I have made some minor changes in the comments of the code and some modifications in documentation. It is very useful when we have a crystal clear comments to explain the code which makes it well documented and clean code.

README.md

@@ -39,7 +39,7 @@ $ pip install nni
 
 To update NNI to the latest version, add `--upgrade` flag to the above commands.
 
-## NNI capabilities in a glance
+## NNI capabilities at a glance
 
 <img src="docs/img/overview.svg" width="100%"/>
 
@@ -220,20 +220,20 @@ To update NNI to the latest version, add `--upgrade` flag to the above commands.
 
 ## Contribution guidelines
 
-If you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions of submitting feedbacks, best coding practices, and code of conduct.
+If you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions for submitting feedback, best coding practices, and code of conduct.
 
 We use [GitHub issues](https://github.com/microsoft/nni/issues) to track tracking requests and bugs.
 Please use [NNI Discussion](https://github.com/microsoft/nni/discussions) for general questions and new ideas.
-For questions of specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).
+For questions about specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).
 
-Participating discussions via the following IM groups is also welcomed.
+Participating in discussions via the following IM groups is also welcomed.
 
 |Gitter||WeChat|
 |----|----|----|
 |![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
 
 Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.
-We appreciate all contributions from community to make NNI thrive.
+We appreciate all contributions from the community to make NNI thrive.
 
 <img src="https://img.shields.io/github/contributors-anon/microsoft/nni"/>
 
@@ -266,15 +266,15 @@ We appreciate all contributions from community to make NNI thrive.
 
 ## Related Projects
 
-Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) had also released few other open source projects.
+Targeting openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) has also released a few other open-source projects.
 
-* [OpenPAI](https://github.com/Microsoft/pai) : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
-* [FrameworkController](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
-* [MMdnn](https://github.com/Microsoft/MMdnn) : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
-* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
-* [nn-Meter](https://github.com/microsoft/nn-Meter) : An accurate inference latency predictor for DNN models on diverse edge devices.
+* [OpenPAI](https://github.com/Microsoft/pai): an open-source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud, and hybrid environments in various scales.
+* [FrameworkController](https://github.com/Microsoft/frameworkcontroller): an open-source general-purpose Kubernetes Pod Controller that orchestrates all kinds of applications on Kubernetes by a single controller.
+* [MMdnn](https://github.com/Microsoft/MMdnn): A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
+* [SPTAG](https://github.com/Microsoft/SPTAG): Space Partition Tree And Graph (SPTAG) is an open-source library for large-scale vector approximate nearest neighbor search scenarios.
+* [nn-Meter](https://github.com/microsoft/nn-Meter): An accurate inference latency predictor for DNN models on diverse edge devices.
 
-We encourage researchers and students leverage these projects to accelerate the AI development and research.
+We encourage researchers and students to leverage these projects to accelerate AI development and research.
 
 ## License
 

docs/source/feature_engineering/gbdt_selector.rst

@@ -12,7 +12,7 @@ For now, we support the ``importance_type`` is ``split`` and ``gain``. But we wi
 Usage
 ^^^^^
 
-First you need to install dependency:
+First you need to install the dependency:
 
 .. code-block:: bash
 
@@ -32,8 +32,8 @@ Then
    fgs = GBDTSelector()
    # fit data
    fgs.fit(X_train, y_train, ...)
-   # get improtant features
-   # will return the index with important feature here.
+   # get important features
+   # will return the index with an important feature here.
    print(fgs.get_selected_features(10))
 
    ...
@@ -53,7 +53,7 @@ And you could reference the examples in ``/examples/feature_engineering/gbdt_sel
   **lgb_params** (dict, require) - The parameters for lightgbm model. The detail you could reference `here <https://lightgbm.readthedocs.io/en/latest/Parameters.html>`__
 
 * 
-  **eval_ratio** (float, require) - The ratio of data size. It's used for split the eval data and train data from self.X.
+  **eval_ratio** (float, require) - The ratio of data size. It's used to split the eval data and train data from self.X.
 
 * 
   **early_stopping_rounds** (int, require) - The early stopping setting in lightgbm. The detail you could reference `here <https://lightgbm.readthedocs.io/en/latest/Parameters.html>`__.
@@ -62,9 +62,9 @@ And you could reference the examples in ``/examples/feature_engineering/gbdt_sel
   **importance_type** (str, require) - could be 'split' or 'gain'. The 'split' means ' result contains numbers of times the feature is used in a model' and the 'gain' means 'result contains total gains of splits which use the feature'. The detail you could reference in `here <https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.feature_importance>`__.
 
 * 
-  **num_boost_round** (int, require) - number of boost round. The detail you could reference `here <https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html#lightgbm.train>`__.
+  **num_boost_round** (int, require) - number of boost rounds. The detail you could reference `here <https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html#lightgbm.train>`__.
 
 **Requirement of get_selected_features FuncArgs**
 
 
-* **topk** (int, require) - the topK impotance features you want to selected.
+* **topk** (int, require) - the topK important features you want to select.

docs/source/feature_engineering/gradient_feature_selector.rst

@@ -24,29 +24,29 @@ Usage
    ...
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
 
-   # initlize a selector
+   # initialize a selector
    fgs = FeatureGradientSelector(n_features=10)
    # fit data
    fgs.fit(X_train, y_train)
-   # get improtant features
-   # will return the index with important feature here.
+   # get important features
+   # will return the index with an important feature here.
    print(fgs.get_selected_features())
 
    ...
 
-And you could reference the examples in ``/examples/feature_engineering/gradient_feature_selector/``\ , too.
+And you could reference the examples in ``/examples/feature_engineering/gradient_feature_selector/``\, too.
 
 **Parameters of class FeatureGradientSelector constructor**
 
 
 * 
-  **order** (int, optional, default = 4) - What order of interactions to include. Higher orders may be more accurate but increase the run time. 12 is the maximum allowed order.
+  **order** (int, optional, default = 4) - What order of interactions to include? Higher orders may be more accurate but increase the run time. 12 is the maximum allowed order.
 
 * 
   **penalty** (int, optional, default = 1) - Constant that multiplies the regularization term.
 
 * 
-  **n_features** (int, optional, default = None) - If None, will automatically choose number of features based on search. Otherwise, the number of top features to select.
+  **n_features** (int, optional, default = None) - If None, will automatically choose a number of features based on search. Otherwise, the number of top features to select.
 
 * 
   **max_features** (int, optional, default = None) - If not None, will use the 'elbow method' to determine the number of features with max_features as the upper limit.
@@ -64,7 +64,7 @@ And you could reference the examples in ``/examples/feature_engineering/gradient
   **shuffle** (bool, optional, default = True) - Shuffle "rows" prior to an epoch.
 
 * 
-  **batch_size** (int, optional, default = 1000) - Nnumber of "rows" to process at a time.
+  **batch_size** (int, optional, default = 1000) - N number of "rows" to process at a time.
 
 * 
   **target_batch_size** (int, optional, default = 1000) - Number of "rows" to accumulate gradients over. Useful when many rows will not fit into memory but are needed for accurate estimation.
@@ -94,10 +94,10 @@ And you could reference the examples in ``/examples/feature_engineering/gradient
 
 
 * 
-  **X** (array-like, require) - The training input samples which shape = [n_samples, n_features]. `np.ndarry` recommended.
+  **X** (array-like, require) - The training input samples which shape = [n_samples, n_features]. `np.ndarray` recommended.
 
 * 
-  **y** (array-like, require) - The target values (class labels in classification, real numbers in regression) which shape = [n_samples]. `np.ndarry` recommended.
+  **y** (array-like, require) - The target values (class labels in classification, real numbers in regression) which shape = [n_samples]. `np.ndarray` recommended.
 
 * 
   **groups** (array-like, optional, default = None) - Groups of columns that must be selected as a unit. e.g. [0, 0, 1, 2] specifies the first two columns are part of a group. Which shape is [n_features].