A python making machine of sklearn pipeline
Project description
Straw-Machine
A python making machine of sklearn pipeline. This python package tempt to lower the code level in constructing a complicated sklearn pipeline in actual data ETL or ML feature engineering.
Install
pip install straw_machine==0.0.1
Usage
from straw_machine.util import (
generate_estimator,
generate_transformer,
generate_pipeline
)
# udf function: add num to all df col
def add(df, num:int, outcol:str):
df[outcol] = df + num
return df
# estimator:
# send ['a'] from dataframe to estimator inputs
# estimator with function add num(1) to all inputs('a') and get outputs columns (['a', 'a+1'], if origin column is need)
estimator1 =generate_estimator(
name='add1',
func=add,
inputs=['a'],
outputs=['a','a+1'],
kw_args={
'num': 1,
'outcol': 'a+1'
}
)
estimator2 =generate_estimator(
name='add2',
func=add,
inputs=['a'],
outputs=['a+2'],
kw_args={
'num': 2,
'outcol': 'a+2'
}
)
# generate transformer with name specific and estimators list
t1 = generate_transformer(
name='trans1',
estimators=[estimator1, estimator2]
)
pl = generate_pipeline(transformers=[t1])
TODO
- low code pipeline building
- with streamlit / gradio support
Project details
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