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.
329 lines
12 KiB
Python
329 lines
12 KiB
Python
import json
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import os
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import tempfile
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from pathlib import Path
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import numpy as np
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import pytest
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import xgboost as xgb
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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 TestBasic:
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def test_compat(self):
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from xgboost.compat import lazy_isinstance
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a = np.array([1, 2, 3])
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assert lazy_isinstance(a, 'numpy', 'ndarray')
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assert not lazy_isinstance(a, 'numpy', 'dataframe')
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def test_basic(self):
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dtrain, dtest = tm.load_agaricus(__file__)
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param = {'max_depth': 2, 'eta': 1,
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'objective': 'binary:logistic'}
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# specify validations set to watch performance
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watchlist = [(dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist, verbose_eval=True)
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preds = bst.predict(dtrain)
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labels = dtrain.get_label()
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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with tempfile.TemporaryDirectory() as tmpdir:
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dtest_path = os.path.join(tmpdir, 'dtest.dmatrix')
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# save dmatrix into binary buffer
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dtest.save_binary(dtest_path)
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# save model
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model_path = os.path.join(tmpdir, 'model.booster')
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bst.save_model(model_path)
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# load model and data in
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bst2 = xgb.Booster(model_file=model_path)
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dtest2 = xgb.DMatrix(dtest_path)
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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def test_metric_config(self):
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# Make sure that the metric configuration happens in booster so the
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# string `['error', 'auc']` doesn't get passed down to core.
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dtrain, dtest = tm.load_agaricus(__file__)
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param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
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'objective': 'binary:logistic', 'eval_metric': ['error', 'auc']}
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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booster = xgb.train(param, dtrain, num_round, watchlist)
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predt_0 = booster.predict(dtrain)
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, 'model.json')
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booster.save_model(path)
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booster = xgb.Booster(params=param, model_file=path)
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predt_1 = booster.predict(dtrain)
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np.testing.assert_allclose(predt_0, predt_1)
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def test_multiclass(self):
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dtrain, dtest = tm.load_agaricus(__file__)
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param = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'num_class': 2}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds))
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if preds[i] != labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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with tempfile.TemporaryDirectory() as tmpdir:
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dtest_path = os.path.join(tmpdir, 'dtest.buffer')
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model_path = os.path.join(tmpdir, 'xgb.model')
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# save dmatrix into binary buffer
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dtest.save_binary(dtest_path)
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# save model
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bst.save_model(model_path)
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# load model and data in
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bst2 = xgb.Booster(model_file=model_path)
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dtest2 = xgb.DMatrix(dtest_path)
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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def test_dump(self):
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data = np.random.randn(100, 2)
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target = np.array([0, 1] * 50)
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features = ['Feature1', 'Feature2']
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dm = xgb.DMatrix(data, label=target, feature_names=features)
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params = {'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'eta': 0.3,
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'max_depth': 1}
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bst = xgb.train(params, dm, num_boost_round=1)
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# number of feature importances should == number of features
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dump1 = bst.get_dump()
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assert len(dump1) == 1, 'Expected only 1 tree to be dumped.'
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len(dump1[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
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dump2 = bst.get_dump(with_stats=True)
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assert dump2[0].count('\n') == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
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msg = 'Expected more info when with_stats=True is given.'
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assert dump2[0].find('\n') > dump1[0].find('\n'), msg
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dump3 = bst.get_dump(dump_format="json")
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dump3j = json.loads(dump3[0])
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assert dump3j['nodeid'] == 0, 'Expected the root node on top.'
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dump4 = bst.get_dump(dump_format="json", with_stats=True)
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dump4j = json.loads(dump4[0])
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assert 'gain' in dump4j, "Expected 'gain' to be dumped in JSON."
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with pytest.raises(ValueError):
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bst.get_dump(fmap="foo")
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def test_feature_score(self):
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rng = np.random.RandomState(0)
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data = rng.randn(100, 2)
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target = np.array([0, 1] * 50)
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features = ["F0"]
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with pytest.raises(ValueError):
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xgb.DMatrix(data, label=target, feature_names=features)
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params = {"objective": "binary:logistic"}
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dm = xgb.DMatrix(data, label=target, feature_names=["F0", "F1"])
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booster = xgb.train(params, dm, num_boost_round=1)
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# no error since feature names might be assigned before the booster seeing data
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# and booster doesn't known about the actual number of features.
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booster.feature_names = ["F0"]
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with pytest.raises(ValueError):
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booster.get_fscore()
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booster.feature_names = None
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# Use JSON to make sure the output has native Python type
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scores = json.loads(json.dumps(booster.get_fscore()))
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np.testing.assert_allclose(scores["f0"], 6.0)
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def test_load_file_invalid(self):
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with pytest.raises(xgb.core.XGBoostError):
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xgb.Booster(model_file='incorrect_path')
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with pytest.raises(xgb.core.XGBoostError):
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xgb.Booster(model_file=u'不正なパス')
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def test_dmatrix_numpy_init_omp(self):
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rows = [1000, 11326, 15000]
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cols = 50
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for row in rows:
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X = np.random.randn(row, cols)
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y = np.random.randn(row).astype('f')
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dm = xgb.DMatrix(X, y, nthread=0)
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np.testing.assert_array_equal(dm.get_label(), y)
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assert dm.num_row() == row
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assert dm.num_col() == cols
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dm = xgb.DMatrix(X, y, nthread=10)
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np.testing.assert_array_equal(dm.get_label(), y)
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assert dm.num_row() == row
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assert dm.num_col() == cols
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def test_cv(self):
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dm, _ = tm.load_agaricus(__file__)
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params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
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'objective': 'binary:logistic'}
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# return np.ndarray
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
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assert isinstance(cv, dict)
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assert len(cv) == (4)
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def test_cv_no_shuffle(self):
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dm, _ = tm.load_agaricus(__file__)
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params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
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'objective': 'binary:logistic'}
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# return np.ndarray
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cv = xgb.cv(params, dm, num_boost_round=10, shuffle=False, nfold=10,
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as_pandas=False)
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assert isinstance(cv, dict)
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assert len(cv) == (4)
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def test_cv_explicit_fold_indices(self):
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dm, _ = tm.load_agaricus(__file__)
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params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
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'binary:logistic'}
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folds = [
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# Train Test
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([1, 3], [5, 8]),
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([7, 9], [23, 43]),
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]
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# return np.ndarray
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cv = xgb.cv(params, dm, num_boost_round=10, folds=folds,
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as_pandas=False)
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assert isinstance(cv, dict)
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assert len(cv) == (4)
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@pytest.mark.skipif(**tm.skip_s390x())
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def test_cv_explicit_fold_indices_labels(self):
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params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
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'reg:squarederror'}
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N = 100
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F = 3
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dm = xgb.DMatrix(data=np.random.randn(N, F), label=np.arange(N))
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folds = [
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# Train Test
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([1, 3], [5, 8]),
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([7, 9], [23, 43, 11]),
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]
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# Use callback to log the test labels in each fold
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class Callback(xgb.callback.TrainingCallback):
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def __init__(self) -> None:
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super().__init__()
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def after_iteration(
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self, model,
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epoch: int,
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evals_log: xgb.callback.TrainingCallback.EvalsLog
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):
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print([fold.dtest.get_label() for fold in model.cvfolds])
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cb = Callback()
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# Run cross validation and capture standard out to test callback result
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with tm.captured_output() as (out, err):
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xgb.cv(
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params, dm, num_boost_round=1, folds=folds, callbacks=[cb],
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as_pandas=False
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)
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output = out.getvalue().strip()
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solution = ('[array([5., 8.], dtype=float32), array([23., 43., 11.],' +
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' dtype=float32)]')
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assert output == solution
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class TestBasicPathLike:
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"""Unit tests using pathlib.Path for file interaction."""
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def test_DMatrix_init_from_path(self):
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"""Initialization from the data path."""
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dtrain, _ = tm.load_agaricus(__file__)
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assert dtrain.num_row() == 6513
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assert dtrain.num_col() == 127
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def test_DMatrix_save_to_path(self):
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"""Saving to a binary file using pathlib from a DMatrix."""
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data = np.random.randn(100, 2)
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target = np.array([0, 1] * 50)
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features = ['Feature1', 'Feature2']
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dm = xgb.DMatrix(data, label=target, feature_names=features)
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# save, assert exists, remove file
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binary_path = Path("dtrain.bin")
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dm.save_binary(binary_path)
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assert binary_path.exists()
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Path.unlink(binary_path)
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def test_Booster_init_invalid_path(self):
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"""An invalid model_file path should raise XGBoostError."""
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with pytest.raises(xgb.core.XGBoostError):
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xgb.Booster(model_file=Path("invalidpath"))
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def test_Booster_save_and_load(self):
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"""Saving and loading model files from paths."""
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save_path = Path("saveload.model")
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data = np.random.randn(100, 2)
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target = np.array([0, 1] * 50)
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features = ['Feature1', 'Feature2']
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dm = xgb.DMatrix(data, label=target, feature_names=features)
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params = {'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'eta': 0.3,
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'max_depth': 1}
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bst = xgb.train(params, dm, num_boost_round=1)
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# save, assert exists
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bst.save_model(save_path)
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assert save_path.exists()
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def dump_assertions(dump):
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"""Assertions for the expected dump from Booster"""
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assert len(dump) == 1, 'Exepcted only 1 tree to be dumped.'
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assert len(dump[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines.'
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# load the model again using Path
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bst2 = xgb.Booster(model_file=save_path)
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dump2 = bst2.get_dump()
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dump_assertions(dump2)
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# load again using load_model
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bst3 = xgb.Booster()
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bst3.load_model(save_path)
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dump3 = bst3.get_dump()
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dump_assertions(dump3)
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# remove file
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Path.unlink(save_path)
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