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test_basic.py
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test_basic.py
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# -*- coding: utf-8 -*-
import numpy as np
import os
import xgboost as xgb
import pytest
import json
from pathlib import Path
import tempfile
import testing as tm
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestBasic:
def test_compat(self):
from xgboost.compat import lazy_isinstance
a = np.array([1, 2, 3])
assert lazy_isinstance(a, 'numpy', 'ndarray')
assert not lazy_isinstance(a, 'numpy', 'dataframe')
def test_basic(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1,
'objective': 'binary:logistic'}
# specify validations set to watch performance
watchlist = [(dtrain, 'train')]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist, verbose_eval=True)
preds = bst.predict(dtrain)
labels = dtrain.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
# error must be smaller than 10%
assert err < 0.1
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
# error must be smaller than 10%
assert err < 0.1
with tempfile.TemporaryDirectory() as tmpdir:
dtest_path = os.path.join(tmpdir, 'dtest.dmatrix')
# save dmatrix into binary buffer
dtest.save_binary(dtest_path)
# save model
model_path = os.path.join(tmpdir, 'model.booster')
bst.save_model(model_path)
# load model and data in
bst2 = xgb.Booster(model_file=model_path)
dtest2 = xgb.DMatrix(dtest_path)
preds2 = bst2.predict(dtest2)
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_metric_config(self):
# Make sure that the metric configuration happens in booster so the
# string `['error', 'auc']` doesn't get passed down to core.
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic', 'eval_metric': ['error', 'auc']}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
booster = xgb.train(param, dtrain, num_round, watchlist)
predt_0 = booster.predict(dtrain)
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, 'model.json')
booster.save_model(path)
booster = xgb.Booster(params=param, model_file=path)
predt_1 = booster.predict(dtrain)
np.testing.assert_allclose(predt_0, predt_1)
def test_record_results(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic', 'eval_metric': 'error'}
# specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
result = {}
res2 = {}
xgb.train(param, dtrain, num_round, watchlist,
callbacks=[xgb.callback.record_evaluation(result)])
xgb.train(param, dtrain, num_round, watchlist,
evals_result=res2)
assert result['train']['error'][0] < 0.1
assert res2 == result
def test_multiclass(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'num_class': 2}
# specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)
# this is prediction
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if preds[i] != labels[i]) / float(len(preds))
# error must be smaller than 10%
assert err < 0.1
with tempfile.TemporaryDirectory() as tmpdir:
dtest_path = os.path.join(tmpdir, 'dtest.buffer')
model_path = os.path.join(tmpdir, 'xgb.model')
# save dmatrix into binary buffer
dtest.save_binary(dtest_path)
# save model
bst.save_model(model_path)
# load model and data in
bst2 = xgb.Booster(model_file=model_path)
dtest2 = xgb.DMatrix(dtest_path)
preds2 = bst2.predict(dtest2)
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_dump(self):
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
dm = xgb.DMatrix(data, label=target, feature_names=features)
params = {'objective': 'binary:logistic',
'eval_metric': 'logloss',
'eta': 0.3,
'max_depth': 1}
bst = xgb.train(params, dm, num_boost_round=1)
# number of feature importances should == number of features
dump1 = bst.get_dump()
assert len(dump1) == 1, 'Expected only 1 tree to be dumped.'
len(dump1[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
dump2 = bst.get_dump(with_stats=True)
assert dump2[0].count('\n') == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
msg = 'Expected more info when with_stats=True is given.'
assert dump2[0].find('\n') > dump1[0].find('\n'), msg
dump3 = bst.get_dump(dump_format="json")
dump3j = json.loads(dump3[0])
assert dump3j['nodeid'] == 0, 'Expected the root node on top.'
dump4 = bst.get_dump(dump_format="json", with_stats=True)
dump4j = json.loads(dump4[0])
assert 'gain' in dump4j, "Expected 'gain' to be dumped in JSON."
def test_feature_score(self):
rng = np.random.RandomState(0)
data = rng.randn(100, 2)
target = np.array([0, 1] * 50)
features = ["F0"]
with pytest.raises(ValueError):
xgb.DMatrix(data, label=target, feature_names=features)
params = {"objective": "binary:logistic"}
dm = xgb.DMatrix(data, label=target, feature_names=["F0", "F1"])
booster = xgb.train(params, dm, num_boost_round=1)
# no error since feature names might be assigned before the booster seeing data
# and booster doesn't known about the actual number of features.
booster.feature_names = ["F0"]
with pytest.raises(ValueError):
booster.get_fscore()
booster.feature_names = None
# Use JSON to make sure the output has native Python type
scores = json.loads(json.dumps(booster.get_fscore()))
np.testing.assert_allclose(scores["f0"], 6.0)
def test_load_file_invalid(self):
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file='incorrect_path')
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=u'不正なパス')
def test_dmatrix_numpy_init_omp(self):
rows = [1000, 11326, 15000]
cols = 50
for row in rows:
X = np.random.randn(row, cols)
y = np.random.randn(row).astype('f')
dm = xgb.DMatrix(X, y, nthread=0)
np.testing.assert_array_equal(dm.get_label(), y)
assert dm.num_row() == row
assert dm.num_col() == cols
dm = xgb.DMatrix(X, y, nthread=10)
np.testing.assert_array_equal(dm.get_label(), y)
assert dm.num_row() == row
assert dm.num_col() == cols
def test_cv(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == (4)
def test_cv_no_shuffle(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, shuffle=False, nfold=10,
as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == (4)
def test_cv_explicit_fold_indices(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
'binary:logistic'}
folds = [
# Train Test
([1, 3], [5, 8]),
([7, 9], [23, 43]),
]
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, folds=folds,
as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == (4)
@pytest.mark.skipif(**tm.skip_s390x())
def test_cv_explicit_fold_indices_labels(self):
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
'reg:squarederror'}
N = 100
F = 3
dm = xgb.DMatrix(data=np.random.randn(N, F), label=np.arange(N))
folds = [
# Train Test
([1, 3], [5, 8]),
([7, 9], [23, 43, 11]),
]
# Use callback to log the test labels in each fold
def cb(cbackenv):
print([fold.dtest.get_label() for fold in cbackenv.cvfolds])
# Run cross validation and capture standard out to test callback result
with tm.captured_output() as (out, err):
xgb.cv(
params, dm, num_boost_round=1, folds=folds, callbacks=[cb],
as_pandas=False
)
output = out.getvalue().strip()
solution = ('[array([5., 8.], dtype=float32), array([23., 43., 11.],' +
' dtype=float32)]')
assert output == solution
class TestBasicPathLike:
"""Unit tests using pathlib.Path for file interaction."""
def test_DMatrix_init_from_path(self):
"""Initialization from the data path."""
dpath = Path('demo/data')
dtrain = xgb.DMatrix(dpath / 'agaricus.txt.train')
assert dtrain.num_row() == 6513
assert dtrain.num_col() == 127
def test_DMatrix_save_to_path(self):
"""Saving to a binary file using pathlib from a DMatrix."""
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
dm = xgb.DMatrix(data, label=target, feature_names=features)
# save, assert exists, remove file
binary_path = Path("dtrain.bin")
dm.save_binary(binary_path)
assert binary_path.exists()
Path.unlink(binary_path)
def test_Booster_init_invalid_path(self):
"""An invalid model_file path should raise XGBoostError."""
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=Path("invalidpath"))
def test_Booster_save_and_load(self):
"""Saving and loading model files from paths."""
save_path = Path("saveload.model")
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
features = ['Feature1', 'Feature2']
dm = xgb.DMatrix(data, label=target, feature_names=features)
params = {'objective': 'binary:logistic',
'eval_metric': 'logloss',
'eta': 0.3,
'max_depth': 1}
bst = xgb.train(params, dm, num_boost_round=1)
# save, assert exists
bst.save_model(save_path)
assert save_path.exists()
def dump_assertions(dump):
"""Assertions for the expected dump from Booster"""
assert len(dump) == 1, 'Exepcted only 1 tree to be dumped.'
assert len(dump[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines.'
# load the model again using Path
bst2 = xgb.Booster(model_file=save_path)
dump2 = bst2.get_dump()
dump_assertions(dump2)
# load again using load_model
bst3 = xgb.Booster()
bst3.load_model(save_path)
dump3 = bst3.get_dump()
dump_assertions(dump3)
# remove file
Path.unlink(save_path)