samples = collections.defaultdict(list)
for d, dec in enumerate(dec_paths):
for i in range(estimator.tree_.node_count):
if dec.toarray()[0][i] == 1:
samples[i].append(d)
'''
estimator.fit(X_train, y_train)
names = dir(estimator)
for name in names:
print(name)
try:
print(estimator.__getattribute__(name))
except TypeError:
pass
exit()
# The decision estimator has an attribute called tree_ which stores the entire
# tree structure and allows access to low level attributes. The binary tree
# tree_ is represented as a number of parallel arrays. The i-th element of each
# array holds information about the node `i`. Node 0 is the tree's root. NOTE:
# Some of the arrays only apply to either leaves or split nodes, resp. In this
# case the values of nodes of the other type are arbitrary!
#
# Among those arrays, we have:
