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 .head(2000) .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.
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