(item):
+    """Check whether the type is str."""
+    return isinstance(item, str)
+
+
 _VALID_CONFIG_CHECKLIST = {
     "knn": {
         "n_neighbors": [_is_positive_int],
-        "weights": [{"uniform", "distance"}],
+        "weights": [{"uniform", "distance"}, callable],
         "algorithm": [{"auto", "ball_tree", "kd_tree", "brute"}],
         "leaf_size": [_is_positive_int],
         "p": [_is_positive_int],
-        "metric": None,
-        "metric_params": None,
+        "metric": [_is_str, callable],
+        "metric_params": [_is_dict, {None}]
     },
     "lr": {
         "penalty": [{"l1", "l2", "elasticnet", "none"}],
@@ -102,7 +104,7 @@ _VALID_CONFIG_CHECKLIST = {
     "mlp": {
         "hidden_layer_sizes": [_is_positive_int_tuple],
         "activation": [{"identity", "logistic", "tanh", "relu"}],
-        "solver": {"lbfgs", "sgd", "adam"},
+        "solver": [{"lbfgs", "sgd", "adam"}],
         "alpha": [_is_positive_float],
         "batch_size": [{"auto"}, _is_positive_int],
         "learning_rate": [{"constant", "invscaling", "adaptive"}],
@@ -117,9 +119,9 @@ _VALID_CONFIG_CHECKLIST = {
         "momentum": [_is_positive_float],
         "nesterovs_momentum": [{True, False}],
         "early_stopping": [{True, False}],
-        "validation_fraction": [_is_positive_float],
-        "beta_1": [_is_positive_float],
-        "beta_2": [_is_positive_float],
+        "validation_fraction": [_is_range_0_1_float],
+        "beta_1": [_is_range_0_1_float],
+        "beta_2": [_is_range_0_1_float],
         "epsilon": [_is_positive_float],
         "n_iter_no_change": [_is_positive_int],
         "max_fun": [_is_positive_int]
@@ -133,7 +135,7 @@ _VALID_CONFIG_CHECKLIST = {
         "min_weight_fraction_leaf": [_is_non_negative_float],
         "max_features": [{"auto", "sqrt", "log2", None}, _is_positive_float],
         "max_leaf_nodes": [_is_positive_int, {None}],
-        "min_impurity_decrease": {_is_non_negative_float},
+        "min_impurity_decrease": [_is_non_negative_float],
         "min_impurity_split": [{None}, _is_positive_float],
         "bootstrap": [{True, False}],
         "oob_scroe": [{True, False}],
@@ -141,9 +143,9 @@ _VALID_CONFIG_CHECKLIST = {
         "random_state": None,
         "verbose": [_is_non_negative_int],
         "warm_start": [{True, False}],
-        "class_weight": None,
+        "class_weight": [{"balanced", "balanced_subsample"}, _is_dict, _is_list],
         "ccp_alpha": [_is_non_negative_float],
-        "max_samples": [_is_positive_float]
+        "max_samples": [{None}, _is_positive_int, _is_range_0_1_float]
     }
 }
 

mindarmour/utils/_check_param.py

@@ -129,7 +129,7 @@ def check_model(model_name, model, model_type):
                 model_type,
                 type(model).__name__)
     LOGGER.error(TAG, msg)
-    raise ValueError(msg)
+    raise TypeError(msg)
 
 
 def check_numpy_param(arg_name, arg_value):

tests/ut/python/detectors/test_region_based_detector.py

@@ -84,7 +84,7 @@ def test_value_error():
     adv = np.random.rand(4, 4).astype(np.float32)
     model = Model(Net())
     # model should be mindspore model
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         assert RegionBasedDetector(Net())
 
     with pytest.raises(ValueError):