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BagDT.py
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BagDT.py
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from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
'''
Random Forest Classifier created using combination of HistGradientBoostingClassifier and BaggingClassifier
'''
class BaggedDecisionTreeClassifier():
def __init__(self, n_estimators=20, bootstrap=True, bootstrap_features=False,
oob_score=False, max_depth=None, min_samples_leaf=20, warm_start=False,
n_jobs=None,
early_stopping='auto',
verbose=0,
random_state=None):
self.tree = DecisionTreeClassifier(max_depth=max_depth, min_samples_leaf=min_samples_leaf)
self.BagDT = BaggingClassifier(base_estimator=self.tree, n_estimators=n_estimators,
bootstrap=bootstrap, bootstrap_features=bootstrap_features, oob_score=oob_score,
warm_start=warm_start, n_jobs=n_jobs, random_state=random_state, verbose=verbose)
def decision_function(self, X):
return self.BagDT.decision_function(X)
def fit(self, X, y, sample_weight=None):
self.BagDT.fit(X, y, sample_weight=sample_weight)
return self.BagDT
def get_params(self, deep=True):
return self.BagDT.get_params(deep=deep)
def predict(self, X):
return self.BagDT.predict(X)
def predict_log_proba(self, X):
return self.BagDT.predict_log_proba(X)
def predict_proba(self, X):
return self.BagDT.predict_proba(X)
def score(self, X, y, sample_weight=None):
return self.BagDT.score(X, y, sample_weight=sample_weight)
def set_params(self, **params):
return self.BagDT.set_params(**params)