# Scikit-learn in NNI¶

Scikit-learn is a popular machine learning tool for data mining and data analysis. It supports many kinds of machine learning models like LinearRegression, LogisticRegression, DecisionTree, SVM etc. How to make the use of scikit-learn more efficiency is a valuable topic.

NNI supports many kinds of tuning algorithms to search the best models and/or hyper-parameters for scikit-learn, and support many kinds of environments like local machine, remote servers and cloud.

## 1. How to run the example¶

To start using NNI, you should install the NNI package, and use the command line tool nnictl to start an experiment. For more information about installation and preparing for the environment, please refer here.

After you installed NNI, you could enter the corresponding folder and start the experiment using following commands:

nnictl create --config ./config.yml


## 2. Description of the example¶

### 2.1 classification¶

This example uses the dataset of digits, which is made up of 1797 8x8 images, and each image is a hand-written digit, the goal is to classify these images into 10 classes.

In this example, we use SVC as the model, and choose some parameters of this model, including "C", "kernel", "degree", "gamma" and "coef0". For more information of these parameters, please refer.

### 2.2 regression¶

This example uses the Boston Housing Dataset, this dataset consists of price of houses in various places in Boston and the information such as Crime (CRIM), areas of non-retail business in the town (INDUS), the age of people who own the house (AGE) etc., to predict the house price of Boston.

In this example, we tune different kinds of regression models including "LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor" and some parameters like "svr_kernel", "knr_weights". You could get more details about these models from here.

## 3. How to write scikit-learn code using NNI¶

It is easy to use NNI in your scikit-learn code, there are only a few steps.

• step 1

Prepare a search_space.json to storage your choose spaces. For example, if you want to choose different models, you may try:

{
"model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]}
}


If you want to choose different models and parameters, you could put them together in a search_space.json file.

{
"model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]},
"svr_kernel": {"_type":"choice","_value":["linear", "poly", "rbf"]},
"knr_weights": {"_type":"choice","_value":["uniform", "distance"]}
}


Then you could read these values as a dict from your python code, please get into the step 2.

• step 2

At the beginning of your python code, you should import nni to insure the packages works normally.

First, you should use nni.get_next_parameter() function to get your parameters given by NNI. Then you could use these parameters to update your code. For example, if you define your search_space.json like following format:

{
"C": {"_type":"uniform","_value":[0.1, 1]},
"kernel": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]},
"degree": {"_type":"choice","_value":[1, 2, 3, 4]},
"gamma": {"_type":"uniform","_value":[0.01, 0.1]},
"coef0 ": {"_type":"uniform","_value":[0.01, 0.1]}
}


You may get a parameter dict like this:

params = {
'C': 1.0,
'kernel': 'linear',
'degree': 3,
'gamma': 0.01,
'coef0': 0.01
}


Then you could use these variables to write your scikit-learn code.

• step 3

After you finished your training, you could get your own score of the model, like your precision, recall or MSE etc. NNI needs your score to tuner algorithms and generate next group of parameters, please report the score back to NNI and start next trial job.

You just need to use nni.report_final_result(score) to communicate with NNI after you process your scikit-learn code. Or if you have multiple scores in the steps of training, you could also report them back to NNI using nni.report_intemediate_result(score). Note, you may not report intermediate result of your job, but you must report back your final result.