Add Docs for NNI AutoFeatureEng

docs/en_US/CommunitySharings/NNI_AutoFeatureEng.md

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+\#\*\*Original Link\*\*:  [如何看待微软最新发布的AutoML平台NNI？by Garvin
+Li](https://www.zhihu.com/question/297982959/answer/964961829?utm_source=wechat_session&utm_medium=social&utm_oi=28812108627968&from=singlemessage&isappinstalled=0))
+
+\# \*\*01 Overview of AutoML\*\*
+
+In author's opinion, AutoML is not only about hyperparameter optimization, but
+also a process that can target various stages of the machine learning process,
+including feature engineering, NAS, HPO, etc.
+
+\# \*\*02 Overview of NNI\*\*
+
+NNI (Neural Network Intelligence) is an open source AutoML toolkit from
+Microsoft, to help users design and tune machine learning models, neural network
+architectures, or a complex system’s parameters in an efficient and automatic
+way.
+
+\*\*Address\*\*:[ https://github.com/SpongebBob/tabular_automl_NNI](https://github.com/SpongebBob/tabular_automl_NNI)
+
+In general, most of Microsoft tools have one prominent characteristic: the
+design is highly reasonable (regardless of the technology innovation degree).
+NNI's AutoFeatureENG basically meets all user requirements of AutoFeatureENG
+with a very reasonable underlying framework design.
+
+\#\*\*03 Details of NNI-AutoFeatureENG\*\*
+
+Each new user could do AutoFeatureENG on NNI easily and efficiently:
+
+Firstly, install the require of files
+
+Then, install NNI through pip
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-b2219460951f6a12.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+NNI treats AutoFeatureENG as a two steps task, feature generation exploration
+and feature selection. Feature generation exploration is mainly about feature
+derivation and high-order feature combination.
+
+\# \*\*04 Feature Exploration\*\*
+
+For feature derivation, NNI offers many operations which could automatically
+generate new features, which list [as
+following](https://github.com/SpongebBob/tabular_automl_NNI/blob/master/AutoFEOp.md) :
+
+\*\*count:\*\*Count encoding is based on replacing categories with their counts
+computed on the train set, also named frequency encoding.
+
+\*\*target:\*\*Target encoding is based on encoding categorical variable values
+with the mean of target variable per value.
+
+\*\*embedding:\*\*Regard features as sentences, generate vectors using
+\*Word2Vec.\*
+
+\*\*crosscout:\*\*Count encoding on more than one-dimension, alike CTR (Click
+Through Rate).
+
+\*\*aggregete:\*\*Decide the aggregation functions of the features, including
+min/max /mean/var.
+
+\*\*nunique:\*\*Statistics of the number of unique features.
+
+\*\*Histsta:\*\*Statistics of feature buckets, like histogram statistics.
+
+Search space could be defined in a\* \*JSON file\*\*: to define how specific
+features intersect, which two columns intersect and how features generate from
+corresponding columns.
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-0534cc8ea51e4382.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+The picture shows us the procedure of defining search space. NNI provides count
+encoding for 1-order-op, as well as cross count encoding, aggerate
+statistics(min max var mean median nunique) for 2-order-op.
+
+For example, we want to search the features which are a frequency encoding
+(valuecount) features on columns name {“C1”, ...,” C26”}, in the following way:
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-d49c0ead372d4ac0.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+we can define a cross frequency encoding (value count on cross dims) method on
+columns {"C1",...,"C26"} x {"C1",...,"C26"} in the following way:
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-c58c8d498559c4f0.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+The purpose of Exploration is to generate new features.You can use
+\*\*\*get_next_parameter\*\*\* function to get received feature candidates of
+one trial.
+
+RECEIVED_PARAMS = nni.get_next_parameter()
+
+\# \*\*05 Feature selection\*\*
+
+To avoid feature explosion and overfitting, feature selection is necessary. In
+the feature selection of NNI-AutoFeatureENG, LightGBM (Light Gradient Boosting
+Machine), a gradient boosting framework developed by Microsoft, is mainly
+promoted.
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-3dbf914e7b48da01.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+If you have used \*XGBoost\* or \*GBDT\*, you would know the algorithm based on
+tree structure can easily calculate the importance of each feature on results.
+LightGBM is able to make feature selection naturally.
+
+The issue is that selected features might be applicable to \*GBDT\* (Gradient
+Boosting Decision Tree), but not to the linear algorithm like \*LR\* (Logistic
+Regression).
+
+![image](https://upload-images.jianshu.io/upload_images/20947594-1c23ae3edc07d9e5.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
+
+\# \*\*06 Summary\*\*
+
+NNI's AutoFeatureEng sets a well-established standard, showing us the operation
+procedure, available modules, which is highly convenient to use. However, a
+simple model is probably not enough for good results.
+
+\#\*\*Suggestions to NNI\*\*
+
+\*\*About Exploration\*\*: If consider using DNN (like xDeepFM) to extract
+high-order feature would be better.
+
+\*\*About Selection\*\*: There could be more intelligent options, such as
+automatic selection system based on downstream models.
+
+\*\*Conclusion\*\*: NNI could offer users some inspirations of design and it is
+a good open source project. I suggest researchers leverage it to accelerate the
+AI research.
+
+\*\*Tips\*\*: Because the scripts of open source projects are compiled based on
+gcc7, Mac system may encounter problems of gcc (GNU Compiler Collection). The
+solution is as follows:
+
+brew install libomp
+