Competition-oriented framework for interactive feature engineering and building pipelines
Project description
# Kaggle Tool Set
kts is a working title, highly likely it will be changed to avoid legal consequences.
## Getting started
To install the package, just clone the repo to a directory included in `PYTHONPATH`.
## What works by now
- Base of feature engineering submodule
## How it works
First of all, you need to import the module:
``` python
import kts
from kts import *
```
Then you should define a function to make new features based on your input dataframe:
``` python
def make_new_features(df):
...
```
To test it out, use `@test` decorator from `kts` or `kts.feature`:
``` python
@test
def make_new_features(df):
...
```
When you're sure that your function works fine, `@register` it:
``` python
@register
def make_new_features(df):
...
```
Since registering source of the function is stored in `storage/features` and calls are cached unless `no_cache=True` is used.
The function will also be contained in `kts.storage.feature_constructors`. If you want to separate feature engineering from other steps of your pipeline, you can easily define all registered functions in a new notebook via
``` python
kts.storage.feature_constructors.define_in_scope(globals())
```
To learn more, read source and example notebook.
kts is a working title, highly likely it will be changed to avoid legal consequences.
## Getting started
To install the package, just clone the repo to a directory included in `PYTHONPATH`.
## What works by now
- Base of feature engineering submodule
## How it works
First of all, you need to import the module:
``` python
import kts
from kts import *
```
Then you should define a function to make new features based on your input dataframe:
``` python
def make_new_features(df):
...
```
To test it out, use `@test` decorator from `kts` or `kts.feature`:
``` python
@test
def make_new_features(df):
...
```
When you're sure that your function works fine, `@register` it:
``` python
@register
def make_new_features(df):
...
```
Since registering source of the function is stored in `storage/features` and calls are cached unless `no_cache=True` is used.
The function will also be contained in `kts.storage.feature_constructors`. If you want to separate feature engineering from other steps of your pipeline, you can easily define all registered functions in a new notebook via
``` python
kts.storage.feature_constructors.define_in_scope(globals())
```
To learn more, read source and example notebook.
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