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feat: Modify README.md #30

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35 changes: 30 additions & 5 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ add "ssh://[email protected]/cursorinsight/FeatureScreeningDemo.jl"#master
```

### Set up for non-julia users
Install Julia v >= 1.6.0. You can install it with [asdf](https://asdf-vm.com/).
Install Julia >= v1.6.0. You can install it with [asdf](https://asdf-vm.com/).

Clone the project
```bash
Expand All @@ -33,9 +33,33 @@ $ sudo apt install hdf5-tools
```

## Usage
This application demonstrates the usage and usefulness of our feature screening
method. You can run a complete `demo` to test all functionality, run some
`benchmark` to measure your feature set or `screen` you feature set.
This application demonstrates the usage and usefulness of our random-forest
based feature screening method. You can run a complete `demo` to test all
functionality with random feature sets, run `benchmark` to measure your data set
accuracy or `screen` you feature set to get a reduced one.

### Generate synthetic data
You can use our
[BiometricBlender](https://github.com/cursorinsight/biometricblender) to
generate synthetic data. You can find more information its project `README.md`.
To test the funcionallity you can generate a data set with the following
parameters:

```bash
python -m biometric_blender \
--n-labels 100 \
--n-samples-per-label 64 \
--n-true-features 100 \
--n-fake-features 300 \
--min-usefulness 0.5 \
--max-usefulness 1 \
--location-sharing-extent 100 \
--location-ordering-extent 50 \
--n-features-out 10000 \
--blending-mode logarithmic \
--min-count 4 \
--max-count 8
```

### Create a random feature dataset
If you don't have a valid dataset to test the features, you can generate random
Expand Down Expand Up @@ -86,7 +110,8 @@ If you don't have valid dataset, you can generate random training feature set.
You can find the steps for that under `Create random feature dataset` heading.

### `screen` command
This command screens the given feature set and generate a screened one.
This command screens the given feature set and splits that into a train and a
test data.

Usage:
```bash
Expand Down