"""
.. module:: CClassifierRandomForest
:synopsis: Random Forest classifier
.. moduleauthor:: Battista Biggio <battista.biggio@unica.it>
.. moduleauthor:: Marco Melis <marco.melis@unica.it>
"""
from secml.ml.classifiers import CClassifierSkLearn
from sklearn.ensemble import RandomForestClassifier
[docs]class CClassifierRandomForest(CClassifierSkLearn):
"""Random Forest classifier.
Parameters
----------
n_estimators : int, optional
The number of trees in the forest. Default 10.
criterion : str, optional
The function to measure the quality of a split. Supported criteria are
'gini' (default) for the Gini impurity and 'entropy' for the
information gain.
max_depth : int or None, optional
The maximum depth of the tree. If None (default), then nodes are
expanded until all leaves are pure or until all leaves contain less
than min_samples_split samples.
min_samples_split : int or float, optional
The minimum number of samples required to split an internal node.
If int, then consider `min_samples_split` as the minimum number.
If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum number of samples
for each split. Default 2.
random_state : int, RandomState or None, optional
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`. Default None.
preprocess : CPreProcess or str or None, optional
Features preprocess to be applied to input data.
Can be a CPreProcess subclass or a string with the type of the
desired preprocessor. If None, input data is used as is.
Attributes
----------
class_type : 'random-forest'
"""
__class_type = 'random-forest'
def __init__(self, n_estimators=10, criterion='gini',
max_depth=None, min_samples_split=2,
random_state=None, preprocess=None):
rf = RandomForestClassifier(
n_estimators=n_estimators,
criterion=criterion,
max_depth=max_depth,
min_samples_split=min_samples_split,
random_state=random_state
)
CClassifierSkLearn.__init__(self, sklearn_model=rf,
preprocess=preprocess)