Package evaluation to test Lale on Julia 1.11.7 (58327cce5e*) started at 2025-10-29T00:37:14.302


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.45s


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

Installing Lale...
   Resolving package versions...
   Installed Conda ── v1.10.3
   Installed Lale ─── v0.1.9
   Installed PyCall ─ v1.96.4
    Updating `~/.julia/environments/v1.11/Project.toml`
  [25676c37] + Lale v0.1.9
    Updating `~/.julia/environments/v1.11/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 v4.18.1
  [8f4d0f93] + Conda v1.10.3
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [a93c6f00] + DataFrames v1.8.1
  [864edb3b] + DataStructures v0.19.1
  [e2d170a0] + DataValueInterfaces v1.0.0
  [e7dc6d0d] + DataValues v0.4.13
  [7806a523] + DecisionTree v0.12.4
  [8bb1440f] + DelimitedFiles v1.9.1
  [ffbed154] + DocStringExtensions v0.9.5
  [48062228] + FilePathsBase v0.9.24
  [842dd82b] + InlineStrings v1.4.5
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.6
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [682c06a0] + JSON v1.2.0
  [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 v1.5.0
  [08abe8d2] + PrettyTables v3.1.0
  [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 v1.2.2
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
  [2913bbd2] + StatsBase v0.34.7
  [892a3eda] + StringManipulation v0.4.1
  [ec057cc2] + StructUtils v2.5.1
  [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 v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [f43a241f] + Downloads v1.6.0
  [7b1f6079] + FileWatching v1.11.0
  [9fa8497b] + Future v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.11.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.11.0
  [de0858da] + Printf v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.11.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.6.0+0
  [e37daf67] + LibGit2_jll v1.7.2+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.6+0
  [14a3606d] + MozillaCACerts_jll v2023.12.12
  [4536629a] + OpenBLAS_jll v0.3.27+1
  [bea87d4a] + SuiteSparse_jll v7.7.0+0
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.59.0+0
  [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/8f06b0cfa4c514c7b9546756dbae91fcfbc92dc9/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 123.28s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 40.68s


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

     Testing Lale
      Status `/tmp/jl_2OrUbY/Project.toml`
  [e3c3008a] AMLPipelineBase v0.1.18
  [336ed68f] CSV v0.10.15
  [a93c6f00] DataFrames v1.8.1
  [25676c37] Lale v0.1.9
  [eadc2687] Pandas v1.6.1
  [438e738f] PyCall v1.96.4
  [10745b16] Statistics v1.11.1
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_2OrUbY/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 v4.18.1
  [8f4d0f93] Conda v1.10.3
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.8.1
  [864edb3b] DataStructures v0.19.1
  [e2d170a0] DataValueInterfaces v1.0.0
  [e7dc6d0d] DataValues v0.4.13
  [7806a523] DecisionTree v0.12.4
  [8bb1440f] DelimitedFiles v1.9.1
  [ffbed154] DocStringExtensions v0.9.5
  [48062228] FilePathsBase v0.9.24
  [842dd82b] InlineStrings v1.4.5
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.6
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [682c06a0] JSON v1.2.0
  [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 v1.5.0
  [08abe8d2] PrettyTables v3.1.0
  [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 v1.2.2
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
  [2913bbd2] StatsBase v0.34.7
  [892a3eda] StringManipulation v0.4.1
  [ec057cc2] StructUtils v2.5.1
  [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 v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching v1.11.0
  [9fa8497b] Future v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.11.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [a63ad114] Mmap v1.11.0
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.11.0
  [de0858da] Printf v1.11.0
  [3fa0cd96] REPL v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.11.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.6.0+0
  [e37daf67] LibGit2_jll v1.7.2+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.6+0
  [14a3606d] MozillaCACerts_jll v2023.12.12
  [4536629a] OpenBLAS_jll v0.3.27+1
  [bea87d4a] SuiteSparse_jll v7.7.0+0
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.59.0+0
  [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
Precompiling Lale...
Info Given Lale was explicitly requested, output will be shown live 
WARNING: could not import AbsTypes.lalepropertynames into LaleOps
  18691.1 ms  ✓ Lale
  1 dependency successfully precompiled in 21 seconds. 93 already precompiled.
  1 dependency had output during precompilation:
┌ Lale
│  [Output was shown above]
└  
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  4.4s
SVR
Ridge
SGDRegressor
KNeighborsRegressor
DecisionTreeRegressor
RandomForestRegressor
ExtraTreesRegressor
GradientBoostingRegressor
AdaBoostRegressor
Test Summary:   | Pass  Total  Time
lale regressors |   18     18  2.0s
Test Summary:            | Pass  Total  Time
lale autogen classifiers |    9      9  0.6s
Test Summary:           | Pass  Total  Time
lale autogen regressors |   10     10  0.6s
Test Summary:               | Pass  Total  Time
lale preprocessors fit test |   19     19  0.3s
Test Summary:                     | Pass  Total  Time
lale preprocessors transform test |   19     19  0.7s
Test Summary:                                         | Pass  Total   Time
scikit preprocessor fit/transform test with real data |    9      9  44.1s
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,  2.51trial/s, best loss: 58.89799637488588]
100%|██████████| 3/3 [00:00<00:00,  4.98trial/s, best loss: 29.294951428296784]
100%|██████████| 3/3 [00:00<00:00,  4.53trial/s, best loss: 29.294951428296784]

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 33%|███▎      | 1/3 [00:00<00:00,  2.55trial/s, best loss: 60.067454801501675]
100%|██████████| 3/3 [00:00<00:00,  3.91trial/s, best loss: 32.67127477017288] 
100%|██████████| 3/3 [00:00<00:00,  3.71trial/s, best loss: 32.67127477017288]

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 33%|███▎      | 1/3 [00:00<00:00,  3.33trial/s, best loss: 53.93127659704387]
 67%|██████▋   | 2/3 [00:00<00:00,  4.28trial/s, best loss: 33.82828122145906]
100%|██████████| 3/3 [00:00<00:00,  5.49trial/s, best loss: 33.82828122145906]
100%|██████████| 3/3 [00:00<00:00,  4.93trial/s, best loss: 33.82828122145906]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:01,  1.83trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:00<00:00,  2.22trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  3.09trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.72trial/s, best loss: -0.9533333333333333]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:00,  2.04trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:00<00:00,  2.04trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.68trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.47trial/s, best loss: -0.9533333333333333]fold: 1, 0.1948032664282382
fold: 2, 0.14553785997184754
fold: 3, 0.1724159487145148
fold: 4, 0.28819505451073324
fold: 5, 0.1922497395384655
fold: 6, 0.18747452103429174
fold: 7, 0.15320971617240794
fold: 8, 0.1344129950590373
fold: 9, 0.2180074497537831
fold: 10, 0.19818055483526847
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  32.5s
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.


  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:00,  2.47trial/s, best loss: 55.762545659275645]
 67%|██████▋   | 2/3 [00:00<00:00,  4.39trial/s, best loss: 55.762545659275645]
100%|██████████| 3/3 [00:00<00:00,  4.82trial/s, best loss: 41.95473921097493] 
100%|██████████| 3/3 [00:00<00:00,  4.33trial/s, best loss: 41.95473921097493]

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 33%|███▎      | 1/3 [00:00<00:00,  2.47trial/s, best loss: 59.58863143573669]
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100%|██████████| 3/3 [00:00<00:00,  3.63trial/s, best loss: 43.81439594939426]
100%|██████████| 3/3 [00:00<00:00,  3.32trial/s, best loss: 43.81439594939426]

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100%|██████████| 3/3 [00:00<00:00,  4.83trial/s, best loss: 39.88723889458449] 
100%|██████████| 3/3 [00:00<00:00,  4.36trial/s, best loss: 39.88723889458449]

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 33%|███▎      | 1/3 [00:00<00:01,  1.61trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:01<00:00,  1.79trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  1.80trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  1.78trial/s, best loss: -0.9533333333333333]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:01,  1.27trial/s, best loss: -0.9533333333333333]
 67%|██████▋   | 2/3 [00:01<00:00,  1.57trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  2.18trial/s, best loss: -0.9533333333333333]
100%|██████████| 3/3 [00:01<00:00,  1.92trial/s, best loss: -0.9533333333333333]fold: 1, 0.18322864081473406
fold: 2, 0.1479099732444879
fold: 3, 0.17570136617019833
fold: 4, 0.29116133649640086
fold: 5, 0.2021155149175108
fold: 6, 0.1792083895605585
fold: 7, 0.14095100304453081
fold: 8, 0.12917966049338128
fold: 9, 0.22021892831654827
fold: 10, 0.18071902096081957
errors: 0
fold: 1, 93.33333333333333
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  15.5s


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 20%|██        | 2/10 [00:00<00:00, 16.86trial/s, best loss: -0.6566558441558441]
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100%|██████████| 10/10 [00:00<00:00, 19.22trial/s, best loss: -0.6566558441558441]

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100%|██████████| 10/10 [00:00<00:00, 16.70trial/s, best loss: -0.6566558441558441]
100%|██████████| 10/10 [00:00<00:00, 16.49trial/s, best loss: -0.6566558441558441]

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 60%|██████    | 6/10 [00:00<00:00, 14.08trial/s, best loss: -0.6573051948051948]
 80%|████████  | 8/10 [00:00<00:00, 14.83trial/s, best loss: -0.6573051948051948]
100%|██████████| 10/10 [00:00<00:00, 15.14trial/s, best loss: -0.6573051948051948]
100%|██████████| 10/10 [00:00<00:00, 15.02trial/s, best loss: -0.6573051948051948]

  0%|          | 0/10 [00:00<?, ?trial/s, best loss=?]
 20%|██        | 2/10 [00:00<00:00, 15.09trial/s, best loss: -0.6566558441558441]
 40%|████      | 4/10 [00:00<00:00, 16.14trial/s, best loss: -0.6566558441558441]
 60%|██████    | 6/10 [00:00<00:00, 14.38trial/s, best loss: -0.6566558441558441]
 80%|████████  | 8/10 [00:00<00:00, 15.01trial/s, best loss: -0.6566558441558441]
100%|██████████| 10/10 [00:00<00:00, 15.19trial/s, best loss: -0.6566558441558441]
100%|██████████| 10/10 [00:00<00:00, 15.12trial/s, best loss: -0.6566558441558441]

  0%|          | 0/10 [00:00<?, ?trial/s, best loss=?]
 20%|██        | 2/10 [00:00<00:00, 16.11trial/s, best loss: -0.6566558441558441]
 40%|████      | 4/10 [00:00<00:00, 17.00trial/s, best loss: -0.6566558441558441]
 60%|██████    | 6/10 [00:00<00:00, 15.16trial/s, best loss: -0.6566558441558441]
 80%|████████  | 8/10 [00:00<00:00, 15.88trial/s, best loss: -0.6566558441558441]
100%|██████████| 10/10 [00:00<00:00, 15.91trial/s, best loss: -0.6566558441558441]
100%|██████████| 10/10 [00:00<00:00, 15.90trial/s, best loss: -0.6566558441558441]Test Summary:      | Pass  Total   Time
Imbalanced Dataset |    5      5  12.0s


  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:00<00:01,  1.77trial/s, best loss: -0.6875005866726743]
 67%|██████▋   | 2/3 [00:01<00:00,  1.88trial/s, best loss: -0.6875005866726743]
100%|██████████| 3/3 [00:01<00:00,  1.99trial/s, best loss: -0.7037537664385684]
100%|██████████| 3/3 [00:01<00:00,  1.95trial/s, best loss: -0.7037537664385684]

  0%|          | 0/3 [00:00<?, ?trial/s, best loss=?]
 33%|███▎      | 1/3 [00:01<00:03,  1.93s/trial, best loss: -0.7412820440614646]
 67%|██████▋   | 2/3 [00:04<00:02,  2.20s/trial, best loss: -0.7412820440614646]
100%|██████████| 3/3 [00:06<00:00,  1.99s/trial, best loss: -0.7412820440614646]
100%|██████████| 3/3 [00:06<00:00,  2.02s/trial, best loss: -0.7412820440614646]Test Summary:   | Pass  Total   Time
Lale Projection |    2      2  20.5s

     Testing Lale tests passed 

Testing completed after 234.7s

PkgEval succeeded after 442.64s