✨ Add BalancedModel

.github/workflows/CI.yml

@@ -19,11 +19,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.0'
           - '1.8'
-          - 'nightly'
-        os:
-          - ubuntu-latest
+        os: [ubuntu-latest, windows-latest, macOS-latest]
         arch:
           - x64
     steps:

Project.toml

@@ -10,17 +10,18 @@ OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
-julia = "1"
 MLJBase = "0.21"
 MLJModelInterface = "1.9"
 OrderedCollections = "1.6"
+julia = "1"
 
 [extras]
+MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Imbalance = "c709b415-507b-45b7-9a3d-1767c89fde68"
 MLJLIBSVMInterface = "61c7150f-6c77-4bb1-949c-13197eac2a52"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Imbalance", "DataFrames", "MLJLIBSVMInterface", "MLJLinearModels"]
+test = ["Test", "Imbalance", "DataFrames", "MLJLIBSVMInterface", "MLJLinearModels", "MLJ"]

README.md

@@ -1,2 +1,46 @@
 # MLJBalancing
 A package with exported learning networks that combine resampling methods from Imbalance.jl and classification models from MLJ
+
+## 🚅 Sequential Resampling
+
+This package allows chaining of resampling methods from Imbalance.jl with classification models from MLJ. Simply construct a `BalancedModel` object while specifying the model and an arbitrart number of resamplers.
+
+### 📖 Example
+
+#### Construct the resamplers and the model
+```julia
+SMOTENC = @load SMOTENC pkg=Imbalance verbosity=0
+TomekUndersampler = @load TomekUndersampler pkg=Imbalance verbosity=0
+
+oversampler = SMOTENC(k=5, ratios=1.0, rng=42)
+undersampler = TomekUndersampler(min_ratios=0.5, rng=42)
+
+logistic_model = LogisticClassifier()
+```
+
+#### Wrap them all in BalancedModel
+```julia
+balanced_model = BalancedModel(model=logistic_model, balancer1=oversampler, balancer2=undersampler)
+```
+Here data will be passed to balancer1 then balancer2 and then the model. In general, there can be any number of balancers.
+
+#### At this point, they behave like one single model
+You can fit, predict, cross-validate and finetune it like any other MLJ model. Here is an example for finetuning
+```julia
+r1 = range(balanced_model, :(balancer1.k), lower=3, upper=10)
+r2 = range(balanced_model, :(balancer2.min_ratios), lower=0.1, upper=0.9)
+
+tuned_balanced_model = TunedModel(model=balanced_model,
+									  tuning=Grid(goal=4),
+									  resampling=CV(nfolds=4),
+									  range=[r1, r2],
+									  measure=cross_entropy);
+
+mach = machine(tuned_balanced_model, X, y);
+fit!(mach, verbosity=0);
+fitted_params(mach).best_model
+```
+
+## 🚆🚆 Parallel Resampling with EasyEnsemble
+
+Coming soon...