1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
import numpy as np
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import pytest
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import xgboost
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from xgboost import testing as tm
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dpath = 'demo/data/'
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rng = np.random.RandomState(1994)
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class TestInteractionConstraints:
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def run_interaction_constraints(
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self, tree_method, feature_names=None, interaction_constraints='[[0, 1]]'
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):
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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, feature_names=feature_names)
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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': interaction_constraints,
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'tree_method': tree_method
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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))), feature_names=feature_names)
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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_exact_interaction_constraints(self):
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self.run_interaction_constraints(tree_method='exact')
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def test_hist_interaction_constraints(self):
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self.run_interaction_constraints(tree_method='hist')
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def test_approx_interaction_constraints(self):
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self.run_interaction_constraints(tree_method='approx')
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def test_interaction_constraints_feature_names(self):
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with pytest.raises(ValueError):
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constraints = [('feature_0', 'feature_1')]
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self.run_interaction_constraints(tree_method='exact',
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interaction_constraints=constraints)
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with pytest.raises(ValueError):
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constraints = [('feature_0', 'feature_3')]
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feature_names = ['feature_0', 'feature_1', 'feature_2']
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self.run_interaction_constraints(tree_method='exact',
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feature_names=feature_names,
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interaction_constraints=constraints)
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constraints = [('feature_0', 'feature_1')]
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feature_names = ['feature_0', 'feature_1', 'feature_2']
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self.run_interaction_constraints(tree_method='exact',
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feature_names=feature_names,
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interaction_constraints=constraints)
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constraints = [['feature_0', 'feature_1'], ['feature_2']]
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feature_names = ['feature_0', 'feature_1', 'feature_2']
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self.run_interaction_constraints(tree_method='exact',
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feature_names=feature_names,
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interaction_constraints=constraints)
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@pytest.mark.skipif(**tm.no_sklearn())
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def training_accuracy(self, tree_method):
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"""Test accuracy, reused by GPU tests."""
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from sklearn.metrics import accuracy_score
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dtrain = xgboost.DMatrix(
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dpath + "agaricus.txt.train?indexing_mode=1&format=libsvm"
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)
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dtest = xgboost.DMatrix(
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dpath + "agaricus.txt.test?indexing_mode=1&format=libsvm"
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)
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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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@pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
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def test_hist_training_accuracy(self, tree_method):
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self.training_accuracy(tree_method=tree_method)
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