From 901012eb90bf7c0dbb3d2566e5d8c0eda1b5b249 Mon Sep 17 00:00:00 2001
From: liuzhe-lz <40699903+liuzhe-lz@users.noreply.github.com>
Date: Sat, 9 Nov 2019 12:43:02 +0800
Subject: [PATCH] docstring fix (#1691)

---
 src/sdk/pynni/nni/assessor.py | 14 +++++++-------
 src/sdk/pynni/nni/tuner.py    | 20 ++++++++++----------
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/sdk/pynni/nni/assessor.py b/src/sdk/pynni/nni/assessor.py
index 0f1dc95619..01a2abcbe9 100644
--- a/src/sdk/pynni/nni/assessor.py
+++ b/src/sdk/pynni/nni/assessor.py
@@ -53,14 +53,14 @@ class Assessor(Recoverable):
     to tell whether this trial can be early stopped or not.
 
     This is the abstract base class for all assessors.
-    Early stopping algorithms should derive this class and override :meth:`assess_trial` method,
+    Early stopping algorithms should inherit this class and override :meth:`assess_trial` method,
     which receives intermediate results from trials and give an assessing result.
 
     If :meth:`assess_trial` returns :obj:`AssessResult.Bad` for a trial,
     it hints NNI framework that the trial is likely to result in a poor final accuracy,
     and therefore should be killed to save resource.
 
-    If an accessor want's to get notified when a trial ends, it can also override :meth:`trial_end`.
+    If an accessor want's to be notified when a trial ends, it can also override :meth:`trial_end`.
 
     To write a new assessor, you can reference :class:`~nni.medianstop_assessor.MedianstopAssessor`'s code as an example.
 
@@ -77,7 +77,7 @@ def assess_trial(self, trial_job_id, trial_history):
 
         The NNI framework has little guarantee on ``trial_history``.
         This method is not guaranteed to be invoked for each time ``trial_history`` get updated.
-        It is also possible that a trial's history keeps updateing after receiving a bad result.
+        It is also possible that a trial's history keeps updating after receiving a bad result.
         And if the trial failed and retried, ``trial_history`` may be inconsistent with its previous value.
 
         The only guarantee is that ``trial_history`` is always growing.
@@ -96,9 +96,9 @@ def assess_trial(self, trial_job_id, trial_history):
 
         Parameters
         ----------
-        trial_job_id: str
+        trial_job_id : str
             Unique identifier of the trial.
-        trial_history: list
+        trial_history : list
             Intermediate results of this trial. The element type is decided by trial code.
 
         Returns
@@ -114,9 +114,9 @@ def trial_end(self, trial_job_id, success):
 
         Parameters
         ----------
-        trial_job_id: str
+        trial_job_id : str
             Unique identifier of the trial.
-        success: bool
+        success : bool
             True if the trial successfully completed; False if failed or terminated.
         """
 
diff --git a/src/sdk/pynni/nni/tuner.py b/src/sdk/pynni/nni/tuner.py
index f011022151..177232b7ed 100644
--- a/src/sdk/pynni/nni/tuner.py
+++ b/src/sdk/pynni/nni/tuner.py
@@ -42,7 +42,7 @@ class Tuner(Recoverable):
     A new trial will run with this configuration.
 
     This is the abstract base class for all tuners.
-    Tuning algorithms should derive this class and override :meth:`update_search_space`, :meth:`receive_trial_result`,
+    Tuning algorithms should inherit this class and override :meth:`update_search_space`, :meth:`receive_trial_result`,
     as well as :meth:`generate_parameters` or :meth:`generate_multiple_parameters`.
 
     After initializing, NNI will first call :meth:`update_search_space` to tell tuner the feasible region,
@@ -96,9 +96,9 @@ def generate_parameters(self, parameter_id, **kwargs):
 
         Parameters
         ----------
-        parameter_id: int
+        parameter_id : int
             Unique identifier for requested hyper-parameters. This will later be used in :meth:`receive_trial_result`.
-        **kwargs:
+        **kwargs
             Unstable parameters which should be ignored by normal users.
 
         Returns
@@ -129,10 +129,10 @@ def generate_multiple_parameters(self, parameter_id_list, **kwargs):
 
         Parameters
         ----------
-        parameter_id_list: list of int
+        parameter_id_list : list of int
             Unique identifiers for each set of requested hyper-parameters.
             These will later be used in :meth:`receive_trial_result`.
-        **kwargs:
+        **kwargs
             Unstable parameters which should be ignored by normal users.
 
         Returns
@@ -159,13 +159,13 @@ def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
 
         Parameters
         ----------
-        parameter_id: int
+        parameter_id : int
             Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
         parameters
             Hyper-parameters generated by :meth:`generate_parameters`.
         value
             Result from trial (the return value of :func:`nni.report_final_result`).
-        **kwargs:
+        **kwargs
             Unstable parameters which should be ignored by normal users.
         """
         raise NotImplementedError('Tuner: receive_trial_result not implemented')
@@ -186,11 +186,11 @@ def trial_end(self, parameter_id, success, **kwargs):
 
         Parameters
         ----------
-        parameter_id: int
+        parameter_id : int
             Unique identifier for hyper-parameters used by this trial.
-        success: bool
+        success : bool
             True if the trial successfully completed; False if failed or terminated.
-        **kwargs:
+        **kwargs
             Unstable parameters which should be ignored by normal users.
         """