Merge pull request #473 from alan-turing-institute/dev

For a 0.10.1 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.10"
+version = "0.10.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -24,7 +24,7 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "^0.7"
 ComputationalResources = "^0.3"
 Distributions = "^0.21,^0.22"
-MLJBase = "^0.12"
+MLJBase = "^0.12.1"
 MLJModels = "^0.9"
 MLJScientificTypes = "^0.2.1"
 MLJTuning = "^0.2"

README.md

@@ -130,7 +130,7 @@ the most up-to-date list, run `using MLJ; models()`.
 [MultivariateStats.jl] | RidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA, BayesianSubspaceLDA | high | †
 [NaiveBayes.jl] | GaussianNBClassifier, MultinomialNBClassifier, HybridNBClassifier | low |
 [NearestNeighbors.jl] | KNNClassifier, KNNRegressor | high |
-[ScikitLearn.jl] | SVMClassifier, SVMRegressor, SVMNuClassifier, SVMNuRegressor, SVMLClassifier, SVMLRegressor, ARDRegressor, BayesianRidgeRegressor, ElasticNetRegressor, ElasticNetCVRegressor, HuberRegressor, LarsRegressor, LarsCVRegressor, LassoRegressor, LassoCVRegressor, LassoLarsRegressor, LassoLarsCVRegressor, LassoLarsICRegressor, LinearRegressor, OrthogonalMatchingPursuitRegressor, OrthogonalMatchingPursuitCVRegressor, PassiveAggressiveRegressor, RidgeRegressor, RidgeCVRegressor, SGDRegressor, TheilSenRegressor, LogisticClassifier, LogisticCVClassifier, PerceptronClassifier, RidgeClassifier, RidgeCVClassifier, PassiveAggressiveClassifier, SGDClassifier, GaussianProcessRegressor, GaussianProcessClassifier, AdaBoostRegressor, AdaBoostClassifier, BaggingRegressor, BaggingClassifier, GradientBoostingRegressor, GradientBoostingClassifier, RandomForestRegressor, RandomForestClassifier, GaussianNB, MultinomialNB, ComplementNB, BayesianLDA, BayesianQDA | high | †
+[ScikitLearn.jl] | ARDRegressor, AdaBoostClassifier, AdaBoostRegressor, AffinityPropagation, AgglomerativeClustering, BaggingClassifier, BaggingRegressor, BayesianLDA, BayesianQDA, BayesianRidgeRegressor, BernoulliNBClassifier, Birch, ComplementNBClassifier, DBSCAN, DummyClassifier, DummyRegressor, ElasticNetCVRegressor, ElasticNetRegressor, ExtraTreesClassifier, ExtraTreesRegressor, FeatureAgglomeration, GaussianNBClassifier, GaussianProcessClassifier, GaussianProcessRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HuberRegressor, KMeans, KNeighborsClassifier, KNeighborsRegressor, LarsCVRegressor, LarsRegressor, LassoCVRegressor, LassoLarsCVRegressor, LassoLarsICRegressor, LassoLarsRegressor, LassoRegressor, LinearRegressor, LogisticCVClassifier, LogisticClassifier, MeanShift, MiniBatchKMeans, MultiTaskElasticNetCVRegressor, MultiTaskElasticNetRegressor, MultiTaskLassoCVRegressor, MultiTaskLassoRegressor, MultinomialNBClassifier, OPTICS, OrthogonalMatchingPursuitCVRegressor, OrthogonalMatchingPursuitRegressor, PassiveAggressiveClassifier, PassiveAggressiveRegressor, PerceptronClassifier, ProbabilisticSGDClassifier, RANSACRegressor, RandomForestClassifier, RandomForestRegressor, RidgeCVClassifier, RidgeCVRegressor, RidgeClassifier, RidgeRegressor, SGDClassifier, SGDRegressor, SVMClassifier, SVMLClassifier, SVMLRegressor, SVMNuClassifier, SVMNuRegressor, SVMRegressor, SpectralClustering, TheilSenRegressor | high | †
 [XGBoost.jl] | XGBoostRegressor, XGBoostClassifier, XGBoostCount | high |
 
 **Note** (†): some models are missing, your help is welcome to complete the interface. Get in touch with Thibaut Lienart on Slack if you would like to help, thanks!

docs/src/machines.md

@@ -59,13 +59,62 @@ fit!(mach, rows=1:100);
 fit!(mach, rows=1:100);
 ```
 
+## Inspecting machines
+
+There are two methods for inspecting the outcomes of training in
+MLJ. To obtain a named-tuple describing the learned parameters (in a
+user-friendly way where possible) use `fitted_params(mach)`. All other
+training-related outcomes are inspected with `report(mach)`.
+
+```@example machines
+X, y = @load_iris
+pca = @load PCA
+mach = machine(pca, X)
+fit!(mach)
+```
+
+```@repl machines
+fitted_params(mach)
+report(mach)
+```
+
+## Saving machines
+
+To save a machine to file, use the [`MLJ.save`](@ref) command:
+
+```julia
+tree = @load DecisionTreeClassifier
+mach = fit!(machine(tree, X, y))
+MLJ.save("my_machine.jlso", mach)
+```
+
+To de-serialize, one uses the `machine` constructor:
+
+```julia
+mach2 = machine("my_machine.jlso")
+predict(mach2, Xnew);
+```
+
+The machine `mach2` cannot be retrained; however, by providing data to
+the constructor one can enable retraining using the saved model
+hyperparameters (which overwrites the saved learned parameters):
+
+```julia
+mach3 = machine("my_machine.jlso", Xnew, ynew)
+fit!(mach3)
+```
+
+
+## Internals
+
 For a supervised machine the `predict` method calls a lower-level
 `MLJBase.predict` method, dispatched on the underlying model and the
 `fitresult` (see below). To see `predict` in action, as well as its
 unsupervised cousins `transform` and `inverse_transform`, see
 [Getting Started](index.md).
 
-Here is a complete list of the fields of a machine:
+The fields of a `Machine` instance (which should not generally be
+accessed byt the user) are:
 
 - `model` - the struct containing the hyperparameters to be used in
   calls to `fit!`
@@ -84,33 +133,14 @@ Here is a complete list of the fields of a machine:
 
 Instead of data `X` and `y`, the `machine` constructor can be provided
 `Node` or `Source` objects ("dynamic data") to obtain a
-`NodalMachine`, rather than a regular `Machine` object, which includes
-the same fields listed above.
-See [Composing Models](composing_models.md) for more on this advanced feature.
-
-
-## Inspecting machines
-
-There are two methods for inspecting the outcomes of training in
-MLJ. To obtain a named-tuple describing the learned parameters, in a
-user-friendly way if possible, use `fitted_params(mach)`. All other
-training-related outcomes are inspected with `report(mach)`.
-
-```@example machines
-X, y = @load_iris
-pca = @load PCA
-mach = machine(pca, X)
-fit!(mach)
-```
-
-```@repl machines
-fitted_params(mach)
-report(mach)
-```
+`NodalMachine`, rather than a regular `Machine` object, which has the
+fields listed above and some others. See [Composing
+Models](composing_models.md) for more on this advanced feature.
 
 
 ## API Reference
 
 ```@docs
 fit!
+MLJBase.save
 ```

src/MLJ.jl

@@ -107,6 +107,7 @@ import Pkg.TOML
 
 # from the MLJ universe:
 using MLJBase
+import MLJBase.save
 using MLJTuning
 using MLJModels