BaggingClassifier take all dataset each time - machine-learning

Question

from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, RandomForestClassifier
import numpy as np
import random
from sklearn.svm import SVC
X=np.random.rand(1000,2)
Y=[random.randint(0,1) for x in range(0,1000)]
svm=BaggingClassifier(SVC(kernel='rbf', random_state=123, gamma=.000001, C=100000, class_weight='balanced'), max_samples=1/5.0, n_estimators=5, n_jobs=-1,random_state=123)
classfier=svm.fit(X,Y)
print(len(svm.estimators_samples_))
print(len(svm.estimators_samples_[0]))# here I expect 0.05*400 samples. but the result is 1000.
In this code, I try to apply BaggingClassifier with SVM. Normally as discussed in the documentation of sckitlearn, the max_samples fix the maximal number of samples to be used for each estimators. However, I remark that each estimator (n_estimators=5) take all the dataset!!! Is it a bug ?

Answer 1

svm.estimators_samples_[0] will return an array equal to the length of the data. This array is populated with boolean values, those values equal to True are the data points used in the estimator (in terms of index value).
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, RandomForestClassifier
import numpy as np
import random
from sklearn.svm import SVC
X=np.random.rand(1000,2)
Y=[random.randint(0,1) for x in range(0,1000)]
svm=BaggingClassifier(SVC(kernel='rbf', random_state=123, gamma=.000001, C=100000, class_weight='balanced'), max_samples=1/5.0, n_estimators=5, n_jobs=-1,random_state=123)
classfier=svm.fit(X,Y)
print(len([i for i in svm.estimators_samples_[0] if i == True]))
Running the above code I get:
181