1dac5e2410
* add back train method but mark as deprecated * add back train method but mark as deprecated * add back train method but mark as deprecated * fix scalastyle error * fix scalastyle error * fix scalastyle error * fix scalastyle error * more changes * temp * update * udpate rabit * change the histogram * update kfactor * sync per node stats * temp * update * final * code clean * update rabit * more cleanup * fix errors * fix failed tests * enforce c++11 * broadcast subsampled feature correctly * init col * temp * col sampling * fix histmastrix init * fix col sampling * remove cout * fix out of bound access * fix core dump remove core dump file * update * add fid * update * revert some changes * temp * temp * pass all tests * bring back some tests * recover some changes * fix lint issue * enable monotone and interaction constraints * don't specify default for monotone and interactions * recover column init part * more recovery * fix core dumps * code clean * revert some changes * fix test compilation issue * fix lint issue * resolve compilation issue * fix issues of lint caused by rebase * fix stylistic changes and change variable names * modularize depth width * address the comments * fix failed tests * wrap perf timers with class * temp * pass all lossguide * pass tests * add comments * more changes * use separate flow for single and tests * add test for lossguide hist * remove duplications * syncing stats for only once * recover more changes * recover more changes * fix root-stats * simplify code * remove outdated comments
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
import numpy as np
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import xgboost as xgb
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import unittest
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def is_increasing(y):
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return np.count_nonzero(np.diff(y) < 0.0) == 0
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def is_decreasing(y):
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return np.count_nonzero(np.diff(y) > 0.0) == 0
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def is_correctly_constrained(learner):
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n = 100
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variable_x = np.linspace(0, 1, n).reshape((n, 1))
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fixed_xs_values = np.linspace(0, 1, n)
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for i in range(n):
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fixed_x = fixed_xs_values[i] * np.ones((n, 1))
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monotonically_increasing_x = np.column_stack((variable_x, fixed_x))
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monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x)
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monotonically_increasing_y = learner.predict(
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monotonically_increasing_dset
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)
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monotonically_decreasing_x = np.column_stack((fixed_x, variable_x))
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monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x)
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monotonically_decreasing_y = learner.predict(
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monotonically_decreasing_dset
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)
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if not (
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is_increasing(monotonically_increasing_y) and
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is_decreasing(monotonically_decreasing_y)
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):
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return False
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return True
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number_of_dpoints = 1000
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x1_positively_correlated_with_y = np.random.random(size=number_of_dpoints)
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x2_negatively_correlated_with_y = np.random.random(size=number_of_dpoints)
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x = np.column_stack((
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x1_positively_correlated_with_y, x2_negatively_correlated_with_y
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))
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zs = np.random.normal(loc=0.0, scale=0.01, size=number_of_dpoints)
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y = (
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5 * x1_positively_correlated_with_y +
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np.sin(10 * np.pi * x1_positively_correlated_with_y) -
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5 * x2_negatively_correlated_with_y -
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np.cos(10 * np.pi * x2_negatively_correlated_with_y) +
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zs
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)
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training_dset = xgb.DMatrix(x, label=y)
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class TestMonotoneConstraints(unittest.TestCase):
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def test_monotone_constraints_for_exact_tree_method(self):
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# first check monotonicity for the 'exact' tree method
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params_for_constrained_exact_method = {
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'tree_method': 'exact', 'verbosity': 1,
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'monotone_constraints': '(1, -1)'
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}
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constrained_exact_method = xgb.train(
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params_for_constrained_exact_method, training_dset
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)
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assert is_correctly_constrained(constrained_exact_method)
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def test_monotone_constraints_for_depthwise_hist_tree_method(self):
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# next check monotonicity for the 'hist' tree method
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params_for_constrained_hist_method = {
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'tree_method': 'hist', 'verbosity': 1,
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'monotone_constraints': '(1, -1)'
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}
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constrained_hist_method = xgb.train(
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params_for_constrained_hist_method, training_dset
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)
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assert is_correctly_constrained(constrained_hist_method)
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def test_monotone_constraints_for_lossguide_hist_tree_method(self):
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# next check monotonicity for the 'hist' tree method
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params_for_constrained_hist_method = {
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'tree_method': 'hist', 'verbosity': 1,
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'grow_policy': 'lossguide',
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'monotone_constraints': '(1, -1)'
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}
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constrained_hist_method = xgb.train(
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params_for_constrained_hist_method, training_dset
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)
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assert is_correctly_constrained(constrained_hist_method)
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