Merge pull request #47 from rekcurd/fix/update-doc-for-v1.0.0

Merged

Author: keigohtr

README.md

@@ -5,17 +5,17 @@
 [![codecov](https://codecov.io/gh/rekcurd/rekcurd-python/branch/master/graph/badge.svg)](https://codecov.io/gh/rekcurd/rekcurd-python "Non-generated packages only")
 [![pypi supported versions](https://img.shields.io/pypi/pyversions/rekcurd.svg)](https://pypi.python.org/pypi/rekcurd)
 
-Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like [Flask](http://flask.pocoo.org/). 
+Rekcurd is the Project for serving ML module. This is a gRPC micro-framework and it can be used like [Django](https://docs.djangoproject.com/) and [Flask](http://flask.pocoo.org/).
 
 
 ## Parent Project
 https://github.com/rekcurd/community
 
 
 ## Components
-- [Rekcurd](https://github.com/rekcurd/rekcurd-python) (here): Project for serving ML module.
+- [Rekcurd](https://github.com/rekcurd/rekcurd-python): Project for serving ML module.
 - [Rekcurd-dashboard](https://github.com/rekcurd/dashboard): Project for managing ML model and deploying ML module.
-- [Rekcurd-client](https://github.com/rekcurd/python-client): Project for integrating ML module. 
+- [Rekcurd-client](https://github.com/rekcurd/python-client): Project for integrating ML module.
 
 
 ## Installation
@@ -34,10 +34,13 @@ $ pip install rekcurd
 ```
 
 ## How to use
-Example code is available [here](https://github.com/rekcurd/rekcurd-example/tree/master/python/sklearn-digits). You can generate Rekcurd application template by a following command.
+Example is available [here](https://github.com/rekcurd/rekcurd-example/tree/master/python/sklearn-digits). You can generate Rekcurd template and implement necessary methods.
 
 ```bash
 $ rekcurd startapp {Your application name}
+$ cd {Your application name}
+$ vi app.py
+$ python app.py
 ```
 
 
@@ -46,5 +49,51 @@ $ rekcurd startapp {Your application name}
 $ python -m unittest
 ```
 
+
 ## Kubernetes support
-Rekcurd can be run on Kubernetes. See [here](https://github.com/rekcurd/community).
+Rekcurd can be run on Kubernetes. See [community repository](https://github.com/rekcurd/community).
+
+
+## Type definition
+### `PredictLabel` type
+*V* is the length of feature vector.
+
+|Field |Type |Description |
+|:---|:---|:---|
+|input <BR>(required) |One of below<BR>- string<BR>- bytes<BR>- string[*V*]<BR>- int[*V*]<BR>- double[*V*] |Input data for inference.<BR>- "Nice weather." for a sentiment analysis.<BR>- PNG file for an image transformation.<BR>- ["a", "b"] for a text summarization.<BR>- [1, 2] for a sales forcast.<BR>- [0.9, 0.1] for mnist data. |
+|option |string| Option field. Must be json format. |
+
+The "option" field needs to be a json format. Any style is Ok but we have some reserved fields below.
+
+|Field |Type |Description |
+|:---|:---|:---|
+|suppress_log_input |bool |True: NOT print the input and output to the log message. <BR>False (default): Print the input and outpu to the log message. |
+|YOUR KEY |any |YOUR VALUE |
+
+### `PredictResult` type
+*M* is the number of classes. If your algorithm is a binary classifier, you set *M* to 1. If your algorithm is a multi-class classifier, you set *M* to the number of classes.
+
+|Field |Type |Description |
+|:---|:---|:---|
+|label<BR>(required) |One of below<BR> -string<BR> -bytes<BR> -string[*M*]<BR> -int[*M*]<BR> -double[*M*] |Result of inference.<BR> -"positive" for a sentiment analysis.<BR> -PNG file for an image transformation.<BR> -["a", "b"] for a multi-class classification.<BR> -[1, 2] for a multi-class classification.<BR> -[0.9, 0.1] for a multi-class classification. |
+|score<BR>(required) |One of below<BR> -double<BR> -double[*M*] |Score of result.<BR> -0.98 for a binary classification.<BR> -[0.9, 0.1] for a multi-class classification. |
+|option |string |Option field. Must be json format. |
+
+### `EvaluateResult` type
+`EvaluateResult` is the evaluation score. *N* is the number of evaluation data. *M* is the number of classes. If your algorithm is a binary classifier, you set *M* to 1. If your algorithm is a multi-class classifier, you set *M* to the number of classes.
+
+|Field |Type |Description |
+|:---|:---|:---|
+|num<BR>(required)|int |Number of evaluation data. |
+|accuracy<BR>(required) |double |Accuracy. |
+|precision<BR>(required) |double[*M*] |Precision. |
+|recall<BR>(required) |double[*M*] |Recall. |
+|fvalue<BR>(required) |double[*M*] |F1 value. |
+
+### `EvaluateDetail` type
+`EvaluateDetail` is the details of evaluation result.
+
+|Field |Type |Description |
+|:---|:---|:---|
+|result<BR>(required) |PredictResult |Prediction result. |
+|is_correct<BR>(required) |bool |Correct or not. |