1.5.0-DEV-7206b56e94.log

Julia Version 1.5.0-DEV.485
Commit 7206b56e94 (2020-03-18 17:25 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
  JULIA_DEPOT_PATH = ::/usr/local/share/julia
  JULIA_NUM_THREADS = 2

  Resolving package versions...
  Installed Reexport ─────────── v0.2.0
  Installed SortingAlgorithms ── v0.3.1
  Installed PenaltyFunctions ─── v0.1.2
  Installed DataAPI ──────────── v1.1.0
  Installed SweepOperator ────── v0.3.0
  Installed RecipesBase ──────── v0.8.0
  Installed SparseRegression ─── v0.2.0
  Installed DataStructures ───── v0.17.10
  Installed LearnBase ────────── v0.2.2
  Installed Missings ─────────── v0.4.3
  Installed LearningStrategies ─ v0.4.0
  Installed OrderedCollections ─ v1.1.0
  Installed LossFunctions ────── v0.5.1
  Installed StatsBase ────────── v0.32.2
   Updating `~/.julia/environments/v1.5/Project.toml`
   ca6142a6 + SparseRegression v0.2.0
   Updating `~/.julia/environments/v1.5/Manifest.toml`
   9a962f9c + DataAPI v1.1.0
   864edb3b + DataStructures v0.17.10
   7f8f8fb0 + LearnBase v0.2.2
   4e8a8231 + LearningStrategies v0.4.0
   30fc2ffe + LossFunctions v0.5.1
   e1d29d7a + Missings v0.4.3
   bac558e1 + OrderedCollections v1.1.0
   06bb1623 + PenaltyFunctions v0.1.2
   3cdcf5f2 + RecipesBase v0.8.0
   189a3867 + Reexport v0.2.0
   a2af1166 + SortingAlgorithms v0.3.1
   ca6142a6 + SparseRegression v0.2.0
   2913bbd2 + StatsBase v0.32.2
   7522ee7d + SweepOperator v0.3.0
   2a0f44e3 + Base64
   ade2ca70 + Dates
   8ba89e20 + Distributed
   b77e0a4c + InteractiveUtils
   76f85450 + LibGit2
   8f399da3 + Libdl
   37e2e46d + LinearAlgebra
   56ddb016 + Logging
   d6f4376e + Markdown
   44cfe95a + Pkg
   de0858da + Printf
   3fa0cd96 + REPL
   9a3f8284 + Random
   ea8e919c + SHA
   9e88b42a + Serialization
   6462fe0b + Sockets
   2f01184e + SparseArrays
   10745b16 + Statistics
   8dfed614 + Test
   cf7118a7 + UUIDs
   4ec0a83e + Unicode
    Testing SparseRegression
     Status `/tmp/jl_DLGmSZ/Project.toml`
   7f8f8fb0 LearnBase v0.2.2
   4e8a8231 LearningStrategies v0.4.0
   30fc2ffe LossFunctions v0.5.1
   06bb1623 PenaltyFunctions v0.1.2
   3cdcf5f2 RecipesBase v0.8.0
   ca6142a6 SparseRegression v0.2.0
   2913bbd2 StatsBase v0.32.2
   7522ee7d SweepOperator v0.3.0
   37e2e46d LinearAlgebra
   8dfed614 Test
     Status `/tmp/jl_DLGmSZ/Manifest.toml`
   9a962f9c DataAPI v1.1.0
   864edb3b DataStructures v0.17.10
   7f8f8fb0 LearnBase v0.2.2
   4e8a8231 LearningStrategies v0.4.0
   30fc2ffe LossFunctions v0.5.1
   e1d29d7a Missings v0.4.3
   bac558e1 OrderedCollections v1.1.0
   06bb1623 PenaltyFunctions v0.1.2
   3cdcf5f2 RecipesBase v0.8.0
   189a3867 Reexport v0.2.0
   a2af1166 SortingAlgorithms v0.3.1
   ca6142a6 SparseRegression v0.2.0
   2913bbd2 StatsBase v0.32.2
   7522ee7d SweepOperator v0.3.0
   2a0f44e3 Base64
   ade2ca70 Dates
   8ba89e20 Distributed
   b77e0a4c InteractiveUtils
   76f85450 LibGit2
   8f399da3 Libdl
   37e2e46d LinearAlgebra
   56ddb016 Logging
   d6f4376e Markdown
   44cfe95a Pkg
   de0858da Printf
   3fa0cd96 REPL
   9a3f8284 Random
   ea8e919c SHA
   9e88b42a Serialization
   6462fe0b Sockets
   2f01184e SparseArrays
   10745b16 Statistics
   8dfed614 Test
   cf7118a7 UUIDs
   4ec0a83e Unicode
SModel
  > abs.(β)  :      
  > λ factor : [0.1 0.1 0.1 0.1 0.1]
  > Loss     : 0.5 * (L2DistLoss)
  > Penalty  : L2Penalty
  > Data
    - x : 1000×5 Array{Float64,2}
    - y : 1000-element Array{Float64,1}
    - w : Nothing

Test Summary: |
Sanity Check  | No tests
[ Info: SparseRegression.ProxGrad
  > L2DistLoss
  > 0.5 * (L2DistLoss)
  > HuberLoss with $\alpha$ = 2.0
  > QuantileLoss with $\tau$ = 0.7
  > LogitMarginLoss
  > DWDMarginLoss with q = 2.0
  > L2HingeLoss
  > ExpLoss

[ Info: SparseRegression.Fista
  > L2DistLoss
  > 0.5 * (L2DistLoss)
  > HuberLoss with $\alpha$ = 2.0
  > QuantileLoss with $\tau$ = 0.7
  > LogitMarginLoss
  > DWDMarginLoss with q = 2.0
  > L2HingeLoss
  > ExpLoss

[ Info: SparseRegression.AdaptiveProxGrad
  > L2DistLoss
  > 0.5 * (L2DistLoss)
  > HuberLoss with $\alpha$ = 2.0
  > QuantileLoss with $\tau$ = 0.7
  > LogitMarginLoss
  > DWDMarginLoss with q = 2.0
  > L2HingeLoss
  > ExpLoss

[ Info: SparseRegression.GradientDescent
  > L2DistLoss
  > 0.5 * (L2DistLoss)
  > HuberLoss with $\alpha$ = 2.0
  > QuantileLoss with $\tau$ = 0.7
  > LogitMarginLoss
  > DWDMarginLoss with q = 2.0
  > L2HingeLoss
  > ExpLoss

Test Summary: | Pass  Total
Sanity Check  |  272    272
Test Summary:     | Pass  Total
Linear Regression |    5      5
Test Summary: | Pass  Total
LineSearch    |    3      3
Test Summary: | Pass  Total
SModel        |    2      2
    Testing SparseRegression tests passed