Proximity Matrix in sklearn.ensemble.RandomForestC

问题:

I'm trying to perform clustering in Python using Random Forests. In the R implementation of Random Forests, there is a flag you can set to get the proximity matrix. I can't seem to find anything similar in the python scikit version of Random Forest. Does anyone know if there is an equivalent calculation for the python version?

回答1:

We don't implement proximity matrix in Scikit-Learn (yet).

However, this could be done by relying on the apply function provided in our implementation of decision trees. That is, for all pairs of samples in your dataset, iterate over the decision trees in the forest (through forest.estimators_) and count the number of times they fall in the same leaf, i.e., the number of times apply give the same node id for both samples in the pair.

Hope this helps.

回答2:

Based on Gilles Louppe answer I have written a function. I don't know if it is effective, but it works. Best regards.

def proximityMatrix(model, X, normalize=True):      

    terminals = model.apply(X)
    nTrees = terminals.shape[1]

    a = terminals[:,0]
    proxMat = 1*np.equal.outer(a, a)

    for i in range(1, nTrees):
        a = terminals[:,i]
        proxMat += 1*np.equal.outer(a, a)

    if normalize:
        proxMat = proxMat / nTrees

    return proxMat   

from sklearn.ensemble import  RandomForestClassifier
from sklearn.datasets import load_breast_cancer
train = load_breast_cancer()

model = RandomForestClassifier(n_estimators=500, max_features=2, min_samples_leaf=40)
model.fit(train.data, train.target)
proximityMatrix(model, train.data, normalize=True)
## array([[ 1.   ,  0.414,  0.77 , ...,  0.146,  0.79 ,  0.002],
##        [ 0.414,  1.   ,  0.362, ...,  0.334,  0.296,  0.008],
##        [ 0.77 ,  0.362,  1.   , ...,  0.218,  0.856,  0.   ],
##        ..., 
##        [ 0.146,  0.334,  0.218, ...,  1.   ,  0.21 ,  0.028],
##        [ 0.79 ,  0.296,  0.856, ...,  0.21 ,  1.   ,  0.   ],
##        [ 0.002,  0.008,  0.   , ...,  0.028,  0.   ,  1.   ]])

回答3:

There is nothing currently implemented for this in python. I took a first try at it here. It would be great if somebody would be interested in adding these methods to scikit.