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ML4T/assess_learners/RTLearner.py

31 lines
982 B
Python

import numpy as np
from AbstractTreeLearner import AbstractTreeLearner
class RTLearner(AbstractTreeLearner):
def __init__(self, leaf_size = 1, verbose = False):
self.leaf_size = leaf_size
self.verbose = verbose
def get_i_and_split_value(self, xs, y):
"""
@summary: Pick a random i and split value.
Make sure that not all X are the same for i and also pick
different values to average the split_value from.
"""
i = np.random.randint(0, xs.shape[1])
while np.all(xs[0,i] == xs[:,i]):
i = np.random.randint(0, xs.shape[1])
# I don't know about the performance of this, but at least it
# terminates reliably. If the two elements are the same something is
# wrong.
a = np.array(list(set(xs[:, i])))
r1, r2 = np.random.choice(a, size = 2, replace = False)
assert(r1 != r2)
split_value = (r1 + r2) / 2.0
return i, split_value