!343 Fix Math Formula Issue

Merge pull request !343 from 张澍坤/master

mindarmour/adv_robustness/evaluations/defense_evaluation.py

@@ -70,9 +70,6 @@ class DefenseEvaluate:
 
         Returns:
             float, the higher, the more successful the defense is.
-
-        Examples:
-            >>> def_eval.cav()
         """
         def_succ_num = np.sum(np.argmax(self._def_preds, axis=1)
                               == self._true_labels)
@@ -87,9 +84,6 @@ class DefenseEvaluate:
 
         Returns:
             float, the higher, the more successful the defense is.
-
-        Examples:
-            >>> def_eval.crr()
         """
         cond1 = np.argmax(self._def_preds, axis=1) == self._true_labels
         cond2 = np.argmax(self._raw_preds, axis=1) != self._true_labels
@@ -118,9 +112,6 @@ class DefenseEvaluate:
             - float, the lower, the more successful the defense is.
 
               - If return value == -1, len(idxes) == 0.
-
-        Examples:
-            >>> def_eval.ccv()
         """
         idxes = np.arange(self._num_samples)
         cond1 = np.argmax(self._def_preds, axis=1) == self._true_labels
@@ -147,9 +138,6 @@ class DefenseEvaluate:
                   more successful the defense.
 
                 - If return value == -1, idxes == 0.
-
-        Examples:
-            >>> def_eval.cos()
         """
         idxes = np.arange(self._num_samples)
         cond1 = np.argmax(self._def_preds, axis=1) == self._true_labels

mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py

@@ -39,7 +39,7 @@ class ClipMechanismsFactory:
     Wrapper of clip noise generating mechanisms. It supports Adaptive Clipping with
     Gaussian Random Noise for now.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     """
 
@@ -49,7 +49,7 @@ class ClipMechanismsFactory:
     @staticmethod
     def create(mech_name, decay_policy='Linear', learning_rate=0.001,
                target_unclipped_quantile=0.9, fraction_stddev=0.01, seed=0):
-        """
+        r"""
         Args:
             mech_name(str): Clip noise generated strategy, support 'Gaussian' now.
             decay_policy(str): Decay policy of adaptive clipping, decay_policy must
@@ -57,7 +57,7 @@ class ClipMechanismsFactory:
             learning_rate(float): Learning rate of update norm clip. Default: 0.001.
             target_unclipped_quantile(float): Target quantile of norm clip. Default: 0.9.
             fraction_stddev(float): The stddev of Gaussian normal which used in
-                empirical_fraction, the formula is :math:`empirical_fraction + N(0, fraction_stddev)`.
+                empirical_fraction, the formula is :math:`empirical\_fraction + N(0, fraction\_stddev)`.
                 Default: 0.01.
             seed(int): Original random seed, if seed=0 random normal will use secure
                 random number. IF seed!=0 random normal will generate values using
@@ -100,7 +100,7 @@ class NoiseMechanismsFactory:
     Wrapper of noise generating mechanisms. It supports Gaussian Random Noise and
     Adaptive Gaussian Random Noise for now.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     """
     def __init__(self):
@@ -168,9 +168,9 @@ class _Mechanisms(Cell):
 
 
 class NoiseGaussianRandom(_Mechanisms):
-    """
+    r"""
     Generate noise in Gaussian Distribution with :math:`mean=0` and
-    :math:`standard deviation = norm_bound * initial_noise_multiplier`.
+    :math:`standard\_deviation = norm\_bound * initial\_noise\_multiplier`.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
@@ -347,11 +347,11 @@ class _MechanismsParamsUpdater(Cell):
 
 
 class AdaClippingWithGaussianRandom(Cell):
-    """
-    Adaptive clipping. If `decay_policy` is 'Linear', the update formula :math:`norm_bound = norm_bound -
-    learning_rate*(beta - target_unclipped_quantile)`.
-    If `decay_policy` is 'Geometric', the update formula is :math:`norm_bound =
-    norm_bound*exp(-learning_rate*(empirical_fraction - target_unclipped_quantile))`.
+    r"""
+    Adaptive clipping. If `decay_policy` is 'Linear', the update formula :math:`norm\_bound = norm\_bound -
+    learning\_rate*(beta - target\_unclipped\_quantile)`.
+    If `decay_policy` is 'Geometric', the update formula is :math:`norm\_bound =
+    norm\_bound*exp(-learning\_rate*(empirical\_fraction - target\_unclipped\_quantile))`.
     where beta is the empirical fraction of samples with the value at most
     `target_unclipped_quantile`.
 

mindarmour/privacy/diff_privacy/monitor/monitor.py

@@ -28,7 +28,7 @@ TAG = 'DP monitor'
 class PrivacyMonitorFactory:
     """
     Factory class of DP training's privacy monitor.
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     """
 
@@ -77,7 +77,7 @@ class RDPMonitor(Callback):
     .. math::
         (ε'+\frac{log(1/δ)}{α-1}, δ)
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     Reference: `Rényi Differential Privacy of the Sampled Gaussian Mechanism
     <https://arxiv.org/abs/1908.10530>`_
@@ -370,7 +370,7 @@ class ZCDPMonitor(Callback):
     noise mechanisms(such as NoiseAdaGaussianRandom and NoiseGaussianRandom).
     The matching noise mechanism of ZCDP will be developed in the future.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     Reference: `Concentrated Differentially Private Gradient Descent with
     Adaptive per-Iteration Privacy Budget <https://arxiv.org/abs/1808.09501>`_

mindarmour/privacy/diff_privacy/train/model.py

@@ -70,7 +70,7 @@ class DPModel(Model):
     DPModel is used for constructing a model for differential privacy training.
     This class is overload mindspore.train.model.Model.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_.
 
     Args:
         micro_batches (int): The number of small batches split from an original

mindarmour/privacy/evaluation/membership_inference.py

@@ -98,7 +98,7 @@ class MembershipInference:
     for inferring user's privacy data. It requires loss or logits results of the training samples.
     (Privacy refers to some sensitive attributes of a single user).
 
-    For details, please refer to the `Tutorial <https://mindspore.cn/mindarmour/docs/en/master/test_model_security_membership_inference.html>`_
+    For details, please refer to the `Tutorial <https://mindspore.cn/mindarmour/docs/en/master/test_model_security_membership_inference.html>`_.
 
     References: `Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov.
     Membership Inference Attacks against Machine Learning Models. 2017.

mindarmour/privacy/sup_privacy/mask_monitor/masker.py

@@ -27,7 +27,7 @@ TAG = 'suppress masker'
 class SuppressMasker(Callback):
     """
     Periodicity check suppress privacy function status and toggle suppress operation.
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_.
 
     Args:
         model (SuppressModel):  SuppressModel instance.

mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py

@@ -32,7 +32,11 @@ TAG = 'Suppression training.'
 
 
 class SuppressPrivacyFactory:
-    """ Factory class of SuppressCtrl mechanisms"""
+    """
+    Factory class of SuppressCtrl mechanisms.
+
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_.
+    """
 
     def __init__(self):
         pass
@@ -41,8 +45,6 @@ class SuppressPrivacyFactory:
     def create(networks, mask_layers, policy="local_train", end_epoch=10, batch_num=20, start_epoch=3,
                mask_times=1000, lr=0.05, sparse_end=0.90, sparse_start=0.0):
         """
-        For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_
-
         Args:
             networks (Cell): The training network.
                 This networks parameter should be same as 'network' parameter of SuppressModel().
@@ -116,7 +118,7 @@ class SuppressCtrl(Cell):
     finding the parameters that should be suppressed, and suppress these
     parameters permanently.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_.
 
     Args:
         networks (Cell): The training network.

mindarmour/privacy/sup_privacy/train/model.py

@@ -59,7 +59,7 @@ class SuppressModel(Model):
     Complete model train function. The suppress privacy function is embedded into the overload
     mindspore.train.model.Model.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html>`_.
 
     Args:
         network (Cell): The training network.

mindarmour/reliability/concept_drift/concept_drift_check_images.py

@@ -89,7 +89,7 @@ class OodDetectorFeatureCluster(OodDetector):
     the testing data features and the clustering centers determines whether an image is an out-of-distribution(OOD)
     image or not.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/concept_drift_images.html>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/concept_drift_images.html>`_.
 
     Args:
         model (Model):The training model.

mindarmour/reliability/concept_drift/concept_drift_check_time_series.py

@@ -23,7 +23,7 @@ from mindarmour.utils._check_param import check_param_type, check_param_in_range
 class ConceptDriftCheckTimeSeries:
     r"""
     ConceptDriftCheckTimeSeries is used for example series distribution change detection.
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/concept_drift_time_series.html>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/concept_drift_time_series.html>`_.
 
     Args:
         window_size(int): Size of a concept window, no less than 10. If given the input data,

mindarmour/reliability/model_fault_injection/fault_injection.py

@@ -31,7 +31,7 @@ class FaultInjector:
     Fault injection module simulates various fault scenarios for deep neural networks and evaluates
     performance and reliability of the model.
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/fault_injection.html>`_
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/fault_injection.html>`_.
 
     Args:
         model (Model): The model need to be evaluated.
@@ -196,7 +196,7 @@ class FaultInjector:
 
     def kick_off(self, ds_data, ds_label, iter_times=100):
         """
-        Startup and return final results.
+        Startup and return final results after Fault Injection.
 
         Args:
             ds_data(np.ndarray): Input data for testing. The evaluation is based on this data.
@@ -241,9 +241,10 @@ class FaultInjector:
 
     def metrics(self):
         """
-        metrics of final result.
+        Metrics of final result.
+
         Returns:
-            list, the summary of result.
+            - list, the summary of result.
         """
         result_summary = []
         single_layer_acc = []