sklearn-cv-pandas
RandomizedSearchCV/GridSearchCV with pandas.DataFrame interface
Installation
pip install sklearn_cv_pandas
Usage
To tune hyper parameters, instantiate CV as the same as original ones, and use methods fit_sv_pandas or fit_cv_pandas
from scipy import stats
from sklearn import linear_model
from sklearn_cv_pandas import RandomizedSearchCV
estimator = linear_model.Lasso()
param_dist = dict(alpha=stats.loguniform(1e-5, 10))
cv = RandomizedSearchCV(estimator, param_dist, scoring="mean_absolute_error")
model = cv.fit_cv_pandas(
df, target_column="y", feature_columns=["x{}".format(i) for i in range(100)], n_fold=5
)
To make prediction, use method predict of the output of fit_sv_pandas or fit_cv_pandas
model.predict(df)
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