ae05948e32
* GPU hist Interaction Constraints. * Duplicate related parameters. * Add tests for CPU interaction constraint. * Add better error reporting. * Thorough tests.
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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import xgboost
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import unittest
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from sklearn.metrics import accuracy_score
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dpath = 'demo/data/'
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rng = np.random.RandomState(1994)
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class TestInteractionConstraints(unittest.TestCase):
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def test_interaction_constraints(self, tree_method='hist'):
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x1 = np.random.normal(loc=1.0, scale=1.0, size=1000)
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x2 = np.random.normal(loc=1.0, scale=1.0, size=1000)
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x3 = np.random.choice([1, 2, 3], size=1000, replace=True)
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y = x1 + x2 + x3 + x1 * x2 * x3 \
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+ np.random.normal(
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loc=0.001, scale=1.0, size=1000) + 3 * np.sin(x1)
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X = np.column_stack((x1, x2, x3))
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dtrain = xgboost.DMatrix(X, label=y)
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params = {
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'max_depth': 3,
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'eta': 0.1,
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'nthread': 2,
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'interaction_constraints': '[[0, 1]]',
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'tree_method': tree_method,
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'verbosity': 2
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}
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num_boost_round = 12
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# Fit a model that only allows interaction between x1 and x2
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bst = xgboost.train(
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params, dtrain, num_boost_round, evals=[(dtrain, 'train')])
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# Set all observations to have the same x3 values then increment
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# by the same amount
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def f(x):
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tmat = xgboost.DMatrix(
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np.column_stack((x1, x2, np.repeat(x, 1000))))
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return bst.predict(tmat)
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preds = [f(x) for x in [1, 2, 3]]
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# Check incrementing x3 has the same effect on all observations
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# since x3 is constrained to be independent of x1 and x2
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# and all observations start off from the same x3 value
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diff1 = preds[1] - preds[0]
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assert np.all(np.abs(diff1 - diff1[0]) < 1e-4)
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diff2 = preds[2] - preds[1]
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assert np.all(np.abs(diff2 - diff2[0]) < 1e-4)
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def test_training_accuracy(self, tree_method='hist'):
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dtrain = xgboost.DMatrix(dpath + 'agaricus.txt.train?indexing_mode=1')
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dtest = xgboost.DMatrix(dpath + 'agaricus.txt.test?indexing_mode=1')
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params = {
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'eta': 1,
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'max_depth': 6,
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'objective': 'binary:logistic',
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'tree_method': tree_method,
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'interaction_constraints': '[[1,2], [2,3,4]]'
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}
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num_boost_round = 5
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params['grow_policy'] = 'lossguide'
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bst = xgboost.train(params, dtrain, num_boost_round)
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pred_dtest = (bst.predict(dtest) < 0.5)
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assert accuracy_score(dtest.get_label(), pred_dtest) < 0.1
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params['grow_policy'] = 'depthwise'
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bst = xgboost.train(params, dtrain, num_boost_round)
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pred_dtest = (bst.predict(dtest) < 0.5)
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assert accuracy_score(dtest.get_label(), pred_dtest) < 0.1
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