0s autopkgtest [18:41:55]: starting date and time: 2025-05-01 18:41:55+0000
  0s autopkgtest [18:41:55]: git checkout: 9986aa8c Merge branch 'skia/fix_network_interface' into 'ubuntu/production'
  0s autopkgtest [18:41:55]: host juju-7f2275-prod-proposed-migration-environment-23; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.k_8grp1_/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:ca-certificates --apt-upgrade xgboost --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=ca-certificates/20250419 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-cpu2-ram4-disk20-s390x --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-23@sto01-s390x-9.secgroup --name adt-questing-s390x-xgboost-20250501-184155-juju-7f2275-prod-proposed-migration-environment-23-4154552c-b107-4303-afab-dc3b8e4abf6d --image adt/ubuntu-questing-s390x-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-23 --net-id=net_prod-autopkgtest-workers-s390x -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/
115s autopkgtest [18:43:50]: testbed dpkg architecture: s390x
116s autopkgtest [18:43:51]: testbed apt version: 3.0.0
116s autopkgtest [18:43:51]: @@@@@@@@@@@@@@@@@@@@ test bed setup
116s autopkgtest [18:43:51]: testbed release detected to be: None
117s autopkgtest [18:43:52]: updating testbed package index (apt update)
117s Get:1 http://ftpmaster.internal/ubuntu questing-proposed InRelease [110 kB]
117s Hit:2 http://ftpmaster.internal/ubuntu questing InRelease
117s Hit:3 http://ftpmaster.internal/ubuntu questing-updates InRelease
117s Hit:4 http://ftpmaster.internal/ubuntu questing-security InRelease
117s Get:5 http://ftpmaster.internal/ubuntu questing-proposed/universe Sources [1149 kB]
118s Get:6 http://ftpmaster.internal/ubuntu questing-proposed/main Sources [126 kB]
118s Get:7 http://ftpmaster.internal/ubuntu questing-proposed/multiverse Sources [27.6 kB]
119s Get:8 http://ftpmaster.internal/ubuntu questing-proposed/main s390x Packages [49.5 kB]
119s Get:9 http://ftpmaster.internal/ubuntu questing-proposed/universe s390x Packages [243 kB]
119s Get:10 http://ftpmaster.internal/ubuntu questing-proposed/multiverse s390x Packages [1804 B]
119s Fetched 1707 kB in 2s (888 kB/s)
122s Reading package lists...
124s autopkgtest [18:43:59]: upgrading testbed (apt dist-upgrade and autopurge)
124s Reading package lists...
125s Building dependency tree...
125s Reading state information...
126s Calculating upgrade...Starting pkgProblemResolver with broken count: 0
127s Starting 2 pkgProblemResolver with broken count: 0
127s Done
129s Entering ResolveByKeep
129s 
130s Calculating upgrade...
131s The following packages will be upgraded:
131s   ca-certificates libperl5.40 perl perl-base perl-modules-5.40
131s 5 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
131s Need to get 10.6 MB of archives.
131s After this operation, 11.3 kB disk space will be freed.
131s Get:1 http://ftpmaster.internal/ubuntu questing-proposed/main s390x libperl5.40 s390x 5.40.1-3 [4972 kB]
137s Get:2 http://ftpmaster.internal/ubuntu questing-proposed/main s390x perl s390x 5.40.1-3 [262 kB]
137s Get:3 http://ftpmaster.internal/ubuntu questing-proposed/main s390x perl-base s390x 5.40.1-3 [1954 kB]
139s Get:4 http://ftpmaster.internal/ubuntu questing-proposed/main s390x perl-modules-5.40 all 5.40.1-3 [3217 kB]
143s Get:5 http://ftpmaster.internal/ubuntu questing-proposed/main s390x ca-certificates all 20250419 [163 kB]
143s Preconfiguring packages ...
143s Fetched 10.6 MB in 12s (913 kB/s)
144s (Reading database ... 
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(Reading database ... 35%
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(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 59826 files and directories currently installed.)
144s Preparing to unpack .../libperl5.40_5.40.1-3_s390x.deb ...
144s Unpacking libperl5.40:s390x (5.40.1-3) over (5.40.1-2ubuntu0.1) ...
144s Preparing to unpack .../perl_5.40.1-3_s390x.deb ...
144s Unpacking perl (5.40.1-3) over (5.40.1-2ubuntu0.1) ...
144s Preparing to unpack .../perl-base_5.40.1-3_s390x.deb ...
144s Unpacking perl-base (5.40.1-3) over (5.40.1-2ubuntu0.1) ...
144s Setting up perl-base (5.40.1-3) ...
144s (Reading database ... 
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(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 59826 files and directories currently installed.)
144s Preparing to unpack .../perl-modules-5.40_5.40.1-3_all.deb ...
144s Unpacking perl-modules-5.40 (5.40.1-3) over (5.40.1-2ubuntu0.1) ...
145s Preparing to unpack .../ca-certificates_20250419_all.deb ...
145s Unpacking ca-certificates (20250419) over (20241223) ...
145s Setting up ca-certificates (20250419) ...
153s Updating certificates in /etc/ssl/certs...
156s rehash: warning: skipping ca-certificates.crt, it does not contain exactly one certificate or CRL
156s 2 added, 4 removed; done.
156s Setting up perl-modules-5.40 (5.40.1-3) ...
156s Setting up libperl5.40:s390x (5.40.1-3) ...
156s Setting up perl (5.40.1-3) ...
156s Processing triggers for man-db (2.13.0-1) ...
162s Processing triggers for libc-bin (2.41-6ubuntu1) ...
162s Processing triggers for ca-certificates (20250419) ...
162s Updating certificates in /etc/ssl/certs...
165s 0 added, 0 removed; done.
165s Running hooks in /etc/ca-certificates/update.d...
165s done.
167s Reading package lists...
168s Building dependency tree...
168s Reading state information...
169s Starting pkgProblemResolver with broken count: 0
170s Starting 2 pkgProblemResolver with broken count: 0
170s Done
170s Solving dependencies...
171s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
174s autopkgtest [18:44:49]: testbed running kernel: Linux 6.14.0-15-generic #15-Ubuntu SMP Sun Apr  6 13:39:00 UTC 2025
175s autopkgtest [18:44:50]: @@@@@@@@@@@@@@@@@@@@ apt-source xgboost
180s Get:1 http://ftpmaster.internal/ubuntu questing/universe xgboost 2.1.4-1build1 (dsc) [2340 B]
180s Get:2 http://ftpmaster.internal/ubuntu questing/universe xgboost 2.1.4-1build1 (tar) [2138 kB]
180s Get:3 http://ftpmaster.internal/ubuntu questing/universe xgboost 2.1.4-1build1 (diff) [23.2 kB]
180s gpgv: Signature made Wed Mar  5 01:20:21 2025 UTC
180s gpgv:                using RSA key 25E3FF2D7F469DBE7D0D4E50AFCFEC8E669CE1C2
180s gpgv: Can't check signature: No public key
180s dpkg-source: warning: cannot verify inline signature for ./xgboost_2.1.4-1build1.dsc: no acceptable signature found
180s autopkgtest [18:44:55]: testing package xgboost version 2.1.4-1build1
181s autopkgtest [18:44:56]: build not needed
182s autopkgtest [18:44:57]: test run-demos: preparing testbed
182s Reading package lists...
183s Building dependency tree...
183s Reading state information...
184s Starting pkgProblemResolver with broken count: 0
185s Starting 2 pkgProblemResolver with broken count: 0
185s Done
186s The following NEW packages will be installed:
186s   blt fontconfig-config fonts-dejavu-core fonts-dejavu-mono fonts-lyx libblas3
186s   libdeflate0 libdmlc0t64 libfontconfig1 libfreetype6 libgfortran5 libgomp1
186s   libgraphite2-3 libharfbuzz0b libimagequant0 libjbig0 libjpeg-turbo8 libjpeg8
186s   libjs-jquery libjs-jquery-ui liblapack3 liblbfgsb0 liblcms2-2 libopenjp2-7
186s   libqhull-r8.0 libraqm0 libsharpyuv0 libtcl8.6 libtiff6 libtk8.6 libwebp7
186s   libwebpdemux2 libwebpmux3 libxft2 libxgboost-dev libxgboost0 libxrender1
186s   libxslt1.1 libxss1 python-matplotlib-data python3-brotli python3-contourpy
186s   python3-cycler python3-decorator python3-fonttools python3-fs python3-joblib
186s   python3-kiwisolver python3-lxml python3-lz4 python3-matplotlib
186s   python3-mpmath python3-numpy python3-numpy-dev python3-pandas
186s   python3-pandas-lib python3-pil python3-pil.imagetk python3-platformdirs
186s   python3-pytz python3-scipy python3-sklearn python3-sklearn-lib python3-sympy
186s   python3-threadpoolctl python3-tk python3-tz python3-ufolib2
186s   python3-unicodedata2 python3-xgboost python3.13-tk tk8.6-blt2.5 unicode-data
186s   x11-common
186s 0 upgraded, 74 newly installed, 0 to remove and 0 not upgraded.
186s Need to get 89.7 MB of archives.
186s After this operation, 373 MB of additional disk space will be used.
186s Get:1 http://ftpmaster.internal/ubuntu questing/main s390x python3-numpy-dev s390x 1:2.2.3+ds-5 [147 kB]
186s Get:2 http://ftpmaster.internal/ubuntu questing/main s390x libblas3 s390x 3.12.1-2 [252 kB]
186s Get:3 http://ftpmaster.internal/ubuntu questing/main s390x libgfortran5 s390x 15-20250404-0ubuntu1 [621 kB]
187s Get:4 http://ftpmaster.internal/ubuntu questing/main s390x liblapack3 s390x 3.12.1-2 [2971 kB]
190s Get:5 http://ftpmaster.internal/ubuntu questing/main s390x python3-numpy s390x 1:2.2.3+ds-5 [4396 kB]
195s Get:6 http://ftpmaster.internal/ubuntu questing/main s390x libtcl8.6 s390x 8.6.16+dfsg-1 [1034 kB]
196s Get:7 http://ftpmaster.internal/ubuntu questing/main s390x libfreetype6 s390x 2.13.3+dfsg-1 [431 kB]
196s Get:8 http://ftpmaster.internal/ubuntu questing/main s390x fonts-dejavu-mono all 2.37-8 [502 kB]
196s Get:9 http://ftpmaster.internal/ubuntu questing/main s390x fonts-dejavu-core all 2.37-8 [835 kB]
197s Get:10 http://ftpmaster.internal/ubuntu questing/main s390x fontconfig-config s390x 2.15.0-2.2ubuntu1 [37.9 kB]
197s Get:11 http://ftpmaster.internal/ubuntu questing/main s390x libfontconfig1 s390x 2.15.0-2.2ubuntu1 [150 kB]
197s Get:12 http://ftpmaster.internal/ubuntu questing/main s390x libxrender1 s390x 1:0.9.10-1.1build1 [20.4 kB]
197s Get:13 http://ftpmaster.internal/ubuntu questing/main s390x libxft2 s390x 2.3.6-1build1 [49.6 kB]
197s Get:14 http://ftpmaster.internal/ubuntu questing/main s390x x11-common all 1:7.7+23ubuntu4 [21.8 kB]
197s Get:15 http://ftpmaster.internal/ubuntu questing/main s390x libxss1 s390x 1:1.2.3-1build3 [7396 B]
197s Get:16 http://ftpmaster.internal/ubuntu questing/main s390x libtk8.6 s390x 8.6.16-1 [830 kB]
198s Get:17 http://ftpmaster.internal/ubuntu questing/main s390x tk8.6-blt2.5 s390x 2.5.3+dfsg-8 [657 kB]
199s Get:18 http://ftpmaster.internal/ubuntu questing/main s390x blt s390x 2.5.3+dfsg-8 [4826 B]
199s Get:19 http://ftpmaster.internal/ubuntu questing/universe s390x fonts-lyx all 2.4.3-1 [171 kB]
199s Get:20 http://ftpmaster.internal/ubuntu questing/main s390x libdeflate0 s390x 1.23-1 [46.1 kB]
199s Get:21 http://ftpmaster.internal/ubuntu questing/main s390x libgomp1 s390x 15-20250404-0ubuntu1 [152 kB]
199s Get:22 http://ftpmaster.internal/ubuntu questing/universe s390x libdmlc0t64 s390x 0.5+git20240614.1334185-1 [195 kB]
199s Get:23 http://ftpmaster.internal/ubuntu questing/main s390x libgraphite2-3 s390x 1.3.14-2ubuntu1 [79.8 kB]
199s Get:24 http://ftpmaster.internal/ubuntu questing/main s390x libharfbuzz0b s390x 10.2.0-1 [538 kB]
200s Get:25 http://ftpmaster.internal/ubuntu questing/main s390x libimagequant0 s390x 2.18.0-1build1 [43.3 kB]
200s Get:26 http://ftpmaster.internal/ubuntu questing/main s390x libjpeg-turbo8 s390x 2.1.5-3ubuntu2 [147 kB]
200s Get:27 http://ftpmaster.internal/ubuntu questing/main s390x libjpeg8 s390x 8c-2ubuntu11 [2146 B]
200s Get:28 http://ftpmaster.internal/ubuntu questing/main s390x libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [328 kB]
200s Get:29 http://ftpmaster.internal/ubuntu questing/universe s390x libjs-jquery-ui all 1.13.2+dfsg-1 [252 kB]
200s Get:30 http://ftpmaster.internal/ubuntu questing/universe s390x liblbfgsb0 s390x 3.0+dfsg.4-1build1 [32.4 kB]
200s Get:31 http://ftpmaster.internal/ubuntu questing/main s390x liblcms2-2 s390x 2.16-2 [175 kB]
200s Get:32 http://ftpmaster.internal/ubuntu questing/universe s390x libqhull-r8.0 s390x 2020.2-6build1 [199 kB]
200s Get:33 http://ftpmaster.internal/ubuntu questing/main s390x libraqm0 s390x 0.10.2-1 [15.8 kB]
200s Get:34 http://ftpmaster.internal/ubuntu questing/main s390x libsharpyuv0 s390x 1.5.0-0.1 [16.7 kB]
200s Get:35 http://ftpmaster.internal/ubuntu questing/main s390x libjbig0 s390x 2.1-6.1ubuntu2 [33.1 kB]
200s Get:36 http://ftpmaster.internal/ubuntu questing/main s390x libwebp7 s390x 1.5.0-0.1 [210 kB]
200s Get:37 http://ftpmaster.internal/ubuntu questing/main s390x libtiff6 s390x 4.5.1+git230720-4ubuntu4 [217 kB]
200s Get:38 http://ftpmaster.internal/ubuntu questing/main s390x libwebpdemux2 s390x 1.5.0-0.1 [12.6 kB]
200s Get:39 http://ftpmaster.internal/ubuntu questing/main s390x libwebpmux3 s390x 1.5.0-0.1 [25.8 kB]
200s Get:40 http://ftpmaster.internal/ubuntu questing/universe s390x libxgboost0 s390x 2.1.4-1build1 [1816 kB]
202s Get:41 http://ftpmaster.internal/ubuntu questing/universe s390x libxgboost-dev s390x 2.1.4-1build1 [74.3 kB]
202s Get:42 http://ftpmaster.internal/ubuntu questing/main s390x libxslt1.1 s390x 1.1.39-0exp1ubuntu4 [170 kB]
202s Get:43 http://ftpmaster.internal/ubuntu questing/universe s390x python-matplotlib-data all 3.8.3-7build1 [2934 kB]
205s Get:44 http://ftpmaster.internal/ubuntu questing/universe s390x python3-brotli s390x 1.1.0-2build4 [380 kB]
205s Get:45 http://ftpmaster.internal/ubuntu questing/universe s390x python3-contourpy s390x 1.3.1-1build1 [197 kB]
205s Get:46 http://ftpmaster.internal/ubuntu questing/universe s390x python3-cycler all 0.12.1-1 [9716 B]
205s Get:47 http://ftpmaster.internal/ubuntu questing/main s390x python3-decorator all 5.1.1-5 [10.1 kB]
205s Get:48 http://ftpmaster.internal/ubuntu questing/main s390x python3-platformdirs all 4.3.6-1 [16.8 kB]
205s Get:49 http://ftpmaster.internal/ubuntu questing/universe s390x python3-fs all 2.4.16-7 [90.8 kB]
205s Get:50 http://ftpmaster.internal/ubuntu questing/main s390x python3-lxml s390x 5.3.2-1 [1370 kB]
207s Get:51 http://ftpmaster.internal/ubuntu questing/universe s390x python3-lz4 s390x 4.4.0+dfsg-1build1 [26.6 kB]
207s Get:52 http://ftpmaster.internal/ubuntu questing/universe s390x python3-scipy s390x 1.14.1-4ubuntu2 [18.0 MB]
227s Get:53 http://ftpmaster.internal/ubuntu questing/universe s390x python3-mpmath all 1.3.0-1 [425 kB]
227s Get:54 http://ftpmaster.internal/ubuntu questing/universe s390x python3-sympy all 1.13.3-5 [4229 kB]
231s Get:55 http://ftpmaster.internal/ubuntu questing/universe s390x python3-ufolib2 all 0.17.0+dfsg1-1 [33.5 kB]
231s Get:56 http://ftpmaster.internal/ubuntu questing/universe s390x python3-unicodedata2 s390x 15.1.0+ds-1build3 [361 kB]
231s Get:57 http://ftpmaster.internal/ubuntu questing/universe s390x unicode-data all 15.1.0-1 [8878 kB]
241s Get:58 http://ftpmaster.internal/ubuntu questing/universe s390x python3-fonttools s390x 4.55.3-2build1 [1496 kB]
242s Get:59 http://ftpmaster.internal/ubuntu questing/universe s390x python3-joblib all 1.4.2-3 [205 kB]
242s Get:60 http://ftpmaster.internal/ubuntu questing/universe s390x python3-kiwisolver s390x 1.4.7-3build1 [55.0 kB]
242s Get:61 http://ftpmaster.internal/ubuntu questing/main s390x libopenjp2-7 s390x 2.5.3-2 [207 kB]
242s Get:62 http://ftpmaster.internal/ubuntu questing/main s390x python3-pil s390x 11.1.0-5build1 [498 kB]
242s Get:63 http://ftpmaster.internal/ubuntu questing/main s390x python3.13-tk s390x 3.13.3-1 [108 kB]
242s Get:64 http://ftpmaster.internal/ubuntu questing/main s390x python3-tk s390x 3.13.3-1 [9854 B]
242s Get:65 http://ftpmaster.internal/ubuntu questing/universe s390x python3-pil.imagetk s390x 11.1.0-5build1 [9668 B]
242s Get:66 http://ftpmaster.internal/ubuntu questing/universe s390x python3-matplotlib s390x 3.8.3-7build1 [17.3 MB]
261s Get:67 http://ftpmaster.internal/ubuntu questing/main s390x python3-pytz all 2025.1-3 [162 kB]
261s Get:68 http://ftpmaster.internal/ubuntu questing/main s390x python3-tz all 2025.1-3 [1866 B]
261s Get:69 http://ftpmaster.internal/ubuntu questing/universe s390x python3-pandas-lib s390x 2.2.3+dfsg-8build1 [4995 kB]
267s Get:70 http://ftpmaster.internal/ubuntu questing/universe s390x python3-pandas all 2.2.3+dfsg-8build1 [3112 kB]
270s Get:71 http://ftpmaster.internal/ubuntu questing/universe s390x python3-threadpoolctl all 3.1.0-1 [21.3 kB]
270s Get:72 http://ftpmaster.internal/ubuntu questing/universe s390x python3-sklearn-lib s390x 1.4.2+dfsg-8 [4262 kB]
275s Get:73 http://ftpmaster.internal/ubuntu questing/universe s390x python3-sklearn all 1.4.2+dfsg-8 [2258 kB]
277s Get:74 http://ftpmaster.internal/ubuntu questing/universe s390x python3-xgboost s390x 2.1.4-1build1 [119 kB]
281s Fetched 89.7 MB in 1min 31s (981 kB/s)
281s Selecting previously unselected package python3-numpy-dev:s390x.
281s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 59824 files and directories currently installed.)
281s Preparing to unpack .../00-python3-numpy-dev_1%3a2.2.3+ds-5_s390x.deb ...
281s Unpacking python3-numpy-dev:s390x (1:2.2.3+ds-5) ...
281s Selecting previously unselected package libblas3:s390x.
281s Preparing to unpack .../01-libblas3_3.12.1-2_s390x.deb ...
281s Unpacking libblas3:s390x (3.12.1-2) ...
281s Selecting previously unselected package libgfortran5:s390x.
281s Preparing to unpack .../02-libgfortran5_15-20250404-0ubuntu1_s390x.deb ...
281s Unpacking libgfortran5:s390x (15-20250404-0ubuntu1) ...
282s Selecting previously unselected package liblapack3:s390x.
282s Preparing to unpack .../03-liblapack3_3.12.1-2_s390x.deb ...
282s Unpacking liblapack3:s390x (3.12.1-2) ...
283s Selecting previously unselected package python3-numpy.
283s Preparing to unpack .../04-python3-numpy_1%3a2.2.3+ds-5_s390x.deb ...
283s Unpacking python3-numpy (1:2.2.3+ds-5) ...
286s Selecting previously unselected package libtcl8.6:s390x.
286s Preparing to unpack .../05-libtcl8.6_8.6.16+dfsg-1_s390x.deb ...
286s Unpacking libtcl8.6:s390x (8.6.16+dfsg-1) ...
286s Selecting previously unselected package libfreetype6:s390x.
286s Preparing to unpack .../06-libfreetype6_2.13.3+dfsg-1_s390x.deb ...
286s Unpacking libfreetype6:s390x (2.13.3+dfsg-1) ...
286s Selecting previously unselected package fonts-dejavu-mono.
286s Preparing to unpack .../07-fonts-dejavu-mono_2.37-8_all.deb ...
286s Unpacking fonts-dejavu-mono (2.37-8) ...
286s Selecting previously unselected package fonts-dejavu-core.
286s Preparing to unpack .../08-fonts-dejavu-core_2.37-8_all.deb ...
286s Unpacking fonts-dejavu-core (2.37-8) ...
286s Selecting previously unselected package fontconfig-config.
286s Preparing to unpack .../09-fontconfig-config_2.15.0-2.2ubuntu1_s390x.deb ...
287s Unpacking fontconfig-config (2.15.0-2.2ubuntu1) ...
288s Selecting previously unselected package libfontconfig1:s390x.
288s Preparing to unpack .../10-libfontconfig1_2.15.0-2.2ubuntu1_s390x.deb ...
288s Unpacking libfontconfig1:s390x (2.15.0-2.2ubuntu1) ...
288s Selecting previously unselected package libxrender1:s390x.
288s Preparing to unpack .../11-libxrender1_1%3a0.9.10-1.1build1_s390x.deb ...
288s Unpacking libxrender1:s390x (1:0.9.10-1.1build1) ...
288s Selecting previously unselected package libxft2:s390x.
288s Preparing to unpack .../12-libxft2_2.3.6-1build1_s390x.deb ...
288s Unpacking libxft2:s390x (2.3.6-1build1) ...
288s Selecting previously unselected package x11-common.
288s Preparing to unpack .../13-x11-common_1%3a7.7+23ubuntu4_all.deb ...
288s Unpacking x11-common (1:7.7+23ubuntu4) ...
288s Selecting previously unselected package libxss1:s390x.
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290s Selecting previously unselected package libjpeg-turbo8:s390x.
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297s Unpacking python3-sympy (1.13.3-5) ...
299s Selecting previously unselected package python3-ufolib2.
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299s Selecting previously unselected package python3-unicodedata2.
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299s Unpacking unicode-data (15.1.0-1) ...
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313s Unpacking python3-xgboost (2.1.4-1build1) ...
313s Setting up libgraphite2-3:s390x (1.3.14-2ubuntu1) ...
313s Setting up liblcms2-2:s390x (2.16-2) ...
313s Setting up libsharpyuv0:s390x (1.5.0-0.1) ...
313s Setting up libxrender1:s390x (1:0.9.10-1.1build1) ...
313s Setting up python3-joblib (1.4.2-3) ...
314s Setting up python3-lz4 (4.4.0+dfsg-1build1) ...
314s Setting up python3-unicodedata2 (15.1.0+ds-1build3) ...
314s Setting up fonts-lyx (2.4.3-1) ...
314s Setting up python3-threadpoolctl (3.1.0-1) ...
315s Setting up x11-common (1:7.7+23ubuntu4) ...
316s Setting up libdeflate0:s390x (1.23-1) ...
316s Setting up libqhull-r8.0:s390x (2020.2-6build1) ...
316s Setting up python3-pytz (2025.1-3) ...
316s Setting up libgomp1:s390x (15-20250404-0ubuntu1) ...
316s Setting up libjbig0:s390x (2.1-6.1ubuntu2) ...
316s Setting up python3-platformdirs (4.3.6-1) ...
316s Setting up python3-tz (2025.1-3) ...
316s Setting up python3-fs (2.4.16-7) ...
318s Setting up unicode-data (15.1.0-1) ...
318s Setting up python3-decorator (5.1.1-5) ...
319s Setting up libblas3:s390x (3.12.1-2) ...
319s update-alternatives: using /usr/lib/s390x-linux-gnu/blas/libblas.so.3 to provide /usr/lib/s390x-linux-gnu/libblas.so.3 (libblas.so.3-s390x-linux-gnu) in auto mode
319s Setting up libfreetype6:s390x (2.13.3+dfsg-1) ...
319s Setting up python3-brotli (1.1.0-2build4) ...
320s Setting up python3-cycler (0.12.1-1) ...
321s Setting up libimagequant0:s390x (2.18.0-1build1) ...
321s Setting up fonts-dejavu-mono (2.37-8) ...
321s Setting up python3-kiwisolver (1.4.7-3build1) ...
321s Setting up python3-numpy-dev:s390x (1:2.2.3+ds-5) ...
322s Setting up libtcl8.6:s390x (8.6.16+dfsg-1) ...
322s Setting up fonts-dejavu-core (2.37-8) ...
322s Setting up libjpeg-turbo8:s390x (2.1.5-3ubuntu2) ...
322s Setting up libgfortran5:s390x (15-20250404-0ubuntu1) ...
322s Setting up libwebp7:s390x (1.5.0-0.1) ...
322s Setting up libxslt1.1:s390x (1.1.39-0exp1ubuntu4) ...
322s Setting up libopenjp2-7:s390x (2.5.3-2) ...
322s Setting up libharfbuzz0b:s390x (10.2.0-1) ...
322s Setting up libxss1:s390x (1:1.2.3-1build3) ...
322s Setting up libjs-jquery (3.6.1+dfsg+~3.5.14-1) ...
322s Setting up python3-mpmath (1.3.0-1) ...
325s Setting up python-matplotlib-data (3.8.3-7build1) ...
325s Setting up libwebpmux3:s390x (1.5.0-0.1) ...
325s Setting up libjpeg8:s390x (8c-2ubuntu11) ...
325s Setting up python3-sympy (1.13.3-5) ...
361s Setting up liblapack3:s390x (3.12.1-2) ...
361s update-alternatives: using /usr/lib/s390x-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/s390x-linux-gnu/liblapack.so.3 (liblapack.so.3-s390x-linux-gnu) in auto mode
361s Setting up libdmlc0t64:s390x (0.5+git20240614.1334185-1) ...
361s Setting up fontconfig-config (2.15.0-2.2ubuntu1) ...
362s Setting up libwebpdemux2:s390x (1.5.0-0.1) ...
362s Setting up libjs-jquery-ui (1.13.2+dfsg-1) ...
362s Setting up libraqm0:s390x (0.10.2-1) ...
362s Setting up python3-numpy (1:2.2.3+ds-5) ...
374s Setting up python3-lxml:s390x (5.3.2-1) ...
375s Setting up libtiff6:s390x (4.5.1+git230720-4ubuntu4) ...
375s Setting up libxgboost0 (2.1.4-1build1) ...
375s Setting up python3-contourpy (1.3.1-1build1) ...
375s Setting up libfontconfig1:s390x (2.15.0-2.2ubuntu1) ...
375s Setting up liblbfgsb0:s390x (3.0+dfsg.4-1build1) ...
375s Setting up libxft2:s390x (2.3.6-1build1) ...
375s Setting up python3-scipy (1.14.1-4ubuntu2) ...
388s Setting up libtk8.6:s390x (8.6.16-1) ...
388s Setting up python3-pandas-lib:s390x (2.2.3+dfsg-8build1) ...
388s Setting up python3-sklearn-lib:s390x (1.4.2+dfsg-8) ...
388s Setting up python3.13-tk (3.13.3-1) ...
388s Setting up python3-pil:s390x (11.1.0-5build1) ...
389s Setting up python3-xgboost (2.1.4-1build1) ...
389s Setting up python3-pandas (2.2.3+dfsg-8build1) ...
415s Setting up libxgboost-dev (2.1.4-1build1) ...
415s Setting up python3-sklearn (1.4.2+dfsg-8) ...
426s Setting up tk8.6-blt2.5 (2.5.3+dfsg-8) ...
426s Setting up blt (2.5.3+dfsg-8) ...
426s Setting up python3-tk:s390x (3.13.3-1) ...
426s Setting up python3-pil.imagetk:s390x (11.1.0-5build1) ...
426s Setting up python3-fonttools (4.55.3-2build1) ...
431s Setting up python3-ufolib2 (0.17.0+dfsg1-1) ...
432s Setting up python3-matplotlib (3.8.3-7build1) ...
439s Processing triggers for libc-bin (2.41-6ubuntu1) ...
440s Processing triggers for man-db (2.13.0-1) ...
452s autopkgtest [18:49:27]: test run-demos: [-----------------------
454s ==========================================
454s Running case basic_walkthrough.py...
459s [0]	eval-logloss:0.22646	train-logloss:0.23316
459s [1]	eval-logloss:0.13776	train-logloss:0.13654
459s error=0.021726
460s ==========================================
460s Running case boost_from_prediction.py...
464s /usr/lib/python3/dist-packages/xgboost/core.py:723: FutureWarning: Pass `evals` as keyword args.
464s   warnings.warn(msg, FutureWarning)
464s start running example to start from a initial prediction
464s [0]	eval-logloss:0.22646	train-logloss:0.23316
464s this is result of running from initial prediction
464s [0]	eval-logloss:0.13776	train-logloss:0.13654
465s ==========================================
465s Running case callbacks.py...
470s /usr/lib/python3/dist-packages/xgboost/core.py:158: UserWarning: [18:51:05] WARNING: ./src/context.cc:196: XGBoost is not compiled with CUDA support.
470s   warnings.warn(smsg, UserWarning)
470s [0]	Train-error:0.03052	Train-rmse:0.35870	Valid-error:0.06993	Valid-rmse:0.37215
470s [1]	Train-error:0.01174	Train-rmse:0.27804	Valid-error:0.04895	Valid-rmse:0.29761
470s [2]	Train-error:0.01174	Train-rmse:0.21911	Valid-error:0.04895	Valid-rmse:0.25064
470s [3]	Train-error:0.00939	Train-rmse:0.17913	Valid-error:0.02797	Valid-rmse:0.21447
470s [4]	Train-error:0.00939	Train-rmse:0.14893	Valid-error:0.02797	Valid-rmse:0.19740
470s [5]	Train-error:0.00939	Train-rmse:0.12816	Valid-error:0.03497	Valid-rmse:0.18098
470s [6]	Train-error:0.00704	Train-rmse:0.11060	Valid-error:0.03497	Valid-rmse:0.17131
470s [7]	Train-error:0.00704	Train-rmse:0.09947	Valid-error:0.03497	Valid-rmse:0.16077
470s [8]	Train-error:0.00469	Train-rmse:0.08816	Valid-error:0.02797	Valid-rmse:0.14865
470s [9]	Train-error:0.00469	Train-rmse:0.07863	Valid-error:0.03497	Valid-rmse:0.15073
470s [10]	Train-error:0.00469	Train-rmse:0.07164	Valid-error:0.02098	Valid-rmse:0.14161
470s [11]	Train-error:0.00469	Train-rmse:0.06692	Valid-error:0.01399	Valid-rmse:0.14028
471s [12]	Train-error:0.00235	Train-rmse:0.06216	Valid-error:0.01399	Valid-rmse:0.13727
471s [13]	Train-error:0.00235	Train-rmse:0.05811	Valid-error:0.01399	Valid-rmse:0.13495
471s [14]	Train-error:0.00000	Train-rmse:0.05532	Valid-error:0.02098	Valid-rmse:0.13500
471s [15]	Train-error:0.00000	Train-rmse:0.05178	Valid-error:0.02098	Valid-rmse:0.13226
471s [16]	Train-error:0.00000	Train-rmse:0.04924	Valid-error:0.02098	Valid-rmse:0.13329
471s [17]	Train-error:0.00000	Train-rmse:0.04656	Valid-error:0.01399	Valid-rmse:0.13183
471s [18]	Train-error:0.00000	Train-rmse:0.04437	Valid-error:0.01399	Valid-rmse:0.13353
471s [19]	Train-error:0.00000	Train-rmse:0.04272	Valid-error:0.02098	Valid-rmse:0.13468
471s [20]	Train-error:0.00000	Train-rmse:0.04030	Valid-error:0.02098	Valid-rmse:0.13419
471s [21]	Train-error:0.00000	Train-rmse:0.03869	Valid-error:0.02797	Valid-rmse:0.13543
471s [22]	Train-error:0.00000	Train-rmse:0.03548	Valid-error:0.03497	Valid-rmse:0.13316
471s [23]	Train-error:0.00000	Train-rmse:0.03444	Valid-error:0.03497	Valid-rmse:0.13637
471s [24]	Train-error:0.00000	Train-rmse:0.03369	Valid-error:0.04196	Valid-rmse:0.13663
471s [25]	Train-error:0.00000	Train-rmse:0.03213	Valid-error:0.03497	Valid-rmse:0.13386
472s [26]	Train-error:0.00000	Train-rmse:0.03100	Valid-error:0.03497	Valid-rmse:0.13180
472s [27]	Train-error:0.00000	Train-rmse:0.03036	Valid-error:0.03497	Valid-rmse:0.13263
472s [28]	Train-error:0.00000	Train-rmse:0.02957	Valid-error:0.04196	Valid-rmse:0.13493
472s [29]	Train-error:0.00000	Train-rmse:0.02866	Valid-error:0.03497	Valid-rmse:0.13487
472s [30]	Train-error:0.00000	Train-rmse:0.02851	Valid-error:0.04196	Valid-rmse:0.13384
472s [31]	Train-error:0.00000	Train-rmse:0.02721	Valid-error:0.02797	Valid-rmse:0.13117
472s [32]	Train-error:0.00000	Train-rmse:0.02615	Valid-error:0.03497	Valid-rmse:0.13188
472s [33]	Train-error:0.00000	Train-rmse:0.02559	Valid-error:0.03497	Valid-rmse:0.13096
473s [34]	Train-error:0.00000	Train-rmse:0.02520	Valid-error:0.04196	Valid-rmse:0.13283
473s [35]	Train-error:0.00000	Train-rmse:0.02438	Valid-error:0.03497	Valid-rmse:0.13220
473s [36]	Train-error:0.00000	Train-rmse:0.02356	Valid-error:0.03497	Valid-rmse:0.13030
473s [37]	Train-error:0.00000	Train-rmse:0.02348	Valid-error:0.03497	Valid-rmse:0.12942
473s [38]	Train-error:0.00000	Train-rmse:0.02337	Valid-error:0.03497	Valid-rmse:0.12955
473s [39]	Train-error:0.00000	Train-rmse:0.02265	Valid-error:0.02797	Valid-rmse:0.12785
473s [40]	Train-error:0.00000	Train-rmse:0.02283	Valid-error:0.03497	Valid-rmse:0.12889
473s [41]	Train-error:0.00000	Train-rmse:0.02255	Valid-error:0.03497	Valid-rmse:0.13066
474s [42]	Train-error:0.00000	Train-rmse:0.02190	Valid-error:0.03497	Valid-rmse:0.12905
474s [43]	Train-error:0.00000	Train-rmse:0.02173	Valid-error:0.03497	Valid-rmse:0.12833
474s [44]	Train-error:0.00000	Train-rmse:0.02180	Valid-error:0.03497	Valid-rmse:0.12884
474s [45]	Train-error:0.00000	Train-rmse:0.02153	Valid-error:0.03497	Valid-rmse:0.13050
474s [46]	Train-error:0.00000	Train-rmse:0.02149	Valid-error:0.03497	Valid-rmse:0.12927
474s [47]	Train-error:0.00000	Train-rmse:0.02134	Valid-error:0.02797	Valid-rmse:0.13012
474s [48]	Train-error:0.00000	Train-rmse:0.02119	Valid-error:0.03497	Valid-rmse:0.12936
474s [49]	Train-error:0.00000	Train-rmse:0.02124	Valid-error:0.02797	Valid-rmse:0.12989
474s [50]	Train-error:0.00000	Train-rmse:0.02116	Valid-error:0.02797	Valid-rmse:0.13014
474s [51]	Train-error:0.00000	Train-rmse:0.02107	Valid-error:0.02797	Valid-rmse:0.12953
474s [52]	Train-error:0.00000	Train-rmse:0.02121	Valid-error:0.02797	Valid-rmse:0.12976
474s [53]	Train-error:0.00000	Train-rmse:0.02080	Valid-error:0.02797	Valid-rmse:0.12903
474s [54]	Train-error:0.00000	Train-rmse:0.02060	Valid-error:0.02797	Valid-rmse:0.13032
474s [55]	Train-error:0.00000	Train-rmse:0.02069	Valid-error:0.02797	Valid-rmse:0.13027
474s [56]	Train-error:0.00000	Train-rmse:0.02039	Valid-error:0.02797	Valid-rmse:0.12900
474s [57]	Train-error:0.00000	Train-rmse:0.02031	Valid-error:0.02797	Valid-rmse:0.12845
474s [58]	Train-error:0.00000	Train-rmse:0.02047	Valid-error:0.02797	Valid-rmse:0.12928
475s [59]	Train-error:0.00000	Train-rmse:0.02013	Valid-error:0.02797	Valid-rmse:0.12865
475s [60]	Train-error:0.00000	Train-rmse:0.02027	Valid-error:0.02797	Valid-rmse:0.12884
475s [61]	Train-error:0.00000	Train-rmse:0.02023	Valid-error:0.02797	Valid-rmse:0.12917
475s [62]	Train-error:0.00000	Train-rmse:0.02013	Valid-error:0.02797	Valid-rmse:0.12947
475s [63]	Train-error:0.00000	Train-rmse:0.01986	Valid-error:0.02797	Valid-rmse:0.12827
475s [64]	Train-error:0.00000	Train-rmse:0.01980	Valid-error:0.02797	Valid-rmse:0.12780
475s [65]	Train-error:0.00000	Train-rmse:0.01971	Valid-error:0.02797	Valid-rmse:0.12888
476s [66]	Train-error:0.00000	Train-rmse:0.01980	Valid-error:0.02797	Valid-rmse:0.12928
476s [67]	Train-error:0.00000	Train-rmse:0.01977	Valid-error:0.02797	Valid-rmse:0.12962
476s [68]	Train-error:0.00000	Train-rmse:0.01969	Valid-error:0.02797	Valid-rmse:0.12991
476s [69]	Train-error:0.00000	Train-rmse:0.01940	Valid-error:0.02797	Valid-rmse:0.12920
476s [70]	Train-error:0.00000	Train-rmse:0.01941	Valid-error:0.02797	Valid-rmse:0.12825
476s [71]	Train-error:0.00000	Train-rmse:0.01936	Valid-error:0.02797	Valid-rmse:0.12785
476s [72]	Train-error:0.00000	Train-rmse:0.01950	Valid-error:0.02797	Valid-rmse:0.12858
476s [73]	Train-error:0.00000	Train-rmse:0.01921	Valid-error:0.02797	Valid-rmse:0.12791
476s [74]	Train-error:0.00000	Train-rmse:0.01914	Valid-error:0.02797	Valid-rmse:0.12832
476s [75]	Train-error:0.00000	Train-rmse:0.01912	Valid-error:0.02797	Valid-rmse:0.12821
477s [76]	Train-error:0.00000	Train-rmse:0.01906	Valid-error:0.02797	Valid-rmse:0.12814
477s [77]	Train-error:0.00000	Train-rmse:0.01897	Valid-error:0.02797	Valid-rmse:0.12845
477s [78]	Train-error:0.00000	Train-rmse:0.01875	Valid-error:0.02797	Valid-rmse:0.12734
477s [79]	Train-error:0.00000	Train-rmse:0.01873	Valid-error:0.02797	Valid-rmse:0.12765
477s [80]	Train-error:0.00000	Train-rmse:0.01866	Valid-error:0.02797	Valid-rmse:0.12793
477s [81]	Train-error:0.00000	Train-rmse:0.01869	Valid-error:0.02797	Valid-rmse:0.12708
477s [82]	Train-error:0.00000	Train-rmse:0.01844	Valid-error:0.02797	Valid-rmse:0.12642
477s [83]	Train-error:0.00000	Train-rmse:0.01857	Valid-error:0.02797	Valid-rmse:0.12709
477s [84]	Train-error:0.00000	Train-rmse:0.01853	Valid-error:0.02797	Valid-rmse:0.12674
477s [85]	Train-error:0.00000	Train-rmse:0.01862	Valid-error:0.02797	Valid-rmse:0.12712
477s [86]	Train-error:0.00000	Train-rmse:0.01853	Valid-error:0.02797	Valid-rmse:0.12754
478s [87]	Train-error:0.00000	Train-rmse:0.01829	Valid-error:0.02797	Valid-rmse:0.12693
478s [88]	Train-error:0.00000	Train-rmse:0.01821	Valid-error:0.02797	Valid-rmse:0.12788
478s [89]	Train-error:0.00000	Train-rmse:0.01816	Valid-error:0.02797	Valid-rmse:0.12782
478s [90]	Train-error:0.00000	Train-rmse:0.01816	Valid-error:0.02797	Valid-rmse:0.12768
478s [91]	Train-error:0.00000	Train-rmse:0.01809	Valid-error:0.02797	Valid-rmse:0.12797
478s [92]	Train-error:0.00000	Train-rmse:0.01807	Valid-error:0.02797	Valid-rmse:0.12768
478s [93]	Train-error:0.00000	Train-rmse:0.01798	Valid-error:0.02797	Valid-rmse:0.12858
478s [94]	Train-error:0.00000	Train-rmse:0.01776	Valid-error:0.02797	Valid-rmse:0.12801
478s [95]	Train-error:0.00000	Train-rmse:0.01786	Valid-error:0.02797	Valid-rmse:0.12804
478s [96]	Train-error:0.00000	Train-rmse:0.01779	Valid-error:0.02797	Valid-rmse:0.12844
478s [97]	Train-error:0.00000	Train-rmse:0.01778	Valid-error:0.02797	Valid-rmse:0.12875
478s [98]	Train-error:0.00000	Train-rmse:0.01759	Valid-error:0.02797	Valid-rmse:0.12806
479s [99]	Train-error:0.00000	Train-rmse:0.01771	Valid-error:0.02797	Valid-rmse:0.12865
479s ==========================================
479s Skip case cat_in_the_dat.py...
479s ==========================================
479s Running case cat_pipeline.py...
482s ==========================================
482s Running case categorical.py...
484s /usr/lib/python3/dist-packages/xgboost/core.py:158: UserWarning: [18:51:20] WARNING: ./src/context.cc:196: XGBoost is not compiled with CUDA support.
484s   warnings.warn(smsg, UserWarning)
484s [0]	validation_0-rmse:2.94171
484s [1]	validation_0-rmse:2.40095
484s [2]	validation_0-rmse:1.96121
484s [3]	validation_0-rmse:1.61285
484s [4]	validation_0-rmse:1.35854
484s [5]	validation_0-rmse:1.12286
484s [6]	validation_0-rmse:0.96789
484s [7]	validation_0-rmse:0.79707
484s [8]	validation_0-rmse:0.66201
484s [9]	validation_0-rmse:0.55282
484s [10]	validation_0-rmse:0.46038
484s [11]	validation_0-rmse:0.38278
484s [12]	validation_0-rmse:0.32840
484s [13]	validation_0-rmse:0.27462
484s [14]	validation_0-rmse:0.23398
484s [15]	validation_0-rmse:0.19823
484s [16]	validation_0-rmse:0.16984
484s [17]	validation_0-rmse:0.14636
484s [18]	validation_0-rmse:0.12447
484s [19]	validation_0-rmse:0.10444
484s [20]	validation_0-rmse:0.08984
484s [21]	validation_0-rmse:0.07610
484s [22]	validation_0-rmse:0.06622
484s [23]	validation_0-rmse:0.05653
484s [24]	validation_0-rmse:0.04906
484s [25]	validation_0-rmse:0.04173
484s [26]	validation_0-rmse:0.03550
484s [27]	validation_0-rmse:0.03046
484s [28]	validation_0-rmse:0.02585
484s [29]	validation_0-rmse:0.02187
484s [30]	validation_0-rmse:0.01863
484s [31]	validation_0-rmse:0.01597
484s [32]	validation_0-rmse:0.01361
484s [33]	validation_0-rmse:0.01166
484s [34]	validation_0-rmse:0.01041
484s [35]	validation_0-rmse:0.00909
484s [36]	validation_0-rmse:0.00780
484s [37]	validation_0-rmse:0.00666
484s [38]	validation_0-rmse:0.00569
484s [39]	validation_0-rmse:0.00493
484s [40]	validation_0-rmse:0.00426
484s [41]	validation_0-rmse:0.00365
484s [42]	validation_0-rmse:0.00314
484s [43]	validation_0-rmse:0.00268
484s [44]	validation_0-rmse:0.00233
484s [45]	validation_0-rmse:0.00200
484s [46]	validation_0-rmse:0.00170
484s [47]	validation_0-rmse:0.00148
484s [48]	validation_0-rmse:0.00130
484s [49]	validation_0-rmse:0.00116
484s [50]	validation_0-rmse:0.00100
484s [51]	validation_0-rmse:0.00090
484s [52]	validation_0-rmse:0.00082
484s [53]	validation_0-rmse:0.00075
484s [54]	validation_0-rmse:0.00075
484s [55]	validation_0-rmse:0.00075
484s [56]	validation_0-rmse:0.00075
484s [57]	validation_0-rmse:0.00075
484s [58]	validation_0-rmse:0.00075
484s [59]	validation_0-rmse:0.00075
484s [60]	validation_0-rmse:0.00075
484s [61]	validation_0-rmse:0.00075
484s [62]	validation_0-rmse:0.00075
484s [63]	validation_0-rmse:0.00075
484s [64]	validation_0-rmse:0.00075
484s [65]	validation_0-rmse:0.00075
484s [66]	validation_0-rmse:0.00075
484s [67]	validation_0-rmse:0.00075
484s [68]	validation_0-rmse:0.00075
484s [69]	validation_0-rmse:0.00075
484s /usr/lib/python3/dist-packages/xgboost/core.py:158: UserWarning: [18:51:20] WARNING: ./src/context.cc:196: XGBoost is not compiled with CUDA support.
484s   warnings.warn(smsg, UserWarning)
484s [70]	validation_0-rmse:0.00075
484s [71]	validation_0-rmse:0.00075
484s [72]	validation_0-rmse:0.00075
484s [73]	validation_0-rmse:0.00075
484s [74]	validation_0-rmse:0.00075
484s [75]	validation_0-rmse:0.00075
484s [76]	validation_0-rmse:0.00075
484s [77]	validation_0-rmse:0.00075
484s [78]	validation_0-rmse:0.00075
484s [79]	validation_0-rmse:0.00075
484s [80]	validation_0-rmse:0.00075
484s [81]	validation_0-rmse:0.00075
484s [82]	validation_0-rmse:0.00075
484s [83]	validation_0-rmse:0.00075
484s [84]	validation_0-rmse:0.00075
484s [85]	validation_0-rmse:0.00075
484s [86]	validation_0-rmse:0.00075
484s [87]	validation_0-rmse:0.00075
484s [88]	validation_0-rmse:0.00075
484s [89]	validation_0-rmse:0.00075
484s [90]	validation_0-rmse:0.00075
484s [91]	validation_0-rmse:0.00075
484s [92]	validation_0-rmse:0.00075
484s [93]	validation_0-rmse:0.00075
484s [94]	validation_0-rmse:0.00075
484s [95]	validation_0-rmse:0.00075
484s [96]	validation_0-rmse:0.00075
484s [97]	validation_0-rmse:0.00075
484s [98]	validation_0-rmse:0.00075
484s [99]	validation_0-rmse:0.00075
484s [0]	validation_0-rmse:2.94171
484s [1]	validation_0-rmse:2.40095
484s [2]	validation_0-rmse:1.96121
484s [3]	validation_0-rmse:1.61285
484s [4]	validation_0-rmse:1.35854
484s [5]	validation_0-rmse:1.12286
484s [6]	validation_0-rmse:0.96789
484s [7]	validation_0-rmse:0.79707
484s [8]	validation_0-rmse:0.66201
484s [9]	validation_0-rmse:0.55282
484s [10]	validation_0-rmse:0.46038
484s [11]	validation_0-rmse:0.38278
484s [12]	validation_0-rmse:0.32840
484s [13]	validation_0-rmse:0.27462
484s [14]	validation_0-rmse:0.23398
484s [15]	validation_0-rmse:0.19823
484s [16]	validation_0-rmse:0.16984
484s [17]	validation_0-rmse:0.14636
484s [18]	validation_0-rmse:0.12447
484s [19]	validation_0-rmse:0.10444
484s [20]	validation_0-rmse:0.08984
484s [21]	validation_0-rmse:0.07610
484s [22]	validation_0-rmse:0.06622
484s [23]	validation_0-rmse:0.05653
484s [24]	validation_0-rmse:0.04906
484s [25]	validation_0-rmse:0.04173
484s [26]	validation_0-rmse:0.03550
484s [27]	validation_0-rmse:0.03046
484s [28]	validation_0-rmse:0.02585
484s [29]	validation_0-rmse:0.02187
484s [30]	validation_0-rmse:0.01863
484s [31]	validation_0-rmse:0.01597
484s [32]	validation_0-rmse:0.01361
484s [33]	validation_0-rmse:0.01166
484s [34]	validation_0-rmse:0.01041
484s [35]	validation_0-rmse:0.00909
484s [36]	validation_0-rmse:0.00780
484s [37]	validation_0-rmse:0.00666
484s [38]	validation_0-rmse:0.00569
484s [39]	validation_0-rmse:0.00493
484s [40]	validation_0-rmse:0.00426
484s [41]	validation_0-rmse:0.00365
484s [42]	validation_0-rmse:0.00314
484s [43]	validation_0-rmse:0.00268
484s [44]	validation_0-rmse:0.00233
484s [45]	validation_0-rmse:0.00200
484s [46]	validation_0-rmse:0.00170
484s [47]	validation_0-rmse:0.00148
484s [48]	validation_0-rmse:0.00130
484s [49]	validation_0-rmse:0.00116
484s [50]	validation_0-rmse:0.00100
484s [51]	validation_0-rmse:0.00090
484s [52]	validation_0-rmse:0.00082
484s [53]	validation_0-rmse:0.00075
484s [54]	validation_0-rmse:0.00075
484s [55]	validation_0-rmse:0.00075
484s [56]	validation_0-rmse:0.00075
484s [57]	validation_0-rmse:0.00075
484s [58]	validation_0-rmse:0.00075
484s [59]	validation_0-rmse:0.00075
484s [60]	validation_0-rmse:0.00075
484s [61]	validation_0-rmse:0.00075
484s [62]	validation_0-rmse:0.00075
484s [63]	validation_0-rmse:0.00075
484s [64]	validation_0-rmse:0.00075
484s [65]	validation_0-rmse:0.00075
484s [66]	validation_0-rmse:0.00075
484s [67]	validation_0-rmse:0.00075
484s [68]	validation_0-rmse:0.00075
484s [69]	validation_0-rmse:0.00075
484s [70]	validation_0-rmse:0.00075
484s [71]	validation_0-rmse:0.00075
484s [72]	validation_0-rmse:0.00075
484s [73]	validation_0-rmse:0.00075
484s [74]	validation_0-rmse:0.00075
484s [75]	validation_0-rmse:0.00075
484s [76]	validation_0-rmse:0.00075
484s [77]	validation_0-rmse:0.00075
484s [78]	validation_0-rmse:0.00075
484s [79]	validation_0-rmse:0.00075
484s [80]	validation_0-rmse:0.00075
484s [81]	validation_0-rmse:0.00075
484s [82]	validation_0-rmse:0.00075
484s [83]	validation_0-rmse:0.00075
484s [84]	validation_0-rmse:0.00075
484s [85]	validation_0-rmse:0.00075
484s [86]	validation_0-rmse:0.00075
484s [87]	validation_0-rmse:0.00075
484s [88]	validation_0-rmse:0.00075
484s [89]	validation_0-rmse:0.00075
484s [90]	validation_0-rmse:0.00075
484s [91]	validation_0-rmse:0.00075
484s [92]	validation_0-rmse:0.00075
484s [93]	validation_0-rmse:0.00075
484s [94]	validation_0-rmse:0.00075
484s [95]	validation_0-rmse:0.00075
484s [96]	validation_0-rmse:0.00075
484s [97]	validation_0-rmse:0.00075
484s [98]	validation_0-rmse:0.00075
484s [99]	validation_0-rmse:0.00075
485s ==========================================
485s Running case continuation.py...
490s [0]	validation_0-logloss:0.43281
490s [1]	validation_0-logloss:0.30816
490s [2]	validation_0-logloss:0.22816
490s [3]	validation_0-logloss:0.17470
490s [4]	validation_0-logloss:0.13438
490s [5]	validation_0-logloss:0.10538
490s [6]	validation_0-logloss:0.08467
490s [7]	validation_0-logloss:0.06909
490s [8]	validation_0-logloss:0.05766
490s [9]	validation_0-logloss:0.04842
490s [10]	validation_0-logloss:0.04121
490s [11]	validation_0-logloss:0.03545
490s [12]	validation_0-logloss:0.03077
490s [13]	validation_0-logloss:0.02742
490s [14]	validation_0-logloss:0.02444
490s [15]	validation_0-logloss:0.02208
491s [16]	validation_0-logloss:0.01993
491s [17]	validation_0-logloss:0.01819
491s [18]	validation_0-logloss:0.01676
491s [19]	validation_0-logloss:0.01575
491s [20]	validation_0-logloss:0.01461
491s [21]	validation_0-logloss:0.01383
491s [22]	validation_0-logloss:0.01304
491s [23]	validation_0-logloss:0.01243
491s [24]	validation_0-logloss:0.01179
491s [25]	validation_0-logloss:0.01142
491s [26]	validation_0-logloss:0.01103
491s [27]	validation_0-logloss:0.01066
491s [28]	validation_0-logloss:0.01037
491s [29]	validation_0-logloss:0.00993
491s [30]	validation_0-logloss:0.00966
491s [31]	validation_0-logloss:0.00920
491s [32]	validation_0-logloss:0.00900
491s [33]	validation_0-logloss:0.00876
491s [34]	validation_0-logloss:0.00852
491s [35]	validation_0-logloss:0.00835
491s [36]	validation_0-logloss:0.00810
491s [37]	validation_0-logloss:0.00791
491s [38]	validation_0-logloss:0.00764
491s [39]	validation_0-logloss:0.00753
491s [40]	validation_0-logloss:0.00739
491s [41]	validation_0-logloss:0.00723
491s [42]	validation_0-logloss:0.00706
491s [43]	validation_0-logloss:0.00695
491s [44]	validation_0-logloss:0.00688
491s [45]	validation_0-logloss:0.00671
491s [46]	validation_0-logloss:0.00662
491s [47]	validation_0-logloss:0.00650
491s [48]	validation_0-logloss:0.00637
491s [49]	validation_0-logloss:0.00628
491s [50]	validation_0-logloss:0.00623
491s [51]	validation_0-logloss:0.00618
491s [52]	validation_0-logloss:0.00614
491s [53]	validation_0-logloss:0.00609
491s [54]	validation_0-logloss:0.00600
491s [55]	validation_0-logloss:0.00590
491s [56]	validation_0-logloss:0.00582
491s [57]	validation_0-logloss:0.00578
491s [58]	validation_0-logloss:0.00570
491s [59]	validation_0-logloss:0.00566
491s [60]	validation_0-logloss:0.00563
491s [61]	validation_0-logloss:0.00559
491s [62]	validation_0-logloss:0.00556
491s [63]	validation_0-logloss:0.00553
491s [64]	validation_0-logloss:0.00549
491s [65]	validation_0-logloss:0.00546
491s [66]	validation_0-logloss:0.00543
491s [67]	validation_0-logloss:0.00540
491s [68]	validation_0-logloss:0.00537
491s [69]	validation_0-logloss:0.00534
491s [70]	validation_0-logloss:0.00531
491s [71]	validation_0-logloss:0.00528
491s [72]	validation_0-logloss:0.00525
491s [73]	validation_0-logloss:0.00523
491s [74]	validation_0-logloss:0.00520
491s [75]	validation_0-logloss:0.00517
491s [76]	validation_0-logloss:0.00515
491s [77]	validation_0-logloss:0.00512
491s [78]	validation_0-logloss:0.00510
491s [79]	validation_0-logloss:0.00507
491s [80]	validation_0-logloss:0.00505
491s [81]	validation_0-logloss:0.00502
491s [82]	validation_0-logloss:0.00500
491s [83]	validation_0-logloss:0.00498
491s [84]	validation_0-logloss:0.00496
491s [85]	validation_0-logloss:0.00493
491s [86]	validation_0-logloss:0.00491
491s [87]	validation_0-logloss:0.00489
491s [88]	validation_0-logloss:0.00486
491s [89]	validation_0-logloss:0.00484
491s [90]	validation_0-logloss:0.00482
491s [91]	validation_0-logloss:0.00480
491s [92]	validation_0-logloss:0.00478
491s [93]	validation_0-logloss:0.00476
491s [94]	validation_0-logloss:0.00474
491s [95]	validation_0-logloss:0.00472
491s [96]	validation_0-logloss:0.00470
491s [97]	validation_0-logloss:0.00468
491s [98]	validation_0-logloss:0.00466
491s [99]	validation_0-logloss:0.00464
491s [100]	validation_0-logloss:0.00463
491s [101]	validation_0-logloss:0.00461
491s [102]	validation_0-logloss:0.00459
491s [103]	validation_0-logloss:0.00457
491s [104]	validation_0-logloss:0.00456
491s [105]	validation_0-logloss:0.00454
491s [106]	validation_0-logloss:0.00452
491s [107]	validation_0-logloss:0.00451
491s [108]	validation_0-logloss:0.00449
491s [109]	validation_0-logloss:0.00448
491s [110]	validation_0-logloss:0.00446
491s [111]	validation_0-logloss:0.00444
491s [112]	validation_0-logloss:0.00443
491s [113]	validation_0-logloss:0.00441
491s [114]	validation_0-logloss:0.00440
491s [115]	validation_0-logloss:0.00438
491s [116]	validation_0-logloss:0.00437
491s [117]	validation_0-logloss:0.00435
491s [118]	validation_0-logloss:0.00434
491s [119]	validation_0-logloss:0.00433
491s [120]	validation_0-logloss:0.00431
491s [121]	validation_0-logloss:0.00430
491s [122]	validation_0-logloss:0.00429
491s [123]	validation_0-logloss:0.00427
491s [124]	validation_0-logloss:0.00426
491s [125]	validation_0-logloss:0.00425
491s [126]	validation_0-logloss:0.00423
491s [127]	validation_0-logloss:0.00422
491s [128]	validation_0-logloss:0.00421
491s [129]	validation_0-logloss:0.00420
491s [130]	validation_0-logloss:0.00419
491s [131]	validation_0-logloss:0.00417
491s [132]	validation_0-logloss:0.00416
491s [133]	validation_0-logloss:0.00415
491s [134]	validation_0-logloss:0.00414
491s [135]	validation_0-logloss:0.00413
491s [136]	validation_0-logloss:0.00412
491s [137]	validation_0-logloss:0.00411
491s [138]	validation_0-logloss:0.00410
491s [139]	validation_0-logloss:0.00409
491s [140]	validation_0-logloss:0.00408
491s [141]	validation_0-logloss:0.00407
491s [142]	validation_0-logloss:0.00406
491s [143]	validation_0-logloss:0.00405
491s [144]	validation_0-logloss:0.00404
491s [145]	validation_0-logloss:0.00403
491s [146]	validation_0-logloss:0.00402
491s [147]	validation_0-logloss:0.00401
491s [148]	validation_0-logloss:0.00400
491s [149]	validation_0-logloss:0.00399
491s [150]	validation_0-logloss:0.00398
491s [151]	validation_0-logloss:0.00397
491s [152]	validation_0-logloss:0.00396
491s [153]	validation_0-logloss:0.00396
491s [154]	validation_0-logloss:0.00395
491s [155]	validation_0-logloss:0.00394
491s [156]	validation_0-logloss:0.00393
491s [157]	validation_0-logloss:0.00392
491s [158]	validation_0-logloss:0.00391
491s [159]	validation_0-logloss:0.00391
491s [160]	validation_0-logloss:0.00390
491s [161]	validation_0-logloss:0.00389
491s [162]	validation_0-logloss:0.00388
491s [163]	validation_0-logloss:0.00388
491s [164]	validation_0-logloss:0.00387
491s [165]	validation_0-logloss:0.00386
491s [166]	validation_0-logloss:0.00385
491s [167]	validation_0-logloss:0.00385
491s [168]	validation_0-logloss:0.00384
491s [169]	validation_0-logloss:0.00383
491s [170]	validation_0-logloss:0.00383
491s [171]	validation_0-logloss:0.00382
491s [172]	validation_0-logloss:0.00381
491s [173]	validation_0-logloss:0.00381
491s [174]	validation_0-logloss:0.00380
491s [175]	validation_0-logloss:0.00380
491s [176]	validation_0-logloss:0.00379
491s [177]	validation_0-logloss:0.00379
491s [178]	validation_0-logloss:0.00378
491s [179]	validation_0-logloss:0.00378
491s [180]	validation_0-logloss:0.00377
491s [181]	validation_0-logloss:0.00377
491s [182]	validation_0-logloss:0.00376
491s [183]	validation_0-logloss:0.00376
491s [184]	validation_0-logloss:0.00375
491s [185]	validation_0-logloss:0.00375
491s [186]	validation_0-logloss:0.00374
491s [187]	validation_0-logloss:0.00374
491s [188]	validation_0-logloss:0.00373
491s [189]	validation_0-logloss:0.00373
491s [190]	validation_0-logloss:0.00373
491s [191]	validation_0-logloss:0.00372
491s [192]	validation_0-logloss:0.00372
491s [193]	validation_0-logloss:0.00372
491s [194]	validation_0-logloss:0.00371
491s [195]	validation_0-logloss:0.00371
491s [196]	validation_0-logloss:0.00371
491s [197]	validation_0-logloss:0.00371
491s [198]	validation_0-logloss:0.00371
491s Total boosted rounds: 195
491s [0]	validation_0-logloss:0.43281
491s [1]	validation_0-logloss:0.30816
491s [2]	validation_0-logloss:0.22816
491s [3]	validation_0-logloss:0.17470
491s [4]	validation_0-logloss:0.13438
491s [5]	validation_0-logloss:0.10538
491s [6]	validation_0-logloss:0.08467
491s [7]	validation_0-logloss:0.06909
491s [8]	validation_0-logloss:0.05766
491s [9]	validation_0-logloss:0.04842
491s [10]	validation_0-logloss:0.04121
491s [11]	validation_0-logloss:0.03545
491s [12]	validation_0-logloss:0.03077
491s [13]	validation_0-logloss:0.02742
491s [14]	validation_0-logloss:0.02444
491s [15]	validation_0-logloss:0.02208
491s [16]	validation_0-logloss:0.01993
491s [17]	validation_0-logloss:0.01819
491s [18]	validation_0-logloss:0.01676
491s [19]	validation_0-logloss:0.01575
491s [20]	validation_0-logloss:0.01461
491s [21]	validation_0-logloss:0.01383
491s [22]	validation_0-logloss:0.01304
491s [23]	validation_0-logloss:0.01243
491s [24]	validation_0-logloss:0.01179
491s [25]	validation_0-logloss:0.01142
491s [26]	validation_0-logloss:0.01103
491s [27]	validation_0-logloss:0.01066
491s [28]	validation_0-logloss:0.01037
491s [29]	validation_0-logloss:0.00993
491s [30]	validation_0-logloss:0.00966
491s [31]	validation_0-logloss:0.00920
491s [32]	validation_0-logloss:0.00900
491s [33]	validation_0-logloss:0.00876
491s [34]	validation_0-logloss:0.00852
491s [35]	validation_0-logloss:0.00835
491s [36]	validation_0-logloss:0.00810
491s [37]	validation_0-logloss:0.00791
491s [38]	validation_0-logloss:0.00764
491s [39]	validation_0-logloss:0.00753
491s [40]	validation_0-logloss:0.00739
491s [41]	validation_0-logloss:0.00723
491s [42]	validation_0-logloss:0.00706
491s [43]	validation_0-logloss:0.00695
491s [44]	validation_0-logloss:0.00688
491s [45]	validation_0-logloss:0.00671
491s [46]	validation_0-logloss:0.00662
491s [47]	validation_0-logloss:0.00650
491s [48]	validation_0-logloss:0.00637
491s [49]	validation_0-logloss:0.00628
491s [50]	validation_0-logloss:0.00623
491s [51]	validation_0-logloss:0.00618
491s [52]	validation_0-logloss:0.00614
491s [53]	validation_0-logloss:0.00609
491s [54]	validation_0-logloss:0.00600
491s [55]	validation_0-logloss:0.00590
491s [56]	validation_0-logloss:0.00582
491s [57]	validation_0-logloss:0.00578
491s [58]	validation_0-logloss:0.00570
491s [59]	validation_0-logloss:0.00566
491s [60]	validation_0-logloss:0.00563
491s [61]	validation_0-logloss:0.00559
491s [62]	validation_0-logloss:0.00556
491s [63]	validation_0-logloss:0.00553
491s [64]	validation_0-logloss:0.00549
491s [65]	validation_0-logloss:0.00546
491s [66]	validation_0-logloss:0.00543
491s [67]	validation_0-logloss:0.00540
491s [68]	validation_0-logloss:0.00537
491s [69]	validation_0-logloss:0.00534
491s [70]	validation_0-logloss:0.00531
491s [71]	validation_0-logloss:0.00528
491s [72]	validation_0-logloss:0.00525
491s [73]	validation_0-logloss:0.00523
491s [74]	validation_0-logloss:0.00520
491s [75]	validation_0-logloss:0.00517
491s [76]	validation_0-logloss:0.00515
491s [77]	validation_0-logloss:0.00512
491s [78]	validation_0-logloss:0.00510
491s [79]	validation_0-logloss:0.00507
491s [80]	validation_0-logloss:0.00505
491s [81]	validation_0-logloss:0.00502
491s [82]	validation_0-logloss:0.00500
491s [83]	validation_0-logloss:0.00498
491s [84]	validation_0-logloss:0.00496
491s [85]	validation_0-logloss:0.00493
491s [86]	validation_0-logloss:0.00491
491s [87]	validation_0-logloss:0.00489
491s [88]	validation_0-logloss:0.00486
491s [89]	validation_0-logloss:0.00484
491s [90]	validation_0-logloss:0.00482
491s [91]	validation_0-logloss:0.00480
491s [92]	validation_0-logloss:0.00478
491s [93]	validation_0-logloss:0.00476
491s [94]	validation_0-logloss:0.00474
491s [95]	validation_0-logloss:0.00472
491s [96]	validation_0-logloss:0.00470
491s [97]	validation_0-logloss:0.00468
491s [98]	validation_0-logloss:0.00466
491s [99]	validation_0-logloss:0.00464
491s [100]	validation_0-logloss:0.00463
491s [101]	validation_0-logloss:0.00461
491s [102]	validation_0-logloss:0.00459
491s [103]	validation_0-logloss:0.00457
491s [104]	validation_0-logloss:0.00456
491s [105]	validation_0-logloss:0.00454
491s [106]	validation_0-logloss:0.00452
491s [107]	validation_0-logloss:0.00451
491s [108]	validation_0-logloss:0.00449
491s [109]	validation_0-logloss:0.00448
491s [110]	validation_0-logloss:0.00446
491s [111]	validation_0-logloss:0.00444
491s [112]	validation_0-logloss:0.00443
491s [113]	validation_0-logloss:0.00441
491s [114]	validation_0-logloss:0.00440
491s [115]	validation_0-logloss:0.00438
491s [116]	validation_0-logloss:0.00437
491s [117]	validation_0-logloss:0.00435
491s [118]	validation_0-logloss:0.00434
491s [119]	validation_0-logloss:0.00433
491s [120]	validation_0-logloss:0.00431
491s [121]	validation_0-logloss:0.00430
491s [122]	validation_0-logloss:0.00429
491s [123]	validation_0-logloss:0.00427
491s [124]	validation_0-logloss:0.00426
491s [125]	validation_0-logloss:0.00425
491s [126]	validation_0-logloss:0.00423
491s [127]	validation_0-logloss:0.00422
492s [0]	validation_0-logloss:0.00421
492s [1]	validation_0-logloss:0.00420
492s [2]	validation_0-logloss:0.00419
492s [3]	validation_0-logloss:0.00417
492s [4]	validation_0-logloss:0.00416
492s [5]	validation_0-logloss:0.00415
492s [6]	validation_0-logloss:0.00414
492s [7]	validation_0-logloss:0.00413
492s [8]	validation_0-logloss:0.00412
492s [9]	validation_0-logloss:0.00411
492s [10]	validation_0-logloss:0.00410
492s [11]	validation_0-logloss:0.00409
492s [12]	validation_0-logloss:0.00408
492s [13]	validation_0-logloss:0.00407
492s [14]	validation_0-logloss:0.00406
492s [15]	validation_0-logloss:0.00405
492s [16]	validation_0-logloss:0.00404
492s [17]	validation_0-logloss:0.00403
492s [18]	validation_0-logloss:0.00402
492s [19]	validation_0-logloss:0.00401
492s [20]	validation_0-logloss:0.00400
492s [21]	validation_0-logloss:0.00399
492s [22]	validation_0-logloss:0.00398
492s [23]	validation_0-logloss:0.00397
492s [24]	validation_0-logloss:0.00396
492s [25]	validation_0-logloss:0.00396
492s [26]	validation_0-logloss:0.00395
492s [27]	validation_0-logloss:0.00394
492s [28]	validation_0-logloss:0.00393
492s [29]	validation_0-logloss:0.00392
492s [30]	validation_0-logloss:0.00391
492s [31]	validation_0-logloss:0.00391
492s [32]	validation_0-logloss:0.00390
492s [33]	validation_0-logloss:0.00389
492s [34]	validation_0-logloss:0.00388
492s [35]	validation_0-logloss:0.00388
492s [36]	validation_0-logloss:0.00387
492s [37]	validation_0-logloss:0.00386
492s [38]	validation_0-logloss:0.00385
492s [39]	validation_0-logloss:0.00385
492s [40]	validation_0-logloss:0.00384
492s [41]	validation_0-logloss:0.00383
492s [42]	validation_0-logloss:0.00383
492s [43]	validation_0-logloss:0.00382
492s [44]	validation_0-logloss:0.00381
492s [45]	validation_0-logloss:0.00381
492s [46]	validation_0-logloss:0.00380
492s [47]	validation_0-logloss:0.00380
492s [48]	validation_0-logloss:0.00379
492s [49]	validation_0-logloss:0.00379
492s [50]	validation_0-logloss:0.00378
492s [51]	validation_0-logloss:0.00378
492s [52]	validation_0-logloss:0.00377
492s [53]	validation_0-logloss:0.00377
492s [54]	validation_0-logloss:0.00376
492s [55]	validation_0-logloss:0.00376
492s [56]	validation_0-logloss:0.00375
492s [57]	validation_0-logloss:0.00375
492s [58]	validation_0-logloss:0.00374
492s [59]	validation_0-logloss:0.00374
492s [60]	validation_0-logloss:0.00373
492s [61]	validation_0-logloss:0.00373
492s [62]	validation_0-logloss:0.00373
492s [63]	validation_0-logloss:0.00372
492s [64]	validation_0-logloss:0.00372
492s [65]	validation_0-logloss:0.00372
492s [66]	validation_0-logloss:0.00371
492s [67]	validation_0-logloss:0.00371
492s [68]	validation_0-logloss:0.00371
492s [69]	validation_0-logloss:0.00371
492s [70]	validation_0-logloss:0.00371
492s [71]	validation_0-logloss:0.00371
492s Total boosted rounds: 195
492s [0]	validation_0-logloss:0.43281
492s [1]	validation_0-logloss:0.30816
493s [2]	validation_0-logloss:0.22816
493s [3]	validation_0-logloss:0.17470
493s [4]	validation_0-logloss:0.13438
493s [5]	validation_0-logloss:0.10538
493s [6]	validation_0-logloss:0.08467
493s [7]	validation_0-logloss:0.06909
493s [8]	validation_0-logloss:0.05766
493s [9]	validation_0-logloss:0.04842
493s [10]	validation_0-logloss:0.04121
493s [11]	validation_0-logloss:0.03545
493s [12]	validation_0-logloss:0.03077
493s [13]	validation_0-logloss:0.02742
493s [14]	validation_0-logloss:0.02444
493s [15]	validation_0-logloss:0.02208
493s [16]	validation_0-logloss:0.01993
493s [17]	validation_0-logloss:0.01819
493s [18]	validation_0-logloss:0.01676
493s [19]	validation_0-logloss:0.01575
493s [20]	validation_0-logloss:0.01461
493s [21]	validation_0-logloss:0.01383
493s [22]	validation_0-logloss:0.01304
493s [23]	validation_0-logloss:0.01243
493s [24]	validation_0-logloss:0.01179
493s [25]	validation_0-logloss:0.01142
493s [26]	validation_0-logloss:0.01103
493s [27]	validation_0-logloss:0.01066
493s [28]	validation_0-logloss:0.01037
493s [29]	validation_0-logloss:0.00993
493s [30]	validation_0-logloss:0.00966
493s [31]	validation_0-logloss:0.00920
493s [32]	validation_0-logloss:0.00900
493s [33]	validation_0-logloss:0.00876
493s [34]	validation_0-logloss:0.00852
493s [35]	validation_0-logloss:0.00835
493s [36]	validation_0-logloss:0.00810
493s [37]	validation_0-logloss:0.00791
493s [38]	validation_0-logloss:0.00764
493s [39]	validation_0-logloss:0.00753
493s [40]	validation_0-logloss:0.00739
493s [41]	validation_0-logloss:0.00723
493s [42]	validation_0-logloss:0.00706
493s [43]	validation_0-logloss:0.00695
493s [44]	validation_0-logloss:0.00688
493s [45]	validation_0-logloss:0.00671
493s [46]	validation_0-logloss:0.00662
493s [47]	validation_0-logloss:0.00650
493s [48]	validation_0-logloss:0.00637
493s [49]	validation_0-logloss:0.00628
493s [50]	validation_0-logloss:0.00623
493s [51]	validation_0-logloss:0.00618
493s [52]	validation_0-logloss:0.00614
493s [53]	validation_0-logloss:0.00609
493s [54]	validation_0-logloss:0.00600
493s [55]	validation_0-logloss:0.00590
493s [56]	validation_0-logloss:0.00582
493s [57]	validation_0-logloss:0.00578
493s [58]	validation_0-logloss:0.00570
493s [59]	validation_0-logloss:0.00566
493s [60]	validation_0-logloss:0.00563
493s [61]	validation_0-logloss:0.00559
493s [62]	validation_0-logloss:0.00556
493s [63]	validation_0-logloss:0.00553
493s [64]	validation_0-logloss:0.00549
493s [65]	validation_0-logloss:0.00546
493s [66]	validation_0-logloss:0.00543
493s [67]	validation_0-logloss:0.00540
493s [68]	validation_0-logloss:0.00537
493s [69]	validation_0-logloss:0.00534
493s [70]	validation_0-logloss:0.00531
493s [71]	validation_0-logloss:0.00528
493s [72]	validation_0-logloss:0.00525
493s [73]	validation_0-logloss:0.00523
493s [74]	validation_0-logloss:0.00520
493s [75]	validation_0-logloss:0.00517
493s [76]	validation_0-logloss:0.00515
493s [77]	validation_0-logloss:0.00512
493s [78]	validation_0-logloss:0.00510
493s [79]	validation_0-logloss:0.00507
493s [80]	validation_0-logloss:0.00505
493s [81]	validation_0-logloss:0.00502
493s [82]	validation_0-logloss:0.00500
493s [83]	validation_0-logloss:0.00498
493s [84]	validation_0-logloss:0.00496
493s [85]	validation_0-logloss:0.00493
493s [86]	validation_0-logloss:0.00491
493s [87]	validation_0-logloss:0.00489
493s [88]	validation_0-logloss:0.00486
493s [89]	validation_0-logloss:0.00484
493s [90]	validation_0-logloss:0.00482
493s [91]	validation_0-logloss:0.00480
493s [92]	validation_0-logloss:0.00478
493s [93]	validation_0-logloss:0.00476
493s [94]	validation_0-logloss:0.00474
493s [95]	validation_0-logloss:0.00472
493s [96]	validation_0-logloss:0.00470
493s [97]	validation_0-logloss:0.00468
493s [98]	validation_0-logloss:0.00466
493s [99]	validation_0-logloss:0.00464
493s [100]	validation_0-logloss:0.00463
493s [101]	validation_0-logloss:0.00461
493s [102]	validation_0-logloss:0.00459
493s [103]	validation_0-logloss:0.00457
493s [104]	validation_0-logloss:0.00456
493s [105]	validation_0-logloss:0.00454
493s [106]	validation_0-logloss:0.00452
493s [107]	validation_0-logloss:0.00451
493s [108]	validation_0-logloss:0.00449
493s [109]	validation_0-logloss:0.00448
493s [110]	validation_0-logloss:0.00446
493s [111]	validation_0-logloss:0.00444
493s [112]	validation_0-logloss:0.00443
493s [113]	validation_0-logloss:0.00441
493s [114]	validation_0-logloss:0.00440
493s [115]	validation_0-logloss:0.00438
493s [116]	validation_0-logloss:0.00437
493s [117]	validation_0-logloss:0.00435
493s [118]	validation_0-logloss:0.00434
493s [119]	validation_0-logloss:0.00433
493s [120]	validation_0-logloss:0.00431
493s [121]	validation_0-logloss:0.00430
493s [122]	validation_0-logloss:0.00429
493s [123]	validation_0-logloss:0.00427
493s [124]	validation_0-logloss:0.00426
493s [125]	validation_0-logloss:0.00425
493s [126]	validation_0-logloss:0.00423
493s [127]	validation_0-logloss:0.00422
493s [128]	validation_0-logloss:0.00421
493s [129]	validation_0-logloss:0.00420
493s [130]	validation_0-logloss:0.00419
493s [131]	validation_0-logloss:0.00417
493s [132]	validation_0-logloss:0.00416
493s [133]	validation_0-logloss:0.00415
493s [134]	validation_0-logloss:0.00414
493s [135]	validation_0-logloss:0.00413
493s [136]	validation_0-logloss:0.00412
493s [137]	validation_0-logloss:0.00411
493s [138]	validation_0-logloss:0.00410
493s [139]	validation_0-logloss:0.00409
493s [140]	validation_0-logloss:0.00408
493s [141]	validation_0-logloss:0.00407
493s [142]	validation_0-logloss:0.00406
493s [143]	validation_0-logloss:0.00405
493s [144]	validation_0-logloss:0.00404
493s [145]	validation_0-logloss:0.00403
493s [146]	validation_0-logloss:0.00402
493s [147]	validation_0-logloss:0.00401
493s [148]	validation_0-logloss:0.00400
493s [149]	validation_0-logloss:0.00399
493s [150]	validation_0-logloss:0.00398
493s [151]	validation_0-logloss:0.00397
493s [152]	validation_0-logloss:0.00396
493s [153]	validation_0-logloss:0.00396
493s [154]	validation_0-logloss:0.00395
493s [155]	validation_0-logloss:0.00394
493s [156]	validation_0-logloss:0.00393
493s [157]	validation_0-logloss:0.00392
493s [158]	validation_0-logloss:0.00391
493s [159]	validation_0-logloss:0.00391
493s [160]	validation_0-logloss:0.00390
493s [161]	validation_0-logloss:0.00389
493s [162]	validation_0-logloss:0.00388
493s [163]	validation_0-logloss:0.00388
493s [164]	validation_0-logloss:0.00387
493s [165]	validation_0-logloss:0.00386
493s [166]	validation_0-logloss:0.00385
493s [167]	validation_0-logloss:0.00385
493s [168]	validation_0-logloss:0.00384
493s [169]	validation_0-logloss:0.00383
493s [170]	validation_0-logloss:0.00383
493s [171]	validation_0-logloss:0.00382
493s [172]	validation_0-logloss:0.00381
493s [173]	validation_0-logloss:0.00381
493s [174]	validation_0-logloss:0.00380
493s [175]	validation_0-logloss:0.00380
493s [176]	validation_0-logloss:0.00379
493s [177]	validation_0-logloss:0.00379
493s [178]	validation_0-logloss:0.00378
493s [179]	validation_0-logloss:0.00378
493s [180]	validation_0-logloss:0.00377
493s [181]	validation_0-logloss:0.00377
493s [182]	validation_0-logloss:0.00376
493s [183]	validation_0-logloss:0.00376
493s [184]	validation_0-logloss:0.00375
493s [185]	validation_0-logloss:0.00375
493s [186]	validation_0-logloss:0.00374
493s [187]	validation_0-logloss:0.00374
493s [188]	validation_0-logloss:0.00373
493s [189]	validation_0-logloss:0.00373
493s [190]	validation_0-logloss:0.00373
493s [191]	validation_0-logloss:0.00372
493s [192]	validation_0-logloss:0.00372
493s [193]	validation_0-logloss:0.00372
493s [194]	validation_0-logloss:0.00371
493s [195]	validation_0-logloss:0.00371
493s [196]	validation_0-logloss:0.00371
493s [197]	validation_0-logloss:0.00371
493s [198]	validation_0-logloss:0.00371
493s [199]	validation_0-logloss:0.00371
493s Total boosted rounds: 195
493s [0]	validation_0-logloss:0.43281
493s [1]	validation_0-logloss:0.30816
493s [2]	validation_0-logloss:0.22816
493s [3]	validation_0-logloss:0.17470
493s [4]	validation_0-logloss:0.13438
493s [5]	validation_0-logloss:0.10538
493s [6]	validation_0-logloss:0.08467
493s [7]	validation_0-logloss:0.06909
493s [8]	validation_0-logloss:0.05766
493s [9]	validation_0-logloss:0.04842
493s [10]	validation_0-logloss:0.04121
493s [11]	validation_0-logloss:0.03545
493s [12]	validation_0-logloss:0.03077
493s [13]	validation_0-logloss:0.02742
493s [14]	validation_0-logloss:0.02444
493s [15]	validation_0-logloss:0.02208
493s [16]	validation_0-logloss:0.01993
493s [17]	validation_0-logloss:0.01819
493s [18]	validation_0-logloss:0.01676
493s [19]	validation_0-logloss:0.01575
493s [20]	validation_0-logloss:0.01461
493s [21]	validation_0-logloss:0.01383
493s [22]	validation_0-logloss:0.01304
493s [23]	validation_0-logloss:0.01243
493s [24]	validation_0-logloss:0.01179
493s [25]	validation_0-logloss:0.01142
493s [26]	validation_0-logloss:0.01103
493s [27]	validation_0-logloss:0.01066
493s [28]	validation_0-logloss:0.01037
493s [29]	validation_0-logloss:0.00993
493s [30]	validation_0-logloss:0.00966
493s [31]	validation_0-logloss:0.00920
493s [32]	validation_0-logloss:0.00900
493s [33]	validation_0-logloss:0.00876
493s [34]	validation_0-logloss:0.00852
493s [35]	validation_0-logloss:0.00835
493s [36]	validation_0-logloss:0.00810
493s [37]	validation_0-logloss:0.00791
493s [38]	validation_0-logloss:0.00764
493s [39]	validation_0-logloss:0.00753
493s [40]	validation_0-logloss:0.00739
493s [41]	validation_0-logloss:0.00723
493s [42]	validation_0-logloss:0.00706
493s [43]	validation_0-logloss:0.00695
493s [44]	validation_0-logloss:0.00688
493s [45]	validation_0-logloss:0.00671
493s [46]	validation_0-logloss:0.00662
493s [47]	validation_0-logloss:0.00650
493s [48]	validation_0-logloss:0.00637
493s [49]	validation_0-logloss:0.00628
493s [50]	validation_0-logloss:0.00623
493s [51]	validation_0-logloss:0.00618
493s [52]	validation_0-logloss:0.00614
493s [53]	validation_0-logloss:0.00609
493s [54]	validation_0-logloss:0.00600
493s [55]	validation_0-logloss:0.00590
493s [56]	validation_0-logloss:0.00582
493s [57]	validation_0-logloss:0.00578
493s [58]	validation_0-logloss:0.00570
493s [59]	validation_0-logloss:0.00566
493s [60]	validation_0-logloss:0.00563
493s [61]	validation_0-logloss:0.00559
493s [62]	validation_0-logloss:0.00556
493s [63]	validation_0-logloss:0.00553
493s [64]	validation_0-logloss:0.00549
493s [65]	validation_0-logloss:0.00546
493s [66]	validation_0-logloss:0.00543
493s [67]	validation_0-logloss:0.00540
493s [68]	validation_0-logloss:0.00537
493s [69]	validation_0-logloss:0.00534
493s [70]	validation_0-logloss:0.00531
493s [71]	validation_0-logloss:0.00528
493s [72]	validation_0-logloss:0.00525
493s [73]	validation_0-logloss:0.00523
493s [74]	validation_0-logloss:0.00520
493s [75]	validation_0-logloss:0.00517
493s [76]	validation_0-logloss:0.00515
493s [77]	validation_0-logloss:0.00512
493s [78]	validation_0-logloss:0.00510
493s [79]	validation_0-logloss:0.00507
493s [80]	validation_0-logloss:0.00505
493s [81]	validation_0-logloss:0.00502
493s [82]	validation_0-logloss:0.00500
493s [83]	validation_0-logloss:0.00498
493s [84]	validation_0-logloss:0.00496
493s [85]	validation_0-logloss:0.00493
493s [86]	validation_0-logloss:0.00491
493s [87]	validation_0-logloss:0.00489
493s [88]	validation_0-logloss:0.00486
493s [89]	validation_0-logloss:0.00484
493s [90]	validation_0-logloss:0.00482
493s [91]	validation_0-logloss:0.00480
493s [92]	validation_0-logloss:0.00478
493s [93]	validation_0-logloss:0.00476
493s [94]	validation_0-logloss:0.00474
493s [95]	validation_0-logloss:0.00472
493s [96]	validation_0-logloss:0.00470
493s [97]	validation_0-logloss:0.00468
493s [98]	validation_0-logloss:0.00466
493s [99]	validation_0-logloss:0.00464
493s [100]	validation_0-logloss:0.00463
493s [101]	validation_0-logloss:0.00461
493s [102]	validation_0-logloss:0.00459
493s [103]	validation_0-logloss:0.00457
493s [104]	validation_0-logloss:0.00456
493s [105]	validation_0-logloss:0.00454
493s [106]	validation_0-logloss:0.00452
493s [107]	validation_0-logloss:0.00451
493s [108]	validation_0-logloss:0.00449
493s [109]	validation_0-logloss:0.00448
493s [110]	validation_0-logloss:0.00446
493s [111]	validation_0-logloss:0.00444
493s [112]	validation_0-logloss:0.00443
493s [113]	validation_0-logloss:0.00441
493s [114]	validation_0-logloss:0.00440
493s [115]	validation_0-logloss:0.00438
493s [116]	validation_0-logloss:0.00437
493s [117]	validation_0-logloss:0.00435
493s [118]	validation_0-logloss:0.00434
493s [119]	validation_0-logloss:0.00433
493s [120]	validation_0-logloss:0.00431
493s [121]	validation_0-logloss:0.00430
493s [122]	validation_0-logloss:0.00429
493s [123]	validation_0-logloss:0.00427
493s [124]	validation_0-logloss:0.00426
493s [125]	validation_0-logloss:0.00425
493s [126]	validation_0-logloss:0.00423
493s [127]	validation_0-logloss:0.00422
494s [0]	validation_0-logloss:0.00421
494s [1]	validation_0-logloss:0.00420
494s [2]	validation_0-logloss:0.00419
494s [3]	validation_0-logloss:0.00417
494s [4]	validation_0-logloss:0.00416
494s [5]	validation_0-logloss:0.00415
494s [6]	validation_0-logloss:0.00414
494s [7]	validation_0-logloss:0.00413
494s [8]	validation_0-logloss:0.00412
494s [9]	validation_0-logloss:0.00411
494s [10]	validation_0-logloss:0.00410
494s [11]	validation_0-logloss:0.00409
494s [12]	validation_0-logloss:0.00408
494s [13]	validation_0-logloss:0.00407
494s [14]	validation_0-logloss:0.00406
494s [15]	validation_0-logloss:0.00405
494s [16]	validation_0-logloss:0.00404
494s [17]	validation_0-logloss:0.00403
494s [18]	validation_0-logloss:0.00402
494s [19]	validation_0-logloss:0.00401
494s [20]	validation_0-logloss:0.00400
494s [21]	validation_0-logloss:0.00399
494s [22]	validation_0-logloss:0.00398
494s [23]	validation_0-logloss:0.00397
494s [24]	validation_0-logloss:0.00396
494s [25]	validation_0-logloss:0.00396
494s [26]	validation_0-logloss:0.00395
494s [27]	validation_0-logloss:0.00394
494s [28]	validation_0-logloss:0.00393
494s [29]	validation_0-logloss:0.00392
494s [30]	validation_0-logloss:0.00391
494s [31]	validation_0-logloss:0.00391
494s [32]	validation_0-logloss:0.00390
494s [33]	validation_0-logloss:0.00389
494s [34]	validation_0-logloss:0.00388
494s [35]	validation_0-logloss:0.00388
494s [36]	validation_0-logloss:0.00387
494s [37]	validation_0-logloss:0.00386
494s [38]	validation_0-logloss:0.00385
494s [39]	validation_0-logloss:0.00385
494s [40]	validation_0-logloss:0.00384
494s [41]	validation_0-logloss:0.00383
494s [42]	validation_0-logloss:0.00383
494s [43]	validation_0-logloss:0.00382
494s [44]	validation_0-logloss:0.00381
494s [45]	validation_0-logloss:0.00381
494s [46]	validation_0-logloss:0.00380
494s [47]	validation_0-logloss:0.00380
494s [48]	validation_0-logloss:0.00379
494s [49]	validation_0-logloss:0.00379
494s [50]	validation_0-logloss:0.00378
494s [51]	validation_0-logloss:0.00378
494s [52]	validation_0-logloss:0.00377
494s [53]	validation_0-logloss:0.00377
494s [54]	validation_0-logloss:0.00376
494s [55]	validation_0-logloss:0.00376
494s [56]	validation_0-logloss:0.00375
494s [57]	validation_0-logloss:0.00375
494s [58]	validation_0-logloss:0.00374
494s [59]	validation_0-logloss:0.00374
494s [60]	validation_0-logloss:0.00373
494s [61]	validation_0-logloss:0.00373
494s [62]	validation_0-logloss:0.00373
494s [63]	validation_0-logloss:0.00372
494s [64]	validation_0-logloss:0.00372
494s [65]	validation_0-logloss:0.00372
494s [66]	validation_0-logloss:0.00371
494s [67]	validation_0-logloss:0.00371
494s [68]	validation_0-logloss:0.00371
494s [69]	validation_0-logloss:0.00371
494s [70]	validation_0-logloss:0.00371
494s [71]	validation_0-logloss:0.00371
494s Total boosted rounds: 195
494s [0]	validation_0-logloss:0.43281
494s [1]	validation_0-logloss:0.30816
494s [2]	validation_0-logloss:0.22816
494s [3]	validation_0-logloss:0.17470
494s [4]	validation_0-logloss:0.13438
494s [5]	validation_0-logloss:0.10538
494s [6]	validation_0-logloss:0.08467
494s [7]	validation_0-logloss:0.06909
494s [8]	validation_0-logloss:0.05766
494s [9]	validation_0-logloss:0.04842
494s [10]	validation_0-logloss:0.04121
494s [11]	validation_0-logloss:0.03545
494s [12]	validation_0-logloss:0.03077
494s [13]	validation_0-logloss:0.02742
494s [14]	validation_0-logloss:0.02444
494s [15]	validation_0-logloss:0.02208
494s [16]	validation_0-logloss:0.01993
494s [17]	validation_0-logloss:0.01819
494s [18]	validation_0-logloss:0.01676
494s [19]	validation_0-logloss:0.01575
494s [20]	validation_0-logloss:0.01461
494s [21]	validation_0-logloss:0.01383
494s [22]	validation_0-logloss:0.01304
494s [23]	validation_0-logloss:0.01243
494s [24]	validation_0-logloss:0.01179
494s [25]	validation_0-logloss:0.01142
494s [26]	validation_0-logloss:0.01103
494s [27]	validation_0-logloss:0.01066
494s [28]	validation_0-logloss:0.01037
494s [29]	validation_0-logloss:0.00993
494s [30]	validation_0-logloss:0.00966
494s [31]	validation_0-logloss:0.00920
494s [32]	validation_0-logloss:0.00900
494s [33]	validation_0-logloss:0.00876
494s [34]	validation_0-logloss:0.00852
494s [35]	validation_0-logloss:0.00835
494s [36]	validation_0-logloss:0.00810
494s [37]	validation_0-logloss:0.00791
494s [38]	validation_0-logloss:0.00764
494s [39]	validation_0-logloss:0.00753
494s [40]	validation_0-logloss:0.00739
494s [41]	validation_0-logloss:0.00723
494s [42]	validation_0-logloss:0.00706
494s [43]	validation_0-logloss:0.00695
494s [44]	validation_0-logloss:0.00688
494s [45]	validation_0-logloss:0.00671
494s [46]	validation_0-logloss:0.00662
494s [47]	validation_0-logloss:0.00650
494s [48]	validation_0-logloss:0.00637
494s [49]	validation_0-logloss:0.00628
494s [50]	validation_0-logloss:0.00623
494s [51]	validation_0-logloss:0.00618
494s [52]	validation_0-logloss:0.00614
494s [53]	validation_0-logloss:0.00609
494s [54]	validation_0-logloss:0.00600
494s [55]	validation_0-logloss:0.00590
494s [56]	validation_0-logloss:0.00582
494s [57]	validation_0-logloss:0.00578
494s [58]	validation_0-logloss:0.00570
494s [59]	validation_0-logloss:0.00566
494s [60]	validation_0-logloss:0.00563
494s [61]	validation_0-logloss:0.00559
494s [62]	validation_0-logloss:0.00556
494s [63]	validation_0-logloss:0.00553
494s [64]	validation_0-logloss:0.00549
494s [65]	validation_0-logloss:0.00546
494s [66]	validation_0-logloss:0.00543
494s [67]	validation_0-logloss:0.00540
494s [68]	validation_0-logloss:0.00537
494s [69]	validation_0-logloss:0.00534
494s [70]	validation_0-logloss:0.00531
494s [71]	validation_0-logloss:0.00528
494s [72]	validation_0-logloss:0.00525
494s [73]	validation_0-logloss:0.00523
494s [74]	validation_0-logloss:0.00520
494s [75]	validation_0-logloss:0.00517
494s [76]	validation_0-logloss:0.00515
494s [77]	validation_0-logloss:0.00512
494s [78]	validation_0-logloss:0.00510
494s [79]	validation_0-logloss:0.00507
494s [80]	validation_0-logloss:0.00505
494s [81]	validation_0-logloss:0.00502
494s [82]	validation_0-logloss:0.00500
494s [83]	validation_0-logloss:0.00498
494s [84]	validation_0-logloss:0.00496
494s [85]	validation_0-logloss:0.00493
494s [86]	validation_0-logloss:0.00491
494s [87]	validation_0-logloss:0.00489
494s [88]	validation_0-logloss:0.00486
494s [89]	validation_0-logloss:0.00484
494s [90]	validation_0-logloss:0.00482
494s [91]	validation_0-logloss:0.00480
494s [92]	validation_0-logloss:0.00478
494s [93]	validation_0-logloss:0.00476
494s [94]	validation_0-logloss:0.00474
494s [95]	validation_0-logloss:0.00472
494s [96]	validation_0-logloss:0.00470
494s [97]	validation_0-logloss:0.00468
494s [98]	validation_0-logloss:0.00466
494s [99]	validation_0-logloss:0.00464
494s [100]	validation_0-logloss:0.00463
494s [101]	validation_0-logloss:0.00461
494s [102]	validation_0-logloss:0.00459
494s [103]	validation_0-logloss:0.00457
494s [104]	validation_0-logloss:0.00456
494s [105]	validation_0-logloss:0.00454
494s [106]	validation_0-logloss:0.00452
494s [107]	validation_0-logloss:0.00451
494s [108]	validation_0-logloss:0.00449
494s [109]	validation_0-logloss:0.00448
494s [110]	validation_0-logloss:0.00446
494s [111]	validation_0-logloss:0.00444
494s [112]	validation_0-logloss:0.00443
494s [113]	validation_0-logloss:0.00441
494s [114]	validation_0-logloss:0.00440
494s [115]	validation_0-logloss:0.00438
494s [116]	validation_0-logloss:0.00437
494s [117]	validation_0-logloss:0.00435
494s [118]	validation_0-logloss:0.00434
494s [119]	validation_0-logloss:0.00433
494s [120]	validation_0-logloss:0.00431
494s [121]	validation_0-logloss:0.00430
494s [122]	validation_0-logloss:0.00429
494s [123]	validation_0-logloss:0.00427
494s [124]	validation_0-logloss:0.00426
494s [125]	validation_0-logloss:0.00425
494s [126]	validation_0-logloss:0.00423
494s [127]	validation_0-logloss:0.00422
494s Total boosted rounds: 128
494s [0]	validation_0-logloss:0.43281
494s [1]	validation_0-logloss:0.30816
494s [2]	validation_0-logloss:0.22816
494s [3]	validation_0-logloss:0.17470
494s [4]	validation_0-logloss:0.13438
494s [5]	validation_0-logloss:0.10538
494s [6]	validation_0-logloss:0.08467
494s [7]	validation_0-logloss:0.06909
494s [8]	validation_0-logloss:0.05766
494s [9]	validation_0-logloss:0.04842
494s [10]	validation_0-logloss:0.04121
494s [11]	validation_0-logloss:0.03545
494s [12]	validation_0-logloss:0.03077
494s [13]	validation_0-logloss:0.02742
494s [14]	validation_0-logloss:0.02444
494s [15]	validation_0-logloss:0.02208
494s [16]	validation_0-logloss:0.01993
494s [17]	validation_0-logloss:0.01819
494s [18]	validation_0-logloss:0.01676
494s [19]	validation_0-logloss:0.01575
494s [20]	validation_0-logloss:0.01461
494s [21]	validation_0-logloss:0.01383
494s [22]	validation_0-logloss:0.01304
494s [23]	validation_0-logloss:0.01243
494s [24]	validation_0-logloss:0.01179
494s [25]	validation_0-logloss:0.01142
494s [26]	validation_0-logloss:0.01103
494s [27]	validation_0-logloss:0.01066
494s [28]	validation_0-logloss:0.01037
494s [29]	validation_0-logloss:0.00993
494s [30]	validation_0-logloss:0.00966
494s [31]	validation_0-logloss:0.00920
494s [0]	validation_0-logloss:0.00900
494s [1]	validation_0-logloss:0.00876
494s [2]	validation_0-logloss:0.00852
494s [3]	validation_0-logloss:0.00835
494s [4]	validation_0-logloss:0.00810
494s [5]	validation_0-logloss:0.00791
494s [6]	validation_0-logloss:0.00764
494s [7]	validation_0-logloss:0.00753
494s [8]	validation_0-logloss:0.00739
494s [9]	validation_0-logloss:0.00723
494s [10]	validation_0-logloss:0.00706
494s [11]	validation_0-logloss:0.00695
494s [12]	validation_0-logloss:0.00688
494s [13]	validation_0-logloss:0.00671
494s [14]	validation_0-logloss:0.00662
494s [15]	validation_0-logloss:0.00650
494s [16]	validation_0-logloss:0.00637
494s [17]	validation_0-logloss:0.00628
494s [18]	validation_0-logloss:0.00623
494s [19]	validation_0-logloss:0.00618
494s [20]	validation_0-logloss:0.00614
494s [21]	validation_0-logloss:0.00609
494s [22]	validation_0-logloss:0.00600
494s [23]	validation_0-logloss:0.00590
494s [24]	validation_0-logloss:0.00582
494s [25]	validation_0-logloss:0.00578
494s [26]	validation_0-logloss:0.00570
494s [27]	validation_0-logloss:0.00566
494s [28]	validation_0-logloss:0.00563
494s [29]	validation_0-logloss:0.00559
494s [30]	validation_0-logloss:0.00556
494s [31]	validation_0-logloss:0.00553
494s [32]	validation_0-logloss:0.00549
494s [33]	validation_0-logloss:0.00546
494s [34]	validation_0-logloss:0.00543
494s [35]	validation_0-logloss:0.00540
494s [36]	validation_0-logloss:0.00537
494s [37]	validation_0-logloss:0.00534
494s [38]	validation_0-logloss:0.00531
494s [39]	validation_0-logloss:0.00528
494s [40]	validation_0-logloss:0.00525
494s [41]	validation_0-logloss:0.00523
494s [42]	validation_0-logloss:0.00520
494s [43]	validation_0-logloss:0.00517
494s [44]	validation_0-logloss:0.00515
495s [45]	validation_0-logloss:0.00512
495s [46]	validation_0-logloss:0.00510
495s [47]	validation_0-logloss:0.00507
495s [48]	validation_0-logloss:0.00505
495s [49]	validation_0-logloss:0.00502
495s [50]	validation_0-logloss:0.00500
495s [51]	validation_0-logloss:0.00498
495s [52]	validation_0-logloss:0.00496
495s [53]	validation_0-logloss:0.00493
495s [54]	validation_0-logloss:0.00491
495s [55]	validation_0-logloss:0.00489
495s [56]	validation_0-logloss:0.00486
495s [57]	validation_0-logloss:0.00484
495s [58]	validation_0-logloss:0.00482
495s [59]	validation_0-logloss:0.00480
495s [60]	validation_0-logloss:0.00478
495s [61]	validation_0-logloss:0.00476
495s [62]	validation_0-logloss:0.00474
495s [63]	validation_0-logloss:0.00472
495s [64]	validation_0-logloss:0.00470
495s [65]	validation_0-logloss:0.00468
495s [66]	validation_0-logloss:0.00466
495s [67]	validation_0-logloss:0.00464
495s [68]	validation_0-logloss:0.00463
495s [69]	validation_0-logloss:0.00461
495s [70]	validation_0-logloss:0.00459
495s [71]	validation_0-logloss:0.00457
495s [72]	validation_0-logloss:0.00456
495s [73]	validation_0-logloss:0.00454
495s [74]	validation_0-logloss:0.00452
495s [75]	validation_0-logloss:0.00451
495s [76]	validation_0-logloss:0.00449
495s [77]	validation_0-logloss:0.00448
495s [78]	validation_0-logloss:0.00446
495s [79]	validation_0-logloss:0.00444
495s [80]	validation_0-logloss:0.00443
495s [81]	validation_0-logloss:0.00441
495s [82]	validation_0-logloss:0.00440
495s [83]	validation_0-logloss:0.00438
495s [84]	validation_0-logloss:0.00437
495s [85]	validation_0-logloss:0.00435
495s [86]	validation_0-logloss:0.00434
495s [87]	validation_0-logloss:0.00433
495s [88]	validation_0-logloss:0.00431
495s [89]	validation_0-logloss:0.00430
495s [90]	validation_0-logloss:0.00429
495s [91]	validation_0-logloss:0.00427
495s [92]	validation_0-logloss:0.00426
495s [93]	validation_0-logloss:0.00425
495s [94]	validation_0-logloss:0.00423
495s [95]	validation_0-logloss:0.00422
495s Total boosted rounds: 128
495s [0]	validation_0-logloss:0.43281
495s [1]	validation_0-logloss:0.30816
495s [2]	validation_0-logloss:0.22816
495s [3]	validation_0-logloss:0.17470
495s [4]	validation_0-logloss:0.13438
495s [5]	validation_0-logloss:0.10538
495s [6]	validation_0-logloss:0.08467
495s [7]	validation_0-logloss:0.06909
495s [8]	validation_0-logloss:0.05766
495s [9]	validation_0-logloss:0.04842
495s [10]	validation_0-logloss:0.04121
495s [11]	validation_0-logloss:0.03545
495s [12]	validation_0-logloss:0.03077
495s [13]	validation_0-logloss:0.02742
495s [14]	validation_0-logloss:0.02444
495s [15]	validation_0-logloss:0.02208
495s [16]	validation_0-logloss:0.01993
495s [17]	validation_0-logloss:0.01819
495s [18]	validation_0-logloss:0.01676
495s [19]	validation_0-logloss:0.01575
495s [20]	validation_0-logloss:0.01461
495s [21]	validation_0-logloss:0.01383
495s [22]	validation_0-logloss:0.01304
495s [23]	validation_0-logloss:0.01243
495s [24]	validation_0-logloss:0.01179
495s [25]	validation_0-logloss:0.01142
495s [26]	validation_0-logloss:0.01103
495s [27]	validation_0-logloss:0.01066
495s [28]	validation_0-logloss:0.01037
495s [29]	validation_0-logloss:0.00993
495s [30]	validation_0-logloss:0.00966
495s [31]	validation_0-logloss:0.00920
495s [32]	validation_0-logloss:0.00900
495s [33]	validation_0-logloss:0.00876
495s [34]	validation_0-logloss:0.00852
495s [35]	validation_0-logloss:0.00835
495s [36]	validation_0-logloss:0.00810
495s [37]	validation_0-logloss:0.00791
495s [38]	validation_0-logloss:0.00764
495s [39]	validation_0-logloss:0.00753
495s [40]	validation_0-logloss:0.00739
495s [41]	validation_0-logloss:0.00723
495s [42]	validation_0-logloss:0.00706
495s [43]	validation_0-logloss:0.00695
495s [44]	validation_0-logloss:0.00688
495s [45]	validation_0-logloss:0.00671
495s [46]	validation_0-logloss:0.00662
495s [47]	validation_0-logloss:0.00650
495s [48]	validation_0-logloss:0.00637
495s [49]	validation_0-logloss:0.00628
495s [50]	validation_0-logloss:0.00623
495s [51]	validation_0-logloss:0.00618
495s [52]	validation_0-logloss:0.00614
495s [53]	validation_0-logloss:0.00609
495s [54]	validation_0-logloss:0.00600
495s [55]	validation_0-logloss:0.00590
495s [56]	validation_0-logloss:0.00582
495s [57]	validation_0-logloss:0.00578
495s [58]	validation_0-logloss:0.00570
495s [59]	validation_0-logloss:0.00566
495s [60]	validation_0-logloss:0.00563
495s [61]	validation_0-logloss:0.00559
495s [62]	validation_0-logloss:0.00556
495s [63]	validation_0-logloss:0.00553
495s [64]	validation_0-logloss:0.00549
495s [65]	validation_0-logloss:0.00546
495s [66]	validation_0-logloss:0.00543
495s [67]	validation_0-logloss:0.00540
495s [68]	validation_0-logloss:0.00537
495s [69]	validation_0-logloss:0.00534
495s [70]	validation_0-logloss:0.00531
495s [71]	validation_0-logloss:0.00528
495s [72]	validation_0-logloss:0.00525
495s [73]	validation_0-logloss:0.00523
495s [74]	validation_0-logloss:0.00520
495s [75]	validation_0-logloss:0.00517
495s [76]	validation_0-logloss:0.00515
495s [77]	validation_0-logloss:0.00512
495s [78]	validation_0-logloss:0.00510
495s [79]	validation_0-logloss:0.00507
495s [80]	validation_0-logloss:0.00505
495s [81]	validation_0-logloss:0.00502
495s [82]	validation_0-logloss:0.00500
495s [83]	validation_0-logloss:0.00498
495s [84]	validation_0-logloss:0.00496
495s [85]	validation_0-logloss:0.00493
495s [86]	validation_0-logloss:0.00491
495s [87]	validation_0-logloss:0.00489
495s [88]	validation_0-logloss:0.00486
495s [89]	validation_0-logloss:0.00484
495s [90]	validation_0-logloss:0.00482
495s [91]	validation_0-logloss:0.00480
495s [92]	validation_0-logloss:0.00478
495s [93]	validation_0-logloss:0.00476
495s [94]	validation_0-logloss:0.00474
495s [95]	validation_0-logloss:0.00472
495s [96]	validation_0-logloss:0.00470
495s [97]	validation_0-logloss:0.00468
495s [98]	validation_0-logloss:0.00466
495s [99]	validation_0-logloss:0.00464
495s [100]	validation_0-logloss:0.00463
495s [101]	validation_0-logloss:0.00461
495s [102]	validation_0-logloss:0.00459
495s [103]	validation_0-logloss:0.00457
495s [104]	validation_0-logloss:0.00456
495s [105]	validation_0-logloss:0.00454
495s [106]	validation_0-logloss:0.00452
495s [107]	validation_0-logloss:0.00451
495s [108]	validation_0-logloss:0.00449
495s [109]	validation_0-logloss:0.00448
495s [110]	validation_0-logloss:0.00446
495s [111]	validation_0-logloss:0.00444
495s [112]	validation_0-logloss:0.00443
495s [113]	validation_0-logloss:0.00441
495s [114]	validation_0-logloss:0.00440
495s [115]	validation_0-logloss:0.00438
495s [116]	validation_0-logloss:0.00437
495s [117]	validation_0-logloss:0.00435
495s [118]	validation_0-logloss:0.00434
495s [119]	validation_0-logloss:0.00433
495s [120]	validation_0-logloss:0.00431
495s [121]	validation_0-logloss:0.00430
495s [122]	validation_0-logloss:0.00429
495s [123]	validation_0-logloss:0.00427
495s [124]	validation_0-logloss:0.00426
495s [125]	validation_0-logloss:0.00425
495s [126]	validation_0-logloss:0.00423
495s [127]	validation_0-logloss:0.00422
495s Total boosted rounds: 128
495s [0]	validation_0-logloss:0.43281
495s [1]	validation_0-logloss:0.30816
495s [2]	validation_0-logloss:0.22816
495s [3]	validation_0-logloss:0.17470
495s [4]	validation_0-logloss:0.13438
495s [5]	validation_0-logloss:0.10538
495s [6]	validation_0-logloss:0.08467
495s [7]	validation_0-logloss:0.06909
495s [8]	validation_0-logloss:0.05766
496s [9]	validation_0-logloss:0.04842
496s [10]	validation_0-logloss:0.04121
496s [11]	validation_0-logloss:0.03545
496s [12]	validation_0-logloss:0.03077
496s [13]	validation_0-logloss:0.02742
496s [14]	validation_0-logloss:0.02444
496s [15]	validation_0-logloss:0.02208
496s [16]	validation_0-logloss:0.01993
496s [17]	validation_0-logloss:0.01819
496s [18]	validation_0-logloss:0.01676
496s [19]	validation_0-logloss:0.01575
496s [20]	validation_0-logloss:0.01461
496s [21]	validation_0-logloss:0.01383
496s [22]	validation_0-logloss:0.01304
496s [23]	validation_0-logloss:0.01243
496s [24]	validation_0-logloss:0.01179
496s [25]	validation_0-logloss:0.01142
496s [26]	validation_0-logloss:0.01103
496s [27]	validation_0-logloss:0.01066
496s [28]	validation_0-logloss:0.01037
496s [29]	validation_0-logloss:0.00993
496s [30]	validation_0-logloss:0.00966
496s [31]	validation_0-logloss:0.00920
496s [0]	validation_0-logloss:0.00900
496s [1]	validation_0-logloss:0.00876
496s [2]	validation_0-logloss:0.00852
496s [3]	validation_0-logloss:0.00835
496s [4]	validation_0-logloss:0.00810
496s [5]	validation_0-logloss:0.00791
496s [6]	validation_0-logloss:0.00764
496s [7]	validation_0-logloss:0.00753
496s [8]	validation_0-logloss:0.00739
496s [9]	validation_0-logloss:0.00723
496s [10]	validation_0-logloss:0.00706
496s [11]	validation_0-logloss:0.00695
496s [12]	validation_0-logloss:0.00688
496s [13]	validation_0-logloss:0.00671
496s [14]	validation_0-logloss:0.00662
496s [15]	validation_0-logloss:0.00650
496s [16]	validation_0-logloss:0.00637
496s [17]	validation_0-logloss:0.00628
496s [18]	validation_0-logloss:0.00623
496s [19]	validation_0-logloss:0.00618
496s [20]	validation_0-logloss:0.00614
496s [21]	validation_0-logloss:0.00609
496s [22]	validation_0-logloss:0.00600
496s [23]	validation_0-logloss:0.00590
496s [24]	validation_0-logloss:0.00582
496s [25]	validation_0-logloss:0.00578
496s [26]	validation_0-logloss:0.00570
496s [27]	validation_0-logloss:0.00566
496s [28]	validation_0-logloss:0.00563
496s [29]	validation_0-logloss:0.00559
496s [30]	validation_0-logloss:0.00556
496s [31]	validation_0-logloss:0.00553
496s [32]	validation_0-logloss:0.00549
496s [33]	validation_0-logloss:0.00546
496s [34]	validation_0-logloss:0.00543
496s [35]	validation_0-logloss:0.00540
496s [36]	validation_0-logloss:0.00537
496s [37]	validation_0-logloss:0.00534
496s [38]	validation_0-logloss:0.00531
496s [39]	validation_0-logloss:0.00528
496s [40]	validation_0-logloss:0.00525
496s [41]	validation_0-logloss:0.00523
496s [42]	validation_0-logloss:0.00520
496s [43]	validation_0-logloss:0.00517
496s [44]	validation_0-logloss:0.00515
496s [45]	validation_0-logloss:0.00512
496s [46]	validation_0-logloss:0.00510
496s [47]	validation_0-logloss:0.00507
496s [48]	validation_0-logloss:0.00505
496s [49]	validation_0-logloss:0.00502
496s [50]	validation_0-logloss:0.00500
496s [51]	validation_0-logloss:0.00498
496s [52]	validation_0-logloss:0.00496
496s [53]	validation_0-logloss:0.00493
496s [54]	validation_0-logloss:0.00491
496s [55]	validation_0-logloss:0.00489
496s [56]	validation_0-logloss:0.00486
496s [57]	validation_0-logloss:0.00484
496s [58]	validation_0-logloss:0.00482
496s [59]	validation_0-logloss:0.00480
496s [60]	validation_0-logloss:0.00478
496s [61]	validation_0-logloss:0.00476
496s [62]	validation_0-logloss:0.00474
496s [63]	validation_0-logloss:0.00472
496s [64]	validation_0-logloss:0.00470
496s [65]	validation_0-logloss:0.00468
496s [66]	validation_0-logloss:0.00466
496s [67]	validation_0-logloss:0.00464
496s [68]	validation_0-logloss:0.00463
496s [69]	validation_0-logloss:0.00461
496s [70]	validation_0-logloss:0.00459
496s [71]	validation_0-logloss:0.00457
496s [72]	validation_0-logloss:0.00456
496s [73]	validation_0-logloss:0.00454
496s [74]	validation_0-logloss:0.00452
496s [75]	validation_0-logloss:0.00451
496s [76]	validation_0-logloss:0.00449
496s [77]	validation_0-logloss:0.00448
496s [78]	validation_0-logloss:0.00446
496s [79]	validation_0-logloss:0.00444
496s [80]	validation_0-logloss:0.00443
496s [81]	validation_0-logloss:0.00441
496s [82]	validation_0-logloss:0.00440
496s [83]	validation_0-logloss:0.00438
496s [84]	validation_0-logloss:0.00437
496s [85]	validation_0-logloss:0.00435
496s [86]	validation_0-logloss:0.00434
496s [87]	validation_0-logloss:0.00433
496s [88]	validation_0-logloss:0.00431
496s [89]	validation_0-logloss:0.00430
496s [90]	validation_0-logloss:0.00429
496s [91]	validation_0-logloss:0.00427
496s [92]	validation_0-logloss:0.00426
496s [93]	validation_0-logloss:0.00425
496s [94]	validation_0-logloss:0.00423
496s [95]	validation_0-logloss:0.00422
497s Total boosted rounds: 128
497s ==========================================
498s Running case cross_validation.py...
501s running cross validation
501s [0]	train-error:0.05067+0.00920	test-error:0.05573+0.01589
501s [1]	train-error:0.02130+0.00206	test-error:0.02119+0.00365
502s running cross validation, disable standard deviation display
502s [0]	train-error:0.05067	test-error:0.05573
502s [1]	train-error:0.02130	test-error:0.02119
502s [2]	train-error:0.00994	test-error:0.00998
502s [3]	train-error:0.01413	test-error:0.01443
502s [4]	train-error:0.00599	test-error:0.00630
502s [5]	train-error:0.00203	test-error:0.00169
502s [6]	train-error:0.00123	test-error:0.00123
502s [7]	train-error:0.00123	test-error:0.00123
502s [8]	train-error:0.00092	test-error:0.00123
502s /usr/lib/python3/dist-packages/xgboost/training.py:38: UserWarning: `feval` is deprecated, use `custom_metric` instead.  They have different behavior when custom objective is also used.See https://xgboost.readthedocs.io/en/latest/tutorials/custom_metric_obj.html for details on the `custom_metric`.
502s   warnings.warn(
503s    train-error-mean  train-error-std  test-error-mean  test-error-std
503s 0          0.050668         0.009201         0.055732        0.015889
503s 1          0.021303         0.002055         0.021188        0.003653
503s 2          0.009942         0.006076         0.009979        0.004828
503s 3          0.014126         0.001706         0.014433        0.003517
503s 4          0.005988         0.001878         0.006295        0.003123
503s 5          0.002034         0.001469         0.001689        0.000574
503s 6          0.001228         0.000260         0.001228        0.001041
503s running cross validation, with preprocessing function
503s running cross validation, with customized loss function
503s ==========================================
503s Running case custom_rmsle.py...
503s /tmp/autopkgtest.jlLNaQ/build.N8X/src/demo/guide-python/custom_rmsle.py:139: SyntaxWarning: invalid escape sequence '\s'
503s   :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`
509s Squared Error
509s [0]	dtrain-rmse:682.95908	dtest-rmse:745.95555
509s [1]	dtrain-rmse:627.66199	dtest-rmse:751.49583
509s [2]	dtrain-rmse:596.74396	dtest-rmse:766.33054
509s [3]	dtrain-rmse:555.92371	dtest-rmse:776.92703
509s [4]	dtrain-rmse:519.58613	dtest-rmse:789.74145
509s [5]	dtrain-rmse:501.26007	dtest-rmse:805.22881
509s [6]	dtrain-rmse:453.81757	dtest-rmse:810.70817
509s [7]	dtrain-rmse:423.51474	dtest-rmse:822.61798
509s [8]	dtrain-rmse:383.34479	dtest-rmse:826.43243
509s [9]	dtrain-rmse:371.08179	dtest-rmse:827.23674
509s [10]	dtrain-rmse:353.11776	dtest-rmse:828.84174
509s [11]	dtrain-rmse:316.42149	dtest-rmse:832.98904
509s [12]	dtrain-rmse:300.24359	dtest-rmse:834.90637
509s [13]	dtrain-rmse:285.81935	dtest-rmse:836.55001
509s [14]	dtrain-rmse:278.47432	dtest-rmse:838.29965
509s [15]	dtrain-rmse:266.08310	dtest-rmse:840.16158
509s [16]	dtrain-rmse:261.48875	dtest-rmse:841.45332
509s [17]	dtrain-rmse:253.15765	dtest-rmse:841.96773
509s [18]	dtrain-rmse:246.41335	dtest-rmse:842.26433
510s [19]	dtrain-rmse:242.90377	dtest-rmse:843.24280
510s Finished Squared Error in: 0.7308712005615234 
510s 
510s Squared Log Error
510s [0]	dtrain-rmsle:1.31939	dtest-rmsle:1.26056
510s [1]	dtrain-rmsle:1.22093	dtest-rmsle:1.16061
510s [2]	dtrain-rmsle:1.13670	dtest-rmsle:1.07549
510s [3]	dtrain-rmsle:1.06691	dtest-rmsle:1.00568
510s [4]	dtrain-rmsle:1.01079	dtest-rmsle:0.95190
510s [5]	dtrain-rmsle:0.96709	dtest-rmsle:0.91206
510s [6]	dtrain-rmsle:0.93453	dtest-rmsle:0.88458
510s [7]	dtrain-rmsle:0.90925	dtest-rmsle:0.86580
510s [8]	dtrain-rmsle:0.89139	dtest-rmsle:0.85326
510s [9]	dtrain-rmsle:0.87620	dtest-rmsle:0.84621
510s [10]	dtrain-rmsle:0.86538	dtest-rmsle:0.84136
510s [11]	dtrain-rmsle:0.85715	dtest-rmsle:0.83849
510s [12]	dtrain-rmsle:0.84866	dtest-rmsle:0.83767
510s [13]	dtrain-rmsle:0.84230	dtest-rmsle:0.83693
510s [14]	dtrain-rmsle:0.83639	dtest-rmsle:0.83785
510s [15]	dtrain-rmsle:0.83076	dtest-rmsle:0.83853
510s [16]	dtrain-rmsle:0.82671	dtest-rmsle:0.83887
510s [17]	dtrain-rmsle:0.82297	dtest-rmsle:0.83883
510s [18]	dtrain-rmsle:0.81919	dtest-rmsle:0.83945
510s [19]	dtrain-rmsle:0.81638	dtest-rmsle:0.83981
510s Finished Squared Log Error in: 0.37279844284057617
510s [0]	dtrain-PyRMSLE:1.37154	dtest-PyRMSLE:1.31491
510s [1]	dtrain-PyRMSLE:1.26642	dtest-PyRMSLE:1.20792
510s [2]	dtrain-PyRMSLE:1.17525	dtest-PyRMSLE:1.11539
510s [3]	dtrain-PyRMSLE:1.09848	dtest-PyRMSLE:1.03800
510s [4]	dtrain-PyRMSLE:1.03565	dtest-PyRMSLE:0.97673
510s [5]	dtrain-PyRMSLE:0.98595	dtest-PyRMSLE:0.93063
510s [6]	dtrain-PyRMSLE:0.94774	dtest-PyRMSLE:0.89723
510s [7]	dtrain-PyRMSLE:0.91977	dtest-PyRMSLE:0.87448
510s [8]	dtrain-PyRMSLE:0.89796	dtest-PyRMSLE:0.85932
510s [9]	dtrain-PyRMSLE:0.88141	dtest-PyRMSLE:0.85028
510s [10]	dtrain-PyRMSLE:0.86816	dtest-PyRMSLE:0.84450
510s [11]	dtrain-PyRMSLE:0.85991	dtest-PyRMSLE:0.84015
510s [12]	dtrain-PyRMSLE:0.85120	dtest-PyRMSLE:0.83900
510s [13]	dtrain-PyRMSLE:0.84384	dtest-PyRMSLE:0.83775
510s [14]	dtrain-PyRMSLE:0.83741	dtest-PyRMSLE:0.83732
510s [15]	dtrain-PyRMSLE:0.83253	dtest-PyRMSLE:0.83800
510s [16]	dtrain-PyRMSLE:0.82842	dtest-PyRMSLE:0.83827
510s [17]	dtrain-PyRMSLE:0.82535	dtest-PyRMSLE:0.83844
510s [18]	dtrain-PyRMSLE:0.82169	dtest-PyRMSLE:0.83953
510s [19]	dtrain-PyRMSLE:0.81857	dtest-PyRMSLE:0.84046
511s ==========================================
511s Running case custom_softmax.py...
516s /tmp/autopkgtest.jlLNaQ/build.N8X/src/demo/guide-python/custom_softmax.py:76: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
516s   grad[r, c] = g
516s [0]	train-PyMError:0.14000
516s [1]	train-PyMError:0.00000
516s [2]	train-PyMError:0.01000
516s [3]	train-PyMError:0.00000
517s [4]	train-PyMError:0.00000
517s [5]	train-PyMError:0.00000
517s [6]	train-PyMError:0.00000
517s [7]	train-PyMError:0.00000
517s [8]	train-PyMError:0.00000
517s [9]	train-PyMError:0.00000
517s [0]	train-merror:0.14000
517s [1]	train-merror:0.00000
517s [2]	train-merror:0.01000
517s [3]	train-merror:0.00000
517s [4]	train-merror:0.00000
517s [5]	train-merror:0.00000
517s [6]	train-merror:0.00000
517s [7]	train-merror:0.00000
517s [8]	train-merror:0.00000
517s [9]	train-merror:0.00000
518s ==========================================
518s Running case evals_result.py...
521s /usr/lib/python3/dist-packages/xgboost/core.py:723: FutureWarning: Pass `evals` as keyword args.
521s   warnings.warn(msg, FutureWarning)
521s [0]	eval-logloss:0.47977	eval-error:0.04283	train-logloss:0.48196	train-error:0.04652
521s [1]	eval-logloss:0.35740	eval-error:0.04283	train-logloss:0.35919	train-error:0.04652
521s Access logloss metric directly from evals_result:
521s [0.4797663698279288, 0.35739665886839306]
521s 
521s Access metrics through a loop:
521s - eval
521s    - logloss
521s       - [0.4797663698279288, 0.35739665886839306]
521s    - error
521s       - [0.04283054003724395, 0.04283054003724395]
521s - train
521s    - logloss
521s       - [0.4819583435789261, 0.35919429156594646]
521s    - error
521s       - [0.04652233993551359, 0.04652233993551359]
521s 
521s Access complete dictionary:
521s {'eval': OrderedDict({'logloss': [0.4797663698279288, 0.35739665886839306], 'error': [0.04283054003724395, 0.04283054003724395]}), 'train': OrderedDict({'logloss': [0.4819583435789261, 0.35919429156594646], 'error': [0.04652233993551359, 0.04652233993551359]})}
522s ==========================================
522s Running case external_memory.py...
528s [0]	Train-rmse:170.67905
528s [1]	Train-rmse:166.88130
529s [2]	Train-rmse:163.67251
529s [3]	Train-rmse:160.83024
530s [4]	Train-rmse:158.26351
530s [5]	Train-rmse:155.91569
530s [6]	Train-rmse:153.74380
530s [7]	Train-rmse:151.74583
530s [8]	Train-rmse:149.92400
531s [9]	Train-rmse:148.14658
532s ==========================================
532s Running case feature_weights.py...
539s [0]	d-rmse:1.00430
539s [1]	d-rmse:0.99638
539s [2]	d-rmse:0.98161
539s [3]	d-rmse:0.97086
539s [4]	d-rmse:0.96269
539s [5]	d-rmse:0.94829
539s [6]	d-rmse:0.93914
539s [7]	d-rmse:0.93187
539s [8]	d-rmse:0.92274
539s [9]	d-rmse:0.91749
541s ==========================================
541s Skip case gamma_regression.py...
541s ==========================================
541s Running case generalized_linear_model.py...
545s /usr/lib/python3/dist-packages/xgboost/core.py:723: FutureWarning: Pass `evals` as keyword args.
545s   warnings.warn(msg, FutureWarning)
545s [0]	eval-logloss:0.57140	train-logloss:0.56882
545s [1]	eval-logloss:0.53011	train-logloss:0.52651
545s [2]	eval-logloss:0.51441	train-logloss:0.51042
545s [3]	eval-logloss:0.50818	train-logloss:0.50404
545s error=0.118560
546s ==========================================
546s Running case individual_trees.py...
551s ==========================================
551s Skip case learning_to_rank.py...
551s ==========================================
551s Running case multioutput_regression.py...
555s [0]	validation_0-rmse:0.29417
555s [1]	validation_0-rmse:0.26360
555s [2]	validation_0-rmse:0.23619
555s [3]	validation_0-rmse:0.22068
555s [4]	validation_0-rmse:0.20681
555s [5]	validation_0-rmse:0.19295
555s [6]	validation_0-rmse:0.17849
555s [7]	validation_0-rmse:0.16562
555s [8]	validation_0-rmse:0.15492
555s [9]	validation_0-rmse:0.14493
555s [10]	validation_0-rmse:0.13892
555s [11]	validation_0-rmse:0.13172
555s [12]	validation_0-rmse:0.12253
555s [13]	validation_0-rmse:0.11744
555s [14]	validation_0-rmse:0.10981
555s [15]	validation_0-rmse:0.10176
555s [16]	validation_0-rmse:0.09476
555s [17]	validation_0-rmse:0.08947
555s [18]	validation_0-rmse:0.08399
555s [19]	validation_0-rmse:0.07952
555s [20]	validation_0-rmse:0.07442
555s [21]	validation_0-rmse:0.07048
555s [22]	validation_0-rmse:0.06749
555s [23]	validation_0-rmse:0.06505
555s [24]	validation_0-rmse:0.06060
555s [25]	validation_0-rmse:0.05737
555s [26]	validation_0-rmse:0.05539
555s [27]	validation_0-rmse:0.05326
555s [28]	validation_0-rmse:0.05026
555s [29]	validation_0-rmse:0.04692
555s [30]	validation_0-rmse:0.04501
555s [31]	validation_0-rmse:0.04175
555s [32]	validation_0-rmse:0.03970
555s [33]	validation_0-rmse:0.03683
555s [34]	validation_0-rmse:0.03430
555s [35]	validation_0-rmse:0.03219
555s [36]	validation_0-rmse:0.03058
555s [37]	validation_0-rmse:0.02914
555s [38]	validation_0-rmse:0.02778
555s [39]	validation_0-rmse:0.02611
555s [40]	validation_0-rmse:0.02504
555s [41]	validation_0-rmse:0.02322
555s [42]	validation_0-rmse:0.02212
555s [43]	validation_0-rmse:0.02129
555s [44]	validation_0-rmse:0.01992
555s [45]	validation_0-rmse:0.01930
555s [46]	validation_0-rmse:0.01864
555s [47]	validation_0-rmse:0.01744
556s [48]	validation_0-rmse:0.01618
556s [49]	validation_0-rmse:0.01521
556s [50]	validation_0-rmse:0.01428
556s [51]	validation_0-rmse:0.01346
556s [52]	validation_0-rmse:0.01276
556s [53]	validation_0-rmse:0.01209
556s [54]	validation_0-rmse:0.01154
556s [55]	validation_0-rmse:0.01094
556s [56]	validation_0-rmse:0.01023
556s [57]	validation_0-rmse:0.00979
556s [58]	validation_0-rmse:0.00934
556s [59]	validation_0-rmse:0.00879
556s [60]	validation_0-rmse:0.00845
556s [61]	validation_0-rmse:0.00822
556s [62]	validation_0-rmse:0.00790
556s [63]	validation_0-rmse:0.00742
556s [64]	validation_0-rmse:0.00711
556s [65]	validation_0-rmse:0.00682
556s [66]	validation_0-rmse:0.00644
556s [67]	validation_0-rmse:0.00615
556s [68]	validation_0-rmse:0.00577
556s [69]	validation_0-rmse:0.00553
556s [70]	validation_0-rmse:0.00514
556s [71]	validation_0-rmse:0.00493
556s [72]	validation_0-rmse:0.00468
556s [73]	validation_0-rmse:0.00443
556s [74]	validation_0-rmse:0.00426
556s [75]	validation_0-rmse:0.00389
556s [76]	validation_0-rmse:0.00362
556s [77]	validation_0-rmse:0.00340
556s [78]	validation_0-rmse:0.00323
556s [79]	validation_0-rmse:0.00303
556s [80]	validation_0-rmse:0.00288
556s [81]	validation_0-rmse:0.00275
556s [82]	validation_0-rmse:0.00264
556s [83]	validation_0-rmse:0.00253
556s [84]	validation_0-rmse:0.00240
556s [85]	validation_0-rmse:0.00230
556s [86]	validation_0-rmse:0.00219
556s [87]	validation_0-rmse:0.00208
556s [88]	validation_0-rmse:0.00198
556s [89]	validation_0-rmse:0.00191
556s [90]	validation_0-rmse:0.00183
556s [91]	validation_0-rmse:0.00173
556s [92]	validation_0-rmse:0.00169
556s [93]	validation_0-rmse:0.00161
556s [94]	validation_0-rmse:0.00154
556s [95]	validation_0-rmse:0.00148
556s [96]	validation_0-rmse:0.00145
556s [97]	validation_0-rmse:0.00141
556s [98]	validation_0-rmse:0.00138
556s [99]	validation_0-rmse:0.00134
556s [100]	validation_0-rmse:0.00131
556s [101]	validation_0-rmse:0.00128
556s [102]	validation_0-rmse:0.00122
556s [103]	validation_0-rmse:0.00121
556s [104]	validation_0-rmse:0.00119
556s [105]	validation_0-rmse:0.00117
556s [106]	validation_0-rmse:0.00113
556s [107]	validation_0-rmse:0.00111
556s [108]	validation_0-rmse:0.00108
556s [109]	validation_0-rmse:0.00104
556s [110]	validation_0-rmse:0.00104
556s [111]	validation_0-rmse:0.00102
556s [112]	validation_0-rmse:0.00100
556s [113]	validation_0-rmse:0.00100
556s [114]	validation_0-rmse:0.00098
556s [115]	validation_0-rmse:0.00098
556s [116]	validation_0-rmse:0.00097
556s [117]	validation_0-rmse:0.00097
556s [118]	validation_0-rmse:0.00097
556s [119]	validation_0-rmse:0.00097
556s [120]	validation_0-rmse:0.00097
556s [121]	validation_0-rmse:0.00096
556s [122]	validation_0-rmse:0.00096
556s [123]	validation_0-rmse:0.00095
556s [124]	validation_0-rmse:0.00093
556s [125]	validation_0-rmse:0.00093
556s [126]	validation_0-rmse:0.00093
556s [127]	validation_0-rmse:0.00092
556s [0]	validation_0-rmse:0.29633
556s [1]	validation_0-rmse:0.27038
556s [2]	validation_0-rmse:0.24670
556s [3]	validation_0-rmse:0.22485
556s [4]	validation_0-rmse:0.20795
556s [5]	validation_0-rmse:0.19109
556s [6]	validation_0-rmse:0.17936
556s [7]	validation_0-rmse:0.17053
556s [8]	validation_0-rmse:0.16198
556s [9]	validation_0-rmse:0.15406
556s [10]	validation_0-rmse:0.14510
556s [11]	validation_0-rmse:0.13641
556s [12]	validation_0-rmse:0.12550
556s [13]	validation_0-rmse:0.11761
556s [14]	validation_0-rmse:0.11132
556s [15]	validation_0-rmse:0.10186
556s [16]	validation_0-rmse:0.09376
556s [17]	validation_0-rmse:0.08897
556s [18]	validation_0-rmse:0.08226
556s [19]	validation_0-rmse:0.07742
556s [20]	validation_0-rmse:0.07209
556s [21]	validation_0-rmse:0.06959
556s [22]	validation_0-rmse:0.06703
556s [23]	validation_0-rmse:0.06274
556s [24]	validation_0-rmse:0.05932
556s [25]	validation_0-rmse:0.05540
556s [26]	validation_0-rmse:0.05157
556s [27]	validation_0-rmse:0.04830
556s [28]	validation_0-rmse:0.04540
556s [29]	validation_0-rmse:0.04313
556s [30]	validation_0-rmse:0.04098
556s [31]	validation_0-rmse:0.03869
556s [32]	validation_0-rmse:0.03728
556s [33]	validation_0-rmse:0.03536
556s [34]	validation_0-rmse:0.03372
556s [35]	validation_0-rmse:0.03235
556s [36]	validation_0-rmse:0.03093
556s [37]	validation_0-rmse:0.02889
556s [38]	validation_0-rmse:0.02686
556s [39]	validation_0-rmse:0.02547
556s [40]	validation_0-rmse:0.02372
556s [41]	validation_0-rmse:0.02209
556s [42]	validation_0-rmse:0.02130
556s [43]	validation_0-rmse:0.02008
556s [44]	validation_0-rmse:0.01901
556s [45]	validation_0-rmse:0.01851
556s [46]	validation_0-rmse:0.01714
556s [47]	validation_0-rmse:0.01644
556s [48]	validation_0-rmse:0.01585
556s [49]	validation_0-rmse:0.01493
556s [50]	validation_0-rmse:0.01370
556s [51]	validation_0-rmse:0.01309
556s [52]	validation_0-rmse:0.01257
556s [53]	validation_0-rmse:0.01193
556s [54]	validation_0-rmse:0.01136
556s [55]	validation_0-rmse:0.01080
556s [56]	validation_0-rmse:0.01010
556s [57]	validation_0-rmse:0.00947
556s [58]	validation_0-rmse:0.00871
556s [59]	validation_0-rmse:0.00818
556s [60]	validation_0-rmse:0.00766
556s [61]	validation_0-rmse:0.00737
556s [62]	validation_0-rmse:0.00695
556s [63]	validation_0-rmse:0.00658
556s [64]	validation_0-rmse:0.00616
556s [65]	validation_0-rmse:0.00595
556s [66]	validation_0-rmse:0.00566
556s [67]	validation_0-rmse:0.00526
556s [68]	validation_0-rmse:0.00487
556s [69]	validation_0-rmse:0.00469
556s [70]	validation_0-rmse:0.00446
556s [71]	validation_0-rmse:0.00422
556s [72]	validation_0-rmse:0.00403
556s [73]	validation_0-rmse:0.00381
556s [74]	validation_0-rmse:0.00360
556s [75]	validation_0-rmse:0.00346
556s [76]	validation_0-rmse:0.00330
556s [77]	validation_0-rmse:0.00312
556s [78]	validation_0-rmse:0.00296
556s [79]	validation_0-rmse:0.00284
556s [80]	validation_0-rmse:0.00275
556s [81]	validation_0-rmse:0.00264
556s [82]	validation_0-rmse:0.00251
556s [83]	validation_0-rmse:0.00242
556s [84]	validation_0-rmse:0.00231
556s [85]	validation_0-rmse:0.00222
556s [86]	validation_0-rmse:0.00212
556s [87]	validation_0-rmse:0.00203
556s [88]	validation_0-rmse:0.00192
556s [89]	validation_0-rmse:0.00181
556s [90]	validation_0-rmse:0.00174
556s [91]	validation_0-rmse:0.00168
556s [92]	validation_0-rmse:0.00159
556s [93]	validation_0-rmse:0.00153
556s [94]	validation_0-rmse:0.00147
556s [95]	validation_0-rmse:0.00142
556s [96]	validation_0-rmse:0.00138
556s [97]	validation_0-rmse:0.00130
556s [98]	validation_0-rmse:0.00123
556s [99]	validation_0-rmse:0.00118
556s [100]	validation_0-rmse:0.00114
556s [101]	validation_0-rmse:0.00112
556s [102]	validation_0-rmse:0.00110
556s [103]	validation_0-rmse:0.00106
556s [104]	validation_0-rmse:0.00103
556s [105]	validation_0-rmse:0.00102
556s [106]	validation_0-rmse:0.00099
556s [107]	validation_0-rmse:0.00096
556s [108]	validation_0-rmse:0.00094
556s [109]	validation_0-rmse:0.00093
556s [110]	validation_0-rmse:0.00091
556s [111]	validation_0-rmse:0.00087
556s [112]	validation_0-rmse:0.00086
556s [113]	validation_0-rmse:0.00084
556s [114]	validation_0-rmse:0.00084
556s [115]	validation_0-rmse:0.00083
556s [116]	validation_0-rmse:0.00083
556s [117]	validation_0-rmse:0.00082
556s [118]	validation_0-rmse:0.00082
556s [119]	validation_0-rmse:0.00081
556s [120]	validation_0-rmse:0.00080
556s [121]	validation_0-rmse:0.00079
556s [122]	validation_0-rmse:0.00079
556s [123]	validation_0-rmse:0.00078
556s [124]	validation_0-rmse:0.00078
556s [125]	validation_0-rmse:0.00077
556s [126]	validation_0-rmse:0.00077
556s [127]	validation_0-rmse:0.00076
556s [0]	Train-rmse:0.28597	Train-PyRMSE:4.04424
556s [1]	Train-rmse:0.25494	Train-PyRMSE:3.60534
556s [2]	Train-rmse:0.23359	Train-PyRMSE:3.30341
556s [3]	Train-rmse:0.21640	Train-PyRMSE:3.06038
556s [4]	Train-rmse:0.20574	Train-PyRMSE:2.90962
556s [5]	Train-rmse:0.19311	Train-PyRMSE:2.73098
556s [6]	Train-rmse:0.18435	Train-PyRMSE:2.60710
556s [7]	Train-rmse:0.17502	Train-PyRMSE:2.47516
556s [8]	Train-rmse:0.16830	Train-PyRMSE:2.38017
556s [9]	Train-rmse:0.15957	Train-PyRMSE:2.25667
556s [10]	Train-rmse:0.15430	Train-PyRMSE:2.18211
556s [11]	Train-rmse:0.14604	Train-PyRMSE:2.06533
556s [12]	Train-rmse:0.13629	Train-PyRMSE:1.92740
556s [13]	Train-rmse:0.13249	Train-PyRMSE:1.87376
556s [14]	Train-rmse:0.12557	Train-PyRMSE:1.77588
556s [15]	Train-rmse:0.12198	Train-PyRMSE:1.72505
556s [16]	Train-rmse:0.11972	Train-PyRMSE:1.69314
556s [17]	Train-rmse:0.11345	Train-PyRMSE:1.60444
556s [18]	Train-rmse:0.10926	Train-PyRMSE:1.54524
556s [19]	Train-rmse:0.10367	Train-PyRMSE:1.46616
556s [20]	Train-rmse:0.09938	Train-PyRMSE:1.40550
556s [21]	Train-rmse:0.09543	Train-PyRMSE:1.34963
556s [22]	Train-rmse:0.09302	Train-PyRMSE:1.31552
556s [23]	Train-rmse:0.08848	Train-PyRMSE:1.25132
556s [24]	Train-rmse:0.08575	Train-PyRMSE:1.21274
556s [25]	Train-rmse:0.08280	Train-PyRMSE:1.17092
556s [26]	Train-rmse:0.07694	Train-PyRMSE:1.08803
556s [27]	Train-rmse:0.07375	Train-PyRMSE:1.04299
556s [28]	Train-rmse:0.07137	Train-PyRMSE:1.00927
556s [29]	Train-rmse:0.06737	Train-PyRMSE:0.95281
556s [30]	Train-rmse:0.06433	Train-PyRMSE:0.90978
556s [31]	Train-rmse:0.06239	Train-PyRMSE:0.88235
556s [32]	Train-rmse:0.06093	Train-PyRMSE:0.86164
556s [33]	Train-rmse:0.05800	Train-PyRMSE:0.82031
556s [34]	Train-rmse:0.05568	Train-PyRMSE:0.78745
556s [35]	Train-rmse:0.05430	Train-PyRMSE:0.76797
556s [36]	Train-rmse:0.05124	Train-PyRMSE:0.72468
556s [37]	Train-rmse:0.04849	Train-PyRMSE:0.68580
556s [38]	Train-rmse:0.04754	Train-PyRMSE:0.67231
556s [39]	Train-rmse:0.04704	Train-PyRMSE:0.66519
556s [40]	Train-rmse:0.04449	Train-PyRMSE:0.62916
556s [41]	Train-rmse:0.04374	Train-PyRMSE:0.61865
556s [42]	Train-rmse:0.04281	Train-PyRMSE:0.60545
556s [43]	Train-rmse:0.04101	Train-PyRMSE:0.58000
556s [44]	Train-rmse:0.03921	Train-PyRMSE:0.55448
556s [45]	Train-rmse:0.03761	Train-PyRMSE:0.53186
556s [46]	Train-rmse:0.03628	Train-PyRMSE:0.51314
556s [47]	Train-rmse:0.03566	Train-PyRMSE:0.50431
556s [48]	Train-rmse:0.03379	Train-PyRMSE:0.47782
556s [49]	Train-rmse:0.03265	Train-PyRMSE:0.46180
556s [50]	Train-rmse:0.03208	Train-PyRMSE:0.45373
556s [51]	Train-rmse:0.03132	Train-PyRMSE:0.44294
556s [52]	Train-rmse:0.03024	Train-PyRMSE:0.42768
556s [53]	Train-rmse:0.02897	Train-PyRMSE:0.40968
556s [54]	Train-rmse:0.02838	Train-PyRMSE:0.40136
556s [55]	Train-rmse:0.02777	Train-PyRMSE:0.39273
556s [56]	Train-rmse:0.02668	Train-PyRMSE:0.37727
556s [57]	Train-rmse:0.02579	Train-PyRMSE:0.36475
556s [58]	Train-rmse:0.02436	Train-PyRMSE:0.34444
556s [59]	Train-rmse:0.02411	Train-PyRMSE:0.34092
556s [60]	Train-rmse:0.02344	Train-PyRMSE:0.33144
556s [61]	Train-rmse:0.02296	Train-PyRMSE:0.32477
556s [62]	Train-rmse:0.02203	Train-PyRMSE:0.31151
556s [63]	Train-rmse:0.02109	Train-PyRMSE:0.29828
556s [64]	Train-rmse:0.02058	Train-PyRMSE:0.29102
556s [65]	Train-rmse:0.02017	Train-PyRMSE:0.28528
556s [66]	Train-rmse:0.01945	Train-PyRMSE:0.27500
556s [67]	Train-rmse:0.01861	Train-PyRMSE:0.26320
556s [68]	Train-rmse:0.01835	Train-PyRMSE:0.25955
556s [69]	Train-rmse:0.01763	Train-PyRMSE:0.24935
556s [70]	Train-rmse:0.01697	Train-PyRMSE:0.24001
556s [71]	Train-rmse:0.01633	Train-PyRMSE:0.23098
556s [72]	Train-rmse:0.01582	Train-PyRMSE:0.22368
556s [73]	Train-rmse:0.01562	Train-PyRMSE:0.22096
556s [74]	Train-rmse:0.01507	Train-PyRMSE:0.21311
556s [75]	Train-rmse:0.01488	Train-PyRMSE:0.21037
556s [76]	Train-rmse:0.01417	Train-PyRMSE:0.20036
556s [77]	Train-rmse:0.01362	Train-PyRMSE:0.19265
556s [78]	Train-rmse:0.01314	Train-PyRMSE:0.18588
556s [79]	Train-rmse:0.01263	Train-PyRMSE:0.17868
556s [80]	Train-rmse:0.01246	Train-PyRMSE:0.17623
556s [81]	Train-rmse:0.01228	Train-PyRMSE:0.17369
556s [82]	Train-rmse:0.01171	Train-PyRMSE:0.16556
556s [83]	Train-rmse:0.01120	Train-PyRMSE:0.15845
556s [84]	Train-rmse:0.01081	Train-PyRMSE:0.15289
556s [85]	Train-rmse:0.01031	Train-PyRMSE:0.14579
556s [86]	Train-rmse:0.00992	Train-PyRMSE:0.14032
556s [87]	Train-rmse:0.00976	Train-PyRMSE:0.13809
556s [88]	Train-rmse:0.00950	Train-PyRMSE:0.13438
556s [89]	Train-rmse:0.00914	Train-PyRMSE:0.12930
556s [90]	Train-rmse:0.00878	Train-PyRMSE:0.12417
556s [91]	Train-rmse:0.00845	Train-PyRMSE:0.11946
556s [92]	Train-rmse:0.00833	Train-PyRMSE:0.11779
556s [93]	Train-rmse:0.00816	Train-PyRMSE:0.11542
556s [94]	Train-rmse:0.00786	Train-PyRMSE:0.11115
556s [95]	Train-rmse:0.00751	Train-PyRMSE:0.10622
556s [96]	Train-rmse:0.00742	Train-PyRMSE:0.10500
556s [97]	Train-rmse:0.00718	Train-PyRMSE:0.10148
556s [98]	Train-rmse:0.00707	Train-PyRMSE:0.10001
556s [99]	Train-rmse:0.00685	Train-PyRMSE:0.09681
556s [100]	Train-rmse:0.00676	Train-PyRMSE:0.09558
556s [101]	Train-rmse:0.00654	Train-PyRMSE:0.09255
556s [102]	Train-rmse:0.00628	Train-PyRMSE:0.08888
556s [103]	Train-rmse:0.00601	Train-PyRMSE:0.08495
556s [104]	Train-rmse:0.00583	Train-PyRMSE:0.08240
556s [105]	Train-rmse:0.00576	Train-PyRMSE:0.08150
556s [106]	Train-rmse:0.00568	Train-PyRMSE:0.08027
556s [107]	Train-rmse:0.00542	Train-PyRMSE:0.07665
557s [108]	Train-rmse:0.00521	Train-PyRMSE:0.07373
557s [109]	Train-rmse:0.00502	Train-PyRMSE:0.07100
557s [110]	Train-rmse:0.00486	Train-PyRMSE:0.06869
557s [111]	Train-rmse:0.00468	Train-PyRMSE:0.06612
557s [112]	Train-rmse:0.00452	Train-PyRMSE:0.06399
557s [113]	Train-rmse:0.00447	Train-PyRMSE:0.06324
557s [114]	Train-rmse:0.00429	Train-PyRMSE:0.06062
557s [115]	Train-rmse:0.00414	Train-PyRMSE:0.05856
557s [116]	Train-rmse:0.00403	Train-PyRMSE:0.05697
557s [117]	Train-rmse:0.00389	Train-PyRMSE:0.05498
557s [118]	Train-rmse:0.00377	Train-PyRMSE:0.05330
557s [119]	Train-rmse:0.00371	Train-PyRMSE:0.05251
557s [120]	Train-rmse:0.00358	Train-PyRMSE:0.05058
557s [121]	Train-rmse:0.00349	Train-PyRMSE:0.04940
557s [122]	Train-rmse:0.00336	Train-PyRMSE:0.04751
557s [123]	Train-rmse:0.00332	Train-PyRMSE:0.04702
557s [124]	Train-rmse:0.00324	Train-PyRMSE:0.04584
557s [125]	Train-rmse:0.00321	Train-PyRMSE:0.04542
557s [126]	Train-rmse:0.00318	Train-PyRMSE:0.04502
557s [127]	Train-rmse:0.00308	Train-PyRMSE:0.04355
557s [0]	Train-rmse:0.29790	Train-PyRMSE:4.21300
557s [1]	Train-rmse:0.26995	Train-PyRMSE:3.81773
557s [2]	Train-rmse:0.24596	Train-PyRMSE:3.47845
557s [3]	Train-rmse:0.22962	Train-PyRMSE:3.24727
557s [4]	Train-rmse:0.22042	Train-PyRMSE:3.11716
557s [5]	Train-rmse:0.20740	Train-PyRMSE:2.93305
557s [6]	Train-rmse:0.20148	Train-PyRMSE:2.84940
557s [7]	Train-rmse:0.19043	Train-PyRMSE:2.69310
557s [8]	Train-rmse:0.18596	Train-PyRMSE:2.62991
557s [9]	Train-rmse:0.17844	Train-PyRMSE:2.52354
557s [10]	Train-rmse:0.17484	Train-PyRMSE:2.47255
557s [11]	Train-rmse:0.15904	Train-PyRMSE:2.24910
557s [12]	Train-rmse:0.15243	Train-PyRMSE:2.15566
557s [13]	Train-rmse:0.14346	Train-PyRMSE:2.02886
557s [14]	Train-rmse:0.13921	Train-PyRMSE:1.96873
557s [15]	Train-rmse:0.13760	Train-PyRMSE:1.94597
557s [16]	Train-rmse:0.13004	Train-PyRMSE:1.83909
557s [17]	Train-rmse:0.12588	Train-PyRMSE:1.78025
557s [18]	Train-rmse:0.12472	Train-PyRMSE:1.76382
557s [19]	Train-rmse:0.11921	Train-PyRMSE:1.68592
557s [20]	Train-rmse:0.11146	Train-PyRMSE:1.57621
557s [21]	Train-rmse:0.10750	Train-PyRMSE:1.52031
557s [22]	Train-rmse:0.10276	Train-PyRMSE:1.45326
557s [23]	Train-rmse:0.10194	Train-PyRMSE:1.44162
557s [24]	Train-rmse:0.09842	Train-PyRMSE:1.39186
557s [25]	Train-rmse:0.09715	Train-PyRMSE:1.37398
557s [26]	Train-rmse:0.09312	Train-PyRMSE:1.31692
557s [27]	Train-rmse:0.09226	Train-PyRMSE:1.30482
557s [28]	Train-rmse:0.08662	Train-PyRMSE:1.22503
557s [29]	Train-rmse:0.08231	Train-PyRMSE:1.16407
557s [30]	Train-rmse:0.08159	Train-PyRMSE:1.15381
557s [31]	Train-rmse:0.07851	Train-PyRMSE:1.11031
557s [32]	Train-rmse:0.07481	Train-PyRMSE:1.05796
557s [33]	Train-rmse:0.07150	Train-PyRMSE:1.01120
557s [34]	Train-rmse:0.07080	Train-PyRMSE:1.00132
557s [35]	Train-rmse:0.06830	Train-PyRMSE:0.96591
557s [36]	Train-rmse:0.06769	Train-PyRMSE:0.95733
557s [37]	Train-rmse:0.06484	Train-PyRMSE:0.91694
557s [38]	Train-rmse:0.06165	Train-PyRMSE:0.87181
557s [39]	Train-rmse:0.05937	Train-PyRMSE:0.83965
557s [40]	Train-rmse:0.05873	Train-PyRMSE:0.83051
557s [41]	Train-rmse:0.05667	Train-PyRMSE:0.80140
557s [42]	Train-rmse:0.05596	Train-PyRMSE:0.79141
557s [43]	Train-rmse:0.05416	Train-PyRMSE:0.76589
557s [44]	Train-rmse:0.05299	Train-PyRMSE:0.74934
557s [45]	Train-rmse:0.05052	Train-PyRMSE:0.71453
557s [46]	Train-rmse:0.04922	Train-PyRMSE:0.69609
557s [47]	Train-rmse:0.04883	Train-PyRMSE:0.69062
557s [48]	Train-rmse:0.04610	Train-PyRMSE:0.65199
557s [49]	Train-rmse:0.04417	Train-PyRMSE:0.62469
557s [50]	Train-rmse:0.04190	Train-PyRMSE:0.59254
557s [51]	Train-rmse:0.04114	Train-PyRMSE:0.58181
557s [52]	Train-rmse:0.03957	Train-PyRMSE:0.55954
557s [53]	Train-rmse:0.03922	Train-PyRMSE:0.55469
557s [54]	Train-rmse:0.03852	Train-PyRMSE:0.54478
557s [55]	Train-rmse:0.03717	Train-PyRMSE:0.52562
557s [56]	Train-rmse:0.03584	Train-PyRMSE:0.50680
557s [57]	Train-rmse:0.03429	Train-PyRMSE:0.48498
557s [58]	Train-rmse:0.03400	Train-PyRMSE:0.48083
557s [59]	Train-rmse:0.03295	Train-PyRMSE:0.46597
557s [60]	Train-rmse:0.03237	Train-PyRMSE:0.45775
557s [61]	Train-rmse:0.03091	Train-PyRMSE:0.43707
557s [62]	Train-rmse:0.02998	Train-PyRMSE:0.42399
557s [63]	Train-rmse:0.02849	Train-PyRMSE:0.40286
557s [64]	Train-rmse:0.02729	Train-PyRMSE:0.38595
557s [65]	Train-rmse:0.02692	Train-PyRMSE:0.38069
557s [66]	Train-rmse:0.02618	Train-PyRMSE:0.37018
557s [67]	Train-rmse:0.02588	Train-PyRMSE:0.36600
557s [68]	Train-rmse:0.02491	Train-PyRMSE:0.35229
557s [69]	Train-rmse:0.02428	Train-PyRMSE:0.34344
557s [70]	Train-rmse:0.02398	Train-PyRMSE:0.33907
557s [71]	Train-rmse:0.02309	Train-PyRMSE:0.32650
557s [72]	Train-rmse:0.02219	Train-PyRMSE:0.31381
557s [73]	Train-rmse:0.02094	Train-PyRMSE:0.29616
557s [74]	Train-rmse:0.02079	Train-PyRMSE:0.29398
557s [75]	Train-rmse:0.02028	Train-PyRMSE:0.28685
557s [76]	Train-rmse:0.01931	Train-PyRMSE:0.27311
557s [77]	Train-rmse:0.01919	Train-PyRMSE:0.27140
557s [78]	Train-rmse:0.01862	Train-PyRMSE:0.26336
557s [79]	Train-rmse:0.01743	Train-PyRMSE:0.24652
557s [80]	Train-rmse:0.01699	Train-PyRMSE:0.24033
557s [81]	Train-rmse:0.01644	Train-PyRMSE:0.23256
557s [82]	Train-rmse:0.01565	Train-PyRMSE:0.22127
557s [83]	Train-rmse:0.01526	Train-PyRMSE:0.21582
557s [84]	Train-rmse:0.01503	Train-PyRMSE:0.21258
557s [85]	Train-rmse:0.01450	Train-PyRMSE:0.20508
558s [86]	Train-rmse:0.01410	Train-PyRMSE:0.19936
558s [87]	Train-rmse:0.01349	Train-PyRMSE:0.19080
558s [88]	Train-rmse:0.01288	Train-PyRMSE:0.18211
558s [89]	Train-rmse:0.01278	Train-PyRMSE:0.18071
558s [90]	Train-rmse:0.01220	Train-PyRMSE:0.17252
558s [91]	Train-rmse:0.01179	Train-PyRMSE:0.16679
558s [92]	Train-rmse:0.01166	Train-PyRMSE:0.16490
558s [93]	Train-rmse:0.01132	Train-PyRMSE:0.16008
558s [94]	Train-rmse:0.01088	Train-PyRMSE:0.15388
558s [95]	Train-rmse:0.01060	Train-PyRMSE:0.14993
558s [96]	Train-rmse:0.01006	Train-PyRMSE:0.14229
558s [97]	Train-rmse:0.00998	Train-PyRMSE:0.14109
558s [98]	Train-rmse:0.00954	Train-PyRMSE:0.13496
558s [99]	Train-rmse:0.00948	Train-PyRMSE:0.13406
558s [100]	Train-rmse:0.00926	Train-PyRMSE:0.13098
558s [101]	Train-rmse:0.00901	Train-PyRMSE:0.12747
558s [102]	Train-rmse:0.00885	Train-PyRMSE:0.12510
558s [103]	Train-rmse:0.00847	Train-PyRMSE:0.11979
558s [104]	Train-rmse:0.00833	Train-PyRMSE:0.11784
558s [105]	Train-rmse:0.00791	Train-PyRMSE:0.11187
558s [106]	Train-rmse:0.00787	Train-PyRMSE:0.11125
558s [107]	Train-rmse:0.00755	Train-PyRMSE:0.10682
558s [108]	Train-rmse:0.00724	Train-PyRMSE:0.10245
558s [109]	Train-rmse:0.00705	Train-PyRMSE:0.09976
558s [110]	Train-rmse:0.00683	Train-PyRMSE:0.09662
558s [111]	Train-rmse:0.00656	Train-PyRMSE:0.09274
558s [112]	Train-rmse:0.00641	Train-PyRMSE:0.09058
558s [113]	Train-rmse:0.00631	Train-PyRMSE:0.08921
558s [114]	Train-rmse:0.00606	Train-PyRMSE:0.08569
558s [115]	Train-rmse:0.00583	Train-PyRMSE:0.08241
558s [116]	Train-rmse:0.00578	Train-PyRMSE:0.08175
558s [117]	Train-rmse:0.00562	Train-PyRMSE:0.07946
558s [118]	Train-rmse:0.00553	Train-PyRMSE:0.07818
558s [119]	Train-rmse:0.00533	Train-PyRMSE:0.07539
558s [120]	Train-rmse:0.00514	Train-PyRMSE:0.07262
558s [121]	Train-rmse:0.00510	Train-PyRMSE:0.07211
558s [122]	Train-rmse:0.00501	Train-PyRMSE:0.07088
558s [123]	Train-rmse:0.00489	Train-PyRMSE:0.06912
558s [124]	Train-rmse:0.00475	Train-PyRMSE:0.06722
558s [125]	Train-rmse:0.00473	Train-PyRMSE:0.06690
558s [126]	Train-rmse:0.00467	Train-PyRMSE:0.06603
558s [127]	Train-rmse:0.00443	Train-PyRMSE:0.06259
559s ==========================================
559s Running case predict_first_ntree.py...
564s /usr/lib/python3/dist-packages/xgboost/core.py:723: FutureWarning: Pass `evals` as keyword args.
564s   warnings.warn(msg, FutureWarning)
565s [0]	eval-logloss:0.22646	train-logloss:0.23316
565s [1]	eval-logloss:0.13776	train-logloss:0.13654
565s [2]	eval-logloss:0.08036	train-logloss:0.08243
567s start testing prediction from first n trees
567s error of ypred1=0.042831
567s error of ypred2=0.006207
567s [0]	validation_0-logloss:0.22646
567s [1]	validation_0-logloss:0.13776
567s [2]	validation_0-logloss:0.08036
567s start testing prediction from first n trees
567s error of ypred1=0.042831
567s error of ypred2=0.006207
567s ==========================================
567s Running case predict_leaf_indices.py...
573s /usr/lib/python3/dist-packages/xgboost/core.py:723: FutureWarning: Pass `evals` as keyword args.
573s   warnings.warn(msg, FutureWarning)
573s [0]	eval-logloss:0.22646	train-logloss:0.23316
573s [1]	eval-logloss:0.13776	train-logloss:0.13654
573s [2]	eval-logloss:0.08036	train-logloss:0.08243
573s start testing predict the leaf indices
573s (1611, 2, 1, 1)
573s [[[[5.]]
573s 
573s   [[4.]]]
573s 
573s 
573s  [[[6.]]
573s 
573s   [[4.]]]
573s 
573s 
573s  [[[5.]]
573s 
573s   [[4.]]]
573s 
573s 
573s  ...
573s 
573s 
573s  [[[6.]]
573s 
573s   [[4.]]]
573s 
573s 
573s  [[[4.]]
573s 
573s   [[3.]]]
573s 
573s 
573s  [[[6.]]
573s 
573s   [[4.]]]]
573s (1611, 3)
574s ==========================================
574s Skip case quantile_data_iterator.py...
574s ==========================================
574s Running case quantile_regression.py...
580s [0]	Train-quantile:1.50159	Test-quantile:1.45489
580s [1]	Train-quantile:1.41395	Test-quantile:1.36960
580s [2]	Train-quantile:1.33178	Test-quantile:1.28702
580s [3]	Train-quantile:1.25429	Test-quantile:1.21133
580s [4]	Train-quantile:1.18373	Test-quantile:1.13959
580s [5]	Train-quantile:1.11898	Test-quantile:1.07261
580s [6]	Train-quantile:1.05770	Test-quantile:1.01264
580s [7]	Train-quantile:1.00142	Test-quantile:0.96139
580s [8]	Train-quantile:0.94844	Test-quantile:0.91339
580s [9]	Train-quantile:0.89917	Test-quantile:0.86934
580s [10]	Train-quantile:0.85692	Test-quantile:0.82830
580s [11]	Train-quantile:0.81823	Test-quantile:0.79270
580s [12]	Train-quantile:0.78400	Test-quantile:0.75817
580s [13]	Train-quantile:0.75333	Test-quantile:0.72983
580s [14]	Train-quantile:0.72555	Test-quantile:0.70335
580s [15]	Train-quantile:0.69885	Test-quantile:0.67960
580s [16]	Train-quantile:0.67482	Test-quantile:0.65947
580s [17]	Train-quantile:0.65359	Test-quantile:0.64055
580s [18]	Train-quantile:0.63363	Test-quantile:0.62346
580s [19]	Train-quantile:0.61495	Test-quantile:0.60855
580s [20]	Train-quantile:0.59783	Test-quantile:0.59489
580s [21]	Train-quantile:0.58197	Test-quantile:0.58130
580s [22]	Train-quantile:0.56682	Test-quantile:0.56949
580s [23]	Train-quantile:0.55237	Test-quantile:0.55744
580s [24]	Train-quantile:0.53809	Test-quantile:0.54622
580s [25]	Train-quantile:0.52441	Test-quantile:0.53571
580s [26]	Train-quantile:0.51174	Test-quantile:0.52520
580s [27]	Train-quantile:0.49974	Test-quantile:0.51546
580s [28]	Train-quantile:0.48865	Test-quantile:0.50628
580s [29]	Train-quantile:0.47833	Test-quantile:0.49784
580s [30]	Train-quantile:0.46895	Test-quantile:0.48966
580s [31]	Train-quantile:0.45900	Test-quantile:0.48302
580s [0]	Train-rmse:4.32523	Test-rmse:4.63143
580s [1]	Train-rmse:4.21367	Test-rmse:4.53399
580s [2]	Train-rmse:4.10813	Test-rmse:4.44222
580s [3]	Train-rmse:4.00795	Test-rmse:4.35559
580s [4]	Train-rmse:3.91314	Test-rmse:4.27507
580s [5]	Train-rmse:3.82342	Test-rmse:4.19994
580s [6]	Train-rmse:3.73860	Test-rmse:4.12864
580s [7]	Train-rmse:3.65855	Test-rmse:4.06333
580s [8]	Train-rmse:3.58302	Test-rmse:3.99920
580s [9]	Train-rmse:3.51166	Test-rmse:3.94100
580s [10]	Train-rmse:3.44443	Test-rmse:3.88581
580s [11]	Train-rmse:3.38085	Test-rmse:3.83491
580s [12]	Train-rmse:3.32121	Test-rmse:3.78659
580s [13]	Train-rmse:3.26491	Test-rmse:3.74137
580s [14]	Train-rmse:3.21217	Test-rmse:3.69918
580s [15]	Train-rmse:3.16233	Test-rmse:3.66047
580s [16]	Train-rmse:3.11625	Test-rmse:3.62292
580s [17]	Train-rmse:3.07250	Test-rmse:3.58980
580s [18]	Train-rmse:3.03120	Test-rmse:3.55971
580s [19]	Train-rmse:2.99260	Test-rmse:3.53039
580s [20]	Train-rmse:2.95471	Test-rmse:3.50273
580s [21]	Train-rmse:2.92082	Test-rmse:3.47765
580s [22]	Train-rmse:2.88707	Test-rmse:3.45409
580s [23]	Train-rmse:2.85719	Test-rmse:3.43196
580s [24]	Train-rmse:2.82955	Test-rmse:3.41116
580s [25]	Train-rmse:2.80145	Test-rmse:3.39262
580s [26]	Train-rmse:2.77526	Test-rmse:3.37544
580s [27]	Train-rmse:2.75237	Test-rmse:3.35703
580s [28]	Train-rmse:2.73071	Test-rmse:3.34065
580s [29]	Train-rmse:2.70895	Test-rmse:3.32714
580s [30]	Train-rmse:2.69007	Test-rmse:3.31295
580s [31]	Train-rmse:2.67125	Test-rmse:3.30119
581s ==========================================
581s Running case sklearn_evals_result.py...
586s [0]	validation_0-logloss:0.63169	validation_1-logloss:0.65974
586s [1]	validation_0-logloss:0.58449	validation_1-logloss:0.62161
586s Access logloss metric directly from validation_0:
586s [0.6316909282654524, 0.5844871088303626]
586s 
586s Access metrics through a loop:
586s - validation_0
586s    - logloss
586s       - [0.6316909282654524, 0.5844871088303626]
586s - validation_1
586s    - logloss
586s       - [0.6597448766976595, 0.6216080508381129]
586s 
586s Access complete dict:
586s {'validation_0': OrderedDict({'logloss': [0.6316909282654524, 0.5844871088303626]}), 'validation_1': OrderedDict({'logloss': [0.6597448766976595, 0.6216080508381129]})}
586s ==========================================
586s Running case sklearn_examples.py...
605s Zeros and Ones from the Digits dataset: binary classification
605s [[87  0]
605s  [ 1 92]]
605s [[91  0]
605s  [ 3 86]]
605s Iris: multiclass classification
605s [[19  0  0]
605s  [ 1 31  2]
605s  [ 0  3 19]]
605s [[31  0  0]
605s  [ 0 16  0]
605s  [ 0  1 27]]
605s California Housing: regression
605s 0.2310805601814026
605s 0.23533764411819863
605s Parameter optimization
605s Fitting 3 folds for each of 4 candidates, totalling 12 fits
605s 0.6838972456687431
605s {'max_depth': 4, 'n_estimators': 100}
605s Pickling sklearn API models
605s True
605s [0]	validation_0-auc:0.99020
605s [1]	validation_0-auc:0.99975
605s [2]	validation_0-auc:0.99975
605s [3]	validation_0-auc:0.99975
605s [4]	validation_0-auc:0.99975
605s [5]	validation_0-auc:0.99975
605s [6]	validation_0-auc:1.00000
605s [7]	validation_0-auc:1.00000
605s [8]	validation_0-auc:1.00000
605s [9]	validation_0-auc:1.00000
605s [10]	validation_0-auc:1.00000
605s [11]	validation_0-auc:1.00000
605s [12]	validation_0-auc:1.00000
605s [13]	validation_0-auc:1.00000
605s [14]	validation_0-auc:1.00000
605s [15]	validation_0-auc:1.00000
605s [16]	validation_0-auc:1.00000
606s ==========================================
606s Running case sklearn_parallel.py...
609s Parallel Parameter optimization
609s Fitting 5 folds for each of 9 candidates, totalling 45 fits
645s 0.688555675183378
645s {'max_depth': 4, 'n_estimators': 50}
645s Exception ignored in: <function ResourceTracker.__del__ at 0x3ff7d7f3060>
645s Traceback (most recent call last):
645s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 82, in __del__
645s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 91, in _stop
645s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 116, in _stop_locked
645s ChildProcessError: [Errno 10] No child processes
646s Exception ignored in: <function ResourceTracker.__del__ at 0x3ffa0af3060>
646s Traceback (most recent call last):
646s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 82, in __del__
646s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 91, in _stop
646s   File "/usr/lib/python3.13/multiprocessing/resource_tracker.py", line 116, in _stop_locked
646s ChildProcessError: [Errno 10] No child processes
647s ==========================================
647s Skip case spark_estimator_examples.py...
647s ==========================================
647s Skip case update_process.py...
647s ==========================================
647s All cases passed.
647s /tmp/autopkgtest.jlLNaQ/wrapper.sh: Killing leaked background processes: 9044 
647s     PID TTY      STAT   TIME COMMAND
647s /tmp/autopkgtest.jlLNaQ/wrapper.sh: 235: kill: No such process
647s 
647s /tmp/autopkgtest.jlLNaQ/wrapper.sh: 237: kill: No such process
647s 
648s autopkgtest [18:52:43]: test run-demos: -----------------------]
648s autopkgtest [18:52:43]: test run-demos:  - - - - - - - - - - results - - - - - - - - - -
648s run-demos            PASS
648s autopkgtest [18:52:43]: @@@@@@@@@@@@@@@@@@@@ summary
648s run-demos            PASS
664s nova [W] Using flock in prodstack7-s390x
664s Creating nova instance adt-questing-s390x-xgboost-20250501-184155-juju-7f2275-prod-proposed-migration-environment-23-4154552c-b107-4303-afab-dc3b8e4abf6d from image adt/ubuntu-questing-s390x-server-20250501.img (UUID 2a693762-e567-4bbe-9838-630e77f0775f)...
664s nova [W] Timed out waiting for 6e503dd0-8170-4b72-b5e4-03f2244a720f to get deleted.