pretools 0.3.0

Preparation tools for machine learning

pretools

Preparation tools for machine learning.

Examples

from pretools.estimators import *
from sklearn.datasets import load_boston
from sklearn.pipeline import make_pipeline

X, y = load_boston(return_X_y=True)
model = ModifiedCatBoostRegressor(random_state=0, verbose=100)
model = make_pipeline(
    Profiler(),
    Astype(),
    NUniqueThreshold(max_freq=None),
    DropCollinearFeatures(method="spearman", random_state=0),
    ClippedFeatures(),
    ModifiedStandardScaler(),
    ModifiedSelectFromModel(model, random_state=0, threshold=1e-06),
    CombinedFeatures(include_data=True),
    ModifiedSelectFromModel(model, random_state=0, threshold=1e-06),
    model,
)

model.fit(X, y)

Installation

pip install pretools

Testing

python setup.py test