Merge pull request #11 from JuliaAI/dev

For a 0.2.0 release

.gitignore

@@ -1,4 +1,4 @@
-Manifest.toml
+/Manifest.toml
 /docs/Manifest.toml
 /test/Manifest.toml
 .ipynb_checkpoints

Project.toml

@@ -1,11 +1,15 @@
 name = "MLJTestIntegration"
 uuid = "697918b4-fdc1-4f9e-8ff9-929724cee270"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
+MLJTuning = "03970b2e-30c4-11ea-3135-d1576263f10f"
+NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 MLJ = "0.18"

README.md

@@ -15,9 +15,9 @@ Pkg.add("MLJTestIntegration")
 
 # Usage
 
-This package provides a single method for testing a collection of
-`models` (types or named tuples with keys `:name` and `:package_name`)
-using the specified training `data`:
+This package provides a method for testing a collection of `models`
+(types or named tuples with keys `:name` and `:package_name`) using
+the specified training `data`:
 
 ```julia
 MLJTestIntegration.test(models, data...; mod=Main, level=2, throw=false, verbosity=1) 
@@ -26,6 +26,16 @@ MLJTestIntegration.test(models, data...; mod=Main, level=2, throw=false, verbosi
 
 For detailed documentation, run `using MLJTestIntegration; @doc MLJTestIntegration.test`.
 
+For convenience, a number of specializations of this method are also provided: 
+
+- `test_single_target_classifiers`
+- `test_single_target_regressors`
+- `test_single_target_count_regressors`
+- `test_continuous_table_transformers`
+
+Query the document strings for details, or see
+[examples/bigtest/notebook.jl](examples/bigtest/notebook.jl).
+
 
 # Examples
 
@@ -60,11 +70,11 @@ regressors = MLJTestIntegration.MLJ.models(matching(X, y)) do m
 end
 
 # to test code loading *and* load code:
-MLJTestIntegration.test(regressors, X, y, verbosity=1, mod=@__MODULE__, level=1)
+MLJTestIntegration.test(regressors, X, y, verbosity=1, mod=@__MODULE__, level=4)
 
 # comprehensive tests:
 failures, summary =
-    MLJTestIntegration.test(regressors, X, y, verbosity=3, mod=@__MODULE__, level=1)
+    MLJTestIntegration.test(regressors, X, y, verbosity=3, mod=@__MODULE__, level=4)
 
 summary |> DataFrame
 ```