🎨 Some polishing

Author: EssamWisam

Project.toml

@@ -4,7 +4,6 @@ authors = ["Essam <[email protected]> and contributors"]
 version = "1.0.0-DEV"
 
 [deps]
-JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
@@ -16,6 +15,9 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 MLJBase = "0.21"
 OrderedCollections = "1.6"
 julia = "1.6"
+MLJModelInterface = "1.9"
+MLUtils = "0.4"
+StatsBase = "0.34"
 
 [extras]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

README.md

@@ -1,5 +1,5 @@
 # MLJBalancing
-A package providing composite models wrapping class imbalance algorithms from [Imbalance.jl](https://github.com/JuliaAI/Imbalance.jl).
+A package providing composite models wrapping class imbalance algorithms from [Imbalance.jl](https://github.com/JuliaAI/Imbalance.jl) with classifiers from [MLJ](https://github.com/alan-turing-institute/MLJ.jl). 
 
 ## ⏬ Instalattion
 ```julia
@@ -51,6 +51,24 @@ fit!(mach, verbosity=0);
 fitted_params(mach).best_model
 ```
 
-## 🚆🚆 Parallel Resampling with EasyEnsemble
+## 🚆🚆 Parallel Resampling with Balanced Bagging
 
-Coming soon...
+The package also offers an implementation of bagging over probabilistic classifiers where the majority class is repeatedly undersampled `T` times down to the size of the minority class. This undersampling scheme was proposed in the *EasyEnsemble* algorithm found in the paper *Exploratory Undersampling for Class-Imbalance Learning.* by *Xu-Ying Liu, Jianxin Wu, & Zhi-Hua Zhou* where an Adaboost model was used and the output scores were averaged.
+
+
+#### Construct a BalancedBaggingClassifier
+In this you must specify the model, and optionally specify the number of bags `T` and the random number generator `rng`. If `T` is not specified it is set as the ratio between the majority and minority counts. If `rng` isn't specified then `default_rng()` is used.
+
+```julia
+LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels verbosity=0
+logistic_model = LogisticClassifier()
+bagging_model = BalancedBaggingClassifier(model=logistic_model, T=10, rng=Random.Xoshiro(42))
+```
+
+#### Now it behaves like one single model
+You can fit, predict, cross-validate and finetune it like any other probabilistic MLJ model where `X` must be a table input (e.g., a dataframe).
+```julia
+mach = machine(bagging_model, X, y)
+fit!(mach)
+pred = predict(mach, X)
+```

examples/BalancedBagging.ipynb example/BalancedBagging.ipynbexamples/BalancedBagging.ipynb renamed to example/BalancedBagging.ipynb

@@ -2,16 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/Documents/GitHub/MLJBalancing/example`\n"
+     ]
+    }
+   ],
    "source": [
     "ENV[\"JULIA_PKG_SERVER\"] = \"\"\n",
     "using Pkg\n",
     "Pkg.activate(@__DIR__)\n",
     "Pkg.instantiate()\n",
     "\n",
-    "\n",
     "using MLJBalancing\n",
     "using Imbalance\n",
     "using MLJ\n",
@@ -27,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -56,9 +63,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: using StaticArrays.setindex in module FiniteDiff conflicts with an existing identifier.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -82,7 +96,7 @@
    "source": [
     "LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels verbosity=0\n",
     "logistic_model = LogisticClassifier()\n",
-    "model = BalancedBaggingClassifier(classifier=logistic_model, T=10, rng=Random.Xoshiro(42))"
+    "model = BalancedBaggingClassifier(model=logistic_model, T=10, rng=Random.Xoshiro(42))"
    ]
   },
   {
@@ -94,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {