sklearn-cv-pandas
RandomizedSearchCV/GridSearchCV with pandas.DataFrame interface
Installation
pip install sklearn_cv_pandas
Usage
Configure CV object
Instantiate CV in the same manner as original ones.
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")
fit with pandas.DataFrame
Our CV object has new methods fit_holdout_pandas and fit_cv_pandas.
Original ones requires x and y as numpy.array.
Instead of numpy array, you can specify one pandas.DataFrame
and column names for x (feature_columns), and column name of y (target_column).
model = cv.fit_cv_pandas(
df, target_column="y", feature_columns=["x{}".format(i) for i in range(100)], n_fold=5
)
predict with pandas.DataFrame
You can run prediction with pandas.DataFrame interface as well.
Output of fit_holdout_pandas and fit_cv_pandas stores feature_columns and target_column.
You can just input pandas.DataFrame for prediction into the method predict.
model.predict(df)
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