remove parameter 'iid' in GridSearchCV. Increase support for parallel training. Modify ut case. Add attack config verfication.

mindarmour/diff_privacy/evaluation/_check_config.py

@@ -0,0 +1,220 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Verify attack config
+"""
+
+from mindarmour.utils.logger import LogUtil
+
+LOGGER = LogUtil.get_instance()
+
+TAG = "check_params"
+
+
+def _is_positive_int(item):
+    """
+    Verify that the value is a positive integer.
+    """
+    if not isinstance(item, int) or item <= 0:
+        return False
+    return True
+
+
+def _is_non_negative_int(item):
+    """
+    Verify that the value is a non-negative integer.
+    """
+    if not isinstance(item, int) or item < 0:
+        return False
+    return True
+
+
+def _is_positive_float(item):
+    """
+    Verify that value is a positive number.
+    """
+    if not isinstance(item, (int, float)) or item <= 0:
+        return False
+    return True
+
+
+def _is_non_negative_float(item):
+    """
+    Verify that value is a non-negative number.
+    """
+    if not isinstance(item, (int, float)) or item < 0:
+        return False
+    return True
+
+
+def _is_positive_int_tuple(item):
+    """
+    Verify that the input parameter is a positive integer tuple.
+    """
+    if not isinstance(item, tuple):
+        return False
+    for i in item:
+        if not _is_positive_int(i):
+            return False
+    return True
+
+
+def _is_dict(item):
+    """
+    Check whether the type is dict.
+    """
+    return isinstance(item, dict)
+
+
+VALID_PARAMS_DICT = {
+    "knn": {
+        "n_neighbors": [_is_positive_int],
+        "weights": [{"uniform", "distance"}],
+        "algorithm": [{"auto", "ball_tree", "kd_tree", "brute"}],
+        "leaf_size": [_is_positive_int],
+        "p": [_is_positive_int],
+        "metric": None,
+        "metric_params": None,
+    },
+    "lr": {
+        "penalty": [{"l1", "l2", "elasticnet", "none"}],
+        "dual": [{True, False}],
+        "tol": [_is_positive_float],
+        "C": [_is_positive_float],
+        "fit_intercept": [{True, False}],
+        "intercept_scaling": [_is_positive_float],
+        "class_weight": [{"balanced", None}, _is_dict],
+        "random_state": None,
+        "solver": [{"newton-cg", "lbfgs", "liblinear", "sag", "saga"}]
+    },
+    "mlp": {
+        "hidden_layer_sizes": [_is_positive_int_tuple],
+        "activation": [{"identity", "logistic", "tanh", "relu"}],
+        "solver": {"lbfgs", "sgd", "adam"},
+        "alpha": [_is_positive_float],
+        "batch_size": [{"auto"}, _is_positive_int],
+        "learning_rate": [{"constant", "invscaling", "adaptive"}],
+        "learning_rate_init": [_is_positive_float],
+        "power_t": [_is_positive_float],
+        "max_iter": [_is_positive_int],
+        "shuffle": [{True, False}],
+        "random_state": None,
+        "tol": [_is_positive_float],
+        "verbose": [{True, False}],
+        "warm_start": [{True, False}],
+        "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],
+        "epsilon": [_is_positive_float],
+        "n_iter_no_change": [_is_positive_int],
+        "max_fun": [_is_positive_int]
+    },
+    "rf": {
+        "n_estimators": [_is_positive_int],
+        "criterion": [{"gini", "entropy"}],
+        "max_depth": [_is_positive_int],
+        "min_samples_split": [_is_positive_float],
+        "min_samples_leaf": [_is_positive_float],
+        "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_split": [{None}, _is_positive_float],
+        "bootstrap": [{True, False}],
+        "oob_scroe": [{True, False}],
+        "n_jobs": [_is_positive_int, {None}],
+        "random_state": None,
+        "verbose": [_is_non_negative_int],
+        "warm_start": [{True, False}],
+        "class_weight": None,
+        "ccp_alpha": [_is_non_negative_float],
+        "max_samples": [_is_positive_float]
+    }
+}
+
+
+
+def _check_config(config_list, check_params):
+    """
+    Verify that config_list is valid.
+    Check_params is the valid value range of the parameter.
+    """
+    if not isinstance(config_list, (list, tuple)):
+        msg = "Type of parameter 'config_list' must be list, but got {}.".format(type(config_list))
+        LOGGER.error(TAG, msg)
+        raise TypeError(msg)
+
+    for config in config_list:
+        if not isinstance(config, dict):
+            msg = "Type of each config in config_list must be dict, but got {}.".format(type(config))
+            LOGGER.error(TAG, msg)
+            raise TypeError(msg)
+
+        if set(config.keys()) != {"params", "method"}:
+            msg = "Keys of each config in config_list must be {}," \
+                "but got {}.".format({'method', 'params'}, set(config.keys()))
+            LOGGER.error(TAG, msg)
+            raise KeyError(msg)
+
+        method = str.lower(config["method"])
+        params = config["params"]
+
+        if method not in check_params.keys():
+            msg = "Method {} is not supported.".format(method)
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+
+        if not params.keys() <= check_params[method].keys():
+            msg = "Params in method {} is not accepted, the parameters " \
+                "that can be set are {}.".format(method, set(check_params[method].keys()))
+
+            LOGGER.error(TAG, msg)
+            raise KeyError(msg)
+
+        for param_key in params.keys():
+            param_value = params[param_key]
+            candidate_values = check_params[method][param_key]
+
+            if not isinstance(param_value, list):
+                msg = "The parameter '{}' in method '{}' setting must within the range of " \
+                "changeable parameters.".format(param_key, method)
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+
+            if candidate_values is None:
+                continue
+
+            for item_value in param_value:
+                flag = False
+                for candidate_value in candidate_values:
+                    if isinstance(candidate_value, set) and item_value in candidate_value:
+                        flag = True
+                        break
+                    elif candidate_value(item_value):
+                        flag = True
+                        break
+
+                if not flag:
+                    msg = "Setting of parmeter {} in method {} is invalid".format(param_key, method)
+                    raise ValueError(msg)
+
+
+def check_config_params(config_list):
+    """
+    External interfaces to verify attack config.
+    """
+    _check_config(config_list, VALID_PARAMS_DICT)

mindarmour/diff_privacy/evaluation/attacker.py

@@ -27,7 +27,7 @@ LOGGER = LogUtil.get_instance()
 TAG = "Attacker"
 
 
-def _attack_knn(features, labels, param_grid):
+def _attack_knn(features, labels, param_grid, n_jobs):
     """
     Train and return a KNN model.
 
@@ -35,20 +35,21 @@ def _attack_knn(features, labels, param_grid):
         features (numpy.ndarray): Loss and logits characteristics of each sample.
         labels (numpy.ndarray): Labels of each sample whether belongs to training set.
         param_grid (dict): Setting of GridSearchCV.
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Returns:
         sklearn.model_selection.GridSearchCV, trained model.
     """
     knn_model = KNeighborsClassifier()
     knn_model = GridSearchCV(
-        knn_model, param_grid=param_grid, cv=3, n_jobs=1, iid=False,
-        verbose=0,
+        knn_model, param_grid=param_grid, cv=3, n_jobs=n_jobs, verbose=0,
     )
     knn_model.fit(X=features, y=labels)
     return knn_model
 
 
-def _attack_lr(features, labels, param_grid):
+def _attack_lr(features, labels, param_grid, n_jobs):
     """
     Train and return a LR model.
 
@@ -56,20 +57,21 @@ def _attack_lr(features, labels, param_grid):
         features (numpy.ndarray): Loss and logits characteristics of each sample.
         labels (numpy.ndarray): Labels of each sample whether belongs to training set.
         param_grid (dict): Setting of GridSearchCV.
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Returns:
         sklearn.model_selection.GridSearchCV, trained model.
     """
-    lr_model = LogisticRegression(C=1.0, penalty="l2", max_iter=1000)
+    lr_model = LogisticRegression(C=1.0, penalty="l2", max_iter=300)
     lr_model = GridSearchCV(
-        lr_model, param_grid=param_grid, cv=3, n_jobs=1, iid=False,
-        verbose=0,
+        lr_model, param_grid=param_grid, cv=3, n_jobs=n_jobs, verbose=0,
     )
     lr_model.fit(X=features, y=labels)
     return lr_model
 
 
-def _attack_mlpc(features, labels, param_grid):
+def _attack_mlpc(features, labels, param_grid, n_jobs):
     """
     Train and return a MLPC model.
 
@@ -77,20 +79,21 @@ def _attack_mlpc(features, labels, param_grid):
         features (numpy.ndarray): Loss and logits characteristics of each sample.
         labels (numpy.ndarray): Labels of each sample whether belongs to training set.
         param_grid (dict): Setting of GridSearchCV.
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Returns:
         sklearn.model_selection.GridSearchCV, trained model.
     """
     mlpc_model = MLPClassifier(random_state=1, max_iter=300)
     mlpc_model = GridSearchCV(
-        mlpc_model, param_grid=param_grid, cv=3, n_jobs=1, iid=False,
-        verbose=0,
+        mlpc_model, param_grid=param_grid, cv=3, n_jobs=n_jobs, verbose=0,
     )
     mlpc_model.fit(features, labels)
     return mlpc_model
 
 
-def _attack_rf(features, labels, random_grid):
+def _attack_rf(features, labels, random_grid, n_jobs):
     """
     Train and return a RF model.
 
@@ -98,20 +101,22 @@ def _attack_rf(features, labels, random_grid):
         features (numpy.ndarray): Loss and logits characteristics of each sample.
         labels (numpy.ndarray): Labels of each sample whether belongs to training set.
         random_grid (dict): Setting of RandomizedSearchCV.
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Returns:
         sklearn.model_selection.RandomizedSearchCV, trained model.
     """
     rf_model = RandomForestClassifier(max_depth=2, random_state=0)
     rf_model = RandomizedSearchCV(
-        rf_model, param_distributions=random_grid, n_iter=7, cv=3, n_jobs=1,
-        iid=False, verbose=0,
+        rf_model, param_distributions=random_grid, n_iter=7, cv=3, n_jobs=n_jobs,
+        verbose=0,
     )
     rf_model.fit(features, labels)
     return rf_model
 
 
-def get_attack_model(features, labels, config):
+def get_attack_model(features, labels, config, n_jobs=-1):
     """
     Get trained attack model specify by config.
 
@@ -123,6 +128,8 @@ def get_attack_model(features, labels, config):
             params of each method must within the range of changeable parameters.
             Tips of params implement can be found in
             "https://scikit-learn.org/0.16/modules/generated/sklearn.grid_search.GridSearchCV.html".
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Returns:
         sklearn.BaseEstimator, trained model specify by config["method"].
@@ -136,13 +143,13 @@ def get_attack_model(features, labels, config):
     method = str.lower(config["method"])
 
     if method == "knn":
-        return _attack_knn(features, labels, config["params"])
+        return _attack_knn(features, labels, config["params"], n_jobs)
     if method == "lr":
-        return _attack_lr(features, labels, config["params"])
+        return _attack_lr(features, labels, config["params"], n_jobs)
     if method == "mlp":
-        return _attack_mlpc(features, labels, config["params"])
+        return _attack_mlpc(features, labels, config["params"], n_jobs)
     if method == "rf":
-        return _attack_rf(features, labels, config["params"])
+        return _attack_rf(features, labels, config["params"], n_jobs)
 
     msg = "Method {} is not supported.".format(config["method"])
     LOGGER.error(TAG, msg)

mindarmour/diff_privacy/evaluation/membership_inference.py

@@ -15,14 +15,16 @@
 Membership Inference
 """
 
+from multiprocessing import cpu_count
 import numpy as np
 
 import mindspore as ms
 from mindspore.train import Model
 from mindspore.dataset.engine import Dataset
 from mindspore import Tensor
-from mindarmour.diff_privacy.evaluation.attacker import get_attack_model
 from mindarmour.utils.logger import LogUtil
+from .attacker import get_attack_model
+from ._check_config import check_config_params
 
 LOGGER = LogUtil.get_instance()
 TAG = "MembershipInference"
@@ -101,13 +103,15 @@ class MembershipInference:
 
     Args:
         model (Model): Target model.
+        n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
+            otherwise the value of n_jobs must be a positive integer.
 
     Examples:
         >>> train_1, train_2 are non-overlapping datasets from training dataset of target model.
         >>> test_1, test_2 are non-overlapping datasets from test dataset of target model.
         >>> We use train_1, test_1 to train attack model, and use train_2, test_2 to evaluate attack model.
         >>> model = Model(network=net, loss_fn=loss, optimizer=opt, metrics={'acc', 'loss'})
-        >>> inference_model = MembershipInference(model)
+        >>> inference_model = MembershipInference(model, n_jobs=-1)
         >>> config = [{"method": "KNN", "params": {"n_neighbors": [3, 5, 7]}}]
         >>> inference_model.train(train_1, test_1, config)
         >>> metrics = ["precision", "recall", "accuracy"]
@@ -115,15 +119,26 @@ class MembershipInference:
 
     Raises:
         TypeError: If type of model is not mindspore.train.Model.
+        TypeError: If type of n_jobs is not int.
+        ValueError: The value of n_jobs is neither -1 nor a positive integer.
     """
 
-    def __init__(self, model):
+    def __init__(self, model, n_jobs=-1):
         if not isinstance(model, Model):
             msg = "Type of parameter 'model' must be Model, but got {}.".format(type(model))
             LOGGER.error(TAG, msg)
             raise TypeError(msg)
+        if not isinstance(n_jobs, int):
+            msg = "Type of parameter 'n_jobs' must be int, but got {}".format(type(n_jobs))
+            LOGGER.error(TAG, msg)
+            raise TypeError(msg)
+        if not (n_jobs == -1 or n_jobs > 0):
+            msg = "Value of n_jobs must be either -1 or positive integer, but got {}.".format(n_jobs)
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
 
         self.model = model
+        self.n_jobs = min(n_jobs, cpu_count())
         self.method_list = ["knn", "lr", "mlp", "rf"]
         self.attack_list = []
 
@@ -162,24 +177,13 @@ class MembershipInference:
             LOGGER.error(TAG, msg)
             raise TypeError(msg)
 
-        for config in attack_config:
-            if not isinstance(config, dict):
-                msg = "Type of each config in 'attack_config' must be dict, but got {}.".format(type(config))
-                LOGGER.error(TAG, msg)
-                raise TypeError(msg)
-            if {"params", "method"} != set(config.keys()):
-                msg = "Each config in attack_config must have keys 'method' and 'params'," \
-                      "but your key value is {}.".format(set(config.keys()))
-                LOGGER.error(TAG, msg)
-                raise KeyError(msg)
-            if str.lower(config["method"]) not in self.method_list:
-                msg = "Method {} is not support.".format(config["method"])
-                LOGGER.error(TAG, msg)
-                raise ValueError(msg)
+        check_config_params(attack_config) # Verify attack config.
 
         features, labels = self._transform(dataset_train, dataset_test)
+
         for config in attack_config:
-            self.attack_list.append(get_attack_model(features, labels, config))
+            self.attack_list.append(get_attack_model(features, labels, config, n_jobs=self.n_jobs))
+
 
     def eval(self, dataset_train, dataset_test, metrics):
         """

tests/ut/python/diff_privacy/test_attacker.py

@@ -35,7 +35,7 @@ def test_get_knn_model():
             "n_neighbors": [3],
         }
     }
-    knn_attacker = get_attack_model(features, labels, config_knn)
+    knn_attacker = get_attack_model(features, labels, config_knn, -1)
     pred = knn_attacker.predict(features)
     assert pred is not None
 
@@ -54,7 +54,7 @@ def test_get_lr_model():
             "C": np.logspace(-4, 2, 10),
         }
     }
-    lr_attacker = get_attack_model(features, labels, config_lr)
+    lr_attacker = get_attack_model(features, labels, config_lr, -1)
     pred = lr_attacker.predict(features)
     assert pred is not None
 
@@ -75,7 +75,7 @@ def test_get_mlp_model():
             "alpha": [0.0001, 0.001, 0.01],
         }
     }
-    mlpc_attacker = get_attack_model(features, labels, config_mlpc)
+    mlpc_attacker = get_attack_model(features, labels, config_mlpc, -1)
     pred = mlpc_attacker.predict(features)
     assert pred is not None
 
@@ -98,6 +98,6 @@ def test_get_rf_model():
             "min_samples_leaf": [1, 2, 4],
         }
     }
-    rf_attacker = get_attack_model(features, labels, config_rf)
+    rf_attacker = get_attack_model(features, labels, config_rf, -1)
     pred = rf_attacker.predict(features)
     assert pred is not None

tests/ut/python/diff_privacy/test_membership_inference.py

@@ -24,6 +24,7 @@ import numpy as np
 import mindspore.dataset as ds
 from mindspore import nn
 from mindspore.train import Model
+import mindspore.context as context
 
 from mindarmour.diff_privacy.evaluation.membership_inference import MembershipInference
 
@@ -31,6 +32,8 @@ sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../"))
 from defenses.mock_net import Net
 
 
+context.set_context(mode=context.GRAPH_MODE)
+
 def dataset_generator(batch_size, batches):
     """mock training data."""
     data = np.random.randn(batches*batch_size, 1, 32, 32).astype(
@@ -51,7 +54,7 @@ def test_get_membership_inference_object():
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
     model = Model(network=net, loss_fn=loss, optimizer=opt)
-    inference_model = MembershipInference(model)
+    inference_model = MembershipInference(model, -1)
     assert isinstance(inference_model, MembershipInference)
 
 
@@ -65,7 +68,7 @@ def test_membership_inference_object_train():
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
     model = Model(network=net, loss_fn=loss, optimizer=opt)
-    inference_model = MembershipInference(model)
+    inference_model = MembershipInference(model, -1)
     assert isinstance(inference_model, MembershipInference)
 
     config = [{
@@ -95,7 +98,7 @@ def test_membership_inference_eval():
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
     model = Model(network=net, loss_fn=loss, optimizer=opt)
-    inference_model = MembershipInference(model)
+    inference_model = MembershipInference(model, -1)
     assert isinstance(inference_model, MembershipInference)
 
     batch_size = 16