[doc] misc refactor (#4638)

docs/source/CommunitySharings/perf_compare.rst → docs/source/misc/perf_compare.rst

-################################################
 Performance Measurement, Comparison and Analysis
-################################################
+================================================
 
 Performance comparison and analysis can help users decide a proper algorithm (e.g., tuner, NAS algorithm) for their scenario. The following are some measurement and comparison data for users' reference.
 
 ..  toctree::
     :maxdepth: 1
 
-    Neural Architecture Search Comparison <NasComparison>
-    Hyper-parameter Tuning Algorithm Comparsion <HpoComparison>
-    Model Compression Algorithm Comparsion <ModelCompressionComparison>
\ No newline at end of file
+    Neural Architecture Search Comparison <nas_comparison>
+    Hyper-parameter Tuning Algorithm Comparsion <hpo_comparison>
+    Model Compression Algorithm Comparsion <model_compress_comp>
\ No newline at end of file

docs/source/CommunitySharings/perf_compare_zh.rst → docs/source/misc/perf_compare_zh.rst

-.. 7d625bd21018f53834e9db08a619c494
+.. 8f6869e44d85db2e2a969194c1828580
 
-################################################
 性能测量，比较和分析
-################################################
+====================
 
 性能比较和分析可以帮助用户在他们的场景中选择适合的算法（例如 Tuner，NAS 算法）。 以下是一些供用户参考的测量和比较数据。
 
 ..  toctree::
     :maxdepth: 1
 
-    神经网络结构搜索（NAS）的对比<NasComparison>
-    超参调优算法的对比<HpoComparison>
-    模型压缩算法的对比<ModelCompressionComparison>
\ No newline at end of file
+    神经网络结构搜索（NAS）的对比<nas_comparison>
+    超参调优算法的对比<hpo_comparison>
+    模型压缩算法的对比<model_compress_comp>
\ No newline at end of file

nni/algorithms/hpo/tpe_tuner.py

@@ -49,7 +49,7 @@ class TpeArguments(NamedTuple):
 
         How each liar works is explained in paper's section 6.1.
         In general "best" suit for small trial number and "worst" suit for large trial number.
-        (:doc:`experiment result </CommunitySharings/ParallelizingTpeSearch>`)
+        (:doc:`experiment result </misc/parallelizing_tpe_search>`)
 
     n_startup_jobs
         The first N hyperparameters are generated fully randomly for warming up.