Merge pull request #582 from alan-turing-institute/dev

For a 0.12.0 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.11.5"
+version = "0.12.0"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -24,10 +24,10 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "^0.8"
 ComputationalResources = "^0.3"
 Distributions = "^0.21,^0.22,^0.23"
-MLJBase = "^0.13.6"
-MLJModels = "^0.10"
+MLJBase = "^0.14"
+MLJModels = "^0.11"
 MLJScientificTypes = "^0.2.1"
-MLJTuning = "^0.3.1"
+MLJTuning = "^0.4"
 ProgressMeter = "^1.1"
 StatsBase = "^0.32,^0.33"
 Tables = "^0.2,^1.0"

README.md

@@ -94,10 +94,11 @@ the most up-to-date list, run `using MLJ; models()`.
 [DecisionTree.jl] | DecisionTreeClassifier, DecisionTreeRegressor, AdaBoostStumpClassifier | high | †
 [EvoTrees.jl] | EvoTreeRegressor, EvoTreeClassifier, EvoTreeCount, EvoTreeGaussian | medium | gradient boosting models
 [GLM.jl] | LinearRegressor, LinearBinaryClassifier, LinearCountRegressor | medium | †
-[LightGBM.jl] | LightGBMClassifier, LightGBMRegressor | high | 
 [LIBSVM.jl] | LinearSVC, SVC, NuSVC, NuSVR, EpsilonSVR, OneClassSVM | high | also via ScikitLearn.jl
-[MLJModels.jl] (builtins) | StaticTransformer, FeatureSelector, FillImputer, UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer, OneHotEncoder, ContinuousEncoder, ConstantRegressor, ConstantClassifier | medium |
+[LightGBM.jl] | LightGBMClassifier, LightGBMRegressor | high | 
+[MLJFlux.jl] | NeuralNetworkRegressor, NeuralNetworkClassifier, MultitargetNeuralNetworkRegressor, ImageClassifier | experimental |
 [MLJLinearModels.jl] | LinearRegressor, RidgeRegressor, LassoRegressor, ElasticNetRegressor, QuantileRegressor, HuberRegressor, RobustRegressor, LADRegressor, LogisticClassifier, MultinomialClassifier | experimental |
+[MLJModels.jl] (builtins) | StaticTransformer, FeatureSelector, FillImputer, UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer, OneHotEncoder, ContinuousEncoder, ConstantRegressor, ConstantClassifier | medium |
 [MultivariateStats.jl] | RidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA, BayesianSubspaceLDA | high | †
 [NaiveBayes.jl] | GaussianNBClassifier, MultinomialNBClassifier, HybridNBClassifier | experimental |
 [NearestNeighbors.jl] | KNNClassifier, KNNRegressor | high |

binder/Project.toml

@@ -0,0 +1,60 @@
+[deps]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
+Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
+Franklin = "713c75ef-9fc9-4b05-94a9-213340da978e"
+GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
+HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
+LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
+LightGBM = "7acf609c-83a4-11e9-1ffb-b912bcd3b04a"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7"
+MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
+MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
+MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
+MLJScientificTypes = "2e2323e0-db8b-457b-ae0d-bdfb3bc63afd"
+MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
+NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
+PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
+PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
+RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+UrlDownload = "856ac37a-3032-4c1c-9122-f86d88358c8b"
+XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
+ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
+Gadfly = "c91e804a-d5a3-530f-b6f0-dfbca275c004"
+MLJTuning = "03970b2e-30c4-11ea-3135-d1576263f10f"
+TreeParzen = "eb66a70c-a255-11e9-03ea-7ba6b2f22006"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+
+[compat]
+MLJ = "0.11"
+MLJBase = "0.13"
+MLJLinearModels = "0.4"
+MLJModelInterface = "0.2"
+MLJModels = "0.9"
+MLJScientificTypes = "0.2"
+Gadfly = "= 1.2.1"
+TreeParzen = "= 0.1.1"
+
+[extras]
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Test", "Logging"]