When using the built-in Selector, you first need to `import` a feature selector, and `initialize` it. You could call the function `fit` in the selector to pass the data to the selector. After that, you could use `get_seleteced_features` to get important features. The function parameters in different selectors might be different, so you need to check the docs before using it.
# How to customize?
## How to customize?
NNI provides _state-of-the-art_ feature selector algorithm in the builtin-selector. NNI also supports to build a feature selector by yourself.
`Baseline` means without any feature selection, we directly pass the data to LogisticRegression. For this benchmark, we only use 10% data from the train as test data. For the GradientFeatureSelector, we only take the top20 features. The metric is the mean accuracy on the given test data and labels.
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@@ -257,7 +257,7 @@ The dataset of benchmark could be download in [here](https://www.csie.ntu.edu.tw
The code could be refenrence `/examples/feature_engineering/gradient_feature_selector/benchmark_test.py`.
## **Reference and Feedback**
## Reference and Feedback
* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
* To know more about [Neural Architecture Search with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md);
*[Support NNI on Windows](Tutorial/NniOnWindows.md)
*[Support NNI on Windows](Tutorial/InstallationWin.md)
* NNI running on windows for local mode
*[New advisor: BOHB](Tuner/BohbAdvisor.md)
* Support a new advisor BOHB, which is a robust and efficient hyperparameter tuning algorithm, combines the advantages of Bayesian optimization and Hyperband
@@ -4,6 +4,7 @@ NNI TrainingService provides the training platform for running NNI trial jobs. N
NNI not only provides few built-in training service options, but also provides a method for customers to build their own training service easily.
## Built-in TrainingService
|TrainingService|Brief Introduction|
|---|---|
|[__Local__](./LocalMode.md)|NNI supports running an experiment on local machine, called local mode. Local mode means that NNI will run the trial jobs and nniManager process in same machine, and support gpu schedule function for trial jobs.|
If you start a docker image using NNI's offical image `msranni/nni`, you could directly start NNI experiments by using `nnictl` command. Our offical image has NNI's running environment and basic python and deep learning frameworks environment.
If you start your own docker image, you may need to install NNI package first, please [refer](Installation.md).
If you start your own docker image, you may need to install NNI package first, please refer to [NNI installation](InstallationLinux.md).
If you want to run NNI's offical examples, you may need to clone NNI repo in github using
Currently we support installation on Linux, Mac and Windows.
## Installation
## **Installation on Linux & Mac**
Installation on Linux and Mac follow the same instruction below.
* __Install NNI through pip__
### __Install NNI through pip__
Prerequisite: `python >= 3.5`
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@@ -12,7 +12,7 @@ Currently we support installation on Linux, Mac and Windows.
python3 -m pip install--upgrade nni
```
* __Install NNI through source code__
### __Install NNI through source code__
Prerequisite: `python >=3.5`, `git`, `wget`
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@@ -22,33 +22,12 @@ Currently we support installation on Linux, Mac and Windows.
./install.sh
```
* __Install NNI in docker image__
### __Install NNI in docker image__
You can also install NNI in a docker image. Please follow the instructions [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/README.md) to build NNI docker image. The NNI docker image can also be retrieved from Docker Hub through the command `docker pull msranni/nni:latest`.
Below are the minimum system requirements for NNI on Linux. Due to potential programming changes, the minimum system requirements for NNI may change over time.
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@@ -74,17 +53,6 @@ Below are the minimum system requirements for NNI on macOS. Due to potential pro
|**Internet**|Boardband internet connection|
|**Resolution**|1024 x 768 minimum display resolution|
Below are the minimum system requirements for NNI on Windows, Windows 10.1809 is well tested and recommend. Due to potential programming changes, the minimum system requirements for NNI may change over time.
Below are the minimum system requirements for NNI on Windows, Windows 10.1809 is well tested and recommend. Due to potential programming changes, the minimum system requirements for NNI may change over time.
In order to save our computing resources, NNI supports an early stop policy and creates **Assessor** to finish this job.
Assessor receives the intermediate result from Trial and decides whether the Trial should be killed by specific algorithm. Once the Trial experiment meets the early stop conditions(which means assessor is pessimistic about the final results), the assessor will kill the trial and the status of trial will be `"EARLY_STOPPED"`.
Here is an experimental result of MNIST after using 'Curvefitting' Assessor in 'maximize' mode, you can see that assessor successfully **early stopped** many trials with bad hyperparameters in advance. If you use assessor, we may get better hyperparameters under the same computing resources.
In order to save our computing resources, NNI supports an early stop policy and creates **Assessor** to finish this job.
Assessor receives the intermediate result from Trial and decides whether the Trial should be killed by specific algorithm. Once the Trial experiment meets the early stop conditions(which means assessor is pessimistic about the final results), the assessor will kill the trial and the status of trial will be `"EARLY_STOPPED"`.
Here is an experimental result of MNIST after using 'Curvefitting' Assessor in 'maximize' mode, you can see that assessor successfully **early stopped** many trials with bad hyperparameters in advance. If you use assessor, we may get better hyperparameters under the same computing resources.
NNI provides an easy way to adopt an approach to set up parameter tuning algorithms, we call them **Tuner**.
Tuner receives metrics from `Trial` to evaluate the performance of a specific parameters/architecture configures. And tuner sends next hyper-parameter or architecture configure to Trial.
