# LearningStrategies.jl

**Repository Path**: Julialang/LearningStrategies.jl

## Basic Information

- **Project Name**: LearningStrategies.jl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-03-12
- **Last Updated**: 2024-06-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LearningStrategies
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|--------------|---------------|------------|
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**LearningStrategies is a modular framework for building iterative algorithms in Julia**.

## Basics

Many algorithms can be generalized to the following pseudocode:

```
setup
while not finished:
    (update model)
    (iteration logic)
cleanup
```

## MetaStrategy
The core function of LearningStrategies is a straightforward abstract implementation
of the above loop.  A `model` can be learned by an `LearningStrategy` or a collection of
strategies in a `MetaStrategy`.

```julia
function learn!(model, strat::LearningStrategy, data)
    setup!(strat, model[, data])
    for (i, item) in enumerate(data)
        update!(model, strat[, i], item)
        hook(strat, model[, data], i)
        finished(strat, model[, data], i) && break
    end
    cleanup!(strat, model)
    model
end
```

- For a `MetaStrategy`, each function (`setup!`, `update!`, `hook`, `finished`, `cleanup!`) is mapped to the contained strategies.
- To let `item == data`, pass the argument `Iterators.repeated(data)`.

## Built In Strategies

See help (i.e. `?MaxIter`) for more info.

- `MetaStrategy`
- `MaxIter`
- `TimeLimit`
- `Converged`
- `ConvergedTo`
- `IterFunction`
- `Tracer`
- `Breaker`
- `Verbose`

## Examples

### Learning with a single LearningStrategy

```julia
julia> using LearningStrategies

julia> s = Verbose(TimeLimit(2))
Verbose TimeLimit(2.0)

julia> @elapsed learn!(nothing, s)  # data == InfiniteNothing()
INFO: TimeLimit(2.0) finished
2.000225545
```

### Learning with a MetaLearner

```julia
julia> using LearningStrategies

julia> s = strategy(Verbose(MaxIter(5)), TimeLimit(10))
MetaStrategy
  > Verbose MaxIter(5)
  > TimeLimit(10.0)

julia> learn!(nothing, s, 1:100)
INFO: MaxIter: 1/5
INFO: MaxIter: 2/5
INFO: MaxIter: 3/5
INFO: MaxIter: 4/5
INFO: MaxIter: 5/5
INFO: MaxIter(5) finished
```

### Linear Regression Solver

```julia
using LearningStrategies
import LearningStrategies: update!, finished
import Base.Iterators: repeated

struct MyLinearModel
    coef
end

struct MyLinearModelSolver <: LearningStrategy end

update!(model, s::MyLinearModelSolver, xy) = (model.coef[:] = xy[1] \ xy[2])

finished(s::MyLinearModelSolver, model) = true

# generate some fake data
x = randn(100, 5)
y = x * linspace(-1, 1, 5) + randn(100)

data = (x, y)

# Create the model
model = MyLinearModel(zeros(5))

# learn! the model with data (x, y)
learn!(model, MyLinearModelSolver(), repeated(data))

# check that it works
model.coef == x \ y
```


# Acknowledgements
LearningStrategies is partially inspired by [IterationManagers](https://github.com/sglyon/IterationManagers.jl) and (Tom Breloff's) conversations with [Spencer Lyon](https://github.com/sglyon).  This functionality was previously part of the [StochasticOptimization](https://github.com/JuliaML/StochasticOptimization.jl) package, but was split off as a dependency.

Complex LearningStrategy examples (using previous LearningStrategies versions) can be found in [StochasticOptimization](https://github.com/JuliaML/StochasticOptimization.jl) and from Tom Breloff's [blog posts](http://www.breloff.com/JuliaML-and-Plots/).

Examples using the current version can be found in [SparseRegression](https://github.com/joshday/SparseRegression.jl).

## Primary author: [Tom Breloff](https://github.com/tbreloff)