... patsy.

There are many ways to get data from pandas to scikit-learn but when you're hacking in a notebook you may prefer to have something that is expressive. Like a domain specific grammar. The tool patsy offers exactly this by mocking features from the R language.


To use scikit-lego you'll need to install it first;

pip install scikit-lego

You can now use it in the pipeline.

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

from sklego.preprocessing import PatsyTransformer

import matplotlib.pylab as plt

X = (df_clean
    .loc[lambda d: d['n_born'] > 2000]
    .assign(num_date = lambda d: date_to_num(d['date'])))
y = X['n_born']

pipe = Pipeline([
    ("patsy", PatsyTransformer("(cc(yday, df=12) + wday + num_date)**2")),
    ("scale", StandardScaler()),
    ("model", LinearRegression())

np.mean(np.abs(pipe.fit(X, y).predict(X) - y))

The scikit-lego documentation for this can be found here.

Feedback? See an issue? Something unclear? Feel free to mention it here.

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