Package evaluation of Lale on Julia 1.10.9 (96dc2d8c45*) started at 2025-06-06T18:16:41.435


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 5.14s


################################################################################
# Installation
#

Installing Lale...
   Resolving package versions...
   Installed Conda ── v1.10.2
   Installed PyCall ─ v1.96.4
   Installed Lale ─── v0.1.9
    Updating `~/.julia/environments/v1.10/Project.toml`
  [25676c37] + Lale v0.1.9
    Updating `~/.julia/environments/v1.10/Manifest.toml`
  [e3c3008a] + AMLPipelineBase v0.1.18
  [1520ce14] + AbstractTrees v0.4.5
  [66dad0bd] + AliasTables v1.1.3
  [336ed68f] + CSV v0.10.15
  [944b1d66] + CodecZlib v0.7.8
  [34da2185] + Compat v4.16.0
  [8f4d0f93] + Conda v1.10.2
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [a93c6f00] + DataFrames v1.7.0
  [864edb3b] + DataStructures v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [e7dc6d0d] + DataValues v0.4.13
  [7806a523] + DecisionTree v0.12.4
  [8bb1440f] + DelimitedFiles v1.9.1
  [ffbed154] + DocStringExtensions v0.9.4
  [48062228] + FilePathsBase v0.9.24
  [842dd82b] + InlineStrings v1.4.3
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.4
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [682c06a0] + JSON v0.21.4
  [b964fa9f] + LaTeXStrings v1.4.0
  [25676c37] + Lale v0.1.9
  [50d2b5c4] + Lazy v0.15.1
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [f0e99cf1] + MLBase v0.9.2
  [1914dd2f] + MacroTools v0.5.16
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [eadc2687] + Pandas v1.6.1
  [69de0a69] + Parsers v2.8.3
  [2dfb63ee] + PooledArrays v1.4.3
⌅ [aea7be01] + PrecompileTools v1.2.1
  [21216c6a] + Preferences v1.4.3
  [08abe8d2] + PrettyTables v2.4.0
  [43287f4e] + PtrArrays v1.3.0
  [438e738f] + PyCall v1.96.4
  [189a3867] + Reexport v1.2.2
  [6e75b9c4] + ScikitLearnBase v0.5.0
  [91c51154] + SentinelArrays v1.4.8
  [a2af1166] + SortingAlgorithms v1.2.1
  [82ae8749] + StatsAPI v1.7.1
  [2913bbd2] + StatsBase v0.34.5
  [892a3eda] + StringManipulation v0.4.1
  [3783bdb8] + TableTraits v1.0.1
  [382cd787] + TableTraitsUtils v1.0.2
  [bd369af6] + Tables v1.12.1
  [3bb67fe8] + TranscodingStreams v0.11.3
  [81def892] + VersionParsing v1.3.0
  [ea10d353] + WeakRefStrings v1.4.2
  [76eceee3] + WorkerUtilities v1.6.1
  [0dad84c5] + ArgTools v1.1.1
  [56f22d72] + Artifacts
  [2a0f44e3] + Base64
  [ade2ca70] + Dates
  [f43a241f] + Downloads v1.6.0
  [7b1f6079] + FileWatching
  [9fa8497b] + Future
  [b77e0a4c] + InteractiveUtils
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2
  [8f399da3] + Libdl
  [37e2e46d] + LinearAlgebra
  [56ddb016] + Logging
  [d6f4376e] + Markdown
  [a63ad114] + Mmap
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.10.0
  [de0858da] + Printf
  [3fa0cd96] + REPL
  [9a3f8284] + Random
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization
  [6462fe0b] + Sockets
  [2f01184e] + SparseArrays v1.10.0
  [10745b16] + Statistics v1.10.0
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test
  [cf7118a7] + UUIDs
  [4ec0a83e] + Unicode
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.4.0+0
  [e37daf67] + LibGit2_jll v1.6.4+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.2+1
  [14a3606d] + MozillaCACerts_jll v2023.1.10
  [4536629a] + OpenBLAS_jll v0.3.23+4
  [bea87d4a] + SuiteSparse_jll v7.2.1+1
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.52.0+1
  [3f19e933] + p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
    Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log`
    Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log`
    Building Lale ──→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/2c5ab4907cb5e76a1eb2c31312dd3e21ebc9ce81/build.log`

Installation completed after 148.7s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 64.67s


################################################################################
# Testing
#

     Testing Lale
      Status `/tmp/jl_4pq0oT/Project.toml`
  [e3c3008a] AMLPipelineBase v0.1.18
  [336ed68f] CSV v0.10.15
  [a93c6f00] DataFrames v1.7.0
  [25676c37] Lale v0.1.9
  [eadc2687] Pandas v1.6.1
  [438e738f] PyCall v1.96.4
  [9a3f8284] Random
  [10745b16] Statistics v1.10.0
  [8dfed614] Test
      Status `/tmp/jl_4pq0oT/Manifest.toml`
  [e3c3008a] AMLPipelineBase v0.1.18
  [1520ce14] AbstractTrees v0.4.5
  [66dad0bd] AliasTables v1.1.3
  [336ed68f] CSV v0.10.15
  [944b1d66] CodecZlib v0.7.8
  [34da2185] Compat v4.16.0
  [8f4d0f93] Conda v1.10.2
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.7.0
  [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [e7dc6d0d] DataValues v0.4.13
  [7806a523] DecisionTree v0.12.4
  [8bb1440f] DelimitedFiles v1.9.1
  [ffbed154] DocStringExtensions v0.9.4
  [48062228] FilePathsBase v0.9.24
  [842dd82b] InlineStrings v1.4.3
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.4
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [682c06a0] JSON v0.21.4
  [b964fa9f] LaTeXStrings v1.4.0
  [25676c37] Lale v0.1.9
  [50d2b5c4] Lazy v0.15.1
  [2ab3a3ac] LogExpFunctions v0.3.29
  [f0e99cf1] MLBase v0.9.2
  [1914dd2f] MacroTools v0.5.16
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [eadc2687] Pandas v1.6.1
  [69de0a69] Parsers v2.8.3
  [2dfb63ee] PooledArrays v1.4.3
⌅ [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [08abe8d2] PrettyTables v2.4.0
  [43287f4e] PtrArrays v1.3.0
  [438e738f] PyCall v1.96.4
  [189a3867] Reexport v1.2.2
  [6e75b9c4] ScikitLearnBase v0.5.0
  [91c51154] SentinelArrays v1.4.8
  [a2af1166] SortingAlgorithms v1.2.1
  [82ae8749] StatsAPI v1.7.1
  [2913bbd2] StatsBase v0.34.5
  [892a3eda] StringManipulation v0.4.1
  [3783bdb8] TableTraits v1.0.1
  [382cd787] TableTraitsUtils v1.0.2
  [bd369af6] Tables v1.12.1
  [3bb67fe8] TranscodingStreams v0.11.3
  [81def892] VersionParsing v1.3.0
  [ea10d353] WeakRefStrings v1.4.2
  [76eceee3] WorkerUtilities v1.6.1
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
[ Info: suppressing PyCall warnings
LinearSVC
QuadraticDiscriminantAnalysis
MLPClassifier
RandomForestClassifier
SVC
LinearSVC
MLPClassifier
SGDClassifier
KNeighborsClassifier
DecisionTreeClassifier
PassiveAggressiveClassifier
RidgeClassifier
ExtraTreesClassifier
GradientBoostingClassifier
BaggingClassifier
AdaBoostClassifier
GaussianNB
MultinomialNB
Test Summary:    | Pass  Total  Time
lale classifiers |   18     18  3.6s
SVR
Ridge
SGDRegressor
KNeighborsRegressor
DecisionTreeRegressor
RandomForestRegressor
ExtraTreesRegressor
GradientBoostingRegressor
AdaBoostRegressor
Test Summary:   | Pass  Total  Time
lale regressors |   18     18  1.6s
Test Summary:            | Pass  Total  Time
lale autogen classifiers |    9      9  0.6s
Test Summary:           | Pass  Total  Time
lale autogen regressors |   10     10  0.7s
Test Summary:               | Pass  Total  Time
lale preprocessors fit test |   19     19  0.4s
Test Summary:                     | Pass  Total  Time
lale preprocessors transform test |   19     19  0.6s
Test Summary:                                         | Pass  Total   Time
scikit preprocessor fit/transform test with real data |    9      9  20.4s
PCAN is not supported.

syntax: LaleOp(name::String, type="sklearn"; args...)
where 'name can be one of:

AdaBoostClassifier AdaBoostRegressor BaggingClassifier ColumnTransformer DecisionTreeClassifier DecisionTreeRegressor DummyClassifier DummyRegressor ExtraTreesClassifier ExtraTreesRegressor FeatureAgglomeration FunctionTransformer GaussianNB GradientBoostingClassifier GradientBoostingRegressor KNeighborsClassifier KNeighborsRegressor LinearRegression LinearSVC LogisticRegression MinMaxScaler MissingIndicator MLPClassifier MultinomialNB NMF Normalizer Nystroem OneHotEncoder OrdinalEncoder PassiveAggressiveClassifier PCA Pipeline PolynomialFeatures QuadraticDiscriminantAnalysis QuantileTransformer RandomForestClassifier RandomForestRegressor RFE Ridge RidgeClassifier RobustScaler SelectKBest SGDClassifier SGDRegressor SimpleImputer StandardScaler SVC SVR TfidfVectorizer VarianceThreshold VotingClassifier 

and 'args' are the corresponding learner's initial parameters.
Note: Consult Scikitlearn's online help for more details about the learner's arguments.
RandomForestClassifers is not supported.

syntax: LaleOp(name::String, type="sklearn"; args...)
where 'name can be one of:

AdaBoostClassifier AdaBoostRegressor BaggingClassifier ColumnTransformer DecisionTreeClassifier DecisionTreeRegressor DummyClassifier DummyRegressor ExtraTreesClassifier ExtraTreesRegressor FeatureAgglomeration FunctionTransformer GaussianNB GradientBoostingClassifier GradientBoostingRegressor KNeighborsClassifier KNeighborsRegressor LinearRegression LinearSVC LogisticRegression MinMaxScaler MissingIndicator MLPClassifier MultinomialNB NMF Normalizer Nystroem OneHotEncoder OrdinalEncoder PassiveAggressiveClassifier PCA Pipeline PolynomialFeatures QuadraticDiscriminantAnalysis QuantileTransformer RandomForestClassifier RandomForestRegressor RFE Ridge RidgeClassifier RobustScaler SelectKBest SGDClassifier SGDRegressor SimpleImputer StandardScaler SVC SVR TfidfVectorizer VarianceThreshold VotingClassifier 

and 'args' are the corresponding learner's initial parameters.
Note: Consult Scikitlearn's online help for more details about the learner's arguments.

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 33%|███▎      | 1/3 [00:00<00:00,  2.21trial/s, best loss: 62.13107985173078]
100%|██████████| 3/3 [00:00<00:00,  4.53trial/s, best loss: 28.609463194664727]
100%|██████████| 3/3 [00:00<00:00,  4.10trial/s, best loss: 28.609463194664727]

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 33%|███▎      | 1/3 [00:00<00:00,  2.23trial/s, best loss: 60.58570990145693]
100%|██████████| 3/3 [00:00<00:00,  3.49trial/s, best loss: 32.69194053408817]
100%|██████████| 3/3 [00:00<00:00,  3.31trial/s, best loss: 32.69194053408817]

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 33%|███▎      | 1/3 [00:00<00:00,  2.36trial/s, best loss: 59.691995189311314]
 67%|██████▋   | 2/3 [00:00<00:00,  3.18trial/s, best loss: 44.80266201424709] 
100%|██████████| 3/3 [00:00<00:00,  3.89trial/s, best loss: 38.2752478535127] 
100%|██████████| 3/3 [00:00<00:00,  3.52trial/s, best loss: 38.2752478535127]

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 33%|███▎      | 1/3 [00:00<00:01,  1.42trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:01<00:00,  1.61trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.23trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  1.99trial/s, best loss: -0.9533333333333333]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:00,  2.25trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:00<00:00,  2.68trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:00<00:00,  3.28trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:00<00:00,  3.02trial/s, best loss: -0.9533333333333333]fold: 1, 0.18744616654349544
fold: 2, 0.1486341223098339
fold: 3, 0.17777731597162252
fold: 4, 0.2937357030720973
fold: 5, 0.1928439997138196
fold: 6, 0.1780713294451706
fold: 7, 0.1441745676062343
fold: 8, 0.13462302294650882
fold: 9, 0.21699864417565884
fold: 10, 0.18695195956497837
errors: 0
fold: 1, 100.0
fold: 2, 86.66666666666667
fold: 3, 100.0
fold: 4, 100.0
fold: 5, 86.66666666666667
fold: 6, 100.0
fold: 7, 100.0
fold: 8, 86.66666666666667
fold: 9, 86.66666666666667
fold: 10, 86.66666666666667
errors: 0
Test Summary:           | Pass  Total   Time
Lale Pipeline Optimizer |    9      9  26.1s
PCAN is not supported.

syntax: LaleOp(name::String, type="sklearn"; args...)
where 'name can be one of:

AdaBoostClassifier AdaBoostRegressor BaggingClassifier ColumnTransformer DecisionTreeClassifier DecisionTreeRegressor DummyClassifier DummyRegressor ExtraTreesClassifier ExtraTreesRegressor FeatureAgglomeration FunctionTransformer GaussianNB GradientBoostingClassifier GradientBoostingRegressor KNeighborsClassifier KNeighborsRegressor LinearRegression LinearSVC LogisticRegression MinMaxScaler MissingIndicator MLPClassifier MultinomialNB NMF Normalizer Nystroem OneHotEncoder OrdinalEncoder PassiveAggressiveClassifier PCA Pipeline PolynomialFeatures QuadraticDiscriminantAnalysis QuantileTransformer RandomForestClassifier RandomForestRegressor RFE Ridge RidgeClassifier RobustScaler SelectKBest SGDClassifier SGDRegressor SimpleImputer StandardScaler SVC SVR TfidfVectorizer VarianceThreshold VotingClassifier 

and 'args' are the corresponding learner's initial parameters.
Note: Consult Scikitlearn's online help for more details about the learner's arguments.
RandomForestClassifers is not supported.

syntax: LaleOp(name::String, type="sklearn"; args...)
where 'name can be one of:

AdaBoostClassifier AdaBoostRegressor BaggingClassifier ColumnTransformer DecisionTreeClassifier DecisionTreeRegressor DummyClassifier DummyRegressor ExtraTreesClassifier ExtraTreesRegressor FeatureAgglomeration FunctionTransformer GaussianNB GradientBoostingClassifier GradientBoostingRegressor KNeighborsClassifier KNeighborsRegressor LinearRegression LinearSVC LogisticRegression MinMaxScaler MissingIndicator MLPClassifier MultinomialNB NMF Normalizer Nystroem OneHotEncoder OrdinalEncoder PassiveAggressiveClassifier PCA Pipeline PolynomialFeatures QuadraticDiscriminantAnalysis QuantileTransformer RandomForestClassifier RandomForestRegressor RFE Ridge RidgeClassifier RobustScaler SelectKBest SGDClassifier SGDRegressor SimpleImputer StandardScaler SVC SVR TfidfVectorizer VarianceThreshold VotingClassifier 

and 'args' are the corresponding learner's initial parameters.
Note: Consult Scikitlearn's online help for more details about the learner's arguments.


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 33%|███▎      | 1/3 [00:00<00:00,  2.28trial/s, best loss: 57.85225888669439]
100%|██████████| 3/3 [00:00<00:00,  5.24trial/s, best loss: 43.02333886548154]
100%|██████████| 3/3 [00:00<00:00,  4.64trial/s, best loss: 43.02333886548154]

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 33%|███▎      | 1/3 [00:00<00:00,  3.35trial/s, best loss: 58.14400831059003]
100%|██████████| 3/3 [00:00<00:00,  6.62trial/s, best loss: 37.962959771940284]
100%|██████████| 3/3 [00:00<00:00,  6.03trial/s, best loss: 37.962959771940284]

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 33%|███▎      | 1/3 [00:00<00:00,  3.42trial/s, best loss: 59.22601191773965]
100%|██████████| 3/3 [00:00<00:00,  6.66trial/s, best loss: 45.402501287789086]
100%|██████████| 3/3 [00:00<00:00,  6.08trial/s, best loss: 45.402501287789086]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:01,  1.67trial/s, best loss: -0.96]
 67%|██████▋   | 2/3 [00:00<00:00,  2.08trial/s, best loss: -0.96]
100%|██████████| 3/3 [00:01<00:00,  2.03trial/s, best loss: -0.96]
100%|██████████| 3/3 [00:01<00:00,  2.00trial/s, best loss: -0.96]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:00,  2.12trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:00<00:00,  2.07trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.22trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.18trial/s, best loss: -0.9533333333333333]fold: 1, 0.19334255246218468
fold: 2, 0.1561848878258876
fold: 3, 0.18724090661794782
fold: 4, 0.28670027548463023
fold: 5, 0.19651588970718775
fold: 6, 0.18605952492365146
fold: 7, 0.1410950632388338
fold: 8, 0.13181600293450588
fold: 9, 0.2188519392107285
fold: 10, 0.1918678050888938
errors: 0
fold: 1, 93.33333333333333
fold: 2, 93.33333333333333
fold: 3, 100.0
fold: 4, 100.0
fold: 5, 86.66666666666667
fold: 6, 100.0
fold: 7, 100.0
fold: 8, 86.66666666666667
fold: 9, 86.66666666666667
fold: 10, 86.66666666666667
errors: 0
Test Summary:           | Pass  Total   Time
Lale Pipeline Optimizer |    9      9  13.5s


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100%|██████████| 10/10 [00:00<00:00, 12.20trial/s, best loss: -0.6566558441558441]

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100%|██████████| 10/10 [00:00<00:00, 15.93trial/s, best loss: -0.6573051948051948]

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100%|██████████| 10/10 [00:00<00:00, 12.55trial/s, best loss: -0.6566558441558441]Test Summary:      | Pass  Total  Time
Imbalanced Dataset |    5      5  7.7s


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 33%|███▎      | 1/3 [00:01<00:03,  1.94s/trial, best loss: -0.7250241709141768]
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100%|██████████| 3/3 [00:05<00:00,  1.80s/trial, best loss: -0.726225676550928]Test Summary:   | Pass  Total   Time
Lale Projection |    2      2  15.1s

     Testing Lale tests passed 

Testing completed after 156.98s

PkgEval succeeded after 444.92s