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import json
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from pathlib import Path
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
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from xgboost . _c_api import _parse_version
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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 ) :
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 " )
assert not lazy_isinstance ( a , " numpy " , " dataframe " )
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def test_basic ( self , tmp_path : Path ) - > None :
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dtrain , dtest = tm . load_agaricus ( __file__ )
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param = { " max_depth " : 2 , " eta " : 1 , " 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 , evals = watchlist , verbose_eval = True )
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preds = bst . predict ( dtrain )
labels = dtrain . get_label ( )
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err = sum (
1 for i in range ( len ( preds ) ) if int ( preds [ i ] > 0.5 ) != labels [ i ]
) / float ( len ( preds ) )
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# error must be smaller than 10%
assert err < 0.1
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preds = bst . predict ( dtest )
labels = dtest . get_label ( )
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err = sum (
1 for i in range ( len ( preds ) ) if int ( preds [ i ] > 0.5 ) != labels [ i ]
) / float ( len ( preds ) )
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# error must be smaller than 10%
assert err < 0.1
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dtest_path = tmp_path / " dtest.dmatrix "
# save dmatrix into binary buffer
dtest . save_binary ( dtest_path )
# save model
model_path = tmp_path / " model.ubj "
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 , tmp_path : Path ) - > None :
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# Make sure that the metric configuration happens in booster so the 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 ,
" objective " : " binary:logistic " ,
" eval_metric " : [ " error " , " auc " ] ,
}
watchlist = [ ( dtest , " eval " ) , ( dtrain , " train " ) ]
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num_round = 2
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booster = xgb . train ( param , dtrain , num_round , evals = watchlist )
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predt_0 = booster . predict ( dtrain )
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path = tmp_path / " model.json "
booster . save_model ( path )
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booster = xgb . Booster ( params = param , model_file = path )
predt_1 = booster . predict ( dtrain )
np . testing . assert_allclose ( predt_0 , predt_1 )
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def test_multiclass ( self , tmp_path : Path ) - > None :
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dtrain , dtest = tm . load_agaricus ( __file__ )
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param = { " max_depth " : 2 , " eta " : 1 , " 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 , evals = watchlist )
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# this is prediction
preds = bst . predict ( dtest )
labels = dtest . get_label ( )
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err = sum ( 1 for i in range ( len ( preds ) ) if preds [ i ] != labels [ i ] ) / float (
len ( preds )
)
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# error must be smaller than 10%
assert err < 0.1
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dtest_path = tmp_path / " dtest.buffer "
model_path = tmp_path / " model.ubj "
# 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
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def test_dump ( self ) :
data = np . random . randn ( 100 , 2 )
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 " ,
" eval_metric " : " logloss " ,
" eta " : 0.3 ,
" max_depth " : 1 ,
}
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bst = xgb . train ( params , dm , num_boost_round = 1 )
# number of feature importances should == number of features
dump1 = bst . get_dump ( )
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assert len ( dump1 ) == 1 , " Expected only 1 tree to be dumped. "
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assert 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. "
assert dump2 [ 0 ] . find ( " \n " ) > dump1 [ 0 ] . find ( " \n " ) , msg
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dump3 = bst . get_dump ( dump_format = " json " )
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 )
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 ) :
bst . get_dump ( fmap = " foo " )
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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 ( )
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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 )
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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 = " 不正なパス " )
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@pytest.mark.parametrize (
" path " , [ " 모델.ubj " , " がうる・ぐら.json " ] , ids = [ " path-0 " , " path-1 " ]
)
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def test_unicode_path ( self , tmp_path : Path , path : str ) - > None :
model_path = tmp_path / path
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dtrain , _ = tm . load_agaricus ( __file__ )
param = { " max_depth " : 2 , " eta " : 1 , " objective " : " binary:logistic " }
bst = xgb . train ( param , dtrain , num_boost_round = 2 )
bst . save_model ( model_path )
bst2 = xgb . Booster ( model_file = model_path )
assert bst . get_dump ( dump_format = " text " ) == bst2 . get_dump ( dump_format = " text " )
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def test_dmatrix_numpy_init_omp ( self ) :
rows = [ 1000 , 11326 , 15000 ]
cols = 50
for row in rows :
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 )
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
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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 , " objective " : " binary:logistic " }
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# return np.ndarray
cv = xgb . cv ( params , dm , num_boost_round = 10 , nfold = 10 , as_pandas = False )
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assert isinstance ( cv , dict )
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 , " 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 , as_pandas = False
)
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assert isinstance ( cv , dict )
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 , " objective " : " binary:logistic " }
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folds = [
# Train Test
( [ 1 , 3 ] , [ 5 , 8 ] ) ,
( [ 7 , 9 ] , [ 23 , 43 ] ) ,
]
# return np.ndarray
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cv = xgb . cv ( params , dm , num_boost_round = 10 , folds = folds , as_pandas = False )
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assert isinstance ( cv , dict )
assert len ( cv ) == ( 4 )
def test_cv_explicit_fold_indices_labels ( self ) :
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params = { " max_depth " : 2 , " eta " : 1 , " objective " : " reg:squarederror " }
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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
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class Callback ( xgb . callback . TrainingCallback ) :
def __init__ ( self ) - > None :
super ( ) . __init__ ( )
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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) :
print ( [ fold . dtest . get_label ( ) for fold in model . cvfolds ] )
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 ] ,
as_pandas = False ,
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)
output = out . getvalue ( ) . strip ( )
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solution = (
" [array([5., 8.], dtype=float32), array([23., 43., 11.], "
+ " 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 ) :
""" Initialization from the data path. """
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dtrain , _ = tm . load_agaricus ( __file__ )
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assert dtrain . num_row ( ) == 6513
assert dtrain . num_col ( ) == 127
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def test_DMatrix_save_to_path ( self , tmp_path : Path ) - > None :
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""" Saving to a binary file using pathlib from a DMatrix. """
data = np . random . randn ( 100 , 2 )
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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binary_path = tmp_path / " dtrain.bin "
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dm . save_binary ( binary_path )
assert binary_path . exists ( )
def test_Booster_init_invalid_path ( self ) :
""" An invalid model_file path should raise XGBoostError. """
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with pytest . raises ( xgb . core . XGBoostError ) :
xgb . Booster ( model_file = Path ( " invalidpath " ) )
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def test_parse_ver ( ) - > None :
( major , minor , patch ) , post = _parse_version ( " 2.1.0 " )
assert post == " "
( major , minor , patch ) , post = _parse_version ( " 2.1.0-dev " )
assert post == " dev "
( major , minor , patch ) , post = _parse_version ( " 2.1.0rc1 " )
assert post == " rc1 "
( major , minor , patch ) , post = _parse_version ( " 2.1.0.post1 " )
assert post == " post1 "
