Skippa
SciKIt-learn Pre-processing Pipeline in PAndas
Read more in the introduction blog on towardsdatascience
Want to create a machine learning model using pandas & scikit-learn? This should make your life easier.
Skippa helps you to easily create a pre-processing and modeling pipeline, based on scikit-learn transformers but preserving pandas dataframe format throughout all pre-processing. This makes it a lot easier to define a series of subsequent transformation steps, while referring to columns in your intermediate dataframe.
So basically the same idea as scikit-pandas
, but a different (and hopefully better) way to achieve it.
Installation
pip install skippa
Optional, if you want to use the gradio app functionality:
pip install skippa[gradio]
Basic usage
Import Skippa
class and columns
helper function
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from skippa import Skippa, columns
Get some data
df = pd.DataFrame({
'q': [0, 0, 0],
'date': ['2021-11-29', '2021-12-01', '2021-12-03'],
'x': ['a', 'b', 'c'],
'x2': ['m', 'n', 'm'],
'y': [1, 16, 1000],
'z': [0.4, None, 8.7]
})
y = np.array([0, 0, 1])
Define your pipeline:
pipe = (
Skippa()
.select(columns(['x', 'x2', 'y', 'z']))
.cast(columns(['x', 'x2']), 'category')
.impute(columns(dtype_include='number'), strategy='median')
.impute(columns(dtype_include='category'), strategy='most_frequent')
.scale(columns(dtype_include='number'), type='standard')
.onehot(columns(['x', 'x2']))
.model(LogisticRegression())
)
and use it for fitting / predicting like this:
pipe.fit(X=df, y=y)
predictions = pipe.predict_proba(df)
If you want details on your model, use:
model = pipe.get_model()
print(model.coef_)
print(model.intercept_)
(de)serialization
And of course you can save and load your model pipelines (for deployment).
N.B. dill
is used for ser/de because joblib and pickle don't provide enough support.
pipe.save('./models/my_skippa_model_pipeline.dill')
...
my_pipeline = Skippa.load_pipeline('./models/my_skippa_model_pipeline.dill')
predictions = my_pipeline.predict(df_new_data)
See the ./examples directory for more examples:
- 01-standard-pipeline.py
- 02-preprocessing-only.py
- 03a-gridsearch.py
- 03b-hyperopt.py
- 04-gradio-app.py
- 05-PCA.py
To Do
- Support pandas assign for creating new columns based on existing columns
- Support cast / astype transformer
- Support for .apply transformer: wrapper around
pandas.DataFrame.apply
- Check how GridSearch (or other param search) works with Skippa
- Add a method to inspect a fitted pipeline/model by creating a Gradio app defining raw features input and model output
- Support PCA transformer
- Facilitate random seed in Skippa object that is dispatched to all downstream operations
- fit-transform does lazy evaluation > cast to category and then selecting category columns doesn't work > each fit/transform should work on the expected output state of the previous transformer, rather than on the original dataframe
- Investigate if Skippa can directly extend sklearn's Pipeline
- Validation of pipeline steps
- Input validation in transformers
- OneHotEncoder: limit to maximum nr. of different values (n most frequent ones)
- Transformer for replacing values (pandas .replace)
- Support arbitrary transformer (if column-preserving)
- Eliminate the need to call columns explicitly
Credits
- Skippa is powered by Data Science Lab Amsterdam
- This project structure is based on the
audreyr/cookiecutter-pypackage
project template.