diff --git a/.gitignore b/.gitignore index 7d816eca..a72a86ff 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ /Manifest.toml +/docs/Manifest.toml .ipynb_checkpoints *~ #* @@ -7,4 +8,4 @@ sandbox/ docs/build /examples/mnist/mnist_machine* -Manifest.toml \ No newline at end of file +*.jls \ No newline at end of file diff --git a/README.md b/README.md index eed27c3c..bc4c72a2 100644 --- a/README.md +++ b/README.md @@ -21,491 +21,80 @@ learning framework. [coveralls-img-dev]: https://coveralls.io/repos/github/alan-turing-institute/MLJFlux.jl/badge.svg?branch=dev "Code Coverage" [coveralls-url]: https://github.com/FluxML/MLJFlux.jl/actions/workflows/ci.yml -MLJFlux makes it possible to apply the machine learning -meta-algorithms provided by MLJ - such as out-of-sample performance -evaluation, hyper-parameter optimization, and iteration control - to some classes of -**supervised deep learning models**. It does this by providing an -interface to the [Flux](https://fluxml.ai/Flux.jl/stable/) -framework. +[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://fluxml.github.io/MLJFlux.jl/dev/) -The guiding vision of this package is to make evaluating and -optimizing basic Flux models more convenient to users already familiar -with the MLJ workflow. This goal will likely place restrictions of the -class of Flux models that can used, at least in the medium term. For -example, online learning, re-enforcement learning, and adversarial -networks are currently out of scope. -Currently MLJFlux is also limited to training models in the case that all -training data fits into memory. - - -### Basic idea - -Each MLJFlux model has a *builder* hyperparameter, an object encoding -instructions for creating a neural network given the data that the -model eventually sees (e.g., the number of classes in a classification -problem). While each MLJ model has a simple default builder, users -will generally need to define their own builders to get good results, -and this will require familiarity with the [Flux -API](https://fluxml.ai/Flux.jl/stable/) for defining a neural network -chain. - -### Installation +## Code Snippet ```julia -using Pkg -Pkg.activate("my_environment", shared=true) -Pkg.add("MLJ") -Pkg.add("MLJFlux") -Pkg.add("RDatasets") # for the demo below -Pkg.add("Plots") +using MLJ, MLJFlux, RDatasets, Plots ``` -### Example - -Following is an introductory example using a default builder and no -standardization of input features ([notebook/script](/examples/iris)). - -For an example implementing early stopping and snapshots, using MLJ's -[`IteratedModel` -wrapper](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/), -see the [MNIST dataset -example](https://github.com/FluxML/MLJFlux.jl/blob/dev/examples/mnist). - - -#### Loading some data and instantiating a model +Grab some data and split into features and target: ```julia -using MLJ -import RDatasets iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); -NeuralNetworkClassifier = @load NeuralNetworkClassifier - -julia> clf = NeuralNetworkClassifier() -NeuralNetworkClassifier( - builder = Short( - n_hidden = 0, - dropout = 0.5, - σ = NNlib.σ), - finaliser = NNlib.softmax, - optimiser = ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}()), - loss = Flux.crossentropy, - epochs = 10, - batch_size = 1, - lambda = 0.0, - alpha = 0.0, - optimiser_changes_trigger_retraining = false) @ 1…60 +X = Float32.(X); # To optmise for GPUs ``` -#### Incremental training +Load model code and instantiate an MLJFlux model: ```julia -import Random.seed!; seed!(123) -mach = machine(clf, X, y) -fit!(mach) +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux -julia> training_loss = cross_entropy(predict(mach, X), y) |> mean -0.9064070459118777 - -# Increasing learning rate and adding iterations: -clf.optimiser.eta = clf.optimiser.eta * 2 -clf.epochs = clf.epochs + 5 - -julia> fit!(mach, verbosity=2) -[ Info: Updating Machine{NeuralNetworkClassifier{Short,…},…} @804. -[ Info: Loss is 0.8686 -[ Info: Loss is 0.8228 -[ Info: Loss is 0.7706 -[ Info: Loss is 0.7565 -[ Info: Loss is 0.7347 -Machine{NeuralNetworkClassifier{Short,…},…} @804 trained 2 times; caches data - args: - 1: Source @985 ⏎ `Table{AbstractVector{Continuous}}` - 2: Source @367 ⏎ `AbstractVector{Multiclass{3}}` - -julia> training_loss = cross_entropy(predict(mach, X), y) |> mean -0.7347092796453824 -``` - -#### Accessing the Flux chain (model) - -```julia -julia> fitted_params(mach).chain -Chain(Chain(Dense(4, 3, σ), Flux.Dropout{Float64}(0.5, false), Dense(3, 3)), softmax) +clf = NeuralNetworkClassifier( + builder=MLJFlux.MLP(; hidden=(5,4)), + batch_size=8, + epochs=50, + acceleration=CUDALibs() # for training on a GPU +) ``` -#### Evolution of out-of-sample performance +Wrap in "iteration controls": ```julia -r = range(clf, :epochs, lower=1, upper=200, scale=:log10) -curve = learning_curve(clf, X, y, - range=r, - resampling=Holdout(fraction_train=0.7), - measure=cross_entropy) -using Plots -plot(curve.parameter_values, - curve.measurements, - xlab=curve.parameter_name, - xscale=curve.parameter_scale, - ylab = "Cross Entropy") - -``` - -![](examples/iris/iris_history.png) - - -### Models - -In MLJ a *model* is a mutable struct storing hyperparameters for some -learning algorithm indicated by the model name, and that's all. In -particular, an MLJ model does not store learned parameters. - -*Warning:* In Flux the term "model" has another meaning. However, as all -Flux "models" used in MLJFLux are `Flux.Chain` objects, we call them -*chains*, and restrict use of "model" to models in the MLJ sense. - -MLJFlux provides four model types, for use with input features `X` and -targets `y` of the [scientific -type](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/) -indicated in the table below. The parameters `n_in`, `n_out` and `n_channels` -refer to information passed to the builder, as described under -[Defining a new builder](defining-a-new-builder) below. - -model type | prediction type | `scitype(X) <: _` | `scitype(y) <: _` ------------|-----------------|---------------|---------------------------- -`NeuralNetworkRegressor` | `Deterministic` | `Table(Continuous)` with `n_in` columns | `AbstractVector{<:Continuous)` (`n_out = 1`) -`MultitargetNeuralNetworkRegressor` | `Deterministic` | `Table(Continuous)` with `n_in` columns | `<: Table(Continuous)` with `n_out` columns -`NeuralNetworkClassifier` | `Probabilistic` | `<:Table(Continuous)` with `n_in` columns | `AbstractVector{<:Finite}` with `n_out` classes -`ImageClassifier` | `Probabilistic` | `AbstractVector(<:Image{W,H})` with `n_in = (W, H)` | `AbstractVector{<:Finite}` with `n_out` classes - -> Table 1. Input and output types for MLJFlux models - -#### Non-tabular input - -Any `AbstractMatrix{<:AbstractFloat}` object `Xmat` can be forced to -have scitype `Table(Continuous)` by replacing it with ` X = -MLJ.table(Xmat)`. Furthermore, this wrapping, and subsequent -unwrapping under the hood, will compile to a no-op. At present this -includes support for sparse matrix data, but the implementation has -not been optimized for sparse data at this time and so should be used -with caution. - -Instructions for coercing common image formats into some -`AbstractVector{<:Image}` are -[here](https://juliaai.github.io/ScientificTypes.jl/dev/#Type-coercion-for-image-data). - - -### Warm restart - -MLJ machines cache state enabling the "warm restart" of model -training, as demonstrated in the example above. In the case of MLJFlux -models, `fit!(mach)` will use a warm restart if: - -- only `model.epochs` has changed since the last call; or - -- only `model.epochs` or `model.optimiser` have changed since the last - call and `model.optimiser_changes_trigger_retraining == false` (the - default) (the "state" part of the optimiser is ignored in this - comparison). This allows one to dynamically modify learning rates, - for example. - -Here `model=mach.model` is the associated MLJ model. - -The warm restart feature makes it possible to apply early stopping -criteria, as defined in -[EarlyStopping.jl](https://github.com/ablaom/EarlyStopping.jl). For an -example, see [/examples/mnist/](/examples/mnist/). (Eventually, this -will be handled by an MLJ model wrapper for controlling arbitrary -iterative models.) - - -### Training on a GPU - -When instantiating a model for training on a GPU, specify -`acceleration=CUDALibs()`, as in - -```julia -using MLJ -ImageClassifier = @load ImageClassifier -model = ImageClassifier(epochs=10, acceleration=CUDALibs()) -mach = machine(model, X, y) |> fit! -``` - -In this example, the data `X, y` is copied onto the GPU under the hood -on the call to `fit!` and cached for use in any warm restart (see -above). The Flux chain used in training is always copied back to the -CPU at then conclusion of `fit!`, and made available as -`fitted_params(mach)`. - - -### Random number generators and reproducibility - -Every MLJFlux model includes an `rng` hyper-parameter that is passed -to builders for the purposes of weight initialization. This can be -any `AbstractRNG` or the seed (integer) for a `MersenneTwister` that -will be reset on every cold restart of model (machine) training. - -### Built-in builders - -The following builders are provided out-of-the-box. Query their -doc-strings for advanced options and further details. - -|builder | description | -|:-------------------------|:-----------------------------------------------------| -| `MLJFlux.Linear(σ=relu)` | vanilla linear network with activation function `σ` | -| `MLJFlux.Short(n_hidden=0, dropout=0.5, σ=sigmoid)` | fully connected network with one hidden layer and dropout| -| `MLJFlux.MLP(hidden=(10,))` | general multi-layer perceptron | - - -### Model hyperparameters. - -All models share the following hyper-parameters: - -1. `builder`: Default = `MLJFlux.Linear(σ=Flux.relu)` (regressors) or - `MLJFlux.Short(n_hidden=0, dropout=0.5, σ=Flux.σ)` (classifiers) - -2. `optimiser`: The optimiser to use for training. Default = - `Flux.ADAM()` - -3. `loss`: The loss function used for training. Default = `Flux.mse` - (regressors) and `Flux.crossentropy` (classifiers) - -4. `n_epochs`: Number of epochs to train for. Default = `10` - -5. `batch_size`: The batch_size for the data. Default = 1 - -6. `lambda`: The regularization strength. Default = 0. Range = [0, ∞) - -7. `alpha`: The L2/L1 mix of regularization. Default = 0. Range = [0, 1] - -8. `rng`: The random number generator (RNG) passed to builders, for - weight intitialization, for example. Can be any `AbstractRNG` or - the seed (integer) for a `MersenneTwister` that is reset on every - cold restart of model (machine) training. Default = - `GLOBAL_RNG`. - -9. `acceleration`: Use `CUDALibs()` for training on GPU; default is `CPU1()`. - -10. `optimiser_changes_trigger_retraining`: True if fitting an - associated machine should trigger retraining from scratch whenever - the optimiser changes. Default = `false` - -The classifiers have an additional hyperparameter `finaliser` (default -= `Flux.softmax`) which is the operation applied to the unnormalized -output of the final layer to obtain probabilities (outputs summing to -one). Default = `Flux.softmax`. It should return a vector of the same -length as its input. - - - - - - - - - - - -### Defining a new builder - -Following is an example defining a new builder for creating a simple -fully-connected neural network with two hidden layers, with `n1` nodes -in the first hidden layer, and `n2` nodes in the second, for use in -any of the first three models in Table 1. The definition includes one -mutable struct and one method: - -```julia -mutable struct MyBuilder <: MLJFlux.Builder - n1 :: Int - n2 :: Int -end - -function MLJFlux.build(nn::MyBuilder, rng, n_in, n_out) - init = Flux.glorot_uniform(rng) - return Chain(Dense(n_in, nn.n1, init=init), - Dense(nn.n1, nn.n2, init=init), - Dense(nn.n2, n_out, init=init)) -end -``` - -Note here that `n_in` and `n_out` depend on the size of the data (see -Table 1). - -For a concrete image classification example, see -[examples/mnist](examples/mnist). - -More generally, defining a new builder means defining a new struct -sub-typing `MLJFlux.Builder` and defining a new `MLJFlux.build` method -with one of these signatures: - -```julia -MLJFlux.build(builder::MyBuilder, rng, n_in, n_out) -MLJFlux.build(builder::MyBuilder, rng, n_in, n_out, n_channels) # for use with `ImageClassifier` -``` - -This method must return a `Flux.Chain` instance, `chain`, subject to the -following conditions: - -- `chain(x)` must make sense: - - - for any `x <: Array{<:AbstractFloat, 2}` of size `(n_in, - batch_size)` where `batch_size` is any integer (for use with one - of the first three model types); or - - - for any `x <: Array{<:Float32, 4}` of size `(W, H, n_channels, - batch_size)`, where `(W, H) = n_in`, `n_channels` is 1 or 3, and - `batch_size` is any integer (for use with `ImageClassifier`) - -- The object returned by `chain(x)` must be an `AbstractFloat` vector - of length `n_out`. - -Alternatively, use `MLJFlux.@builder(neural_net)` to automatically create a builder for -any valid Flux chain expression `neural_net`, where the symbols `n_in`, `n_out`, -`n_channels` and `rng` can appear literally, with the interpretations explained above. For -example, - -``` -builder = MLJFlux.@builder Chain(Dense(n_in, 128), Dense(128, n_out, tanh)) -``` - -### Loss functions - -Currently, the loss function specified by `loss=...` is applied -internally by Flux and needs to conform to the Flux API. You cannot, -for example, supply one of MLJ's probabilistic loss functions, such as -`MLJ.cross_entropy` to one of the classifier constructors, although -you *should* use MLJ loss functions in MLJ meta-algorithms. - - - - - - - - - - - - -### An image classification example - -An expanded version of this example, with early stopping and -snapshots, is available [here](/examples/mnist). - -We define a builder that builds a chain with six alternating -convolution and max-pool layers, and a final dense layer, which we -apply to the MNIST image dataset. - -First we define a generic builder (working for any image size, color -or gray): - -```julia -using MLJ -using Flux -using MLDatasets - -# helper function -function flatten(x::AbstractArray) - return reshape(x, :, size(x)[end]) -end - -import MLJFlux -mutable struct MyConvBuilder - filter_size::Int - channels1::Int - channels2::Int - channels3::Int -end - -function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels) - - k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3 - - mod(k, 2) == 1 || error("`filter_size` must be odd. ") - - # padding to preserve image size on convolution: - p = div(k - 1, 2) - - front = Chain( - Conv((k, k), n_channels => c1, pad=(p, p), relu), - MaxPool((2, 2)), - Conv((k, k), c1 => c2, pad=(p, p), relu), - MaxPool((2, 2)), - Conv((k, k), c2 => c3, pad=(p, p), relu), - MaxPool((2 ,2)), - flatten) - d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first - return Chain(front, Dense(d, n_out)) -end -``` - -Next, we load some of the MNIST data and check scientific types -conform to those is the table above: - -```julia -N = 500 -Xraw, yraw = MNIST.traindata(); -Xraw = Xraw[:,:,1:N]; -yraw = yraw[1:N]; - -julia> scitype(Xraw) -AbstractArray{Unknown, 3} - -julia> scitype(yraw) -AbstractArray{Count,1} -``` - -Inputs should have element scitype `GrayImage`: - -```julia -X = coerce(Xraw, GrayImage); -``` - -For classifiers, target must have element scitype `<: Finite`: - -```julia -y = coerce(yraw, Multiclass); -``` - -Instantiating an image classifier model: - -```julia -ImageClassifier = @load ImageClassifier -clf = ImageClassifier(builder=MyConvBuilder(3, 16, 32, 32), - epochs=10, - loss=Flux.crossentropy) -``` - -And evaluating the accuracy of the model on a 30% holdout set: - -```julia -mach = machine(clf, X, y) - -julia> evaluate!(mach, - resampling=Holdout(rng=123, fraction_train=0.7), - operation=predict_mode, - measure=misclassification_rate) -┌────────────────────────┬───────────────┬────────────┐ -│ _.measure │ _.measurement │ _.per_fold │ -├────────────────────────┼───────────────┼────────────┤ -│ misclassification_rate │ 0.0467 │ [0.0467] │ -└────────────────────────┴───────────────┴────────────┘ -``` - - -### Adding new models to MLJFlux (advanced) - -This section is mainly for MLJFlux developers. It assumes familiarity -with the [MLJ model -API](https://alan-turing-institute.github.io/MLJ.jl/dev/adding_models_for_general_use/) - -If one subtypes a new model type as either -`MLJFlux.MLJFluxProbabilistic` or `MLJFlux.MLJFluxDeterministic`, then -instead of defining new methods for `MLJModelInterface.fit` and -`MLJModelInterface.update` one can make use of fallbacks by -implementing the lower level methods `shape`, `build`, and -`fitresult`. See the [classifier source code](/src/classifier.jl) for -an example. - -One still needs to implement a new `predict` method. +stop_conditions = [ + Step(1), # Apply controls every epoch + NumberLimit(1000), # Don't train for more than 100 steps + Patience(4), # Stop after 5 iterations of deteriation in validation loss + NumberSinceBest(5), # Or if the best loss occurred 9 iterations ago + TimeLimit(30/60), # Or if 30 minutes passed +] + +validation_losses = [] +train_losses = [] +callbacks = [ + WithLossDo(loss->push!(validation_losses, loss)), + WithTrainingLossesDo(losses->push!(train_losses, losses[end])), +] + +iterated_model = IteratedModel( + model=clf, + resampling=Holdout(fraction_train=0.5); # loss and stopping are based on out-of-sample + measures=log_loss, + controls=vcat(stop_conditions, callbacks), +); +``` + +Train the wrapped model: + +```julia-repl +julia> mach = machine(iterated_model, X, y) +julia> fit!(mach) + +[ Info: Training machine(ProbabilisticIteratedModel(model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …), …). +[ Info: No iteration parameter specified. Using `iteration_parameter=:(epochs)`. +[ Info: final loss: 0.10431026246922499 +[ Info: final training loss: 0.046286315 +[ Info: Stop triggered by Patience(4) stopping criterion. +[ Info: Total of 349 iterations. +``` +Inspect results: + +```julia-repl +julia> plot(train_losses, label="Validation Loss", linewidth=2, size=(800,400)) +julia> plot!(validation_losses, label="Validation Loss", linewidth=2, size=(800,400)) +``` + +![](readme_figure.png) diff --git a/docs/Project.toml b/docs/Project.toml index e4b51035..c86ddfde 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,6 +1,20 @@ [deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +Imbalance = "c709b415-507b-45b7-9a3d-1767c89fde68" +Languages = "8ef0a80b-9436-5d2c-a485-80b904378c43" MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55" +MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728" +MLJXGBoostInterface = "54119dfa-1dab-4055-a167-80440f4f7a91" +MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" +WordTokenizers = "796a5d58-b03d-544a-977e-18100b691f6e" diff --git a/docs/make.jl b/docs/make.jl index 8e3b6736..4c2b0d2c 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -5,48 +5,57 @@ using Flux DocMeta.setdocmeta!(MLJFlux, :DocTestSetup, :(using MLJFlux); recursive=true) makedocs( - sitename = "MLJFlux", - format = Documenter.HTML(; - collapselevel = 1, - assets = [ - "assets/favicon.ico", - asset( - "https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,100;0,300;0,400;0,700;0,900;1,100;1,300;1,400;1,700;1,900&family=Montserrat:ital,wght@0,100..900;1,100..900&display=swap", - class = :css, - ), - asset( - "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css", - class = :css, - ) - ], - repolink="https://github.com/FluxML/MLJFlux.jl" -), - modules = [MLJFlux], - warnonly = true, - pages = ["Introduction" => "index.md", - "Interface"=> Any[ - "Summary"=>"interface/Summary.md", - "Builders"=>"interface/Builders.md", - "Custom Builders"=>"interface/Custom Builders.md", - "Classification"=>"interface/Classification.md", - "Regression"=>"interface/Regression.md", - "Multi-Target Regression"=>"interface/Multitarget Regression.md", - "Image Classification"=>"interface/Image Classification.md", - ], - "Workflow Examples" => Any[ - "Incremental Training"=>"workflow examples/Incremental Training/incremental.md", - "Hyperparameter Tuning"=>"workflow examples/Hyperparameter Tuning/tuning.md", - "Neural Architecture Search"=>"workflow examples/Basic Neural Architecture Search/tuning.md", - "Model Composition"=>"workflow examples/Composition/composition.md", - "Model Comparison"=>"workflow examples/Comparison/comparison.md", - "Early Stopping"=>"workflow examples/Early Stopping/iteration.md", - "Live Training"=>"workflow examples/Live Training/live-training.md", - ], - # "Tutorials"=>Any[ - # "Spam Detection with RNNs"=>"full tutorials/Spam Detection with RNNs/SMS.md" - # ], - "Contributing" => "contributing.md"], - doctest = false, + sitename = "MLJFlux", + format = Documenter.HTML( + collapselevel = 1, + assets = [ + "assets/favicon.ico", + asset( + "https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,100;0,300;0,400;0,700;0,900;1,100;1,300;1,400;1,700;1,900&family=Montserrat:ital,wght@0,100..900;1,100..900&display=swap", + class = :css, + ), + asset( + "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css", + class = :css, + ) + ], + repolink="https://github.com/FluxML/MLJFlux.jl" + ), + modules = [MLJFlux], + warnonly = true, + pages = [ + "Introduction" => "index.md", + "Interface" => Any[ + "Summary" => "interface/Summary.md", + "Builders" => "interface/Builders.md", + "Custom Builders" => "interface/Custom Builders.md", + "Classification" => "interface/Classification.md", + "Regression" => "interface/Regression.md", + "Multi-Target Regression" => "interface/Multitarget Regression.md", + "Image Classification" => "interface/Image Classification.md", + ], + "Common Workflows" => Any[ + "Incremental Training" => + "common_workflows/incremental_training/notebook.md", + "Hyperparameter Tuning" => + "common_workflows/hyperparameter_tuning/notebook.md", + "Model Composition" => + "common_workflows/composition/notebook.md", + "Model Comparison" => + "common_workflows/comparison/notebook.md", + "Early Stopping" => + "common_workflows/early_stopping/notebook.md", + "Live Training" => + "common_workflows/live_training/notebook.md", + "Neural Architecture Search" => + "common_workflows/architecture_search/notebook.md", + ], + "Extended Examples" => Any[ + "MNIST Images" => "extended_examples/MNIST/notebook.md", + "Spam Detection with RNNs" => "extended_examples/spam_detection/notebook.md", + ], + "Contributing" => "contributing.md"], + doctest = false, ) # Documenter can also automatically deploy documentation to gh-pages. diff --git a/docs/Manifest.toml b/docs/src/common_workflows/architecture_search/Manifest.toml similarity index 71% rename from docs/Manifest.toml rename to docs/src/common_workflows/architecture_search/Manifest.toml index 2ec5f8aa..0a20d4e5 100644 --- a/docs/Manifest.toml +++ b/docs/src/common_workflows/architecture_search/Manifest.toml @@ -1,13 +1,14 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.10.0" +julia_version = "1.10.3" manifest_format = "2.0" -project_hash = "760378c053aeb477e203dc95fb0a527c7911c8d1" +project_hash = "0f9d92a558d050b0bba129bd2d0367e7b1953ddf" -[[deps.ANSIColoredPrinters]] -git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c" -uuid = "a4c015fc-c6ff-483c-b24f-f7ea428134e9" -version = "0.0.1" +[[deps.ARFFFiles]] +deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"] +git-tree-sha1 = "e8c8e0a2be6eb4f56b1672e46004463033daa409" +uuid = "da404889-ca92-49ff-9e8b-0aa6b4d38dc8" +version = "1.4.1" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] @@ -20,11 +21,6 @@ weakdeps = ["ChainRulesCore", "Test"] AbstractFFTsChainRulesCoreExt = "ChainRulesCore" AbstractFFTsTestExt = "Test" -[[deps.AbstractTrees]] -git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177" -uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -version = "0.4.5" - [[deps.Adapt]] deps = ["LinearAlgebra", "Requires"] git-tree-sha1 = "6a55b747d1812e699320963ffde36f1ebdda4099" @@ -35,6 +31,12 @@ weakdeps = ["StaticArrays"] [deps.Adapt.extensions] AdaptStaticArraysExt = "StaticArrays" +[[deps.AliasTables]] +deps = ["PtrArrays", "Random"] +git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff" +uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8" +version = "1.1.3" + [[deps.ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" @@ -53,18 +55,6 @@ git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be" uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458" version = "0.1.0" -[[deps.AtomsBase]] -deps = ["LinearAlgebra", "PeriodicTable", "Printf", "Requires", "StaticArrays", "Unitful", "UnitfulAtomic"] -git-tree-sha1 = "995c2b6b17840cd87b722ce9c6cdd72f47bab545" -uuid = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a" -version = "0.3.5" - -[[deps.BFloat16s]] -deps = ["LinearAlgebra", "Printf", "Random", "Test"] -git-tree-sha1 = "2c7cc21e8678eff479978a0a2ef5ce2f51b63dff" -uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b" -version = "0.5.0" - [[deps.BSON]] git-tree-sha1 = "4c3e506685c527ac6a54ccc0c8c76fd6f91b42fb" uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" @@ -103,11 +93,6 @@ git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" version = "0.1.8" -[[deps.BufferedStreams]] -git-tree-sha1 = "4ae47f9a4b1dc19897d3743ff13685925c5202ec" -uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d" -version = "1.2.1" - [[deps.CEnum]] git-tree-sha1 = "389ad5c84de1ae7cf0e28e381131c98ea87d54fc" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" @@ -119,6 +104,12 @@ git-tree-sha1 = "6c834533dc1fabd820c1db03c839bf97e45a3fab" uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" version = "0.10.14" +[[deps.Calculus]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad" +uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" +version = "0.5.1" + [[deps.CategoricalArrays]] deps = ["DataAPI", "Future", "Missings", "Printf", "Requires", "Statistics", "Unicode"] git-tree-sha1 = "1568b28f91293458345dabba6a5ea3f183250a61" @@ -137,67 +128,50 @@ version = "0.10.8" SentinelArrays = "91c51154-3ec4-41a3-a24f-3f23e20d615c" StructTypes = "856f2bd8-1eba-4b0a-8007-ebc267875bd4" +[[deps.CategoricalDistributions]] +deps = ["CategoricalArrays", "Distributions", "Missings", "OrderedCollections", "Random", "ScientificTypes"] +git-tree-sha1 = "926862f549a82d6c3a7145bc7f1adff2a91a39f0" +uuid = "af321ab8-2d2e-40a6-b165-3d674595d28e" +version = "0.1.15" + + [deps.CategoricalDistributions.extensions] + UnivariateFiniteDisplayExt = "UnicodePlots" + + [deps.CategoricalDistributions.weakdeps] + UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228" + [[deps.ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] -git-tree-sha1 = "291821c1251486504f6bae435227907d734e94d2" +git-tree-sha1 = "227985d885b4dbce5e18a96f9326ea1e836e5a03" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" -version = "1.66.0" +version = "1.69.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra"] -git-tree-sha1 = "575cd02e080939a33b6df6c5853d14924c08e35b" +git-tree-sha1 = "71acdbf594aab5bbb2cec89b208c41b4c411e49f" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.23.0" +version = "1.24.0" weakdeps = ["SparseArrays"] [deps.ChainRulesCore.extensions] ChainRulesCoreSparseArraysExt = "SparseArrays" -[[deps.Chemfiles]] -deps = ["AtomsBase", "Chemfiles_jll", "DocStringExtensions", "PeriodicTable", "Unitful", "UnitfulAtomic"] -git-tree-sha1 = "82fe5e341c793cb51149d993307da9543824b206" -uuid = "46823bd8-5fb3-5f92-9aa0-96921f3dd015" -version = "0.10.41" - -[[deps.Chemfiles_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "f3743181e30d87c23d9c8ebd493b77f43d8f1890" -uuid = "78a364fa-1a3c-552a-b4bb-8fa0f9c1fcca" -version = "0.10.4+0" - [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] git-tree-sha1 = 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-git-tree-sha1 = "362a287c3aa50601b0bc359053d5c2468f0e7ce0" -uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" -version = "0.12.11" +[[deps.Combinatorics]] +git-tree-sha1 = "08c8b6831dc00bfea825826be0bc8336fc369860" +uuid = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" +version = "1.0.2" [[deps.CommonSubexpressions]] deps = ["MacroTools", "Test"] @@ -218,7 +192,7 @@ weakdeps = ["Dates", "LinearAlgebra"] [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "1.0.5+1" +version = "1.1.1+0" [[deps.CompositionsBase]] git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" @@ -272,12 +246,6 @@ git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" version = "1.16.0" -[[deps.DataDeps]] -deps = ["HTTP", "Libdl", "Reexport", "SHA", "Scratch", "p7zip_jll"] -git-tree-sha1 = "8ae085b71c462c2cb1cfedcb10c3c877ec6cf03f" -uuid = "124859b0-ceae-595e-8997-d05f6a7a8dfe" -version = "0.7.13" - [[deps.DataFrames]] deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"] git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8" @@ -322,44 +290,70 @@ git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" version = "1.15.1" +[[deps.Distances]] +deps = ["LinearAlgebra", "Statistics", "StatsAPI"] +git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0" +uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" +version = "0.10.11" +weakdeps = ["ChainRulesCore", "SparseArrays"] + + [deps.Distances.extensions] + DistancesChainRulesCoreExt = "ChainRulesCore" + DistancesSparseArraysExt = "SparseArrays" + [[deps.Distributed]] deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" +[[deps.Distributions]] +deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "9c405847cc7ecda2dc921ccf18b47ca150d7317e" +uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" +version = "0.25.109" + + [deps.Distributions.extensions] + DistributionsChainRulesCoreExt = "ChainRulesCore" + DistributionsDensityInterfaceExt = "DensityInterface" + DistributionsTestExt = "Test" + + [deps.Distributions.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d" + Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" + [[deps.DocStringExtensions]] deps = ["LibGit2"] git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d" uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" version = "0.9.3" -[[deps.Documenter]] -deps = ["ANSIColoredPrinters", "AbstractTrees", 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"05882d6995ae5c12bb5f36dd2ed3f61c98cbb172" @@ -440,9 +436,9 @@ weakdeps = ["StaticArrays"] [[deps.Functors]] deps = ["LinearAlgebra"] -git-tree-sha1 = "d3e63d9fa13f8eaa2f06f64949e2afc593ff52c2" +git-tree-sha1 = "8a66c07630d6428eaab3506a0eabfcf4a9edea05" uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196" -version = "0.4.10" +version = "0.4.11" [[deps.Future]] deps = ["Random"] @@ -450,9 +446,9 @@ uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820" [[deps.GPUArrays]] deps = ["Adapt", "GPUArraysCore", "LLVM", "LinearAlgebra", "Printf", "Random", "Reexport", "Serialization", "Statistics"] -git-tree-sha1 = "68e8ff56a4a355a85d2784b94614491f8c900cde" +git-tree-sha1 = "38cb19b8a3e600e509dc36a6396ac74266d108c1" uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7" -version = "10.1.0" +version = "10.1.1" [[deps.GPUArraysCore]] deps = ["Adapt"] @@ -460,70 +456,17 @@ git-tree-sha1 = "ec632f177c0d990e64d955ccc1b8c04c485a0950" uuid = "46192b85-c4d5-4398-a991-12ede77f4527" version = "0.1.6" -[[deps.GZip]] -deps = ["Libdl", "Zlib_jll"] -git-tree-sha1 = "0085ccd5ec327c077ec5b91a5f937b759810ba62" -uuid = "92fee26a-97fe-5a0c-ad85-20a5f3185b63" -version = "0.6.2" - -[[deps.Git]] -deps = ["Git_jll"] -git-tree-sha1 = "51764e6c2e84c37055e846c516e9015b4a291c7d" -uuid = "d7ba0133-e1db-5d97-8f8c-041e4b3a1eb2" -version = "1.3.0" - -[[deps.Git_jll]] -deps = ["Artifacts", "Expat_jll", "JLLWrappers", "LibCURL_jll", "Libdl", "Libiconv_jll", "OpenSSL_jll", "PCRE2_jll", "Zlib_jll"] -git-tree-sha1 = "d8be4aab0f4e043cc40984e9097417307cce4c03" -uuid = "f8c6e375-362e-5223-8a59-34ff63f689eb" -version = "2.36.1+2" - -[[deps.Glob]] -git-tree-sha1 = "97285bbd5230dd766e9ef6749b80fc617126d496" -uuid = "c27321d9-0574-5035-807b-f59d2c89b15c" -version = "1.3.1" - -[[deps.Gumbo]] -deps = ["AbstractTrees", "Gumbo_jll", "Libdl"] -git-tree-sha1 = "a1a138dfbf9df5bace489c7a9d5196d6afdfa140" -uuid = "708ec375-b3d6-5a57-a7ce-8257bf98657a" -version = "0.8.2" - -[[deps.Gumbo_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] 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= "d1d712be3164d61d1fb98e7ce9bcbc6cc06b45ed" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" version = "1.10.8" -[[deps.IOCapture]] -deps = ["Logging", "Random"] -git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c" -uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.4" +[[deps.HypergeometricFunctions]] +deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] +git-tree-sha1 = "f218fe3736ddf977e0e772bc9a586b2383da2685" +uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" +version = "0.3.23" [[deps.IRTools]] deps = ["InteractiveUtils", "MacroTools"] @@ -531,24 +474,6 @@ git-tree-sha1 = "950c3717af761bc3ff906c2e8e52bd83390b6ec2" uuid = "7869d1d1-7146-5819-86e3-90919afe41df" version = "0.4.14" -[[deps.ImageBase]] -deps = ["ImageCore", "Reexport"] -git-tree-sha1 = "eb49b82c172811fd2c86759fa0553a2221feb909" -uuid = "c817782e-172a-44cc-b673-b171935fbb9e" -version = "0.1.7" - -[[deps.ImageCore]] -deps = ["ColorVectorSpace", "Colors", "FixedPointNumbers", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "PrecompileTools", "Reexport"] -git-tree-sha1 = "b2a7eaa169c13f5bcae8131a83bc30eff8f71be0" -uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534" -version = "0.10.2" - -[[deps.ImageShow]] -deps = ["Base64", "ColorSchemes", "FileIO", "ImageBase", "ImageCore", "OffsetArrays", "StackViews"] -git-tree-sha1 = "3b5344bcdbdc11ad58f3b1956709b5b9345355de" -uuid = "4e3cecfd-b093-5904-9786-8bbb286a6a31" -version = "0.3.8" - [[deps.InitialValues]] git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" uuid = "22cec73e-a1b8-11e9-2c92-598750a2cf9c" @@ -564,12 +489,6 @@ version = "1.4.0" deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" -[[deps.InternedStrings]] -deps = ["Random", "Test"] -git-tree-sha1 = "eb05b5625bc5d821b8075a77e4c421933e20c76b" -uuid = "7d512f48-7fb1-5a58-b986-67e6dc259f01" -version = "0.7.0" - [[deps.InvertedIndices]] git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f" @@ -580,16 +499,22 @@ git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" version = "0.2.2" +[[deps.IterationControl]] +deps = ["EarlyStopping", "InteractiveUtils"] +git-tree-sha1 = "e663925ebc3d93c1150a7570d114f9ea2f664726" +uuid = "b3c1a2ee-3fec-4384-bf48-272ea71de57c" +version = "0.5.4" + [[deps.IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" uuid = "82899510-4779-5014-852e-03e436cf321d" version = "1.0.0" [[deps.JLD2]] -deps = ["FileIO", "MacroTools", "Mmap", "OrderedCollections", "Pkg", "PrecompileTools", "Printf", "Reexport", "Requires", "TranscodingStreams", "UUIDs"] -git-tree-sha1 = "dca9ff5abdf5fab4456876bc93f80c59a37b81df" +deps = ["FileIO", "MacroTools", "Mmap", "OrderedCollections", "Pkg", "PrecompileTools", "Reexport", "Requires", "TranscodingStreams", "UUIDs", "Unicode"] +git-tree-sha1 = "bdbe8222d2f5703ad6a7019277d149ec6d78c301" uuid = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -version = "0.4.47" +version = "0.4.48" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] @@ -603,18 +528,6 @@ git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" version = "0.21.4" -[[deps.JSON3]] -deps = ["Dates", "Mmap", "Parsers", "PrecompileTools", "StructTypes", "UUIDs"] -git-tree-sha1 = "eb3edce0ed4fa32f75a0a11217433c31d56bd48b" -uuid = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" -version = "1.14.0" - - [deps.JSON3.extensions] - JSON3ArrowExt = ["ArrowTypes"] - - [deps.JSON3.weakdeps] - ArrowTypes = "31f734f8-188a-4ce0-8406-c8a06bd891cd" - [[deps.JuliaVariables]] deps = ["MLStyle", "NameResolution"] git-tree-sha1 = "49fb3cb53362ddadb4415e9b73926d6b40709e70" @@ -623,9 +536,9 @@ version = "0.2.4" [[deps.KernelAbstractions]] deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] -git-tree-sha1 = "db02395e4c374030c53dc28f3c1d33dec35f7272" +git-tree-sha1 = "8e5a339882cc401688d79b811d923a38ba77d50a" uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" -version = "0.9.19" +version = "0.9.20" [deps.KernelAbstractions.extensions] EnzymeExt = "EnzymeCore" @@ -635,14 +548,16 @@ version = "0.9.19" [[deps.LLVM]] deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Preferences", "Printf", "Requires", "Unicode"] -git-tree-sha1 = "839c82932db86740ae729779e610f07a1640be9a" +git-tree-sha1 = "389aea28d882a40b5e1747069af71bdbd47a1cae" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "6.6.3" -weakdeps = ["BFloat16s"] +version = "7.2.1" [deps.LLVM.extensions] BFloat16sExt = "BFloat16s" + [deps.LLVM.weakdeps] + BFloat16s = "ab4f0b2a-ad5b-11e8-123f-65d77653426b" + [[deps.LLVMExtra_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] git-tree-sha1 = "88b916503aac4fb7f701bb625cd84ca5dd1677bc" @@ -654,19 +569,21 @@ git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" version = "1.3.1" -[[deps.LazilyInitializedFields]] -git-tree-sha1 = "8f7f3cabab0fd1800699663533b6d5cb3fc0e612" -uuid = "0e77f7df-68c5-4e49-93ce-4cd80f5598bf" -version = "1.2.2" +[[deps.LatinHypercubeSampling]] +deps = ["Random", "StableRNGs", "StatsBase", "Test"] +git-tree-sha1 = "825289d43c753c7f1bf9bed334c253e9913997f8" +uuid = "a5e1c1ea-c99a-51d3-a14d-a9a37257b02d" +version = "1.9.0" [[deps.LazyArtifacts]] deps = ["Artifacts", "Pkg"] uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3" -[[deps.LazyModules]] -git-tree-sha1 = "a560dd966b386ac9ae60bdd3a3d3a326062d3c3e" -uuid = "8cdb02fc-e678-4876-92c5-9defec4f444e" -version = "0.3.1" +[[deps.LearnAPI]] +deps = ["InteractiveUtils", "Statistics"] +git-tree-sha1 = "ec695822c1faaaa64cee32d0b21505e1977b4809" +uuid = "92ad9a40-7767-427a-9ee6-6e577f1266cb" +version = "0.1.0" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] @@ -695,21 +612,15 @@ version = "1.11.0+1" [[deps.Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" -[[deps.Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" -uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" -version = "1.17.0+0" - [[deps.LinearAlgebra]] deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"] uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "18144f3e9cbe9b15b070288eef858f71b291ce37" +git-tree-sha1 = "a2d09619db4e765091ee5c6ffe8872849de0feea" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.27" +version = "0.3.28" [deps.LogExpFunctions.extensions] LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" @@ -730,31 +641,75 @@ git-tree-sha1 = "c1dd6d7978c12545b4179fb6153b9250c96b0075" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" version = "1.0.3" -[[deps.MAT]] -deps = ["BufferedStreams", "CodecZlib", "HDF5", "SparseArrays"] -git-tree-sha1 = "1d2dd9b186742b0f317f2530ddcbf00eebb18e96" -uuid = "23992714-dd62-5051-b70f-ba57cb901cac" -version = "0.10.7" +[[deps.MLFlowClient]] +deps = ["Dates", "FilePathsBase", "HTTP", "JSON", "ShowCases", "URIs", "UUIDs"] +git-tree-sha1 = "9abb12b62debc27261c008daa13627255bf79967" +uuid = "64a0f543-368b-4a9a-827a-e71edb2a0b83" +version = "0.5.1" + +[[deps.MLJ]] +deps = ["CategoricalArrays", "ComputationalResources", "Distributed", "Distributions", "FeatureSelection", "LinearAlgebra", "MLJBalancing", "MLJBase", "MLJEnsembles", "MLJFlow", "MLJIteration", "MLJModels", "MLJTuning", "OpenML", "Pkg", "ProgressMeter", "Random", "Reexport", "ScientificTypes", "StatisticalMeasures", "Statistics", "StatsBase", "Tables"] +git-tree-sha1 = "fb2da07c720db5d900bcaa940e1d098de281747a" +uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +version = "0.20.6" + +[[deps.MLJBalancing]] +deps = ["MLJBase", "MLJModelInterface", "MLUtils", "OrderedCollections", "Random", "StatsBase"] +git-tree-sha1 = "f707a01a92d664479522313907c07afa5d81df19" +uuid = "45f359ea-796d-4f51-95a5-deb1a414c586" +version = "0.1.5" -[[deps.MLDatasets]] -deps = ["CSV", "Chemfiles", "DataDeps", "DataFrames", "DelimitedFiles", "FileIO", "FixedPointNumbers", "GZip", "Glob", "HDF5", "ImageShow", "JLD2", "JSON3", "LazyModules", "MAT", "MLUtils", "NPZ", "Pickle", "Printf", "Requires", "SparseArrays", "Statistics", "Tables"] -git-tree-sha1 = "aab72207b3c687086a400be710650a57494992bd" -uuid = "eb30cadb-4394-5ae3-aed4-317e484a6458" -version = "0.7.14" +[[deps.MLJBase]] +deps = ["CategoricalArrays", "CategoricalDistributions", "ComputationalResources", "Dates", "DelimitedFiles", "Distributed", "Distributions", "InteractiveUtils", "InvertedIndices", "LearnAPI", "LinearAlgebra", "MLJModelInterface", "Missings", "OrderedCollections", "Parameters", "PrettyTables", "ProgressMeter", "Random", "RecipesBase", "Reexport", "ScientificTypes", "Serialization", "StatisticalMeasuresBase", "StatisticalTraits", "Statistics", "StatsBase", "Tables"] +git-tree-sha1 = "24e5d28b2ea86b3feb6af5a5735f012d62e27b65" +uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d" +version = "1.4.0" +weakdeps = ["StatisticalMeasures"] + + [deps.MLJBase.extensions] + DefaultMeasuresExt = "StatisticalMeasures" + +[[deps.MLJEnsembles]] +deps = ["CategoricalArrays", "CategoricalDistributions", "ComputationalResources", "Distributed", "Distributions", "MLJModelInterface", "ProgressMeter", "Random", "ScientificTypesBase", "StatisticalMeasuresBase", "StatsBase"] +git-tree-sha1 = "84a5be55a364bb6b6dc7780bbd64317ebdd3ad1e" +uuid = "50ed68f4-41fd-4504-931a-ed422449fee0" +version = "0.4.3" + +[[deps.MLJFlow]] +deps = ["MLFlowClient", "MLJBase", "MLJModelInterface"] +git-tree-sha1 = "508bff8071d7d1902d6f1b9d1e868d58821f1cfe" +uuid = "7b7b8358-b45c-48ea-a8ef-7ca328ad328f" +version = "0.5.0" [[deps.MLJFlux]] -deps = ["CategoricalArrays", "ColorTypes", "ComputationalResources", "Flux", "MLJModelInterface", "Metalhead", "ProgressMeter", "Random", "Statistics", "Tables"] -git-tree-sha1 = "933cc8ec638bd6735c2a05a349f94eb75e59357c" -repo-rev = "docs" -repo-url = ".." +deps = ["CategoricalArrays", "ColorTypes", "ComputationalResources", "Flux", "MLJModelInterface", "Metalhead", "Optimisers", "ProgressMeter", "Random", "Statistics", "Tables"] +git-tree-sha1 = "2fcdce39d979f2865aaa82d5750c6ee4ce543f4d" uuid = "094fc8d1-fd35-5302-93ea-dabda2abf845" -version = "0.4.0" +version = "0.5.0" + +[[deps.MLJIteration]] +deps = ["IterationControl", "MLJBase", "Random", "Serialization"] +git-tree-sha1 = "f93f381a82fc1768c1a99c27a84b7ea1b1ee186d" +uuid = "614be32b-d00c-4edb-bd02-1eb411ab5e55" +version = "0.6.2" [[deps.MLJModelInterface]] deps = ["Random", "ScientificTypesBase", "StatisticalTraits"] -git-tree-sha1 = "d2a45e1b5998ba3fdfb6cfe0c81096d4c7fb40e7" +git-tree-sha1 = "88ef480f46e0506143681b3fb14d86742f3cecb1" uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" -version = "1.9.6" +version = "1.10.0" + +[[deps.MLJModels]] +deps = ["CategoricalArrays", "CategoricalDistributions", "Combinatorics", "Dates", "Distances", "Distributions", "InteractiveUtils", "LinearAlgebra", "MLJModelInterface", "Markdown", "OrderedCollections", "Parameters", "Pkg", "PrettyPrinting", "REPL", "Random", "RelocatableFolders", "ScientificTypes", "StatisticalTraits", "Statistics", "StatsBase", "Tables"] +git-tree-sha1 = "42bcff728e44bcb682885a8f9900f9f4b4891c18" +uuid = "d491faf4-2d78-11e9-2867-c94bc002c0b7" +version = "0.17.1" + +[[deps.MLJTuning]] +deps = ["ComputationalResources", "Distributed", "Distributions", "LatinHypercubeSampling", "MLJBase", "ProgressMeter", "Random", "RecipesBase", "StatisticalMeasuresBase"] +git-tree-sha1 = "97f959ae512736b02c69a08af45afc5321bcef91" +uuid = "03970b2e-30c4-11ea-3135-d1576263f10f" +version = "0.8.7" [[deps.MLStyle]] git-tree-sha1 = "bc38dff0548128765760c79eb7388a4b37fae2c8" @@ -767,33 +722,16 @@ git-tree-sha1 = "b45738c2e3d0d402dffa32b2c1654759a2ac35a4" uuid = "f1d291b0-491e-4a28-83b9-f70985020b54" version = "0.4.4" -[[deps.MPIPreferences]] -deps = ["Libdl", "Preferences"] -git-tree-sha1 = "c105fe467859e7f6e9a852cb15cb4301126fac07" -uuid = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267" -version = "0.1.11" - [[deps.MacroTools]] deps = ["Markdown", "Random"] git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" version = "0.5.13" -[[deps.MappedArrays]] -git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" -uuid = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900" -version = "0.4.2" - [[deps.Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" -[[deps.MarkdownAST]] -deps = ["AbstractTrees", "Markdown"] -git-tree-sha1 = "465a70f0fc7d443a00dcdc3267a497397b8a3899" -uuid = "d0879d2d-cac2-40c8-9cee-1863dc0c7391" -version = "0.1.2" - [[deps.MbedTLS]] deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"] git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf" @@ -832,11 +770,11 @@ version = "1.2.0" [[deps.Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" -[[deps.MosaicViews]] -deps = ["MappedArrays", "OffsetArrays", "PaddedViews", "StackViews"] -git-tree-sha1 = "7b86a5d4d70a9f5cdf2dacb3cbe6d251d1a61dbe" -uuid = "e94cdb99-869f-56ef-bcf0-1ae2bcbe0389" -version = "0.3.4" +[[deps.Mocking]] +deps = ["Compat", "ExprTools"] +git-tree-sha1 = "bf17d9cb4f0d2882351dfad030598f64286e5936" +uuid = "78c3b35d-d492-501b-9361-3d52fe80e533" +version = "0.7.8" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" @@ -844,9 +782,9 @@ version = "2023.1.10" [[deps.NNlib]] deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "e0cea7ec219ada9ac80ec2e82e374ab2f154ae05" +git-tree-sha1 = "3d4617f943afe6410206a5294a95948c8d1b35bd" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.9.16" +version = "0.9.17" [deps.NNlib.extensions] NNlibAMDGPUExt = "AMDGPU" @@ -860,12 +798,6 @@ version = "0.9.16" EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869" cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" -[[deps.NPZ]] -deps = ["FileIO", "ZipFile"] -git-tree-sha1 = "60a8e272fe0c5079363b28b0953831e2dd7b7e6f" -uuid = "15e1cf62-19b3-5cfa-8e77-841668bca605" -version = "0.4.3" - [[deps.NaNMath]] deps = ["OpenLibm_jll"] git-tree-sha1 = "0877504529a3e5c3343c6f8b4c0381e57e4387e4" @@ -882,15 +814,6 @@ version = "0.1.5" uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" version = "1.2.0" -[[deps.OffsetArrays]] -git-tree-sha1 = "e64b4f5ea6b7389f6f046d13d4896a8f9c1ba71e" -uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" -version = "1.14.0" -weakdeps = ["Adapt"] - - [deps.OffsetArrays.extensions] - OffsetArraysAdaptExt = "Adapt" - [[deps.OneHotArrays]] deps = ["Adapt", "ChainRulesCore", "Compat", "GPUArraysCore", "LinearAlgebra", "NNlib"] git-tree-sha1 = "963a3f28a2e65bb87a68033ea4a616002406037d" @@ -900,18 +823,18 @@ version = "0.2.5" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.23+2" +version = "0.3.23+4" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] uuid = "05823500-19ac-5b8b-9628-191a04bc5112" version = "0.8.1+2" -[[deps.OpenSSH_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenSSL_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "1b2f042897343a9dfdcc9366e4ecbd3d00780c49" -uuid = "9bd350c2-7e96-507f-8002-3f2e150b4e1b" -version = "8.9.0+1" +[[deps.OpenML]] +deps = ["ARFFFiles", "HTTP", "JSON", "Markdown", "Pkg", "Scratch"] +git-tree-sha1 = "6efb039ae888699d5a74fb593f6f3e10c7193e33" +uuid = "8b6db2d4-7670-4922-a472-f9537c81ab66" +version = "0.3.1" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] @@ -921,9 +844,9 @@ version = "1.4.3" [[deps.OpenSSL_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200" +git-tree-sha1 = "a028ee3cb5641cccc4c24e90c36b0a4f7707bdf5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.23+0" +version = "3.0.14+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -942,22 +865,17 @@ git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" version = "1.6.3" -[[deps.PCRE2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15" -version = "10.42.0+1" - -[[deps.PackageExtensionCompat]] -git-tree-sha1 = "fb28e33b8a95c4cee25ce296c817d89cc2e53518" -uuid = "65ce6f38-6b18-4e1d-a461-8949797d7930" -version = "1.0.2" -weakdeps = ["Requires", "TOML"] +[[deps.PDMats]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65" +uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" +version = "0.11.31" -[[deps.PaddedViews]] -deps = ["OffsetArrays"] -git-tree-sha1 = "0fac6313486baae819364c52b4f483450a9d793f" -uuid = "5432bcbf-9aad-5242-b902-cca2824c8663" -version = "0.5.12" +[[deps.Parameters]] +deps = ["OrderedCollections", "UnPack"] +git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe" +uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a" +version = "0.12.3" [[deps.Parsers]] deps = ["Dates", "PrecompileTools", "UUIDs"] @@ -971,18 +889,6 @@ git-tree-sha1 = "47b49a4dbc23b76682205c646252c0f9e1eb75af" uuid = "570af359-4316-4cb7-8c74-252c00c2016b" version = "1.2.0" -[[deps.PeriodicTable]] -deps = ["Base64", "Unitful"] -git-tree-sha1 = "238aa6298007565529f911b734e18addd56985e1" -uuid = "7b2266bf-644c-5ea3-82d8-af4bbd25a884" -version = "1.2.1" - -[[deps.Pickle]] -deps = ["BFloat16s", "DataStructures", "InternedStrings", "Mmap", "Serialization", "SparseArrays", "StridedViews", "StringEncodings", "ZipFile"] -git-tree-sha1 = "e99da19b86b7e1547b423fc1721b260cfbe83acb" -uuid = "fbb45041-c46e-462f-888f-7c521cafbc2c" -version = "0.3.5" - [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" @@ -1011,11 +917,16 @@ git-tree-sha1 = "632eb4abab3449ab30c5e1afaa874f0b98b586e4" uuid = "8162dcfd-2161-5ef2-ae6c-7681170c5f98" version = "0.2.0" +[[deps.PrettyPrinting]] +git-tree-sha1 = "142ee93724a9c5d04d78df7006670a93ed1b244e" +uuid = "54e16d92-306c-5ea0-a30b-337be88ac337" +version = "0.4.2" + [[deps.PrettyTables]] deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "88b895d13d53b5577fd53379d913b9ab9ac82660" +git-tree-sha1 = "66b20dd35966a748321d3b2537c4584cf40387c7" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.3.1" +version = "2.3.2" [[deps.Printf]] deps = ["Unicode"] @@ -1033,6 +944,29 @@ git-tree-sha1 = "763a8ceb07833dd51bb9e3bbca372de32c0605ad" uuid = "92933f4c-e287-5a05-a399-4b506db050ca" version = "1.10.0" +[[deps.PtrArrays]] +git-tree-sha1 = "f011fbb92c4d401059b2212c05c0601b70f8b759" +uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d" +version = "1.2.0" + +[[deps.QuadGK]] +deps = ["DataStructures", "LinearAlgebra"] +git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e" +uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" +version = "2.9.4" + +[[deps.RData]] +deps = ["CategoricalArrays", "CodecZlib", "DataFrames", "Dates", "FileIO", "Requires", "TimeZones", "Unicode"] +git-tree-sha1 = "19e47a495dfb7240eb44dc6971d660f7e4244a72" +uuid = "df47a6cb-8c03-5eed-afd8-b6050d6c41da" +version = "0.8.3" + +[[deps.RDatasets]] +deps = ["CSV", "CodecZlib", "DataFrames", "FileIO", "Printf", "RData", "Reexport"] +git-tree-sha1 = "2720e6f6afb3e562ccb70a6b62f8f308ff810333" +uuid = "ce6b1742-4840-55fa-b093-852dadbb1d8b" +version = "0.7.7" + [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" @@ -1047,16 +981,22 @@ git-tree-sha1 = "9f0a1b71baaf7650f4fa8a1d168c7fb6ee41f0c9" uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9" version = "0.1.0" +[[deps.RecipesBase]] +deps = ["PrecompileTools"] +git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" +uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" +version = "1.3.4" + [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" uuid = "189a3867-3050-52da-a836-e630ba90ab69" version = "1.2.2" -[[deps.RegistryInstances]] -deps = ["LazilyInitializedFields", "Pkg", "TOML", "Tar"] -git-tree-sha1 = "ffd19052caf598b8653b99404058fce14828be51" -uuid = "2792f1a3-b283-48e8-9a74-f99dce5104f3" -version = "0.1.0" +[[deps.RelocatableFolders]] +deps = ["SHA", "Scratch"] +git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864" +uuid = "05181044-ff0b-4ac5-8273-598c1e38db00" +version = "1.0.1" [[deps.Requires]] deps = ["UUIDs"] @@ -1064,15 +1004,27 @@ git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7" uuid = "ae029012-a4dd-5104-9daa-d747884805df" version = "1.3.0" +[[deps.Rmath]] +deps = ["Random", "Rmath_jll"] +git-tree-sha1 = "f65dcb5fa46aee0cf9ed6274ccbd597adc49aa7b" +uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa" +version = "0.7.1" + +[[deps.Rmath_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d483cd324ce5cf5d61b77930f0bbd6cb61927d21" +uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f" +version = "0.4.2+0" + [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" -[[deps.Sass]] -deps = ["libsass_jll"] -git-tree-sha1 = "aa841c3738cec78b5dbccd56dda332710f35f6a5" -uuid = "322a6be2-4ae8-5d68-aaf1-3e960788d1d9" -version = "0.2.0" +[[deps.ScientificTypes]] +deps = ["CategoricalArrays", "ColorTypes", "Dates", "Distributions", "PrettyTables", "Reexport", "ScientificTypesBase", "StatisticalTraits", "Tables"] +git-tree-sha1 = "75ccd10ca65b939dab03b812994e571bf1e3e1da" +uuid = "321657f4-b219-11e9-178b-2701a2544e81" +version = "3.0.2" [[deps.ScientificTypesBase]] git-tree-sha1 = "a8e18eb383b5ecf1b5e6fc237eb39255044fd92b" @@ -1152,17 +1104,17 @@ git-tree-sha1 = "e08a62abc517eb79667d0a29dc08a3b589516bb5" uuid = "171d559e-b47b-412a-8079-5efa626c420e" version = "0.1.15" -[[deps.StackViews]] -deps = ["OffsetArrays"] -git-tree-sha1 = "46e589465204cd0c08b4bd97385e4fa79a0c770c" -uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15" -version = "0.1.1" +[[deps.StableRNGs]] +deps = ["Random"] +git-tree-sha1 = "83e6cce8324d49dfaf9ef059227f91ed4441a8e5" +uuid = "860ef19b-820b-49d6-a774-d7a799459cd3" +version = "1.0.2" [[deps.StaticArrays]] deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"] -git-tree-sha1 = "bf074c045d3d5ffd956fa0a461da38a44685d6b2" +git-tree-sha1 = "6e00379a24597be4ae1ee6b2d882e15392040132" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.9.3" +version = "1.9.5" weakdeps = ["ChainRulesCore", "Statistics"] [deps.StaticArrays.extensions] @@ -1170,15 +1122,35 @@ weakdeps = ["ChainRulesCore", "Statistics"] StaticArraysStatisticsExt = "Statistics" [[deps.StaticArraysCore]] -git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" +git-tree-sha1 = "192954ef1208c7019899fbf8049e717f92959682" uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" -version = "1.4.2" +version = "1.4.3" + +[[deps.StatisticalMeasures]] +deps = ["CategoricalArrays", "CategoricalDistributions", "Distributions", "LearnAPI", "LinearAlgebra", "MacroTools", "OrderedCollections", "PrecompileTools", "ScientificTypesBase", "StatisticalMeasuresBase", "Statistics", "StatsBase"] +git-tree-sha1 = "8b5a165b0ee2b361d692636bfb423b19abfd92b3" +uuid = "a19d573c-0a75-4610-95b3-7071388c7541" +version = "0.1.6" + + [deps.StatisticalMeasures.extensions] + LossFunctionsExt = "LossFunctions" + ScientificTypesExt = "ScientificTypes" + + [deps.StatisticalMeasures.weakdeps] + LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7" + ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81" + +[[deps.StatisticalMeasuresBase]] +deps = ["CategoricalArrays", "InteractiveUtils", "MLUtils", "MacroTools", "OrderedCollections", "PrecompileTools", "ScientificTypesBase", "Statistics"] +git-tree-sha1 = "17dfb22e2e4ccc9cd59b487dce52883e0151b4d3" +uuid = "c062fc1d-0d66-479b-b6ac-8b44719de4cc" +version = "0.1.1" [[deps.StatisticalTraits]] deps = ["ScientificTypesBase"] -git-tree-sha1 = "30b9236691858e13f167ce829490a68e1a597782" +git-tree-sha1 = "983c41a0ddd6c19f5607ca87271d7c7620ab5d50" uuid = "64bff920-2084-43da-a3e6-9bb72801c0c9" -version = "3.2.0" +version = "3.3.0" [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] @@ -1197,23 +1169,19 @@ git-tree-sha1 = "5cf7606d6cef84b543b483848d4ae08ad9832b21" uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" version = "0.34.3" -[[deps.StridedViews]] -deps = ["LinearAlgebra", "PackageExtensionCompat"] -git-tree-sha1 = "5b765c4e401693ab08981989f74a36a010aa1d8e" -uuid = "4db3bf67-4bd7-4b4e-b153-31dc3fb37143" -version = "0.2.2" - - [deps.StridedViews.extensions] - StridedViewsCUDAExt = "CUDA" +[[deps.StatsFuns]] +deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] +git-tree-sha1 = "cef0472124fab0695b58ca35a77c6fb942fdab8a" +uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c" +version = "1.3.1" - [deps.StridedViews.weakdeps] - CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + [deps.StatsFuns.extensions] + StatsFunsChainRulesCoreExt = "ChainRulesCore" + StatsFunsInverseFunctionsExt = "InverseFunctions" -[[deps.StringEncodings]] -deps = ["Libiconv_jll"] -git-tree-sha1 = "b765e46ba27ecf6b44faf70df40c57aa3a547dcb" -uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" -version = "0.3.7" + [deps.StatsFuns.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.StringManipulation]] deps = ["PrecompileTools"] @@ -1234,12 +1202,6 @@ weakdeps = ["Adapt", "GPUArraysCore", "SparseArrays", "StaticArrays"] StructArraysSparseArraysExt = "SparseArrays" StructArraysStaticArraysExt = "StaticArrays" -[[deps.StructTypes]] -deps = ["Dates", "UUIDs"] -git-tree-sha1 = "ca4bccb03acf9faaf4137a9abc1881ed1841aa70" -uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4" -version = "1.10.0" - [[deps.SuiteSparse]] deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" @@ -1254,6 +1216,12 @@ deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" version = "1.0.3" +[[deps.TZJData]] +deps = ["Artifacts"] +git-tree-sha1 = "1607ad46cf8d642aa779a1d45af1c8620dbf6915" +uuid = "dc5dba14-91b3-4cab-a142-028a31da12f7" +version = "1.2.0+2024a" + [[deps.TableTraits]] deps = ["IteratorInterfaceExtensions"] git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39" @@ -1271,20 +1239,24 @@ deps = ["ArgTools", "SHA"] uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" version = "1.10.0" -[[deps.TensorCore]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6" -uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50" -version = "0.1.1" - [[deps.Test]] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +[[deps.TimeZones]] +deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"] +git-tree-sha1 = "a6ae8d7a27940c33624f8c7bde5528de21ba730d" +uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53" +version = "1.17.0" +weakdeps = ["RecipesBase"] + + [deps.TimeZones.extensions] + TimeZonesRecipesBaseExt = "RecipesBase" + [[deps.TranscodingStreams]] -git-tree-sha1 = "5d54d076465da49d6746c647022f3b3674e64156" +git-tree-sha1 = "a947ea21087caba0a798c5e494d0bb78e3a1a3a0" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.10.8" +version = "0.10.9" weakdeps = ["Random", "Test"] [deps.TranscodingStreams.extensions] @@ -1319,29 +1291,14 @@ version = "1.5.1" deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" +[[deps.UnPack]] +git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b" +uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed" +version = "1.0.2" + [[deps.Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" -[[deps.Unitful]] -deps = ["Dates", "LinearAlgebra", "Random"] -git-tree-sha1 = "dd260903fdabea27d9b6021689b3cd5401a57748" -uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" -version = "1.20.0" - - [deps.Unitful.extensions] - ConstructionBaseUnitfulExt = "ConstructionBase" - InverseFunctionsUnitfulExt = "InverseFunctions" - - [deps.Unitful.weakdeps] - ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9" - InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" - -[[deps.UnitfulAtomic]] -deps = ["Unitful"] -git-tree-sha1 = "903be579194534af1c4b4778d1ace676ca042238" -uuid = "a7773ee8-282e-5fa2-be4e-bd808c38a91a" -version = "1.0.0" - [[deps.UnsafeAtomics]] git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" @@ -1349,9 +1306,9 @@ version = "0.2.1" [[deps.UnsafeAtomicsLLVM]] deps = ["LLVM", "UnsafeAtomics"] -git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" +git-tree-sha1 = "d9f5962fecd5ccece07db1ff006fb0b5271bdfdd" uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" -version = "0.1.3" +version = "0.1.4" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] @@ -1364,12 +1321,6 @@ git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7" uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60" version = "1.6.1" -[[deps.ZipFile]] -deps = ["Libdl", "Printf", "Zlib_jll"] -git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a" -uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" -version = "0.10.1" - [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" @@ -1402,12 +1353,6 @@ deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.8.0+1" -[[deps.libsass_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "941afb93587dcec07f89e511057f5efc0bec6f0d" -uuid = "47bcb7c8-5119-555a-9eeb-0afcc36cd728" -version = "3.6.4+0" - [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" diff --git a/docs/src/workflow examples/Basic Neural Architecture Search/Project.toml b/docs/src/common_workflows/architecture_search/Project.toml similarity index 82% rename from docs/src/workflow examples/Basic Neural Architecture Search/Project.toml rename to docs/src/common_workflows/architecture_search/Project.toml index 49fe3e47..4226930c 100644 --- a/docs/src/workflow examples/Basic Neural Architecture Search/Project.toml +++ b/docs/src/common_workflows/architecture_search/Project.toml @@ -3,4 +3,5 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/architecture_search/README.md b/docs/src/common_workflows/architecture_search/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/architecture_search/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/architecture_search/generate.jl b/docs/src/common_workflows/architecture_search/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/architecture_search/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.ipynb b/docs/src/common_workflows/architecture_search/notebook.ipynb similarity index 59% rename from docs/src/workflow examples/Basic Neural Architecture Search/tuning.ipynb rename to docs/src/common_workflows/architecture_search/notebook.ipynb index d2869628..958109de 100644 --- a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.ipynb +++ b/docs/src/common_workflows/architecture_search/notebook.ipynb @@ -10,23 +10,18 @@ { "cell_type": "markdown", "source": [ - "Neural Architecture Search is (NAS) is an instance of hyperparameter tuning concerned with tuning model hyperparameters\n", - "defining the architecture itself. Although it's typically performed with sophisticated search algorithms for efficiency,\n", - "in this example we will be using a simple random search." + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/architecture_search)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ - "**Julia version** is assumed to be 1.10.*" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Basic Imports" + "Neural Architecture Search (NAS) is an instance of hyperparameter tuning concerned\n", + "with tuning model hyperparameters defining the architecture itself. Although it's\n", + "typically performed with sophisticated search algorithms for efficiency, in this example\n", + "we will be using a simple random search." ], "metadata": {} }, @@ -36,22 +31,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "┌ Warning: The project dependencies or compat requirements have changed since the manifest was last resolved.\n", - "│ It is recommended to `Pkg.resolve()` or consider `Pkg.update()` if necessary.\n", - "└ @ Pkg.API /Applications/Julia-1.10.app/Contents/Resources/julia/share/julia/stdlib/v1.10/Pkg/src/API.jl:1800\n", - "[ Info: Precompiling RDatasets [ce6b1742-4840-55fa-b093-852dadbb1d8b]\n" + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/common_workflows/architecture_search`\n" ] } ], "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", "source": [ "using MLJ # Has MLJFlux models\n", "using Flux # For more flexibility\n", "using RDatasets: RDatasets # Dataset source\n", - "using DataFrames # To view tuning results in a table" + "using DataFrames # To view tuning results in a table\n", + "import Optimisers # native Flux.jl optimisers no longer supported" ], "metadata": {}, - "execution_count": 1 + "execution_count": 2 }, { "cell_type": "markdown", @@ -71,7 +89,7 @@ ] }, "metadata": {}, - "execution_count": 2 + "execution_count": 3 } ], "cell_type": "code", @@ -82,7 +100,7 @@ "first(X, 5)" ], "metadata": {}, - "execution_count": 2 + "execution_count": 3 }, { "cell_type": "markdown", @@ -94,7 +112,8 @@ { "cell_type": "markdown", "source": [ - "Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start)." + "Now let's construct our model. This follows a similar setup the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." ], "metadata": {} }, @@ -111,33 +130,41 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (1, 1, 1), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Flux.Optimise.Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (1, 1, 1), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" }, "metadata": {}, - "execution_count": 3 + "execution_count": 4 } ], "cell_type": "code", "source": [ "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = \"MLJFlux\"\n", "clf = NeuralNetworkClassifier(\n", - "\tbuilder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu),\n", - "\toptimiser = Flux.ADAM(0.01),\n", - "\tbatch_size = 8,\n", - "\tepochs = 10,\n", - "\trng = 42,\n", + " builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu),\n", + " optimiser = Optimisers.ADAM(0.01),\n", + " batch_size = 8,\n", + " epochs = 10,\n", + " rng = 42,\n", ")" ], "metadata": {}, - "execution_count": 3 + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "### Generating Network Architectures" + ], + "metadata": {} }, { "cell_type": "markdown", "source": [ - "### Generating Network Architectures\n", - "We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define a network with $k$ hidden layers and\n", - "where the ith layer has $z_i$ neurons. We will proceed by defining a function that can generate all possible networks with a\n", - "specific number of hidden layers, a minimum and maximum number of neurons per layer and increments to consider for the number of neurons." + "We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define\n", + "a network with $k$ hidden layers and where the ith layer has $z_i$ neurons. We will\n", + "proceed by defining a function that can generate all possible networks with a specific\n", + "number of hidden layers, a minimum and maximum number of neurons per layer and\n", + "increments to consider for the number of neurons." ], "metadata": {} }, @@ -149,53 +176,54 @@ "text/plain": "generate_networks (generic function with 1 method)" }, "metadata": {}, - "execution_count": 4 + "execution_count": 5 } ], "cell_type": "code", "source": [ - "function generate_networks(;\n", - "\tmin_neurons::Int,\n", - "\tmax_neurons::Int,\n", - "\tneuron_step::Int,\n", - "\tnum_layers::Int,\n", - ")\n", - "\t# Define the range of neurons\n", - "\tneuron_range = min_neurons:neuron_step:max_neurons\n", + "function generate_networks(\n", + " ;min_neurons::Int,\n", + " max_neurons::Int,\n", + " neuron_step::Int,\n", + " num_layers::Int,\n", + " )\n", + " # Define the range of neurons\n", + " neuron_range = min_neurons:neuron_step:max_neurons\n", "\n", - "\t# Empty list to store the network configurations\n", - "\tnetworks = Vector{Tuple{Vararg{Int, num_layers}}}()\n", + " # Empty list to store the network configurations\n", + " networks = Vector{Tuple{Vararg{Int, num_layers}}}()\n", "\n", - "\t# Recursive helper function to generate all combinations of tuples\n", - "\tfunction generate_tuple(current_layers, remaining_layers)\n", - "\t\tif remaining_layers > 0\n", - "\t\t\tfor n in neuron_range\n", - "\t\t\t\t# current_layers =[] then current_layers=[(min_neurons)],\n", - "\t\t\t\t# [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],...\n", - "\t\t\t\t# for each of these we call generate_layers again which appends\n", - "\t\t\t\t# the n combinations for each one of them\n", - "\t\t\t\tgenerate_tuple(vcat(current_layers, [n]), remaining_layers - 1)\n", - "\t\t\tend\n", - "\t\telse\n", - "\t\t\t# in the base case, no more layers to \"recurse on\"\n", - "\t\t\t# and we just append the current_layers as a tuple\n", - "\t\t\tpush!(networks, tuple(current_layers...))\n", - "\t\tend\n", - "\tend\n", + " # Recursive helper function to generate all combinations of tuples\n", + " function generate_tuple(current_layers, remaining_layers)\n", + " if remaining_layers > 0\n", + " for n in neuron_range\n", + " # current_layers =[] then current_layers=[(min_neurons)],\n", + " # [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],...\n", + " # for each of these we call generate_layers again which appends\n", + " # the n combinations for each one of them\n", + " generate_tuple(vcat(current_layers, [n]), remaining_layers - 1)\n", + " end\n", + " else\n", + " # in the base case, no more layers to \"recurse on\"\n", + " # and we just append the current_layers as a tuple\n", + " push!(networks, tuple(current_layers...))\n", + " end\n", + " end\n", "\n", - "\t# Generate networks for the given number of layers\n", - "\tgenerate_tuple([], num_layers)\n", + " # Generate networks for the given number of layers\n", + " generate_tuple([], num_layers)\n", "\n", - "\treturn networks\n", + " return networks\n", "end" ], "metadata": {}, - "execution_count": 4 + "execution_count": 5 }, { "cell_type": "markdown", "source": [ - "Now let's generate an array of all possible neural networks with three hidden layers and number of neurons per layer ∈ [1,64] with a step of 4" + "Now let's generate an array of all possible neural networks with three hidden layers and\n", + "number of neurons per layer ∈ [1,64] with a step of 4" ], "metadata": {} }, @@ -207,18 +235,23 @@ "text/plain": "5-element Vector{Tuple{Int64, Int64, Int64}}:\n (1, 1, 1)\n (1, 1, 5)\n (1, 1, 9)\n (1, 1, 13)\n (1, 1, 17)" }, "metadata": {}, - "execution_count": 5 + "execution_count": 6 } ], "cell_type": "code", "source": [ "networks_space =\n", - "\tgenerate_networks(min_neurons = 1, max_neurons = 64, neuron_step = 4, num_layers = 3)\n", + " generate_networks(\n", + " min_neurons = 1,\n", + " max_neurons = 64,\n", + " neuron_step = 4,\n", + " num_layers = 3,\n", + " )\n", "\n", "networks_space[1:5]" ], "metadata": {}, - "execution_count": 5 + "execution_count": 6 }, { "cell_type": "markdown", @@ -230,7 +263,8 @@ { "cell_type": "markdown", "source": [ - "Let's use this array to define the range of hyperparameters and pass it along with the model to the `TunedModel` constructor." + "Let's use this array to define the range of hyperparameters and pass it along with the\n", + "model to the `TunedModel` constructor." ], "metadata": {} }, @@ -241,16 +275,16 @@ "r1 = range(clf, :(builder.hidden), values = networks_space)\n", "\n", "tuned_clf = TunedModel(\n", - "\tmodel = clf,\n", - "\ttuning = RandomSearch(),\n", - "\tresampling = CV(nfolds = 4, rng = 42),\n", - "\trange = [r1],\n", - "\tmeasure = cross_entropy,\n", - "\tn = 100, # searching over 100 random samples are enough\n", + " model = clf,\n", + " tuning = RandomSearch(),\n", + " resampling = CV(nfolds = 4, rng = 42),\n", + " range = [r1],\n", + " measure = cross_entropy,\n", + " n = 100, # searching over 100 random samples are enough\n", ");" ], "metadata": {}, - "execution_count": 6 + "execution_count": 7 }, { "cell_type": "markdown", @@ -262,7 +296,8 @@ { "cell_type": "markdown", "source": [ - "Similar to the last workflow example, all we need now is to fit our model and the search will take place automatically:" + "Similar to the last workflow example, all we need now is to fit our model and the search\n", + "will take place automatically:" ], "metadata": {} }, @@ -271,10 +306,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (25, 53, 45), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Flux.Optimise.Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (21, 57, 25), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" }, "metadata": {}, - "execution_count": 7 + "execution_count": 8 } ], "cell_type": "code", @@ -284,7 +319,7 @@ "fitted_params(mach).best_model" ], "metadata": {}, - "execution_count": 7 + "execution_count": 8 }, { "cell_type": "markdown", @@ -296,7 +331,8 @@ { "cell_type": "markdown", "source": [ - "Let's analyze the search results by converting the history array to a dataframe and viewing it:" + "Let's analyze the search results by converting the history array to a dataframe and\n", + "viewing it:" ], "metadata": {} }, @@ -305,26 +341,26 @@ { "output_type": "execute_result", "data": { - "text/plain": "\u001b[1m10×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m mlp \u001b[0m\u001b[1m measurement \u001b[0m\n │\u001b[90m MLP… \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────────────────────\n 1 │ MLP(hidden = (25, 53, 45), …) 0.0865692\n 2 │ MLP(hidden = (49, 41, 49), …) 0.0870145\n 3 │ MLP(hidden = (25, 61, 21), …) 0.0870776\n 4 │ MLP(hidden = (45, 21, 41), …) 0.0921284\n 5 │ MLP(hidden = (49, 13, 33), …) 0.0941658\n 6 │ MLP(hidden = (21, 49, 53), …) 0.100384\n 7 │ MLP(hidden = (33, 57, 61), …) 0.101213\n 8 │ MLP(hidden = (33, 49, 9), …) 0.10241\n 9 │ MLP(hidden = (17, 37, 17), …) 0.10542\n 10 │ MLP(hidden = (29, 49, 17), …) 0.108438", + "text/plain": "\u001b[1m10×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m mlp \u001b[0m\u001b[1m measurement \u001b[0m\n │\u001b[90m MLP… \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────────────────────\n 1 │ MLP(hidden = (21, 57, 25), …) 0.0867019\n 2 │ MLP(hidden = (45, 17, 13), …) 0.0929803\n 3 │ MLP(hidden = (33, 13, 49), …) 0.0973896\n 4 │ MLP(hidden = (21, 41, 61), …) 0.0981502\n 5 │ MLP(hidden = (57, 49, 61), …) 0.100331\n 6 │ MLP(hidden = (25, 25, 29), …) 0.101083\n 7 │ MLP(hidden = (29, 61, 21), …) 0.101466\n 8 │ MLP(hidden = (29, 61, 5), …) 0.107513\n 9 │ MLP(hidden = (21, 61, 17), …) 0.107874\n 10 │ MLP(hidden = (45, 49, 61), …) 0.111292", "text/html": [ - 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9MLP(hidden = (21, 61, 17), …)0.107874
10MLP(hidden = (45, 49, 61), …)0.111292
" ] }, "metadata": {}, - "execution_count": 8 + "execution_count": 9 } ], "cell_type": "code", "source": [ "history = report(mach).history\n", "history_df = DataFrame(\n", - "\tmlp = [x[:model].builder for x in history],\n", - "\tmeasurement = [x[:measurement][1] for x in history],\n", + " mlp = [x[:model].builder for x in history],\n", + " measurement = [x[:measurement][1] for x in history],\n", ")\n", "first(sort!(history_df, [order(:measurement)]), 10)" ], "metadata": {}, - "execution_count": 8 + "execution_count": 9 }, { "cell_type": "markdown", @@ -342,11 +378,11 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.10.0" + "version": "1.10.3" }, "kernelspec": { "name": "julia-1.10", - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.3", "language": "julia" } }, diff --git a/docs/src/common_workflows/architecture_search/notebook.jl b/docs/src/common_workflows/architecture_search/notebook.jl new file mode 100644 index 00000000..61ba5d49 --- /dev/null +++ b/docs/src/common_workflows/architecture_search/notebook.jl @@ -0,0 +1,136 @@ +# # Neural Architecture Search with MLJFlux + +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/architecture_search). + +# Neural Architecture Search (NAS) is an instance of hyperparameter tuning concerned +# with tuning model hyperparameters defining the architecture itself. Although it's +# typically performed with sophisticated search algorithms for efficiency, in this example +# we will be using a simple random search. + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md + +# **Julia version** is assumed to be 1.10.* + +# ### Basic Imports + +using MLJ # Has MLJFlux models +using Flux # For more flexibility +using RDatasets: RDatasets # Dataset source +using DataFrames # To view tuning results in a table +import Optimisers # native Flux.jl optimisers no longer supported + +# ### Loading and Splitting the Data + +iris = RDatasets.dataset("datasets", "iris"); +y, X = unpack(iris, ==(:Species), colname -> true, rng = 123); +X = Float32.(X); # To be compatible with type of network network parameters +first(X, 5) + + +# ### Instantiating the model + +# Now let's construct our model. This follows a similar setup the one followed in the +# [Quick Start](../../index.md#Quick-Start). + +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = "MLJFlux" +clf = NeuralNetworkClassifier( + builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu), + optimiser = Optimisers.ADAM(0.01), + batch_size = 8, + epochs = 10, + rng = 42, +) + + +# ### Generating Network Architectures + +# We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define +# a network with $k$ hidden layers and where the ith layer has $z_i$ neurons. We will +# proceed by defining a function that can generate all possible networks with a specific +# number of hidden layers, a minimum and maximum number of neurons per layer and +# increments to consider for the number of neurons. + +function generate_networks( + ;min_neurons::Int, + max_neurons::Int, + neuron_step::Int, + num_layers::Int, + ) + ## Define the range of neurons + neuron_range = min_neurons:neuron_step:max_neurons + + ## Empty list to store the network configurations + networks = Vector{Tuple{Vararg{Int, num_layers}}}() + + ## Recursive helper function to generate all combinations of tuples + function generate_tuple(current_layers, remaining_layers) + if remaining_layers > 0 + for n in neuron_range + ## current_layers =[] then current_layers=[(min_neurons)], + ## [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],... + ## for each of these we call generate_layers again which appends + ## the n combinations for each one of them + generate_tuple(vcat(current_layers, [n]), remaining_layers - 1) + end + else + ## in the base case, no more layers to "recurse on" + ## and we just append the current_layers as a tuple + push!(networks, tuple(current_layers...)) + end + end + + ## Generate networks for the given number of layers + generate_tuple([], num_layers) + + return networks +end + + +# Now let's generate an array of all possible neural networks with three hidden layers and +# number of neurons per layer ∈ [1,64] with a step of 4 +networks_space = + generate_networks( + min_neurons = 1, + max_neurons = 64, + neuron_step = 4, + num_layers = 3, + ) + +networks_space[1:5] + +# ### Wrapping the Model for Tuning + +# Let's use this array to define the range of hyperparameters and pass it along with the +# model to the `TunedModel` constructor. +r1 = range(clf, :(builder.hidden), values = networks_space) + +tuned_clf = TunedModel( + model = clf, + tuning = RandomSearch(), + resampling = CV(nfolds = 4, rng = 42), + range = [r1], + measure = cross_entropy, + n = 100, # searching over 100 random samples are enough +); + +# ### Performing the Search + +# Similar to the last workflow example, all we need now is to fit our model and the search +# will take place automatically: +mach = machine(tuned_clf, X, y); +fit!(mach, verbosity = 0); +fitted_params(mach).best_model + +# ### Analyzing the Search Results + +# Let's analyze the search results by converting the history array to a dataframe and +# viewing it: +history = report(mach).history +history_df = DataFrame( + mlp = [x[:model].builder for x in history], + measurement = [x[:measurement][1] for x in history], +) +first(sort!(history_df, [order(:measurement)]), 10) diff --git a/docs/src/common_workflows/architecture_search/notebook.md b/docs/src/common_workflows/architecture_search/notebook.md new file mode 100644 index 00000000..e995c68f --- /dev/null +++ b/docs/src/common_workflows/architecture_search/notebook.md @@ -0,0 +1,159 @@ +```@meta +EditURL = "notebook.jl" +``` + +# Neural Architecture Search with MLJFlux + +This demonstration is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/architecture_search). + +Neural Architecture Search (NAS) is an instance of hyperparameter tuning concerned +with tuning model hyperparameters defining the architecture itself. Although it's +typically performed with sophisticated search algorithms for efficiency, in this example +we will be using a simple random search. + +**Julia version** is assumed to be 1.10.* + +### Basic Imports + +````@example architecture_search +using MLJ # Has MLJFlux models +using Flux # For more flexibility +using RDatasets: RDatasets # Dataset source +using DataFrames # To view tuning results in a table +import Optimisers # native Flux.jl optimisers no longer supported +```` + +### Loading and Splitting the Data + +````@example architecture_search +iris = RDatasets.dataset("datasets", "iris"); +y, X = unpack(iris, ==(:Species), colname -> true, rng = 123); +X = Float32.(X); # To be compatible with type of network network parameters +first(X, 5) +```` + +### Instantiating the model + +Now let's construct our model. This follows a similar setup the one followed in the +[Quick Start](../../index.md#Quick-Start). + +````@example architecture_search +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = "MLJFlux" +clf = NeuralNetworkClassifier( + builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu), + optimiser = Optimisers.ADAM(0.01), + batch_size = 8, + epochs = 10, + rng = 42, +) +```` + +### Generating Network Architectures + +We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define +a network with $k$ hidden layers and where the ith layer has $z_i$ neurons. We will +proceed by defining a function that can generate all possible networks with a specific +number of hidden layers, a minimum and maximum number of neurons per layer and +increments to consider for the number of neurons. + +````@example architecture_search +function generate_networks( + ;min_neurons::Int, + max_neurons::Int, + neuron_step::Int, + num_layers::Int, + ) + # Define the range of neurons + neuron_range = min_neurons:neuron_step:max_neurons + + # Empty list to store the network configurations + networks = Vector{Tuple{Vararg{Int, num_layers}}}() + + # Recursive helper function to generate all combinations of tuples + function generate_tuple(current_layers, remaining_layers) + if remaining_layers > 0 + for n in neuron_range + # current_layers =[] then current_layers=[(min_neurons)], + # [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],... + # for each of these we call generate_layers again which appends + # the n combinations for each one of them + generate_tuple(vcat(current_layers, [n]), remaining_layers - 1) + end + else + # in the base case, no more layers to "recurse on" + # and we just append the current_layers as a tuple + push!(networks, tuple(current_layers...)) + end + end + + # Generate networks for the given number of layers + generate_tuple([], num_layers) + + return networks +end +```` + +Now let's generate an array of all possible neural networks with three hidden layers and +number of neurons per layer ∈ [1,64] with a step of 4 + +````@example architecture_search +networks_space = + generate_networks( + min_neurons = 1, + max_neurons = 64, + neuron_step = 4, + num_layers = 3, + ) + +networks_space[1:5] +```` + +### Wrapping the Model for Tuning + +Let's use this array to define the range of hyperparameters and pass it along with the +model to the `TunedModel` constructor. + +````@example architecture_search +r1 = range(clf, :(builder.hidden), values = networks_space) + +tuned_clf = TunedModel( + model = clf, + tuning = RandomSearch(), + resampling = CV(nfolds = 4, rng = 42), + range = [r1], + measure = cross_entropy, + n = 100, # searching over 100 random samples are enough +); +nothing #hide +```` + +### Performing the Search + +Similar to the last workflow example, all we need now is to fit our model and the search +will take place automatically: + +````@example architecture_search +mach = machine(tuned_clf, X, y); +fit!(mach, verbosity = 0); +fitted_params(mach).best_model +```` + +### Analyzing the Search Results + +Let's analyze the search results by converting the history array to a dataframe and +viewing it: + +````@example architecture_search +history = report(mach).history +history_df = DataFrame( + mlp = [x[:model].builder for x in history], + measurement = [x[:measurement][1] for x in history], +) +first(sort!(history_df, [order(:measurement)]), 10) +```` + +--- + +*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* + diff --git a/docs/src/common_workflows/architecture_search/notebook.unexecuted.ipynb b/docs/src/common_workflows/architecture_search/notebook.unexecuted.ipynb new file mode 100644 index 00000000..85b68135 --- /dev/null +++ b/docs/src/common_workflows/architecture_search/notebook.unexecuted.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Neural Architecture Search with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/architecture_search)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Neural Architecture Search (NAS) is an instance of hyperparameter tuning concerned\n", + "with tuning model hyperparameters defining the architecture itself. Although it's\n", + "typically performed with sophisticated search algorithms for efficiency, in this example\n", + "we will be using a simple random search." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "using RDatasets: RDatasets # Dataset source\n", + "using DataFrames # To view tuning results in a table\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng = 123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters\n", + "first(X, 5)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's construct our model. This follows a similar setup the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = \"MLJFlux\"\n", + "clf = NeuralNetworkClassifier(\n", + " builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu),\n", + " optimiser = Optimisers.ADAM(0.01),\n", + " batch_size = 8,\n", + " epochs = 10,\n", + " rng = 42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Generating Network Architectures" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define\n", + "a network with $k$ hidden layers and where the ith layer has $z_i$ neurons. We will\n", + "proceed by defining a function that can generate all possible networks with a specific\n", + "number of hidden layers, a minimum and maximum number of neurons per layer and\n", + "increments to consider for the number of neurons." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "function generate_networks(\n", + " ;min_neurons::Int,\n", + " max_neurons::Int,\n", + " neuron_step::Int,\n", + " num_layers::Int,\n", + " )\n", + " # Define the range of neurons\n", + " neuron_range = min_neurons:neuron_step:max_neurons\n", + "\n", + " # Empty list to store the network configurations\n", + " networks = Vector{Tuple{Vararg{Int, num_layers}}}()\n", + "\n", + " # Recursive helper function to generate all combinations of tuples\n", + " function generate_tuple(current_layers, remaining_layers)\n", + " if remaining_layers > 0\n", + " for n in neuron_range\n", + " # current_layers =[] then current_layers=[(min_neurons)],\n", + " # [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],...\n", + " # for each of these we call generate_layers again which appends\n", + " # the n combinations for each one of them\n", + " generate_tuple(vcat(current_layers, [n]), remaining_layers - 1)\n", + " end\n", + " else\n", + " # in the base case, no more layers to \"recurse on\"\n", + " # and we just append the current_layers as a tuple\n", + " push!(networks, tuple(current_layers...))\n", + " end\n", + " end\n", + "\n", + " # Generate networks for the given number of layers\n", + " generate_tuple([], num_layers)\n", + "\n", + " return networks\n", + "end" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now let's generate an array of all possible neural networks with three hidden layers and\n", + "number of neurons per layer ∈ [1,64] with a step of 4" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "networks_space =\n", + " generate_networks(\n", + " min_neurons = 1,\n", + " max_neurons = 64,\n", + " neuron_step = 4,\n", + " num_layers = 3,\n", + " )\n", + "\n", + "networks_space[1:5]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping the Model for Tuning" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's use this array to define the range of hyperparameters and pass it along with the\n", + "model to the `TunedModel` constructor." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "r1 = range(clf, :(builder.hidden), values = networks_space)\n", + "\n", + "tuned_clf = TunedModel(\n", + " model = clf,\n", + " tuning = RandomSearch(),\n", + " resampling = CV(nfolds = 4, rng = 42),\n", + " range = [r1],\n", + " measure = cross_entropy,\n", + " n = 100, # searching over 100 random samples are enough\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Performing the Search" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Similar to the last workflow example, all we need now is to fit our model and the search\n", + "will take place automatically:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(tuned_clf, X, y);\n", + "fit!(mach, verbosity = 0);\n", + "fitted_params(mach).best_model" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Analyzing the Search Results" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's analyze the search results by converting the history array to a dataframe and\n", + "viewing it:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "history = report(mach).history\n", + "history_df = DataFrame(\n", + " mlp = [x[:model].builder for x in history],\n", + " measurement = [x[:measurement][1] for x in history],\n", + ")\n", + "first(sort!(history_df, [order(:measurement)]), 10)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/common_workflows/comparison/Manifest.toml b/docs/src/common_workflows/comparison/Manifest.toml new file mode 100644 index 00000000..cc7f5095 --- /dev/null +++ b/docs/src/common_workflows/comparison/Manifest.toml @@ -0,0 +1,2089 @@ +# This file is machine-generated - editing it directly is not advised + 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"8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.52.0+1" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" + +[[deps.x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[deps.x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+1" diff --git a/docs/src/workflow examples/Comparison/Project.toml b/docs/src/common_workflows/comparison/Project.toml similarity index 92% rename from docs/src/workflow examples/Comparison/Project.toml rename to docs/src/common_workflows/comparison/Project.toml index 69b34c38..49b9b810 100644 --- a/docs/src/workflow examples/Comparison/Project.toml +++ b/docs/src/common_workflows/comparison/Project.toml @@ -8,5 +8,6 @@ MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728" MLJXGBoostInterface = "54119dfa-1dab-4055-a167-80440f4f7a91" MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/comparison/README.md b/docs/src/common_workflows/comparison/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/comparison/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/comparison/generate.jl b/docs/src/common_workflows/comparison/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/comparison/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/workflow examples/Comparison/comparison.ipynb b/docs/src/common_workflows/comparison/notebook.ipynb similarity index 68% rename from docs/src/workflow examples/Comparison/comparison.ipynb rename to docs/src/common_workflows/comparison/notebook.ipynb index e6d5f3b1..8163b302 100644 --- a/docs/src/workflow examples/Comparison/comparison.ipynb +++ b/docs/src/common_workflows/comparison/notebook.ipynb @@ -10,10 +10,38 @@ { "cell_type": "markdown", "source": [ - "In this workflow example, we see how we can compare different machine learning models with a neural network from MLJFlux." + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/comparison)." ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we see how we can compare different machine learning models\n", + "with a neural network from MLJFlux." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/common_workflows/comparison`\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, { "cell_type": "markdown", "source": [ @@ -35,10 +63,11 @@ "using MLJ # Has MLJFlux models\n", "using Flux # For more flexibility\n", "import RDatasets # Dataset source\n", - "using DataFrames # To visualize hyperparameter search results" + "using DataFrames # To visualize hyperparameter search results\n", + "import Optimisers # native Flux.jl optimisers no longer supported" ], "metadata": {}, - "execution_count": 1 + "execution_count": 2 }, { "cell_type": "markdown", @@ -55,13 +84,13 @@ "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);" ], "metadata": {}, - "execution_count": 2 + "execution_count": 3 }, { "cell_type": "markdown", "source": [ - "### Instantiating the models\n", - "Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start)." + "### Instantiating the models Now let's construct our model. This follows a similar setup\n", + "to the one followed in the [Quick Start](../../index.md#Quick-Start)." ], "metadata": {} }, @@ -78,10 +107,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" }, "metadata": {}, - "execution_count": 3 + "execution_count": 4 } ], "cell_type": "code", @@ -90,14 +119,14 @@ "\n", "clf1 = NeuralNetworkClassifier(\n", " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", + " optimiser=Optimisers.Adam(0.01),\n", " batch_size=8,\n", " epochs=50,\n", " rng=42\n", " )" ], "metadata": {}, - "execution_count": 3 + "execution_count": 4 }, { "cell_type": "markdown", @@ -131,14 +160,21 @@ "clf4 = XGBoostClassifier();" ], "metadata": {}, - "execution_count": 4 + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping One of the Models in a TunedModel" + ], + "metadata": {} }, { "cell_type": "markdown", "source": [ - "### Wrapping One of the Models in a TunedModel\n", - "Instead of just comparing with four models with the default/given hyperparameters, we will give `XGBoostClassifier` an unfair advantage\n", - "By wrapping it in a `TunedModel` that considers the best learning rate η for the model." + "Instead of just comparing with four models with the default/given hyperparameters, we\n", + "will give `XGBoostClassifier` an unfair advantage By wrapping it in a `TunedModel` that\n", + "considers the best learning rate η for the model." ], "metadata": {} }, @@ -156,19 +192,26 @@ ");" ], "metadata": {}, - "execution_count": 5 + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "Of course, one can wrap each of the four in a TunedModel if they are interested in\n", + "comparing the models over a large set of their hyperparameters." + ], + "metadata": {} }, { "cell_type": "markdown", "source": [ - "Of course, one can wrap each of the four in a TunedModel if they are interested in comparing the models over a large set of their hyperparameters." + "### Comparing the models" ], "metadata": {} }, { "cell_type": "markdown", "source": [ - "### Comparing the models\n", "We simply pass the four models to the `models` argument of the `TunedModel` construct" ], "metadata": {} @@ -185,7 +228,7 @@ ");" ], "metadata": {}, - "execution_count": 6 + "execution_count": 7 }, { "cell_type": "markdown", @@ -214,7 +257,7 @@ "fit!(mach, verbosity=0);" ], "metadata": {}, - "execution_count": 7 + "execution_count": 8 }, { "cell_type": "markdown", @@ -228,23 +271,26 @@ { "output_type": "execute_result", "data": { - "text/plain": "\u001b[1m4×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m mlp \u001b[0m\u001b[1m measurement \u001b[0m\n │\u001b[90m Probabil… \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────────────────────────\n 1 │ BayesianLDA(method = gevd, …) 0.0610826\n 2 │ RandomForestClassifier(max_depth… 0.0996176\n 3 │ NeuralNetworkClassifier(builder … 0.113266\n 4 │ ProbabilisticTunedModel(model = … 0.221056", + "text/plain": "\u001b[1m4×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m mlp \u001b[0m\u001b[1m measurement \u001b[0m\n │\u001b[90m Probabil… \u001b[0m\u001b[90m Float64 \u001b[0m\n─────┼────────────────────────────────────────────────\n 1 │ BayesianLDA(method = gevd, …) 0.0610826\n 2 │ NeuralNetworkClassifier(builder … 0.0857014\n 3 │ RandomForestClassifier(max_depth… 0.102881\n 4 │ ProbabilisticTunedModel(model = … 0.221056", "text/html": [ - "
4×2 DataFrame
Rowmlpmeasurement
Probabil…Float64
1BayesianLDA(method = gevd, …)0.0610826
2RandomForestClassifier(max_depth = -1, …)0.0996176
3NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …)0.113266
4ProbabilisticTunedModel(model = XGBoostClassifier(test = 1, …), …)0.221056
" + "
4×2 DataFrame
Rowmlpmeasurement
Probabil…Float64
1BayesianLDA(method = gevd, …)0.0610826
2NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …)0.0857014
3RandomForestClassifier(max_depth = -1, …)0.102881
4ProbabilisticTunedModel(model = XGBoostClassifier(test = 1, …), …)0.221056
" ] }, "metadata": {}, - "execution_count": 8 + "execution_count": 9 } ], "cell_type": "code", "source": [ "history = report(mach).history\n", - "history_df = DataFrame(mlp = [x[:model] for x in history], measurement = [x[:measurement][1] for x in history])\n", + "history_df = DataFrame(\n", + " mlp = [x[:model] for x in history],\n", + " measurement = [x[:measurement][1] for x in history],\n", + ")\n", "sort!(history_df, [order(:measurement)])" ], "metadata": {}, - "execution_count": 8 + "execution_count": 9 }, { "cell_type": "markdown", @@ -269,11 +315,11 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.10.0" + "version": "1.10.3" }, "kernelspec": { "name": "julia-1.10", - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.3", "language": "julia" } }, diff --git a/docs/src/workflow examples/Comparison/comparison.jl b/docs/src/common_workflows/comparison/notebook.jl similarity index 67% rename from docs/src/workflow examples/Comparison/comparison.jl rename to docs/src/common_workflows/comparison/notebook.jl index b780958b..4d75c49d 100644 --- a/docs/src/workflow examples/Comparison/comparison.jl +++ b/docs/src/common_workflows/comparison/notebook.jl @@ -1,18 +1,24 @@ # # Model Comparison with MLJFlux -# In this workflow example, we see how we can compare different machine learning models with a neural network from MLJFlux. -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/comparison). + +# In this workflow example, we see how we can compare different machine learning models +# with a neural network from MLJFlux. +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md # **Julia version** is assumed to be 1.10.* + # ### Basic Imports using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source using DataFrames # To visualize hyperparameter search results +import Optimisers # native Flux.jl optimisers no longer supported # ### Loading and Splitting the Data @@ -20,17 +26,16 @@ iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); - -# ### Instantiating the models -# Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). +# ### Instantiating the models Now let's construct our model. This follows a similar setup +# to the one followed in the [Quick Start](../../index.md#Quick-Start). NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf1 = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=50, + epochs=50, rng=42 ) @@ -44,8 +49,10 @@ clf4 = XGBoostClassifier(); # ### Wrapping One of the Models in a TunedModel -# Instead of just comparing with four models with the default/given hyperparameters, we will give `XGBoostClassifier` an unfair advantage -# By wrapping it in a `TunedModel` that considers the best learning rate η for the model. + +# Instead of just comparing with four models with the default/given hyperparameters, we +# will give `XGBoostClassifier` an unfair advantage By wrapping it in a `TunedModel` that +# considers the best learning rate η for the model. r1 = range(clf4, :eta, lower=0.01, upper=0.5, scale=:log10) tuned_model_xg = TunedModel( @@ -56,10 +63,13 @@ tuned_model_xg = TunedModel( measure=cross_entropy, ); -# Of course, one can wrap each of the four in a TunedModel if they are interested in comparing the models over a large set of their hyperparameters. +# Of course, one can wrap each of the four in a TunedModel if they are interested in +# comparing the models over a large set of their hyperparameters. # ### Comparing the models + # We simply pass the four models to the `models` argument of the `TunedModel` construct + tuned_model = TunedModel( models=[clf1, clf2, clf3, tuned_model_xg], tuning=Explicit(), @@ -68,16 +78,16 @@ tuned_model = TunedModel( ); # Then wrapping our tuned model in a machine and fitting it. + mach = machine(tuned_model, X, y); fit!(mach, verbosity=0); # Now let's see the history for more details on the performance for each of the models history = report(mach).history -history_df = DataFrame(mlp = [x[:model] for x in history], measurement = [x[:measurement][1] for x in history]) +history_df = DataFrame( + mlp = [x[:model] for x in history], + measurement = [x[:measurement][1] for x in history], +) sort!(history_df, [order(:measurement)]) # This is Occam's razor in practice. - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=true) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src diff --git a/docs/src/common_workflows/comparison/notebook.md b/docs/src/common_workflows/comparison/notebook.md new file mode 100644 index 00000000..1419ab55 --- /dev/null +++ b/docs/src/common_workflows/comparison/notebook.md @@ -0,0 +1,119 @@ +```@meta +EditURL = "notebook.jl" +``` + +# Model Comparison with MLJFlux + +This demonstration is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/comparison). + +In this workflow example, we see how we can compare different machine learning models +with a neural network from MLJFlux. + +**Julia version** is assumed to be 1.10.* + +### Basic Imports + +````@example comparison +using MLJ # Has MLJFlux models +using Flux # For more flexibility +import RDatasets # Dataset source +using DataFrames # To visualize hyperparameter search results +import Optimisers # native Flux.jl optimisers no longer supported +```` + +### Loading and Splitting the Data + +````@example comparison +iris = RDatasets.dataset("datasets", "iris"); +y, X = unpack(iris, ==(:Species), colname -> true, rng=123); +nothing #hide +```` + +### Instantiating the models Now let's construct our model. This follows a similar setup +to the one followed in the [Quick Start](../../index.md#Quick-Start). + +````@example comparison +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux + +clf1 = NeuralNetworkClassifier( + builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), + optimiser=Optimisers.Adam(0.01), + batch_size=8, + epochs=50, + rng=42 + ) +```` + +Let's as well load and construct three other classical machine learning models: + +````@example comparison +BayesianLDA = @load BayesianLDA pkg=MultivariateStats +clf2 = BayesianLDA() +RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree +clf3 = RandomForestClassifier() +XGBoostClassifier = @load XGBoostClassifier pkg=XGBoost +clf4 = XGBoostClassifier(); +nothing #hide +```` + +### Wrapping One of the Models in a TunedModel + +Instead of just comparing with four models with the default/given hyperparameters, we +will give `XGBoostClassifier` an unfair advantage By wrapping it in a `TunedModel` that +considers the best learning rate η for the model. + +````@example comparison +r1 = range(clf4, :eta, lower=0.01, upper=0.5, scale=:log10) +tuned_model_xg = TunedModel( + model=clf4, + ranges=[r1], + tuning=Grid(resolution=10), + resampling=CV(nfolds=5, rng=42), + measure=cross_entropy, +); +nothing #hide +```` + +Of course, one can wrap each of the four in a TunedModel if they are interested in +comparing the models over a large set of their hyperparameters. + +### Comparing the models + +We simply pass the four models to the `models` argument of the `TunedModel` construct + +````@example comparison +tuned_model = TunedModel( + models=[clf1, clf2, clf3, tuned_model_xg], + tuning=Explicit(), + resampling=CV(nfolds=5, rng=42), + measure=cross_entropy, +); +nothing #hide +```` + +Then wrapping our tuned model in a machine and fitting it. + +````@example comparison +mach = machine(tuned_model, X, y); +fit!(mach, verbosity=0); +nothing #hide +```` + +Now let's see the history for more details on the performance for each of the models + +````@example comparison +history = report(mach).history +history_df = DataFrame( + mlp = [x[:model] for x in history], + measurement = [x[:measurement][1] for x in history], +) +sort!(history_df, [order(:measurement)]) +```` + +This is Occam's razor in practice. + +--- + +*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* + diff --git a/docs/src/common_workflows/comparison/notebook.unexecuted.ipynb b/docs/src/common_workflows/comparison/notebook.unexecuted.ipynb new file mode 100644 index 00000000..b8517a90 --- /dev/null +++ b/docs/src/common_workflows/comparison/notebook.unexecuted.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Model Comparison with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/comparison)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we see how we can compare different machine learning models\n", + "with a neural network from MLJFlux." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "using DataFrames # To visualize hyperparameter search results\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the models Now let's construct our model. This follows a similar setup\n", + "to the one followed in the [Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "\n", + "clf1 = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=50,\n", + " rng=42\n", + " )" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Let's as well load and construct three other classical machine learning models:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "BayesianLDA = @load BayesianLDA pkg=MultivariateStats\n", + "clf2 = BayesianLDA()\n", + "RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree\n", + "clf3 = RandomForestClassifier()\n", + "XGBoostClassifier = @load XGBoostClassifier pkg=XGBoost\n", + "clf4 = XGBoostClassifier();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping One of the Models in a TunedModel" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Instead of just comparing with four models with the default/given hyperparameters, we\n", + "will give `XGBoostClassifier` an unfair advantage By wrapping it in a `TunedModel` that\n", + "considers the best learning rate η for the model." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "r1 = range(clf4, :eta, lower=0.01, upper=0.5, scale=:log10)\n", + "tuned_model_xg = TunedModel(\n", + " model=clf4,\n", + " ranges=[r1],\n", + " tuning=Grid(resolution=10),\n", + " resampling=CV(nfolds=5, rng=42),\n", + " measure=cross_entropy,\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Of course, one can wrap each of the four in a TunedModel if they are interested in\n", + "comparing the models over a large set of their hyperparameters." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Comparing the models" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We simply pass the four models to the `models` argument of the `TunedModel` construct" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "tuned_model = TunedModel(\n", + " models=[clf1, clf2, clf3, tuned_model_xg],\n", + " tuning=Explicit(),\n", + " resampling=CV(nfolds=5, rng=42),\n", + " measure=cross_entropy,\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Then wrapping our tuned model in a machine and fitting it." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(tuned_model, X, y);\n", + "fit!(mach, verbosity=0);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now let's see the history for more details on the performance for each of the models" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "history = report(mach).history\n", + "history_df = DataFrame(\n", + " mlp = [x[:model] for x in history],\n", + " measurement = [x[:measurement][1] for x in history],\n", + ")\n", + "sort!(history_df, [order(:measurement)])" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "This is Occam's razor in practice." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + 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b/docs/src/common_workflows/composition/Project.toml similarity index 87% rename from docs/src/workflow examples/Composition/Project.toml rename to docs/src/common_workflows/composition/Project.toml index d10ca3c4..8b6bea93 100644 --- a/docs/src/workflow examples/Composition/Project.toml +++ b/docs/src/common_workflows/composition/Project.toml @@ -5,4 +5,5 @@ Imbalance = "c709b415-507b-45b7-9a3d-1767c89fde68" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJBalancing = "45f359ea-796d-4f51-95a5-deb1a414c586" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/composition/README.md b/docs/src/common_workflows/composition/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/composition/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/composition/generate.jl b/docs/src/common_workflows/composition/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/composition/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/workflow examples/Composition/composition.ipynb b/docs/src/common_workflows/composition/notebook.ipynb similarity index 52% rename from docs/src/workflow examples/Composition/composition.ipynb rename to docs/src/common_workflows/composition/notebook.ipynb index 710660f7..ced33e3c 100644 --- a/docs/src/workflow examples/Composition/composition.ipynb +++ b/docs/src/common_workflows/composition/notebook.ipynb @@ -10,11 +10,39 @@ { "cell_type": "markdown", "source": [ - "In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux models. We will assume a\n", - "class imbalance setting and wrap an oversampler with a deep learning model from MLJFlux." + "This tutorial is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/composition)." ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux\n", + "models. We will assume a class imbalance setting and wrap an oversampler with a deep\n", + "learning model from MLJFlux." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/common_workflows/composition`\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, { "cell_type": "markdown", "source": [ @@ -37,10 +65,11 @@ "using Flux # For more flexibility\n", "import RDatasets # Dataset source\n", "import Random # To create imbalance\n", - "import Imbalance # To solve the imbalance" + "import Imbalance # To solve the imbalance\n", + "import Optimisers # native Flux.jl optimisers no longer supported" ], "metadata": {}, - "execution_count": 1 + "execution_count": 2 }, { "cell_type": "markdown", @@ -58,7 +87,7 @@ "X = Float32.(X); # To be compatible with type of network network parameters" ], "metadata": {}, - "execution_count": 2 + "execution_count": 3 }, { "cell_type": "markdown", @@ -87,7 +116,7 @@ "Imbalance.checkbalance(y)" ], "metadata": {}, - "execution_count": 3 + "execution_count": 4 }, { "cell_type": "markdown", @@ -99,7 +128,8 @@ { "cell_type": "markdown", "source": [ - "Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize it." + "Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize\n", + "it." ], "metadata": {} }, @@ -116,34 +146,58 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" + "text/plain": "MLJFlux.NeuralNetworkClassifier" }, "metadata": {}, - "execution_count": 4 + "execution_count": 5 } ], "cell_type": "code", "source": [ "BorderlineSMOTE1 = @load BorderlineSMOTE1 pkg=Imbalance verbosity=0\n", - "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", - "# We didn't need to load Standardizer because it is a local model for MLJ (see `localmodels()`)\n", - "\n", + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "We didn't need to load Standardizer because it is a local model for MLJ (see\n", + "`localmodels()`)" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ "clf = NeuralNetworkClassifier(\n", " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", + " optimiser=Optimisers.Adam(0.01),\n", " batch_size=8,\n", " epochs=50,\n", - " rng=42\n", - " )" + " rng=42,\n", + ")" ], "metadata": {}, - "execution_count": 4 + "execution_count": 6 }, { "cell_type": "markdown", "source": [ - "First we wrap the oversampler with the neural network via the `BalancedModel` construct. This comes from `MLJBalancing`\n", - "And allows combining resampling methods with MLJ models in a sequential pipeline." + "First we wrap the oversampler with the neural network via the `BalancedModel`\n", + "construct. This comes from `MLJBalancing` And allows combining resampling methods with\n", + "MLJ models in a sequential pipeline." ], "metadata": {} }, @@ -155,7 +209,7 @@ "text/plain": "Standardizer(\n features = Symbol[], \n ignore = false, \n ordered_factor = false, \n count = false)" }, "metadata": {}, - "execution_count": 5 + "execution_count": 7 } ], "cell_type": "code", @@ -165,7 +219,7 @@ "standarizer = Standardizer()" ], "metadata": {}, - "execution_count": 5 + "execution_count": 7 }, { "cell_type": "markdown", @@ -182,7 +236,7 @@ "text/plain": "ProbabilisticPipeline(\n standardizer = Standardizer(\n features = Symbol[], \n ignore = false, \n ordered_factor = false, \n count = false), \n balanced_model_probabilistic = BalancedModelProbabilistic(\n model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), \n balancer1 = BorderlineSMOTE1(m = 5, …)), \n cache = true)" }, "metadata": {}, - "execution_count": 6 + "execution_count": 8 } ], "cell_type": "code", @@ -190,21 +244,27 @@ "pipeline = standarizer |> balanced_model" ], "metadata": {}, - "execution_count": 6 + "execution_count": 8 }, { "cell_type": "markdown", "source": [ - "By this, any training data will be standardized then oversampled then passed to the model. Meanwhile,\n", - "for inference, the standardizer will automatically use the training set's mean and std and the oversampler\n", - "will be transparent." + "By this, any training data will be standardized then oversampled then passed to the\n", + "model. Meanwhile, for inference, the standardizer will automatically use the training\n", + "set's mean and std and the oversampler will be transparent." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Training the Composed Model" ], "metadata": {} }, { "cell_type": "markdown", "source": [ - "### Training the Composed Model\n", "It's indistinguishable from training a single model." ], "metadata": {} @@ -221,13 +281,10 @@ "[ Info: Training machine(BorderlineSMOTE1(m = 5, …), …).\n", "[ Info: Training machine(:model, …).\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", - "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", - "┌ Warning: Layer with Float32 parameters got Float64 input.\n", - "│ The input will be converted, but any earlier layers may be very slow.\n", - "│ layer = Dense(4 => 5, relu) # 25 parameters\n", - "│ summary(x) = \"4×8 Matrix{Float64}\"\n", - "└ @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/stateless.jl:60\n", - "\rOptimising neural net: 4%[> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 47%[===========> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 49%[============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 51%[============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 53%[=============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 55%[=============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 57%[==============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 59%[==============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 61%[===============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 63%[===============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 65%[================> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 67%[================> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 69%[=================> ] ETA: 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96%[========================>] ETA: 0:00:00\u001b[K\rOptimising neural net: 98%[========================>] ETA: 0:00:00\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:00\u001b[K\n", + "\rProgress: 13%|███████▏ | ETA: 0:00:01\u001b[K\rProgress: 100%|█████████████████████████████████████████████████████| Time: 0:00:00\u001b[K\n", + "\rProgress: 67%|███████████████████████████████████▍ | ETA: 0:00:01\u001b[K\r\n", + " class: virginica\u001b[K\r\u001b[A[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", + "\rOptimising neural net: 4%[> ] ETA: 0:05:10\u001b[K\rOptimising neural net: 6%[=> ] ETA: 0:03:22\u001b[K\rOptimising neural net: 8%[=> ] ETA: 0:02:29\u001b[K\rOptimising neural net: 10%[==> ] ETA: 0:01:56\u001b[K\rOptimising neural net: 12%[==> ] ETA: 0:01:35\u001b[K\rOptimising neural net: 14%[===> ] ETA: 0:01:20\u001b[K\rOptimising neural net: 16%[===> ] ETA: 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neural net: 49%[============> ] ETA: 0:00:13\u001b[K\rOptimising neural net: 51%[============> ] ETA: 0:00:12\u001b[K\rOptimising neural net: 53%[=============> ] ETA: 0:00:11\u001b[K\rOptimising neural net: 55%[=============> ] ETA: 0:00:10\u001b[K\rOptimising neural net: 57%[==============> ] ETA: 0:00:10\u001b[K\rOptimising neural net: 59%[==============> ] ETA: 0:00:09\u001b[K\rOptimising neural net: 61%[===============> ] ETA: 0:00:08\u001b[K\rOptimising neural net: 63%[===============> ] ETA: 0:00:08\u001b[K\rOptimising neural net: 82%[====================> ] ETA: 0:00:03\u001b[K\rOptimising neural net: 84%[=====================> ] ETA: 0:00:02\u001b[K\rOptimising neural net: 86%[=====================> ] ETA: 0:00:02\u001b[K\rOptimising neural net: 88%[======================> ] ETA: 0:00:02\u001b[K\rOptimising neural net: 90%[======================> ] ETA: 0:00:01\u001b[K\rOptimising neural net: 92%[=======================> ] ETA: 0:00:01\u001b[K\rOptimising neural net: 94%[=======================> ] ETA: 0:00:01\u001b[K\rOptimising neural net: 96%[========================>] ETA: 0:00:01\u001b[K\rOptimising neural net: 98%[========================>] ETA: 0:00:00\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:12\u001b[K\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 3, \"versicolor\" => 1).\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 3, \"versicolor\" => 1).\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"versicolor\" => 2).\n", @@ -236,26 +293,32 @@ "[ Info: After filtering, the mapping from each class to number of borderline points is (\"versicolor\" => 2).\n", "┌ Warning: Cannot oversample a class with no borderline points. Skipping.\n", "└ @ Imbalance ~/.julia/packages/Imbalance/knJL1/src/oversampling_methods/borderline_smote1/borderline_smote1.jl:67\n", - "\rEvaluating over 5 folds: 40%[==========> ] ETA: 0:00:00\u001b[K[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", + "┌ Warning: Layer with Float32 parameters got Float64 input.\n", + "│ The input will be converted, but any earlier layers may be very slow.\n", + "│ layer = Dense(4 => 5, relu) # 25 parameters\n", + "│ summary(x) = \"4×8 Matrix{Float64}\"\n", + "└ @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/stateless.jl:60\n", + "\rEvaluating over 5 folds: 40%[==========> ] ETA: 0:00:16\u001b[K[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1, \"versicolor\" => 2).\n", - "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1).\n", + "\rEvaluating over 5 folds: 60%[===============> ] ETA: 0:00:07\u001b[K[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1).\n", "┌ Warning: Cannot oversample a class with no borderline points. Skipping.\n", "└ @ Imbalance ~/.julia/packages/Imbalance/knJL1/src/oversampling_methods/borderline_smote1/borderline_smote1.jl:67\n", - "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1).\n", + "\rProgress: 67%|███████████████████████████████████▍ | ETA: 0:00:00\u001b[K\r\n", + " class: virginica\u001b[K\r\u001b[A[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 1).\n", "┌ Warning: Cannot oversample a class with no borderline points. Skipping.\n", "└ @ Imbalance ~/.julia/packages/Imbalance/knJL1/src/oversampling_methods/borderline_smote1/borderline_smote1.jl:67\n", + "\rEvaluating over 5 folds: 80%[====================> ] ETA: 0:00:03\u001b[K[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 3, \"versicolor\" => 3).\n", "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 3, \"versicolor\" => 3).\n", - "[ Info: After filtering, the mapping from each class to number of borderline points is (\"virginica\" => 3, \"versicolor\" => 3).\n", - "\rEvaluating over 5 folds: 100%[=========================] Time: 0:00:00\u001b[K\n" + "\rEvaluating over 5 folds: 100%[=========================] Time: 0:00:11\u001b[K\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "PerformanceEvaluation object with these fields:\n model, measure, operation, measurement, per_fold,\n per_observation, fitted_params_per_fold,\n report_per_fold, train_test_rows, resampling, repeats\nExtract:\n┌────────────┬──────────────┬─────────────┬─────────┬───────────────────────────\n│\u001b[22m measure \u001b[0m│\u001b[22m operation \u001b[0m│\u001b[22m measurement \u001b[0m│\u001b[22m 1.96*SE \u001b[0m│\u001b[22m per_fold \u001b[0m ⋯\n├────────────┼──────────────┼─────────────┼─────────┼───────────────────────────\n│ Accuracy() │ predict_mode │ 0.98 │ 0.0268 │ [1.0, 1.0, 0.95, 0.95, 1 ⋯\n└────────────┴──────────────┴─────────────┴─────────┴───────────────────────────\n\u001b[36m 1 column omitted\u001b[0m\n" + "text/plain": "PerformanceEvaluation object with these fields:\n model, measure, operation,\n measurement, per_fold, per_observation,\n fitted_params_per_fold, report_per_fold,\n train_test_rows, resampling, repeats\nExtract:\n┌────────────┬──────────────┬─────────────┐\n│\u001b[22m measure \u001b[0m│\u001b[22m operation \u001b[0m│\u001b[22m measurement \u001b[0m│\n├────────────┼──────────────┼─────────────┤\n│ Accuracy() │ predict_mode │ 0.99 │\n└────────────┴──────────────┴─────────────┘\n┌────────────────────────────┬─────────┐\n│\u001b[22m per_fold \u001b[0m│\u001b[22m 1.96*SE \u001b[0m│\n├────────────────────────────┼─────────┤\n│ [1.0, 1.0, 0.95, 1.0, 1.0] │ 0.0219 │\n└────────────────────────────┴─────────┘\n" }, "metadata": {}, - "execution_count": 7 + "execution_count": 9 } ], "cell_type": "code", @@ -266,7 +329,7 @@ "evaluate!(mach, resampling=cv, measure=accuracy)" ], "metadata": {}, - "execution_count": 7 + "execution_count": 9 }, { "cell_type": "markdown", @@ -284,11 +347,11 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.10.0" + "version": "1.10.3" }, "kernelspec": { "name": "julia-1.10", - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.3", "language": "julia" } }, diff --git a/docs/src/workflow examples/Composition/composition.jl b/docs/src/common_workflows/composition/notebook.jl similarity index 61% rename from docs/src/workflow examples/Composition/composition.jl rename to docs/src/common_workflows/composition/notebook.jl index 27f07917..182021eb 100644 --- a/docs/src/workflow examples/Composition/composition.jl +++ b/docs/src/common_workflows/composition/notebook.jl @@ -1,14 +1,19 @@ # # Model Composition with MLJFlux -# In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux models. We will assume a -# class imbalance setting and wrap an oversampler with a deep learning model from MLJFlux. +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/composition). -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +# In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux +# models. We will assume a class imbalance setting and wrap an oversampler with a deep +# learning model from MLJFlux. + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md # **Julia version** is assumed to be 1.10.* + # ### Basic Imports using MLJ # Has MLJFlux models @@ -16,6 +21,7 @@ using Flux # For more flexibility import RDatasets # Dataset source import Random # To create imbalance import Imbalance # To solve the imbalance +import Optimisers # native Flux.jl optimisers no longer supported # ### Loading and Splitting the Data @@ -27,48 +33,48 @@ X = Float32.(X); # To be compatible with type of network network parameters Random.seed!(803429) subset_indices = rand(1:size(X, 1), 100) X, y = X[subset_indices, :], y[subset_indices] -Imbalance.checkbalance(y) - +Imbalance.checkbalance(y) # ### Instantiating the model -# Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize it. +# Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize +# it. + BorderlineSMOTE1 = @load BorderlineSMOTE1 pkg=Imbalance verbosity=0 NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux -## We didn't need to load Standardizer because it is a local model for MLJ (see `localmodels()`) + +# We didn't need to load Standardizer because it is a local model for MLJ (see +# `localmodels()`) clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=50, - rng=42 - ) + epochs=50, + rng=42, +) + +# First we wrap the oversampler with the neural network via the `BalancedModel` +# construct. This comes from `MLJBalancing` And allows combining resampling methods with +# MLJ models in a sequential pipeline. -# First we wrap the oversampler with the neural network via the `BalancedModel` construct. This comes from `MLJBalancing` -# And allows combining resampling methods with MLJ models in a sequential pipeline. oversampler = BorderlineSMOTE1(k=5, ratios=1.0, rng=42) balanced_model = BalancedModel(model=clf, balancer1=oversampler) standarizer = Standardizer() # Now let's compose the balanced model with a standardizer. pipeline = standarizer |> balanced_model -# By this, any training data will be standardized then oversampled then passed to the model. Meanwhile, -# for inference, the standardizer will automatically use the training set's mean and std and the oversampler -# will be transparent. +# By this, any training data will be standardized then oversampled then passed to the +# model. Meanwhile, for inference, the standardizer will automatically use the training +# set's mean and std and the oversampler will be transparent. # ### Training the Composed Model + # It's indistinguishable from training a single model. mach = machine(pipeline, X, y) fit!(mach) cv=CV(nfolds=5) -evaluate!(mach, resampling=cv, measure=accuracy) - - - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=false) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src +evaluate!(mach, resampling=cv, measure=accuracy) diff --git a/docs/src/workflow examples/Composition/composition.md b/docs/src/common_workflows/composition/notebook.md similarity index 69% rename from docs/src/workflow examples/Composition/composition.md rename to docs/src/common_workflows/composition/notebook.md index 43d5137d..0ef30b3b 100644 --- a/docs/src/workflow examples/Composition/composition.md +++ b/docs/src/common_workflows/composition/notebook.md @@ -1,11 +1,15 @@ ```@meta -EditURL = "composition.jl" +EditURL = "notebook.jl" ``` # Model Composition with MLJFlux -In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux models. We will assume a -class imbalance setting and wrap an oversampler with a deep learning model from MLJFlux. +This tutorial is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/composition). + +In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux +models. We will assume a class imbalance setting and wrap an oversampler with a deep +learning model from MLJFlux. **Julia version** is assumed to be 1.10.* @@ -17,6 +21,7 @@ using Flux # For more flexibility import RDatasets # Dataset source import Random # To create imbalance import Imbalance # To solve the imbalance +import Optimisers # native Flux.jl optimisers no longer supported ```` ### Loading and Splitting the Data @@ -39,24 +44,30 @@ Imbalance.checkbalance(y) ### Instantiating the model -Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize it. +Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize +it. ````@example composition BorderlineSMOTE1 = @load BorderlineSMOTE1 pkg=Imbalance verbosity=0 NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux -# We didn't need to load Standardizer because it is a local model for MLJ (see `localmodels()`) +```` + +We didn't need to load Standardizer because it is a local model for MLJ (see +`localmodels()`) +````@example composition clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=50, - rng=42 - ) + rng=42, +) ```` -First we wrap the oversampler with the neural network via the `BalancedModel` construct. This comes from `MLJBalancing` -And allows combining resampling methods with MLJ models in a sequential pipeline. +First we wrap the oversampler with the neural network via the `BalancedModel` +construct. This comes from `MLJBalancing` And allows combining resampling methods with +MLJ models in a sequential pipeline. ````@example composition oversampler = BorderlineSMOTE1(k=5, ratios=1.0, rng=42) @@ -70,11 +81,12 @@ Now let's compose the balanced model with a standardizer. pipeline = standarizer |> balanced_model ```` -By this, any training data will be standardized then oversampled then passed to the model. Meanwhile, -for inference, the standardizer will automatically use the training set's mean and std and the oversampler -will be transparent. +By this, any training data will be standardized then oversampled then passed to the +model. Meanwhile, for inference, the standardizer will automatically use the training +set's mean and std and the oversampler will be transparent. ### Training the Composed Model + It's indistinguishable from training a single model. ````@example composition diff --git a/docs/src/common_workflows/composition/notebook.unexecuted.ipynb b/docs/src/common_workflows/composition/notebook.unexecuted.ipynb new file mode 100644 index 00000000..54b2439a --- /dev/null +++ b/docs/src/common_workflows/composition/notebook.unexecuted.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Model Composition with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/composition)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we see how MLJFlux enables composing MLJ models with MLJFlux\n", + "models. We will assume a class imbalance setting and wrap an oversampler with a deep\n", + "learning model from MLJFlux." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "import Random # To create imbalance\n", + "import Imbalance # To solve the imbalance\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "To simulate an imbalanced dataset, we will take a random sample:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "Random.seed!(803429)\n", + "subset_indices = rand(1:size(X, 1), 100)\n", + "X, y = X[subset_indices, :], y[subset_indices]\n", + "Imbalance.checkbalance(y)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's load `BorderlineSMOTE1` to oversample the data and `Standardizer` to standardize\n", + "it." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "BorderlineSMOTE1 = @load BorderlineSMOTE1 pkg=Imbalance verbosity=0\n", + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "We didn't need to load Standardizer because it is a local model for MLJ (see\n", + "`localmodels()`)" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=50,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "First we wrap the oversampler with the neural network via the `BalancedModel`\n", + "construct. This comes from `MLJBalancing` And allows combining resampling methods with\n", + "MLJ models in a sequential pipeline." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "oversampler = BorderlineSMOTE1(k=5, ratios=1.0, rng=42)\n", + "balanced_model = BalancedModel(model=clf, balancer1=oversampler)\n", + "standarizer = Standardizer()" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now let's compose the balanced model with a standardizer." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "pipeline = standarizer |> balanced_model" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "By this, any training data will be standardized then oversampled then passed to the\n", + "model. Meanwhile, for inference, the standardizer will automatically use the training\n", + "set's mean and std and the oversampler will be transparent." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Training the Composed Model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "It's indistinguishable from training a single model." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(pipeline, X, y)\n", + "fit!(mach)\n", + "cv=CV(nfolds=5)\n", + "evaluate!(mach, resampling=cv, measure=accuracy)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + 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@@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/early_stopping/README.md b/docs/src/common_workflows/early_stopping/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/early_stopping/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/early_stopping/generate.jl b/docs/src/common_workflows/early_stopping/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/early_stopping/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/common_workflows/early_stopping/notebook.ipynb b/docs/src/common_workflows/early_stopping/notebook.ipynb new file mode 100644 index 00000000..bbdda628 --- /dev/null +++ b/docs/src/common_workflows/early_stopping/notebook.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Early Stopping with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/early_stopping)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we learn how MLJFlux enables us to easily use early stopping\n", + "when training MLJFlux models." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/common_workflows/early_stopping`\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "using Plots # To visualize training\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model Now let's construct our model. This follows a similar setup\n", + "to the one followed in the [Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ Info: For silent loading, specify `verbosity=0`. \n", + "import MLJFlux ✔\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "\n", + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=50,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping it in an IteratedModel" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's start by defining the condition that can cause the model to early stop." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "5-element Vector{Any}:\n IterationControl.Step(1)\n EarlyStopping.NumberLimit(100)\n EarlyStopping.Patience(5)\n EarlyStopping.NumberSinceBest(9)\n EarlyStopping.TimeLimit(Dates.Millisecond(1800000))" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "stop_conditions = [\n", + " Step(1), # Repeatedly train for one iteration\n", + " NumberLimit(100), # Don't train for more than 100 iterations\n", + " Patience(5), # Stop after 5 iterations of disimprovement in validation loss\n", + " NumberSinceBest(9), # Or if the best loss occurred 9 iterations ago\n", + " TimeLimit(30/60), # Or if 30 minutes passed\n", + "]" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "We can also define callbacks. Here we want to store the validation loss for each iteration" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "1-element Vector{IterationControl.WithLossDo{Main.var\"##351\".var\"#3#4\"}}:\n IterationControl.WithLossDo{Main.var\"##351\".var\"#3#4\"}(Main.var\"##351\".var\"#3#4\"(), false, nothing)" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "validation_losses = []\n", + "callbacks = [\n", + " WithLossDo(loss->push!(validation_losses, loss)),\n", + "]" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "Construct the iterated model and pass to it the stop_conditions and the callbacks:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iterated_model = IteratedModel(\n", + " model=clf,\n", + " resampling=Holdout(fraction_train=0.7); # loss and stopping are based on out-of-sample\n", + " measures=log_loss,\n", + " iteration_parameter=:(epochs),\n", + " controls=vcat(stop_conditions, callbacks),\n", + " retrain=false # no need to retrain on all data at the end\n", + ");" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "You can see more advanced stopping conditions as well as how to involve callbacks in the\n", + "[documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Training with Early Stopping" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "At this point, all we need is to fit the model and iteration controls will be\n", + "automatically handled" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ Info: Training machine(ProbabilisticIteratedModel(model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …), …).\n", + "[ Info: final loss: 0.05287897645527522\n", + "[ Info: final training loss: 0.045833383\n", + "[ Info: Stop triggered by EarlyStopping.NumberLimit(100) stopping criterion. \n", + "[ Info: Total of 100 iterations. \n" + ] + } + ], + "cell_type": "code", + "source": [ + "mach = machine(iterated_model, X, y)\n", + "fit!(mach)\n", + "# We can get the training losses like so\n", + "training_losses = report(mach)[:model_report].training_losses;" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "### Results" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We can see that the model converged after 100 iterations." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=2}", + "image/png": 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"plot!(validation_losses, label=\"Validation Loss\", linewidth=2, size=(800,400))" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/workflow examples/Early Stopping/iteration.jl b/docs/src/common_workflows/early_stopping/notebook.jl similarity index 64% rename from docs/src/workflow examples/Early Stopping/iteration.jl rename to docs/src/common_workflows/early_stopping/notebook.jl index 1af02f8c..a6c59da3 100644 --- a/docs/src/workflow examples/Early Stopping/iteration.jl +++ b/docs/src/common_workflows/early_stopping/notebook.jl @@ -1,19 +1,25 @@ -# # Early Stopping with MLJFlux +# # Early Stopping with MLJ -# In this workflow example, we learn how MLJFlux enables us to easily use early stopping when training MLJFlux models. +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/early_stopping). -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +# In this workflow example, we learn how MLJFlux enables us to easily use early stopping +# when training MLJFlux models. + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md + +# **Julia version** is assumed to be 1.10.* -# **Julia version** is assumed to be 1.10.* # ### Basic Imports using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source -using Plots # To visualize training +using Plots # To visualize training +import Optimisers # native Flux.jl optimisers no longer supported # ### Loading and Splitting the Data @@ -22,22 +28,22 @@ y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X); # To be compatible with type of network network parameters -# ### Instantiating the model -# Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). +# ### Instantiating the model Now let's construct our model. This follows a similar setup +# to the one followed in the [Quick Start](../../index.md#Quick-Start). NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=50, - rng=42 - ) + epochs=50, + rng=42, +) # ### Wrapping it in an IteratedModel -# Let's start by defining the condition that can cause the model to early stop. +# Let's start by defining the condition that can cause the model to early stop. stop_conditions = [ Step(1), # Repeatedly train for one iteration NumberLimit(100), # Don't train for more than 100 iterations @@ -47,7 +53,7 @@ stop_conditions = [ ] # We can also define callbacks. Here we want to store the validation loss for each iteration -validation_losses = [] +validation_losses = [] callbacks = [ WithLossDo(loss->push!(validation_losses, loss)), ] @@ -55,17 +61,20 @@ callbacks = [ # Construct the iterated model and pass to it the stop_conditions and the callbacks: iterated_model = IteratedModel( model=clf, - resampling=CV(nfolds=6), # Split the data internally into 0.7 training and 0.3 validation + resampling=Holdout(fraction_train=0.7); # loss and stopping are based on out-of-sample measures=log_loss, iteration_parameter=:(epochs), controls=vcat(stop_conditions, callbacks), - retrain=false # no need to retrain on all data at the end + retrain=false # no need to retrain on all data at the end ); -# You can see more advanced stopping conditions as well as how to involve callbacks in the [documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models) +# You can see more advanced stopping conditions as well as how to involve callbacks in the +# [documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models) # ### Training with Early Stopping -# At this point, all we need is to fit the model and iteration controls will be automatically handled + +# At this point, all we need is to fit the model and iteration controls will be +# automatically handled mach = machine(iterated_model, X, y) fit!(mach) @@ -73,13 +82,8 @@ fit!(mach) training_losses = report(mach)[:model_report].training_losses; # ### Results + # We can see that the model converged after 100 iterations. plot(training_losses, label="Training Loss", linewidth=2) plot!(validation_losses, label="Validation Loss", linewidth=2, size=(800,400)) - -#- - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=false) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src diff --git a/docs/src/workflow examples/Early Stopping/iteration.md b/docs/src/common_workflows/early_stopping/notebook.md similarity index 62% rename from docs/src/workflow examples/Early Stopping/iteration.md rename to docs/src/common_workflows/early_stopping/notebook.md index 10f33e7c..e6738259 100644 --- a/docs/src/workflow examples/Early Stopping/iteration.md +++ b/docs/src/common_workflows/early_stopping/notebook.md @@ -1,51 +1,56 @@ ```@meta -EditURL = "iteration.jl" +EditURL = "notebook.jl" ``` # Early Stopping with MLJFlux -In this workflow example, we learn how MLJFlux enables us to easily use early stopping when training MLJFlux models. +This demonstration is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/early_stopping). + +In this workflow example, we learn how MLJFlux enables us to easily use early stopping +when training MLJFlux models. **Julia version** is assumed to be 1.10.* ### Basic Imports -````@example iteration +````@example early_stopping using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source -using Plots # To visualize training +using Plots # To visualize training +import Optimisers # native Flux.jl optimisers no longer supported ```` ### Loading and Splitting the Data -````@example iteration +````@example early_stopping iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X); # To be compatible with type of network network parameters nothing #hide ```` -### Instantiating the model -Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). +### Instantiating the model Now let's construct our model. This follows a similar setup +to the one followed in the [Quick Start](../../index.md#Quick-Start). -````@example iteration +````@example early_stopping NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=50, - rng=42 - ) + rng=42, +) ```` ### Wrapping it in an IteratedModel Let's start by defining the condition that can cause the model to early stop. -````@example iteration +````@example early_stopping stop_conditions = [ Step(1), # Repeatedly train for one iteration NumberLimit(100), # Don't train for more than 100 iterations @@ -57,8 +62,8 @@ stop_conditions = [ We can also define callbacks. Here we want to store the validation loss for each iteration -````@example iteration -validation_losses = [] +````@example early_stopping +validation_losses = [] callbacks = [ WithLossDo(loss->push!(validation_losses, loss)), ] @@ -66,23 +71,27 @@ callbacks = [ Construct the iterated model and pass to it the stop_conditions and the callbacks: -````@example iteration -iterated_model = IteratedModel(model=clf, - resampling=CV(nfolds=6), # Split the data internally into 0.7 training and 0.3 validation - measures=log_loss, - iteration_parameter=:(epochs), - controls=vcat(stop_conditions, callbacks), - retrain=false # no need to retrain on all data at the end - ); +````@example early_stopping +iterated_model = IteratedModel( + model=clf, + resampling=Holdout(fraction_train=0.7); # loss and stopping are based on out-of-sample + measures=log_loss, + iteration_parameter=:(epochs), + controls=vcat(stop_conditions, callbacks), + retrain=false # no need to retrain on all data at the end +); nothing #hide ```` -You can see more advanced stopping conditions as well as how to involve callbacks in the [documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models) +You can see more advanced stopping conditions as well as how to involve callbacks in the +[documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models) ### Training with Early Stopping -At this point, all we need is to fit the model and iteration controls will be automatically handled -````@example iteration +At this point, all we need is to fit the model and iteration controls will be +automatically handled + +````@example early_stopping mach = machine(iterated_model, X, y) fit!(mach) # We can get the training losses like so @@ -91,17 +100,14 @@ nothing #hide ```` ### Results + We can see that the model converged after 100 iterations. -````@example iteration +````@example early_stopping plot(training_losses, label="Training Loss", linewidth=2) plot!(validation_losses, label="Validation Loss", linewidth=2, size=(800,400)) ```` -````@example iteration -using Literate #src -```` - --- *This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* diff --git a/docs/src/common_workflows/early_stopping/notebook.unexecuted.ipynb b/docs/src/common_workflows/early_stopping/notebook.unexecuted.ipynb new file mode 100644 index 00000000..5effdb73 --- /dev/null +++ b/docs/src/common_workflows/early_stopping/notebook.unexecuted.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Early Stopping with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/early_stopping)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example, we learn how MLJFlux enables us to easily use early stopping\n", + "when training MLJFlux models." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "using Plots # To visualize training\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model Now let's construct our model. This follows a similar setup\n", + "to the one followed in the [Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "\n", + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=50,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping it in an IteratedModel" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's start by defining the condition that can cause the model to early stop." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "stop_conditions = [\n", + " Step(1), # Repeatedly train for one iteration\n", + " NumberLimit(100), # Don't train for more than 100 iterations\n", + " Patience(5), # Stop after 5 iterations of disimprovement in validation loss\n", + " NumberSinceBest(9), # Or if the best loss occurred 9 iterations ago\n", + " TimeLimit(30/60), # Or if 30 minutes passed\n", + "]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "We can also define callbacks. Here we want to store the validation loss for each iteration" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "validation_losses = []\n", + "callbacks = [\n", + " WithLossDo(loss->push!(validation_losses, loss)),\n", + "]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Construct the iterated model and pass to it the stop_conditions and the callbacks:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iterated_model = IteratedModel(\n", + " model=clf,\n", + " resampling=Holdout(fraction_train=0.7); # loss and stopping are based on out-of-sample\n", + " measures=log_loss,\n", + " iteration_parameter=:(epochs),\n", + " controls=vcat(stop_conditions, callbacks),\n", + " retrain=false # no need to retrain on all data at the end\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "You can see more advanced stopping conditions as well as how to involve callbacks in the\n", + "[documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Training with Early Stopping" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "At this point, all we need is to fit the model and iteration controls will be\n", + "automatically handled" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(iterated_model, X, y)\n", + "fit!(mach)\n", + "# We can get the training losses like so\n", + "training_losses = report(mach)[:model_report].training_losses;" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Results" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We can see that the model converged after 100 iterations." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "plot(training_losses, label=\"Training Loss\", linewidth=2)\n", + "plot!(validation_losses, label=\"Validation Loss\", linewidth=2, size=(800,400))" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/common_workflows/hyperparameter_tuning/Manifest.toml b/docs/src/common_workflows/hyperparameter_tuning/Manifest.toml new file mode 100644 index 00000000..7de851af --- /dev/null +++ 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"Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+1" diff --git a/docs/src/workflow examples/Early Stopping/Project.toml b/docs/src/common_workflows/hyperparameter_tuning/Project.toml similarity index 82% rename from docs/src/workflow examples/Early Stopping/Project.toml rename to docs/src/common_workflows/hyperparameter_tuning/Project.toml index 74f46e95..b95d41bd 100644 --- a/docs/src/workflow examples/Early Stopping/Project.toml +++ b/docs/src/common_workflows/hyperparameter_tuning/Project.toml @@ -2,5 +2,6 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/hyperparameter_tuning/README.md b/docs/src/common_workflows/hyperparameter_tuning/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/hyperparameter_tuning/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/hyperparameter_tuning/generate.jl b/docs/src/common_workflows/hyperparameter_tuning/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/hyperparameter_tuning/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/workflow examples/Hyperparameter Tuning/tuning.jl b/docs/src/common_workflows/hyperparameter_tuning/notebook.jl similarity index 60% rename from docs/src/workflow examples/Hyperparameter Tuning/tuning.jl rename to docs/src/common_workflows/hyperparameter_tuning/notebook.jl index e00ae6fc..aa39830d 100644 --- a/docs/src/workflow examples/Hyperparameter Tuning/tuning.jl +++ b/docs/src/common_workflows/hyperparameter_tuning/notebook.jl @@ -1,19 +1,25 @@ # # Hyperparameter Tuning with MLJFlux -# In this workflow example we learn how to tune different hyperparameters of MLJFlux models with emphasis on training hyperparameters. +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/hyperparameter_tuning). -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +# In this workflow example we learn how to tune different hyperparameters of MLJFlux +# models with emphasis on training hyperparameters. + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md # **Julia version** is assumed to be 1.10.* + # ### Basic Imports using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source using Plots # To plot tuning results +import Optimisers # native Flux.jl optimisers no longer supported # ### Loading and Splitting the Data @@ -24,25 +30,32 @@ X = Float32.(X); # To be compatible with type of network network parameters # ### Instantiating the model -# Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start). + +# Now let's construct our model. This follows a similar setup the one followed in the +# [Quick Start](../../index.md#Quick-Start). NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=10, - rng=42 - ) + epochs=10, + rng=42, +) # ### Hyperparameter Tuning Example -# Let's tune the batch size and the learning rate. We will use grid search and 5-fold cross-validation. + +# Let's tune the batch size and the learning rate. We will use grid search and 5-fold +# cross-validation. # We start by defining the hyperparameter ranges r1 = range(clf, :batch_size, lower=1, upper=64) -r2 = range(clf, :(optimiser.eta), lower=10^-4, upper=10^0, scale=:log10) +etas = [10^x for x in range(-4, stop=0, length=4)] +optimisers = [Optimisers.Adam(eta) for eta in etas] +r2 = range(clf, :optimiser, values=optimisers) -# Then passing the ranges along with the model and other arguments to the `TunedModel` constructor. +# Then passing the ranges along with the model and other arguments to the `TunedModel` +# constructor. tuned_model = TunedModel( model=clf, @@ -58,26 +71,28 @@ fit!(mach, verbosity=0); # Let's check out the best performing model: fitted_params(mach).best_model -# We can visualize the hyperparameter search results as follows -plot(mach) # ### Learning Curves -# With learning curves, it's possible to center our focus on the effects of a single hyperparameter of the model + +# With learning curves, it's possible to center our focus on the effects of a single +# hyperparameter of the model # First define the range and wrap it in a learning curve r = range(clf, :epochs, lower=1, upper=200, scale=:log10) -curve = learning_curve(clf, X, y, - range=r, - resampling=CV(nfolds=4, rng=42), - measure=cross_entropy) +curve = learning_curve( + clf, + X, + y, + range=r, + resampling=CV(nfolds=4, rng=42), + measure=cross_entropy, +) # Then plot the curve -plot(curve.parameter_values, - curve.measurements, - xlab=curve.parameter_name, - xscale=curve.parameter_scale, - ylab = "Cross Entropy") - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=false) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src +plot( + curve.parameter_values, + curve.measurements, + xlab=curve.parameter_name, + xscale=curve.parameter_scale, + ylab = "Cross Entropy", +) diff --git a/docs/src/workflow examples/Hyperparameter Tuning/tuning.md b/docs/src/common_workflows/hyperparameter_tuning/notebook.md similarity index 58% rename from docs/src/workflow examples/Hyperparameter Tuning/tuning.md rename to docs/src/common_workflows/hyperparameter_tuning/notebook.md index c9ab4989..ae50dd14 100644 --- a/docs/src/workflow examples/Hyperparameter Tuning/tuning.md +++ b/docs/src/common_workflows/hyperparameter_tuning/notebook.md @@ -1,25 +1,30 @@ ```@meta -EditURL = "tuning.jl" +EditURL = "notebook.jl" ``` # Hyperparameter Tuning with MLJFlux -In this workflow example we learn how to tune different hyperparameters of MLJFlux models with emphasis on training hyperparameters. +This demonstration is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/hyperparameter_tuning). + +In this workflow example we learn how to tune different hyperparameters of MLJFlux +models with emphasis on training hyperparameters. **Julia version** is assumed to be 1.10.* ### Basic Imports -````@example Tuning +````@example hyperparameter_tuning using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source using Plots # To plot tuning results +import Optimisers # native Flux.jl optimisers no longer supported ```` ### Loading and Splitting the Data -````@example Tuning +````@example hyperparameter_tuning iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X); # To be compatible with type of network network parameters @@ -27,32 +32,39 @@ nothing #hide ```` ### Instantiating the model -Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start). -````@example Tuning +Now let's construct our model. This follows a similar setup the one followed in the +[Quick Start](../../index.md#Quick-Start). + +````@example hyperparameter_tuning NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=10, - rng=42 - ) + rng=42, +) ```` ### Hyperparameter Tuning Example -Let's tune the batch size and the learning rate. We will use grid search and 5-fold cross-validation. + +Let's tune the batch size and the learning rate. We will use grid search and 5-fold +cross-validation. We start by defining the hyperparameter ranges -````@example Tuning +````@example hyperparameter_tuning r1 = range(clf, :batch_size, lower=1, upper=64) -r2 = range(clf, :(optimiser.eta), lower=10^-4, upper=10^0, scale=:log10) +etas = [10^x for x in range(-4, stop=0, length=4)] +optimisers = [Optimisers.Adam(eta) for eta in etas] +r2 = range(clf, :optimiser, values=optimisers) ```` -Then passing the ranges along with the model and other arguments to the `TunedModel` constructor. +Then passing the ranges along with the model and other arguments to the `TunedModel` +constructor. -````@example Tuning +````@example hyperparameter_tuning tuned_model = TunedModel( model=clf, tuning=Grid(goal=25), @@ -65,7 +77,7 @@ nothing #hide Then wrapping our tuned model in a machine and fitting it. -````@example Tuning +````@example hyperparameter_tuning mach = machine(tuned_model, X, y); fit!(mach, verbosity=0); nothing #hide @@ -73,37 +85,39 @@ nothing #hide Let's check out the best performing model: -````@example Tuning +````@example hyperparameter_tuning fitted_params(mach).best_model ```` -We can visualize the hyperparameter search results as follows - -````@example Tuning -plot(mach) -```` - ### Learning Curves -With learning curves, it's possible to center our focus on the effects of a single hyperparameter of the model + +With learning curves, it's possible to center our focus on the effects of a single +hyperparameter of the model First define the range and wrap it in a learning curve -````@example Tuning +````@example hyperparameter_tuning r = range(clf, :epochs, lower=1, upper=200, scale=:log10) -curve = learning_curve(clf, X, y, - range=r, - resampling=CV(nfolds=4, rng=42), - measure=cross_entropy) +curve = learning_curve( + clf, + X, + y, + range=r, + resampling=CV(nfolds=4, rng=42), + measure=cross_entropy, +) ```` Then plot the curve -````@example Tuning -plot(curve.parameter_values, - curve.measurements, - xlab=curve.parameter_name, - xscale=curve.parameter_scale, - ylab = "Cross Entropy") +````@example hyperparameter_tuning +plot( + curve.parameter_values, + curve.measurements, + xlab=curve.parameter_name, + xscale=curve.parameter_scale, + ylab = "Cross Entropy", +) ```` --- diff --git a/docs/src/common_workflows/hyperparameter_tuning/notebook.unexecuted.ipynb b/docs/src/common_workflows/hyperparameter_tuning/notebook.unexecuted.ipynb new file mode 100644 index 00000000..2060f391 --- /dev/null +++ b/docs/src/common_workflows/hyperparameter_tuning/notebook.unexecuted.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Hyperparameter Tuning with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/hyperparameter_tuning)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example we learn how to tune different hyperparameters of MLJFlux\n", + "models with emphasis on training hyperparameters." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "using Plots # To plot tuning results\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's construct our model. This follows a similar setup the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=10,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Hyperparameter Tuning Example" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Let's tune the batch size and the learning rate. We will use grid search and 5-fold\n", + "cross-validation." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We start by defining the hyperparameter ranges" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "r1 = range(clf, :batch_size, lower=1, upper=64)\n", + "etas = [10^x for x in range(-4, stop=0, length=4)]\n", + "optimisers = [Optimisers.Adam(eta) for eta in etas]\n", + "r2 = range(clf, :optimiser, values=optimisers)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Then passing the ranges along with the model and other arguments to the `TunedModel`\n", + "constructor." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "tuned_model = TunedModel(\n", + " model=clf,\n", + " tuning=Grid(goal=25),\n", + " resampling=CV(nfolds=5, rng=42),\n", + " range=[r1, r2],\n", + " measure=cross_entropy,\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Then wrapping our tuned model in a machine and fitting it." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(tuned_model, X, y);\n", + "fit!(mach, verbosity=0);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Let's check out the best performing model:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "fitted_params(mach).best_model" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Learning Curves" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "With learning curves, it's possible to center our focus on the effects of a single\n", + "hyperparameter of the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "First define the range and wrap it in a learning curve" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "r = range(clf, :epochs, lower=1, upper=200, scale=:log10)\n", + "curve = learning_curve(\n", + " clf,\n", + " X,\n", + " y,\n", + " range=r,\n", + " resampling=CV(nfolds=4, rng=42),\n", + " measure=cross_entropy,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Then plot the curve" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "plot(\n", + " curve.parameter_values,\n", + " curve.measurements,\n", + " xlab=curve.parameter_name,\n", + " xscale=curve.parameter_scale,\n", + " ylab = \"Cross Entropy\",\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + 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= "5.8.0+1" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.52.0+1" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" diff --git a/docs/src/workflow examples/Incremental Training/Project.toml b/docs/src/common_workflows/incremental_training/Project.toml similarity index 79% rename from docs/src/workflow examples/Incremental Training/Project.toml rename to docs/src/common_workflows/incremental_training/Project.toml index b4afe33e..631dd106 100644 --- a/docs/src/workflow examples/Incremental Training/Project.toml +++ b/docs/src/common_workflows/incremental_training/Project.toml @@ -2,4 +2,5 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/incremental_training/README.md b/docs/src/common_workflows/incremental_training/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/incremental_training/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/incremental_training/generate.jl b/docs/src/common_workflows/incremental_training/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/common_workflows/incremental_training/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/workflow examples/Incremental Training/incremental.ipynb b/docs/src/common_workflows/incremental_training/notebook.ipynb similarity index 54% rename from docs/src/workflow examples/Incremental Training/incremental.ipynb rename to docs/src/common_workflows/incremental_training/notebook.ipynb index 6bb51aaa..b85e848b 100644 --- a/docs/src/workflow examples/Incremental Training/incremental.ipynb +++ b/docs/src/common_workflows/incremental_training/notebook.ipynb @@ -3,15 +3,42 @@ { "cell_type": "markdown", "source": [ - "# Incremental Training with MLJFlux\n", + "# Incremental Training with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ "In this workflow example we explore how to incrementally train MLJFlux models." ], "metadata": {} }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/common_workflows/incremental_training`\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, { "cell_type": "markdown", "source": [ - "**Julia version** is assumed to be 1.10.*" + "**Julia version** is assumed to be 1.10.* This tutorial is available as a Jupyter\n", + "notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/incremental_training)." ], "metadata": {} }, @@ -28,10 +55,11 @@ "source": [ "using MLJ # Has MLJFlux models\n", "using Flux # For more flexibility\n", - "import RDatasets # Dataset source" + "import RDatasets # Dataset source\n", + "import Optimisers # native Flux.jl optimisers no longer supported" ], "metadata": {}, - "execution_count": 1 + "execution_count": 2 }, { "cell_type": "markdown", @@ -47,19 +75,28 @@ "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", "X = Float32.(X) # To be compatible with type of network network parameters\n", - "(X_train, X_test), (y_train, y_test) = partition((X, y), 0.8,\n", - " multi = true,\n", - " shuffle = true,\n", - " rng=42);" + "(X_train, X_test), (y_train, y_test) = partition(\n", + " (X, y), 0.8,\n", + " multi = true,\n", + " shuffle = true,\n", + " rng=42,\n", + ");" ], "metadata": {}, - "execution_count": 2 + "execution_count": 3 }, { "cell_type": "markdown", "source": [ - "### Instantiating the model\n", - "Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#quick-start)." + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's construct our model. This follows a similar setup to the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." ], "metadata": {} }, @@ -76,10 +113,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Flux.Optimise.Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" }, "metadata": {}, - "execution_count": 3 + "execution_count": 4 } ], "cell_type": "code", @@ -87,20 +124,27 @@ "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", "clf = NeuralNetworkClassifier(\n", " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", + " optimiser=Optimisers.Adam(0.01),\n", " batch_size=8,\n", " epochs=10,\n", - " rng=42\n", - " )" + " rng=42,\n", + ")" ], "metadata": {}, - "execution_count": 3 + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "### Initial round of training" + ], + "metadata": {} }, { "cell_type": "markdown", "source": [ - "### Initial round of training\n", - "Now let's train the model. Calling fit! will automatically train it for 100 epochs as specified above." + "Now let's train the model. Calling fit! will automatically train it for 100 epochs as\n", + "specified above." ], "metadata": {} }, @@ -111,16 +155,16 @@ "output_type": "stream", "text": [ "[ Info: Training machine(NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …).\n", - "\rOptimising neural net: 18%[====> ] ETA: 0:00:21\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:05\u001b[K\n" + "\rOptimising neural net: 18%[====> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 27%[======> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 36%[=========> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 45%[===========> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 55%[=============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 64%[===============> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 73%[==================> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 82%[====================> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 91%[======================> ] ETA: 0:00:00\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:00\u001b[K\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "trained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …)\n args: \n 1:\tSource @655 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}\n 2:\tSource @902 ⏎ AbstractVector{ScientificTypesBase.Multiclass{3}}\n" + "text/plain": "trained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …)\n args: \n 1:\tSource @068 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}\n 2:\tSource @767 ⏎ AbstractVector{ScientificTypesBase.Multiclass{3}}\n" }, "metadata": {}, - "execution_count": 4 + "execution_count": 5 } ], "cell_type": "code", @@ -129,7 +173,7 @@ "fit!(mach)" ], "metadata": {}, - "execution_count": 4 + "execution_count": 5 }, { "cell_type": "markdown", @@ -143,10 +187,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.5187556517212482" + "text/plain": "0.4392339631006042" }, "metadata": {}, - "execution_count": 5 + "execution_count": 6 } ], "cell_type": "code", @@ -154,17 +198,17 @@ "training_loss = cross_entropy(predict(mach, X_train), y_train)" ], "metadata": {}, - "execution_count": 5 + "execution_count": 6 }, { "outputs": [ { "output_type": "execute_result", "data": { - "text/plain": "0.5333333333333333" + "text/plain": "0.9" }, "metadata": {}, - "execution_count": 6 + "execution_count": 7 } ], "cell_type": "code", @@ -172,15 +216,28 @@ "val_acc = accuracy(predict_mode(mach, X_test), y_test)" ], "metadata": {}, - "execution_count": 6 + "execution_count": 7 }, { "cell_type": "markdown", "source": [ - "Poor performance it seems.\n", - "### Incremental Training\n", - "Now let's train it for another 30 epochs at half the original learning rate. All we need to do is changes these\n", - "hyperparameters and call fit again. It won't reset the model parameters before training." + "Poor performance it seems." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Incremental Training" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's train it for another 30 epochs at half the original learning rate. All we need\n", + "to do is changes these hyperparameters and call fit again. It won't reset the model\n", + "parameters before training." ], "metadata": {} }, @@ -191,47 +248,47 @@ "output_type": "stream", "text": [ "[ Info: Updating machine(NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …).\n", - "[ Info: Loss is 0.5195\n", - "[ Info: Loss is 0.5113\n", - "[ Info: Loss is 0.5056\n", - "[ Info: Loss is 0.501\n", - "[ Info: Loss is 0.497\n", - "[ Info: Loss is 0.4944\n", - "[ Info: Loss is 0.4909\n", - "[ Info: Loss is 0.4881\n", - "[ Info: Loss is 0.4855\n", - "[ Info: Loss is 0.4833\n", - "[ Info: Loss is 0.4813\n", - "[ Info: Loss is 0.4794\n", - "[ Info: Loss is 0.4777\n", - "[ Info: Loss is 0.476\n", - "[ Info: Loss is 0.4744\n", - "[ Info: Loss is 0.4729\n", - "[ Info: Loss is 0.471\n", - "[ Info: Loss is 0.4685\n", - "[ Info: Loss is 0.4357\n", - "[ Info: Loss is 0.3986\n", - "[ Info: Loss is 0.354\n", - "[ Info: Loss is 0.3212\n", - "[ Info: Loss is 0.294\n", - "[ Info: Loss is 0.2832\n", - "[ Info: Loss is 0.2727\n", - "[ Info: Loss is 0.247\n", - "[ Info: Loss is 0.2285\n", - "[ Info: Loss is 0.2153\n", - "[ Info: Loss is 0.2024\n", - "[ Info: Loss is 0.1928\n" + "[ Info: Loss is 0.4393\n", + "[ Info: Loss is 0.4317\n", + "[ Info: Loss is 0.4244\n", + "[ Info: Loss is 0.4171\n", + "[ Info: Loss is 0.4096\n", + "[ Info: Loss is 0.4017\n", + "[ Info: Loss is 0.3931\n", + "[ Info: Loss is 0.3838\n", + "[ Info: Loss is 0.3737\n", + "[ Info: Loss is 0.3626\n", + "[ Info: Loss is 0.3505\n", + "[ Info: Loss is 0.3382\n", + "[ Info: Loss is 0.3244\n", + "[ Info: Loss is 0.3095\n", + "[ Info: Loss is 0.2954\n", + "[ Info: Loss is 0.2813\n", + "[ Info: Loss is 0.2654\n", + "[ Info: Loss is 0.25\n", + "[ Info: Loss is 0.235\n", + "[ Info: Loss is 0.2203\n", + "[ Info: Loss is 0.2118\n", + "[ Info: Loss is 0.196\n", + "[ Info: Loss is 0.179\n", + "[ Info: Loss is 0.1674\n", + "[ Info: Loss is 0.1586\n", + "[ Info: Loss is 0.1469\n", + "[ Info: Loss is 0.1353\n", + "[ Info: Loss is 0.1251\n", + "[ Info: Loss is 0.1173\n", + "[ Info: Loss is 0.1102\n" ] } ], "cell_type": "code", "source": [ - "clf.optimiser.eta = clf.optimiser.eta / 2\n", + "clf.optimiser = Optimisers.Adam(clf.optimiser.eta/2)\n", "clf.epochs = clf.epochs + 30\n", "fit!(mach, verbosity=2);" ], "metadata": {}, - "execution_count": 7 + "execution_count": 8 }, { "cell_type": "markdown", @@ -245,10 +302,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.18276122841169196" + "text/plain": "0.10519664737051289" }, "metadata": {}, - "execution_count": 8 + "execution_count": 9 } ], "cell_type": "code", @@ -256,17 +313,17 @@ "training_loss = cross_entropy(predict(mach, X_train), y_train)" ], "metadata": {}, - "execution_count": 8 + "execution_count": 9 }, { "outputs": [ { "output_type": "execute_result", "data": { - "text/plain": "0.9333333333333333" + "text/plain": "0.9666666666666667" }, "metadata": {}, - "execution_count": 9 + "execution_count": 10 } ], "cell_type": "code", @@ -274,12 +331,13 @@ "training_acc = accuracy(predict_mode(mach, X_test), y_test)" ], "metadata": {}, - "execution_count": 9 + "execution_count": 10 }, { "cell_type": "markdown", "source": [ - "That's much better. If we are rather interested in resetting the model parameters before fitting, we can do `fit(mach, force=true)`." + "That's much better. If we are rather interested in resetting the model parameters before\n", + "fitting, we can do `fit(mach, force=true)`." ], "metadata": {} }, @@ -299,11 +357,11 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.10.0" + "version": "1.10.3" }, "kernelspec": { "name": "julia-1.10", - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.3", "language": "julia" } }, diff --git a/docs/src/workflow examples/Incremental Training/incremental.jl b/docs/src/common_workflows/incremental_training/notebook.jl similarity index 58% rename from docs/src/workflow examples/Incremental Training/incremental.jl rename to docs/src/common_workflows/incremental_training/notebook.jl index 1718a1a3..20d38b53 100644 --- a/docs/src/workflow examples/Incremental Training/incremental.jl +++ b/docs/src/common_workflows/incremental_training/notebook.jl @@ -1,9 +1,13 @@ # # Incremental Training with MLJFlux + +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/incremental_training). + # In this workflow example we explore how to incrementally train MLJFlux models. -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md # **Julia version** is assumed to be 1.10.* @@ -12,59 +16,72 @@ Pkg.instantiate(); #src using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source +import Optimisers # native Flux.jl optimisers no longer supported + # ### Loading and Splitting the Data iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X) # To be compatible with type of network network parameters -(X_train, X_test), (y_train, y_test) = partition((X, y), 0.8, - multi = true, - shuffle = true, - rng=42); +(X_train, X_test), (y_train, y_test) = partition( + (X, y), 0.8, + multi = true, + shuffle = true, + rng=42, +); # ### Instantiating the model -# Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). + +# Now let's construct our model. This follows a similar setup to the one followed in the +# [Quick Start](../../index.md#Quick-Start). NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=10, - rng=42 - ) + epochs=10, + rng=42, +) # ### Initial round of training -# Now let's train the model. Calling fit! will automatically train it for 100 epochs as specified above. + +# Now let's train the model. Calling fit! will automatically train it for 100 epochs as +# specified above. mach = machine(clf, X_train, y_train) fit!(mach) # Let's evaluate the training loss and validation accuracy -training_loss = cross_entropy(predict(mach, X_train), y_train) +training_loss = cross_entropy(predict(mach, X_train), y_train) + #- + val_acc = accuracy(predict_mode(mach, X_test), y_test) # Poor performance it seems. + # ### Incremental Training -# Now let's train it for another 30 epochs at half the original learning rate. All we need to do is changes these -# hyperparameters and call fit again. It won't reset the model parameters before training. -clf.optimiser.eta = clf.optimiser.eta / 2 +# Now let's train it for another 30 epochs at half the original learning rate. All we need +# to do is changes these hyperparameters and call fit again. It won't reset the model +# parameters before training. + +clf.optimiser = Optimisers.Adam(clf.optimiser.eta/2) clf.epochs = clf.epochs + 30 fit!(mach, verbosity=2); # Let's evaluate the training loss and validation accuracy -training_loss = cross_entropy(predict(mach, X_train), y_train) +training_loss = cross_entropy(predict(mach, X_train), y_train) + #- + training_acc = accuracy(predict_mode(mach, X_test), y_test) -#- -# That's much better. If we are rather interested in resetting the model parameters before fitting, we can do `fit(mach, force=true)`. +#- -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=false) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src +# That's much better. If we are rather interested in resetting the model parameters before +# fitting, we can do `fit(mach, force=true)`. diff --git a/docs/src/workflow examples/Incremental Training/incremental.md b/docs/src/common_workflows/incremental_training/notebook.md similarity index 59% rename from docs/src/workflow examples/Incremental Training/incremental.md rename to docs/src/common_workflows/incremental_training/notebook.md index 2a04a14c..94be1207 100644 --- a/docs/src/workflow examples/Incremental Training/incremental.md +++ b/docs/src/common_workflows/incremental_training/notebook.md @@ -1,72 +1,85 @@ ```@meta -EditURL = "incremental.jl" +EditURL = "notebook.jl" ``` # Incremental Training with MLJFlux + In this workflow example we explore how to incrementally train MLJFlux models. -**Julia version** is assumed to be 1.10.* +**Julia version** is assumed to be 1.10.* This tutorial is available as a Jupyter +notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/incremental_training). ### Basic Imports -````@example incremental +````@example incremental_training using MLJ # Has MLJFlux models using Flux # For more flexibility import RDatasets # Dataset source +import Optimisers # native Flux.jl optimisers no longer supported ```` ### Loading and Splitting the Data -````@example incremental +````@example incremental_training iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X) # To be compatible with type of network network parameters -(X_train, X_test), (y_train, y_test) = partition((X, y), 0.8, - multi = true, - shuffle = true, - rng=42); +(X_train, X_test), (y_train, y_test) = partition( + (X, y), 0.8, + multi = true, + shuffle = true, + rng=42, +); nothing #hide ```` ### Instantiating the model -Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#quick-start). -````@example incremental +Now let's construct our model. This follows a similar setup to the one followed in the +[Quick Start](../../index.md#Quick-Start). + +````@example incremental_training NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=10, - rng=42 - ) + rng=42, +) ```` ### Initial round of training -Now let's train the model. Calling fit! will automatically train it for 100 epochs as specified above. -````@example incremental +Now let's train the model. Calling fit! will automatically train it for 100 epochs as +specified above. + +````@example incremental_training mach = machine(clf, X_train, y_train) fit!(mach) ```` Let's evaluate the training loss and validation accuracy -````@example incremental +````@example incremental_training training_loss = cross_entropy(predict(mach, X_train), y_train) ```` -````@example incremental +````@example incremental_training val_acc = accuracy(predict_mode(mach, X_test), y_test) ```` Poor performance it seems. + ### Incremental Training -Now let's train it for another 30 epochs at half the original learning rate. All we need to do is changes these -hyperparameters and call fit again. It won't reset the model parameters before training. -````@example incremental -clf.optimiser.eta = clf.optimiser.eta / 2 +Now let's train it for another 30 epochs at half the original learning rate. All we need +to do is changes these hyperparameters and call fit again. It won't reset the model +parameters before training. + +````@example incremental_training +clf.optimiser = Optimisers.Adam(clf.optimiser.eta/2) clf.epochs = clf.epochs + 30 fit!(mach, verbosity=2); nothing #hide @@ -74,15 +87,16 @@ nothing #hide Let's evaluate the training loss and validation accuracy -````@example incremental +````@example incremental_training training_loss = cross_entropy(predict(mach, X_train), y_train) ```` -````@example incremental +````@example incremental_training training_acc = accuracy(predict_mode(mach, X_test), y_test) ```` -That's much better. If we are rather interested in resetting the model parameters before fitting, we can do `fit(mach, force=true)`. +That's much better. If we are rather interested in resetting the model parameters before +fitting, we can do `fit(mach, force=true)`. --- diff --git a/docs/src/common_workflows/incremental_training/notebook.unexecuted.ipynb b/docs/src/common_workflows/incremental_training/notebook.unexecuted.ipynb new file mode 100644 index 00000000..4d12d4d7 --- /dev/null +++ b/docs/src/common_workflows/incremental_training/notebook.unexecuted.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Incremental Training with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this workflow example we explore how to incrementally train MLJFlux models." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.* This tutorial is available as a Jupyter\n", + "notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/incremental_training)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ # Has MLJFlux models\n", + "using Flux # For more flexibility\n", + "import RDatasets # Dataset source\n", + "import Optimisers # native Flux.jl optimisers no longer supported" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X) # To be compatible with type of network network parameters\n", + "(X_train, X_test), (y_train, y_test) = partition(\n", + " (X, y), 0.8,\n", + " multi = true,\n", + " shuffle = true,\n", + " rng=42,\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's construct our model. This follows a similar setup to the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=10,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Initial round of training" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's train the model. Calling fit! will automatically train it for 100 epochs as\n", + "specified above." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(clf, X_train, y_train)\n", + "fit!(mach)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Let's evaluate the training loss and validation accuracy" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "training_loss = cross_entropy(predict(mach, X_train), y_train)" + ], + "metadata": {}, + "execution_count": null + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "val_acc = accuracy(predict_mode(mach, X_test), y_test)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Poor performance it seems." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Incremental Training" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's train it for another 30 epochs at half the original learning rate. All we need\n", + "to do is changes these hyperparameters and call fit again. It won't reset the model\n", + "parameters before training." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "clf.optimiser = Optimisers.Adam(clf.optimiser.eta/2)\n", + "clf.epochs = clf.epochs + 30\n", + "fit!(mach, verbosity=2);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Let's evaluate the training loss and validation accuracy" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "training_loss = cross_entropy(predict(mach, X_train), y_train)" + ], + "metadata": {}, + "execution_count": null + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "training_acc = accuracy(predict_mode(mach, X_test), y_test)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "That's much better. If we are rather interested in resetting the model parameters before\n", + "fitting, we can do `fit(mach, force=true)`." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/common_workflows/live_training/Manifest.toml b/docs/src/common_workflows/live_training/Manifest.toml new file mode 100644 index 00000000..7de851af --- /dev/null +++ b/docs/src/common_workflows/live_training/Manifest.toml @@ -0,0 +1,1985 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.10.3" 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examples/Hyperparameter Tuning/Project.toml b/docs/src/common_workflows/live_training/Project.toml similarity index 82% rename from docs/src/workflow examples/Hyperparameter Tuning/Project.toml rename to docs/src/common_workflows/live_training/Project.toml index 74f46e95..b95d41bd 100644 --- a/docs/src/workflow examples/Hyperparameter Tuning/Project.toml +++ b/docs/src/common_workflows/live_training/Project.toml @@ -2,5 +2,6 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b" diff --git a/docs/src/common_workflows/live_training/README.md b/docs/src/common_workflows/live_training/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/common_workflows/live_training/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/common_workflows/live_training/generate.jl b/docs/src/common_workflows/live_training/generate.jl new file mode 100644 index 00000000..daf1a1a1 --- /dev/null +++ b/docs/src/common_workflows/live_training/generate.jl @@ -0,0 +1,5 @@ +# Execute this julia file in a new julia process to generate the notebooks from +# ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=false, pluto=false) diff --git a/docs/src/workflow examples/Live Training/live-training.jl b/docs/src/common_workflows/live_training/notebook.jl similarity index 52% rename from docs/src/workflow examples/Live Training/live-training.jl rename to docs/src/common_workflows/live_training/notebook.jl index 5715d1c8..16bae98a 100644 --- a/docs/src/workflow examples/Live Training/live-training.jl +++ b/docs/src/common_workflows/live_training/notebook.jl @@ -1,17 +1,24 @@ # # Live Training with MLJFlux -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/live_training). + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md # **Julia version** is assumed to be 1.10.* # ### Basic Imports -using MLJ # Has MLJFlux models -using Flux # For more flexibility -import RDatasets # Dataset source -using Plots # For training plot +using MLJ +using Flux +import RDatasets +import Optimisers + +#- + +using Plots # ### Loading and Splitting the Data @@ -21,20 +28,24 @@ X = Float32.(X); # To be compatible with type of network network parameters # ### Instantiating the model -# Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). + +# Now let's construct our model. This follows a similar setup to the one followed in the +# [Quick Start](../../index.md#Quick-Start). NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, - epochs=50, - rng=42 - ) + epochs=50, + rng=42, +) -# Now let's wrap this in an iterated model. We will use a callback that makes a plot for validation losses each iteration. +# Now let's wrap this in an iterated model. We will use a callback that makes a plot for +# validation losses each iteration. + stop_conditions = [ Step(1), # Repeatedly train for one iteration NumberLimit(100), # Don't train for more than 100 iterations @@ -45,29 +56,23 @@ gr(reuse=true) # use the same window for plots function plot_loss(loss) push!(validation_losses, loss) display(plot(validation_losses, label="validation loss", xlim=(1, 100))) - sleep(.01) # to catch up with the plots while they are being generated + sleep(.01) # to catch up with the plots while they are being generated end callbacks = [ WithLossDo(plot_loss),] -iterated_model = IteratedModel(model=clf, - resampling=Holdout(), - measures=log_loss, - iteration_parameter=:(epochs), - controls=vcat(stop_conditions, callbacks), - retrain=true - ) +iterated_model = IteratedModel( + model=clf, + resampling=Holdout(), + measures=log_loss, + iteration_parameter=:(epochs), + controls=vcat(stop_conditions, callbacks), + retrain=true, +) # ### Live Training # Simply fitting the model is all we need mach = machine(iterated_model, X, y) -fit!(mach, force=true) - - -#- - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute=false) #src -Literate.notebook(@__FILE__, @__DIR__, execute=true) #src +fit!(mach, force=true) diff --git a/docs/src/workflow examples/Live Training/live-training.md b/docs/src/common_workflows/live_training/notebook.md similarity index 53% rename from docs/src/workflow examples/Live Training/live-training.md rename to docs/src/common_workflows/live_training/notebook.md index 2248c190..edc1b140 100644 --- a/docs/src/workflow examples/Live Training/live-training.md +++ b/docs/src/common_workflows/live_training/notebook.md @@ -1,23 +1,30 @@ ```@meta -EditURL = "live-training.jl" +EditURL = "notebook.jl" ``` -# Incremental Training with MLJFlux +# Live Training with MLJFlux + +This tutorial is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/live_training). **Julia version** is assumed to be 1.10.* ### Basic Imports -````@example live-training -using MLJ # Has MLJFlux models -using Flux # For more flexibility -import RDatasets # Dataset source -using Plots # For training plot +````@example live_training +using MLJ +using Flux +import RDatasets +import Optimisers +```` + +````@example live_training +using Plots ```` ### Loading and Splitting the Data -````@example live-training +````@example live_training iris = RDatasets.dataset("datasets", "iris"); y, X = unpack(iris, ==(:Species), colname -> true, rng=123); X = Float32.(X); # To be compatible with type of network network parameters @@ -25,23 +32,26 @@ nothing #hide ```` ### Instantiating the model -Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). -````@example live-training +Now let's construct our model. This follows a similar setup to the one followed in the +[Quick Start](../../index.md#Quick-Start). + +````@example live_training NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=50, - rng=42 - ) + rng=42, +) ```` -Now let's wrap this in an iterated model. We will use a callback that makes a plot for validation losses each iteration. +Now let's wrap this in an iterated model. We will use a callback that makes a plot for +validation losses each iteration. -````@example live-training +````@example live_training stop_conditions = [ Step(1), # Repeatedly train for one iteration NumberLimit(100), # Don't train for more than 100 iterations @@ -52,32 +62,29 @@ gr(reuse=true) # use the same window for plots function plot_loss(loss) push!(validation_losses, loss) display(plot(validation_losses, label="validation loss", xlim=(1, 100))) - sleep(.01) # to catch up with the plots while they are being generated + sleep(.01) # to catch up with the plots while they are being generated end callbacks = [ WithLossDo(plot_loss),] -iterated_model = IteratedModel(model=clf, - resampling=Holdout(), # Split the data internally into 0.7 training and 0.3 validation - measures=log_loss, - iteration_parameter=:(epochs), - controls=vcat(stop_conditions, callbacks), - retrain=true # no need to retrain on all data at the end - ) +iterated_model = IteratedModel( + model=clf, + resampling=Holdout(), + measures=log_loss, + iteration_parameter=:(epochs), + controls=vcat(stop_conditions, callbacks), + retrain=true, +) ```` ### Live Training Simply fitting the model is all we need -````@example live-training +````@example live_training mach = machine(iterated_model, X, y) fit!(mach, force=true) ```` -````@example live-training -using Literate #src -```` - --- *This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* diff --git a/docs/src/common_workflows/live_training/notebook.unexecuted.ipynb b/docs/src/common_workflows/live_training/notebook.unexecuted.ipynb new file mode 100644 index 00000000..a647a39a --- /dev/null +++ b/docs/src/common_workflows/live_training/notebook.unexecuted.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Live Training with MLJFlux" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/common_workflows/live_training)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ\n", + "using Flux\n", + "import RDatasets\n", + "import Optimisers" + ], + "metadata": {}, + "execution_count": null + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Plots" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Loading and Splitting the Data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", + "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", + "X = Float32.(X); # To be compatible with type of network network parameters" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiating the model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now let's construct our model. This follows a similar setup to the one followed in the\n", + "[Quick Start](../../index.md#Quick-Start)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", + "\n", + "clf = NeuralNetworkClassifier(\n", + " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", + " optimiser=Optimisers.Adam(0.01),\n", + " batch_size=8,\n", + " epochs=50,\n", + " rng=42,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now let's wrap this in an iterated model. We will use a callback that makes a plot for\n", + "validation losses each iteration." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "stop_conditions = [\n", + " Step(1), # Repeatedly train for one iteration\n", + " NumberLimit(100), # Don't train for more than 100 iterations\n", + "]\n", + "\n", + "validation_losses = []\n", + "gr(reuse=true) # use the same window for plots\n", + "function plot_loss(loss)\n", + " push!(validation_losses, loss)\n", + " display(plot(validation_losses, label=\"validation loss\", xlim=(1, 100)))\n", + " sleep(.01) # to catch up with the plots while they are being generated\n", + "end\n", + "\n", + "callbacks = [ WithLossDo(plot_loss),]\n", + "\n", + "iterated_model = IteratedModel(\n", + " model=clf,\n", + " resampling=Holdout(),\n", + " measures=log_loss,\n", + " iteration_parameter=:(epochs),\n", + " controls=vcat(stop_conditions, callbacks),\n", + " retrain=true,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Live Training\n", + "Simply fitting the model is all we need" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(iterated_model, X, y)\n", + "fit!(mach, force=true)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/full tutorials/Boston.md b/docs/src/extended_examples/Boston.md similarity index 100% rename from docs/src/full tutorials/Boston.md 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"052768ef-5323-5732-b1bb-66c8b64840ba" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" +MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" +MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" +MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55" +MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" diff --git a/docs/src/extended_examples/MNIST/README.md b/docs/src/extended_examples/MNIST/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/extended_examples/MNIST/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/extended_examples/MNIST/generate.jl b/docs/src/extended_examples/MNIST/generate.jl new file mode 100644 index 00000000..f68699de --- /dev/null +++ b/docs/src/extended_examples/MNIST/generate.jl @@ -0,0 +1,5 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=false, pluto=false) + diff --git a/docs/src/extended_examples/MNIST/loss.png b/docs/src/extended_examples/MNIST/loss.png new file mode 100644 index 00000000..c77e097a Binary files /dev/null and b/docs/src/extended_examples/MNIST/loss.png differ diff --git a/docs/src/extended_examples/MNIST/notebook.ipynb b/docs/src/extended_examples/MNIST/notebook.ipynb new file mode 100644 index 00000000..617be38e --- /dev/null +++ b/docs/src/extended_examples/MNIST/notebook.ipynb @@ -0,0 +1,2111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using MLJ to classifiy the MNIST image dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/extended_examples/MNIST`\n" + ] + } + ], + "source": [ + "using Pkg\n", + "const DIR = @__DIR__\n", + "Pkg.activate(DIR)\n", + "Pkg.instantiate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Julia version** is assumed to be 1.10.*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "using MLJ\n", + "using Flux\n", + "import MLJFlux\n", + "import MLUtils\n", + "import MLJIteration # for `skip`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If running on a GPU, you will also need to `import CUDA` and `import cuDNN`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "using Plots\n", + "gr(size=(600, 300*(sqrt(5)-1)));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Downloading the MNIST image dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import MLDatasets: MNIST\n", + "\n", + "ENV[\"DATADEPS_ALWAYS_ACCEPT\"] = true\n", + "images, labels = MNIST(split=:train)[:];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In MLJ, integers cannot be used for encoding categorical data, so we\n", + "must force the labels to have the `Multiclass` [scientific\n", + "type](https://juliaai.github.io/ScientificTypes.jl/dev/). For\n", + "more on this, see [Working with Categorical\n", + "Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "labels = coerce(labels, Multiclass);\n", + "images = coerce(images, GrayImage);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking scientific types:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "@assert scitype(images) <: AbstractVector{<:Image}\n", + "@assert scitype(labels) <: AbstractVector{<:Finite}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For general instructions on coercing image data, see [Type coercion\n", + "for image\n", + "data](https://juliaai.github.io/ScientificTypes.jl/dev/#Type-coercion-for-image-data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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This is a recipe\n", + "for building the neural network. Our builder will work for images of\n", + "any (constant) size, whether they be color or black and white (ie,\n", + "single or multi-channel). The architecture always consists of six\n", + "alternating convolution and max-pool layers, and a final dense\n", + "layer; the filter size and the number of channels after each\n", + "convolution layer is customisable." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import MLJFlux\n", + "struct MyConvBuilder\n", + " filter_size::Int\n", + " channels1::Int\n", + " channels2::Int\n", + " channels3::Int\n", + "end\n", + "\n", + "function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels)\n", + " k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3\n", + " mod(k, 2) == 1 || error(\"`filter_size` must be odd. \")\n", + " p = div(k - 1, 2) # padding to preserve image size\n", + " init = Flux.glorot_uniform(rng)\n", + " front = Chain(\n", + " Conv((k, k), n_channels => c1, pad=(p, p), relu, init=init),\n", + " MaxPool((2, 2)),\n", + " Conv((k, k), c1 => c2, pad=(p, p), relu, init=init),\n", + " MaxPool((2, 2)),\n", + " Conv((k, k), c2 => c3, pad=(p, p), relu, init=init),\n", + " MaxPool((2 ,2)),\n", + " MLUtils.flatten)\n", + " d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first\n", + " return Chain(front, Dense(d, n_out, init=init))\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notes.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- There is no final `softmax` here, as this is applied by default in all MLJFLux\n", + " classifiers. Customisation of this behaviour is controlled using using the `finaliser`\n", + " hyperparameter of the classifier.\n", + "\n", + "- Instead of calculating the padding `p`, Flux can infer the required padding in each\n", + " dimension, which you enable by replacing `pad = (p, p)` with `pad = SamePad()`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define the MLJ model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mFor silent loading, specify `verbosity=0`. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "import MLJFlux ✔\n" + ] + }, + { + "data": { + "text/plain": [ + "ImageClassifier(\n", + " builder = MyConvBuilder(3, 16, 32, 32), \n", + " finaliser = NNlib.softmax, \n", + " optimiser = Adam(0.001, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n", + " loss = Flux.Losses.crossentropy, \n", + " epochs = 10, \n", + " batch_size = 50, \n", + " lambda = 0.0, \n", + " alpha = 0.0, \n", + " rng = 123, \n", + " optimiser_changes_trigger_retraining = false, \n", + " acceleration = CPU1{Nothing}(nothing))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ImageClassifier = @load ImageClassifier\n", + "clf = ImageClassifier(\n", + " builder=MyConvBuilder(3, 16, 32, 32),\n", + " batch_size=50,\n", + " epochs=10,\n", + " rng=123,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can add Flux options `optimiser=...` and `loss=...` in the above constructor\n", + "call. At present, `loss` must be a Flux-compatible loss, not an MLJ measure. To run on a\n", + "GPU, add to the constructor `acceleration=CUDALib()` and omit `rng`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For illustration purposes, we won't use all the data here:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "501:1000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = 1:500\n", + "test = 501:1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Binding the model with data in an MLJ machine:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "mach = machine(clf, images, labels);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training for 10 epochs on the first 500 images:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTraining machine(ImageClassifier(builder = MyConvBuilder(3, 16, 32, 32), …), …).\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 2.291\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 2.208\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 2.049\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 1.685\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 1.075\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 0.628\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 0.4639\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 0.361\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 0.2921\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoss is 0.2478\n" + ] + } + ], + "source": [ + "fit!(mach, rows=train, verbosity=2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(training_losses = Float32[2.3242702, 2.2908378, 2.20822, 2.0489829, 1.6850392, 1.0751165, 0.6279615, 0.46388212, 0.36103815, 0.29207793, 0.2478443],)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "report(mach)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(chain = Chain(Chain(Chain(Conv((3, 3), 1 => 16, relu, pad=1), MaxPool((2, 2)), Conv((3, 3), 16 => 32, relu, pad=1), MaxPool((2, 2)), Conv((3, 3), 32 => 32, relu, pad=1), MaxPool((2, 2)), flatten), Dense(288 => 10)), softmax),)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain = fitted_params(mach)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16-element Vector{Float32}:\n", + " 0.011803599\n", + " 0.05579675\n", + " 8.461591f-5\n", + " 0.013422165\n", + " -0.001925053\n", + " 0.011568692\n", + " -0.00051727734\n", + " -0.0003228416\n", + " 0.03614383\n", + " 0.06365696\n", + " -0.0005846103\n", + " -0.004092362\n", + " 0.0036211032\n", + " 0.0031117066\n", + " 0.02764553\n", + " 0.05152524" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Flux.params(chain)[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding 20 more epochs:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUpdating machine(ImageClassifier(builder = MyConvBuilder(3, 16, 32, 32), …), …).\n", + "\u001b[33mOptimising neural net: 100%[=========================] Time: 0:00:30\u001b[39m\n" + ] + } + ], + "source": [ + "clf.epochs = clf.epochs + 20\n", + "fit!(mach, rows=train);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computing an out-of-sample estimate of the loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.36284237158113225" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_labels = predict(mach, rows=test);\n", + "cross_entropy(predicted_labels, labels[test])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or to fit and predict, in one line:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PerformanceEvaluation object with these fields:\n", + " model, measure, operation,\n", + " measurement, per_fold, per_observation,\n", + " fitted_params_per_fold, report_per_fold,\n", + " train_test_rows, resampling, repeats\n", + "Extract:\n", + "┌──────────────────────┬───────────┬─────────────┐\n", + "│\u001b[22m measure \u001b[0m│\u001b[22m operation \u001b[0m│\u001b[22m measurement \u001b[0m│\n", + "├──────────────────────┼───────────┼─────────────┤\n", + "│ LogLoss( │ predict │ 0.363 │\n", + "│ tol = 2.22045e-16) │ │ │\n", + "└──────────────────────┴───────────┴─────────────┘\n" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate!(mach,\n", + " resampling=Holdout(fraction_train=0.5),\n", + " measure=cross_entropy,\n", + " rows=1:1000,\n", + " verbosity=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping the MLJFlux model with iteration controls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any iterative MLJFlux model can be wrapped in *iteration controls*,\n", + "as we demonstrate next. For more on MLJ's `IteratedModel` wrapper,\n", + "see the [MLJ\n", + "documentation](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The \"self-iterating\" classifier, called `iterated_clf` below, is for\n", + "iterating the image classifier defined above until one of the\n", + "following stopping criterion apply:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `Patience(3)`: 3 consecutive increases in the loss\n", + "- `InvalidValue()`: an out-of-sample loss, or a training loss, is `NaN`, `Inf`, or `-Inf`\n", + "- `TimeLimit(t=5/60)`: training time has exceeded 5 minutes\n", + "\n", + "These checks (and other controls) will be applied every two epochs\n", + "(because of the `Step(2)` control). Additionally, training a\n", + "machine bound to `iterated_clf` will:\n", + "\n", + "- save a snapshot of the machine every three control cycles (every six epochs)\n", + "- record traces of the out-of-sample loss and training losses for plotting\n", + "- record mean value traces of each Flux parameter for plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a complete list of controls, see [this\n", + "table](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wrapping the classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some helpers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extract Flux params from an MLJFlux machine" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "parameters(mach) = vec.(Flux.params(fitted_params(mach)));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To store the traces:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Any[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "losses = []\n", + "training_losses = []\n", + "parameter_means = Float32[];\n", + "epochs = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To update the traces:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "update_epochs (generic function with 1 method)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "update_loss(loss) = push!(losses, loss)\n", + "update_training_loss(losses) = push!(training_losses, losses[end])\n", + "update_means(mach) = append!(parameter_means, mean.(parameters(mach)));\n", + "update_epochs(epoch) = push!(epochs, epoch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The controls to apply:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "save_control =\n", + " MLJIteration.skip(Save(joinpath(tempdir(), \"mnist.jls\")), predicate=3)\n", + "\n", + "controls=[\n", + " Step(2),\n", + " Patience(3),\n", + " InvalidValue(),\n", + " TimeLimit(5/60),\n", + " save_control,\n", + " WithLossDo(),\n", + " WithLossDo(update_loss),\n", + " WithTrainingLossesDo(update_training_loss),\n", + " Callback(update_means),\n", + " WithIterationsDo(update_epochs),\n", + "];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The \"self-iterating\" classifier:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ProbabilisticIteratedModel(\n", + " model = ImageClassifier(\n", + " builder = MyConvBuilder(3, 16, 32, 32), \n", + " finaliser = NNlib.softmax, \n", + " optimiser = Adam(0.001, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n", + " loss = Flux.Losses.crossentropy, \n", + " epochs = 30, \n", + " batch_size = 50, \n", + " lambda = 0.0, \n", + " alpha = 0.0, \n", + " rng = 123, \n", + " optimiser_changes_trigger_retraining = false, \n", + " acceleration = CPU1{Nothing}(nothing)), \n", + " controls = Any[Step(2), Patience(3), InvalidValue(), TimeLimit(Dates.Millisecond(300000)), IterationControl.Skip{Save{typeof(Serialization.serialize)}, IterationControl.var\"#8#9\"{Int64}}(Save{typeof(Serialization.serialize)}(\"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/mnist.jls\", Serialization.serialize), IterationControl.var\"#8#9\"{Int64}(3)), WithLossDo{IterationControl.var\"#20#22\"}(IterationControl.var\"#20#22\"(), false, nothing), WithLossDo{typeof(update_loss)}(update_loss, false, nothing), WithTrainingLossesDo{typeof(update_training_loss)}(update_training_loss, false, nothing), Callback{typeof(update_means)}(update_means, false, nothing, false), WithIterationsDo{typeof(update_epochs)}(update_epochs, false, nothing)], \n", + " resampling = Holdout(\n", + " fraction_train = 0.7, \n", + " shuffle = false, \n", + " rng = Random._GLOBAL_RNG()), \n", + " measure = LogLoss(tol = 2.22045e-16), \n", + " weights = nothing, \n", + " class_weights = nothing, \n", + " operation = MLJModelInterface.predict, \n", + " retrain = false, \n", + " check_measure = true, \n", + " iteration_parameter = nothing, \n", + " cache = true)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iterated_clf = IteratedModel(\n", + " clf,\n", + " controls=controls,\n", + " resampling=Holdout(fraction_train=0.7),\n", + " measure=log_loss,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binding the wrapped model to data:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "mach = machine(iterated_clf, images, labels);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTraining machine(ProbabilisticIteratedModel(model = ImageClassifier(builder = MyConvBuilder(3, 16, 32, 32), …), …), …).\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mNo iteration parameter specified. Using `iteration_parameter=:(epochs)`. \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 2.2247422992833092\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 1.9681479167178544\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mSaving \"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/mnist1.jls\". \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 1.220910971646785\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.5940933327640742\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.46833501799372196\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mSaving \"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/mnist2.jls\". \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.4241402839593314\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.40840895980242126\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.404754883332919\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mSaving \"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/mnist3.jls\". \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.4097772917650752\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.420399235463716\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.43216415903189187\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mfinal loss: 0.43216415903189187\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mfinal training loss: 0.043363843\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mStop triggered by Patience(3) stopping criterion. \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTotal of 22 iterations. \n" + ] + } + ], + "source": [ + "fit!(mach, rows=train);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of the training and out-of-sample losses:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/loss.png\"" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot(\n", + " epochs,\n", + " losses,\n", + " xlab = \"epoch\",\n", + " ylab = \"cross entropy\",\n", + " label=\"out-of-sample\",\n", + ")\n", + "plot!(epochs, training_losses, label=\"training\")\n", + "\n", + "savefig(joinpath(tempdir(), \"loss.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evolution of weights" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jI2fPnk0O7TwMMlTBoaCgID09XaPRkAmIKSkpCKFnn33WOQUNBsNDJllGRkZZWVmbNm2cU7Dikz6JVq1aURR18uTJB66dRgZfuLSWWrdujRA6fvx4ZdXzhEg9f//9912PBTqQf5chQ4Y4p2BWVtb9ezsfFWnt/f7770ePHu3duzf5cda7d2+e59evX3/16tXOnTs7PhhRUVE6ne7q1asP7KV0QRpkLp9YjPHDf4Zd3PUf2llUVNQbb7zxxx9/DBo0qKCg4OjRo4/3ROCxQRCCh0JRVFRU1K1btxyLkSKEUlNTt2zZ4rJnSkrK+++/r9Ppfv3116CgoB9//JGm6dGjRz/koIAvv/zSuVtp8eLFVqv1hRdeIN+w5GDStWvXnO+ycOHCh1z4OCgoiKKomzdvOn8r7d2798iRIw9z94cREhLyzDPPFBQUfPHFFxVvLS4udlyuW7cuQujmzZvOO7Ru3bply5apqalr1qy5/93dIyIiom/fvtnZ2WQW3b3queu/y8yZMyu3GBKE33zzTVlZWa9evcjGXr16URQ1d+5c5NQvihBiGGbMmDEY42nTplXsqLzPO/nCCy+o1eqff/7Z+aP+008/JScnP17Z5B86MzPTeaPBYKj4U4kMwrp/5zmoCtA1Ch7WqFGjPv744+eff37OnDl169Y9ffr0woULGzZsmJaW5tjHYDCMGDHCYrH88ssvpA3XtWvX2bNnz5o1a/To0bt3777XgRlCoVCcOXNm5MiRb7/9tlKp3Lx581dffaXVaj/99FOyQ8eOHcmXVGRk5MCBA3me/+WXX1atWhUVFfXAcXoIIR8fnzZt2iQmJr744ouTJ09Wq9UHDhz47LPPGjdufOnSpSd6d5wsXbq0Q4cOs2bNSk5OHjJkSHR0dFFR0bVr17Zs2aJUKvfs2UN269Chw86dO0ePHj106FCFQqHRaCZMmEBR1Pfff9+1a9c333zzr7/+6tevX2RkZF5e3uXLl3/99ddWrVrFx8dXVp0Pafny5R07dpw+ffqZM2cGDRoUHR1dUFBw7dq1TZs2+fv7//bbbwihHj16MAzzzTff+Pn59ejRw2g0fvfdd7t37w4ODq7ERmGTJk1CQkKys7NR+eBMhFBQUFCzZs3IxAznIEQIzZkzZ9++fevWrcvIyBg9enTjxo2NRmN6evquXbsuXbp0r14Ef3//xYsXjxs3rkuXLq+88kpUVNSZM2e2bt367LPPPvADfFcdOnRACM2ePfvGjRtkMYcJEyacOHFi9OjRY8aMadeuXWRkpMVi+f3339etWxcYGNi3b99HfQrwpDwxZwN42GNMqMcYcxw3YsQIxyeHYZjZs2e7zCN89dVXEUJTp051fihBEMjftvOKGxWRlWVOnjzpPKgyPDz8+PHjzrvt2LHDediCTqfbsmXLoEGDEELnz58n+zhWlqn4LJcuXXIe5sey7L///e8vv/wSIbR8+XLHbmQeoV6vd77vvHnzEEKrVq1y3kgOQfXt29d5440bNyqudhYSEkLau4TJZHr99dcdr8V5ZZlz586RuQ3O6tWr9/333zv2ISvL3Of9dEZeMsdxzhv//e9/I4TWrVvnvPH8+fPon/MdMcbXrl1zWWkFIRQaGrp48WLHPqtXr3burw4NDf3zzz9btWqFECotLXW8LoTQoEGDXMojk0A2b978wBdCPpaRkZHOG8mUG41G45hQ6FBUVDR69GjnDluEkK+vL1lmyPkxnT/qGONff/3VMYYzLi5u+/bt8+fPRwjFx8c79iGjwHJzc53vSPLVeZo/xvjzzz8PDQ11FJCZmZmUlFRx8HCzZs3uv/oSqCIUhjPUex+9Xl9WVqbRaBxLTVZUXFxsMpmCgoJczo1w7ty55ORkhULx1FNPhYSElJaWkkkLWq1WFEUyFSw8PNzlXkajMTc3l2VZ5yW+XPj6+tI0XVRUZLVajx49mpWVFRIS0r17d5cF1RBCZWVliYmJWVlZwcHBXbt2JQcvTSZTWFgYWcKRVCKTyUivlAue5xMTEy9fvuzr69u5c+eAgICSkpKioiLyKsg+WVlZVqu1Xr16zsMyyW6BgYHOw3lsNltmZqZCoXBZVxMhdOvWrVOnThUXFwcGBkZERDRt2vSugzz1en1+fn7FN+fatWvJycmlpaV16tSJjo52WeX1xo0bNE3fZ+1WZ5mZmTabLSoqyrlBU1xcXFxcHBQU5LzGptVqzcrKUiqVZCEVlwdJTEwsKSkJDAysV69eXFycS/OooKDg9OnTOTk5ERERTz31lEQiuXXrFsdxjrfxXg9eVFRElo2tOGbYBanZ5d0uKysrKCiQSqX3Or1DQUHBiRMn8vPz/f39Q0NDW7Ro4fz5vNdHHSFUWloqkUjIMNcxY8asW7du3759jhYbeXWRkZHOQUs+D3d9Ay0WS05OjiiKERERZDm6GzduXLx4MS8vz8/PLzo6mixxANwPghBUF44g9HQhALi6detWXFycKIq3b9++//rgoCaCY4QAAPAPO3fu/Omnn0aMGNGgQQOLxXL27NnPP/+8tLT0008/hRSslSAIAQDgH2Qy2bZt2zZu3OjYIpfLP/nkk1mzZnmwKlB1IAhBdfHNN988xpA8ACpdv379ioqKTp06lZ2dbbFYQkJCOnXq5LwiK6hl4BghAAAArwYT6gEAAHg1CEIAAABeDYIQAACAV4MgBAAA4NUgCAEAAHi1mhGE77//vssi/d4MY/zAU/yAqmC1Wj1dgpeCd94jbDabl0wrqBlB+Ndff5El5wFCSBRFCEKP4DjO0yV4KXjnPYLneVEUPV2FO9SMIAQAAACqCAQhAAAArwZBCAAAwKtBEAIAAPBqEIQAAAC8GgQhAAAArwZBCAAAoGYo5tDB2/hiSSXPboTzEQIAAKimbhrw2UJ8thCdLcRni3Axh1v4Ue/E0TG+lXnu0lobhIIgtGnTRq/Xe7qQSvD0008vXbrU01UAAEDVEjC6acCpxTipACcV4JP5mKFQnI5q4ksNqUfNb0vH+lJ0FZy9u9YGoc1mu3jxYlpamqcLeVJ///13fHy8p6sAAIDKZxXRlVJ77CUV4DOF2EeK2gRQbQKocTH0d13pYIU7yqi1QYgQoigqOjra01U8qYyMDE+XAAAAlaPEis4X3Um+63ocraFI8g2PolsFUCpPhFJtDkIAAACeddt0J/bSilERh+N0VJsAqk9dakpTupkfJa0GQzYhCAEAAFQOXkSXSnFSAU4rwanFOCEXSxl7V+fohnQTX6qJjqqCY3xPCoIQAADAY9LbUHKhPfaSCnByEY5QUXE6qokOjYuh13SjA+WeLvEhQBACAAB4WLdNOK0YOQZ2ZhlxQx+qia/9IF+bAEpRA1OlBpYMAADAXZwP8iXmY05EJPb61KWmt6iq+QxuBkEIAADAzibaW3unC3FiHpNWKoYocSt/qpU/NbEJ3dKfcs98BjeDIPSw/Pz877777vTp02VlZfv27fN0OQAA78IJ6FwRPl2ITxfg0wU4rQTX11Kt/anWAdTQumKbOhJfOePpGqscBKGH5eXl3bp1q1mzZnPmzPF0LQCA2s9lDjsZ3kIGdr7agG4dQCnLY8FsxlKJR2t1FwhCN9Hr9dOmTfv6668VCgVCKC8vb968eYsXL46Li1u6dOmFCxcgCAEAVYEM7LTP5CvBF0twpPrOHPYaOrylcnn9G+AuGo3mwoULW7ZsGTVqFEJo7dq1eXl5LAvvPwCgkpXZ0LnCu6/eMi6GbhdIyWp/Z+ej8aIvYhOPGm7kzUIln7/jXrb2YXuE/GM01fjx45csWTJq1CiM8apVq1asWOGeSgAAtZvLumVZRtzM787Azhhfiqn5AzurlBcFoZJFF4ezvOimp/OVuW4ZOnTo1KlTU1NTc3JyRFHs2bOnm0oBANQuxdydmXxJBfiWCTfV1bYpDe7kRUGIENJ49MCvRCJ57bXXvvvuu+zs7LfffpuqjisNAQCqI+fJfEkF2MyjOKfkq57rltUg3hWEHjdu3LjWrVtbrVbH+QVFUUxPT8/KykIIXb9+nWGYyMhIj9YIAPA85+Q7lY/p8tPyDY+iF7arpit21lwQhG4VERHRvn17X1/fgIAAsqWsrKxv374IoaioqL59+wYGBp44ccKjNQIAPMA5+f7Owyx957R88V3oEKWn66vVIAjdymKxpKamrl+/3rHF19f32rVrHiwJAOB+Zh6dL8ZnCvHZQpxchFOKcJCCahNAtfan3omjW3en/CsMMgBVB4LQfTZv3rx06dLY2NguXbp4uhYAgFs5D+x0nswXp6OeCac6BtWMszTUVhCE7lOvXr0PP/ywV69eni4EAFDlHF2d5FwNZGAnGeEyLoZuG0h5wcplNQYEofu0bdvW0yUAAKqETUSXS+80+M4U2E9IG6ejnougZreGgZ3VGgShh23fvv3rr79OS0tTKpXDhg1bsGCBTAYHBwCo7kqtKKXozqnYzxbiSLX9hLTvxNEdAumg2niWhtoKgtDD8vLypk2b1rlz57y8vGHDhqnValh0FIBqyKWrk6zeQqY0vNoAVuys2eCfzk30ev24ceO+++47tVqNELp9+/aMGTPWrFnz5ptvkh10Ot2oUaMOHz7sySoBAAghhHgRXSq90+A7kYcltH0mH+nqhNVbahMIQjfRaDRFRUW//vrr2LFjEUKrV69GCDkvuo0xPnDgQKdOnTxWIgBezLFQNUk+cnIi0tU5LoZe1ZWuA12dtZcXBSG2crdnvYQ5s3ueLnDiF7KGLZy3jB8/fsGCBWPHjhVFcfXq1c6zCRFCixYtunnz5rZt29xTHgBejnR1kn5OslB1Qx+qie9dTssHaj0v+qempLK6X3gyZgYOHPjuu++ePn06JydHqVR27tzZcVN8fPySJUv+/PNPlUrlwQoBqK2KOHShBF8oH9hythCrJFRLP9TCnxoYSf27NV1fC6M6vZcXBaHHMQzzxhtvkEW3x48f71h0e+3atfPnzz906BCsMgpApcgy4osl6EKJfep6Wgm2CijGl2riS8X4UgPC6Zb+FExgBw4QhG41duzYuLg4QRDWrl1Ltvzyyy8TJ05ctWpVSUlJUlKSUqmMjY31aI0A1DC3TfYeTnJ4L6UIMzRq4kvF6ahoDTUgnIrTUVEaaPCBe4IgdKuQkJAuXbr4+/v7+vqSLWfPno2NjV20aBG52qhRo59//tlzBQJQrdlElGnEqcV3ku9CCfaR2sdztgmghkfRzfwoGNgCHgkEoVsZjcbExMTt27c7tnzxxRcerAeA6qzUiq6W4et6fCaXvWoUUovxdT0OVVJNdChOR3UJpsbF0K0CKBV8jYEnA58g91m3bt1///vfzp07t2vXztO1AFDtkLOup5Xg63p7m++2CUdrqDgd1UCFhkdT0zV0Ux0lgyU6QWWjn/whSkpKXnjhBa1WGxUVda9uvfnz59epU8fPz2/y5Mk8zzvfdOLEifr16ztOVFuLtW3bdsmSJRs2bPB0IQB43m0T3n8Lx18UpyQIff/HB6+31d9oe/eEcCwH66TUuBj6jwGM8V+S1GHsxt7MR3H88Ci6TQCkIKgSldAinDFjBkVRubm5Z8+e7d+/f+fOnevVq+e8w549e1asWPH3339rtdo+ffqsXLly4sSJ5CaO4yZOnKjRaIqLi5+8kmquSZMm97pJFEWaroQfJQBUQ84H9khrL7kIaySIDGZp4ksNjKDjdBScexZ4ypMGIcdx69evP3LkiEKh6NSpU//+/X/44YfZs2c770MWEiPpOHXq1K+//toRhHPmzBk+fPipU6eesIya648//hg/fvytW7coiurRo0d8fHxYWJiniwLg8eWZ0ZUyfKUUXy7FF0tRWjHOMNpXaWnsg7oGU+Ni6BhfOLAHqpEn/TDevn3baDQ2bdqUXG3WrNmlS5dc9rl8+fLLL79ccYezZ8/u27cvISHhpZdeesIyaq7GjRv//vvvDRo0MBqNr7/++pQpU7Zs2eLpogB4KDlmdKUUXy0j/6GrZfhqKcb1DO0AACAASURBVJYxqIGWauhDNfKhXqmPYnV0Qy0lgf4OUI09aRAWFRXJZDKJREKuarXawsLCivtoNBpyWaPRmEwmjuMYhhk3btyKFSsc972Pq1evOi/C2aJFi6NHj97/LhaL5RFeRtXT6/UvvvjiL7/84uPjgxBKT09/9913N2/eHBERQXZQqVRPP/30ihUrKt5XEAS9Xu981Wq1CoLgnsqBg8Fg8HQJHlNipdKN1A0DSjfQF0qpi2XUNT3F0KieCkepUT21+JQffqMejvPFWgl2ua/FiJ7wr9Gb33kPMpvNUqmUYWr2gVm5XP7AlHnSIAwICLBYLFarVSqVIoRKSkqCgoIq7lNWVkYul5aWarVamUy2aNGi0NBQiqKSkpKKi4tv37595cqVhg0b3vVZGjRosHPnzo4dOz58YXd95SabWcBuyg+1VEWhO1N4NRoNy7Lr16+fMGECQmjVqlWBgYFk0W2DwfC///0vKytrzZo1CxYsqPhQDMM4fkkghARB4DhOqYQjKh7g/A9RWxVz6Lr+ztDN63p8uRSzNIrWUNEaKlqLnqlHfaCjGmgpH6n7qvKGd766YVm2FgThw3jSIAwJCdFqtefOnSOnXz937lzr1q1d9omJiUlOTh42bBhCKDk5uXHjxgghmUxmNBpnzJiBEEpNTc3IyPD19b1rDFQWC8+9umsCx3NV9xQODMXM6Ta9ZVBT543jx4+fMWPGhAkTeJ7//vvvHbMJTSbT/v37s7KyEEKkvQiAezww856LoOJ0dEMfSvvgjhsAaiz8xCZNmvTss88WFRXt3btXo9FkZGRgjK9evTp48GCbzYYx3rdvX3BwcEpKSmZmZlxc3MqVK10eYejQofPmzbvPU3Ts2DEhIeGRqjKbzXK5/BFfStUSRbFhw4YJCQlbt25t0aJFxR1+/vnnsLAwl42HDh3q0aOH8xae541GYxUWCu6hrKzM0yU8viILTswXf7gsTD/JD9/Pt9lmU6+16tZZ22yzDd/Pz07iN14XEvPFMqunC72bGv3O11wmk4nneU9X4Q6VMHJrwYIFkydPjo2NDQwM/PHHH8PDwxFCPM/n5OSQHfr27TtjxozBgwdbrdZXX32VnJDPWXR0dEhIyJNXUs1RFDV27Nj4+Pjs7GzSQeoiLi4uPz+f53nn8xQC8KicZ6ZfL0PX9fhiCZYyiMxViNNRw6NRtIZu5ENpoJ0HAEIUxq5HtquhTp06LV68+JGOEVosFp1OZza76eyDD6mgoKBRo0aiKGZmZpJjHps2bQoODo6KisrOzv744481Gs3WrVud73L48OE5c+YcOnTIsQWOEXqKXq+vbkeqDDZEziVLxm1eKcU39DhIQTXQooZaqoEP1UCLGmipBlpKXpMP9FTDd94b1I7BMg8DWh5uFRAQ0K1bt+DgYMdfNcdxM2fOzMrK8vPzI01nz1YIqjmbiFKK8Ml8fCofn8zH6Qbc3I9q7kc10FLdglFDH7q+BpZfAeDRQBC6VWlp6fHjxw8cOODYMmrUqFGjRnmwJFD9kXOpH8/Fx3LwuSIcrqLaBFBtAqjXGtHtAiH2AHhSEITus3z58sWLFw8aNKh58+aergVUayVWlJiPj+WKSQX47zzM0ogk3/QWdJc6tE7m6foAqF0gCN1nwIABPXv2jImJ8XQhoNox2NDZQpxUYP/vlgk3LT/N0KquNJxdD4AqBUHoPpGRkZ4uAVQXvIguldpj73guTi3GcTrqqTpUn7rU9BZ0Ex2cTh0A94EgrC5GjRq1Z8+etLS04OBgT9cCqsRtk/04X1IBTi7CEeWH+oZHwaE+ADwJgrBa2LJlS2FhYXFxsSiKnq4FVJpsE0osEEmz70QelpQf6vu0NdOpDpx+AYDqAv4W3USv1z/99NM7duzw9/dHCF25cuXtt9/eu3cvy7KFhYWzZs3atm0bHD6s6fQ2lHyPQ32ru9JBcKgPgGrJu4KQK7FhwU0LCMh0Eor+x6LbdevW/eGHH6ZOnYoQio+Pj42NJSvITJkyZfr06XXq1HFPYaASOQ71kT7PTCNu7ke1CYBDfQDUJF4UhKJVTF2ZLvLuCEKKphq+GKqNVjlvHD9+/Pjx49977z2bzbZu3bq9e/cihHbv3n379u3Ro0eXlpa6oTDw5O51qO/VBnT7IEoKJ94DoKbxoiCkpXTr6Xc/zZN79OzZk2XZI0eOZGdn169fv2XLlgihSZMmvf3225s3bzYajQihXbt2DRgwAE5SXw1dKcUfJEj/zLMFyql2gVT7QGpENN3Kn1J60d8QALUT/BG71bhx48ii22+99RbZ0qVLl6SkpKSkJJvNhhDauXNn8+bNIQirFTOPPk8WVlwQp8aKa3rK/GE+OwC1CwShW40ePXr27Nk0TY8YMYJs+fHHH8mFkpKS3377beXKlaGhoZ4rELjalYHfSRDidChpCOsrWjSQggDUOhCEbuXr69ujR4+IiAiFwnUEoUQiGT58eMXtwFNuGfFHp8QT+Xj5U0z/MAohpNd7uiYAQBWAIHSrgoKCP//8MyEhoeJNKpVq48aN7i8JVGQT0bI0cUGyMD6W/q4rC1PdAajdYIib+yxatKhZs2b/+te/Gjdu7OlawD0dzsYtt/L7b4t/D2I/bc1ACgJQ60GL0H1Gjx49atQomC9YbWWb0PSTwl95+NtOzIBwmAEIgLeAIHSfwMBAT5cA7o4X0dI0cd4ZYWwMff4FtkafzB0A8Kiga9TDDhw4wLKsX7kdO3Z4uiKvczQHt/6N35EhHhvILmzHQAoC4G2gReh5zZs3P336tKer8EaFHPr4lLA7Ey9oS49uCD8KAfBS8MfvJnq9vmnTpjk5OeRqampq+/bteZ4nV8vKysiEeuAeIkbrrohNN9vkDLowjIUUBMCbeVeLsLTggii4KW+0/o0Z9s7sa41G06pVq7Vr186YMQMhtHLlyq5du5JFt8+fP9+wYcOysrIBAwbEx8eT01OAqnO6AI8/LihYtH8AG6eDQTEAeDsvCkKBN585MF0UODc8F0WxzXvM8Q9p67xx/Pjxo0aNmjZtGsdx69evP3r0KEKodevWOTk5fn5+RUVFI0aMmDp16g8//OCGCr1TMYc+PS1sSccL2tKvNqQhAwEAyKuCkGEVPV705FCUzp0763S6/fv3Z2dnN2/evEmTJgghnU5HbvXz85s2bdq//vUvD1ZYi2GEfrwizjglDI+i04axWomnCwIAVBteFITVgWPR7cmTJ1e8NTc318fHx/1V1XpnC/HEvwSrgLb3ZdsFQjsQAPAPEIRu9corr3z88ccMwwwZMoRsWbFihVwuj4qKunz58qxZs8gRRFBZSq3o30nCL9fEmS2ZyXE0dIYCACqCIHQrtVrdvXv3Bg0ayGT2cTRhYWG//PJLXl5eUFDQsmXLHAEJntzODHHSX2L3YCptmCRA7ulqAADVFQShW92+ffvgwYOLFi1ybHnuueeee+45D5ZUK10uxZP+EgosaEMvpmMQNAMBAPcD06fcZ+7cubGxsVOnTo2KivJ0LbWWiUefnha67eKfDadPPc9CCgIAHghahO7zwQcfTJ8+3dEpCirdzgzxnQSxax3q3FBJEJzYEQDwcCAI3UepVN7rJovFcu7cOYVCERsbS2bZg0dytQy/kyDcMqKfejBP1YFWIADgEcB3ruft3r379ddfDw0NRQg1bNgQTs/7SMw8+uKcsCRVnBRHf9yXkUJnPwDgEUEQelhmZubLL7+8bdu23r17I4TMZrOnK6pJdmaIUxLEOB06M5QNV0FDEADwOOD3s5vo9fqIiIjMzExy9fTp07GxsYIgrF+/vnv37j179szMzOR5XqGAQ1sPJcuIRx8W3v9bXNGF2dkPUhAA8Pi8qEWIETqaX2AVRfc8XQd/P43T0T6NRtOvX7/Vq1d/+umnCKGVK1cOGTKEYZjLly8LghAbG6tWqzMzM+Pj459//nn3VFhD2US0LE387KwwoQn9XVdWBqcPBAA8GS8KQrMg/OfSFYtbgpClqPlNm7TR+TpvnDRp0sCBA2fNmmU2mzds2JCYmIgQKikpSUhIOH/+fN26dXfs2DFmzJg+ffqo1Wo3FFkTHcrGk44L0Vp0cjBbTwOtQABAJfCiIFQyzM4unTxYQMuWLUNDQ/fs2ZOdnd2pU6cGDRoghEJCQp566qm6desihAYOHMhx3MWLF9u2bfugB/M6t014xknxrzz8bSdmQDhEIACg0lTOMcKffvqpc+fO7du3X7Zs2V13SElJee6551q0aDFhwgS9Xk82rlq1qm/fvi1atOjTp8+GDRsqpZJqbvz48fHx8fHx8W+99RbZ0rFjx9zcXHK5pKSE4zg4H6ELXkT/PS8238KHqtD5F1hIQQBA5aqEIDxy5Mi77747f/78JUuWLFy4cMuWLS47cBzXv3//Hj16bNy4MScnZ9KkSWS7j4/Pxx9//Ouvv7711lvjxo3bv3//kxdTzb344osnTpzIzs52LKs2YsSIwsLCmTNn7t27d8yYMU8//TSsO+PsaA5utY3fmSEeG8gubMfI4YggAKCyVUIQLl++/O233+7Vq1f79u0//PDDio3CrVu3+vv7f/DBB40bN168ePGGDRsKCwsRQsOHD+/Zs2dsbOzw4cO7d+9OjpnVbgqFokuXLm+88YZj1rxMJjt69KjFYvnhhx+6d+9e8WeE18oxo9GHhZGHhA+b0/sHsDG+0BAEAFSJSjhGmJKS8uKLL5LLbdu2nTdvnssO58+fdxz0ioyM9PHxuXz5cqdOnRBCJSUlOTk5ycnJZ8+e/fzzz5+8mGru2rVrBw8eXLJkifPGunXrOi/DDRBCy9LET08LYxvTl4azSi86kA0A8IBK+I7Jz893nE5Wp9MVFBSIokjTd9qaeXl5vr53xk/qdDrHUbFt27b997//vXHjxr/+9a9GjRrd6ylu3LgxdOhQxyqd0dHR27dvv39VFovl8V5O1ZkxY0Z8fPzcuXPJ0JiHJ4qiwWBwXBUEgeM40V3zQNxvzTVmxWXm9162RlosWpDhwfdwE6PRSFHQMPUAeOc9wmw2S6VShqnZByTkcvkD162shCDUarVGo5Fc1uv1Pj4+zimIEPLx8SkrK3Nc1ev1Op2OXH7ttddee+01vV7fv3//L7/8cubMmXd9ivDw8BkzZrRq1Ypc1el0D5xgUA1X7Fy4cOHChQsf4440TTu/XkEQJBLJfVYurdFO5uN5KfzR59gYX6mna3GFMYaZLR4B77xHMAxTC4LwYVRCWkRHR1++fJlcvnz5csWxHlFRUY71M0tKSvLy8lz20Wg0/fv3P3v27D2rZNm6detGR0c/ebXVTWFhYWlpqeMqRVHePFimiEMvHRRWdmHgiCAAwG0qYbDMqFGjVq1aVVZWxnHcsmXLRo0aRbbPmzcvLS0NITRixIikpKS///4bIfTtt98+9dRTERERCKFDhw5hjBFCGRkZGzduJEcNvc33338/olz37t27dOni6Yo8RsTolUP8y/WpofVg5T8AgPtUQotw5MiRx44di4iIoGm6X79+EydOJNt/+umntm3bNmnSJDAwMD4+/tlnn5VIJAEBAZs2bSI7fPzxx8nJyRqNxmKxjB079p133nnyYmqcDz744IMPPiCXhwwZ0rhxY8/W40GzkgSriOa2qf39MACAaqUSgpBhmJUrV37zzTcYY+cDV5cuXXJcHjly5IgRI4xGo2NYDUIoISHBZrOZTCbnjbWVwWAIDw8/deoUWVDmr7/+evHFF9PT0x3977m5uXv27PGGobN3tSsD/3QVJz7PMtAnCgBwr0obUfLA0yawLFsx8CQSiftSECPbmRuYF9zzbJK4MEold1xVq9UjR45cs2bNggULEELx8fFjx451Pgq9bt26Dh06xMTEuKe8aiVdj988ym/uwwbKH7wzAABUrmo3tLLqYBtvTbyKbG4JQppmQnSM6h/f6xMnTuzdu/ecOXNMJtPWrVvPnz/vfOvatWs//PBDd9RWzVgE9MIBYXZrOLM8AMAzvCgIKSmrGtfXgwU0adKkYcOG27dvz87O7tWrFxkxRPz1118ZGRnDhg3zYHmeMuG4EONDvR0LA2QAAJ7hRUFYHZBFt/Py8lyOBa5evfrll1/2wplSKy6IiQX4xCD4HAIAPAZ+hrvVCy+8cO7cuZKSkv79+zs2GgyGTZs2vfHGGx4szCNO5uNPTwtb+zCwiBoAwIPgG8itpFJpx44d27Zt67IE3bx58zp06ODBwtyPzJ1f9hTTQAuHBgEAngQtQrc6c+bMoUOHxo0b57wxOjp6ypQpnirJI0SMRsLceQBA9QAtQveZPHnyjh07/vvf/wYFBXm6Fg+blSTYYO48AKB6gCB0n//7v//7v//7P09X4Xkwdx4AUK1AEHqeyWQ6fPiwXq9v2bJlrV9iDebOAwAeGcai2YgtJpEzYYuR0dVhfAMq8eEhCD0sKyurc+fOzZs3r1ev3pQpUz788MP333/f00VVFZg7DwAQLSbMmUWLEVtMosWEzUbRYhQtJvtVi0m0GLHZKNqvGsn+tFJNyZW0TEnJlaqOT6s69KvEkiAI3UQQBL1e7zhBMca4pKTEx8fn559/btiw4a5duxBCAwYMeP3112txEMLceQBqGWyziia9aDaIJgM260WTAdus2GYVzYY7221WbLOSW0VjGaJpSiKlWCmtVFMKDa1U00o1rVBTEhktV9K6ILKFUmgoiZSSSGmFmtH4IroKhxRAELqJ1Wpt0KDBkSNHmjRpghA6fPjwG2+8cfXqVT8/P4vFQvYxm81+fn4eLbMKLYe58wBUV9hqES1mzJU30TgTtpg5Q5nVZkGcmbTVRIsJc0bRYsJmk2g2iBYTxTCUTEnLlbRCRSlUtFxJyVV0ebtNEuxHyZW0XEXJlbRcSS7TChWiql2HkBd9K2Es5Cf/hkXePU+na9RLqg50XFUoFGPGjFm9evWiRYsQQvHx8ePGjaNpesyYMWfOnOnYsWN4ePiVK1d++ukn95TnZifz8ZzTwrGBLMydB6AKiaJoMYlmA+ZIqpkxZxZNepEzY5JnnFk0G8gFXN5FKZqNlERKy5SUTEErVJRcRcsVlEwhMlJKpWFUGkoXVJ5kSlqupBQqEm8UU0v+nmvJy3gYWBRMuRdF3uKOJ6MYbUQ75BSECKEJEyZ07Njxs88+MxqNu3bt+vrrrxFCKSkpu3fvnjRpUmhoaHx8/A8//NC6dWt3VOhGMHcegEdl7060cY5eR2yzYRsnmgyk1xHz5VcdfZK8VTQbKVZCK9S0Uk1JZIiVOnc8UnKlxNHx6LhVoaZVWoqVVKzBbDZLpVLnk+TUVl4UhDQjrdd/pgcLqF+/fqtWrbZs2ZKdnT1gwICQkBCE0JdffvnSSy+Rc/P27du3Tp0606ZNq1u3rgfrrFxk7vxImDsPvB4WeNFYJhrLRKNeNJaKhlKBXDWViYYy0owTORO2mESzkZLIaLmCdDxSChUtU1JyBS1TUDIlrVSzQeG0TEHJFbRMae+TlClpuYKSwmjsx+FFQVgdvP322998801ubu6yZcvIFoZhrFYruWy1WjHGtez3F5k7PwfmzoPaCwu8aNTb88xUJhpKBWOpPe2cYg/brLRKW/6fD6P2oVUa1i+IDm9IqzT2zkaZotoeSKvFIAjdatCgQVOmTJHJZL169SJbxo4dO2jQIIVCERoa+v333w8ZMiQ4ONizRVYimDsPajpsswplhUJpkX3Qo8kgmg1CWZFQWujokxT0xbRCxWj9nMdA0kqNJCiM8SnfqFAzWj+It+oJgtCtWJZt2bJl9+7dqfK/h549e546dWrnzp25ubnTp08fOnSoZyusRDB3HlRnD5VwhhKKYRmtP631c044aVgDJq49JFytAUHoPiaTKSEh4dixY+vWrXPeHhMTExMT46mqqgjMnQeehLGgLxH1xUJpoWAoEUsLBX2xUFYklhUJ+hLRWCpazMydXkoNrfKh1T6MSsv6B9MqLa3U0motrfah5SpPvxLgDhCE7jN37ty9e/euXr1ap9N5upYqNx7mzoOqhG1WR7AJpQWioUQoKU87fbFoKKVVGlrty/gEMBpf2sefDQiVRTeltX6MxpdW+9AKrzsJNrgPCEL3Wbhw4cKFCz1dhTssvyCeLsAJMHcePAFH16VYVmTNu13KGYWyQtFkEEnvpdlAK9SMj73TktH6S0Kj5D5taa0f4+PH+AbWmiluwA3gswIqGZk7fxzmzoMHEU0GoayQBJtw5/9F2Kzni/ORKJCDc4yPnyBXSwNDJMERtFJNa/0ZrR8clgOVCL6rQGUic+eXP8XUh7nzXs95NIpQWiSUFdmHopDMM5TQciWj9aO1/oyPH6P1Z/1DSO8lrVSzuiBKpnA8lF6v12g0HnwtoHaDIASVxjF3fgjMnfcSGAtkQEppoVCSL5QWCiUFQkmBUFYoFOchimZ8Axi1L+PjT2t0jNZPFtWE1ugYH39Go6PVPp6uHgA7CEJQaWDufK2EBV40lAplRWJZoVBaxBfmlB+6K+SL8iiWJa06NiCERB3jY78KA1JATVGbg5Dn+S+++MLTVTyp69eve7qEhwJz52s055Ep5FgdX5hNrvLFubRcyfqH0Fo/xsef9Q+WBEeQUSqsXx1Y0wvUArU2COVy+dy5c4uLiz1dyJPS6XRTpkzxdBUPAHPnawR72hXmlEddjuOInWg2OEamMFo/xsdfGd6NXGV1dRANfd2gNqu1QYgQ+uijjzxdgleAufPVimgy8IXZpBuTjE/hC7LFskLHOEzGP5gMTpHUiZA3asX4B8MgTODlanMQAvcYf1yI9YW58+4jGkrJEBV7N6a+WCjJF8qKhOJ80VBKq30Y30Ayl47xCZBFN1W26clo/RjfQEoi9XTtAFRHEITgicDc+cqHsWAoEfUlQkmBYCgRivNFQ4lQUuAIP0quJL2XtNaP0fqxAaGy+s0YH39GF8RofBENg5UAeDTw/QUeH8ydf2z3mksulhUJJfmUTE5OZWA/YucbII1oZB+f8s8JdgCAJwdfYOAxwdz5+3uYqKs4lxyWBwPA/eDvDTwOmDuP7ht1fHEeLVdA1AFQI8BfI3gcnyR619x5oTiPu3befPksZygWSgpEfYlo0tNqH8YngNHqaB9/RusnCY2SN25tP3Sn0cEgTABqCghC8Mh2ZeD112r73HmMbXmZ1mvnuevnuevnEW+TRjdl6jZUtexqXzBM4wtRB0DtAEEIHk1tnjsvira8TOv1VMvlM9zVcxTNSKPj5I1aafq8KKkTgShKr9fLYelnAGqdygnCmzdvfvXVV3l5eb17937zzTepCr+UOY775ptvkpKSoqKipk+f7ufnhxBKTU3duHHjlStXfHx8Xn311c6dO1dKMaDqkLnzn7apRXPnRcF66zp3+Qx3PdV6I5VWaGSNWyniOvgMGsv61fF0cQAAd6iEIDSbzd26dRs2bNjIkSNnzpxZWFhYcUmXSZMmXbly5f3339+4ceOzzz6bkJCAEPryyy/r1Knz/PPPp6en9+3bd/v27X369HnyekDVIXPn34qp2QNkMGe23rzIXU/lrqdab16UBIVJo5qo2vXxG/k+rdJ6ujoAgLtVQhBu2rTJ399/0aJFCCE/P78RI0Z88MEHEonEsUN+fv6PP/54+fLliIiIZ555JiQk5NixY126dFm7dq2j7Zienr5hwwYIwuqsRs+dFw0l1psXuetp3PVUPjudDakni47TdB8srf8JLVd5ujoAgCdVwpfaqVOnunbtSi537tw5Pz//5s2bDRo0cOxw9uzZkJCQiIgIhBDLsp07dz558mSXLl2ce1CzsrJatGjx5MWAKlIT584LpYXWG2nc9fPWG2l8/i1JRGNZdJzPM6Ok0U0pVvLg+wMAvEMlfKvl5OQ0a9aMXGYYRqfT5eTkOAdhbm6uv7+/46q/v392drbzI2zZsuXEiRNr1qy511NkZWVNmjRJq7V3W4WFhS1btuzJK6+hBEHgOE4URbc9Y7GVGrFfsrgNX4fmDAa3Pe3jEItz+avn+IyL/I00JNjo0PqSerHS58YqQ6PJIE8bQjYLhxD3GA9uNBorHv8GbgDvvEeYzWapVMowNXuWlFwuZ9kHJF0lBKFSqeS4O18rZrNZpVLdZweLxeK8w4EDB8aPH79jx46AgIB7PYW/v/+YMWNiY2PJVT8/P7Xae8/5KQiCRCJRKpXueToRo2HH+FENqZdjquWSzaJovXXNej2Vu5HKXUmm5SppdBNVwxbS/iMlwZGV+1QYY2/+4HkQvPMewTBMLQjCh1EJQRgWFpaenk4u5+fnm0ymsLAwlx2ysrJ4niexnJ6e3rt3b3LTkSNHRo4cuXnz5o4dO97nKRQKRbt27e6/D6ginyQKfDWbO4+tnDXrqvVGKnc91XojjdHqpNFNFXEdfAe/yeiCPF0dAKCGqYQgHD58eK9evXJycoKDg9euXdujR4/AwECE0MGDBzUaTbt27dq2bavT6bZt2zZ8+PC0tLTk5ORBgwYhhBISEoYPH/7LL79069btycsAVWFXBv75Gj5VDebO/2OoZ/oFSZ1w+1DPVz6glTC3DwDw+CohCFu2bPnaa6+1atWqUaNGV69e3blzJ9m+dOnS6Ojodu3a0TS9ZMmSMWPGLFu27Pz5859//jlJymnTphUXFw8bNozs37t3702bNj15PaCyXC3Drx/hf+vrsbnzQlmxLfMSdz2Nu3zGlpclCY2WRcdp+4yQRsXBqfUAAJWFwhhXygPdvHkzNze3efPmcrn9W1Ov1zMM4ziUVVpaevHixcjIyODgYMcOPM87HkEikdzrMECnTp0WL14MXaMEGSxT1ccILQJ6aic/LoZ2/6xBvjBb/8cG7mqyaDHJouNk9ZtJo+Okdet7/Ex7er1eAyvLeAK88x5ROwbLPIxKGwsfGRkZGfmPsQkuH1wfH58OHTrcZwdQrXhk7jy2QUhuKgAAIABJREFUWfUHNhqO7lD3GOLfc44kKBzW8wQAVLWaMykMuNGyNA/MnbeknijZupINjqjz/hLGD8a8AADcBIIQuDqZj+eecevceb4wu2TrCj4vy3fYRHlsWzc9KwAAIIQgCIELN5933t4XemS7uttg/9c+gQVfAADuB0EI7iDnnX+lgZvOO29JPVGydQUbHFnng6XQFwoA8BQIQnAHmTv/aesqHyTGF2SXbF3O59/yHT5ZHtOmqp8OAADuA4IQ2Lln7vw/+kJfnwV9oQAAj4MgBAi5a+78nb7QD5fCWmgAgGoCghAgM49ePCjMa8t0rrLzzkNfKADeSeSxaBMRQpjHglVECGERC5yIEEIi4i0C2Y03Oy6ICGOEkGARyXovAidiASGERJso8hghpItR+zerzHNoQxACNOGvKpw7D32hAHiQI4cEi4hFjEUkcAJCSLTZt/MWEWGMBaftvIgQ4k0iz/M0TYscRgghjHmz/dRvd0LLIiARIZJVIkYICVYR8xg5hRbNUrSERghRLMVIaYQQRVOMjEYIIRqxcvuIBFbhuECTZTQYOU3OvcXIaEqGEEK0hKVZCiGkCKjkFRYhCL1dlc6dh75QAFwIVhELWOBELGDny66JhZFgcSQWRiR1MCL7Ox4HIcSbBIQQeTRkbz9hVB5Xjhxi5DRFU4iyZw8tsW9n5TSiKIpBjKx8O0sjhFgFjXmKZRlJIIsQQhTFKuy/le+ElpxBNEIkq2gKIcRIaYqlEEK0hCahVSNAEHq1E3lVNXeeL8gu2bqMz78NfaGgJiKxRJpNpJF038uYNIBEq2i/4z8vk3YSiS5aStMMxchoiqEYKU2VX3ZJLIpCTHlisUoaIST3kyAKUTTFyGnklDSskkEIkUdDJJMYCjnF1WODtUZB7XfbhIcfENZ0Yyt37vw/+kLfmE0x8BkDVYt00PFmgRx8IrFkb2xZRCxi3ixgEQkWEQuiYMUiL4rW8gyzJ5ZztmHRJpJYIs2me16WUDRLk6AivXb/2E1avvM/L3v63aoubCJv4S1Gm4njOYvAGaxGTrBaBaveauB4KydwBqvRInBWwWqwGi08xwmcyWY282ZOsA5t/NxLsUMqsRj4kvJSFgEN3S9MakIPCK/Mv0xL6omSLcvZkHrQFwruyp5VJH6songnq0SEMW8WsIAEq0CaWeVhJtisPLIVYhELFnuPIgktsgPpoGMVDDn4ZA8eCUWzNGldkcNOrJKmGIaR0jRL01J7htl3ZsqzqjyxPP0+VXdWwcqR0BKsVt5qsNmzymgzmXmLlbcabSaLYOF4K9nCCVazzWzizRzPmXmL0WaiKUrBKpQSpYyRKli5SqqUMVIZI1NLVTJGJmOkGpk6gPGTMVK1VE22qKRKBSuXMVJ/hX/lvhwIQi816S8hTEVNa1Fpf/B8we2Srcv5/Nu+I96BvtDahDcL5LgUaSqRQ1BOuYUFK2lgYcEikEgrb5mJzmGGRcSbBZJVZNyEPatkNMVQrJxGNMUqGIpGjIyhWYpV0lIflmYpRsZYrGaNn5ocpiJ9gKTtRboWPf0O1TwCFkw2s8lm4gSrmbeQ1hjHc/Y84+15ZrSaOIEz8xZO4Cw8Z7CV72Y1sjQrZ0loOaUXK1VLVHLWHmOBTICMlaok9vRSSBRKViFjZQpWrpQoGKoa9bhCEHqjb86LSQX42MDK6aaBvtBqhQQPbxbI2HR7htmwyIsu/YSkP5AcvuItAiIZZg82UeQxGQpoDyc5Q5pKrrlljyWKVdIynYSiEassb5k5ZRUjZyj68Y9a6fW0RnP3k5V6J6PNZL2TYRzHWw02o5m3WAWr0WYy2cykBWa0mewZZjWSC0abycybRYxVEqVSopAxUgWrUEmVckYmZaQaqdoRY0GqAEqgtAqNWqqSslIFK1dJlDJGKmflKomKrl3nR4PvLK9z4DZemCwkDGJVlfGPf6cvdNoyxjewEh4RlPcf8mZBsIgCJwoWgVzlzaLA3dnIW0TeIggWUbSJgkUUBSxaRftYDHuXIEPGYvyj7aWgKZpilTTNsvagYilGxlCMU+8iybDyoYCgcll4zipYDTYjx3OccKfz0GQzmWxmC8+Rg2cWgeN4Tm81kKgjGcYJVpPNrJQoZIyMhJOcdc4wmUqiVEjkGqlaqVUoJUo5I5OzMrVEJWNlMkaqlqrkrFxCP9QfPwyWAbVTuh6/epj/tRcbpXnSLzi+4HbJluV8YTb0hd4HaZDxZoG32PNMsIgCJ5CQ4y13go03CeQyz4miTWTlDKtgGDnNyGhGzjAymlUwrJxmZLTEX0o2sgqakTOsnKYlNCOnHeMGQZVytMaMNpNjWAcnOC7Y22ekF1FvNZDDafabBKvBarzTncjKpIyEdCfKGZlSolRIFHJGppGpg9VBsgoZpmDlMkamlCg8/R7UNhCEXsRgQ4P+EGa1YnqEPFEKYiunP7jJ3hc61ov6QgWLaLFanRtqPPm/2SXkRN4sCJz9cBojo1kliTR7kjHlIafQsIycZmUMI6fLY49h5DTkWRURsUiGKXLCnfEdJpvZ0alotlkcW0gTzcybOd4eeyT/HD2KSntXoUwlUdpDS6qSMlKNTB2iriNlpOUxJiUtNhkjkzISjRT6eKsdb/kKAxihN44KbQKo8bFP9CXrFX2hGHElNkuh1VxgtRSW/1dgFUVRqpawCoaR0Y7QYuUMq6BlPpLy1htputn3gfGHT0JvNZDoKjWU0bZcq2CzClYLz9lEm8lmFrBgsBpFLJL/G20mXuTNvMUq2DiB4wTOJvAm3iyIgsFm342maHKgy55MrEzGSEnvIulUlLNylVQZrq2rYOVSxj7QQ8pIlRKFozfS0+8KqHwQhN5i3hkxy4gPDnj8f/E7faEvTpE3bl2JtXkQFjDJPEuh1VJov2DK42iGkvtLyX++DVXyjjq5v9Qm4TRajadLrhbIlC9y2WA1YoQRQiSHyEYzbxFEgexGostsM/NYMFqNIsaOhCPDF+0JJ3A2wZ5wRptRxFgjVVMUpZaoaESppCoJw5KGl4SWKCQKlmLUUhWFKI1MTSEqTBvKUoxCopDQEhJsEoZVsAqWZtQSFUVR0BQD9wJB6BV23BTjL4onB5NFlB5Z7egL5c3CneZdeeZZS21SH0l55kkCwrVyf6kiUGZfC/GfbHrObdWaeQsv8gghXhTMvJlsNNpMIhYRQiQ5UHlfH7mV4zmraCOXHeFkE3hLeTiR5hFCCCNssBrJRnIEy34vmxFhhBCyiTYLz5VXYuZFweW5SKOKXFZJlDRFI4RYmlGw9sNXClbO0IyUkcgYmYyVScujSyVR0jQdqgmmEKWWqhiKUUoUEpqVsTLSc0iiy/GYhF6v12jgJwioKjXyGw08kosleNwxYWc/NlT5OIcGLaknircsk4RE1ZS+UCxirti1kWcpsCIKORp56jB5QAut3F8q00keZmBkKVeWZyq4WZDJFrOkZYNIxgj2jDFYjWSlfEdTyTlsHKkmiIKjzWSwOe7CWQUb+mfSKFg5S7Pon+niiAcpIyE5RBpM5FaSN+QyaSohhCQMq5HZW0JBqgDymCSEyEYpI5WVR5paokIUQgixNOvoAyTJhBAi/YoP/e8AQI0BQVjL/X979x3fVnnvD/x7pvayZMsrdpzEsbOnybjZITeDMBIopUDhhkILBQp0hFmgLaWUMloo65YfZRXKCpdASEIWSZw0y3Z24gzHTuIhydpbZ/3+OLIsj0wPeXzfL168jo7OOXqsyPr4ec4z3FG4Zp3wpxKqJP2SU5B31HpWvME7G9JuekgxdFxXFK+DRF6Keds0bDZESIaUA0+dqbCM1cvbFxzExotCY9hpCzpsQbst6LAFHbaQwx50NATtLMlmaNLTWKNaoZJrNtAyLTRsfGSVmWQVNAstwyaRahRJJU6R2+sgKYqST0EIdRsMwr5MlODW7/lr84hlQy+ty0bLttCne0Jb6MU3bKozFOR5e13GhFhj2FUfsNUFGhpDLmfYVRdoqA/YbEGHmlFlazOztNZsbeZAQ97ErLFZWmuuLluuCWEDHUJ9Uuq/4FDXeXiXwInwp5JLuzGY1Bb6BmW0dFHZzkMSJO/JYNgeTeq0yVFKUiUHnoXVF6gzJhqVFpbVne8D7I8F5IRLTrvGsNMfC1pUaVlaq1mVZlGlFZmHzM6flqW1WjXpPWraJ4RQ98Ag7LP+dUL8olradS1NX3RtMN4W6rKlqi3UXxO2l7kbK7yqDIUmS6m0sIbBGjn8zjW0jhM4R9hZH7A1hpzOiLvO31AXaHCGXQ1BO0My2dpMs8pkVqVl6zJnm6fJ4WdWmeT7ZwghBBiEfVWFU/rVTmH9ItpycaOe4m2hpd/o592knX41kN1aMYp5ucZ9Ptsut8hL6eMMYx4crDS3XoHaHws0hl2usLsu0JBIO2fY3Rh2WVRp8bTTZsrVuzSVKUuTocQhXwihi4BB2AfZwnDdOuHvU6mRpouq98hj5NnBI60Pv0HpTF1dvASRE12H/fbdHl9NKG2YruCaTGOhFgjwxwJbq7fX+uvlHiuOUGND0KGmVVZNeoYm3aqxZGqswyxDM9QWqybdpDR2W4ERQn0SBmFfw4nww438nUXkDQUXbhLlG+vcn74iBn2mW5crBo3ohuIBAEjgqw7Z93gaK7zaPFXGRGPx7QNIhhQlcVd9+ZqqDTvry8dZRw8y5o2wFM/Jn27VpFs1GYku/ggh1LkwCPua+7cLJpZ4YtyFUlCSAltX+r77SD/vR93WFhqyRRv3eu27PSRDZJQYJzxWyGhpADjjq11/ZMuaqg1KWjF/0JwHSn5mUOi7oTwIIQQYhH3MG0fELQ3SjmsvsNAg77K5P35J4mIZD7xEp+d0dan4sNC412vf44m4OMto/bBleZocJQAEudDGqi1rT22q9p6elTftjzMfG2Ia1NWFQQihVjAI+45tNun35ULp1bSeOfdBkhT8z2rvt+/pZl+vm30DkF04JbTIS57KgH2Px13pNxXpcuekm4ZpCZIQJemA4/Daqk2bT28fmV58TeGCabmT6e7tnoMQQgkYhH3E6YD0gw38P2fSg/XnrA0KLrvr3y9JsUjG/S/Q1gFdV5jA2bB9t8dR4VWlsxkTjYU35chTd9pDjeurN399fC1LMfMHzfngmteNCkPXFQMhhC4GBmFfEObh+vXCw6OpBbnnTMHQ7vWer/7RpRXBqIdzlHtsOz1AQPo4w5gHBynTWACICrGtp3evPL7muKtqZt7U301/eGja4K4oAEIIXQYMwl5PXmhwmJF4YGT78Sb4XO5PXhH97vT7/8JY8zq9AEJEdB702fd4AmfDljGGwpty9AXxqZkrXSe+Pr52Y83WYnMhNoEihHomDMJe7/l9YqVXKl3c/j9leO9Wzxevq6+Yp7/jiU6eMlQCz/GAfY/HddivH6jOnGIyj8wnKAIAHKHGdUlNoP+65k0c7YcQ6rE655vR6XS+//77brf76quvLikpaXuAKIqffPLJgQMHRo8efeONN5IkKe+srKysqKhQKpVLly7tlJL0N9/VSq8eFndcQ6na/EsKPrfns1d4Z4PlZ88wuZ3ZFCmPgrDtcjMaOmOiseCaTHkUREyIbU9qAn1s6oOj0od34usihFBX6IQgDAaDkyZNmjJlyvDhwxcsWPDBBx8sWrSo1TH33HNPWVnZzTff/MILL2zevPmNN94AgNdee+1Pf/qT2WxmGAaD8DIc80o//p7/fC6dq2l9azBREUz7n8c7qyLIh4TGfV77Hk/UzaWPN468e6AqXSE/JTeBbjpdWpQ2ZH7B7D/N+i2Of0cI9RaEvDRoR7z11lvvvffe9u3bAeAf//jHu+++u23btuQDzp49O2TIkFOnTmVlZdXX1w8aNOjkyZPZ2dkcxzEM8/HHH//lL38pLy8/z0tMmTLl5Zdfnjx5cgeL2jcIghCNRgVGPWUl/+BI8s6iFrcGBb/b89mrvKMu7ZZfM7lDOv5yrUZBZEw0yqMgAKAx5Pyu+vtVJ9aRBLlw8NwFBXPSVN03Q1v3w2WYUgXf+ZQIh8Msy1JU37+v3wl1hU2bNi1YsEDeXrBgwU9/+tNIJKJUNs93vHXr1hEjRmRlZQFAVlbWsGHDtm7d+sMf/pBhzjPeDZ2PKMEtm4S52USrFAzv3epZ8bq6ZF7a7Y91vCJ4rlEQchPo2qqNBxuPzhww9ZEpv8AmUIRQ79UJQdjQ0DBr1ix522q1AkBdXd2gQc1ThNTX12dkZCQeWq3W+vr6S3oJm8327LPPJi6Sl5e3fPnyDha79xIE4clyyRURPpwmRiKcvFMKev1fvik6anW3P07nDIlyPHD85V0/5uVd+/yO3T6SIkyjtMX35ChMDABwUuxwQ9X6mi2bTpcOMuZfmTfj4Ym/UNIKAIhEIp310/VkkUgE/3pLCXznUyISiYii2NtrhAzDXPBH6IQgpChKEAR5W95o9ZGlaVoUxcRDQRBo+tJeV6VSFRcXFxQUyA8tFktv/7fpiK/OEF+cJf6zmFAx8Tchun+b/6u3lBPmam75DUFf5vcFHxE8h4ON5b5gXSRtpG7wjVm6gSr5KWfEvfn09jWnNnAiNztv+uv//bxVnd45P0yvQlFUf/7gpRC+8ylBNUl1QTqEIC68CE8nBGF2dnZdXZ28XVtbS5KkXC9MPqC2tjbxsLa2Njs7+5JeQq/XL126FO8RAsA+l3TfTv7/ZvHZOhUAiKGA95t3YlUHzXf+js0behkXlETJeyKYGAWRNTXNPFIvj4LgBG53Q8Xaqk3ltv2TsyfeN/HO8Zmj+/OStgzDYL0kJfCdTwme5y+mOtUHdEIQLl68+A9/+MPTTz/NMMwXX3wxf/58lmUBYP/+/WazOScnZ+7cubfffvuRI0eGDRt25MiRmpqaOXPmdPx1+yFXFK5fL7wymRiXJgFA5PAu96evKIdfkfHLVwlWcalXE3mpbouzbnOj0sJmTDQOWpJFq+Kf+FPe099VbVpdtT5Xlz1/0JxHpzyAi9wihC6bIEm+NjdrIqIQbmpNTPBzPN+yC6coSV6OS95TpNMNUKs6sXidEIRLly79+9//PmvWrKFDh3799dfffvutvP+ee+5ZsmTJr3/9a5PJ9Pjjj8+fP3/RokXffvvtE088YTQaAWDv3r2/+c1vGhoaampq5s2bV1JS8uyzz3a8PH0VL8IN6/kfDSZuLICIN+j+5u3osYq0Hz+sGDzqkq8lgX2Pp2aNTTdANereAlVGPERdYffGmtLVVRv8Mf/c/BlvzH8hS2s9/5UQQpctLAgRIX7bKCjwsaRbSD6OF5LywB2LJbYFSfLxzaESE8Ug3xwnYUGIJKVLgOe5pOt4OU5seihKkBwwnCQGkq4TEYQgxxEEkWha5EQpwLcOs5AgRMXWYebleLFlmFEEoWdax42SpFRtqps6hqZbNmYSQBjZFu0BP84fcFt+Z06S1QnDJwCA47gNGzY4nc45c+bIvUMB4MCBA2azOdEKWl5efujQoZEjR44bN07e4/f7jx07lriIXq8vLCxs9/o4fAIA7t8unPBJ38yno4d3ej59VTXiCuN1PyXYS66oeY4Fqr9uIGiy4GqrfpAGADiR311fvrZq0+76ipKscdcULujnTaDngp34U+Uy3nk/z/OiBABRUQgJAgBIEniavvcjQnNdJMALnBRPIE+Mk78QeUn0N9VgIqKYdDDPNcWVOxa/Gi9J/qaESM6hRBkAwM3Fkyw5TlQUpaTiHb/VFK1ImgS4VR6Y2OaBuRRB6JO6WbAkqaGb40RFUcqkdNHSNJN0HQPDkE0PCYDkgKEJQpd0WSVFkTzPMAzZVCqGJLRtuneoKUrRZt5EA0OTF3FnrufonCDsahiE7x8Xn90r7pgfldb8v2hlhXrpz/Uj2pnB5/xC9ZHqVbZwYyx/YYZltAEIqPae+er46o3VWwuMeQsHzZ2ZNxWbQM8Dg7AtTmyuRng5TgQJACJCPDmSG7X8fLzJK7kGk6joBAVBrg9xopjICQ8Xr8z4olGBJAGAF8VE5CTqTIkWtuQqjo6maZIAOSeahhKZmr73FSSpbvpO19IUQ8S/640sI39/0wSpa6rBKEkyUXHR0jTTFAyJqyVHiJJqruUkygAAJiaeZDTZIm96MhxHiHqQ/9ilX+8USov3hV9+RTlsouVXr8bg0paPiHq4M+sczoO+AXPTh92RJhGwvW73F5VfV3tOLx4y/62FL2ZqMi58FZRSiVQI8Lxcg0k0rCWHh5fjm9JICAsiAEggeZrqLom84UXJz8d3JhIlKoohoXVEhQQhKogQb5GLnxLgBblixJCktqk6YmAYEggAUFLx5Ehu1ErkTXINJlHR0VCUhmGaLkg3XbCpYhGLmTQaiLewxS+oZ2iKICCphY0kwIB9atClwyDs6epDsOw7/7rYu/rV5aabf6koHCsIAkSjF3k6HxLObmxs2OnKnJQ28bGhUTL6TdV3nx1dqaQUVxfOnz/zCRbnQrsQOVG8HOcMBoEXIH7nPx4J7kSNp+kmf0jg5eRI5EoidSSARCZ5OU5uYgvy8ZtDiYoUJ8WDLbmKY2AYuXahpWk5URINa63DI55GlIoioWUaJfKGJomhtFbemUgUBUmqm/78N7KM3DyeeBUSiETMaGmK6cpVnVvBujjqUhiEPVpEgKc+3f3ViVczRk80Ln+DUFxCRylJkOq3uc6sd5hH6ccvL2wkGt8+/ME3J78baSl+YOJPJ2SO6bpi9xwejosKYlDgfRwfFUU/xwUFISIIXo6X7+W4OS4qiCGB93F8RBQCvBDg+Ygg+Dhe7gXgjnEKklTTlIFhGAA9ywIASTRHgpGJN6YlbuqoKVpBkQCgIElTm9RJtKfp6Xj8aGiaJUlIqkgxRDzYCAKMWMVBqIthEPZcUiy66n//3/11O/P/5yHl0HGXciY07vdWf21TZypG3z/oBJz4494Pym375w2c9fbCv1o1vWMsfFgQ3DFOvv0jV5USD90xTu7s4ObkDbHlw/iRXo5nSVJFkXLTmZIiTSyroiglKW+QSooyMYyJYZSU2sQyKopSklR8f9NDs4JNdGHAeglCfRIGYQ8VrTp06t0XQ6riwY+/qdRcQkXQcyxw6usGkiYH/ShzN7HnL7te4EX+2sJFj055UJ4OrQcKCcIRn/+wz3fI5z/k9R32+esiERVF6mhGSZE6mtbQtJIkDQwj55mRYZQUpaYoq1KppEgtTWtpWkGSBoZRU5SCIk0Mm6hdIYTQ+WEQ9jgSF/Ot+dC9c8OTWT//821T1W2WWDqXwNlw9dcNUS+fNlf9vWLLkxXfDrcMvW9Cj5sOJiaKxwOBw02Zd8jnqwqGBmnUI/T64XrdbQPzhut1xTod1au6XyOEei8Mwp4lduqw66MXY9ZBC4a++tY8U4HuosIg6ubOrHe4j/jZqdJa1Zr/VO+eVzCrh/QF5UTxWJvYy1Yqh+t1E0zGHwzIeUpfjLGHEEohDMKeQq4IhvZsUC6577+rSh4aTs7MunA2JDqFxoaFPp/4hTPkWpJ71S8n3Z2q4YDnib0RBv3i7MyHi4eONOgV3djhECGEzg+DsEeIVR9xffQik12QsfyNm3dqJlrgZ8UXiAq5U+jpdXbPANfHIz82W0w3FV83JaekO1tBOVE8Ew4f8vrL3G6MPYRQL4VBmGKJiqDx+ntVo6f+rlw8GxQ3XXXefxcJ3AcD+9acalQ3fl70+ajC4t8VPzzQMKCri5qIPblXy2Gf76jfb1W0iL0Rep0Su6gghHoVDMJUitUcdX30IpM10Lr8DVKj/6pG/EeluOtaij13Dcpd6T+04pSTc20u+H7ihFGvDnlWz3ZJh/62sVfpD2QoFHLsXWlNf6BwMMYeQhdDFDk+Fkrew8d8kpS0SisfFfjWq1vzXFASuVY7uag/+UQAAJC4qK+9Vwy23im0+yohUYi12snF/CCJgiCQJHme9fxEkW/7KheDi3ou4yw+FhRFHgAKx981eOxPLuMK54JBmBqSwPvX/Tu4/Vvj9T9XjZkGAEc90s9Kha//m85Wt/+xqz/pOPzlqaA7cqTowNgpw14Y8iRJdFqTIy9Jp0Oh88fecL0OBySgbsPHAmLTsgZhfy0pagBAEgUuFpB3ikJU4MPxg7mQKMQzg4t64weIPN/i4EjT/tYhIX/vJx62jaVWX9w8F04OD0kS+Ji/5QWDkti8UANJMjSrTj6AZvVE0u8vRSuoNvf1KVpNtpn4iVHoiNa/+ASj0Lc6jCBphtW22klSCkZhaLVTpc1q+yo0qyUIKhaL0TRNnvvWBknSNKs517PnwSiMl3EWzWpIkgYAhdpyGaef78qdezl0MbjaKtdHL9Lp2dblb5BaAwC4o3D1d8JzJVRJejspWFlddWjVSdUZTf3Is7PuuOIqw39Fo9EOpqAgSRUezxaHc4fTdcjnOxUM5avVIwy64Xr94qzM5UWFQ3VaFu/t9UKiwPFcc+VD5MOC0DwhX3JgAAAf80tS8xo6sYi3+UKSyCV9ubcKj1Z1C54Li8mvEgtKEp/0VKzVy0lScyVGFDmBi19Z4CMCH78OzWrJpmUNKEYvfx0TJJX4ficpBUXHh9jSjJqk4lPwMAo9ANF0sK7pYDaRASTJ0KYWX98Mq4PkWKJYimkxeLfVFzfNqJLDgyAoumXDDM2qSbLXTwmEk26jLiEJfOD7Ff5NKwyLbtNMXSTvFCS45Xt+6UDif4a2CB5RknZU7Tm6pmbA6XymmJ782AiTfioACG2WsrxInCjucXs2Oxq3OBq3O115atXMdMuSnOzfDi8uwti7dIl6gyQ11zwSWcJzAblOkPhyl8R4REmSlKi18LGAnA2JLBFFTogfJnLReBRxUR+ACElJlqgPSWKLxCIphmaaKx8kraKo5okUkgMDAGhWRxDNX3OsMqm6QJBM0pc7STLJf/u3qluotJlk8quwGoKIf7dQtJJqmskh8XIE0VygH7Q3AAAgAElEQVSJIUmGYjRtD06Gc/qgLoVB2H24+mrXv/5C6c3W37xGGcyJ/ct3CbwIz5Y0fx8FudCayo0nN50dXztxUOHASY+NUhkuc1IYXpL2ebzrbfbSRud2pytTqZhmMS8ryH//iokWRX+cbltOF4EPCXyYiwZ4LijyES4W4LmAwEf4WJCPBQQ+zHMhLuYX+IjAhbmoR+AjAh+JRTwEQfCxkChyAEBSLM2ooGWFgFHo5XsqNKMhSAaSWr0IorkdiVXGaxg0oyVICgCUGmv8MJKmGQ20TItEhCTqIiTFUrS61WEIocuAQdgdBK/Tv+mLcNkmw3V3qSfMSX7qwxPi/1VLu66jKQIA4Ky/7suj39aU1c0/u3BW3qDRvx6itFxyXIUEodzt2dboXG937HC6inTa/7KYbxuY994VE9LY3h5+UiziE/iwwIe5qP/iYswrx1hTnsVYpZ6iVRStYhQ6ilZTtJJR6GhGQ9JKhtXSjJZVpdGMmmF1FK2kGBWjMFC0kqKVUY7WarWtKlUIod4Og7Br8c56/4bPwvtKNVfMy1j+OqUzJT9b4ZR+vVPYsIg2sVJZw/7Pj64MV3GLaxdP1c0o/EmuvkB9rsu2FeSF/zidpY3ObU7Xf5zOYp3uSmvGL4YM/mzKFb1o+YJIoMHTeNjrOORrPMpxwfZiLMoqDReIMaWJZjU0q6VoJc2oGYUce0pGYTxXy9tFEv1+VokNdAj1NRiEXYVrqPFv+DRyaJdmysLMx94mNa0br2xhuG6d8NfJXJVz4/PbVmYHs//77CI9r8u/xmoZ07pnV7sCPL/D6Vpvd5Q2Ovd6PHL4PVxU+F+Wyb2ie6ck8n7XCTn5PI5DXvtBADBkjDSmj8jIn8UodPEYo1pUy1JdaoRQX4NB2Pm42pP+71dEK8s1U6/K/O0/SVXrTswAwIlw47raWcZ1H1Wsn6gZd7ftbrqWHTAv3TrJRJDnmxrGx/E7GhvX1jfs9PoOen0jDfppFvPTw4unWcw9f0gfF/N7HUf8rmM+Z6Xbts9jP8go9CbraGPG6CFj79CbizSG/FSXESHU72AQdqZo1SH/hk+5+lO6mUtNNz5AMO3fkDvgOPxo6UoicmCSed7M8B/82yOZk9IG3JpOKdrvt2mLRHe53NuczvU2+/FAoMRkmmTQPz28eEa6pYd39YwEGtz2/T7nMZ+z0mPfH/TWaAz5xozRJuvonMKrjRkjk/s3IoRQSmAQdo7osQrv6g9Ev1s7/VrzsicIup3bcpzAbazZ+u8jXzZGeE9s4Tvm291rPIrRiqHLBzC61v8Q9ZFIaaOztNG5rdF5PBC4Ii3tyoz0v44dPSnNREpSNBpVq3tchIgiF3BXuW375Tqfq76cIGm9uUhvHpqRN6Oo5D5dWmGbscAIIZRiGIQdI0mRwzt9az6SBE43+3r1hDnQpooW4sJlDft211dsPr19VMawuYPuWrEh9wVXoxjjxzw4WGlurjXWhSNyta+00dkYjV2RZppmMb81Ydw4o4FMmuXosscRdjou6vM2HvXY98l1Pq/jkFqfqzcX6dKGFoy6dcK8FxXq9FSXESGELgCD8DJJAh8u/963/hNSrdcvuFk5fBIkZZUoScddJ3fXV+yqLz/urhphKb4ia9xbC1/0nVDt+VfDQ0bP8B/HO4VWBYNytW+dzR4WxOkW839ZzD8dVDDeZOyBC/TJTZ1u236Pfb/PeSwWcevNRXKdL2/YD0zW0diZBSHU62AQXjKJ50K71vm++5gyZRiv+YlyxOTEU+6Id5/94J76vf+p3c1S7ITMMUuLFl+RNV5NqzwnAjX/dByzeblp1pzp1L9t9tJdzs2ORk4Up1nMV1ozflE4eLhe36PCTxS4gKdKjj23bZ/XcZhmtSbraL25KKfw6pHTRuvNQ6EbV31CCKGugEF4CaRoOLhjrX/jZ0zuEPMdT7B5RQAgSMLhxsrtZ3eXNew7668bkzFiau4VPx55o1WTDgBRN2fb6G7YffqUgXvXLB4eEArFjpm2sDPSzfOs6c+MHJbXk271RQINvniXzv2t+rZkDZpnSB+pUKWluowIIdTJMAgvihj0BbauDGxdqSgcY7nnT0xmXl2goezE2j31e8sa9mVrMydkjvnZuNvHZIykSQoARF5y7PNuL6vf4ncdzOb2jPADoWTEtL+MzZ2bMTZb1SPaDyVJ8LtOeOwHPY6DXsdhr+MgEJQxfbghfWTmwNlFJffpTIMJEj8hCKE+Dr/mLkDwuwPffxn8z7fKEZP09z9XCf5tZ9du27EzJnITM8fOzp/260n36pKWOzlU7V6598xWR2OZPqwyUdOHW2bqs2zHzAM1mndmUGmXP6tJJ5BE3uc65rEdcNv3e+wHvI7DKm2mMWOUIX3E0Al3G9KHKzXWVJYPIYRSAYPwnHhnQ2Dzl4GyTQ3jxh+94dpy97GjWx4rNhdOzBr7+xmPFKUNSRxZFQxurnNsPN6w3tfIS9IVCsNVk/PeGJydr1avqBbv3SY8Mob6xcjzjpPvGpLI+90nEzf5kgewF5XcZ84uYZWmC18FIYT6NAzCdnD11Wc3frT3bNm+gsyyMbSWqZrA6W4ovnpi5li2aRGyxFCH1WdskRg/yqucqTb/eszkMSMscveRiAAP/EdYc1b6dgE9ztxNIZgYyZfo3qLW5+AAdoQQOg8MwmZRIbb38IYd+7/eG6l3quixk8aX5E68N3tCetNqyPWRSGl97XqbfZ3NHuT4ElE/ppZ9WRowZUxmxmIjrW6e4eyoR/rhRqHIQOy6ljZ05XoP508+k3VUYuVShBBC7cIghLpAQ1nDvl0ntpQ5D1ujxHjL8Ifm/Gxk5mh5DHtDJPLZ2drEIPcZFsu4mOa5ugH5Z8n0MQbrUpMmu3XPl/ePi7/aKTwxlnpgZOfPoiIKnN91wlFbHvIcddv2yWPYk5IPR/IhhNCl6adB6Iv6y23799Tv3VVfTvL8CI8wwS/dX3J7xtSrgaTs0egXtXXyOPfTofCkNNM0i/lWbXbmfsG5yqsdoMqcYjIv0xNU6wZPPwd3lwp7ndKmq+iRps5pDuVjAY/jsMe+L3lIgzZteHrO+JzCq03WMR1ZVwghhFA/CkJREo+7q8oa9u2p33vUebzYXDiWTHvoNDUoqtDNWhoc+V873N5th47KE1vLc3u+NWHcCErj2uuzfeMWeZ/qCuOERwrbzgsqK2uUbtooTMkgdl9HqzvwvspLNLRKPrnOVzDqx2mZYyWgeuZcowgh1Bv1/SB0hd276yu21+4ua9hnVpmm5lxx87AlhWcc4Q2fhXTRo5NueVltWW+zH1uzfpI5Hn7jjAZCAu+JYMNK994T9WnDdAXXZBoLteeaREUCeOWg+Me9wqtTqR8OuuTm0MSMnXLyhf31ekuxyTomI2/GkHF3GSzDWq2H3nPmGkUIoT6gbwZhVIgddByRR7vbgo6x1pETs8beP+FOM6t37Pzu+399+L6lYNvoJcc5cVKUvtLAyKs6MCQJAGF79PQqu223W2FgMqeYCm/KOdfqSLLGCCzbwtvCsONaepDuoppDWydfoEFvLpKTD5doQAihbtYHg/Cf+z/+9Oj/FZsLS7LGLZ9832BTQYgXtzfUPfv91lJHY6XKOHHMwuk5uX+2mJPX8xMiov2gx77HE7JFMyYYx9w/SGm5cHfPzfXSrd8LSwcSX1xJsecIL54LBj3VAU+1z1npsR9w2/cLXCgxb9mwyb/UGgfijJ0IIZQqnROEDodj3bp1Go1m/vz5SmU7vRY5jvvuu+9cLtfcuXOzs7MT+6urqzdv3pydnT1nzhyqkxZYv27owh8NXyISzPZG54d1ztK9W3Y7XUWBxqkq6veTJs8eOlyRvFKSBL7qkH2Pp3GfVz9QnTnFZB6lP/8a8TJRgr/sF/96UHhnBr1wQPz4WMQd8FQHPacC3pqgpzrgORXwVPMxv9ZYoDHk681FecNuGD3zdxpDXqf8pAghhDquE4Lw8OHDM2fOXLBgQX19/dNPP11aWqrRaJIP4Dhu9uzZADB06NAHH3xw9erVV1xxBQCsXbv25ptvXrp0aUVFRVZW1sqVKwmiEypGq+3+107s2+/1jddrp/oafn5k65T8QemLb6Yt2cmHxbycvczT8B83SRMZJcYJjxQy2ot9N2xhuHODUx09/d3oM2zD6fLKGp+z0uesFIWoHHgaQ745uyRv2PUaQ75aPwCbOhFCqMfqhCD84x//uGzZsueff14UxRkzZrz//vv33HNP8gErVqzweDwVFRUMwzz33HNPP/30t99+CwCPP/74n//85zvvvDMUChUVFW3atGnOnDkdL89AjeZ3eZkj9x8RN25UT5itu/cPlMGceFbkJdchn323x1cTsow2FN8+QJt7gSHn8poMQe/poLcm6K2pb6wJuqt+TFPmtPzQ6XzCkG+yjskderXGkK8x5He8/AghhLpTJwThN998s27dOgAgSXLJkiWrVq1qFYSrVq269tprGYYBgBtuuOHxxx+PxWJOp7OsrOz6668HALVavXDhwm+++aZTgnBU+Xf+zf+nnn619vF3SHXzdNihhqh9j8e2y63JVmZMNBbfNoBseVtPFLhwoC7orUlkXtBb43Meo2iFxpCvSxuqTSvaTl/1BZn3x+sHzxyg73hREUIIpVxHgzAQCPh8vpycHPlhTk5ObW1tq2Nqa2snT56cOEAUxYaGBofDodFoTCZTYv+RI0fO9Soej+e9997buHGj/DA3N/dHP/rRuQ5mx8+2TFlEKFQCgMBxfFhw7ffbd3q5AG8ZZxhxX57CxIhCzO8/FfTW+F3H/K7jId+ZoLcmGrIp1Fa5MVOtz8sctEitz9OaBsvzc54Owq2bwaKEL5ZAmgI4jrvc96yjBEHgOC6FBei38G1PFXznU4LjOIIgRFFMdUE6hKIokrzAzamOBqE8pi1xb4+iKJ7nWx0jimKiHHKPGJ7nBUFIviPY7okJHMf5fD6Xy5U4+Hxj6TQGCQB4wXcy5Kzwu47alANdyrFuVmVz+k+f2Xwm5DstZ55anydnXnrebF3aUJU2u93l9wRB+OoM8cBO4tcjpHuHSQRAagfyCU1SWYh+Cd/2VMF3PiX6xtt+wRSEjgehwWBQq9V2u13uC2qz2ZI7hcoyMzNtNpu83dDQAADZ2dkMwwQCgVAoJM+Q0u6JCenp6ffff3+iWnl+9Ue3n9q52ms7KbJ1HFlH5ZAazUAhWqBR5afnXqE1/lBryFdqMy/yB4wI8PAuYc1ZafVCqtsWkTg/+W+Idnvnoi7FcRy+7SmB73xKSJLEsmxn9efvyTrhHuHs2bPXrFkzduxYAFizZo3cQVSSJLvdbrFYKIqaPXv2u++++9RTTwHA2rVrp06dqlQqc3NzCwsL165du2TJEkEQ1q1b96c//anjhQEA++FqINjCWdel5RVqjQM7suRety0igRBCKFU6IQgfeeSRxYsXx2Kx2traioqKd999FwBcLldmZuaxY8cKCwtvvvnm559//tZbbx0xYsQLL7zw/vvvAwBBEI8//vjPf/7zY8eO7dixQ61WX3311R0vDACMWXpzp1ynSxeRQAgh1EN0wlf8tGnTtmzZAgCFhYVlZWUWiwUAtFrtO++8Y7Va5e1du3ZNnDgxEomsXbv2qquukk+8/fbbP/nkk0AgMG/evC1bttB0T5nmxs/BLZuEP+8TN11FYwoihFDfRkiSlOoyXNiUKVNefvnli7xH2EGJRSTenEZ1ZBGJriMIAq4+kRJ+v1+n06W6FP0RvvMpEQ6H8R5hbxXz1XMhN6OxMJq0dnuBnksHF5FACCHUG/XBIGw8vLrxwEou6OSCTkqhZbQWRp3GatMZjZlObGjiG2TTeu6XsYgEQgihPqAPBmH25DuyJ98hb4t8JOZ3cAE7H/bGAo6Y3x6yV8YCjpjfxkd8MZ9NEmK0Uh9RWitClpsNhskDrcoj6Q6lgdVlMNp0Wmlgdem4NARCCPVhfTAIk5G0UmkaoDQNONcBPBd5tdzzxaGGp8c4x2j9sYA96j4TjByK+e2xgF0Ie/mIn1bqKJWB1WawugxaqWd0Gaw2g1bqWV0Go81gtGaC6Ptt6Agh1Ff18SA8P1sYbvue5kTLpzdZs9XtV/tEIcaHPHzEywUcMb+dj3g5v8PrOCFXMTm/jQu6KIWW0aXTSj2rs7LadEppYHUZrDadVhkYbYbCkHVJtyoRQgh1p/77Bb2+VvqfLcKdRcRvx1HUuds+SYpldRmsLgPSC9s9QBJ5Lujigk4u4OBCLi7ojAXs/jNn5A0+6OJCLlplZDRmWqknKJZW6gmapRgVyapJiqWUOpJiSUZFsRqSZimFlqSVJM1SCh1BMZRCQzIqksLB/Agh1FX6YxDyIjyzV3j3mPTxbGp6Zkfv/xEkHU9KGHaOQyQ5KfmIVxI4PuyVBE7gwmIsJAoxIezj+KjIR4RoQBQ4IRoQ+YjIx4SITxQ4MRYSuJAkcHKIUqyKZNUEQROsllGo5PgkKJZWaElGQdAKWo5PVkMyKpJm5bNIRkWxaoJiaCWumIEQQq31uyCsCUg/2iikq4jyJXSaontek2A0ZkZjvvCB5yaHqBALi7EQz0UiARdDgchFhGhAEjk+GhC5qBj2Rd1nJZETYiExFhIFjo/4JCEmcmEhGhQFToj647VPhYag5NqngqQVBEVTrAYA5IcAQCm0BEEBQVBKHQCQJEOy6pYHaOT23ni4kk1XYJQkzQIAxWoJEm+dIoR6gf4VhCuqxXu3CY+MoX4xkuxdPUFppQEAGA0AgCAI9OUOqBebap9SvPYZFfmoXPUEAJGLiEIMAIRoQJIEkCQh7AMATmw6gI+KfDR+gCgASHzEDwCSmHQFPgYAQkw+IF5ygqQohQYACFohD1mhWHVzlBIEQVJJYRwf00KyKoJikstPNCVuMkqhJYgW4z5JWkEyreZoJmhl6xHZJKNuc/14OdtcH0O9vxO5sPzb0X8I0QgvMhJFAYD8h3iqSxTHai0kc4EF1S9JfwnCxCIS3y6ge8giEikhV+nkcOoefMQLAJIoCNEgAIh8RJKjNBaUk5IPNx0Qkw+IinxEPleMhcWWv3uSyEdiwVYvIUQDktRiyTSRj4pcpOVR8cxucRgXkoQWq9wlytniR4j6Qbq0JdkoVtNVPaRIklZoL3xYG3w0AD1+YTkhFpTEcy7Hllr98G69JEmJxfIIiqHYzsyejsiceGvW5GWdeMF+EYS4iEQKJUKXUaeltiQdd/ETfXXhF7oo8tHAZZxHK7RwEQuzpda5/oDAKdZSAqdY6ztwEQnU/dq233YiWn35K4shhNrqy0Ho5+DuUmGvU9p0FT3S1H+bQxFCCJ1Hn60klTVK47/kKQJ2X4cpiBBC6Jz6YI0QF5FACCF08fpgEH7y//ZlnGncN8pgbjQKpJ5MNxCabhowiBBCqNfpg0E4Z9YAXQ0FDh+383jE7hUdPiBJMkNPZRjIdD2ZYaAy9GS6gTRrgcAmU4QQ6u/6YBBmDE6DwS166kvBqOjwCnaf6PAJVTZu53HB7pU8QcKoSaQjma6nMvSk1UiocIAFQgj1I30wCNsiNApKk0ENzGixlxdEd1Cwe0WHT7R7uWp7xO4TG9xAkWS6XGtMBKSBtOiw+ogQQn1SvwjC9tEUma4n01vPQ92i+njKzu06cc7qY5aJUDDtXhshhFBv0Y+D8BwuofpY5wKGaqf62CZcEUII9VgYhBfnQtVHodbFHzoj2H2i3QscH68+ZqdRuWnxdEzTAoVjORBCqMfBIOyQdquPUjAiJ6Jo9wpVttiO46LdK3pDlEVHZhhIq4GyGkmrgbIaSIse0xEhhFILg7DzERolXaCEgtaNq0KjX7R5RZtHaPBw+6oFu1dyBQijRs5F0mqgMgxkppFK1wPd92e5RQihHgKDsLvQFJVppDKNAPnNOwVRdAUStx5jJxqEpluPVHYalZPW3DEn00QosWMOQgh1PgzClKLIc916FGpdQp2r3XEdRJZJSNcKA9Jx1CNCCHUcBmFPRGgU9NAsemhW8s54x5xaF3/WKVVUB9cfEhs8QBHJ3Vap7DRqgBnTESGELh4GYa+R6JhDCQJEo2q1GlqOeuSP1UU3HRJtHiCJ1oM6ctJIYxeukIcQQr0XBmHvdo5uq23mBDjrlHihvRlz9IAT5iCE+jcMwj6o/XQMRASbV7R7RJuXP1onbjks2L3Ai6TVSCX6rGYYSKsB644IoX4Fg7C/ILRKWquEwdbknVIoKtq8ckBylXXilsOizStFuORcpKwGMsNApumw7ogQ6pMwCPs1Qq2gCjKoNkMe4/PJ1brON9tqThqVacQJARBCvR0GIWojMZ/ciAHNO1vOtho7VifUutqfizw7jWDxc4UQ6jXwCwtdnHZnW205IUBiLnJCraBy0lqkI04IgBDqqTAIUQecY0IA0RMUal0t0rHeDXTTQo/ZaVROGi6DjBDqITAIUecjjZq2XU8TEwIItS5u94mI3df+hAC5aYRakZJiI4T6p04LwuPHj7tcrnHjxrFs+3/jOxyOEydOFBYWWiyW5P3BYFAQBL0e1/Dr49od1CF6gqLNK9q9gs3LHzoTnxCAJql0A6FXEToVqVUSOhWpVxFapfwfqVcRGmWqfgqEUN/TCUEoiuJtt922devWAQMGnD17dv369UOGDGl1zAcffPDQQw+NHj16//79f/vb32655RYAWLly5S9/+cuTJ0+OGzeuvLy84yVBvU687liUnbxT8ocFu0/yhyV/WAxEJF+Yb/CI/rAUiEiBiOgPS6EoqWuKxnhMqkhd00OdkpCfVeBdSYTQhXVCEK5bt27r1q0HDhzQ6/W/+tWvnnzyyY8++ij5gFAo9MADD3z11VfTp0/fsmXL0qVLly5dqlKphg0b9sknnxw6dOivf/1rx4uB+gxCp6J1qvMfI8V4yRsS3UEpGJFCUSkYFT1BsdouBaNSMCrvkUJRQq0gNAr5/6RRQxg1pCaxR0maNKRJg4teIdTPdUIQfvrppzfccIPctrls2bKJEycKgkBRzV8u69evT0tLmz59OgDMmDHDYDBs3LjxqquuKiwsBIBjx451vAyovyFYmmivn04yiROkYCQejcGI6A2J7qBg90mh+E7RExTdQYIAQqNsSko1oVYSGkU8L40a0qghNApSrwYSJxRAqG/qhCA8c+bM2LFj5e2BAwdGo1GbzZadnZ18QEFBQeJhQUHB6dOnL+klwuHw7t27A4GA/NBkMk2YMKHDBUd9HMFQhFEDF5oxTgrHJF9IDEQkfyTeGOsPC2cCbRtjJTXrU7KkWgEsTTAUoVYATREKmlCywFCEiiUUNNAUoVYQDE2wFKFSAEMRCoZQMjjzAEI91kUF4aFDh5566qm2+//617/m5uaGw2GFIt7NT6lUAkAoFEo+LBwOJ/egUSqVrQ64IKfT+d577yU61BQUFPztb3+7pCv0JYIgRKNRURRTXZA+REODRgtWbavdBAABQAKAKEmBSMjhVtGsGI4RnCByAoRjwAnACVIgRAiiFI5BTACOhwgnxXjgRSkUJQRRinIQ5UGSQMkQLC1RJKFRAk0SLA1KBhgKWJpQMhJFEmoFMBQwFKhYgqEkhiJULFAkoWKBpYGhoL+OxQwGgwSBNfLuJn91Jzfv9UZKpZKmL5B0FxWEVqv11ltvbbvfYDAAQGZmptPplPc0NjbKe1qdnjhAPiYrq8VKexeUm5v78ssvT548+ZLO6qsEQWAYRl6GCXUfvQ50Sp1Od5mnC6IU4aQoB7wghaISJ0gxXgrHgOOlKC+FY8ALUjgmBWPAxQ8gYrwUikq8IMoncoIUjhEsDYxc6aSAZQgVS9CkPBwzPuxEQRM0BRRJKFkAIFQskATBUMDQQBLxI5UsQRFAU3J/IvlEuTrbOe9VZ5MkSatt/WcK6moURfWBILwYFxWEFovluuuuO9ezJSUlmzZtevTRRwFg69atI0aMaPWRLSkp2bdvn9/v1+l0Pp9v3759JSUlHSw3Qr0MRRIaBaHp6BBJKcYnkhJinBSOSZwgRTgAkEJRAJCiHPAiCKIUjACA2OgDUZI4HjhBEiUIxwBAisQkQQJekKJJJ0Y4EESgSHkOoHg6sjQwNNGUoKBiCZIAhiIYGgiCUMuxygBFAU22G6uEWiHX5Qi1AggicUAilRFKuU64R3jHHXc8//zzv//974cPH/6b3/wm0Yi6YMGCxYsX33fffcXFxfPmzbvlllvuuuuu//3f/50/f77cTaampubf//73vn37bDbbn//854KCghtvvLHj5UGoDyNYGli644F6ToLYJlYFSZCkSAwAIByTRAk4QeJ4ECWpKVYhwgEvitE2eQwghaKSBPH9kpQ4IHF6cyKSTRlMUyBnKkPJ89YKhBRUKwEAGJpgKICmim8ihuMbJAAQSlbu2USoWTl6yfjbRSTet8SkDYljUH/WCUFosVhKS0tfeeWVysrK55577qabbpL3L1myZPjw4fL2Rx999NJLL33wwQeTJk166KGH5J08z7vd7ry8vB//+Mdutzs9Pb3jhUEIdQhFymnRhVnbSiIRxaYM5gWQM5UTpBgPAKIvwFAMAADHS5wAyUEb4SDCAYDo9IMgAoAUjjUnrgQAIAajAAAgSfGNpjAGkELxgwklCxQB8p8acsSyNDA0ABBNld3kWmxSlCrkFcoIJRtP4kRtWG6Rjkc7DQDNdesWqcwCEMknom5GSPJfaz3blClT8B5hgtxZBu8Rdj+5eT/VpeiPuuGdlyIxECSQI1YO1BgPHA8AEi/KbcjNmZ0cpYlwjcTiJ0Z54AUAkFukIR7tPABITRXulqkcA5Ag0ToNkGigBgBC1RS0iWAmmmMYlAwhd0imKEIRr9g0Z3PTKS0iXMkASUJSPENzpRmAZeJ1bpKIgsgwDJXU7t1X4VyjCCEEct8i6M6q8Hk05yVI4Xi9tjmYpeYYhggnyfibzLgAAAsiSURBVNkpCFKUj5+SiNgYB8EIAIAoiQ2e+M4IB6IIABInQCx+SlOlGSDKSbwQPyUcjcrtxk1Vc4AWIZ1ozY5vJ7I2OaqTt6HNNpG8HX8g9/CKbzfFNiS1flM5aW1nM+4IDEKEEOphmhqoIaXB3P7wCb45cUFoqisDgCg2V5eTozp5G1puB1vub2qelHt4xfc3xTYk1ZiZyYWKGcM7/gMmYBAihBC6aDRFJN3IJOACsyH2CjjbBUIIoX4Ng7D3OXXq1IYNG1Jdiv7o888/93g8qS5Fv+Pz+T777LNUl6I/2rRp04kTJ1Jdiu6AQdj7bNu27eOPP051KfqjN9544+jRo6kuRb9z4sSJV155JdWl6I8+++yzLVu2pLoU3QGDECGEUL+GQYgQQqhfwyBECCHUr/WOmWVyc3P1ej3OpSJzuVw+n2/gwIGpLki/U1lZOWDAAPwcdrNwOFxdXT1s2LBUF6Tfqamp0Wq1ZrM51QXpkKVLlz722GPnP6Z3BOHRo0cDgQAuSCYTBCESiWg0nTmxAroYPp9Pp9Ph57D7+Xy+xHKkqNuEQiGWZS+4mF8PV1BQkJaWdv5jekcQIoQQQl0E7xEihBDq1zAIEUII9WsYhAghhPo1DEKEEEL9Wu/uDtTfnD59es2aNYmHCxYsyMvLS2F5+rZwOLxv377Dhw9PmDBhzJgxif1er/fDDz+02+2LFi2aNGlSCkvYVzU2NpaVldXU1PzgBz8wmUzyzh07duzfvz9xzB133NHbezP2NG63e/Xq1ceOHUtPT7/xxhvT09MTT61atWrHjh1Dhgy5+eabGaYPrtCLNcLe5MCBA08++WRZE6/Xm+oS9WXz5s1btmzZo48+unbt2sTOaDQ6derUTZs2sSy7ePHiL7/8MoUl7JPcbveAAQOeeuqpu+++u66uLrH/888/f/PNNxMffrFpjTrUWZYuXfr5559TFFVaWlpUVHT8+HF5/7PPPvvAAw/odLp33nnnhhtuSG0huwj+SdXLDBo06K233kp1KfqFjRs3six7zTXXJO/8/PPPGYb59NNPSZLMy8v7wx/+sGTJklSVsE8yGo0+n49hmNbrwQIsWrTomWeeSUmp+oOVK1fqdDp5+5prrvnnP//57LPPhkKhF154Ye3atSUlJffee29ubm5FRcW4ceNSW9R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"\n", + "\n", + "\n" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_epochs = length(losses)\n", + "n_parameters = div(length(parameter_means), n_epochs)\n", + "parameter_means2 = reshape(copy(parameter_means), n_parameters, n_epochs)'\n", + "plot(\n", + " epochs,\n", + " parameter_means2,\n", + " title=\"Flux parameter mean weights\",\n", + " xlab = \"epoch\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note.** The higher the number in the plot legend, the deeper the layer we are\n", + "**weight-averaging." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/weights.png\"" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "savefig(joinpath(tempdir(), \"weights.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieving a snapshot for a prediction:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-element CategoricalArrays.CategoricalArray{Int64,1,UInt32}:\n", + " 7\n", + " 9\n", + " 5" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mach2 = machine(joinpath(tempdir(), \"mnist3.jls\"))\n", + "predict_mode(mach2, images[501:503])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Restarting training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mutating `iterated_clf.controls` or `clf.epochs` (which is otherwise\n", + "ignored) will allow you to restart training from where it left off." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUpdating machine(ProbabilisticIteratedModel(model = ImageClassifier(builder = MyConvBuilder(3, 16, 32, 32), …), …), …).\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.4449181129617429\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.4575672614002921\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mSaving \"/var/folders/4n/gvbmlhdc8xj973001s6vdyw00000gq/T/mnist1.jls\". \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.4693455717095324\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.48012884529192995\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mloss: 0.49023152105995377\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mfinal loss: 0.49023152105995377\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mfinal training loss: 0.010609009\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mStop triggered by Patience(4) stopping criterion. \n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTotal of 32 iterations. \n" + ] + }, + { + "data": { + "image/png": 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", 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Patience(4)\n", + "fit!(mach, rows=train)\n", + "\n", + "plot(\n", + " epochs,\n", + " losses,\n", + " xlab = \"epoch\",\n", + " ylab = \"cross entropy\",\n", + " label=\"out-of-sample\",\n", + ")\n", + "plot!(epochs, training_losses, label=\"training\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.10.3", + "language": "julia", + "name": "julia-1.10" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/src/extended_examples/MNIST/notebook.jl b/docs/src/extended_examples/MNIST/notebook.jl new file mode 100644 index 00000000..448f50ee --- /dev/null +++ b/docs/src/extended_examples/MNIST/notebook.jl @@ -0,0 +1,295 @@ +# # Using MLJ to classifiy the MNIST image dataset + +# This tutorial is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST). + +using Pkg #!md +const DIR = @__DIR__ #!md +Pkg.activate(DIR) #!md +Pkg.instantiate() #!md + +# **Julia version** is assumed to be 1.10.* + +using MLJ +using Flux +import MLJFlux +import MLUtils +import MLJIteration # for `skip` + +# If running on a GPU, you will also need to `import CUDA` and `import cuDNN`. + +using Plots +gr(size=(600, 300*(sqrt(5)-1))); + +# ## Basic training + +# Downloading the MNIST image dataset: + +import MLDatasets: MNIST + +ENV["DATADEPS_ALWAYS_ACCEPT"] = true +images, labels = MNIST(split=:train)[:]; + +# In MLJ, integers cannot be used for encoding categorical data, so we +# must force the labels to have the `Multiclass` [scientific +# type](https://juliaai.github.io/ScientificTypes.jl/dev/). For +# more on this, see [Working with Categorical +# Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/). + +labels = coerce(labels, Multiclass); +images = coerce(images, GrayImage); + +# Checking scientific types: + +@assert scitype(images) <: AbstractVector{<:Image} +@assert scitype(labels) <: AbstractVector{<:Finite} + +# Looks good. + +# For general instructions on coercing image data, see [Type coercion +# for image +# data](https://juliaai.github.io/ScientificTypes.jl/dev/#Type-coercion-for-image-data) + +images[1] + +# We start by defining a suitable `Builder` object. This is a recipe +# for building the neural network. Our builder will work for images of +# any (constant) size, whether they be color or black and white (ie, +# single or multi-channel). The architecture always consists of six +# alternating convolution and max-pool layers, and a final dense +# layer; the filter size and the number of channels after each +# convolution layer is customisable. + +import MLJFlux +struct MyConvBuilder + filter_size::Int + channels1::Int + channels2::Int + channels3::Int +end + +function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels) + k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3 + mod(k, 2) == 1 || error("`filter_size` must be odd. ") + p = div(k - 1, 2) # padding to preserve image size + init = Flux.glorot_uniform(rng) + front = Chain( + Conv((k, k), n_channels => c1, pad=(p, p), relu, init=init), + MaxPool((2, 2)), + Conv((k, k), c1 => c2, pad=(p, p), relu, init=init), + MaxPool((2, 2)), + Conv((k, k), c2 => c3, pad=(p, p), relu, init=init), + MaxPool((2 ,2)), + MLUtils.flatten) + d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first + return Chain(front, Dense(d, n_out, init=init)) +end + +# **Notes.** + +# - There is no final `softmax` here, as this is applied by default in all MLJFLux +# classifiers. Customisation of this behaviour is controlled using using the `finaliser` +# hyperparameter of the classifier. +# +# - Instead of calculating the padding `p`, Flux can infer the required padding in each +# dimension, which you enable by replacing `pad = (p, p)` with `pad = SamePad()`. + +# We now define the MLJ model. + +ImageClassifier = @load ImageClassifier +clf = ImageClassifier( + builder=MyConvBuilder(3, 16, 32, 32), + batch_size=50, + epochs=10, + rng=123, +) + +# You can add Flux options `optimiser=...` and `loss=...` in the above constructor +# call. At present, `loss` must be a Flux-compatible loss, not an MLJ measure. To run on a +# GPU, add to the constructor `acceleration=CUDALib()` and omit `rng`. + +# For illustration purposes, we won't use all the data here: + +train = 1:500 +test = 501:1000 + + +# Binding the model with data in an MLJ machine: +mach = machine(clf, images, labels); + +# Training for 10 epochs on the first 500 images: + +fit!(mach, rows=train, verbosity=2); + +# Inspecting: + +report(mach) + +#- + +chain = fitted_params(mach) + +#- + +Flux.params(chain)[2] + +#- + +# Adding 20 more epochs: + +clf.epochs = clf.epochs + 20 +fit!(mach, rows=train); + +# Computing an out-of-sample estimate of the loss: + +predicted_labels = predict(mach, rows=test); +cross_entropy(predicted_labels, labels[test]) + +# Or to fit and predict, in one line: + +evaluate!( + mach, + resampling=Holdout(fraction_train=0.5), + measure=cross_entropy, + rows=1:1000, + verbosity=0, +) + + +# ## Wrapping the MLJFlux model with iteration controls + +# Any iterative MLJFlux model can be wrapped in *iteration controls*, +# as we demonstrate next. For more on MLJ's `IteratedModel` wrapper, +# see the [MLJ +# documentation](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/). + +# The "self-iterating" classifier, called `iterated_clf` below, is for +# iterating the image classifier defined above until one of the +# following stopping criterion apply: + +# - `Patience(3)`: 3 consecutive increases in the loss +# - `InvalidValue()`: an out-of-sample loss, or a training loss, is `NaN`, `Inf`, or `-Inf` +# - `TimeLimit(t=5/60)`: training time has exceeded 5 minutes +# +# These checks (and other controls) will be applied every two epochs +# (because of the `Step(2)` control). Additionally, training a +# machine bound to `iterated_clf` will: +# +# - save a snapshot of the machine every three control cycles (every six epochs) +# - record traces of the out-of-sample loss and training losses for plotting +# - record mean value traces of each Flux parameter for plotting + +# For a complete list of controls, see [this +# table](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided). + +# ### Wrapping the classifier + +# Some helpers + +# To extract Flux params from an MLJFlux machine + +parameters(mach) = vec.(Flux.params(fitted_params(mach))); + +# To store the traces: + +losses = [] +training_losses = [] +parameter_means = Float32[]; +epochs = [] + +# To update the traces: + +update_loss(loss) = push!(losses, loss) +update_training_loss(losses) = push!(training_losses, losses[end]) +update_means(mach) = append!(parameter_means, mean.(parameters(mach))); +update_epochs(epoch) = push!(epochs, epoch) + +# The controls to apply: + +save_control = + MLJIteration.skip(Save(joinpath(tempdir(), "mnist.jls")), predicate=3) + +controls=[ + Step(2), + Patience(3), + InvalidValue(), + TimeLimit(5/60), + save_control, + WithLossDo(), + WithLossDo(update_loss), + WithTrainingLossesDo(update_training_loss), + Callback(update_means), + WithIterationsDo(update_epochs), +]; + +# The "self-iterating" classifier: + +iterated_clf = IteratedModel( + clf, + controls=controls, + resampling=Holdout(fraction_train=0.7), + measure=log_loss, +) + +# ### Binding the wrapped model to data: + +mach = machine(iterated_clf, images, labels); + + +# ### Training + +fit!(mach, rows=train); + +# ### Comparison of the training and out-of-sample losses: + +plot( + epochs, + losses, + xlab = "epoch", + ylab = "cross entropy", + label="out-of-sample", +) +plot!(epochs, training_losses, label="training") + +savefig(joinpath(tempdir(), "loss.png")) + +# ### Evolution of weights + +n_epochs = length(losses) +n_parameters = div(length(parameter_means), n_epochs) +parameter_means2 = reshape(copy(parameter_means), n_parameters, n_epochs)' +plot( + epochs, + parameter_means2, + title="Flux parameter mean weights", + xlab = "epoch", +) + +# **Note.** The higher the number in the plot legend, the deeper the layer we are +# **weight-averaging. + +savefig(joinpath(tempdir(), "weights.png")) + + +# ### Retrieving a snapshot for a prediction: + +mach2 = machine(joinpath(tempdir(), "mnist3.jls")) +predict_mode(mach2, images[501:503]) + + +# ### Restarting training + +# Mutating `iterated_clf.controls` or `clf.epochs` (which is otherwise +# ignored) will allow you to restart training from where it left off. + +iterated_clf.controls[2] = Patience(4) +fit!(mach, rows=train) + +plot( + epochs, + losses, + xlab = "epoch", + ylab = "cross entropy", + label="out-of-sample", +) +plot!(epochs, training_losses, label="training") diff --git a/docs/src/extended_examples/MNIST/notebook.md b/docs/src/extended_examples/MNIST/notebook.md new file mode 100644 index 00000000..ec7cef3f --- /dev/null +++ b/docs/src/extended_examples/MNIST/notebook.md @@ -0,0 +1,362 @@ +```@meta +EditURL = "notebook.jl" +``` + +# Using MLJ to classifiy the MNIST image dataset + +This tutorial is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST). + +**Julia version** is assumed to be 1.10.* + +````@example MNIST +using MLJ +using Flux +import MLJFlux +import MLUtils +import MLJIteration # for `skip` +```` + +If running on a GPU, you will also need to `import CUDA` and `import cuDNN`. + +````@example MNIST +using Plots +gr(size=(600, 300*(sqrt(5)-1))); +nothing #hide +```` + +## Basic training + +Downloading the MNIST image dataset: + +````@example MNIST +import MLDatasets: MNIST + +ENV["DATADEPS_ALWAYS_ACCEPT"] = true +images, labels = MNIST(split=:train)[:]; +nothing #hide +```` + +In MLJ, integers cannot be used for encoding categorical data, so we +must force the labels to have the `Multiclass` [scientific +type](https://juliaai.github.io/ScientificTypes.jl/dev/). For +more on this, see [Working with Categorical +Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/). + +````@example MNIST +labels = coerce(labels, Multiclass); +images = coerce(images, GrayImage); +nothing #hide +```` + +Checking scientific types: + +````@example MNIST +@assert scitype(images) <: AbstractVector{<:Image} +@assert scitype(labels) <: AbstractVector{<:Finite} +```` + +Looks good. + +For general instructions on coercing image data, see [Type coercion +for image +data](https://juliaai.github.io/ScientificTypes.jl/dev/#Type-coercion-for-image-data) + +````@example MNIST +images[1] +```` + +We start by defining a suitable `Builder` object. This is a recipe +for building the neural network. Our builder will work for images of +any (constant) size, whether they be color or black and white (ie, +single or multi-channel). The architecture always consists of six +alternating convolution and max-pool layers, and a final dense +layer; the filter size and the number of channels after each +convolution layer is customisable. + +````@example MNIST +import MLJFlux +struct MyConvBuilder + filter_size::Int + channels1::Int + channels2::Int + channels3::Int +end + +function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels) + k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3 + mod(k, 2) == 1 || error("`filter_size` must be odd. ") + p = div(k - 1, 2) # padding to preserve image size + init = Flux.glorot_uniform(rng) + front = Chain( + Conv((k, k), n_channels => c1, pad=(p, p), relu, init=init), + MaxPool((2, 2)), + Conv((k, k), c1 => c2, pad=(p, p), relu, init=init), + MaxPool((2, 2)), + Conv((k, k), c2 => c3, pad=(p, p), relu, init=init), + MaxPool((2 ,2)), + MLUtils.flatten) + d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first + return Chain(front, Dense(d, n_out, init=init)) +end +```` + +**Notes.** + +- There is no final `softmax` here, as this is applied by default in all MLJFLux + classifiers. Customisation of this behaviour is controlled using using the `finaliser` + hyperparameter of the classifier. + +- Instead of calculating the padding `p`, Flux can infer the required padding in each + dimension, which you enable by replacing `pad = (p, p)` with `pad = SamePad()`. + +We now define the MLJ model. + +````@example MNIST +ImageClassifier = @load ImageClassifier +clf = ImageClassifier( + builder=MyConvBuilder(3, 16, 32, 32), + batch_size=50, + epochs=10, + rng=123, +) +```` + +You can add Flux options `optimiser=...` and `loss=...` in the above constructor +call. At present, `loss` must be a Flux-compatible loss, not an MLJ measure. To run on a +GPU, add to the constructor `acceleration=CUDALib()` and omit `rng`. + +For illustration purposes, we won't use all the data here: + +````@example MNIST +train = 1:500 +test = 501:1000 +```` + +Binding the model with data in an MLJ machine: + +````@example MNIST +mach = machine(clf, images, labels); +nothing #hide +```` + +Training for 10 epochs on the first 500 images: + +````@example MNIST +fit!(mach, rows=train, verbosity=2); +nothing #hide +```` + +Inspecting: + +````@example MNIST +report(mach) +```` + +````@example MNIST +chain = fitted_params(mach) +```` + +````@example MNIST +Flux.params(chain)[2] +```` + +Adding 20 more epochs: + +````@example MNIST +clf.epochs = clf.epochs + 20 +fit!(mach, rows=train); +nothing #hide +```` + +Computing an out-of-sample estimate of the loss: + +````@example MNIST +predicted_labels = predict(mach, rows=test); +cross_entropy(predicted_labels, labels[test]) +```` + +Or to fit and predict, in one line: + +````@example MNIST +evaluate!(mach, + resampling=Holdout(fraction_train=0.5), + measure=cross_entropy, + rows=1:1000, + verbosity=0) +```` + +## Wrapping the MLJFlux model with iteration controls + +Any iterative MLJFlux model can be wrapped in *iteration controls*, +as we demonstrate next. For more on MLJ's `IteratedModel` wrapper, +see the [MLJ +documentation](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/). + +The "self-iterating" classifier, called `iterated_clf` below, is for +iterating the image classifier defined above until one of the +following stopping criterion apply: + +- `Patience(3)`: 3 consecutive increases in the loss +- `InvalidValue()`: an out-of-sample loss, or a training loss, is `NaN`, `Inf`, or `-Inf` +- `TimeLimit(t=5/60)`: training time has exceeded 5 minutes + +These checks (and other controls) will be applied every two epochs +(because of the `Step(2)` control). Additionally, training a +machine bound to `iterated_clf` will: + +- save a snapshot of the machine every three control cycles (every six epochs) +- record traces of the out-of-sample loss and training losses for plotting +- record mean value traces of each Flux parameter for plotting + +For a complete list of controls, see [this +table](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided). + +### Wrapping the classifier + +Some helpers + +To extract Flux params from an MLJFlux machine + +````@example MNIST +parameters(mach) = vec.(Flux.params(fitted_params(mach))); +nothing #hide +```` + +To store the traces: + +````@example MNIST +losses = [] +training_losses = [] +parameter_means = Float32[]; +epochs = [] +```` + +To update the traces: + +````@example MNIST +update_loss(loss) = push!(losses, loss) +update_training_loss(losses) = push!(training_losses, losses[end]) +update_means(mach) = append!(parameter_means, mean.(parameters(mach))); +update_epochs(epoch) = push!(epochs, epoch) +```` + +The controls to apply: + +````@example MNIST +save_control = + MLJIteration.skip(Save(joinpath(tempdir(), "mnist.jls")), predicate=3) + +controls=[ + Step(2), + Patience(3), + InvalidValue(), + TimeLimit(5/60), + save_control, + WithLossDo(), + WithLossDo(update_loss), + WithTrainingLossesDo(update_training_loss), + Callback(update_means), + WithIterationsDo(update_epochs), +]; +nothing #hide +```` + +The "self-iterating" classifier: + +````@example MNIST +iterated_clf = IteratedModel( + clf, + controls=controls, + resampling=Holdout(fraction_train=0.7), + measure=log_loss, +) +```` + +### Binding the wrapped model to data: + +````@example MNIST +mach = machine(iterated_clf, images, labels); +nothing #hide +```` + +### Training + +````@example MNIST +fit!(mach, rows=train); +nothing #hide +```` + +### Comparison of the training and out-of-sample losses: + +````@example MNIST +plot( + epochs, + losses, + xlab = "epoch", + ylab = "cross entropy", + label="out-of-sample", +) +plot!(epochs, training_losses, label="training") + +savefig(joinpath(tempdir(), "loss.png")) +```` + +### Evolution of weights + +````@example MNIST +n_epochs = length(losses) +n_parameters = div(length(parameter_means), n_epochs) +parameter_means2 = reshape(copy(parameter_means), n_parameters, n_epochs)' +plot( + epochs, + parameter_means2, + title="Flux parameter mean weights", + xlab = "epoch", +) +```` + +**Note.** The higher the number in the plot legend, the deeper the layer we are +**weight-averaging. + +````@example MNIST +savefig(joinpath(tempdir(), "weights.png")) +```` + +### Retrieving a snapshot for a prediction: + +````julia +mach2 = machine(joinpath(tempdir(), "mnist3.jls")) +predict_mode(mach2, images[501:503]) +```` + +```` +3-element CategoricalArrays.CategoricalArray{Int64,1,UInt32}: + 7 + 9 + 5 +```` + +### Restarting training + +Mutating `iterated_clf.controls` or `clf.epochs` (which is otherwise +ignored) will allow you to restart training from where it left off. + +````@example MNIST +iterated_clf.controls[2] = Patience(4) +fit!(mach, rows=train) + +plot( + epochs, + losses, + xlab = "epoch", + ylab = "cross entropy", + label="out-of-sample", +) +plot!(epochs, training_losses, label="training") +```` + +--- + +*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* + diff --git a/docs/src/extended_examples/MNIST/notebook.unexecuted.ipynb b/docs/src/extended_examples/MNIST/notebook.unexecuted.ipynb new file mode 100644 index 00000000..f2beaabc --- /dev/null +++ b/docs/src/extended_examples/MNIST/notebook.unexecuted.ipynb @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Using MLJ to classifiy the MNIST image dataset" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "const DIR = @__DIR__\n", + "Pkg.activate(DIR)\n", + "Pkg.instantiate()" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Julia version** is assumed to be 1.10.*" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ\n", + "using Flux\n", + "import MLJFlux\n", + "import MLUtils\n", + "import MLJIteration # for `skip`" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "If running on a GPU, you will also need to `import CUDA` and `import cuDNN`." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Plots\n", + "gr(size=(600, 300*(sqrt(5)-1)));" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "## Basic training" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Downloading the MNIST image dataset:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "import MLDatasets: MNIST\n", + "\n", + "ENV[\"DATADEPS_ALWAYS_ACCEPT\"] = true\n", + "images, labels = MNIST(split=:train)[:];" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "In MLJ, integers cannot be used for encoding categorical data, so we\n", + "must force the labels to have the `Multiclass` [scientific\n", + "type](https://juliaai.github.io/ScientificTypes.jl/dev/). For\n", + "more on this, see [Working with Categorical\n", + "Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "labels = coerce(labels, Multiclass);\n", + "images = coerce(images, GrayImage);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Checking scientific types:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "@assert scitype(images) <: AbstractVector{<:Image}\n", + "@assert scitype(labels) <: AbstractVector{<:Finite}" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Looks good." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For general instructions on coercing image data, see [Type coercion\n", + "for image\n", + "data](https://juliaai.github.io/ScientificTypes.jl/dev/#Type-coercion-for-image-data)" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "images[1]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "We start by defining a suitable `Builder` object. This is a recipe\n", + "for building the neural network. Our builder will work for images of\n", + "any (constant) size, whether they be color or black and white (ie,\n", + "single or multi-channel). The architecture always consists of six\n", + "alternating convolution and max-pool layers, and a final dense\n", + "layer; the filter size and the number of channels after each\n", + "convolution layer is customisable." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "import MLJFlux\n", + "struct MyConvBuilder\n", + " filter_size::Int\n", + " channels1::Int\n", + " channels2::Int\n", + " channels3::Int\n", + "end\n", + "\n", + "function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels)\n", + " k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3\n", + " mod(k, 2) == 1 || error(\"`filter_size` must be odd. \")\n", + " p = div(k - 1, 2) # padding to preserve image size\n", + " init = Flux.glorot_uniform(rng)\n", + " front = Chain(\n", + " Conv((k, k), n_channels => c1, pad=(p, p), relu, init=init),\n", + " MaxPool((2, 2)),\n", + " Conv((k, k), c1 => c2, pad=(p, p), relu, init=init),\n", + " MaxPool((2, 2)),\n", + " Conv((k, k), c2 => c3, pad=(p, p), relu, init=init),\n", + " MaxPool((2 ,2)),\n", + " MLUtils.flatten)\n", + " d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first\n", + " return Chain(front, Dense(d, n_out, init=init))\n", + "end" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Notes.**" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- There is no final `softmax` here, as this is applied by default in all MLJFLux\n", + " classifiers. Customisation of this behaviour is controlled using using the `finaliser`\n", + " hyperparameter of the classifier.\n", + "\n", + "- Instead of calculating the padding `p`, Flux can infer the required padding in each\n", + " dimension, which you enable by replacing `pad = (p, p)` with `pad = SamePad()`." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We now define the MLJ model." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "ImageClassifier = @load ImageClassifier\n", + "clf = ImageClassifier(\n", + " builder=MyConvBuilder(3, 16, 32, 32),\n", + " batch_size=50,\n", + " epochs=10,\n", + " rng=123,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "You can add Flux options `optimiser=...` and `loss=...` in the above constructor\n", + "call. At present, `loss` must be a Flux-compatible loss, not an MLJ measure. To run on a\n", + "GPU, add to the constructor `acceleration=CUDALib()` and omit `rng`." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For illustration purposes, we won't use all the data here:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "train = 1:500\n", + "test = 501:1000" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Binding the model with data in an MLJ machine:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(clf, images, labels);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Training for 10 epochs on the first 500 images:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "fit!(mach, rows=train, verbosity=2);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Inspecting:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "report(mach)" + ], + "metadata": {}, + "execution_count": null + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "chain = fitted_params(mach)" + ], + "metadata": {}, + "execution_count": null + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "Flux.params(chain)[2]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Adding 20 more epochs:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "clf.epochs = clf.epochs + 20\n", + "fit!(mach, rows=train);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Computing an out-of-sample estimate of the loss:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "predicted_labels = predict(mach, rows=test);\n", + "cross_entropy(predicted_labels, labels[test])" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Or to fit and predict, in one line:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "evaluate!(mach,\n", + " resampling=Holdout(fraction_train=0.5),\n", + " measure=cross_entropy,\n", + " rows=1:1000,\n", + " verbosity=0)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "## Wrapping the MLJFlux model with iteration controls" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Any iterative MLJFlux model can be wrapped in *iteration controls*,\n", + "as we demonstrate next. For more on MLJ's `IteratedModel` wrapper,\n", + "see the [MLJ\n", + "documentation](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "The \"self-iterating\" classifier, called `iterated_clf` below, is for\n", + "iterating the image classifier defined above until one of the\n", + "following stopping criterion apply:" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- `Patience(3)`: 3 consecutive increases in the loss\n", + "- `InvalidValue()`: an out-of-sample loss, or a training loss, is `NaN`, `Inf`, or `-Inf`\n", + "- `TimeLimit(t=5/60)`: training time has exceeded 5 minutes\n", + "\n", + "These checks (and other controls) will be applied every two epochs\n", + "(because of the `Step(2)` control). Additionally, training a\n", + "machine bound to `iterated_clf` will:\n", + "\n", + "- save a snapshot of the machine every three control cycles (every six epochs)\n", + "- record traces of the out-of-sample loss and training losses for plotting\n", + "- record mean value traces of each Flux parameter for plotting" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For a complete list of controls, see [this\n", + "table](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Wrapping the classifier" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Some helpers" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "To extract Flux params from an MLJFlux machine" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "parameters(mach) = vec.(Flux.params(fitted_params(mach)));" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "To store the traces:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "losses = []\n", + "training_losses = []\n", + "parameter_means = Float32[];\n", + "epochs = []" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "To update the traces:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "update_loss(loss) = push!(losses, loss)\n", + "update_training_loss(losses) = push!(training_losses, losses[end])\n", + "update_means(mach) = append!(parameter_means, mean.(parameters(mach)));\n", + "update_epochs(epoch) = push!(epochs, epoch)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "The controls to apply:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "save_control =\n", + " MLJIteration.skip(Save(joinpath(tempdir(), \"mnist.jls\")), predicate=3)\n", + "\n", + "controls=[\n", + " Step(2),\n", + " Patience(3),\n", + " InvalidValue(),\n", + " TimeLimit(5/60),\n", + " save_control,\n", + " WithLossDo(),\n", + " WithLossDo(update_loss),\n", + " WithTrainingLossesDo(update_training_loss),\n", + " Callback(update_means),\n", + " WithIterationsDo(update_epochs),\n", + "];" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "The \"self-iterating\" classifier:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iterated_clf = IteratedModel(\n", + " clf,\n", + " controls=controls,\n", + " resampling=Holdout(fraction_train=0.7),\n", + " measure=log_loss,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Binding the wrapped model to data:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach = machine(iterated_clf, images, labels);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Training" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "fit!(mach, rows=train);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Comparison of the training and out-of-sample losses:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "plot(\n", + " epochs,\n", + " losses,\n", + " xlab = \"epoch\",\n", + " ylab = \"cross entropy\",\n", + " label=\"out-of-sample\",\n", + ")\n", + "plot!(epochs, training_losses, label=\"training\")\n", + "\n", + "savefig(joinpath(tempdir(), \"loss.png\"))" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Evolution of weights" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "n_epochs = length(losses)\n", + "n_parameters = div(length(parameter_means), n_epochs)\n", + "parameter_means2 = reshape(copy(parameter_means), n_parameters, n_epochs)'\n", + "plot(\n", + " epochs,\n", + " parameter_means2,\n", + " title=\"Flux parameter mean weights\",\n", + " xlab = \"epoch\",\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "**Note.** The higher the number in the plot legend, the deeper the layer we are\n", + "**weight-averaging." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "savefig(joinpath(tempdir(), \"weights.png\"))" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Retrieving a snapshot for a prediction:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "mach2 = machine(joinpath(tempdir(), \"mnist3.jls\"))\n", + "predict_mode(mach2, images[501:503])" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Restarting training" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Mutating `iterated_clf.controls` or `clf.epochs` (which is otherwise\n", + "ignored) will allow you to restart training from where it left off." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "iterated_clf.controls[2] = Patience(4)\n", + "fit!(mach, rows=train)\n", + "\n", + "plot(\n", + " epochs,\n", + " losses,\n", + " xlab = \"epoch\",\n", + " ylab = \"cross entropy\",\n", + " label=\"out-of-sample\",\n", + ")\n", + "plot!(epochs, training_losses, label=\"training\")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } 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= "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" + +[[deps.ZygoteRules]] +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00" +uuid = "700de1a5-db45-46bc-99cf-38207098b444" +version = "0.2.5" + +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.8.0+1" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.52.0+1" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" diff --git a/docs/src/full tutorials/Spam Detection with RNNs/Project.toml b/docs/src/extended_examples/spam_detection/Project.toml similarity index 74% rename from docs/src/full tutorials/Spam Detection with RNNs/Project.toml rename to docs/src/extended_examples/spam_detection/Project.toml index 97bcaa8b..da092aaa 100644 --- a/docs/src/full tutorials/Spam Detection with RNNs/Project.toml +++ b/docs/src/extended_examples/spam_detection/Project.toml @@ -2,9 +2,11 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" Languages = "8ef0a80b-9436-5d2c-a485-80b904378c43" +MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" MLJText = "5e27fcf9-6bac-46ba-8580-b5712f3d6387" -ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" TextAnalysis = "a2db99b7-8b79-58f8-94bf-bbc811eef33d" WordTokenizers = "796a5d58-b03d-544a-977e-18100b691f6e" diff --git a/docs/src/extended_examples/spam_detection/README.md b/docs/src/extended_examples/spam_detection/README.md new file mode 100644 index 00000000..b68a07e7 --- /dev/null +++ b/docs/src/extended_examples/spam_detection/README.md @@ -0,0 +1,15 @@ +# Contents + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + + +# Important + +Scripts or notebooks in this folder cannot be reliably executed without the accompanying +Manifest.toml and Project.toml files. diff --git a/docs/src/extended_examples/spam_detection/generate.jl b/docs/src/extended_examples/spam_detection/generate.jl new file mode 100644 index 00000000..0f122402 --- /dev/null +++ b/docs/src/extended_examples/spam_detection/generate.jl @@ -0,0 +1,4 @@ +# Execute this julia file to generate the notebooks from ../notebook.jl + +joinpath(@__DIR__, "..", "..", "generate.jl") |> include +generate(@__DIR__, execute=true, pluto=false) diff --git a/docs/src/full tutorials/Spam Detection with RNNs/SMS.ipynb b/docs/src/extended_examples/spam_detection/notebook.ipynb similarity index 57% rename from docs/src/full tutorials/Spam Detection with RNNs/SMS.ipynb rename to docs/src/extended_examples/spam_detection/notebook.ipynb index 3d265f1e..eed3ba38 100644 --- a/docs/src/full tutorials/Spam Detection with RNNs/SMS.ipynb +++ b/docs/src/extended_examples/spam_detection/notebook.ipynb @@ -10,10 +10,50 @@ { "cell_type": "markdown", "source": [ - "In this tutorial we use a custom RNN model from Flux with MLJFlux to classify text messages as spam or ham. We will be using the [SMS Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle." + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/spam_detection)." ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "In this demo we use a custom RNN model from Flux with MLJFlux to classify text\n", + "messages as spam or ham. We will be using the [SMS Collection\n", + "Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "**Warning.** This demo includes some non-idiomatic use of MLJ to allow use of the\n", + "Flux.jl `Embedding` layer. It is not recommended for MLJ beginners." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Activating project at `~/GoogleDrive/Julia/MLJ/MLJFlux/docs/src/extended_examples/spam_detection`\n", + "┌ Warning: The project dependencies or compat requirements have changed since the manifest was last resolved.\n", + "│ It is recommended to `Pkg.resolve()` or consider `Pkg.update()` if necessary.\n", + "└ @ Pkg.API /Applications/Julia-1.10.app/Contents/Resources/julia/share/julia/stdlib/v1.10/Pkg/src/API.jl:1807\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": 1 + }, { "cell_type": "markdown", "source": [ @@ -28,14 +68,14 @@ "using MLJ\n", "using MLJFlux\n", "using Flux\n", + "import Optimisers # Flux.jl native optimisers no longer supported\n", "using CSV # Read data\n", "using DataFrames # Read data\n", - "using ScientificTypes # Type coercion\n", "using WordTokenizers # For tokenization\n", "using Languages # For stop words" ], "metadata": {}, - "execution_count": 1 + "execution_count": 2 }, { "cell_type": "markdown", @@ -44,14 +84,23 @@ ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "We assume the [SMS Collection\n", + "Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) has been\n", + "downloaded and is in a file called \"sms.csv\" in the same directory as the this script." + ], + "metadata": {} + }, { "outputs": [], "cell_type": "code", "source": [ - "df = CSV.read(\"./sms.csv\", DataFrame);" + "df = CSV.read(joinpath(@__DIR__, \"sms.csv\"), DataFrame);" ], "metadata": {}, - "execution_count": 2 + "execution_count": 3 }, { "cell_type": "markdown", @@ -63,29 +112,23 @@ { "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌──────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n", - "│ Category │ Message │\n", - "│ String7 │ String │\n", - "│ Textual │ Textual │\n", - "├──────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n", - "│ ham │ Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat... │\n", - "│ ham │ Ok lar... Joking wif u oni... │\n", - "│ spam │ Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's │\n", - "│ ham │ U dun say so early hor... U c already then say... │\n", - "│ ham │ Nah I don't think he goes to usf, he lives around here though │\n", - "└──────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n" - ] + "output_type": "execute_result", + "data": { + "text/plain": "\u001b[1m5×2 DataFrame\u001b[0m\n\u001b[1m Row \u001b[0m│\u001b[1m Category \u001b[0m\u001b[1m Message \u001b[0m\n │\u001b[90m String7 \u001b[0m\u001b[90m String \u001b[0m\n─────┼─────────────────────────────────────────────\n 1 │ ham Go until jurong point, crazy.. A…\n 2 │ ham Ok lar... Joking wif u oni...\n 3 │ spam Free entry in 2 a wkly comp to w…\n 4 │ ham U dun say so early hor... U c al…\n 5 │ ham Nah I don't think he goes to usf…", + "text/html": [ + "
5×2 DataFrame
RowCategoryMessage
String7String
1hamGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...
2hamOk lar... Joking wif u oni...
3spamFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's
4hamU dun say so early hor... U c already then say...
5hamNah I don't think he goes to usf, he lives around here though
" + ] + }, + "metadata": {}, + "execution_count": 4 } ], "cell_type": "code", "source": [ - "first(df, 5) |> pretty" + "first(df, 5)" ], "metadata": {}, - "execution_count": 3 + "execution_count": 4 }, { "cell_type": "markdown", @@ -99,7 +142,8 @@ { "cell_type": "markdown", "source": [ - "- Remove stop words (i.e., words that are not useful for the analysis, like \"the\", \"a\", etc.)" + "- Remove stop words (i.e., words that are not useful for the analysis, like \"the\", \"a\",\n", + " etc.)" ], "metadata": {} }, @@ -118,36 +162,41 @@ "text/plain": "preprocess_text (generic function with 1 method)" }, "metadata": {}, - "execution_count": 4 + "execution_count": 5 } ], "cell_type": "code", "source": [ + "const STOP_WORDS = Languages.stopwords(Languages.English())\n", + "\n", "function preprocess_text(text)\n", - "\t# (1) Splitting texts into words (so later it can be a sequence of vectors)\n", - "\ttokens = WordTokenizers.tokenize(text)\n", + " # (1) Splitting texts into words (so later it can be a sequence of vectors)\n", + " tokens = WordTokenizers.tokenize(text)\n", "\n", - "\t# (2) Stop word removal\n", - "\tstop_words = Languages.stopwords(Languages.English())\n", - "\tfiltered_tokens = filter(token -> !(token in stop_words), tokens)\n", + " # (2) Stop word removal\n", + " filtered_tokens = filter(token -> !(token in STOP_WORDS), tokens)\n", "\n", - "\treturn filtered_tokens\n", + " return filtered_tokens\n", "end" ], "metadata": {}, - "execution_count": 4 + "execution_count": 5 }, { "cell_type": "markdown", "source": [ - "Define the vocabulary to be the set of all words in our training set. We also need a function that would map each word in a given sequence of words into its index in the dictionary (which is equivalent to representing the words as one-hot vectors)." + "Define the vocabulary to be the set of all words in our training set. We also need a\n", + "function that would map each word in a given sequence of words into its index in the\n", + "dictionary (which is equivalent to representing the words as one-hot vectors)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ - "Now after we do this the sequences will all be numerical vectors but they will be of unequal length. Thus, to facilitate batching of data for the deep learning model, we need to decide on a specific maximum length for all sequences and:" + "Now after we do this the sequences will all be numerical vectors but they will be of\n", + "unequal length. Thus, to facilitate batching of data for the deep learning model, we\n", + "need to decide on a specific maximum length for all sequences and:" ], "metadata": {} }, @@ -168,7 +217,8 @@ { "cell_type": "markdown", "source": [ - "Lastly, we must also handle the case that an incoming text sequence may involve words never seen in training by represent all such out-of-vocabulary words with a new token." + "Lastly, we must also handle the case that an incoming text sequence may involve words\n", + "never seen in training by represent all such out-of-vocabulary words with a new token." ], "metadata": {} }, @@ -187,26 +237,26 @@ "text/plain": "encode_and_equalize (generic function with 1 method)" }, "metadata": {}, - "execution_count": 5 + "execution_count": 6 } ], "cell_type": "code", "source": [ "function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val)\n", - "\t# (1) encode using the vocabulary\n", - "\ttext_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq]\n", + " # (1) encode using the vocabulary\n", + " text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq]\n", "\n", - "\t# (2) truncate sequence if > max_length\n", - "\tlength(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length])\n", + " # (2) truncate sequence if > max_length\n", + " length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length])\n", "\n", - "\t# (3) pad with pad_val\n", - "\ttext_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds)))\n", + " # (3) pad with pad_val\n", + " text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds)))\n", "\n", - "\treturn text_seq_inds\n", + " return text_seq_inds\n", "end" ], "metadata": {}, - "execution_count": 5 + "execution_count": 6 }, { "cell_type": "markdown", @@ -223,13 +273,16 @@ "x_data, y_data = unpack(df, ==(:Message), ==(:Category))\n", "y_data = coerce(y_data, Multiclass);\n", "\n", - "(x_train, x_val), (y_train, y_val) = partition((x_data, y_data), 0.8,\n", - "\tmulti = true,\n", - "\tshuffle = true,\n", - "\trng = 42);" + "(x_train, x_val), (y_train, y_val) = partition(\n", + " (x_data, y_data),\n", + " 0.8,\n", + " multi = true,\n", + " shuffle = true,\n", + " rng = 42,\n", + ");" ], "metadata": {}, - "execution_count": 6 + "execution_count": 7 }, { "cell_type": "markdown", @@ -246,7 +299,7 @@ "x_val_processed = [preprocess_text(text) for text in x_val];" ], "metadata": {}, - "execution_count": 7 + "execution_count": 8 }, { "cell_type": "markdown", @@ -270,7 +323,7 @@ "println(x_train_processed[1], \" is \", y_data[1])" ], "metadata": {}, - "execution_count": 8 + "execution_count": 9 }, { "cell_type": "markdown", @@ -287,7 +340,7 @@ "text/plain": "12" }, "metadata": {}, - "execution_count": 9 + "execution_count": 10 } ], "cell_type": "code", @@ -299,7 +352,7 @@ "max_length = 12 # can choose this more smartly if you wish" ], "metadata": {}, - "execution_count": 9 + "execution_count": 10 }, { "cell_type": "markdown", @@ -316,23 +369,23 @@ "text/plain": "5-element Vector{Vector{Int64}}:\n [1, 2, 3, 4, 5, 10404, 10404, 10404, 10404, 10404, 10404, 10404]\n [6, 7, 8, 9, 10, 11, 12, 13, 11, 14, 15, 16]\n [36, 37, 38, 39, 36, 40, 41, 42, 10404, 10404, 10404, 10404]\n [43, 24, 36, 44, 45, 46, 10404, 10404, 10404, 10404, 10404, 10404]\n [43, 47, 48, 49, 50, 51, 52, 53, 54, 55, 44, 45]" }, "metadata": {}, - "execution_count": 10 + "execution_count": 11 } ], "cell_type": "code", "source": [ "x_train_processed_equalized = [\n", - "\tencode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", - "\tseq in x_train_processed\n", - "]\n", + " encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", + " seq in x_train_processed\n", + " ]\n", "x_val_processed_equalized = [\n", - "\tencode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", - "\tseq in x_val_processed\n", - "]\n", - "x_train_processed_equalized[1:5] # all sequences are encoded and of the same length" + " encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", + " seq in x_val_processed\n", + " ]\n", + "x_train_processed_equalized[1:5] # all sequences are encoded and of the same length" ], "metadata": {}, - "execution_count": 10 + "execution_count": 11 }, { "cell_type": "markdown", @@ -349,7 +402,7 @@ "text/plain": "(4458, 12)" }, "metadata": {}, - "execution_count": 11 + "execution_count": 12 } ], "cell_type": "code", @@ -360,7 +413,7 @@ "size(x_train_processed_equalized_fixed)" ], "metadata": {}, - "execution_count": 11 + "execution_count": 12 }, { "cell_type": "markdown", @@ -372,7 +425,8 @@ { "cell_type": "markdown", "source": [ - "For the model, we will use a RNN from Flux. We will average the hidden states corresponding to any sequence then pass that to a dense layer for classification." + "For the model, we will use a RNN from Flux. We will average the hidden states\n", + "corresponding to any sequence then pass that to a dense layer for classification." ], "metadata": {} }, @@ -392,12 +446,13 @@ "(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim]" ], "metadata": {}, - "execution_count": 12 + "execution_count": 13 }, { "cell_type": "markdown", "source": [ - "For compatibility, we will also define a layer that simply casts the input to integers as the embedding layer in Flux expects integets but the MLJFlux model expects floats:" + "For compatibility, we will also define a layer that simply casts the input to integers\n", + "as the embedding layer in Flux expects integers but the MLJFlux model expects floats:" ], "metadata": {} }, @@ -410,12 +465,12 @@ "(m::Intify)(x) = Int.(x)" ], "metadata": {}, - "execution_count": 13 + "execution_count": 14 }, { "cell_type": "markdown", "source": [ - "Here we define out network:" + "Here we define our network:" ], "metadata": {} }, @@ -427,28 +482,31 @@ "text/plain": "GenericBuilder(apply = #15)\n" }, "metadata": {}, - "execution_count": 14 + "execution_count": 15 } ], "cell_type": "code", "source": [ "builder = MLJFlux.@builder begin\n", - "\tChain(\n", - "\t\tIntify(), # Cast input to integer\n", - "\t\tEmbedding(vocab_size + 2 => 300), # Embedding layer\n", - "\t\tRNN(300, 50, tanh), # RNN layer\n", - "\t\tMean(), # Mean pooling layer\n", - "\t\tDense(50, 2) # Classification dense layer\n", - "\t)\n", + " Chain(\n", + " Intify(), # Cast input to integer\n", + " Embedding(vocab_size + 2 => 300), # Embedding layer\n", + " RNN(300, 50, tanh), # RNN layer\n", + " Mean(), # Mean pooling layer\n", + " Dense(50, 2), # Classification dense layer\n", + " )\n", "end" ], "metadata": {}, - "execution_count": 14 + "execution_count": 15 }, { "cell_type": "markdown", "source": [ - "Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to take into account the padding and out-of-vocabulary tokens. Recall that the indices in our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to map them into meaningful dense vectors (of dimensionality 300 here)." + "Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to\n", + "take into account the padding and out-of-vocabulary tokens. Recall that the indices in\n", + "our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to\n", + "map them into meaningful dense vectors (of dimensionality 300 here)." ], "metadata": {} }, @@ -472,24 +530,24 @@ { "output_type": "execute_result", "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = GenericBuilder(\n apply = Main.var\"##500\".var\"#15#16\"()), \n finaliser = NNlib.softmax, \n optimiser = Flux.Optimise.Adam(0.1, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 128, \n lambda = 0.0, \n alpha = 0.0, \n rng = Random._GLOBAL_RNG(), \n optimiser_changes_trigger_retraining = false, \n acceleration = ComputationalResources.CPU1{Nothing}(nothing))" + "text/plain": "NeuralNetworkClassifier(\n builder = GenericBuilder(\n apply = Main.var\"##1022\".var\"#15#16\"()), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.1, (0.9, 0.999), 1.0e-8), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 128, \n lambda = 0.0, \n alpha = 0.0, \n rng = Random.TaskLocalRNG(), \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" }, "metadata": {}, - "execution_count": 15 + "execution_count": 16 } ], "cell_type": "code", "source": [ "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux\n", "clf = NeuralNetworkClassifier(\n", - "\tbuilder = builder,\n", - "\toptimiser = Flux.ADAM(0.1),\n", - "\tbatch_size = 128,\n", - "\tepochs = 10,\n", + " builder = builder,\n", + " optimiser = Optimisers.Adam(0.1),\n", + " batch_size = 128,\n", + " epochs = 10,\n", ")" ], "metadata": {}, - "execution_count": 15 + "execution_count": 16 }, { "cell_type": "markdown", @@ -503,10 +561,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "untrained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …)\n args: \n 1:\tSource @402 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}\n 2:\tSource @501 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}\n" + "text/plain": "untrained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …)\n args: \n 1:\tSource @796 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}\n 2:\tSource @667 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}\n" }, "metadata": {}, - "execution_count": 16 + "execution_count": 17 } ], "cell_type": "code", @@ -515,7 +573,7 @@ "mach = machine(clf, x_train_processed_equalized_fixed, y_train)" ], "metadata": {}, - "execution_count": 16 + "execution_count": 17 }, { "cell_type": "markdown", @@ -531,16 +589,16 @@ "output_type": "stream", "text": [ "[ Info: Training machine(NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …), …).\n", - "\rOptimising neural net: 18%[====> ] ETA: 0:00:12\u001b[K\rOptimising neural net: 27%[======> ] ETA: 0:00:10\u001b[K\rOptimising neural net: 36%[=========> ] ETA: 0:00:09\u001b[K\rOptimising neural net: 45%[===========> ] ETA: 0:00:07\u001b[K\rOptimising neural net: 55%[=============> ] ETA: 0:00:06\u001b[K\rOptimising neural net: 64%[===============> ] ETA: 0:00:05\u001b[K\rOptimising neural net: 73%[==================> ] ETA: 0:00:04\u001b[K\rOptimising neural net: 82%[====================> ] ETA: 0:00:02\u001b[K\rOptimising neural net: 91%[======================> ] ETA: 0:00:01\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:12\u001b[K\n" + "\rOptimising neural net: 18%[====> ] ETA: 0:00:13\u001b[K\rOptimising neural net: 27%[======> ] ETA: 0:00:13\u001b[K\rOptimising neural net: 36%[=========> ] ETA: 0:00:12\u001b[K\rOptimising neural net: 45%[===========> ] ETA: 0:00:11\u001b[K\rOptimising neural net: 55%[=============> ] ETA: 0:00:09\u001b[K\rOptimising neural net: 64%[===============> ] ETA: 0:00:07\u001b[K\rOptimising neural net: 73%[==================> ] ETA: 0:00:05\u001b[K\rOptimising neural net: 82%[====================> ] ETA: 0:00:04\u001b[K\rOptimising neural net: 91%[======================> ] ETA: 0:00:02\u001b[K\rOptimising neural net: 100%[=========================] Time: 0:00:19\u001b[K\n" ] }, { "output_type": "execute_result", "data": { - "text/plain": "trained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …)\n args: \n 1:\tSource @402 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}\n 2:\tSource @501 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}\n" + "text/plain": "trained Machine; caches model-specific representations of data\n model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …)\n args: \n 1:\tSource @796 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}\n 2:\tSource @667 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}}\n" }, "metadata": {}, - "execution_count": 17 + "execution_count": 18 } ], "cell_type": "code", @@ -548,7 +606,7 @@ "fit!(mach)" ], "metadata": {}, - "execution_count": 17 + "execution_count": 18 }, { "cell_type": "markdown", @@ -562,10 +620,10 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.9370418555201171" + "text/plain": "0.9468762240501374" }, "metadata": {}, - "execution_count": 18 + "execution_count": 19 } ], "cell_type": "code", @@ -574,7 +632,7 @@ "balanced_accuracy(ŷ, y_val)" ], "metadata": {}, - "execution_count": 18 + "execution_count": 19 }, { "cell_type": "markdown", @@ -601,7 +659,7 @@ "z = rand(x_val)\n", "z_processed = preprocess_text(z)\n", "z_encoded_equalized =\n", - "\tencode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val)\n", + " encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val)\n", "z_encoded_equalized_fixed = matrixify([z_encoded_equalized])\n", "z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous)\n", "z_pred = predict_mode(mach, z_encoded_equalized_fixed)\n", @@ -609,7 +667,7 @@ "print(\"SMS: `$(z)` and the prediction is `$(z_pred)`\")" ], "metadata": {}, - "execution_count": 19 + "execution_count": 20 }, { "cell_type": "markdown", @@ -627,11 +685,11 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.10.0" + "version": "1.10.3" }, "kernelspec": { "name": "julia-1.10", - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.3", "language": "julia" } }, diff --git a/docs/src/extended_examples/spam_detection/notebook.jl b/docs/src/extended_examples/spam_detection/notebook.jl new file mode 100644 index 00000000..3d712ebf --- /dev/null +++ b/docs/src/extended_examples/spam_detection/notebook.jl @@ -0,0 +1,202 @@ +# # SMS Spam Detection with RNNs + +# This demonstration is available as a Jupyter notebook or julia script +# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/spam_detection). + +# In this demo we use a custom RNN model from Flux with MLJFlux to classify text +# messages as spam or ham. We will be using the [SMS Collection +# Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle. + +# **Warning.** This demo includes some non-idiomatic use of MLJ to allow use of the +# Flux.jl `Embedding` layer. It is not recommended for MLJ beginners. + +using Pkg #!md +Pkg.activate(@__DIR__); #!md +Pkg.instantiate(); #!md + +# ### Basic Imports +using MLJ +using MLJFlux +using Flux +import Optimisers # Flux.jl native optimisers no longer supported +using CSV # Read data +using DataFrames # Read data +using WordTokenizers # For tokenization +using Languages # For stop words + + +# ### Reading Data + +# We assume the [SMS Collection +# Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) has been +# downloaded and is in a file called "sms.csv" in the same directory as the this script. +df = CSV.read(joinpath(@__DIR__, "sms.csv"), DataFrame); + +# Display the first 5 rows with DataFrames +first(df, 5) + + +# ### Text Preprocessing +# Let's define a function that given an SMS message would: +# - Tokenize it (i.e., convert it into a vector of words) + +# - Remove stop words (i.e., words that are not useful for the analysis, like "the", "a", +# etc.) + +# - Return the filtered vector of words + +const STOP_WORDS = Languages.stopwords(Languages.English()) + +function preprocess_text(text) + ## (1) Splitting texts into words (so later it can be a sequence of vectors) + tokens = WordTokenizers.tokenize(text) + + ## (2) Stop word removal + filtered_tokens = filter(token -> !(token in STOP_WORDS), tokens) + + return filtered_tokens +end + +# Define the vocabulary to be the set of all words in our training set. We also need a +# function that would map each word in a given sequence of words into its index in the +# dictionary (which is equivalent to representing the words as one-hot vectors). + +# Now after we do this the sequences will all be numerical vectors but they will be of +# unequal length. Thus, to facilitate batching of data for the deep learning model, we +# need to decide on a specific maximum length for all sequences and: + +# - If a sequence is longer than the maximum length, we need to truncate it + +# - If a sequence is shorter than the maximum length, we need to pad it with a new token + +# Lastly, we must also handle the case that an incoming text sequence may involve words +# never seen in training by represent all such out-of-vocabulary words with a new token. + +# We will define a function that would do this for us. + +function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val) + ## (1) encode using the vocabulary + text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq] + + ## (2) truncate sequence if > max_length + length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length]) + + ## (3) pad with pad_val + text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds))) + + return text_seq_inds +end + +# ### Preparing Data +# Splitting the data +x_data, y_data = unpack(df, ==(:Message), ==(:Category)) +y_data = coerce(y_data, Multiclass); + +(x_train, x_val), (y_train, y_val) = partition( + (x_data, y_data), + 0.8, + multi = true, + shuffle = true, + rng = 42, +); + +# Now let's process the training and validation sets: +x_train_processed = [preprocess_text(text) for text in x_train] +x_val_processed = [preprocess_text(text) for text in x_val]; + +# sanity check +println(x_train_processed[1], " is ", y_data[1]) + +# Define the vocabulary from the training data +vocab = unique(vcat(x_train_processed...)) +vocab_dict = Dict(word => idx for (idx, word) in enumerate(vocab)) +vocab_size = length(vocab) +pad_val, oov_val = vocab_size + 1, vocab_size + 2 +max_length = 12 # can choose this more smartly if you wish + +# Encode and equalize training and validation data: +x_train_processed_equalized = [ + encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for + seq in x_train_processed + ] +x_val_processed_equalized = [ + encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for + seq in x_val_processed + ] +x_train_processed_equalized[1:5] # all sequences are encoded and of the same length + + +# Convert both structures into matrix form: +matrixify(v) = reduce(hcat, v)' +x_train_processed_equalized_fixed = matrixify(x_train_processed_equalized) +x_val_processed_equalized_fixed = matrixify(x_val_processed_equalized) +size(x_train_processed_equalized_fixed) + +# ### Instantiate Model + +# For the model, we will use a RNN from Flux. We will average the hidden states +# corresponding to any sequence then pass that to a dense layer for classification. + +# For this, we need to define a custom Flux layer to perform the averaging operation: + +struct Mean end +Flux.@layer Mean +(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim] + +# For compatibility, we will also define a layer that simply casts the input to integers +# as the embedding layer in Flux expects integers but the MLJFlux model expects floats: +struct Intify end +Flux.@layer Intify +(m::Intify)(x) = Int.(x) + +# Here we define our network: +builder = MLJFlux.@builder begin + Chain( + Intify(), # Cast input to integer + Embedding(vocab_size + 2 => 300), # Embedding layer + RNN(300, 50, tanh), # RNN layer + Mean(), # Mean pooling layer + Dense(50, 2), # Classification dense layer + ) +end + +# Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to +# take into account the padding and out-of-vocabulary tokens. Recall that the indices in +# our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to +# map them into meaningful dense vectors (of dimensionality 300 here). + +# 1. Load and instantiate model +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux +clf = NeuralNetworkClassifier( + builder = builder, + optimiser = Optimisers.Adam(0.1), + batch_size = 128, + epochs = 10, +) + +# 2. Wrap it in a machine +x_train_processed_equalized_fixed = coerce(x_train_processed_equalized_fixed, Continuous) +mach = machine(clf, x_train_processed_equalized_fixed, y_train) + + +# ## Train the Model +fit!(mach) + +# ## Evaluate the Model +ŷ = predict_mode(mach, x_val_processed_equalized_fixed) +balanced_accuracy(ŷ, y_val) + +# Acceptable performance. Let's see some live examples: + +using Random: Random; +Random.seed!(99); + +z = rand(x_val) +z_processed = preprocess_text(z) +z_encoded_equalized = + encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val) +z_encoded_equalized_fixed = matrixify([z_encoded_equalized]) +z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous) +z_pred = predict_mode(mach, z_encoded_equalized_fixed) + +print("SMS: `$(z)` and the prediction is `$(z_pred)`") diff --git a/docs/src/extended_examples/spam_detection/notebook.md b/docs/src/extended_examples/spam_detection/notebook.md new file mode 100644 index 00000000..12cd2a7e --- /dev/null +++ b/docs/src/extended_examples/spam_detection/notebook.md @@ -0,0 +1,259 @@ +```@meta +EditURL = "notebook.jl" +``` + +# SMS Spam Detection with RNNs + +This demonstration is available as a Jupyter notebook or julia script +[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/spam_detection). + +In this demo we use a custom RNN model from Flux with MLJFlux to classify text +messages as spam or ham. We will be using the [SMS Collection +Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle. + +**Warning.** This demo includes some non-idiomatic use of MLJ to allow use of the +Flux.jl `Embedding` layer. It is not recommended for MLJ beginners. + +### Basic Imports + +````@example spam_detection +using MLJ +using MLJFlux +using Flux +import Optimisers # Flux.jl native optimisers no longer supported +using CSV # Read data +using DataFrames # Read data +using WordTokenizers # For tokenization +using Languages # For stop words +```` + +### Reading Data + +We assume the [SMS Collection +Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) has been +downloaded and is in a file called "sms.csv" in the same directory as the this script. + +````@example spam_detection +df = CSV.read(joinpath(@__DIR__, "sms.csv"), DataFrame); +nothing #hide +```` + +Display the first 5 rows with DataFrames + +````@example spam_detection +first(df, 5) +```` + +### Text Preprocessing +Let's define a function that given an SMS message would: +- Tokenize it (i.e., convert it into a vector of words) + +- Remove stop words (i.e., words that are not useful for the analysis, like "the", "a", + etc.) + +- Return the filtered vector of words + +````@example spam_detection +const STOP_WORDS = Languages.stopwords(Languages.English()) + +function preprocess_text(text) + # (1) Splitting texts into words (so later it can be a sequence of vectors) + tokens = WordTokenizers.tokenize(text) + + # (2) Stop word removal + filtered_tokens = filter(token -> !(token in STOP_WORDS), tokens) + + return filtered_tokens +end +```` + +Define the vocabulary to be the set of all words in our training set. We also need a +function that would map each word in a given sequence of words into its index in the +dictionary (which is equivalent to representing the words as one-hot vectors). + +Now after we do this the sequences will all be numerical vectors but they will be of +unequal length. Thus, to facilitate batching of data for the deep learning model, we +need to decide on a specific maximum length for all sequences and: + +- If a sequence is longer than the maximum length, we need to truncate it + +- If a sequence is shorter than the maximum length, we need to pad it with a new token + +Lastly, we must also handle the case that an incoming text sequence may involve words +never seen in training by represent all such out-of-vocabulary words with a new token. + +We will define a function that would do this for us. + +````@example spam_detection +function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val) + # (1) encode using the vocabulary + text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq] + + # (2) truncate sequence if > max_length + length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length]) + + # (3) pad with pad_val + text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds))) + + return text_seq_inds +end +```` + +### Preparing Data +Splitting the data + +````@example spam_detection +x_data, y_data = unpack(df, ==(:Message), ==(:Category)) +y_data = coerce(y_data, Multiclass); + +(x_train, x_val), (y_train, y_val) = partition( + (x_data, y_data), + 0.8, + multi = true, + shuffle = true, + rng = 42, +); +nothing #hide +```` + +Now let's process the training and validation sets: + +````@example spam_detection +x_train_processed = [preprocess_text(text) for text in x_train] +x_val_processed = [preprocess_text(text) for text in x_val]; +nothing #hide +```` + +sanity check + +````@example spam_detection +println(x_train_processed[1], " is ", y_data[1]) +```` + +Define the vocabulary from the training data + +````@example spam_detection +vocab = unique(vcat(x_train_processed...)) +vocab_dict = Dict(word => idx for (idx, word) in enumerate(vocab)) +vocab_size = length(vocab) +pad_val, oov_val = vocab_size + 1, vocab_size + 2 +max_length = 12 # can choose this more smartly if you wish +```` + +Encode and equalize training and validation data: + +````@example spam_detection +x_train_processed_equalized = [ + encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for + seq in x_train_processed + ] +x_val_processed_equalized = [ + encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for + seq in x_val_processed + ] +x_train_processed_equalized[1:5] # all sequences are encoded and of the same length +```` + +Convert both structures into matrix form: + +````@example spam_detection +matrixify(v) = reduce(hcat, v)' +x_train_processed_equalized_fixed = matrixify(x_train_processed_equalized) +x_val_processed_equalized_fixed = matrixify(x_val_processed_equalized) +size(x_train_processed_equalized_fixed) +```` + +### Instantiate Model + +For the model, we will use a RNN from Flux. We will average the hidden states +corresponding to any sequence then pass that to a dense layer for classification. + +For this, we need to define a custom Flux layer to perform the averaging operation: + +````@example spam_detection +struct Mean end +Flux.@layer Mean +(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim] +```` + +For compatibility, we will also define a layer that simply casts the input to integers +as the embedding layer in Flux expects integers but the MLJFlux model expects floats: + +````@example spam_detection +struct Intify end +Flux.@layer Intify +(m::Intify)(x) = Int.(x) +```` + +Here we define our network: + +````@example spam_detection +builder = MLJFlux.@builder begin + Chain( + Intify(), # Cast input to integer + Embedding(vocab_size + 2 => 300), # Embedding layer + RNN(300, 50, tanh), # RNN layer + Mean(), # Mean pooling layer + Dense(50, 2), # Classification dense layer + ) +end +```` + +Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to +take into account the padding and out-of-vocabulary tokens. Recall that the indices in +our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to +map them into meaningful dense vectors (of dimensionality 300 here). + +1. Load and instantiate model + +````@example spam_detection +NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux +clf = NeuralNetworkClassifier( + builder = builder, + optimiser = Optimisers.Adam(0.1), + batch_size = 128, + epochs = 10, +) +```` + +2. Wrap it in a machine + +````@example spam_detection +x_train_processed_equalized_fixed = coerce(x_train_processed_equalized_fixed, Continuous) +mach = machine(clf, x_train_processed_equalized_fixed, y_train) +```` + +## Train the Model + +````@example spam_detection +fit!(mach) +```` + +## Evaluate the Model + +````@example spam_detection +ŷ = predict_mode(mach, x_val_processed_equalized_fixed) +balanced_accuracy(ŷ, y_val) +```` + +Acceptable performance. Let's see some live examples: + +````@example spam_detection +using Random: Random; +Random.seed!(99); + +z = rand(x_val) +z_processed = preprocess_text(z) +z_encoded_equalized = + encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val) +z_encoded_equalized_fixed = matrixify([z_encoded_equalized]) +z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous) +z_pred = predict_mode(mach, z_encoded_equalized_fixed) + +print("SMS: `$(z)` and the prediction is `$(z_pred)`") +```` + +--- + +*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* + diff --git a/docs/src/extended_examples/spam_detection/notebook.unexecuted.ipynb b/docs/src/extended_examples/spam_detection/notebook.unexecuted.ipynb new file mode 100644 index 00000000..cd5758bf --- /dev/null +++ b/docs/src/extended_examples/spam_detection/notebook.unexecuted.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# SMS Spam Detection with RNNs" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This demonstration is available as a Jupyter notebook or julia script\n", + "[here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/spam_detection)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this demo we use a custom RNN model from Flux with MLJFlux to classify text\n", + "messages as spam or ham. We will be using the [SMS Collection\n", + "Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "**Warning.** This demo includes some non-idiomatic use of MLJ to allow use of the\n", + "Flux.jl `Embedding` layer. It is not recommended for MLJ beginners." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Pkg\n", + "Pkg.activate(@__DIR__);\n", + "Pkg.instantiate();" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Imports" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using MLJ\n", + "using MLJFlux\n", + "using Flux\n", + "import Optimisers # Flux.jl native optimisers no longer supported\n", + "using CSV # Read data\n", + "using DataFrames # Read data\n", + "using WordTokenizers # For tokenization\n", + "using Languages # For stop words" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Reading Data" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We assume the [SMS Collection\n", + "Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) has been\n", + "downloaded and is in a file called \"sms.csv\" in the same directory as the this script." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "df = CSV.read(joinpath(@__DIR__, \"sms.csv\"), DataFrame);" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Display the first 5 rows with DataFrames" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "first(df, 5)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Text Preprocessing\n", + "Let's define a function that given an SMS message would:\n", + "- Tokenize it (i.e., convert it into a vector of words)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- Remove stop words (i.e., words that are not useful for the analysis, like \"the\", \"a\",\n", + " etc.)" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- Return the filtered vector of words" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "const STOP_WORDS = Languages.stopwords(Languages.English())\n", + "\n", + "function preprocess_text(text)\n", + " # (1) Splitting texts into words (so later it can be a sequence of vectors)\n", + " tokens = WordTokenizers.tokenize(text)\n", + "\n", + " # (2) Stop word removal\n", + " filtered_tokens = filter(token -> !(token in STOP_WORDS), tokens)\n", + "\n", + " return filtered_tokens\n", + "end" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Define the vocabulary to be the set of all words in our training set. We also need a\n", + "function that would map each word in a given sequence of words into its index in the\n", + "dictionary (which is equivalent to representing the words as one-hot vectors)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Now after we do this the sequences will all be numerical vectors but they will be of\n", + "unequal length. Thus, to facilitate batching of data for the deep learning model, we\n", + "need to decide on a specific maximum length for all sequences and:" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- If a sequence is longer than the maximum length, we need to truncate it" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "- If a sequence is shorter than the maximum length, we need to pad it with a new token" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Lastly, we must also handle the case that an incoming text sequence may involve words\n", + "never seen in training by represent all such out-of-vocabulary words with a new token." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We will define a function that would do this for us." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val)\n", + " # (1) encode using the vocabulary\n", + " text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq]\n", + "\n", + " # (2) truncate sequence if > max_length\n", + " length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length])\n", + "\n", + " # (3) pad with pad_val\n", + " text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds)))\n", + "\n", + " return text_seq_inds\n", + "end" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Preparing Data\n", + "Splitting the data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "x_data, y_data = unpack(df, ==(:Message), ==(:Category))\n", + "y_data = coerce(y_data, Multiclass);\n", + "\n", + "(x_train, x_val), (y_train, y_val) = partition(\n", + " (x_data, y_data),\n", + " 0.8,\n", + " multi = true,\n", + " shuffle = true,\n", + " rng = 42,\n", + ");" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now let's process the training and validation sets:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "x_train_processed = [preprocess_text(text) for text in x_train]\n", + "x_val_processed = [preprocess_text(text) for text in x_val];" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "sanity check" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "println(x_train_processed[1], \" is \", y_data[1])" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Define the vocabulary from the training data" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "vocab = unique(vcat(x_train_processed...))\n", + "vocab_dict = Dict(word => idx for (idx, word) in enumerate(vocab))\n", + "vocab_size = length(vocab)\n", + "pad_val, oov_val = vocab_size + 1, vocab_size + 2\n", + "max_length = 12 # can choose this more smartly if you wish" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Encode and equalize training and validation data:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "x_train_processed_equalized = [\n", + " encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", + " seq in x_train_processed\n", + " ]\n", + "x_val_processed_equalized = [\n", + " encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for\n", + " seq in x_val_processed\n", + " ]\n", + "x_train_processed_equalized[1:5] # all sequences are encoded and of the same length" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Convert both structures into matrix form:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "matrixify(v) = reduce(hcat, v)'\n", + "x_train_processed_equalized_fixed = matrixify(x_train_processed_equalized)\n", + "x_val_processed_equalized_fixed = matrixify(x_val_processed_equalized)\n", + "size(x_train_processed_equalized_fixed)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "### Instantiate Model" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For the model, we will use a RNN from Flux. We will average the hidden states\n", + "corresponding to any sequence then pass that to a dense layer for classification." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For this, we need to define a custom Flux layer to perform the averaging operation:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "struct Mean end\n", + "Flux.@layer Mean\n", + "(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim]" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "For compatibility, we will also define a layer that simply casts the input to integers\n", + "as the embedding layer in Flux expects integers but the MLJFlux model expects floats:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "struct Intify end\n", + "Flux.@layer Intify\n", + "(m::Intify)(x) = Int.(x)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Here we define our network:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "builder = MLJFlux.@builder begin\n", + " Chain(\n", + " Intify(), # Cast input to integer\n", + " Embedding(vocab_size + 2 => 300), # Embedding layer\n", + " RNN(300, 50, tanh), # RNN layer\n", + " Mean(), # Mean pooling layer\n", + " Dense(50, 2), # Classification dense layer\n", + " )\n", + "end" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to\n", + "take into account the padding and out-of-vocabulary tokens. Recall that the indices in\n", + "our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to\n", + "map them into meaningful dense vectors (of dimensionality 300 here)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "1. Load and instantiate model" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux\n", + "clf = NeuralNetworkClassifier(\n", + " builder = builder,\n", + " optimiser = Optimisers.Adam(0.1),\n", + " batch_size = 128,\n", + " epochs = 10,\n", + ")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "2. Wrap it in a machine" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "x_train_processed_equalized_fixed = coerce(x_train_processed_equalized_fixed, Continuous)\n", + "mach = machine(clf, x_train_processed_equalized_fixed, y_train)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "## Train the Model" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "fit!(mach)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "## Evaluate the Model" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "ŷ = predict_mode(mach, x_val_processed_equalized_fixed)\n", + "balanced_accuracy(ŷ, y_val)" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Acceptable performance. Let's see some live examples:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Random: Random;\n", + "Random.seed!(99);\n", + "\n", + "z = rand(x_val)\n", + "z_processed = preprocess_text(z)\n", + "z_encoded_equalized =\n", + " encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val)\n", + "z_encoded_equalized_fixed = matrixify([z_encoded_equalized])\n", + "z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous)\n", + "z_pred = predict_mode(mach, z_encoded_equalized_fixed)\n", + "\n", + "print(\"SMS: `$(z)` and the prediction is `$(z_pred)`\")" + ], + "metadata": {}, + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.3" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.3", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/docs/src/full tutorials/Spam Detection with RNNs/sms.csv b/docs/src/extended_examples/spam_detection/sms.csv similarity index 100% rename from docs/src/full tutorials/Spam Detection with RNNs/sms.csv rename to docs/src/extended_examples/spam_detection/sms.csv diff --git a/docs/src/full tutorials/MNIST.md b/docs/src/full tutorials/MNIST.md deleted file mode 100644 index 2183f547..00000000 --- a/docs/src/full tutorials/MNIST.md +++ /dev/null @@ -1,100 +0,0 @@ -## Image Classification Example -An expanded version of this example, with early stopping and -snapshots, is available [here](/examples/mnist). - -We define a builder that builds a chain with six alternating -convolution and max-pool layers, and a final dense layer, which we -apply to the MNIST image dataset. - -First we define a generic builder (working for any image size, color -or gray): - -```julia -using MLJ -using Flux -using MLDatasets - -# helper function -function flatten(x::AbstractArray) - return reshape(x, :, size(x)[end]) -end - -import MLJFlux -mutable struct MyConvBuilder - filter_size::Int - channels1::Int - channels2::Int - channels3::Int -end - -function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels) - - k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3 - - mod(k, 2) == 1 || error("`filter_size` must be odd. ") - - # padding to preserve image size on convolution: - p = div(k - 1, 2) - - front = Chain( - Conv((k, k), n_channels => c1, pad=(p, p), relu), - MaxPool((2, 2)), - Conv((k, k), c1 => c2, pad=(p, p), relu), - MaxPool((2, 2)), - Conv((k, k), c2 => c3, pad=(p, p), relu), - MaxPool((2 ,2)), - flatten, - ) - d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first - return Chain(front, Dense(d, n_out)) -end -``` -Next, we load some of the MNIST data and check scientific types -conform to those is the table above: - -```julia -N = 500 -Xraw, yraw = MNIST(split=:train)[:]; -Xraw = Xraw[:,:,1:N]; -yraw = yraw[1:N]; - -scitype(Xraw) -``` -```julia -scitype(yraw) -``` - -Inputs should have element scitype `GrayImage`: - -```julia -X = coerce(Xraw, GrayImage); -``` - -For classifiers, target must have element scitype `<: Finite`: - -```julia -y = coerce(yraw, Multiclass); -``` - -Instantiating an image classifier model: - -```julia -ImageClassifier = @load ImageClassifier -clf = ImageClassifier( - builder=MyConvBuilder(3, 16, 32, 32), - epochs=10, - loss=Flux.crossentropy, - ) -``` - -And evaluating the accuracy of the model on a 30% holdout set: - -```julia -mach = machine(clf, X, y) - -evaluate!( - mach, - resampling=Holdout(rng=123, fraction_train=0.7), - measure=misclassification_rate, - ) -``` diff --git a/docs/src/full tutorials/Spam Detection with RNNs/SMS.jl b/docs/src/full tutorials/Spam Detection with RNNs/SMS.jl deleted file mode 100644 index 4f4bd8dd..00000000 --- a/docs/src/full tutorials/Spam Detection with RNNs/SMS.jl +++ /dev/null @@ -1,180 +0,0 @@ -# # SMS Spam Detection with RNNs - -# In this tutorial we use a custom RNN model from Flux with MLJFlux to classify text messages as spam or ham. We will be using the [SMS Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle. - -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src - -# ### Basic Imports -using MLJ -using MLJFlux -using Flux -using CSV # Read data -using DataFrames # Read data -using ScientificTypes # Type coercion -using WordTokenizers # For tokenization -using Languages # For stop words - - -# ### Reading Data -df = CSV.read("./sms.csv", DataFrame); - -# Display the first 5 rows with DataFrames -first(df, 5) |> pretty - - -# ### Text Preprocessing -# Let's define a function that given an SMS message would: -# - Tokenize it (i.e., convert it into a vector of words) - -# - Remove stop words (i.e., words that are not useful for the analysis, like "the", "a", etc.) - -# - Return the filtered vector of words - -function preprocess_text(text) - ## (1) Splitting texts into words (so later it can be a sequence of vectors) - tokens = WordTokenizers.tokenize(text) - - ## (2) Stop word removal - stop_words = Languages.stopwords(Languages.English()) - filtered_tokens = filter(token -> !(token in stop_words), tokens) - - return filtered_tokens -end - -# Define the vocabulary to be the set of all words in our training set. We also need a function that would map each word in a given sequence of words into its index in the dictionary (which is equivalent to representing the words as one-hot vectors). - -# Now after we do this the sequences will all be numerical vectors but they will be of unequal length. Thus, to facilitate batching of data for the deep learning model, we need to decide on a specific maximum length for all sequences and: - -# - If a sequence is longer than the maximum length, we need to truncate it - -# - If a sequence is shorter than the maximum length, we need to pad it with a new token - -# Lastly, we must also handle the case that an incoming text sequence may involve words never seen in training by represent all such out-of-vocabulary words with a new token. - -# We will define a function that would do this for us. - -function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val) - ## (1) encode using the vocabulary - text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq] - - ## (2) truncate sequence if > max_length - length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length]) - - ## (3) pad with pad_val - text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds))) - - return text_seq_inds -end - -# ### Preparing Data -# Splitting the data -x_data, y_data = unpack(df, ==(:Message), ==(:Category)) -y_data = coerce(y_data, Multiclass); - -(x_train, x_val), (y_train, y_val) = partition((x_data, y_data), 0.8, - multi = true, - shuffle = true, - rng = 42); - -# Now let's process the training and validation sets: -x_train_processed = [preprocess_text(text) for text in x_train] -x_val_processed = [preprocess_text(text) for text in x_val]; - -# sanity check -println(x_train_processed[1], " is ", y_data[1]) - -# Define the vocabulary from the training data -vocab = unique(vcat(x_train_processed...)) -vocab_dict = Dict(word => idx for (idx, word) in enumerate(vocab)) -vocab_size = length(vocab) -pad_val, oov_val = vocab_size + 1, vocab_size + 2 -max_length = 12 # can choose this more smartly if you wish - -# Encode and equalize training and validation data: -x_train_processed_equalized = [ - encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for - seq in x_train_processed -] -x_val_processed_equalized = [ - encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for - seq in x_val_processed -] -x_train_processed_equalized[1:5] # all sequences are encoded and of the same length - - -# Convert both structures into matrix form: -matrixify(v) = reduce(hcat, v)' -x_train_processed_equalized_fixed = matrixify(x_train_processed_equalized) -x_val_processed_equalized_fixed = matrixify(x_val_processed_equalized) -size(x_train_processed_equalized_fixed) - -# ### Instantiate Model - -# For the model, we will use a RNN from Flux. We will average the hidden states corresponding to any sequence then pass that to a dense layer for classification. - -# For this, we need to define a custom Flux layer to perform the averaging operation: - -struct Mean end -Flux.@layer Mean -(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim] - -# For compatibility, we will also define a layer that simply casts the input to integers as the embedding layer in Flux expects integers but the MLJFlux model expects floats: -struct Intify end -Flux.@layer Intify -(m::Intify)(x) = Int.(x) - -# Here we define our network: -builder = MLJFlux.@builder begin - Chain( - Intify(), # Cast input to integer - Embedding(vocab_size + 2 => 300), # Embedding layer - RNN(300, 50, tanh), # RNN layer - Mean(), # Mean pooling layer - Dense(50, 2) # Classification dense layer - ) -end - -# Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to take into account the padding and out-of-vocabulary tokens. Recall that the indices in our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to map them into meaningful dense vectors (of dimensionality 300 here). - -# 1. Load and instantiate model -NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux -clf = NeuralNetworkClassifier( - builder = builder, - optimiser = Flux.ADAM(0.1), - batch_size = 128, - epochs = 10, -) - -# 2. Wrap it in a machine -x_train_processed_equalized_fixed = coerce(x_train_processed_equalized_fixed, Continuous) -mach = machine(clf, x_train_processed_equalized_fixed, y_train) - - -# ## Train the Model -fit!(mach) - -# ## Evaluate the Model -ŷ = predict_mode(mach, x_val_processed_equalized_fixed) -balanced_accuracy(ŷ, y_val) - -# Acceptable performance. Let's see some live examples: - -using Random: Random; -Random.seed!(99); - -z = rand(x_val) -z_processed = preprocess_text(z) -z_encoded_equalized = - encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val) -z_encoded_equalized_fixed = matrixify([z_encoded_equalized]) -z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous) -z_pred = predict_mode(mach, z_encoded_equalized_fixed) - -print("SMS: `$(z)` and the prediction is `$(z_pred)`") - - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute = true) #src -Literate.notebook(@__FILE__, @__DIR__, execute = true) #src diff --git a/docs/src/full tutorials/Spam Detection with RNNs/SMS.md b/docs/src/full tutorials/Spam Detection with RNNs/SMS.md deleted file mode 100644 index 99bc4a15..00000000 --- a/docs/src/full tutorials/Spam Detection with RNNs/SMS.md +++ /dev/null @@ -1,321 +0,0 @@ -```@meta -EditURL = "SMS.jl" -``` - -# SMS Spam Detection with RNNs - -In this tutorial we use a custom RNN model from Flux with MLJFlux to classify text messages as spam or ham. We will be using the [SMS Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) from Kaggle. - -### Basic Imports - -````julia -using MLJ -using MLJFlux -using Flux -using CSV # Read data -using DataFrames # Read data -using ScientificTypes # Type coercion -using WordTokenizers # For tokenization -using Languages # For stop words -```` - -### Reading Data - -````julia -df = CSV.read("./sms.csv", DataFrame); -```` - -Display the first 5 rows with DataFrames - -````julia -first(df, 5) |> pretty -```` - -```` -┌──────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ -│ Category │ Message │ -│ String7 │ String │ -│ Textual │ Textual │ -├──────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ ham │ Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat... │ -│ ham │ Ok lar... Joking wif u oni... │ -│ spam │ Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's │ -│ ham │ U dun say so early hor... U c already then say... │ -│ ham │ Nah I don't think he goes to usf, he lives around here though │ -└──────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ - -```` - -### Text Preprocessing -Let's define a function that given an SMS message would: -- Tokenize it (i.e., convert it into a vector of words) - -- Remove stop words (i.e., words that are not useful for the analysis, like "the", "a", etc.) - -- Return the filtered vector of words - -````julia -function preprocess_text(text) - # (1) Splitting texts into words (so later it can be a sequence of vectors) - tokens = WordTokenizers.tokenize(text) - - # (2) Stop word removal - stop_words = Languages.stopwords(Languages.English()) - filtered_tokens = filter(token -> !(token in stop_words), tokens) - - return filtered_tokens -end -```` - -```` -preprocess_text (generic function with 1 method) -```` - -Define the vocabulary to be the set of all words in our training set. We also need a function that would map each word in a given sequence of words into its index in the dictionary (which is equivalent to representing the words as one-hot vectors). - -Now after we do this the sequences will all be numerical vectors but they will be of unequal length. Thus, to facilitate batching of data for the deep learning model, we need to decide on a specific maximum length for all sequences and: - -- If a sequence is longer than the maximum length, we need to truncate it - -- If a sequence is shorter than the maximum length, we need to pad it with a new token - -Lastly, we must also handle the case that an incoming text sequence may involve words never seen in training by represent all such out-of-vocabulary words with a new token. - -We will define a function that would do this for us. - -````julia -function encode_and_equalize(text_seq, vocab_dict, max_length, pad_val, oov_val) - # (1) encode using the vocabulary - text_seq_inds = [get(vocab_dict, word, oov_val) for word in text_seq] - - # (2) truncate sequence if > max_length - length(text_seq_inds) > max_length && (text_seq_inds = text_seq_inds[1:max_length]) - - # (3) pad with pad_val - text_seq_inds = vcat(text_seq_inds, fill(pad_val, max_length - length(text_seq_inds))) - - return text_seq_inds -end -```` - -```` -encode_and_equalize (generic function with 1 method) -```` - -### Preparing Data -Splitting the data - -````julia -x_data, y_data = unpack(df, ==(:Message), ==(:Category)) -y_data = coerce(y_data, Multiclass); - -(x_train, x_val), (y_train, y_val) = partition((x_data, y_data), 0.8, - multi = true, - shuffle = true, - rng = 42); -```` - -Now let's process the training and validation sets: - -````julia -x_train_processed = [preprocess_text(text) for text in x_train] -x_val_processed = [preprocess_text(text) for text in x_val]; -```` - -sanity check - -````julia -println(x_train_processed[1], " is ", y_data[1]) -```` - -```` -["Que", "pases", "un", "buen", "tiempo"] is ham - -```` - -Define the vocabulary from the training data - -````julia -vocab = unique(vcat(x_train_processed...)) -vocab_dict = Dict(word => idx for (idx, word) in enumerate(vocab)) -vocab_size = length(vocab) -pad_val, oov_val = vocab_size + 1, vocab_size + 2 -max_length = 12 # can choose this more smartly if you wish -```` - -```` -12 -```` - -Encode and equalize training and validation data: - -````julia -x_train_processed_equalized = [ - encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for - seq in x_train_processed -] -x_val_processed_equalized = [ - encode_and_equalize(seq, vocab_dict, max_length, pad_val, oov_val) for - seq in x_val_processed -] -x_train_processed_equalized[1:5] # all sequences are encoded and of the same length -```` - -```` -5-element Vector{Vector{Int64}}: - [1, 2, 3, 4, 5, 10404, 10404, 10404, 10404, 10404, 10404, 10404] - [6, 7, 8, 9, 10, 11, 12, 13, 11, 14, 15, 16] - [36, 37, 38, 39, 36, 40, 41, 42, 10404, 10404, 10404, 10404] - [43, 24, 36, 44, 45, 46, 10404, 10404, 10404, 10404, 10404, 10404] - [43, 47, 48, 49, 50, 51, 52, 53, 54, 55, 44, 45] -```` - -Convert both structures into matrix form: - -````julia -matrixify(v) = reduce(hcat, v)' -x_train_processed_equalized_fixed = matrixify(x_train_processed_equalized) -x_val_processed_equalized_fixed = matrixify(x_val_processed_equalized) -size(x_train_processed_equalized_fixed) -```` - -```` -(4458, 12) -```` - -### Instantiate Model - -For the model, we will use a RNN from Flux. We will average the hidden states corresponding to any sequence then pass that to a dense layer for classification. - -For this, we need to define a custom Flux layer to perform the averaging operation: - -````julia -struct Mean end -Flux.@layer Mean -(m::Mean)(x) = mean(x, dims = 2)[:, 1, :] # [batch_size, seq_len, hidden_dim] => [batch_size, 1, hidden_dim]=> [batch_size, hidden_dim] -```` - -For compatibility, we will also define a layer that simply casts the input to integers as the embedding layer in Flux expects integets but the MLJFlux model expects floats: - -````julia -struct Intify end -Flux.@layer Intify -(m::Intify)(x) = Int.(x) -```` - -Here we define out network: - -````julia -builder = MLJFlux.@builder begin - Chain( - Intify(), # Cast input to integer - Embedding(vocab_size + 2 => 300), # Embedding layer - RNN(300, 50, tanh), # RNN layer - Mean(), # Mean pooling layer - Dense(50, 2) # Classification dense layer - ) -end -```` - -```` -GenericBuilder(apply = #15) - -```` - -Notice that we used an embedding layer with input dimensionality `vocab_size + 2` to take into account the padding and out-of-vocabulary tokens. Recall that the indices in our input correspond to one-hot-vectors and the embedding layer's purpose is to learn to map them into meaningful dense vectors (of dimensionality 300 here). - -1. Load and instantiate model - -````julia -NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = MLJFlux -clf = NeuralNetworkClassifier( - builder = builder, - optimiser = Flux.ADAM(0.1), - batch_size = 128, - epochs = 10, -) -```` - -```` -NeuralNetworkClassifier( - builder = GenericBuilder( - apply = Main.var"##445".var"#15#16"()), - finaliser = NNlib.softmax, - optimiser = Flux.Optimise.Adam(0.1, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), - loss = Flux.Losses.crossentropy, - epochs = 10, - batch_size = 128, - lambda = 0.0, - alpha = 0.0, - rng = Random._GLOBAL_RNG(), - optimiser_changes_trigger_retraining = false, - acceleration = ComputationalResources.CPU1{Nothing}(nothing)) -```` - -2. Wrap it in a machine - -````julia -x_train_processed_equalized_fixed = coerce(x_train_processed_equalized_fixed, Continuous) -mach = machine(clf, x_train_processed_equalized_fixed, y_train) -```` - -```` -untrained Machine; caches model-specific representations of data - model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …) - args: - 1: Source @029 ⏎ AbstractMatrix{ScientificTypesBase.Continuous} - 2: Source @942 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}} - -```` - -## Train the Model - -````julia -fit!(mach) -```` - -```` -trained Machine; caches model-specific representations of data - model: NeuralNetworkClassifier(builder = GenericBuilder(apply = #15), …) - args: - 1: Source @029 ⏎ AbstractMatrix{ScientificTypesBase.Continuous} - 2: Source @942 ⏎ AbstractVector{ScientificTypesBase.Multiclass{2}} - -```` - -## Evaluate the Model - -````julia -ŷ = predict_mode(mach, x_val_processed_equalized_fixed) -balanced_accuracy(ŷ, y_val) -```` - -```` -0.9370418555201171 -```` - -Acceptable performance. Let's see some live examples: - -````julia -using Random: Random; -Random.seed!(99); - -z = rand(x_val) -z_processed = preprocess_text(z) -z_encoded_equalized = - encode_and_equalize(z_processed, vocab_dict, max_length, pad_val, oov_val) -z_encoded_equalized_fixed = matrixify([z_encoded_equalized]) -z_encoded_equalized_fixed = coerce(z_encoded_equalized_fixed, Continuous) -z_pred = predict_mode(mach, z_encoded_equalized_fixed) - -print("SMS: `$(z)` and the prediction is `$(z_pred)`") -```` - -```` -SMS: `Hi elaine, is today's meeting confirmed?` and the prediction is `CategoricalArrays.CategoricalValue{InlineStrings.String7, UInt32}[InlineStrings.String7("ham")]` -```` - ---- - -*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* - diff --git a/docs/src/generate.jl b/docs/src/generate.jl new file mode 100644 index 00000000..a9451d07 --- /dev/null +++ b/docs/src/generate.jl @@ -0,0 +1,52 @@ +function generate(dir; execute=true, pluto=false) + quote + using Pkg + Pkg.activate(temp=true) + Pkg.add("Literate") + using Literate + + OUTDIR = $dir + outdir = splitpath(OUTDIR)[end] + INFILE = joinpath(OUTDIR, "notebook.jl") + + @info "Generating notebooks for $outdir. " + + # generate pluto notebook: + if $pluto + TEMPDIR = tempdir() + Literate.notebook(INFILE, TEMPDIR, flavor=Literate.PlutoFlavor()) + mv("$TEMPDIR/notebook.jl", "$OUTDIR/notebook.pluto.jl", force=true) + else + @warn "Not generating a Pluto notebook for $outdir." + end + + Literate.markdown( + INFILE, + OUTDIR, + execute=false, + # overrides the default ```@example notebook ... ```, which will be ambiguous: + # config=Dict("codefence" => Pair("````@julia", "````" )), + config=Dict("codefence" => Pair("````@example $outdir", "````" )), + ) + + Literate.notebook(INFILE, OUTDIR, execute=false) + mv("$OUTDIR/notebook.ipynb", "$OUTDIR/notebook.unexecuted.ipynb", force=true) + Literate.notebook(INFILE, OUTDIR, execute=$execute) + $execute || @warn "Not generating a pre-executed Jupyter notebook for $outdir. "* + "YOU NEED TO EXECUTE \"notebook.ipynb\" MANUALLY!" + + end |> eval +end + +# Pkg.add("Pluto") +# using Pluto +# Pluto.run(notebook=joinpath(OUTDIR, "notebook.pluto.jl")) + +# Pkg.add("IJulia") +# Pkg.instantiate() +# using IJulia +# IJulia.notebook(dir=OUTDIR) +# Pkg.add("IJulia") +# Pkg.instantiate() +# using IJulia +# IJulia.notebook(dir=OUTDIR) diff --git a/docs/src/index.md b/docs/src/index.md index 8f19784b..aba818d5 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -10,24 +10,31 @@ A Julia package integrating deep learning Flux models with [MLJ](https://juliaai - Make it easier to apply machine learning techniques provided by MLJ, including: out-of-sample performance evaluation, hyper-parameter optimization, iteration control, and more, to deep learning models -!!! note "MLJFlux Coverage" - MLJFlux support is focused on fundamental and widely used deep learning models. Sophisticated architectures or techniques such as online learning, reinforcement learning, and adversarial networks are currently beyond its scope. +!!! note "MLJFlux Scope" -Also note that MLJFlux is limited to training models only when all training data fits into memory, though it still supports automatic batching of data. + MLJFlux support is focused on fundamental deep learning models for common + supervised learning tasks. Sophisticated architectures and approaches, such as online + learning, reinforcement learning, and adversarial networks, are currently outside its + scope. Also, MLJFlux is limited to tasks where all (batches of) training data + fits into memory. ## Installation ```julia import Pkg Pkg.activate("my_environment", shared=true) -Pkg.add(["MLJ", "MLJFlux", "Flux"]) +Pkg.add(["MLJ", "MLJFlux", "Optimisers", "Flux"]) ``` -You only need `Flux` if you need to build a custom architecture or experiment with different optimizers, loss functions and activations. +You only need `Flux` if you need to build a custom architecture, or experiment with different loss or activation functions. Since MLJFlux 0.5, you must use optimisers from Optimisers.jl, as native Flux.jl optimisers are no longer supported. ## Quick Start + +For the following demo, you will need to additionally run `Pkg.add("RDatasets")`. + ```@example using MLJ, Flux, MLJFlux import RDatasets +import Optimisers # 1. Load Data iris = RDatasets.dataset("datasets", "iris"); @@ -37,7 +44,7 @@ y, X = unpack(iris, ==(:Species), colname -> true, rng=123); NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg="MLJFlux" clf = NeuralNetworkClassifier( builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), + optimiser=Optimisers.Adam(0.01), batch_size=8, epochs=100, acceleration=CUDALibs() # For GPU support @@ -50,20 +57,23 @@ mach = machine(clf, X, y) cv=CV(nfolds=5) evaluate!(mach, resampling=cv, measure=accuracy) ``` -As you can see we were able to use MLJ functionality (i.e., cross validation) with a Flux deep learning model. All arguments provided also have defaults. +As you can see we are able to use MLJ meta-functionality (i.e., cross validation) with a Flux deep learning model. All arguments provided have defaults. -Notice that we were also able to define the neural network in a high-level fashion by only specifying the number of neurons in each hidden layer and the activation function. Meanwhile, `MLJFlux` was able to infer the input and output layer as well as use a suitable default for the loss function and output activation given the classification task. Notice as well that we did not need to implement a training or prediction loop as in `Flux`. +Notice that we are also able to define the neural network in a high-level fashion by only +specifying the number of neurons in each hidden layer and the activation +function. Meanwhile, `MLJFlux` is able to infer the input and output layer as well as use +a suitable default for the loss function and output activation given the classification +task. Notice as well that we did not need to manually implement a training or prediction +loop. -## Basic idea +## Basic idea: "builders" for data-dependent architecture -As in the example above, any MLJFlux model has a `builder` hyperparameter, an object encoding -instructions for creating a neural network given the data that the -model eventually sees (e.g., the number of classes in a classification -problem). While each MLJ model has a simple default builder, users -may need to define custom builders to get optimal results, -and this will require familiarity with the [Flux -API](https://fluxml.ai/Flux.jl/stable/) for defining a neural network -chain. +As in the example above, any MLJFlux model has a `builder` hyperparameter, an object +encoding instructions for creating a neural network given the data that the model +eventually sees (e.g., the number of classes in a classification problem). While each MLJ +model has a simple default builder, users may need to define custom builders to get +optimal results, and this will require familiarity with the [Flux +API](https://fluxml.ai/Flux.jl/stable/) for defining a neural network chain. ## Flux or MLJFlux? @@ -82,4 +92,4 @@ chain. A comparable project, [FastAI](https://github.com/FluxML/FastAI.jl)/[FluxTraining](https://github.com/FluxML/FluxTraining.jl), also provides a high-level interface for interacting with Flux models and supports a set of features that may overlap with (but not include all of) those supported by MLJFlux. -Many of the features mentioned above are showcased in the workflow examples that you can access from the sidebar. \ No newline at end of file +Many of the features mentioned above are showcased in the workflow examples that you can access from the sidebar. diff --git a/docs/src/interface/Classification.md b/docs/src/interface/Classification.md index d45d7a2b..82930f2d 100644 --- a/docs/src/interface/Classification.md +++ b/docs/src/interface/Classification.md @@ -1,7 +1,4 @@ ```@docs MLJFlux.NeuralNetworkClassifier -``` - -```@docs MLJFlux.NeuralNetworkBinaryClassifier -``` \ No newline at end of file +``` diff --git a/docs/src/interface/Custom Builders.md b/docs/src/interface/Custom Builders.md index 5a3514c8..42543ed2 100644 --- a/docs/src/interface/Custom Builders.md +++ b/docs/src/interface/Custom Builders.md @@ -25,8 +25,8 @@ end Note here that `n_in` and `n_out` depend on the size of the data (see [Table 1](@ref Models). -For a concrete image classification example, see -the [Image Classification Example](@ref). +For a concrete image classification example, see [Using MLJ to classifiy the MNIST image +dataset](@ref). More generally, defining a new builder means defining a new struct sub-typing `MLJFlux.Builder` and defining a new `MLJFlux.build` method @@ -58,4 +58,4 @@ example, ``` builder = MLJFlux.@builder Chain(Dense(n_in, 128), Dense(128, n_out, tanh)) -``` \ No newline at end of file +``` diff --git a/docs/src/interface/Regression.md b/docs/src/interface/Regression.md index f19f4b8e..543b6e48 100644 --- a/docs/src/interface/Regression.md +++ b/docs/src/interface/Regression.md @@ -1,3 +1,3 @@ ```@docs MLJFlux.NeuralNetworkRegressor -``` \ No newline at end of file +``` diff --git a/docs/src/interface/Summary.md b/docs/src/interface/Summary.md index a8f7b383..cc607e53 100644 --- a/docs/src/interface/Summary.md +++ b/docs/src/interface/Summary.md @@ -5,28 +5,28 @@ targets `y` of the [scientific type](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/) indicated in the table below. The parameters `n_in`, `n_out` and `n_channels` refer to information passed to the builder, as described under -[Defining a new builder](defining-a-new-builder) below. +[Defining Custom Builders](@ref). -Model Type | Prediction type | `scitype(X) <: _` | `scitype(y) <: _` ------------|-----------------|---------------|---------------------------- -`NeuralNetworkRegressor` | `Deterministic` | `Table(Continuous)` with `n_in` columns | `AbstractVector{<:Continuous)` (`n_out = 1`) -`MultitargetNeuralNetworkRegressor` | `Deterministic` | `Table(Continuous)` with `n_in` columns | `<: Table(Continuous)` with `n_out` columns -`NeuralNetworkClassifier` | `Probabilistic` | `<:Table(Continuous)` with `n_in` columns | `AbstractVector{<:Finite}` with `n_out` classes -`NeuralNetworkBinaryClassifier` | `Probabilistic` | `<:Table(Continuous)` with `n_in` columns | `AbstractVector{<:Finite{2}}` (`n_out = 2`) -`ImageClassifier` | `Probabilistic` | `AbstractVector(<:Image{W,H})` with `n_in = (W, H)` | `AbstractVector{<:Finite}` with `n_out` classes +| Model Type | Prediction type | `scitype(X) <: _` | `scitype(y) <: _` | +|---------------------------------------------|-----------------|-----------------------------------------------------|-------------------------------------------------| +| [`NeuralNetworkRegressor`](@ref) | `Deterministic` | `Table(Continuous)` with `n_in` columns | `AbstractVector{<:Continuous)` (`n_out = 1`) | +| [`MultitargetNeuralNetworkRegressor`](@ref) | `Deterministic` | `Table(Continuous)` with `n_in` columns | `<: Table(Continuous)` with `n_out` columns | +| [`NeuralNetworkClassifier`](@ref) | `Probabilistic` | `<:Table(Continuous)` with `n_in` columns | `AbstractVector{<:Finite}` with `n_out` classes | +| [`NeuralNetworkBinaryClassifier`](@ref) | `Probabilistic` | `<:Table(Continuous)` with `n_in` columns | `AbstractVector{<:Finite{2}}` (`n_out = 2`) | +| [`ImageClassifier`](@ref) | `Probabilistic` | `AbstractVector(<:Image{W,H})` with `n_in = (W, H)` | `AbstractVector{<:Finite}` with `n_out` classes | ```@raw html -
See definition of "model" +
What exactly is a "model"? ``` In MLJ a *model* is a mutable struct storing hyper-parameters for some learning algorithm indicated by the model name, and that's all. In particular, an MLJ model does not store learned parameters. !!! warning "Difference in Definition" - In Flux the term "model" has another meaning. However, as all - Flux "models" used in MLJFLux are `Flux.Chain` objects, we call them - *chains*, and restrict use of "model" to models in the MLJ sense. + In Flux the term "model" has another meaning. However, as all + Flux "models" used in MLJFLux are `Flux.Chain` objects, we call them + *chains*, and restrict use of "model" to models in the MLJ sense. ```@raw html
@@ -67,12 +67,10 @@ models, `fit!(mach)` will use a warm restart if: Here `model=mach.model` is the associated MLJ model. -The warm restart feature makes it possible to apply early stopping -criteria, as defined in -[EarlyStopping.jl](https://github.com/ablaom/EarlyStopping.jl). For an -example, see [/examples/mnist/](/examples/mnist/). (Eventually, this -will be handled by an MLJ model wrapper for controlling arbitrary -iterative models.) +The warm restart feature makes it possible to externally control iteration. See, for +example, [Early Stopping with MLJFlux](@ref) and [Using MLJ to classifiy the MNIST image +dataset](@ref). + ```@raw html
``` @@ -81,35 +79,37 @@ iterative models.) ## Model Hyperparameters. -All models share the following hyper-parameters: +All models share the following hyper-parameters. See individual model docstrings for a full list. -| Hyper-parameter | Description | Default | -|----------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------| -| `builder` | Default builder for models. | `MLJFlux.Linear(σ=Flux.relu)` (regressors) or `MLJFlux.Short(n_hidden=0, dropout=0.5, σ=Flux.σ)` (classifiers) | -| `optimiser` | The optimiser to use for training. | `Flux.ADAM()` | -| `loss` | The loss function used for training. | `Flux.mse` (regressors) and `Flux.crossentropy` (classifiers) | -| `n_epochs` | Number of epochs to train for. | `10` | -| `batch_size` | The batch size for the data. | `1` | -| `lambda` | The regularization strength. Range = [0, ∞). | `0` | -| `alpha` | The L2/L1 mix of regularization. Range = [0, 1]. | `0` | -| `rng` | The random number generator (RNG) passed to builders, for weight initialization, for example. Can be any `AbstractRNG` or the seed (integer) for a `MersenneTwister` that is reset on every cold restart of model (machine) training. | `GLOBAL_RNG` | -| `acceleration` | Use `CUDALibs()` for training on GPU; default is `CPU1()`. | `CPU1()` | -| `optimiser_changes_trigger_retraining` | True if fitting an associated machine should trigger retraining from scratch whenever the optimiser changes. | `false` | +| Hyper-parameter | Description | Default | +|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| +| `builder` | Default builder for models. | `MLJFlux.Linear(σ=Flux.relu)` (regressors) or `MLJFlux.Short(n_hidden=0, dropout=0.5, σ=Flux.σ)` (classifiers) | +| `optimiser` | The optimiser to use for training. | `Optimiser.Adam()` | +| `loss` | The loss function used for training. | `Flux.mse` (regressors) and `Flux.crossentropy` (classifiers) | +| `n_epochs` | Number of epochs to train for. | `10` | +| `batch_size` | The batch size for the data. | `1` | +| `lambda` | The regularization strength. Range = [0, ∞). | `0` | +| `alpha` | The L2/L1 mix of regularization. Range = [0, 1]. | `0` | +| `rng` | The random number generator (RNG) passed to builders, for weight initialization, for example. Can be any `AbstractRNG` or the seed (integer) for a `Xoshirio` that is reset on every cold restart of model (machine) training. | `GLOBAL_RNG` | +| `acceleration` | Use `CUDALibs()` for training on GPU; default is `CPU1()`. | `CPU1()` | +| `optimiser_changes_trigger_retraining` | True if fitting an associated machine should trigger retraining from scratch whenever the optimiser changes. | `false` | -The classifiers have an additional hyperparameter `finaliser` (default -= `Flux.softmax`) which is the operation applied to the unnormalized -output of the final layer to obtain probabilities (outputs summing to -one). Default = `Flux.softmax`. It should return a vector of the same -length as its input. +The classifiers have an additional hyperparameter `finaliser` (default is `Flux.softmax`, +or `Flux.σ` in the binary case) which is the operation applied to the unnormalized output +of the final layer to obtain probabilities (outputs summing to one). It should return a +vector of the same length as its input. !!! note "Loss Functions" - Currently, the loss function specified by `loss=...` is applied - internally by Flux and needs to conform to the Flux API. You cannot, - for example, supply one of MLJ's probabilistic loss functions, such as - `MLJ.cross_entropy` to one of the classifier constructors. -That said, you can only use MLJ loss functions or metrics in evaluation meta-algorithms (such as cross validation) and they will work even if the underlying model comes from `MLJFlux`. + Currently, the loss function specified by `loss=...` is applied + internally by Flux and needs to conform to the Flux API. You cannot, + for example, supply one of MLJ's probabilistic loss functions, such as + `MLJ.cross_entropy` to one of the classifier constructors. + +That said, you can only use MLJ loss functions or metrics in evaluation meta-algorithms +(such as cross validation) and they will work even if the underlying model comes from +`MLJFlux`. ```@raw html
More on accelerated training with GPUs @@ -134,14 +134,12 @@ CPU at then conclusion of `fit!`, and made available as ``` -## Built-in builders - -As for the `builder` argument, the following builders are provided out-of-the-box: - -|Builder | Description | -|:-------------------------|:-----------------------------------------------------| -| `MLJFlux.MLP(hidden=(10,))` | General multi-layer perceptron | -| `MLJFlux.Short(n_hidden=0, dropout=0.5, σ=sigmoid)` | Fully connected network with one hidden layer and dropout| -| `MLJFlux.Linear(σ=relu)` | Vanilla linear network with no hidden layers and activation function `σ` | +## Builders -See the following sections to learn more about the interface for the builders and models. +| Builder | Description | +|:--------------------------------------------------------------|:-------------------------------------------------------------------------| +| [`MLJFlux.MLP`](@ref)`(hidden=(10,))` | General multi-layer perceptron | +| [`MLJFlux.Short`](@ref)`(n_hidden=0, dropout=0.5, σ=sigmoid)` | Fully connected network with one hidden layer and dropout | +| [`MLJFlux.Linear`](@ref)`(σ=relu)` | Vanilla linear network with no hidden layers and activation function `σ` | +| [`MLJFlux.@builder`](@ref) | Macro for customized builders | +| | | diff --git a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.jl b/docs/src/workflow examples/Basic Neural Architecture Search/tuning.jl deleted file mode 100644 index 5a61c3e1..00000000 --- a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.jl +++ /dev/null @@ -1,126 +0,0 @@ -# # Neural Architecture Search with MLJFlux - -# Neural Architecture Search is (NAS) is an instance of hyperparameter tuning concerned with tuning model hyperparameters -# defining the architecture itself. Although it's typically performed with sophisticated search algorithms for efficiency, -# in this example we will be using a simple random search. - -using Pkg #src -Pkg.activate(@__DIR__); #src -Pkg.instantiate(); #src - -# **Julia version** is assumed to be 1.10.* - -# ### Basic Imports - -using MLJ # Has MLJFlux models -using Flux # For more flexibility -using RDatasets: RDatasets # Dataset source -using DataFrames # To view tuning results in a table - -# ### Loading and Splitting the Data - -iris = RDatasets.dataset("datasets", "iris"); -y, X = unpack(iris, ==(:Species), colname -> true, rng = 123); -X = Float32.(X); # To be compatible with type of network network parameters -first(X, 5) - - - -# ### Instantiating the model - -# Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start). -NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = "MLJFlux" -clf = NeuralNetworkClassifier( - builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu), - optimiser = Flux.ADAM(0.01), - batch_size = 8, - epochs = 10, - rng = 42, -) - - -# ### Generating Network Architectures -# We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define a network with $k$ hidden layers and -# where the ith layer has $z_i$ neurons. We will proceed by defining a function that can generate all possible networks with a -# specific number of hidden layers, a minimum and maximum number of neurons per layer and increments to consider for the number of neurons. - -function generate_networks(; - min_neurons::Int, - max_neurons::Int, - neuron_step::Int, - num_layers::Int, -) - ## Define the range of neurons - neuron_range = min_neurons:neuron_step:max_neurons - - ## Empty list to store the network configurations - networks = Vector{Tuple{Vararg{Int, num_layers}}}() - - ## Recursive helper function to generate all combinations of tuples - function generate_tuple(current_layers, remaining_layers) - if remaining_layers > 0 - for n in neuron_range - ## current_layers =[] then current_layers=[(min_neurons)], - ## [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],... - ## for each of these we call generate_layers again which appends - ## the n combinations for each one of them - generate_tuple(vcat(current_layers, [n]), remaining_layers - 1) - end - else - ## in the base case, no more layers to "recurse on" - ## and we just append the current_layers as a tuple - push!(networks, tuple(current_layers...)) - end - end - - ## Generate networks for the given number of layers - generate_tuple([], num_layers) - - return networks -end - - -# Now let's generate an array of all possible neural networks with three hidden layers and number of neurons per layer ∈ [1,64] with a step of 4 -networks_space = - generate_networks(min_neurons = 1, max_neurons = 64, neuron_step = 4, num_layers = 3) - -networks_space[1:5] - -# ### Wrapping the Model for Tuning - - -# Let's use this array to define the range of hyperparameters and pass it along with the model to the `TunedModel` constructor. -r1 = range(clf, :(builder.hidden), values = networks_space) - -tuned_clf = TunedModel( - model = clf, - tuning = RandomSearch(), - resampling = CV(nfolds = 4, rng = 42), - range = [r1], - measure = cross_entropy, - n = 100, # searching over 100 random samples are enough -); - -# ### Performing the Search - -# Similar to the last workflow example, all we need now is to fit our model and the search will take place automatically: -mach = machine(tuned_clf, X, y); -fit!(mach, verbosity = 0); -fitted_params(mach).best_model - -# ### Analyzing the Search Results - -# Let's analyze the search results by converting the history array to a dataframe and viewing it: -history = report(mach).history -history_df = DataFrame( - mlp = [x[:model].builder for x in history], - measurement = [x[:measurement][1] for x in history], -) -first(sort!(history_df, [order(:measurement)]), 10) - - - - -using Literate #src -Literate.markdown(@__FILE__, @__DIR__, execute = false) #src -Literate.notebook(@__FILE__, @__DIR__, execute = true) #src diff --git a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.md b/docs/src/workflow examples/Basic Neural Architecture Search/tuning.md deleted file mode 100644 index 308058e8..00000000 --- a/docs/src/workflow examples/Basic Neural Architecture Search/tuning.md +++ /dev/null @@ -1,141 +0,0 @@ -```@meta -EditURL = "tuning.jl" -``` - -# Neural Architecture Search with MLJFlux - -Neural Architecture Search is (NAS) is an instance of hyperparameter tuning concerned with tuning model hyperparameters -defining the architecture itself. Although it's typically performed with sophisticated search algorithms for efficiency, -in this example we will be using a simple random search. - -**Julia version** is assumed to be 1.10.* - -### Basic Imports - -````@example tuning -using MLJ # Has MLJFlux models -using Flux # For more flexibility -using RDatasets: RDatasets # Dataset source -using DataFrames # To view tuning results in a table -```` - -### Loading and Splitting the Data - -````@example tuning -iris = RDatasets.dataset("datasets", "iris"); -y, X = unpack(iris, ==(:Species), colname -> true, rng = 123); -X = Float32.(X); # To be compatible with type of network network parameters -first(X, 5) -```` - -### Instantiating the model - -Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start). - -````@example tuning -NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg = "MLJFlux" -clf = NeuralNetworkClassifier( - builder = MLJFlux.MLP(; hidden = (1, 1, 1), σ = Flux.relu), - optimiser = Flux.ADAM(0.01), - batch_size = 8, - epochs = 10, - rng = 42, -) -```` - -### Generating Network Architectures -We know that the MLP builder takes a tuple of the form $(z_1, z_2, ..., z_k)$ to define a network with $k$ hidden layers and -where the ith layer has $z_i$ neurons. We will proceed by defining a function that can generate all possible networks with a -specific number of hidden layers, a minimum and maximum number of neurons per layer and increments to consider for the number of neurons. - -````@example tuning -function generate_networks(; - min_neurons::Int, - max_neurons::Int, - neuron_step::Int, - num_layers::Int, -) - # Define the range of neurons - neuron_range = min_neurons:neuron_step:max_neurons - - # Empty list to store the network configurations - networks = Vector{Tuple{Vararg{Int, num_layers}}}() - - # Recursive helper function to generate all combinations of tuples - function generate_tuple(current_layers, remaining_layers) - if remaining_layers > 0 - for n in neuron_range - # current_layers =[] then current_layers=[(min_neurons)], - # [(min_neurons+neuron_step)], [(min_neurons+2*neuron_step)],... - # for each of these we call generate_layers again which appends - # the n combinations for each one of them - generate_tuple(vcat(current_layers, [n]), remaining_layers - 1) - end - else - # in the base case, no more layers to "recurse on" - # and we just append the current_layers as a tuple - push!(networks, tuple(current_layers...)) - end - end - - # Generate networks for the given number of layers - generate_tuple([], num_layers) - - return networks -end -```` - -Now let's generate an array of all possible neural networks with three hidden layers and number of neurons per layer ∈ [1,64] with a step of 4 - -````@example tuning -networks_space = - generate_networks(min_neurons = 1, max_neurons = 64, neuron_step = 4, num_layers = 3) - -networks_space[1:5] -```` - -### Wrapping the Model for Tuning - -Let's use this array to define the range of hyperparameters and pass it along with the model to the `TunedModel` constructor. - -````@example tuning -r1 = range(clf, :(builder.hidden), values = networks_space) - -tuned_clf = TunedModel( - model = clf, - tuning = RandomSearch(), - resampling = CV(nfolds = 4, rng = 42), - range = [r1], - measure = cross_entropy, - n = 100, # searching over 100 random samples are enough -); -nothing #hide -```` - -### Performing the Search - -Similar to the last workflow example, all we need now is to fit our model and the search will take place automatically: - -````@example tuning -mach = machine(tuned_clf, X, y); -fit!(mach, verbosity = 0); -fitted_params(mach).best_model -```` - -### Analyzing the Search Results - -Let's analyze the search results by converting the history array to a dataframe and viewing it: - -````@example tuning -history = report(mach).history -history_df = DataFrame( - mlp = [x[:model].builder for x in history], - measurement = [x[:measurement][1] for x in history], -) -first(sort!(history_df, [order(:measurement)]), 10) -```` - ---- - -*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* - diff --git a/docs/src/workflow examples/Comparison/comparison.md b/docs/src/workflow examples/Comparison/comparison.md deleted file mode 100644 index f712ad6d..00000000 --- a/docs/src/workflow examples/Comparison/comparison.md +++ /dev/null @@ -1,142 +0,0 @@ -```@meta -EditURL = "comparison.jl" -``` - -# Model Comparison with MLJFlux - -In this workflow example, we see how we can compare different machine learning models with a neural network from MLJFlux. - -**Julia version** is assumed to be 1.10.* - -### Basic Imports - -````julia -using MLJ # Has MLJFlux models -using Flux # For more flexibility -import RDatasets # Dataset source -using DataFrames # To visualize hyperparameter search results -```` - -### Loading and Splitting the Data - -````julia -iris = RDatasets.dataset("datasets", "iris"); -y, X = unpack(iris, ==(:Species), colname -> true, rng=123); -```` - -### Instantiating the models -Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start). - -````julia -NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux - -clf1 = NeuralNetworkClassifier( - builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu), - optimiser=Flux.ADAM(0.01), - batch_size=8, - epochs=50, - rng=42 - ) -```` - -```` -NeuralNetworkClassifier( - builder = MLP( - hidden = (5, 4), - σ = NNlib.relu), - finaliser = NNlib.softmax, - optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), - loss = Flux.Losses.crossentropy, - epochs = 50, - batch_size = 8, - lambda = 0.0, - alpha = 0.0, - rng = 42, - optimiser_changes_trigger_retraining = false, - acceleration = CPU1{Nothing}(nothing)) -```` - -Let's as well load and construct three other classical machine learning models: - -````julia -BayesianLDA = @load BayesianLDA pkg=MultivariateStats -clf2 = BayesianLDA() -RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree -clf3 = RandomForestClassifier() -XGBoostClassifier = @load XGBoostClassifier pkg=XGBoost -clf4 = XGBoostClassifier(); -```` - -```` -[ Info: For silent loading, specify `verbosity=0`. -import MLJMultivariateStatsInterface ✔ -[ Info: For silent loading, specify `verbosity=0`. -import MLJDecisionTreeInterface ✔ -[ Info: For silent loading, specify `verbosity=0`. -import MLJXGBoostInterface ✔ - -```` - -### Wrapping One of the Models in a TunedModel -Instead of just comparing with four models with the default/given hyperparameters, we will give `XGBoostClassifier` an unfair advantage -By wrapping it in a `TunedModel` that considers the best learning rate η for the model. - -````julia -r1 = range(clf4, :eta, lower=0.01, upper=0.5, scale=:log10) -tuned_model_xg = TunedModel( - model=clf4, - ranges=[r1], - tuning=Grid(resolution=10), - resampling=CV(nfolds=5, rng=42), - measure=cross_entropy, -); -```` - -Of course, one can wrap each of the four in a TunedModel if they are interested in comparing the models over a large set of their hyperparameters. - -### Comparing the models -We simply pass the four models to the `models` argument of the `TunedModel` construct - -````julia -tuned_model = TunedModel( - models=[clf1, clf2, clf3, tuned_model_xg], - tuning=Explicit(), - resampling=CV(nfolds=5, rng=42), - measure=cross_entropy, -); -```` - -Then wrapping our tuned model in a machine and fitting it. - -````julia -mach = machine(tuned_model, X, y); -fit!(mach, verbosity=0); -```` - -```` -┌ Warning: Layer with Float32 parameters got Float64 input. -│ The input will be converted, but any earlier layers may be very slow. -│ layer = Dense(4 => 5, relu) # 25 parameters -│ summary(x) = "4×8 Matrix{Float64}" -└ @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/stateless.jl:60 - -```` - -Now let's see the history for more details on the performance for each of the models - -````julia -history = report(mach).history -history_df = DataFrame(mlp = [x[:model] for x in history], measurement = [x[:measurement][1] for x in history]) -sort!(history_df, [order(:measurement)]) -```` - -```@raw html -
4×2 DataFrame
Rowmlpmeasurement
Probabil…Float64
1BayesianLDA(method = gevd, …)0.0610826
2RandomForestClassifier(max_depth = -1, …)0.106565
3NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …)0.113266
4ProbabilisticTunedModel(model = XGBoostClassifier(test = 1, …), …)0.221056
-``` - -This is Occam's razor in practice. - ---- - -*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).* - diff --git a/docs/src/workflow examples/Early Stopping/iteration.ipynb b/docs/src/workflow examples/Early Stopping/iteration.ipynb deleted file mode 100644 index 31ae9899..00000000 --- a/docs/src/workflow examples/Early Stopping/iteration.ipynb +++ /dev/null @@ -1,403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Early Stopping with MLJFlux" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "In this workflow example, we learn how MLJFlux enables us to easily use early stopping when training MLJFlux models." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "**Julia version** is assumed to be 1.10.*" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Basic Imports" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "using MLJ # Has MLJFlux models\n", - "using Flux # For more flexibility\n", - "import RDatasets # Dataset source\n", - "using Plots # To visualize training" - ], - "metadata": {}, - "execution_count": 1 - }, - { - "cell_type": "markdown", - "source": [ - "### Loading and Splitting the Data" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", - "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", - "X = Float32.(X); # To be compatible with type of network network parameters" - ], - "metadata": {}, - "execution_count": 2 - }, - { - "cell_type": "markdown", - "source": [ - "### Instantiating the model\n", - "Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start)." - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ Info: For silent loading, specify `verbosity=0`. \n", - "import MLJFlux ✔\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" - }, - "metadata": {}, - "execution_count": 3 - } - ], - "cell_type": "code", - "source": [ - "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", - "\n", - "clf = NeuralNetworkClassifier(\n", - " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", - " batch_size=8,\n", - " epochs=50,\n", - " rng=42\n", - " )" - ], - "metadata": {}, - "execution_count": 3 - }, - { - "cell_type": "markdown", - "source": [ - "### Wrapping it in an IteratedModel" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "Let's start by defining the condition that can cause the model to early stop." - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "5-element Vector{Any}:\n Step(1)\n NumberLimit(100)\n Patience(5)\n NumberSinceBest(9)\n TimeLimit(Dates.Millisecond(1800000))" - }, - "metadata": {}, - "execution_count": 4 - } - ], - "cell_type": "code", - "source": [ - "stop_conditions = [\n", - " Step(1), # Repeatedly train for one iteration\n", - " NumberLimit(100), # Don't train for more than 100 iterations\n", - " Patience(5), # Stop after 5 iterations of disimprovement in validation loss\n", - " NumberSinceBest(9), # Or if the best loss occurred 9 iterations ago\n", - " TimeLimit(30/60), # Or if 30 minutes passed\n", - "]" - ], - "metadata": {}, - "execution_count": 4 - }, - { - "cell_type": "markdown", - "source": [ - "We can also define callbacks. Here we want to store the validation loss for each iteration" - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "1-element Vector{WithLossDo{Main.var\"##321\".var\"#3#4\"}}:\n WithLossDo{Main.var\"##321\".var\"#3#4\"}(Main.var\"##321\".var\"#3#4\"(), false, nothing)" - }, - "metadata": {}, - "execution_count": 5 - } - ], - "cell_type": "code", - "source": [ - "validation_losses = []\n", - "callbacks = [\n", - " WithLossDo(loss->push!(validation_losses, loss)),\n", - "]" - ], - "metadata": {}, - "execution_count": 5 - }, - { - "cell_type": "markdown", - "source": [ - "Construct the iterated model and pass to it the stop_conditions and the callbacks:" - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Warning: Training could be very slow unless `resampling` is `Holdout(...)`, `nothing`, or a vector of the form `[(train, test),]`, where `train` and `test` are valid row indices for the data, as in `resampling = [(1:100, 101:150),]`. \n", - "└ @ MLJIteration ~/.julia/packages/MLJIteration/hgNDV/src/constructors.jl:274\n" - ] - } - ], - "cell_type": "code", - "source": [ - "iterated_model = IteratedModel(model=clf,\n", - " resampling=CV(nfolds=6), # Split the data internally into 0.7 training and 0.3 validation\n", - " measures=log_loss,\n", - " iteration_parameter=:(epochs),\n", - " controls=vcat(stop_conditions, callbacks),\n", - " retrain=false # no need to retrain on all data at the end\n", - " );" - ], - "metadata": {}, - "execution_count": 6 - }, - { - "cell_type": "markdown", - "source": [ - "You can see more advanced stopping conditions as well as how to involve callbacks in the [documentation](https://juliaai.github.io/MLJ.jl/stable/controlling_iterative_models/#Controlling-Iterative-Models)" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Training with Early Stopping\n", - "At this point, all we need is to fit the model and iteration controls will be automatically handled" - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "┌ Warning: Training could be very slow unless `resampling` is `Holdout(...)`, `nothing`, or a vector of the form `[(train, test),]`, where `train` and `test` are valid row indices for the data, as in `resampling = [(1:100, 101:150),]`. \n", - "└ @ MLJBase ~/.julia/packages/MLJBase/QyZZM/src/machines.jl:654\n", - "[ Info: Training machine(ProbabilisticIteratedModel(model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …), …).\n", - "[ Info: final loss: 0.0727575172201591\n", - "[ Info: final training loss: 0.08841877\n", - "[ Info: Stop triggered by NumberLimit(100) stopping criterion. \n", - "[ Info: Total of 100 iterations. \n" - ] - } - ], - "cell_type": "code", - "source": [ - "mach = machine(iterated_model, X, y)\n", - "fit!(mach)\n", - "# We can get the training losses like so\n", - "training_losses = report(mach)[:model_report].training_losses;" - ], - "metadata": {}, - "execution_count": 7 - }, - { - "cell_type": "markdown", - "source": [ - "### Results\n", - "We can see that the model converged after 100 iterations." - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Plot{Plots.GRBackend() n=2}", - "image/png": 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DQ/Py8ur8mCkpKR06dPDx8SEijuN69+597ty5sWPHTpo0KTQ0dOjQoSNHjhw7dizHcTNnzuzXr9+AAQOGDh06efLkyjc37cgZAlbU7QcJm9x/AwAA4LjU3fqru/W3dyvu2tSpU+fNm/fggw+mpqaKU1LNnTs3NjZ248aNHMctWrTozgcGRRzHyeVyo9EovqzY2L59+/r1648ePerh4ZGdnR0cHFzLcs5KpbLiQPEkFUtNSyQSccPGVXpqOtWKFStee+21rVu3vvnmm4cPH16yZMkLL7wwZcqUHTt2fPPNNz/99NMff/xhy/kbmsM/RUiYaxQAAOAvI0aMKCkpmTFjxvjx411dXYkoKyurY8eOHMeZTKba13hOSEhYv349EQmCUFGZlZXVokULDw8PIlq7dm1FsYeHR0lJ1UmSOnbsqNVqDxw4QERarXbz5s1V5mq3Xbdu3a5du5aSkkJE4uCq3r17G41Gg8EQHh4+c+bMBQsWJCcnE1Fpaamfn9/jjz/+2WefiXuaAmfowQpUkaecioxUYCBfpb1bAwAAYD9yufyxxx5bsmTJRx99JO7517/+NXPmzM2bN1+8eLFFixa1HLt48eIHH3zwyJEj+fn5FSPchw0b9p///EdcYEeMWaInnnhi1qxZ77333qxZs3r16iXudHNz+/LLL8eOHZuQkJCcnNyvX79Ro0bZ2PL8/PzKC/scPnz4448/TkxMTEhIOH78+OTJk3v37p2WlhYfH9+jRw83N7f9+/d/8sknRNS+ffuYmBh/f//Dhw+/9NJLtv5JNTAmCPbp+Fm7du22bdvWrFljS7FGoxFjeE3iN1uO5wsHh0sTAvAgofMrLy+vPLwRmjlcD1CZVqtVq9U23oQSzZs3z8PD4+WXX264VjUyjUaTl5cXERFR8eeQnp5+7dq16OhohUJhMBj8/f0tFktubq64tmB2draPj4+4nLNGozlz5kxoaKi/v39JSUlwcDARabXaU6dOeXl5dezY8caNGxVnNplMOTk57u7urq6uFWcjopKSkpSUlNDQ0PDwcHFPXl6eQqEQ85lery8qKqq8rCERGY3G3NzcyntCQkIkEklBQYE4ZL4iGpaVlaWkpPA836lTJ09PTyIyGAwpKSlarTYqKkpctfpOx44dmzVrVsXEXY3AGXqwiKitBzueL1wuFRCwAACgmXN1da3SKxEWFhYWFiZuiys0S6XSiohTOZS4urpWdEdVjBavvCpz5U4muVxecdrKgcnT07PiJKKK9aeJSKVSVUlXRKRQKCpOVZmvr2+VU7m7u/fs2bPyHqVS2b179zuPtS9nGINFfz1IeAXDsAAAAKAJcJaA5UmEFQkBAACgaXCWgIWpsAAAAKDJcJKA1cadcYxSywRLjXNzAAAAADQSJwlYKim1cGEmntI06MQCAAAAO3OSgEVEUR5ERFiREAAAAOzOSaZpIKK2HmxXpnC5RBjaAjM1AACArTiOW758eVJSkr0bAg2orKyM4xq1U8mJAhYWzAEAgLv3/PPP9+/veGsOgi0sFgsRiUtT1z6Lfb1zooCFBwkBAODu+fv7Dxo0yN6tgAZhMpmISJykvpE5zxisth5EmGsUAAAAmgDnCVgtXJlaStk6KjXZuykAAADQvDlPwGJEbdwZEV0tQycWAAAA2JPzBCwiaufJiOhCMQIWAAAA2JNTBaxob0ZEyUUIWAAAAGBPThWwYr0ZEZ0pRMACAAAAe3KqgNXZh4goGQELAAAA7MqpAlaoC/NVUqGRMrTIWAAAAGA3ThWwiCjaSxyGZe92AAAAQDPmbAGrsw8j3CUEAAAAu3K2gBXrgwcJAQAAwM6cLmB5owcLAAAA7MzZAlYHLybn6GqZoDHbuykAAADQXDlbwJJz1M6T8QKdx3zuAAAAYCfOFrCoYrpRDMMCAAAAO3HGgIUHCQEAAMCunDBgdcaDhAAAAGBXThuwzhYJPCIWAAAA2IMTBiwfBYW4MI2ZrpcjYQEAAIAdOGHAIqJYbyIMwwIAAAA7cdaAhWFYAAAAYDdOGrB8GBGdKbR3OwAAAKBZsilgCYKwcOHCLl269O7de9OmTdXWLF26tFevXp07d54+fXpubm69NvKuoQcLAAAA7MimgLVs2bJVq1Z99913L7/88pQpU1JSUqoU/Pjjj+++++7ixYu3bNlSXl7+5JNPNkBT70KUB3ORUrpGKDbatyEAAADQHNkUsL744otXX301JiZm6NChjz766FdffVWl4PTp04MGDbr//vvDwsKmT59++vTpBmjqXeAYdfRCJxYAAADYR90By2q1Xrx4MS4uTnwZFxd37ty5KjWjRo3av3//wYMHU1NTv/jiiwkTJtR/S+9SZ8znDgAAAHYirbOiuLjYYrF4eHiILz09PfPy8qrUxMXFDR06dOjQoW5ubu7u7u+9916dp7127drmzZsjIiIq9nz55Zc9e/astlir1dZ5wirauUiIpCdyTZpwy90eC02cVqtljNm7FdBU4HqAynQ6Hc/zuCRAZDKZiEgul9fvadVqNcfV0UVVd8Byd3dnjFVEHI1G4+XlVaXmzTffvHTpUk5Ojkql+uqrrxITEy9fviyRSGo5bevWrQcOHPjRRx/dbodUGhoaWktzXV1d62xqZfHBAp20XCiXuroq7+pAaPoEQbjb6wGcGK4HqIwxplarEbBA1EAByxZ13yKUy+UhISFXrlwRX165ciU8PLxKzaFDh4YNG6ZSqYho3LhxqampOTk5tZ+WMebq6trqL2FhYXWGwbsS4804RueLBTNfj2cFAAAAqJtNmWbSpEmffPKJ1WotKChYvXr1pEmTiEir1b788suFhYVE1LFjx99++81gMBDRzz//7OvrGxgY2KDtrpObjCLcmNFKl0sxDAsAAAAalU0Ba968eVarNTAwsE2bNuPGjXvwwQeJyGAwLFu2rKysjIhef/11uVweGhoaERGxcOHCH374ofb7g/fOWlKgO72PhNrCkzgb1hmMcwcAAIDGVfcYLCJyd3ffsWNHWVmZTCYT7wMSkY+PT0lJibjt7e29detWk8lkNBrd3NwaqrGVlP66XHdyD6dQKTvE11QT68M2plFyoTApshFaBAAAAHDbXQx7cnd3r0hX1ZLL5Y2TrohI6hNIRMarybXUdBaXfMZUWAAAANC4HHUtQkVkDBEZU6vOyFXZXysSImABAABAo3LUgCUP78AkUlNGKm/Q1VQT5sq8FJRvoOwaSwAAAADqn6MGLCZXyFpEEm813bhQYw1RDFZ9BgAAgEbnqAGLiBStbbhL6I0FcwAAAKCxOXTAiibbhmGhBwsAAAAakwMHLHmrjsRx5vQrgslQUw2mwgIAAIDG58ABi1Oq5SGtBavFlHappppOXkzG0ZVSQYcVnwEAAKCxOHDAIiJ5XXcJFRKK8mBWgc4XoxMLAAAAGoljB6y/hmGdraWmsw8jopMFCFgAAADQSBw9YHUixkxplwSzqaaaXgGMiPZkI2ABAABAI3HsgMWp3WSBLQWL2XTrSk01g0IYESVl8jwiFgAAADQKxw5YVLFmzrUah2FFurNwN1ZopLOYrAEAAAAaheMHrNbRRGSqdTas/kGMiJKyELAAAACgMTh+wIqMJsaMN84L1hpnYhgYLAYsvhHbBQAAAM2XwwcsztVT6h8qmIzmW9dqqhkUwjGifdmC0dqYTQMAAIBmyuEDFtmwZk6Aijp4MZ2FjuXjLiEAAAA0OGcKWLXNhoW7hAAAANBonCJgRcYQken6eeJrzE9/BSz0YAEAAECDc4aAJfHwkfoG8QadOet6TTX9gzkZR3/mCWXmxmwaAAAANEfOELCo4i7htRrvErrJqJsvs/B0MAedWAAAANCwnCRg/bXqc0otNRiGBQAAAI3DSQLW/x4kFGrsoBoYwhHR7kz0YAEAAEDDcpKAJfUJlHj587pyc87NmmoSApiLlM4VCbn6xmwaAAAANDtOErCISNG6E9U6DEvOUc8AJhDtzcZdQgAAAGhAzhSw6phulIgGBnOEyRoAAACggTlRwIqMISLjtdqHYTEi2pmBgAUAAAANyHkCltQvROLhw2tKLHkZNdV09WG+SrqpEW6UI2MBAABAQ3GegEV/DcMyXDxeUwHHqE8g7hICAABAw3KqgKWK7U1EulN7a6nBmjkAAADQ0JwqYCk7xHNKF1P6FUt+Zk014jCsP7J4JCwAAABoIE4VsJhMroy+n4h0p/fXVNPWg7VwYXl6OleEiAUAAAANwqkCFhGpu/YjIt3JP2qpGYC7hAAAANCQnC1gKdt25Vw9Lbm3zNlpNdWIdwmTMjHdKAAAADQIZwtYxElUsQlEpDu5p6aSgcGMEe3LEcyIWAAAANAAnC5gVdwlPLWnphlHg9WsnSfTmOlYPu4SAgAAQP1zwoClaNVJ4u1vLcoz3bxcU01iCCOizTfRhQUAAAD1zwkDFjGmvj0hVo13CSe05oho1VXegogFAAAA9c0ZAxaRqmtfItKf2U989QGqhz9r58ly9bQzE3cJAQAAoJ45Z8CSt4iS+oVYy4qN187WVDO5DUdE311FFxYAAADUM+cMWESk7tqXal02Z3IkkzDalMYXGhuvVQAAANAcOHHA6k9E+rMHBYu52oIQFzYohJl4+jEVnVgAAABQn5w2YEkDWshCWvE6jfHyyZpqpuAuIQAAADQApw1Y9L9lc/bWVPBwOOcpp2P5AtYlBAAAgHrk7AGLMX3KEcGor7ZAKaFxrTgiWn0NnVgAAABQb5w5YEm8/OXh7QWTUX/+aE01FXcJMSEWAAAA1BdnDlhEpO7Sl4j0NT9L2DMAE2IBAABAPXP2gNW1L3ESw8UTvK68pprHIzHUHQAAAOqTkwcsztVTERkjWC26kzUumzOlDSbEAgAAgPrk5AGLiFx7DSei8qT1tUyINTCYmXj66To6sQAAAKAeOH/AUkX3kIW0spbk647tqqlmShTuEgIAAEC9cf6ARYy5P/AoEZXtWidYLdWWPBLOecrpaJ6QUoyh7gAAAHCvmkHAIlLF9JIFR1iL82rqxMKEWAAAAFCPmkXAIsbcEx8lorKdP9TUiXV7QqwrmBALAAAA7lXzCFhEqs69ZUHh1uI83fGkagt6BrC2HixHT79jQiwAAAC4N80lYBFjbokTiKh81zrirdWWPNmWI6LXT1p5RCwAAAC4B80mYBGpu/SVBYVbCrO1J/6otmBWB66lKztZIKzE44QAAABwD5pRwCLG3AaNI6LynWur7cRSSendOI6I5h23lpoau3UAAADgNJpTwCJSd+kn9Q+1FGTXNLH7o6253oEsT08Lk6u/jQgAAABQp+YVsIjj3BMnEFHZ79V3YjGij++XcIw+OsdfLcVQLAAAAPgnmlnAIlJ3GyD1C7EUZOlO76u2oKsvezySM/H00nGMxAIAAIB/otkFLOI48XHCsh1riK8+Qr0XL3GX0S9p/C5M2QAAAAB3r/kFLCKX7gOkvkGW/Eztke3VFgSo6KVYCRHN+dOKeUcBAADgbjXHgEWcxGPoVCIq2fSlOTO12pK50VykOztfLHxzGQkLAAAA7s5dBKzc3FydTldLgSAI2dnZWq32nlvV4FRd+rj0HCKYTYXfvsMbqmmwQkKL4jgiWnDCWmRs9PYBAACAI7MpYOXl5fXo0aNr167BwcGvvvpqtTW7d+9u1apVTExMaGjo66+/Xq+NbBCejzwrb9HGUpBV9P0HJFQz1mp0BDcohBUZ6e3TmLIBAAAA7oJNAevVV19t3bp1ZmbmxYsXv/rqqyNHjlQpSE1NHTNmzNKlS/Pz8wsLC6dPn94ATa1nTCrzmfoq5+JuOP9n+d6N1dZ8EC+RMPrsAn8ZUzYAAACAzeoOWDzPr127dvbs2UQUFBQ0duzY1atXV6n58ssvhw0bNmTIEEEQOI4LCQlpkMbWN4m3v/fEucRY6a8rjNdT7izo7MOebMuZeZrwh1VrafwGAgAAgEOqO2Dl5+drNJq2bduKL6Oiom7cuFGl5sKFC0qlskuXLn5+fpygT5YAACAASURBVD169Lhw4UKdp+V5XqPRXK/EYrFDhFF2vM9twBjirUUr37GWFd1Z8F6cpI0HO1MoPLnfil4sAAAAsIW0zoqysjIiUqlU4ksXF5fS0tIqNfn5+UePHj127FhERMQrr7wyYcKEs2fP1n7a69evJyUlDRw48HY7pNLPP/+8R48e1RY36MB5Sd8x0tTzlrQL+SvfdX3iNeL+FjplRD/2Yv12yn66zrdzNb/YAR1Z9qfVahlj9m4FNBW4HqAynU7H8zwuCRCZTCYiksvl9XtatVrNcXV0UdUdsPz8/IiopKTE39+fiIqLi8WNyvz9/aOjoyMiIojo+eeff+edd/Ly8u4sqywyMnLkyJFr1qypswEiV1dXGyv/AdVTr+Z9MMNyPYU/tMl98OQq73ZxpZ8GCkN/t7x9TtItQD6iZbOc26IpEQShQa8HcCy4HqAyxpharUbAAlEDBSxb1J0VPD09w8LCjh8/Lr48fvx4TExMlZrY2NiKGRx0Oh1jrKLHyyFI3Ly8H3+JOK5s5w/6c4fvLHgwlL3ZTcILNGmvNaUYtwoBAACgNjZ1xsyYMeOVV15JTk5et27djh07nnzySSK6detW9+7dxduF06dP37Fjxy+//HL16tW5c+cOGzbMzc2tYRte3xRtYj2GTCFBKFq1yHjl9J0F8zpzj7bmys00Yqe1wND4DQQAAACHUfctQiKaO3eu0WicNm2ap6fnpk2bWrZsSUQymSw8PFy8BxkWFrZp06Z33323pKSkV69er732WsO2umG4DRrPa8vK92wo+OYN36ffVrSOrvwuI1reW3K1VDhRIEzcY/ntQakUtwoBAACgOkyobo7NRrB27dpt27bZOAZLo9E00hgLQShe/6n28G+cUu373CJ5WFSV99M1QtxmS56e5kZzi++TNEaT4A7l5eUO10UKDQfXA1Sm1WoxBgsqNOkxWM0LY15jZ6m79uMNuoIv5pszqq5UGObK1g+Uyjj68Bz/1SUsUwgAAADVQMC6A2Pek/6fKrY3r9fkL1tgyb1V5f0+geyznhIieuag9dPzyFgAAABQFQJWdTjOe/JLyvZxvKYk/4t5lsKcKu9Pb8d9kSBhjGYfsb58HCsVAgAAwN8gYFWPSaQ+U19RtOpoLSko+GK+taSgSsEz7blVfSVSjt5L5pGxAAAAoDIErBoxucJn+lvyFlGWgqzcxTONV85UKXgsklvTTyLj6L1k/rlDVh7TYwEAAAARIWDVjlOqfZ99R9muG68pyV82v+z3tfT3hy7HteI2JUpVUvriIj9ln9WCEVkAAACAgFUnTu3m+/TbHsOfJKKy7avyP59nLS+uXDCkBdv+oNRNRquv8Y/ttZqRsQAAAJo9BCwbMOY2cJzfc4sk7l7Gq2fyPpxlunGh8vt9g9iOh6SecvrpOt9/myWtHDcLAQAAmjUELFspImP8534mDnvP+/TF8qSfKt8u7BnAkoZIQ1zYoVwhdqPl+2voyAIAAGi+ELDugsTDx2/m+279R5PAl/66ouCrVy1FuRXvdvVl50dLJ7bmysw0ea91bJK1yGjHxgIAAIDdIGDdJU7iMfJfPk++yrm4Gy6eyF30dPnejcTf7q/ykNOa/pKfBko85fTzDb7zRsuebNwuBAAAaHYQsP4JVXTPwAXLXXoOEUyG0k1f5X4405R+peLdsRHciVHSHv7sllYY9JvlpWNWE24YAgAANCcIWP8Qp3bzGjfb9+m3pN4B5szreR/PKdn4hWDUi++2dmf7h0nf6CrhGL1/lo/ZYNl2C11ZAAAAzQUC1j1Rto8LePlLt36PEAma/Ztz339On3xQHPwu5ej1rtyBYdJ2nuxyqTDsd8tDOywXShCzAAAAnB8C1r1icqXHqOn+c5bIQltbCrMLv307d/EM/dnDYsy635+de0T6ZS+Jn5J+zxBiN1qePmjNN9i70QAAANCQELDqh7xFm4D/+8Rz7EyJp58583rhijdzF8/UnztCgiDlaHo77uJY2XMdOCL66hLfbr350/M8pn0HAABwVghY9YeTuCYMC3z1W69xsyWevubM1MLl/8l9/1n9mQMkCD4KWtpTcvphaWIIKzLS7CPWNustS1J4jdnezQYAAID6hoBVz5hE6tJzSOCCFZ6PPCvx8DFnpxWufCdn0dOa/Zt5vaaTF9s5WLo5UdLek6WVC//+0xq2zrzghDVHb+92AwAAQP1BwGoQTCZ37TMy8JVvPR9+RuLhY8lNL9n4RfbrjxX/8F9T+uURLbnzY6RbHpAMCmHFRnr3DN/yB/PkvVYMgQcAAHAOUns3wJkxmdy17yjX3sP1KUe1h7cZLp/SHt2pPbpTFhrp2nPI0K79hoep/8wTFp/jf0njv7/Gr0nlBwWzKVHcwy05Ff5mAAAAHBZ+jTc8TqKK6amK6WkpyNYe+U17dKc541rxT5+U/LJM2SE+tmu/9X3iU+NkH57jv7vK78wUdmZaPeTWsRHclDZcQiBj9m4+AAAA3C0mCPa5LbV27dpt27atWbPGlmKNRuPq6trQTWocgsWsTz6oPbLdmHpOnMqBU6qV0T3VXfvqw7uuS2OrrvJ/5t3+S4l0Z5PbcBNbs9buCFr/U15e7ubmZu9WQFOB6wEq02q1arWa4X9OgYiITCYTEcnl8sb/0ejBamxMKlN366/u1t9aWqg7vU9/aq8p/Yru+G7d8d2ci/uETj2e6NA9Pb7zd7fU318TrpUJr520vnaSYrzZqJZsVDjXxQffGgAAAE0derDsz1KQpTu1T39qjzkn/fYuTqKI6KBo1z3Zt9s3JeFbbgklptvvhLuxUS3Zw+FcQgCTNNeshR4LqAzXA1SGHiyozI49WAhYTYg5O81w4bjh0gnT9fOC1SLulLh7y6K6pvrF/MI6fV/gn6W7/fflIad+QdyAYDYwmHX0al5fJfiFCpXheoDKELCgMtwiBCIiWVC4LCjcbeBY3qAzXjltuHjCcPG4taTAemJ3CO2eSfRv74Cy0JhDLtHfmjsdMPhtvslvvklEFKiiAcHcgGDWN4hFYrQWAACAvSFgNUWcUq2KSVDFJBCROTvNeDXZeC3ZmJpiKcpVF+1KpF2JROThl+fT5pQqaou1zR5N5NpU9dpUIiJ/Fd3vz/XwZz0DWHdfpsbfMAAAQKPDr9+mTuzWcu0zkgTBnHXDeC3ZeDXZeD2FL833L81/iA4/RESMlXu0uOjaZh/X+qg2Yo82YstNFyKSchTrze7zZ118WBcfFu3N5JhZFgAAoOEhYDkOxmQhrWQhrVz7PkyCYM67ZU6/Yrp52ZR+xZyZ6laSHl+SHk9JYmWJOvCiKuJPSfi50ta/ZrVcJvfnick46uh1O2zFeLNO3sxHYe8PBQAA4IwQsBwTY7KAMFlAmDpuEBEJVos5M9WUftWccc2ced2ck+apze6hze5Bh8Vys0SRpgq9IA29nBt2TdHiE0WLNHmghUn9VdTJi7X3ZOK/O3oxX6VdPxcAAIBTQMByBkwilYe1lYe1vf2at5rzMsyZqebM6+bM6+acm1Ra2EaT2oZSKw6xMulNReAVeWiqokWqPORLZYtURUg55+KloDburI0Hi/JgbdypjQeLdGeednj8AgAAwIEhYDkjTiILbCkLbEndBog7eL3Gkpthzrlpybtlzrlpyb1FRbmtDBmtDBlEf1Yclyv3uSCPuKCKuKAMX62IuKEMtpCEiLwUFOHGItxYhOvtjXA3CndlWDARAACgWvgN2SxwKld5eDt5eLuKPYLZZMm7Zc7LsOTesuTeMudlWPJuBZgKA0yF/TUnxBqLRJ6mDjuuiFrv3veEof2pgqoTQPgpqYUra+HCWrpSS1fWwpVC1CzMlQJVTIrR9AAA0IwhYDVTTCaXhbSWhbT+3y5BsBTmmDNTzVk3xH+oKCey/Fpk+bVHC36zegRkt+l/MqxfMtfiRrlwU0O3NEK+gfINwqmCqnPVShgFqFiYKwWrWZgrtXFnkR6sjTuFuTbf2ecBAKBZQcCCvzAm9Q2S+gapYnuJO3iDzpJ9Q3/+mO7kHirODT2xLvTEurEt2qi7D1Al9JO4e+Xo6ZZGSNcIt7SUphEytJSpFdI1lKsXsnRClo6I/pa95By1cmeR7tTBkw0K4foEMoXEDh8UAACgoWGpHLCBIBhTU3QnkvTJB3m9hoiYRKq+7wH3QRMk3v53llt4ytEL6RrK1Ak3NXS1VLhWJlwtpQzt3642FykNDOEGh7LBLVhL17vo2sLSKFAZrgeoDEvlQGVYKgeaNsYUkdGKyGjP0c8ZLhzTnUjSp/ypPfyb7uhOddxAt8QJUp+gyuVSjkJdWKgLEf3tO85gvR22jucL2zOE5EJhy01+y00ioo5ebGRLNr+zxAWXJAAAOD78NoO7wGRyVWwvVWwvS2F2edJ67Z+/a//8XXt0pyq2l8eQKVL/0NoPV0oo2ptFe7OHw+ndOMrSCdtvCdszhN2Z/Pli4XyxcDxf+PUBKe4bAgCAo8OzXvBPSH2CvMbNDpz/tUt8IuMk+jMHchZNL1rzgbWsyPaTBKvZU225nwdKCibJdg2WBqpoV6bw6B6rhW+4hgMAADQGBCz456S+wV4T5wbM/8alx2DGON3xpNx3/6U5+Cvd5cA+KUeDQtiuIVJvBf2Sxk/db+XtMzIQAACgfiBgwb2S+gR6jX8+YMFyVXQP3qAt+Xlp3kf/Nmem1n3k33XyYtsfkrrJaPU1fvYRa0M0FQAAoHEgYEH9kHoH+Dz1uu+/3pB4+5vSL+d+OLtk4xeCUX9XJ4n3Y5sSpUoJLb3Av3YSGQsAABwVAhbUJ2XH+wNf+tK13yNEpNm/OWfR04bzf9Z5VGUDgtmPAyRSjt46zX9wFqOxAADAISFgQT1jCpXnqOkB//eJPKyttTiv4Jv/6E7tvaszjGjJfdtHwjF66Zj1q0vIWAAA4HgQsKBByEJb+8/52H3wZBKE4h+XWHJv3dXhkyK5T3pIBKLnDlkP5GDEOwAAOBgELGgwjLk/OFEdN0gw6gu/fVswGe7q6BkduBdjOKtAH6WgEwsAABwMAhY0LK+xM2WBYeacm8XrPr7bY1+MkSgk9Gs6n6lFJxYAADgSBCxoWEyu9HnyNaZQ6U7t1R7ZflfH+ilpVEvOwtM3lxGwAADAkSBgQYOT+od6jX+eiEo2fmHOuHZXxz7dniOiby7zmN4dAAAcCAIWNAZ1134uPYcIZlPhynd4g9b2A/sHsQ6eLEMrbLuFhAUAAA4DAQsaiecjz8pbtLEUZBd9/8FdraUzrR1HRMsuImABAIDDQMCCRsKkMp+pr3JqN8P5P8v3/WL7gVOjOLWUfs8QrpVhJBYAADgGBCxoPBJvf69H/48YK/t1heHSSRuP8pTTuFacQLT8MjqxAADAMSBgQaNSRfdwGzhOsFoKV7xpvJ5i41FPt7s91N2I9QkBAMARIGBBY/MY+oTL/Q8JJmPhV6+Z0q/UXiyYTUR0vz/r6ssKDPRLGjqxAADAASBgQaNjzGv88+pu/XmDrmDZAnN2WrVV1uK8gq/fyHr5Ef3Zw0T0r7YcEX2JpQkBAMARIGCBPTDm/dgLquievK684PN5lvzMv73LWzV7N+Ysetpw/k/Bain+4b+WwuxJkZy7jPZmC+eLMdQdAACaOgQssBNO4j1lnrJdN2t5cf7Sly1FueJu060ruf99vmTTV4JRr+rSR9nxfl6vKVq50IWZJ0ZyRPQ1hroDAECTh4AFdsOkMp+nXldERltL8gs+f9lSkFX664q8j/5tzrgm8fDxefI1nynzvSe9IPUJMt26Urrlm2fbc0T03RVeb2X2bjsAAEBtELDAnphM7jPtP/KwKEtBds47T5Un/UTE3AaMCVywXBXTk4g4lav3E/OYVKbZv7nNrYM9/FmJiTak47oFAIAmDb+owM44pdr3mXdkQeEkCPKwtgFzP/EYMY3JlRUF8hZRHiOmEVHxDx//OziXiFakSuzWXAAAABtI7d0AAOLUbn6zF5tuXla27Uqsmtt/rn1GGlPP6ZMP9t6zMDDg/ROFslMFQldf3CgEAIAmCj1Y0CRwKldlu27VpiuR98S5Uv9Qa+a1b8pXENHicxjqDgAATRcCFjgGplD5PLGAyeSxV7eOLdv703X+SinmawAAgCbK1oCVnp7+/fffJyUl8XxtPQcXLlzYv39/fTQMoCpZcITHyH8R0cKsL0KMOe8loxMLAACaKJsC1q5du7p06bJnz54XX3xx1KhRglB9z0FWVlbfvn3HjBlTry0E+B/XXsNVsb0UFt3Mwg3fX+PTytGJBQAATZFNAeuVV15ZuHDhihUrDhw4cPr06X379lVbNnPmzMmTJ9dr8wCq8hgyhRgbU7JHbdZgJBYAADRNdQes3NzcY8eOjR07lohcXFwGDx7866+/3lm2du1ahUIxYsSI+m8jQCXSgBbSiE4yq3F0yZ7ll/ksHTqxAACgyal7moaMjAy1Wu3l5SW+DA0NvXDhQpWagoKCt956a+/evZcuXbLxB5eVlV29enXZsmUVe4YPHx4YGFhtsdVqtVqtNp4ZnJ60+0DL9XPPlm//1nvYf89a34vDfA3NGr4foDLxemA1P5IMzYr45VDvXxEcx9V5jdUdsKxWK8f9r6NLIpFYLJYqNTNmzHjppZcCAgJsD1h6vb6oqOjEiRO32yGV9uzZ08fHp9pis9lsNpttPDM4PaF1Z87dO7DsVg9dyrJL0f9ub/VVoB+r+cL3A1QmXg8IWCASvxzq/XqQy+X1ELCCgoI0Go1er1epVESUl5cXFBRUueDGjRtbtmzx8PA4cuRIdnZ2eXn5008//eabbwYEBNRy2oCAgPvuu++bb76x4YOQxWJRKpV110HzYDa7KHoMLvt9zYuG7Q+7RH91TfpmN8zt3nyZzWZ8P0AFq9WqVCoRsEAk9hDJ5XI7/Og6K0JDQyMjI3fu3ElEPM/v3r27X79+RGS1WktLS4nI29t7yZIl3bt379atW1RUlEwm69atG77voEG59BzCJNLueUcCzEWfnOdLTPZuEAAAQCV192AxxubNm/fcc8+lpaUdOXKE4zhxJPvRo0cTEhIEQfDw8Jg+fbpYvG/fvlWrVlW8BGggEg8fZcd4/dnDL1t2zzGN+/wCP78zZs0FAICmwqbfSU8++eR3332XlZUVHx9/8OBBmUxGRK1bt648RF0UFRW1ePHi+m8mwB1cEoYR0ci8HRKB/+85qwaDcAAAoMmwdbHnQYMGDRo0qPKegICAp59+ukpZUFAQpsKCxqGM6iL1C6H8zJmS40uM9319mZ/TCZ1YAADQJOAXEjgsxlwShhLR02Xbiej9ZKu+6uOtAAAA9oGABQ7M5b4HmFzhdvPUMHV2jp5WXMHE7gAA0CQgYIED41Su6i59SRBet/5ORO+cserQiQUAAE0AAhY4NnGoe/DF3/v4mLJ19Ol5dGIBAID9IWCBY5OHRclbRPE6zQcuh4loUbK12GjvNgEAQLOHgAUOz6XXUCIKv7BtQDArMdGH57AsHQAA2BkCFjg8ddf+nNrNlHZpceh1RvRxCp+rt3ebAACgeUPAAofHZHJ19wFE1Cpt//CWnNZC755BJxYAANgTAhY4A1Wn+4nIeDX5ne4cx2jZRf56uWDvRgEAQPOFgAXOQB7RkcnkpoxrHeTlE1tzJp7ePo3HCQEAwG4QsMAZMJlc3rIdCYIxNeWtbpyco1VX+Qsl6MQCAAD7QMACJ6Fo05mIjFdOh7uxp9pyVoFeO4lOLAAAsA8ELHASyqjORGS4mkxEr3aRqKW08QZ/NA+dWAAAYAcIWOAk5C3bckq1JTfdWloYpKaZHTiB6LWTeJwQAADsAAELnAUnkbfqSETGq8lE9FKsxFNOOzOFvdnoxAIAgMaGgAXOQxHVhYiMV88QkbeCXoiRENF/TqETCwAAGhsCFjgPZZtYIjJcOSO+nNWR81LQ3mzhYA46sQAAoFEhYIHzkAW34lw9rMV5loIsInKX0eyOHBG9m4xOLAAAaFQIWOBEGFNExhCR8a9OrNkdJW4y2n5LOJ6PTiwAAGg8CFjgVJTibFhXk8WX3gp6tj1HRAuTMScWAAA0HgQscCoKcTasK6dJuN1l9X/RErWUNqXx54rQiQUAAI0EAQucitQvROLtz2vLzNlp4p4AFU1rywnoxAIAgEaEgAXORhEZS0TGK6cr9rwYwykk9NN1/kopOrEAAKAxIGCBs7k9WcNfw7CIKNSFTWnDWQVaVEMn1tki4eFd1j+yEL8AAKB+IGCBs7k93ei1s4LVUrHzpVhOytHqa/xNTdUU9UMq32OLZdNN/tlDVisiFgAA1AcELHA2Eg8fqX+oYNSbb12r2NnKjU1szZl5eq9SJ5aFp//70zpxj1VnIZWUrpQK61IxTgsAAOoBAhY4IaX4LOHV05V3zovlOEYrrvCZWoGICgw0+HfLRym8lKNFcZLPekqI6J0zPI9OLAAAuGcIWOCEFOJsWFeSK+9s58lGh3NGK/03hT9VIMRttuzOFPyUtGuw9KVY7vFILsKNXSwRfr6BTiwAALhXCFjghBRtYokxU9oFwWyqvH9BF44RfXGRT/jVklYuJASw5Edk/YIYEck4ejmWI6K3TqMTCwAA7hUCFjghTu0mC2klmE2mGxcq74/1ZsPCOL2FDFZ6rgP3x1BpkPp/7z4RxYW5spRiYdNNdGIBAMA9QcAC56SM6kJEhqtnquxfGMf1CmTf9ZUs7SmR//3yl3P0UsztTiz0YQEAwL1AwALnpGgTS0TGOwJWRy92YJh0cpvqr/yn2nIhLuxMofArOrEAAOAeIGCBc1K0jmYSqSn9Cq/X3MVREnoxmiOi/6ATCwAA7gECFjgnJlfKW7YlnjdcOnlXBz7dngtWs1MFwo5biFgAAPAPIWCB01K0605ERd+/V7T6fUt+po1HKSU0J5ojojdPWxuwcQAA4NQQsMBpufUf7ZIwlDFOd+KPnIX/Klqz2MaY9Wx7zl9Ff+YJuzLRiQUAAP8EAhY4LSaTe42dFfjKCtc+Ixkn0R3fnfPutMKV79QZs1ykNKeThIjeOIVOLAAA+CcQsMDJSbz8PR95NmD+Ny73P8Q4if7MgZyF07VHd9Z+1IwOnI+CDucK61J5rAANAAB3CwELmgWpd4DXhH8HzP/G5b4HiLeWbFhqzk2vpd5NRnOiJUT06B6r/2rzhD+s317hs3WN1VwAAHBwCFjQjEh9Ar0e/T+X+x4UTMai798TrJZail+I5ubFclEerMhIP17nn9xvDVlr7rzR8tIxa56+0ZoMAAAOCQELmh3P0c9K/ULMGall27+vpUwhoXfjJJfHSlPHSz9PkIxoybnIKLlIeP8sP2pXbckMAAAAAQuaHSZXej/+EpNIy5N+Ml47W2d9Kzf2bHtuc6Kk8HFZ0hCpr5KO5AmHczEyCwAAaoSABc2RPCzKbdB4EoSi1e/zOlunepdzNCCYPd2OI6KPUrCWDgAA1AgBC5op9wcnysPbWUsKSn754q4OnNVRopDQL2n89XJ0YgEAQPUQsKC54iTek15iCpXueJLu1F7bjwtQ0fhWnFWgpRfQiQUAANVDwILmS+ob5DnyX0RUsv4za3Ge7Qe+EM0xoq8v8aWmBmscAAA4MgQsaNZceg5Rxfbm9Zqi1R+QYOstv2hv1jeIlZvp2yvoxAIAgGogYEFz5zV2psTNy5h6rmTDUsFsa5fUnE4cES05z1sQsQAA4A4IWNDcca4eXhPnEifRHNya+8FzprSLthw1vCXXzpOllQubbyJhAQBAVQhYAKRs3z3ghc9koa0teRl5S/6v+KdPBJOh9kMY0cwOmK8BAACqh4AFQEQkC47wn7PEY/iTjJNoD/+W+/6zxtRztR8yNYrzUdChXOFoHuZrAACAv0HAAriNSaRuA8f5Pf9fWWCYpSA7f+lLpb8ur2VUllpK09rdHonViM0EAAAHgIAF8DfysCj/F5a6DRpPROVJ60t+XlpL8awOnIyj9Tf4dA06sQAA4H8QsACqYlKZx7Cp/rM/JE6iO5FkLcmvqTLEhY2J4Cw8Jh0FAIC/QcACqJ48vL26Sx/BatHs31xL2dxojoi+usRrzI3VMgAAaPIQsABq5DZgLDGmObSNN2hrqunmy3oFshITrbyKTiwAALgNAQugRrKQVorIWMGo1x7ZUUuZOOnoKyesyUUYiQUAAEQIWAC1c+s/mog0+34RrJaaah4O5ya05kpN9NB2y/VyZCwAAEDAAqiVsn13WWBLa0mB/sz+mmoY0aq+kodCWY6eHthuzdE3ZgMBAKApQsACqBVjrv0eIaLypPW1rAYt42jDIGkPf5ZaJjyw3VJi65KGAADgnBCwAOqg7j5A4u5tzrphvJpcW5mUtjwgbefJzhUJD++yGKyN1kAAAGhyELAA6sCkMpfew4mofM+G2it9lbRrsCTMle3NFib8YbViOBYAQHOFgAVQN9eE4UyhMlw8bs66UXtlqAv77UGJt4I23+RnHEIvFgBAM4WABVA3Tu3qEp9IRJp9m+os7ujFtjwgVUnpy0v8rMNWXY1PHwIAgNOyNWBlZGS8/PLLTzzxxLp16+58V6/Xr1+//vnnn582bdrXX39tNmNOa3A2rv0eIY7TnfzDWlZUZ3FCAPtxgETK0WcX+E4bLNtu4WYhAEDzYlPA0uv1CQkJWq02MTFxwYIFS5dWXf52//79S5cuDQ8PT0hIWLp06WOPPdYATQWwJ6lPoCo6QbCYNQe22FI/PIw7OEza2YfdKBeG/W4Zk2TN1CJmAQA0F1Jbin788Ud/f/9PP/2UiLy8vGbMmPHss89y3P/CWWJi4oMPPihux8fHx8TEaLVaFxeXhmgxgL249R+tTz6gPbTNPXECkyvrrL/Pnx0fKV16gX/tpHXDDX77Lf6tjvp/mQ7yRTmC2SiYTbxeI5iMgsUk6LXKDnHugyc3wqcAAIBGYFPAOnLkSL9+bdcvSQAAIABJREFU/cTtfv36paWlZWdnh4SEVBRUDlsFBQUuLi5KZd2/fgAcizy8nTyio+nGec3h39z6PWLLIVKOnu/EjY5gi7dfbnF+2/Cz+0t5Y7WVpoxr6u4DpX4h1b4LAACOxaaAlZOTExERIW6r1WoXF5cqAauCXq9//vnnX3nlFYlEUvs5MzIyDhw4MHr06Io9L774YkxMTLXFOp2ucoaDZk6v19d5gTUQecJw043zpVuWW2UqeZe+ddYLRr3p7EHJ0d9fyLpORAJjB1y7HHWLHhYh7+YvZSpXJpMxmcJ4Yrcx+WDJno3qYU81/IdwNna8HqAJ0ul0RMQYs3dDoEkwmUxEZLHU89NGSqWyzlhiU8BSKBRiE4lIEASz2VxtB5XRaHzkkUc6der0wgsv1HlOHx+fli1bjh8//nY7pNKoqKia+r0sFgu6xKBCTVdgI1B27cNy0sp3r9P8/ImH2eDSZ2RNlbyuvPz3Nfrju3mDjog4F3d1fCKLH7wvNfDLS8KSMnqhJXu3OydhREQKb//8s4eMJ//wHPYEp8S99btjx+sBmiCr1apUKhGwQCTGILlc3hCnrZ1NASs0NPTWrVvidk5OjtlsvrP7ymw2jx8/3sXFZeXKlbb8YJVKFRYWNm7cOFsawHEcerCggn2vB49hT0jcPEo2fVW66UtBX17twCndqb2lv3xpLS8mIkWrTi4JQ1WxvZhURkTLgqirLz/rsHXxOSGlmP9hgNRTToqQVoo2scYrZ/THdtl48xEq4PsBKhOvBwQsEIlfDnb5irDpRz788MPbtm0rKSkhotWrV/fv39/Ly4uIDh06dP78eSKyWq2TJ082mUxr1qyRSm0KbQCOy7Xvw94T5xInKft9bcn6zyqvUWgtziv46rWiVYus5cWKyOiAl5b5zV6s7tZfTFei6e24pCFSfxXtyBDu22y5VCIQkVvfUUSkPbCFeL7xPxEAANQvm8JQ7969Bw4c2L179/bt2x87dmzr1q3i/oULF3bu3Pntt9/etGnTunXrWrRo0aFDB/Gt3bt3VwzbAnA+6rhBnNqtcOU7mkNbreVF3pPnMYlUe2R7yeavBaOeU7q4D37ctc9IquF/o3sFsiMjpCN3WlOKhYRfLT8OkA7qcJ/UP9SSl6FPOaKKSWjkjwMAAPWLCYKtc/OcOXMmPz8/Pj7ew8ND3JOdna1QKLy9vbVabW5ubuXiFi1ayGSy6k5z29q1a7dt27ZmzRpbfrRGo3F1dbWxneD0ysvL3dzc7N0KIiJj6rnCr9/gDVpFVBfBqDfdvEREqs69PR95TuLuVefh5WaatNe65SYv5ei9OMm0ol/LfvlC0Trab9YHDd9259F0rgdoCrRarVqtxi1CEIkjyOt9DJYt7uJ2XufOnavsCQoKEjdcXFxatWpVb40CcBCK1tF+M98v+PIV45XTRCTx9PUcM1PV6X4bD3eT0S+DJK+coEXJ/Nyj1o1eA35QrDKmnjNnpMpCWzdkwwEAoGFhZCjAPZGFtvabvVge3t61z8iAl7+yPV2JOEbvxkk2PyBp6coOFSuXuyUSUcEfv9zVSXhNqbW08K4OAQCABoWABXCvpH4h/v/+yPORZzml+p+dYXgYd3GM9PWu3Gq/YVbG6U/vW3askLft7r1gNuUunpnz9lTdiT/+2U8HAIB6h4AF0CSopPRGV8m2CSFn/n979x0eRbU2APw9Z2Z7ymbTswlphHR6ABFQei+KBbFeC1fRzwL2e1VUrHivXgtiu/dar4DYEAFBQCAQQk+DVEjPZpPsbrJ9Zs75/lgMgVQQ2ETO7+HhmZ1MObuZZ+fNe855J3iknArFWzdcuV483Nh9kGXbs0EyG6ngbvridfN3q4BIl6C1DMMwTNdYgMUwvUiiP5p247UAcLtp4yGDe8QP4uPZkr3zEsRUcLf8ugYANKOmIV5m/e1748onidV8yRrMMAzDdIgFWAzTuyji0+VRA3SC6T2fXZTCihwy8Fvx15qOU1nWzJ+kZpM8KiHgxodCHn6T04W4SnINK+53nzx+iZvNMAzDtMUCLIbpdXzGzgaA2RXfHZjLDQtCpc100s/iDb9KDc4zNqOC27p9HQD4Tb8NEJJF9g9d+q4iYbBkaTS++5gta5NXGs8wDMMAC7AYphdSDb2a8wsQak8mm/Oy5vBvjeI0PKw9QdLWCZ8Vn67zbt29XrI0yvslKlMyPGuwxi/o3uU+Y+dQUTB9/ZZ53UrocaE7hmEY5gJiARbD9DqIl2lGzwSAlu3f8hgeSsOHruHHhSGDA27/TZq8UfyxnAhOl3XbNwDgN+2WM/bleO38xbqFS5FMbt31oy37F++8B4ZhmMsbC7AYpjfSXDkTyeTO/KzmzV8BwAB/tGMW/8EYzl8OW6vp3C3S39/7QWox0cik1vRVW+oRkwMWPAwAlu8/YiWyGIZhLj0WYDFMb8T5BuhuexIw17zxs5Zt3wAAAliUhEtukL0xkhvs57qp5lsAuJ1fMH2T+O1JIrR7QrR62ARV+mjisJpW/+vSt59hGOYyxwIshumlVOmjdTctAYQs6z+x7d3oWRmkhKXp+LfAjUGSuUKXuNd/2KYqOn+rpP9KuGun9GM5cbSp6aC9/gGs9nEWZNsPshqkDMMwlxQLsBim91JnTNRecy9Qalr7juPwTs9K6nZat38DAENvuK16oezNUVxqADI64d9FZO4WKfgL4bpfpc9LiMkFnJ/Of+49AGD+dpXUYur4HESyfP+h8e2lQl3FpXpbDMMwf34swGKYXs1n3Fy/mbcDIU1fvO4syAYA6671xGqRxyQrk4bpFPBwGs6bz+dfx7+cwWUEI7sI606Q23ZIIV8KY9aLjwsTjVFDia3ZuGZl+4MTp63hw2dbdnzrKsuv/+eDLNHFMAxzoXDLli3zyolzc3OLi4vnz5/fk43dbrdcLr/YTWL6CrfbrVAovN2KS0cRn04Fl6s0z5GTKdfHWdZ/Qt2ugAWP8EERrdsEK9HYMHRPEr4rEff3QwKBciuctML+BtiA0xaYfuENpQ+VRX7cFHm0iZZboVkAmbnW8eFT7vLjnG+AMnm4UFPmyMkkLWZF4hCEuQ5bQuxWsbGW89FeqrfeI5fb9cB0TRAEmUyGEPJ2Q5heQZIkAOC4jr/TLir+0p+SYZhz5T/rTup0WDN/avh4GVAqj01RJg3rcMtIDVqcghanYLMbDhhpThPNM4V+CX/5a9HKh8o+mIDTN1T6AUCGveDj8pd0UvNJTfRnI5YFhoVM0G5KyvzAmvmTu7Io8I6/c7qQtod1leXb9v7sOLKLCm7ttff5jJt7Kd42wzBMn8UCLIbpCxDSXnc/cdntB7ZBu9pXHdLKYZIeTdIjAICxs+vf2x1UkrONfrxh+GO+eVvnFLzDE2Gb7/D7ox63GlRgIK/ClPSY+A8qXomqKCp+5f71Ix7FSRkpClvSia2qwxtFzwgthAAh83erON8A1ZBxF/ctMwzD9GUswGKYPgIh3cKlWO0LmFMmDj3nfRc8Ynj93qDC7fcq3I6cTADwuWrevBmLUltQSTMtssAxMy2y9L9O9dbyk29ObslesGfZrpwhA2x5MuIWAZrkun1Rk2pSpl5Zvztp338av1wRoPHXDBh0Ud4pwzBM38cCLIbpOzCnvfa+89uVDwr3n3mH+bsPHDmZgLmA+Ys1V84EgGEKGBbUdrRKQKV1mfGXNYG7Ph/XcogCytYO/cR/2i8+GSLi4QQAXPt8YMOdjesrVr3wf+mv0dCYOF8U54difCDaB8X4QrgaIQBiNbvLjyuTM6CT4VwMwzB/bizAYpjLhc+4eY68fUJ1qe72p7rIgUX54KhrF7iHpLtKclVDr742MGymBCXNtMhCiyxQ3Ex/C18Ufcg8sXHXawXPznO8sU0e3HZ3P3A/0vLjzVVrVJKjImpk0fSnIvwVURqI9kHqXv994yZQaKb5JlpogWFBaFY/NlCaYZjz1Ou/8BiGuVAQCl78CpVExMu63VYemyqPTfUsKzhIDUCpAa3RBkenPl6/qjm05OiOxue2z1xRKPiWW6GihSRW7ryv8r+RbiMAOJG8X+W+qq+end/v71asAgCdAvQaFKWBcDWK1IBejSJ+fxmsvEjvuRsUYHMVzTbSvCaab6LFzbRtTfxJevTWKK7NGz9bbhNddZwoMNzSHw8NYtEYwzCnIUqpV0781Vdfbdiw4csvv+zJxlar1cfH52I3iekrWlpafH19vd2Kyx1x2o3vPiZUlcr7JQY/8JpQV27+7kP3iXwAIMH9KsfdXcGHDln/dx+78aRf/6VJz+e6/dpWmT+LHEOYGunVEKpCkZpT/werULgKwtQQrESyzmv2nff1cKiBPrhXyjSc/g7kEMT7oXQditLA58Wk0QU8hsXJeNlQLuDMQhC/1dLXc6SNlae/QNMC0O0D8M3xOFx9Hm358yAUDjbQoUGI81LAabPZ1Go1K9PAeLjdbgDwSqUnFmAxfQ8LsHoJydJo/NcSscnA60JFUz1Qyvnp/GberhkxBRACALHJ0PD+06Kxmg/tF3zfy03KoCobrbLRahvU2mmFDWrttMoGNXZqcp1x5HRHyd2NP6zWTt7jM9CzJkQFIUoUpoZQFQpWQrAShashSAnBSuRLbDFBPppzScc3OOFvB6SPCwmhEKaC2xJwug6lBqBkLVL+PmasyQXPHpQ+OE5EAkFKWD6cuzsRI4AfyslrOWRfPQUADQ93JWKE4KsSYnQCAHAIpkai2xJwWgCqsEKNnVbZoMpGa+y0wgpxfmj5MJyu+9Pe+1sEuHm7tL6CPD0YvzTcO8PvWIDFtMUCrG6wAItpiwVYvYdoqKx/eymxNSO5wnf8fN8J1yOFqu0GUoupYdXfhOoyThcSfN8rfLC+w+M4Jai10xo71Nqp4tCmgXtWcZKbIvRjxOw3wm+vdMml7r6oVDwEKVCICkJUEKRAQUpI0qLBgSgt4IyxXyKB94+R5w5JJhfIMDyYip8dyvl13mWa20QfzpK21VAAGKRDTgkKLRQAgpXwQCp3fwoOVAAACAQ2VpJPi+lPFcTd7sHbbXEI7knCLw7jgrzUK3rxnGihc36R8kwUAJQcHL+ej/bxQpTDAiymLRZgdYMFWExbLMDqVYTqUkfuXs2oaZw2qMMNiMPa8OFz7hP5nG9A0L3LZfr4zg5FBbd57Tu27C0AoEwa5io+SiWRD43SLnzUHJJodNAaOxid1OgEg4MaHGB0UKMTamykyY3snfQ/cggG+KNBgWiwDuk18HoOyW2iADA1Er01ikvS9ug2vO4EeTSbnGyhABDri5am478MwB2O2W90wf9KyVclxOwGvQb0ahTlAxFqFKmBMBX6spSsLCACAa0cnh3KPZCC23d9CgTyTFTNQ6J/XwoRdtbR+VvFBiekBqAYH9hQSW+Kx1+NP+cklk2Ed/LJnGiU0rNfTQdH8GqA9Xi2VGmDz6/i+PN6EN3OOvprNXlmyHnu/gfZRfjuJJkbjX26H6XZAULh25PkqnDsrSGVHWIBVjdYgMW0xQKsPoe6XY3/edF57ABW+fhcNU89fCIfFH7WNmJDbeN/lgvVpUiuDLjxQfWwCUJVSdMXK4S6csCc3+QbfacsRFwHQY3nerCLYHTSegcYndDgpHUOyGuiR5rocfMZ49YBIM4X/XMUnht9bncwhwgfF5JgJVwXi//Ize+4mS7ZJ22spACQ6I/+MYqbEYWKLHS/8dS/w43UKQECuDEev5aB+3kjCXSuPjpOHtgjuQnMjEJfTeAtbpq4VnSKkDWXHxF8Du1vFmDmZnF3He3vh45ey5/HtNPcJnrtFmG8nvtwzPl0UDolWJwp6RTwxsjz2f3tfPLQXgkA3hvNLU4556uk2EKHfy82C/DOaO6Bc98dAMxu2FRJzvsSvXWH9EUJWZSEPzivT++Vo+Tp/dIkPdoy/XzmzxkcMGWjOD0KvZpxPme3izDpZzFQCT9O4dtecyzA6gYLsJi2WIDVF1FJNH2xwn74NwAAhOQxKZqMiaoh47DKBwAceVmmL98gDisfrA+88xlZeMypvQR388bPWravA0plkf11Nz/a+qNWXV8PLgnyTPRoEz3SSEua6dgw/EgaVnq7ONfGSrpkn3TcfGogl61N+g0B9PdHVTbqEEHFw5I0/OQgrn1GwU1gUyX5vIQanfSuRLwgroNkWCuJwoYKUtoCN8ahCPWFjNhEAo9mS//KIwDw2ED8SgbnGdv+twPSy0fImDC0a1ZP77VNLpi6UTzQcOqW9Ega/ueoc/s95TTRST+LnsFw303m5p1jDC0QmL9VWl9BAODbSdw1Mee2++46OuFn0RPNh6ig+AZZF13P7TlEuOJH8WgT9exeeoPsXNNIDhHG/iQebKAvZ3BPDTrnCOt/pWThdgkAZBiOXcfH+53bdXKkkY78QfT0j++YyV8Vfs6X2ZxfpPUVhEOQO59PPvf85f/tkd4tIADww2RuTptfPQuwusECLKYtFmD1VZS6So7a9v/qOLqbuhwAgGRyZcpIzi/Auns9UKoaNCbgpiVYefY0PFdZvunLN8TGWiSTa0ZN04yZJQvt1/rTC389UGo/sI0P1stjki7kYc8kEFhZQJ4/LJlcoNegjCCUEYxGBKPhwUgrhworfWI/WV1KKECEGr00HN+WgDECANhXT78oIV+XkQbn6aNFadBDaXhREvY9865scMDHheTD46TCempo1N2J+IlBOFLTwQ3ME4etPEYONtCb4vETA7G+o81aVdro3TulX6qpgoMPxnC3J5y+q7UIkLBGMDjgm4nc/Njub/ae7EVOE+3vh14fgW/YJhEKu2bxo0N7eqM90kgnbxQbnBDrQ09YUYQaFVzH+/f4ripRWLhdWlNG5BjcBCI1qOA63rfHIU6NnQ77TqxzwGMD8d56uruOnusw/7t2Sv8uIklapJVDVj1dNpR7bug5BEkUYOF26etSAgC+Mii+QRaq6nan0yqsdNC3otkNCf6o2EJv7Y8/u/ocGu+UION7Mc9E+/mgCisdG4Z29jiw9vi4kNyzS/Is3xCHV084t9h6Ww2d9LPoiWaGBqED804nsViA1Q0WYDFtsQCrr6NupyNnj/3Ar86iw0AIAADm/Gf9xXf8fOhk6Ax1Ocw/fGTbuxEoBYQU/Qf5jJ2tShsFmLuw14NkaWz64nVX8VFASDV4rP+sO/nAsAt18PYcIjQL0Nm9cG89fXivlG2kADAsCM2IQmvKqGeUPQAM1KHbErBWDm/mkXwTBQB/OdybjB9MxRFqtLOOvn+MfHvi1KD7BH+U5I82VBJCQcHBnQPwk4NO9z8anfBJIfng+KlxZh4KDu5KxE8MPLubklD4pZquOkZ+qiAShVAVfDupg0jog+Pk3t1SvB8quI6XdxkqVNvopI3ScTNN0aItM7gINfIkwBL90eFreFUP7tSHG+nkn8VGF8zuh/8zyjnnN8UeA+15VxcFuHOn9N8i4i+HLdP5B/ZI2Ub6cBp+s2cpNIHA+A1ipoFOjECbp/MHjPSKH0UlD0XX8x0Gsu39p4jcuVPS8JA1l7e4Ycx60VcGJTfIQnocJL18hPztgOQng3QdyjTQe5JwzztJJQoTNog76+i8aPzmKJy4VpTouaWRHt0n/SOXDPBHv83i074RGl2weTo/Rd/T3U+00EHfii0CrBjJPXNAchM4fA0/sMeTbZsFGLhOLLfSvw3G/ymiNXb6/WSudQyAFwMsbtmyZZf+rACQm5tbXFw8f/78nmzsdru98ukwvZPb7VYoFN1vx/RWiONlEbHq4RM1o6ZxfjrEywJufFA9bHxn0RUAIF6mSh2pGjQGgIqGSrG+ynF4p23fL9TtogEhSl//C9IwR+6ehg+eEesqsI8/ABWqy2x7NlCXQx6diPiL8hUkw9BFT1CUBt2dhPv7of1GKLTQnXW00QXhavhrEl41hls2lBsdioYGoftScEYwrrbTQgtkGui7+eTzEvqPXOKZ0Dc3Gr89mntzFLcwHl8Xi5vckNtE9xvpygJSZQMeoRcPk7t3SZurqCeB8fch3LKhnFWAXBPN/n2z9ACkVaAGJ7xbQO7YKb2TTwotVM7BjXF49QQuraN74ZBAtO4kLbRQnQJdEdLpb/ZECx2/QSpupoMD0a8z+TAVAoAxofi7cnrMTN0EJuu7SeQcbKCTN4pNLpgbjddO4rAkjNPLPymi+430qnAc49vNfZoC/N8e6aPjxEcGm6bxo0LQ8GD0SRHZV09nR+PwHvSoPrRXWneS9vNBv0znfWQQqUHHzHCkkTa5YF4P+hlzmui1WyWRwMdjucl63M8HHWmEnCbqlGBGVI+SWD+Wk79mSgjB2on8XxLxB8fJoQZ6TQwOVfUoRnkth/y7iISrYeM0PkKDau2QbaT1Drg+rkdn/62W3pcpcRh+mson+iNAaGs1LW6mdyf2aHdCYd4WqbgZbozD/xzFNbpgbz2ts8OC+J4m8O7fI22vpRnB6POreQUHm6poUTMsSvIkfEGSJADgOC8MC2AZLKbvYRmsyxxx2u3ZW6yZP4mGSvCEa1EJ8sj+sqgEeVSCLKzfeTwAkQpu83erbHt+BgBlSoZu4VIqSZYN/7Xv3wqUYh9/v2m3+oye7q1HK9pFeDufVFjp3Gg8Sd9pDc9sI30jh3x7kkgUwlRwdxJelISj2iVRjpnpS0fI16WktfgFh2BWP7w4BU/Wnw5yj5np8sNkdRmRKMgwjA1DmQbqkgAAYn3RoiR85wDcdYplYyWdsVkMUEDJDTJdR38TFVnoxJ+lKhsdGYI2TuXbVnPNNtLRP4oAkDmbH9l5fHaggU7ZKJpccE0MXj2Bk+FTswiXH6HPHpQS/NHR7nJgj2dLK3KIkoMNU/kJEadOtCRLejOPZASjvXP4riumflZMbv9NUnKwazY//Pdq/idaaPI3okDgwDx+SGBX+zcLkPG9WGQ5I+dUYKYD14kcgoIejIXKN9ErfhRbBHglg3tyEAaAh/ZKb+eTyXr0Sw8Gmx9ooKN/FEUCm37POVXbaP81okuCw9fyg7pLIzULMOhb8WQLfXYIfn4YBwA2EeJXCwYHrJ/C9+RhU6/nkCeypQg1yp3P6xRgcED8asEuQva8059nFzZU0lmbRSUHh67hk7XIKUH8arFtEotlsLrBMlhMWyyDdZlDvEweneQzZrYiPp267EJ9lWSqd1cUOfP22jI3tGz7xpm/T6gqERuqJUsjdTsAYSxXdpEeE6rLGlY97Tx2APEy7dxF2mvvQwoVVqpV6aNVaaPE+iqxrsJZkG0/shurNXxgWE+eNXRhyTCMCUMz++H+fgh3ftPRa9D1cfjWBHxtLP7nKG6SHvvLO9g6WImujcEL4nGLAG4C9yTiz67m7k3G8X5nfEbBSjQ/Ft8Yj5sFyDXR0mYAgJn90Juj+Lev4MaGIU13H0OCP9proPkmcBOYFnlGQsIlwWcl5NYdUo0dxoWhTdPOHi+l1yCbCLsNdI+B3p3Y8bS4bCOdslE0u2F+LP56AucZ5i8IgkwmGx2Kvy+nx8xUpDCp8xzYC4fJS0eIHMO6yfzUyNPv/spQ/EUJPWamISrUxUTIw42nkk+rxnCz+p0+S4ACmd2wx0BLmultCV1lYm7ZIe2qo0MC0TeT+Nb3GKxEFTY40EDrnXBdlyPYGl0w8WepzgEL4/E/R50KBUeG4I8LSb4JMoLxgC6LfdhEmLpRMjrh4TR8/+/zFv3kqMkFe+tptQ1u6i6NdF+mtKOWDg9Cn159KhKVY+Ax2lxFCy20NY3UmdwmunC7RCismch7CvD6yMDihkwDrbLRm/t3c/YmF0zfJFoFeHXEqYHtPAYZPiOJxTJY3WAZLKYtlsFi2mo21inMde7KEqGy2F1ZLDbWQruvNcTLOG0Q5x+ENX5IrkByJVZpkEyB5ErqcrRsW0sFtyysn+7WJ2X6uPancOTusfz4iWisBs/A/KThqsFjlKmj2o/Hv3iI006dNk4b3P2mF0FJM91RSyfr0bnWDs1pokO/EzkE+dfx/f0QADQ44f1j5L0CyeAAAJiiR99N7rgig1OCId+Jx830qUH45TOn7lfa6Lv55P1jpEWA62PxV+NPF45qrYPlyYEhBPvm8O2fFClReD2HPL1f4hB8PYFrH8d8X06u2SL5y+HYdbIOH3/U5ILh34snWjoe7GVyQf81QpMLNkzlZ0R1/KH9K488nCVp5XBg3tmZqiobHbBWdIpwYF4HjfcQCUzdJG6roRnB6LeZZyTq3sojj2RJyVqUcy3fRcmGv+6WPjxO0nUoey7fdmptvQPi1whWAfZ1WWjD8xGpeDg474wBW04J+q8Rq2103STu2s47Sd0ERnwvHm2i9yXjlVeePn2DE+JWCy0CZM7uZpbDwu3S/0rJ2DC0YybfGsqdlcRiGaxusAwW0xbLYDFtuQlVh0crYlNUg8f6jJvrc/U1ysRhcn0cHxLJ+QVihZISQp12YrdKpnrRUCnUnBAqi90nClwlOa7CQ67SXCCSZvSMwL88wwV0HL7IQqM0V87ktUHUaRcb60RDpSMn0/rbd0JFIUgS1vhd1EiLCm7r9m+a/r28ZetqR14WiAIfGI7kl7SYo06BhgYhbUf5sK6FqlClDfY30GobDNKhvx8gd+yUtlRTmwhDAtGKkdwrGZyik+QCj2FYEPpvEdlTT2dGYU+Bif1G+mg2uXe3tKuOugl45ru1jSE8GSyEkF6DLAJkGuh+I70rEbfegAUCnxYTz5xBjOA/47gO8zRJWnS4EXKaaKUNrm8XfmUa6O2/SXkmOjIErZnQQRCj4kHOoc1V9GgT/Wsybp/J2VVHb9khUQr/m8CNDj17fz85anZDpoGeaKG3dpIDe2ivtLqMhqvh1xm8TnnGCYYGodVltNDSVQbuh3LyWDZRcrB5On9W8Q6NDKwC7DbQCmunZ693wIzNok2EN0ackb0DAB6DkoOfK2mBCe5Nxp3ljv+WrLs+AAAXIElEQVR+QFp3kib4o3WTzpgGoebBLsHOOlpu7Sr/980J8uxBouFh0zQ+sM3b5zHIMWysooUWWJSMCctgdY1lsJi2WAaLaasn1wMV3JLZKFkaic1C3W4quIjDSt1OKriJy6FKHaVMyejh6aTmJkdOpuPILldZ3qkpkABIJueDIvigcD4wnPMsBEXwupA/OmaLUvuBbZYN/5XMRgBAciV1OwEAcbwyebh6xGRl6sgOi6/2KrV2GLBWsAqAERAKGMGMKLQknRvfs1JJS/dJ/8wl6Tr0zBD8rzzieTi3DMN1sXhJOm4/TKdtJXebCAPXiWUt1DM+yVMt9o3cU0Ur4nzRayNwF31wFVaa8o1oE2HjNH7a7x2I22ro8sPS9loKABFqtG8u19lUQTeBlG/E0mb64RjunqTTZ9lXT1/LIT+UE0JhaTrurKipyQXxawSTC36Zzk8+c0aewQGvHpXeyiNKDrbP5Ed1NEbth3Iyb4sUpITiG2TadgmK0mY66kexwQlvjeIeSuvgE2hyQdxqweKG32bx48I6OP7cLdKP5WRiBNoyg2//YzeBAWvEciv933iuw+HqmQZ61U8iAOyazbefA2F2Q+zXgtkN22fyV3d0nRgckL5OMDph5ZXcfclnH79tEmt6uAisTEMXWIDFtMUCLKYtb10PUovJkZPpOJop1JQRq6X9BojjOV0oH6znQyL5oAg+OILTBmGVD1b5IFn3X/eu4iPmHz4WqkoAQBYZr51zjzwu1ZmfZcve6jx2AIgEAFjjp0zJQLKzE7pY7atMGqaIS+06wqOSSKwWzj/wHN72eVl+mDxzUFLxcFt//Eg6PqenANlFGPydWPx7cYoABSxKwg+kdFzKC9o9KmdrNZ2yUVTysCQNf1xIPP2SqQHoqUH4xrjuK56/kUse2yfF+aLc+fxvtXT5EWmPgQKAVg4PpuKH0rgOB++3WlNGbtwmhamg+AaZRgabKunrOdKO2lM1yf6ajFeM4LqoELsihzyeLQ0LQvt/L+xU1kJX5JD/FhFPuf9Pr+Zu7Xyg0vgN4o5aelYMV2WjLx8hnxQSN4GpkWjjtA7CI48XDpPnDkrjwtBvZxa12lpNXzws7ayjWjnkzOfbz6Lw8JS2StKivPlnTxTYVkPv3iWdaKF/G4yXd1ItzHPNdFhSq8kFt+4Qf66kk/Vo8/SO2/9OPnlwrzQkEO2dSdCZARYFWF9OEILZ/br79f8xLMBi+h4WYDFt9YbrgTjtUkOt2FgjNtSe+tdYI5mM7UeDeSBehlU+SKXBKh+sVCOVBis1WKVBSjVWapBC5cjJdBZkAwCnDfafebt6+MS2g/SJ1Ww/uN2WvVWoLu2iVVjlo0wapkwdqUwejjV+rU11nyhwn8h3lea5Kwqp4OaD9arUkcrUkYr4tM4CMmK3SpYGPiSyi4QZFQVX8VFH7h6hskQel6IZNa217L5IYFMVHRWCzu8R15kGOmGDGO2LHkrFdwzAmi5zdu2fRXjnTuk/RadyjRnB6OnBeE6/DvrsOiQSGP69eLSJhqrAE5wFKeGRNO6BVNyTQu0UYPSPYlY9nR+LSyzUU6hdK4fFKfjBVK7bWqAOERLXipU2+r/xXHIAeu0oWVNGJAoYwdxo/OQg3PWTiA430uHfi/zvsxFr7PSVI+SjQuKSgENwUzx+6wousPMAsVmAuK/PKGq1sZK+eFjaW08BQKeA/17FdRGjCASSvxFLm+mnV3Genj6XBF+Vkn/lEc/nMCwI7ZnTaY20FgHiVgsNzjNKatU74M086b0C0iKAvxxyOw/vWpNY34yns6NOBVie0Oq5Q+RII5VhcP5F1sPL4PywAIvpe3rDDZXpPXrt9UBFQWyoEY3VorHGsyA1m6jDShxWKri73R0r1b6TbvS56pou0l1CzQl3RZEnm9WW2FjrzM8W6sp/PxaWRyfJwmPc5YVC7YnWnk1ACCtUxGk/tZXaR5k0TJl2hSImRTQZREOlUFcu1JWLdRVScxMAIJlcFtlfHp0kj05UxCRzASEAQJw2Z8F+Z+5e57H9rYfykEcnaUZNVQ+9GinODiUkS6NQXSoYKjn/QFlEnCwkEnBX6QSbCCoOenI7bB9gmVww8WdRK4c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"plot!(validation_losses, label=\"Validation Loss\", linewidth=2, size=(800,400))" - ], - "metadata": {}, - "execution_count": 8 - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "using Literate #src" - ], - "metadata": {}, - "execution_count": 9 - }, - { - "cell_type": "markdown", - "source": [ - "---\n", - "\n", - "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" - ], - "metadata": {} - } - ], - "nbformat_minor": 3, - "metadata": { - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - }, - "kernelspec": { - "name": "julia-1.10", - "display_name": "Julia 1.10.0", - "language": "julia" - } - }, - "nbformat": 4 -} diff --git a/docs/src/workflow examples/Hyperparameter Tuning/tuning.ipynb b/docs/src/workflow examples/Hyperparameter Tuning/tuning.ipynb deleted file mode 100644 index 3b199e70..00000000 --- a/docs/src/workflow examples/Hyperparameter Tuning/tuning.ipynb +++ /dev/null @@ -1,897 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Hyperparameter Tuning with MLJFlux" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "In this workflow example we learn how to tune different hyperparameters of MLJFlux models with emphasis on training hyperparameters." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "**Julia version** is assumed to be 1.10.*" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Basic Imports" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "using MLJ # Has MLJFlux models\n", - "using Flux # For more flexibility\n", - "import RDatasets # Dataset source\n", - "using Plots # To plot tuning results" - ], - "metadata": {}, - "execution_count": 1 - }, - { - "cell_type": "markdown", - "source": [ - "### Loading and Splitting the Data" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", - "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", - "X = Float32.(X); # To be compatible with type of network network parameters" - ], - "metadata": {}, - "execution_count": 2 - }, - { - "cell_type": "markdown", - "source": [ - "### Instantiating the model\n", - "Now let's construct our model. This follows a similar setup the one followed in the [Quick Start](../../index.md#Quick-Start)." - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ Info: For silent loading, specify `verbosity=0`. \n", - "import MLJFlux ✔\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" - }, - "metadata": {}, - "execution_count": 3 - } - ], - "cell_type": "code", - "source": [ - "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", - "clf = NeuralNetworkClassifier(\n", - " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", - " batch_size=8,\n", - " epochs=10,\n", - " rng=42\n", - " )" - ], - "metadata": {}, - "execution_count": 3 - }, - { - "cell_type": "markdown", - "source": [ - "### Hyperparameter Tuning Example\n", - "Let's tune the batch size and the learning rate. We will use grid search and 5-fold cross-validation." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "We start by defining the hyperparameter ranges" - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "NumericRange(0.0001 ≤ optimiser.eta ≤ 1.0; origin=0.5, unit=0.5; on log10 scale)" - }, - "metadata": {}, - "execution_count": 4 - } - ], - "cell_type": "code", - "source": [ - "r1 = range(clf, :batch_size, lower=1, upper=64)\n", - "r2 = range(clf, :(optimiser.eta), lower=10^-4, upper=10^0, scale=:log10)" - ], - "metadata": {}, - "execution_count": 4 - }, - { - "cell_type": "markdown", - "source": [ - "Then passing the ranges along with the model and other arguments to the `TunedModel` constructor." - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "tuned_model = TunedModel(\n", - " model=clf,\n", - " tuning=Grid(goal=25),\n", - " resampling=CV(nfolds=5, rng=42),\n", - " range=[r1, r2],\n", - " measure=cross_entropy,\n", - ");" - ], - "metadata": {}, - "execution_count": 5 - }, - { - "cell_type": "markdown", - "source": [ - "Then wrapping our tuned model in a machine and fitting it." - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "mach = machine(tuned_model, X, y);\n", - "fit!(mach, verbosity=0);" - ], - "metadata": {}, - "execution_count": 6 - }, - { - "cell_type": "markdown", - "source": [ - "Let's check out the best performing model:" - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.1, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 10, \n batch_size = 32, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" - }, - "metadata": {}, - "execution_count": 7 - } - ], - "cell_type": "code", - "source": [ - "fitted_params(mach).best_model" - ], - "metadata": {}, - "execution_count": 7 - }, - { - "cell_type": "markdown", - "source": [ - "We can visualize the hyperparameter search results as follows" - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Plot{Plots.GRBackend() n=4}", - "image/png": 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"cell_type": "code", - "source": [ - "plot(mach)" - ], - "metadata": {}, - "execution_count": 8 - }, - { - "cell_type": "markdown", - "source": [ - "### Learning Curves\n", - "With learning curves, it's possible to center our focus on the effects of a single hyperparameter of the model" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "First define the range and wrap it in a learning curve" - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ Info: Training machine(ProbabilisticTunedModel(model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …), …).\n", - "[ Info: Attempting to evaluate 25 models.\n", - "\rEvaluating over 25 metamodels: 0%[> ] ETA: N/A\u001b[K\rEvaluating over 25 metamodels: 4%[=> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 8%[==> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 12%[===> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 16%[====> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 20%[=====> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 24%[======> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 28%[=======> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 32%[========> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 36%[=========> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 40%[==========> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 44%[===========> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 48%[============> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 52%[=============> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 56%[==============> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 60%[===============> ] ETA: 0:00:01\u001b[K\rEvaluating over 25 metamodels: 64%[================> ] ETA: 0:00:01\u001b[K\rEvaluating over 25 metamodels: 68%[=================> ] ETA: 0:00:01\u001b[K\rEvaluating over 25 metamodels: 72%[==================> ] ETA: 0:00:01\u001b[K\rEvaluating over 25 metamodels: 76%[===================> ] ETA: 0:00:01\u001b[K\rEvaluating over 25 metamodels: 80%[====================> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 84%[=====================> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 88%[======================> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 92%[=======================> ] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 96%[========================>] ETA: 0:00:00\u001b[K\rEvaluating over 25 metamodels: 100%[=========================] Time: 0:00:04\u001b[K\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": "(parameter_name = \"epochs\",\n parameter_scale = :log10,\n parameter_values = [1, 2, 3, 4, 5, 6, 7, 9, 11, 13 … 39, 46, 56, 67, 80, 96, 116, 139, 167, 200],\n measurements = [0.8062291224242571, 0.7349032636328473, 0.6831822864090799, 0.6499205331218364, 0.6248770254396706, 0.606830885162984, 0.592554407591952, 0.5716582179222147, 0.5568372147591829, 0.5458850958793409 … 0.20880982517086102, 0.17360248501543618, 0.1304176223923372, 0.10766664152601196, 0.10348057744910813, 0.10307123308456925, 0.09357906967304538, 0.09787030345670497, 0.10027104135450549, 0.09926870681190969],)" - }, - "metadata": {}, - "execution_count": 9 - } - ], - "cell_type": "code", - "source": [ - "r = range(clf, :epochs, lower=1, upper=200, scale=:log10)\n", - "curve = learning_curve(clf, X, y,\n", - " range=r,\n", - " resampling=CV(nfolds=4, rng=42),\n", - " measure=cross_entropy)" - ], - "metadata": {}, - "execution_count": 9 - }, - { - "cell_type": "markdown", - "source": [ - "Then plot the curve" - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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"execution_count": 10 - }, - { - "cell_type": "markdown", - "source": [ - "---\n", - "\n", - "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" - ], - "metadata": {} - } - ], - "nbformat_minor": 3, - "metadata": { - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - }, - "kernelspec": { - "name": "julia-1.10", - "display_name": "Julia 1.10.0", - "language": "julia" - } - }, - "nbformat": 4 -} diff --git a/docs/src/workflow examples/Live Training/live-training.ipynb b/docs/src/workflow examples/Live Training/live-training.ipynb deleted file mode 100644 index 283a9b47..00000000 --- a/docs/src/workflow examples/Live Training/live-training.ipynb +++ /dev/null @@ -1,11077 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Incremental Training with MLJFlux" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "**Julia version** is assumed to be 1.10.*" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Basic Imports" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "using MLJ # Has MLJFlux models\n", - "using Flux # For more flexibility\n", - "import RDatasets # Dataset source\n", - "using Plots # For training plot" - ], - "metadata": {}, - "execution_count": 1 - }, - { - "cell_type": "markdown", - "source": [ - "### Loading and Splitting the Data" - ], - "metadata": {} - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "iris = RDatasets.dataset(\"datasets\", \"iris\");\n", - "y, X = unpack(iris, ==(:Species), colname -> true, rng=123);\n", - "X = Float32.(X); # To be compatible with type of network network parameters" - ], - "metadata": {}, - "execution_count": 2 - }, - { - "cell_type": "markdown", - "source": [ - "### Instantiating the model\n", - "Now let's construct our model. This follows a similar setup to the one followed in the [Quick Start](../../index.md#Quick-Start)." - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ Info: For silent loading, specify `verbosity=0`. \n", - "import MLJFlux ✔\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": "NeuralNetworkClassifier(\n builder = MLP(\n hidden = (5, 4), \n σ = NNlib.relu), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing))" - }, - "metadata": {}, - "execution_count": 3 - } - ], - "cell_type": "code", - "source": [ - "NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux\n", - "\n", - "clf = NeuralNetworkClassifier(\n", - " builder=MLJFlux.MLP(; hidden=(5,4), σ=Flux.relu),\n", - " optimiser=Flux.ADAM(0.01),\n", - " batch_size=8,\n", - " epochs=50,\n", - " rng=42\n", - " )" - ], - "metadata": {}, - "execution_count": 3 - }, - { - "cell_type": "markdown", - "source": [ - "Now let's wrap this in an iterated model. We will use a callback that makes a plot for validation losses each iteration." - ], - "metadata": {} - }, - { - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "ProbabilisticIteratedModel(\n model = NeuralNetworkClassifier(\n builder = MLP(hidden = (5, 4), …), \n finaliser = NNlib.softmax, \n optimiser = Adam(0.01, (0.9, 0.999), 1.0e-8, IdDict{Any, Any}()), \n loss = Flux.Losses.crossentropy, \n epochs = 50, \n batch_size = 8, \n lambda = 0.0, \n alpha = 0.0, \n rng = 42, \n optimiser_changes_trigger_retraining = false, \n acceleration = CPU1{Nothing}(nothing)), \n controls = Any[Step(1), NumberLimit(100), WithLossDo{typeof(Main.var\"##365\".plot_loss)}(Main.var\"##365\".plot_loss, false, nothing)], \n resampling = Holdout(\n fraction_train = 0.7, \n shuffle = false, \n rng = Random._GLOBAL_RNG()), \n measure = LogLoss(tol = 2.22045e-16), \n weights = nothing, \n class_weights = nothing, \n operation = nothing, \n retrain = true, \n check_measure = true, \n iteration_parameter = :epochs, \n cache = true)" - }, - "metadata": {}, - "execution_count": 4 - } - ], - "cell_type": "code", - "source": [ - "stop_conditions = [\n", - " Step(1), # Repeatedly train for one iteration\n", - " NumberLimit(100), # Don't train for more than 100 iterations\n", - "]\n", - "\n", - "validation_losses = []\n", - "gr(reuse=true) # use the same window for plots\n", - "function plot_loss(loss)\n", - " push!(validation_losses, loss)\n", - " display(plot(validation_losses, label=\"validation loss\", xlim=(1, 100)))\n", - " sleep(.01) # to catch up with the plots while they are being generated\n", - "end\n", - "\n", - "callbacks = [ WithLossDo(plot_loss),]\n", - "\n", - "iterated_model = IteratedModel(model=clf,\n", - " resampling=Holdout(), # Split the data internally into 0.7 training and 0.3 validation\n", - " measures=log_loss,\n", - " iteration_parameter=:(epochs),\n", - " controls=vcat(stop_conditions, callbacks),\n", - " retrain=true # no need to retrain on all data at the end\n", - " )" - ], - "metadata": {}, - "execution_count": 4 - }, - { - "cell_type": "markdown", - "source": [ - "### Live Training\n", - "Simply fitting the model is all we need" - ], - "metadata": {} - }, - { - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ Info: Training machine(ProbabilisticIteratedModel(model = NeuralNetworkClassifier(builder = MLP(hidden = (5, 4), …), …), …), …).\n", - "[ Info: final loss: 0.11905657006943889\n", - "[ Info: final training loss: 0.07196077\n", - "[ Info: Stop triggered by NumberLimit(100) stopping criterion. \n", - "[ Info: Retraining on all provided data. To suppress, specify `retrain=false`. \n", - "[ Info: Total of 100 iterations. \n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "Plot{Plots.GRBackend() n=1}", - "image/png": 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AgFuoMwiZIwQAuImf401LS0uzsrJ0Ot2QIUNCQ0Nr/evFixcvXbpUc0+XLl30en1+fn5xcbGyR6/Xd+nS5bZfRI8QAOA2jgbhxYsXe/Xq1a1bN4vF8tJLL/34449NmjSp2WD79u3r1q1TtgsKCq5cuXLlyhUhxNy5cw8fPpyUlCSECA0N/eyzz277XWH+BCEAwE0cDcIlS5b07dv3o48+EkI8/PDDy5Yte+2112o2mDhx4sSJE5XtCRMmREdH+/n99uHPPvvsH//4R8drokcIAHAbR+cIt27dOnbsWGV77NixW7duratlSUnJ559/PnXqVPueX3/9dfv27fn5+Q5+F3OEAAC3cbRHeOHChcTERGU7KSmpoKCgrpbr169v27atfS7Qz89vz549hw8f3rt375gxY1auXClJ0i3fWF1dvWbNmpCQELPQ3/DLyMraOXDgwIb8LD7KarVarVZPV6F5yjHU6dR4+Zi2cEI6BYfRKWw2myzfvlvlaBBarVa9Xq9s6/V6i8VSV8vVq1c/8cQT9pdr1qxRxkgvXbrUtWvXTz/9dPTo0XV9xeHDhwMCAoQQtp4Zuafz+/VjhPT2zGaz2cyBaiyz2Wyz2TxdhTfghHQKDqNT2Gw2R36vHQ3ChISEwsJCZbuwsDAhIeGWzY4ePXrixIlx48b9/xf8e6YwISFh4MCBhw4dqisIg4KC/vrXv0ZFRQkhPl1nHv/o9MAAB6vzaWazOTAw0NNVeAO9Xm8wGDxdheZxQjqFxWLhMDaezWarqKi4bTNHB4L69++flZWlbGdlZaWnpyvbJSUlNfvvK1eufOSRRyIjI2/+BKvVevToUeXy0dsKZ5oQAOAWjvYIZ8+e3adPn/DwcIvFsmHDhv379yv7mzRpkpWV1b9/fyFEdXX1hg0bNm7caH9XdXX1oEGDBg8eHBoa+sUXX1gsloyMDEe+jgtHAQDu4WiPMDU1de/evTabzc/Pb//+/a1atVL2L1u2rHXr1sr29evXFy1aNGDAAPu7DAbDc889Z7Var127NnXq1Ozs7JtX4t8SQQgAcA/JkStq3CM6OjovL0+ZIxz2D8sLnfRDE299fSlqMhqNYWFhnq5C8yorK5kjdApOSKcwmUwOdhtQD2WOMCQkpP5mKr1YPMxfMlarJaEBAF5MrUHIQ+oBAG6h3iBkjhAA4AYEIQDAp6k2CFlHCABwB9UGIT1CAIA7qDgIqz1dBADAB6g4COkRAgBcT7VByBwhAMAdVBuE9AgBAO6g1iD0JwgBAO6g1iCkRwgAcAvVBiFzhAAAd1BtEAqTWZCEAABXU2kQ6iXhrxcVFk/XAQDwdioNQiFEONOEAADXU28QMk0IAHADNQchPUIAgMsRhAAAn0YQAgB8moqD0F8qrWaOEADgWioOQnqEAADXIwgBAD5N5UHI0CgAwLXUG4R3+UslPKQeAOBi6g3CSH9xvcrTRQAAvJ2KgzBAXKdHCABwMTUHoXS9ijlCAIBrqTgI/UUxQ6MAABdTbxBGBTBHCABwOfUGYWSAVMzQKADAxdQbhMF+QghRafV0HQAAr6beIBRCRLCCAgDgYqoOQkZHAQCupuog5HoZAICrqToIIwPEdZ7EBABwJXUHob/EUkIAgEupOgijAhkaBQC4lqqDMNKfu6wBAFxL3UHIfbcBAC6m+iBkaBQA4EqqDsIo1hECAFxM1UHIs3kBAK6m7iBkjhAA4GKqDkKGRgEArqbqIORiGQCAq6k6CP11wqATJrOn6wAAeC9VB6EQIipA4najAADXUXsQMjoKAHAp1Qehv+C+2wAA11F7EEYFcLtRAIALqT0IWUoIAHApDQQhQ6MAANdRfRDyJCYAgCupPQijuGoUAOBKag9C5ggBAC6l/iCUiisZGgUAuIragzCKHiEAwJXUHoQ8khAA4FKqD0IW1AMAXEn9QShKzMJGFAIAXEPtQaiXRLBeGHkSEwDANdQehEKIqECeUw8AcBUNBCHXywAAXKcBQWi1WvPy8oqLixv6HVVVVXl5eUajsaFvVLCmHgDgOo4GYW5ubps2bX7/+9+3bNlywYIFNzcYOXJk1L8FBgYOGDBA2b9r166UlJTRo0enpKSsW7fuDkqMDGBoFADgKo4G4Ysvvjh27Njjx48fPnz4L3/5y4kTJ2o12Lp1a/G/tWvX7rHHHhNCyLI8ffr0RYsWHTly5Ouvv/7jH/9YUlLS0BK53SgAwHUcCsIbN2784x//ePrpp4UQzZo1GzFixIYNG+pqfPDgwdzc3Mcff1zZvnz58oQJE4QQPXv2bN269ZYtWxpaInOEAADX8XOk0fnz5/V6fbNmzZSXLVu2PHv2bF2NP/jggzFjxkRERAghzp0717x5c4PB4MgbbTbbzz//HB4erryMi4tLSkoSypr6aoZGAQAu4VAQGo3GoKAg+8vg4ODS0tJbtqysrNywYcOnn35a1xvruWSmvLx81qxZfn6/ldSnT59FixYJIYKFPtckmUwVjpTqg8rKyiRJ8nQVmldZWanX6+1/tOGOcUI6RVlZmadL8AY2m02Wb9+PcigI4+LijEajxWJRUqq4uDg+Pv6WLT/99NOIiIj+/fvb33j9+nX7vxYXF6emptb1LaGhoTt37oyKiqq1PyHctueaHBoa6EipPkiW5dDQUE9XoXl+fn4EoVNwQjoLh7HxbDZbRcXtO1EOzREmJydHRUXt379febl3796uXbvesuXq1aunTZtm/3uwY8eO586dKywsVArav39/XW+sBw+pBwC4jkNB6O/vP2PGjNmzZ//4449/+ctfcnJyxo0bJ4TIzs5u27atvdnZs2d3796dkZFh35OUlDRq1KgZM2b89NNPs2fPjomJsS+rcFxkgCjmYhkAgGs4unxi3rx5I0eOnDNnzk8//bRjxw7lkpawsLDevXvb25w5c2b+/PnJyck137h69eq77777hRdeMBqN27Ztu4PJAx5JCABwHcmRiUT3iI6OzsvLu3mO8Ea1SNlovjGRyZtbMxqNYWFhnq5C87hYxlk4IZ3CZDIxR9h4yhxhSEhI/c00cK/RcIMoswiLzdN1AAC8kQaCUCeJcIMo4UlMAAAX0EAQCp5TDwBwGW0EYRQXjgIAXEMbQcjtRgEALqKRIGRoFADgGloJQoZGAQAuoY0gZE09AMBFtBGEDI0CAFxEI0HIxTIAANfQSBAyRwgAcA1tBGEUD6kHALiGNoIwMoChUQCAS2gkCP0ZGgUAuIQ2gjCKq0YBAK6hjSAMNYhqm6iyeroOAIDX0UYQCiEiA8QN1tQDAJxNO0Hoz+goAMD5tBOELCUEALiAZoKQ240CAFxBM0HI7UYBAK6gnSDkdqMAABfQTBBGMUcIAHABzQRhJLcbBQC4gIaCkKFRAIDzaScImSMEALiAdoIwQCrmqlEAgLNpJgijGBoFALiAZoIwMkBwsQwAwOk0E4RRARLLJwAATqeZIAzUC4NOGM2ergMA4F00E4RCiMRg6WI5o6MAAGfSUhA2DRYFZZ4uAgDgXbQUhIkhUgE9QgCAU2krCOkRAgCcTFNBGCwVlNEjBAA4k6aCMEQUlHu6CACAd9FUENIjBAA4m6aCkB4hAMDZtBSECcHS1QrZSp8QAOA8WgpCvSSiA8WVCpIQAOA0WgpC8ds0oaeLAAB4Ea0FIWvqAQBOpbkgZE09AMCZtBaErKAAADiV1oKQFRQAAKfSWhDSIwQAOJXWgpAeIQDAqTQXhPQIAQDOpLEgDDcIIYTR7Ok6AADeQmNBKIRoyjQhAMB5tBeETBMCAJxIg0FIjxAA4DwaDEJ6hAAA59FiENIjBAA4jQaDMJjbjQIAnEaDQcgDKAAAzqPBIKRHCABwHu0FYXywdK1Sttg8XQcAwCtoLwj1kogJlK5UMDoKAHAC7QWhYAUFAMB5tBmErKkHADiJNoOQHiEAwEk0GoT0CAEAzqHNIGQFBQDASRwNQlmW/+u//isxMTEpKen111+/ZZvCwsKMjIy4uLimTZu+9NJLys65c+d2/7eBAwc6pWjW1AMAnMXPwXYfffTRxo0bf/jhB6vVOnDgwPbt2z/88MM1G5jN5vvvv79nz54HDhwwGAx5eXnK/vz8/CFDhowZM0YIodfrnVI0PUIAgLM4GoQrV66cNWtW8+bNhRDPPPPMqlWragXhli1bSktL33nnHSXt4uPj7f+UnJyclpbmvJrpEQIAnMbRodGTJ0927txZ2e7cufPJkydrNcjOzk5LS3v22Wc7duz48MMP12ywePHitm3bjhw5cu/evU4pOswgJCFKzU75MACAT3O0R1hcXBwWFqZsh4eHFxUV1Wpw8eLFTz755L333nv11VeXL19+//33nzhxIigoaPLkyS+99FJwcPAXX3wxaNCg7Ozs1q1b3/IrSktLo6Oj7S/Hjh37wQcf1FVPQpB/3tWyNuH0C0VZWZkkSZ6uQvMqKyv1er3BYPB0IZrHCekUZWVM/ziBzWaT5dvHhKNBGBUVVVpaqmzXSix7g9TU1CeeeEIIMW/evLfffjs7O7t3797Dhg1TGrRt2/af//znJ5988vLLL9/yK8LDw/Py8qKiohypJznUcl0ODg3l903IshwaGurpKjTPz8+PIHQKTkhn4TA2ns1mq6iouG0zR4dGW7duffz4cWX72LFjrVq1urmBv7//bx+q0xkMBovFUqtNUFDQzTvvDNOEAACncDQIp06d+s477xQWFl68eHH58uVTpkxR9k+ZMkWZDnz88cfz8vK2b98uhFi1apVer+/WrZvVav3444+vX79eVVW1YcOGbdu2jRgxwil1c+EoAMApHB0anThx4rFjx9q1a6fT6aZPn/7oo48q+7Ozs00mkxAiPDw8MzPzueeeKygoaN++/ZYtW0JDQy0Wy9KlS6dPn15VVdWhQ4cNGzY46/LRxBDpxA16hACAxpIcmUh0j+joaMfnCD8/Z/vbL/LnQ5yzMFHTjEaj/Tom3DEulnEWTkinMJlMzBE2njJHGBISUn8zTd5iTfAACgCAk2g2CEMEF8sAABpPq0EYFyQVVwmLzdN1AAA0TqtBqJdETKB0uYJOIQCgUbQahIIVFAAAZ9ByELKmHgDQaFoOQnqEAIBG03AQJodKv5roEQIAGkXDQZgSKs6aPF0EAEDjNByEd4dJ+UZ6hACARtFwEKaESWcJQgBA42g4CJsEimqbKKn2dB0AAC3TcBAKIZqHSue4XgYA0AjaDkKmCQEAjaTtIEwJE/lGTxcBANAybQfh3WHSWYZGAQCNoPUgpEcIAGgUbQdhSigrKAAAjaLtIORiGQBAI2k7CO/yF346UVTl6ToAAJql7SAUdAoBAI2j+SBkmhAA0BiaD0IuHAUANIbmgzCFoVEAQCNoPghZUw8AaAwvCEKGRgEAd077QRgqnTPJdAkBAHdG80EY5CfCDeJyuafrAABok+aDUCiPqmeaEABwR7whCFlTDwC4Y14RhKFcLwMAuEPeEIQpYdxcBgBwh7whCBkaBQDcMW8IwpRQcdbk6SIAANrkDUHYPEy6UCZb6RMCABrOG4LQXyeaBEoFZSQhAKDBvCEIBTdaAwDcKS8JwpRQrpcBANwJLwnCu8MEN5cBANwBrwlCiaFRAMAd8JIgZE09AODOeEkQcrEMAODOeEkQJoVIVyvlKqun6wAAaI2XBKFeEk2DpfMsJQQANJCXBKEQIiVMnGV0FADQQN4ThNx6GwBwB7wnCFlTDwC4A94ThHeH8QwKAECDeVMQ0iMEADQYQQgA8GneE4TxwaLKKoqrPF0HAEBTvCcIJSHaR0rHr9MpBAA0gPcEoRAiNVI6WkwQAgAawNuC8Bg9QgBAQ3hXEEYRhACAhvGqIOwYKR0tlklCAIDjvCoIYwJFoF4UcOttAIDDvCoIxW+jo54uAgCgHd4WhB25XgYA0BDeFoSpUdIxVlAAABzmdUEYKR2lRwgAcJi3BWGHSCn3hmwlCgEAjvG2IAzxE/HB0ulSkhAA4BBvC0LBjdYAAA3hhUHYMUqwggIA4DXpi74AAA48SURBVCBHg9Bqtb711lv333//5MmTc3Nzb9nm2rVrc+bMGT58+Pjx4/fs2aPsLCsr+/Of/zxs2LCZM2deuXLFOVXXq0MEKygAAI5yNAhff/31devWvfjiiy1atEhPTzeZTLUalJaW9unT5+rVq88+++yoUaOMRqOyf8aMGYcOHZo7d251dfWIESNk198BrWMUQ6MAAEf5OdLIbDYvW7YsMzPzvvvuGzRo0LZt2zZu3Dht2rSabd56662UlJTVq1fX3Hnx4sXMzMyzZ88mJCT07du3adOme/bsue+++5z5E9ykzV3S+TK5wiKCHPrhAAA+zaEe4YULF65evdq7d2/lZe/evQ8ePFirzffffz9s2LCFCxdOmzZt7dq1Ss/vyJEjzZo1S0hIEELo9fqePXve/Ean89OJFuHSyRI6hQCA23Oo03T58uWwsDA/v98aR0dHnzp1qlabc+fOvfnmmy+88MLw4cPnzZuXk5OzaNGiK1euREZG2ttER0dfunSprm8xmUzp6el6vV55mZ6ePn/+/Ib9NP/WNtRw4FJ1S3/rnb1dW24epsYdqKys1Ov1BoPB04VoHiekU5SVlblhIsnr2Ww2Rw6jQ0EYGhpaWVlpf1leXh4WFlarTXBwcL9+/V588UUhRExMzEMPPbRw4UJH3mgXFBT017/+NTw8XHnZpEmTehrXr2uc7VS5HBamv7O3a84dHyjYGQwGgtBZOCEbT5Kk0NBQT1eheTabraKi4rbNHArCpKQki8VSUFCQmJgohMjPz2/WrFmtNvYhUCFE06ZNTSZTdXV1cnLy+fPnLRaL0pvMz88fMWJEXd+i1+s7d+4cFRXlSEn1S40Uy3P4YwoAcHsOzRFGRkYOGTJk5cqVQoiLFy9+9dVXjz32mBDi8uXLy5YtU9pMmDBh27ZtVVVVQojPP/+8S5cuAQEB3bt3j4qKyszMFEJkZ2efOHFi1KhRrvpRaugYKR0rdsP3AAA0z9HlE4sXL16zZk2PHj26dOny1FNPderUSQhx5syZmTNnKg3GjBnTpk2bNm3a9OrV67333lu1apUQQqfTLV++fNasWb169Ro8ePCSJUtqThm6TvMwqaRavlHthq8CAGib5Ph8rNlszsnJiYuLi4uLU/Yow68hISH2NufPny8vL2/RooX9yhohRHl5+S+//JKSkhIREVHP50dHR+fl5TllaFQIce8Wy5u/0/eNl5zyaWpmNBqZkmk8LpZxFk5IpzCZTMwRNt7NIXVLDVhqZzAYlI6gnU6nq/UFycnJN78xODi4S5cujn+RU6RGSseuy74QhACAxvDCe40qUnlUPQDAAV4bhB2jCEIAwO15bRB24o6jAAAHeG0QxgQKP524WE4WAgDq47VBKJTVhDyYEABQL28Owk5R0uEieoQAgPp4cxD+rom0r5AgBADUx5uDsFes9GOhzdNVAABUzZuDMCVMkoT0q4lOIQCgTt4chEKI3zWR9jI6CgCom5cHYc9Yad9VghAAUCcvD8JesfQIAQD18fIg7NFEOlIsV3PFDACgDl4ehCF+omW49DOrCQEAdfDyIBRC9OR6GQBA3XwgCLleBgBQN+8PQq6XAQDUw/uDsG2EVFwlX6nwdB0AAFXy/iCUhPhdE2n/Va4cBQDcgvcHoRCiVyx33wYA3JpPBGHPJjqmCQEAt+QTQXhvnPTTVdlKFAIAbuITQRjhLxKCpRPXSUIAQG0+EYRCmSZkNSEA4Ca+EoQ9uV4GAHArvhKELKsHANySrwRhx0jpnEkuqfZ0HQAAlfGVIPTTia7R0k9MEwIA/pOvBKFgdBQAcCs+FIQ9Y6V93GgNAPCffCgI+8TpqqyeLgIAoDI+FIRxQeLb+/08XQUAQF18KAgBALgZQQgA8GkEIQDApxGEmvfmm2/KMstCGuvbb789ePCgp6vQPJPJ9O6773q6Cm+wfv36goICT1ehefn5+Zs2bbptM4JQ8xYtWmS1cjlsY2VlZf3www+erkLzLl68uGrVKk9X4Q0+/vjjEydOeLoKzTty5Mjnn39+22YEIQDApxGEAACfRhACAHyapJ7rLIKCguLj43U6srlhzp49m5KS4ukqNK+oqMhgMISHh3u6EG2zWCyXL19OSkrydCGad/ny5YiIiMDAQE8Xom3l5eWRkZG3nW1V0Z1WTp06VVVV5ekqtKeqqiogIMDTVWiexWKRJEmv13u6EM3jhHQKDqNTyLIcHR1922Yq6hECAOB+jEMCAHwaQQgA8GkEIQDApxGEAACfpqKrRnFbsizv27fvu+++u379eqdOncaNG2cwGJR/unbt2urVq69cuTJixIjBgwd7tk4Nyc7O/umnnx5//HH7womdO3du3bo1JiZm6tSpcXFxni1PE3Jzczdt2qSck5MmTVJWQJ09e/Zvf/tbeXn5o48+2r17d0/XqAE7duz47rvvJEkaMmRI//79lZ1Wq3Xt2rVHjx5t27btlClT7L/vqCk/P//gwYPFxcWPPvpoRESEfX92dvbGjRsDAwMnTZp0zz33KDvNZvMHH3yQm5vbqVOnjIwM5UJxeoRacvr06fHjx5eWliYlJS1dunTYsGE2m00IUVlZ2adPn+PHj999990TJ0788MMPPV2pNpSWlmZkZMyYMaOwsFDZ8+mnn44dO7ZZs2Znzpzp1auX0Wj0bIXq98033/Ts2bOkpCQlJeW7775Tbnt76dKlHj16mEym2NjYwYMHf//9954uU+1WrVo1YcKE+Pj42NjYsWPHrl27Vtn/zDPPLF++vHXr1h9++OHkyZM9WqNKXbt2rVu3bu+9996MGTMuX75s379v377+/ftHRUVVVVX16NHj/Pnzyv6JEyeuX7++VatWy5Yte+65535rLUM7qqurLRaLsl1cXOzn53f8+HFZltetW9elSxebzSbLcmZmZrt27ZRt1G/GjBnLli0TQuTl5Sl70tLS1qxZo2z36dPnvffe81hxWmA2m5OSkj7++ONa++fNmzdmzBhle9GiRSNHjnR7aRozbNiwxYsXK9sLFy5UjtilS5cCAgLOnz8vy3JxcXFgYODp06c9WaUq2f9fJ4TIycmx7x89evS8efOU7QkTJrz88suyLOfl5QUGBhYXF8uyfP78+cDAwMuXL8uyTI9QSwwGg33Ft9lsttlsoaGhQojdu3cPGTJEkiQhxJAhQ3JycuxdHNRl586dubm5TzzxhH1PWVnZwYMH7QPLQ4YM2bVrl4eq04YjR44Yjca0tLRly5b97W9/s3egd+3aNWTIEGWbw+iI9u3bHz58WPnf+uHDhzt06CCE+OGHH1q2bKncpicyMrJbt2579uzxdKWqo/x/72a7du0aOnSosm0/Cffs2ZOWlhYZGSmESEpKuueee3788UfB0Kh2zZo16/HHH2/WrJkQ4tKlS02aNFH233XXXQEBAZcuXfJodWpXXl4+c+bMd999t+ZvkXLQYmNjlZdxcXEXL170TH0akZ+f7+fn9+ijj964cePLL7/s2rVraWmp+M8TMjY2trS0tKyszKOVqt2iRYuuXLmSnJycnJxsNBrnz58vhLh8+bL9MApOyIaorq4uKiqqeRIqv911HVKCUJPmzp2bk5OzdOlS5aWfn5/FYlG2ZVm2Wq3+/v6eq04D5s6dO2HChHbt2tXcqVyJYD+SZrOZe1zVT6fTFRUVrVy58pVXXtm8eXNUVJQyuVXzhFTuXefnx3V59Zk/f35VVdWWLVu2bNlSUlKycOFCIYSfn1/NR42azWZ+rx2k1+t1Ol3Nk1A5dHUdUoJQe1577bUvv/xy+/btSgdfCJGYmGj/U/Hy5csWiyUhIcFzBWrA+vXrN27c2L1793vvvVcI8fDDD3/55ZfKPd/tjwUvKCjgMNavadOmQoj27dsrL9u3b3/u3DnxnydkQUFBTEwMf1LU7/3333/ttde6deuWlpb26quvvv/++0KIpk2b1nxIfUFBgXLAcVt6vT4uLq7m77Jy6BITE295SAlCjVm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nCNk4CgBQgeMEISNCAIAKHCUIw7wZEQIAVOAoQRjuI+UbGBECAOzNcYKQESEAQAWOEoQRrBECANTgKEHooxNaSZTWqF0HAMDNOEoQCiHCfaQCBoUAAPtyqCBkmRAAYG8OFYQsEwIA7M2RgpDHjQIA7M6RgpARIQDA7hwqCFkjBADYW+OC8Pz58xcuXGiiUhgRAgDsz9ogNBgMw4cPT0xM7Nev33333VdVVVWnwYIFC4L+y8/Pz9PTs7KyceM7HjcKALA/a4NwyZIlpaWlZ86cOXPmTHFx8dKlS+s0eO6554r+a+bMmSNGjPDx8WlUKTxuFABgf9YG4bp161JTU/V6vV6vT01NXbt27c1amkymjz76aMqUKY0tpbmnMJiEwdTYnwMA4PbprGx37ty5jh07KsedOnU6d+7czVp+/vnnWq126NChN2tgNBovX76ckZGhfJQkKTY2VqvVSkKEekuXDHI7f8nq+gEAuCPWBmF5ebm3t7dy7OPjU1paerOWH3744W9/+1utVnuzBjk5OV9//XVWVpbyUZKkpUuXxsTECCFaeupziowhksXa8t1PRUWFJPEfCrZBZ9oQnWlDdKatWCwWa3rS2iAMDQ0tLi5WjouKisLCwuptVlBQsGPHjv/7v/+7xaViYmLGjx+/cOHCG/+olb+5RNb7+TnQTR2ORpZlPz8/tatwEXSmDdGZNkRn2orFYjEYGn5Qi7WRExcXl56erhynp6f37Nmz3mYrVqxISkrq1KmTlZetg1sJAQB2Zu2I8Mknn3z44Yfj4+OFEAsXLty0aZNyPi4ubvHixQkJCcrHf/7zny+99NJtVxPuzcZRAIBdWRuEAwYMWLJkyTvvvCOEWLp0aUpKinI+ISEhICBAOc7Pz09OTh49evRtVxPuI364fNs/DQBAo1kbhEKIhx9++OGHH65zcvHixbXH4eHh13+8DeE+Un4lO2UAAPbjWNtSWCMEANiZYwVhmDePGwUA2JVjBWFLb1FULcxEIQDAXhwrCLWSCPIUl3k9LwDAXhwrCAUvYwIA2JcDBqHIZ0QIALAXBwxCqYARIQDAXhwwCLmDAgBgP44XhDxlDQBgR44XhIwIAQB25IBByK5RAID9OGAQMiIEANiPwwVhmLd0ySAzJAQA2IfDBaGnVnjrRHG12nUAANyDwwWhUDaOskwIALALhwxClgkBAPbimEHIrYQAADtxzCAUBYwIAQB24ZhByBohAMBOHDIIvUUeI0IAgF04YhB2CpBOXWNECACwB0cMwpgA6eQ17qkHANiDIwahv17460VeBVEIAGhyjhiEQoiYQCnrmtpFAADcgKMGYYCUVcKIEADQ5Bw4CNkvAwBoeo4ahIEEIQDAHhw1CAPESdYIAQBNz0GDsL2/VGCQq8xq1wEAcHUOGoRaSbT1k06XMjsKAGhaDhqEgo2jAAC7cOQgFNxKCABoag4chGwcBQA0PQcOwgDpJEEIAGhiDh2EJ1gjBAA0MccNwhAvoZHElSq16wAAuDTHDUIhRDQbRwEATcyhg5AnjgIAmppjByEbRwEATcyxg5BbCQEATczBg5A7KAAATcuhg7BTM+lsmVxjUbsOAIDrcugg9NSKCB/pbBmDQgBAU3HoIBRCRLNMCABoSo4ehGwcBQA0KYcPQm4lBAA0JWcIQh4uAwBoMo4fhII7KAAATcfRgzDCV6owiRKj2nUAAFyUowehJERUgHSKQSEAoGk4ehAK5R0UBCEAoGk4QRDGBAiCEADQRJwiCKWsErWLAAC4KCcJQkaEAICm4QxBGCidLpUtRCEAoAk4QRD66kRzT+lCBUkIALA9JwhCIUTXQHG0WO0iAACuyDmCMDZIOlLEiBAAYHtOEoTB0mGCEADQBJwkCIOkw1cJQgCA7TlHEHYJlM6Vy5UmtesAALgc5whCnUZEB0jHeB8TAMDWnCMIBbOjAICm4TxByH4ZAEATcJ4gDJL+zYgQAGBrzhOEwdKRIpkkBADYltMEYbCn8NNL58qIQgCALTlNEAohYoMEy4QAANtyriCUDhepXQQAwLU4VRAGcwcFAMDGnCoIg7iDAgBgY84UhFEBUkGlXFqjdh0AABfiTEGolUTX5tJRBoUAANtxpiAUzI4CAGzN2YKQ/TIAAJtytiBkRAgAsClnC8Jg6WixbCEKAQA24mRB2EwvWnhJ2aUkIQDANpwsCAWzowAAm3LCIAzmiaMAAJtxwiAMkg5fVbsIAICrcMIg5FX1AADbcb4gbO8vXTPKxdVq1wEAcAnOF4SSEN2bS0cYFAIAbMH5glAwOwoAsB3nDMIgHrQGALANpwzCXiHST4UEIQDABpwyCOOCpHPlcmGV2nUAAJyfUwahTiOSQqW9BRa1CwEAOD2nDEIhRHK4Jq2A2VEAwJ1y1iBMCZd2XyQIAQB3ylmDsFewdJZlQgDAHXPWIGSZEABgE84ahIJlQgCALThxELJMCAC4c40IQpPJlJmZWVhYeIs2BoPh6NGjBQUFd1xYw1gmBADcOWuD8NixY1FRUb/5zW86d+78pz/9qd42y5cvj4yMHDNmTHx8/Ouvv267IuvHMiEA4M5ZG4TPP//8hAkTDh8+fOjQoXfeeefo0aN1Guzbt+/555//9ttvjx8/npeXN23aNFuXWg+WCQEAd8iqICwpKfnqq69mzJghhGjduvW999778ccf12mzePHi1NTUHj16lJaWCiGaN29u81pvxDIhAOAO6axplJubq9PpWrVqpXzs2LHj2bNn67Q5ceKETqfr1KlTTU1NUFDQunXrunTpUu/VjEbj5cuXMzIyas/ExsbqdFZVUkftMmGI1238NAAA1gVhRUWFt7d37UcfHx9l2He9oqKi9PT0jIyMwMDAF154ITU1dd++ffVe7ezZs19//fWJEyeUj3q9/v3334+Ojr6t+sVdwfp/5RhHtHKXlcLy8nK1S3AddKYN0Zk2RGfaisVikSSpwWZWBWFoaGhpaanJZFLGbVevXg0LC7uxTUpKSmBgoBBiypQpb7/9ttFo9PDwuPFq0dHR48ePX7hwoTVf3aCBrS0/XpN/00Vrk6s5BX9/f7VLcB10pg3RmTZEZ9qExWIxGAwNNrNqjbB169YhISH79+9XPqanp/fq1atOm/j4+OLiYuW4uLjY29tbr9c3puDbxDIhAOBOWBWEer1+xowZs2fP3rt379/+9reTJ0+OHz9eCHHw4MGOHTsqbZ544okNGzasW7fu+++/nzNnzuTJk60ZkN457iYEANwJa7eo/OlPf/Lz85s/f37Lli2//fZbZdgeGBg4aNAgpUHnzp23bNmyaNGiysrKMWPGzJo1q6lK/qXauwkfaufET8kBAKhFkmV7zysuWrQoOzvbVmuEQog3jlguVsp/T3CLZcKysjIWD2yFzrQhOtOG6ExbUdYIfX19b93MFUZRLBMCAG6bKwQhy4QAgNvmCkGo04j+odK3+e5yKyEAwIZcIQiFECPaaj47x+woAKDRXCQIR7bVfHHeYmRMCABoJBcJwlBv0TlASstnUAgAaBwXCUIhxMh2mi3nGBICABrHdYJwVDtpy1m73xQJAHByrhOEnZpJgR7iwBWiEADQCK4ThEKIh9pJW84yOwoAaASXCsKR7TSbzjIiBAA0gksFYXyIZDCJEyVkIQDAWi4VhJIQD7aVtnBnPQDAai4VhIKbKAAAjeRqQZgcJp0plS9UMCgEAFjF1YJQpxHDW2u25RKEAACruFoQCmWZkJsoAADWccEgHNZKs/+KXFytdh0AAGfggkHooxMp4ZovzjMoBAA0zAWDUAgxkpsoAADWcc0gfKCt5uuLlhKj2nUAAByeawZhkKcY2kqz5jSzowCABrhmEAohpsZolp4gCAEADXDZIBwQIRnM4kfeygQAuCWXDUJJiCnRmg8YFAIAbsllg1AIMTlas+mspbRG7ToAAA7MlYMw1FsMCNesz2ZQCAC4KVcOQiHE1M6aD7IIQgDATbl4EA6JlK5UiUNX2TIDAKifiwehRhJTojXLGBQCAG7CxYNQCDElWvo421JpUrsOAIBDcv0gjPSVEkOlDTkMCgEA9XD9IBRCTI3hhkIAQP3cIgjvba05Wy4yi9kyAwCoyy2CUKcRU2M072QyKAQA1OUWQSiEeLKbZmOOJb9S7ToAAA7GXYIw2FOM76h5J9OsdiEAAMfiLkEohJjzK83SE5ZrvK0XAHAdNwrCdv7S0FY8cQ0A8AtuFIRCiBdjNQuPWoxEIQDgv9wrCHsESd2aizWnSUIAwM/cKwiFEC/Gal8/bLFwSyEAQAjhhkE4IFxq7im25TIoBAAI4YZBKISY8yvNa4cJQgCAEO4ZhKPaaYqqxXcFTI8CANwyCDWSeO5XmtePcHM9AMAtg1AIMTFKc6RI7GFQCABuz02D0Esr3uireWKf2cRaIQC4NzcNQiHEuA6aEC+xjAfNAIB7c98gFEK8m6idd9B8tVrtOgAA6nHrIOzWXHq4veaVDHbNAID7cusgFEL8JV67IcdypIhdMwDgptw9CJt7ij/21D7xvZkkBAD35O5BKISY0UVTZhSbctg1AwDuiCAUWkm8m6ids99SYVK7FACA3RGEQgjRP0xKaCm9+m92zQCA2yEIf/ZuonblSTn9MmuFAOBeCMKftfAS7yVpHt1tLq9RuxQAgB0RhP/zYFtN/1DpxQNMkAKAGyEIf2FRovaL8/IX55kgBQB3QRD+QjO9WJ2snf4dz10DAHdBENbVP0wa016atpcJUgBwCwRhPV7roz1VKq/L5hZ7AHB9BGE9PLViVbL26XTz2TIWCwHAxRGE9esZLL0Uqx2502zgcTMA4NIIwpua3V3TI0h6Op3FQgBwZQThrbyXpP2uQF5xksVCAHBZBOGt+OnF5sHa3x8wHyxksRAAXBNB2IDoAOmdftrRX5uLuLMQAFwRQdiwsR00I9tKk9JMjAoBwPUQhFZ5o6+2uFr8v4NsnAEAV0MQWkWvERsH6T7Olt84wsYZAHApBKG1wrxF2v2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gq6++euqpp7TX5eXl586dy8rKCgsLs72UOH/lVLHV9vYAADSerVOjaWlpubm5Bw4c2L17d8eOHV999dU6DaZMmXLy/3vggQdGjBhRrxQUrhECAPRgaxAuWbJk2rRpnp6eIjJjxowlS5ZcraWqqp988snvfve7+pbS0kssVimsqu+fAwCg4WydGk1PT4+Li9Nex8XFZWRkWCyWK14C3LBhw4ULF8aNG3e1U1VVVeXk5Kxfv772yE033eTn5ycisf7KqWL1xhClHt8BAACNYGsQlpWVacNBEfHy8qqpqamqqvL29r685YIFC6ZOnerl5XW1U2VnZx88ePCFF16oPfLMM8/Ex8eLSLS3+XBeVQdPrhReVWlpqaLwi4J90Jl2RGfaEZ1pL1ar1ZaetDUIw8PDCwoKtNf5+flBQUFXTMGLFy/+85//3LJlyzVOFRMTM2LEiEWLFl3+pQ4tLNnVZj8/B7qpw9GoqqqNntF4dKYd0Zl2RGfai9VqLS8vv24zWyOnT58+P/74o/b6xx9/7NOnzxWbffbZZ126dOnVq5eNp62DWwkBAM3M1hHhQw891L9//xtvvLFly5Z/+ctfFi5cqB3v3bv3U089NX78eO3tggULGrBMplacv/KvDOZFAQDNx9Yg7NKly7fffvv2229XVFS8//77t956q3b89ttvj42N1V4XFxePHDly8uTJDa6G7UYBAM2sHjvLDBw4cODAgXUO/ulPf6p97e/v/9JLLzWmmrb+ypkS1aqKgevEAIBm4VjLUrxNEmCWnHIuEwIAmoljBaGItPVXmB0FADQbhwtCFo4CAJqTAwah/MqIEADQXBwwCBkRAgCaj8MFYdsAghAA0HwcLgjj/LiVEADQfBwuCNv4KbnlaqVF7zoAAO7B4YLQqEikj3K2lNlRAEBzcLggFBaOAgCakSMGIetlAADNxhGDkDsoAADNxiGDkIWjAIDm4ohB2DlIOXSBESEAoDk4YhB2a6GcLlFLa/SuAwDgBhwxCM0G6dZC+SWfQSEAoMk5YhCKSJ+Wys48ghAA0OQcNAh7t1R2nScIAQBNzkGDsE9LZSdBCABoeg4ahF1bKBmlalG13nUAAFydgwahUZGewcoeBoUAgCbmoEEoIn1CmR0FADQ5xw1C1ssAAJqB4wYh62UAAM3AcYOwU6ByrkwtqNS7DgCAS3PcIDQocmNLZQ/7ywAAmpLjBqGwvwwAoOk5dBCyXgYA0NQcOghZLwMAaGoOHYTtA5XCKjWvQu86AACuy6GDUBGJD1F2MygEADQZhw5CYX8ZAEATc/QgZL0MAKBJOXoQcgcFAKBJOXoQxvkrFRY1p1zvOgAALsrRg1BE4pkdBQA0GScIwt7MjgIAmoxzBCEjQgBAE3GCIOzTUvk5z6p3FQAA1+QEQRjtp4hIZimDQgCA/TlBEIpI75bKT1wmBAA0AecIwsRww+YcghAAYH/OEYRDIpSNBCEAoAk4RxD2aamcuKgWVuldBwDA5ThHEJoN0q+VsuUcg0IAgJ05RxCKyJBww8ZsbqIAANiZ0wTh4AhlYzYjQgCAnTlNEPYPVQ4VqsXVetcBAHAtThOEnkbp3VLZymVCAIBdOU0QisiQcGVTDpcJAQD25FRBGGHgMiEAwL6cKQgHtFL2FqhlNXrXAQBwIc4UhN4muSFY2Z7LoBAAYDfOFISi7bXG3YQAAPtxsiAcHG5g01EAgB05WRAmhCm7zqsVFr3rAAC4CicLQj+zdA1SfubZhAAAO3GyIJTfLhMShAAA+3DGIDRs5LZ6AICdOF8QJoYpO3LVKqIQAGAPzheEgR7SIVDZdZ7ZUQCAHThfEIrI4HAuEwIA7MMpg3Aot9UDAOzESYPQsC2XZxMCAOzAKYMw0EMGhSsr0xkUAgAayymDUEQmxRm+/JXLhACAxnLWIBwfa/g+21rE7CgAoHGcNQgDzDI43LCK2VEAQOM4axCKyKS2CrOjAIBGsjUIS0tLp02bFhQUFBMTs2DBgiu2OXDgwIgRI3x8fCIjI9988037FXll42IMPzA7CgBoHFuD8LnnnsvNzc3IyFi2bNnjjz9+8ODBOg3OnTs3YsSI8ePHZ2dn79mzZ9CgQfYuta4AswyOMKw8w+woAKDhbApCVVU//PDD//u///Pz84uPj58wYcJHH31Up83bb7990003zZ49OzAwMCwsrFevXk1QbV2T4pQvTzE7CgBoOJuCMD8/Pz8/v2fPntrbHj16HDt2rE6bvXv3tmvXbvz48R07dpwyZUp2dradK72ScTGGjdnWi1XN8FEAANdksqVRQUGBiPj7+2tvAwMDz58/X6dNVlbWhg0bli9f3r179zlz5tx5550bN2684tmOHTuWlpaWlpamvTWbzStWrEhMTGxA9YrIwFDzl8fK7ox1lwnS0tJSRVH0rsJF0Jl2RGfaEZ1pL1ar1ZaetCkIW7ZsKSJFRUUhISEiUlhYGBoaWqdNSEjIuHHjhg4dKiJ//vOfW7dufeHChRYtWlx+Nm3IuGjRIls++romd7B+ecp0X3ejXc7m+FRV9fPz07sKF0Fn2hGdaUd0pr1Yrdby8vLrNrNparRFixahoaH79+/X3u7fv79jx4512nTu3Nlo/C2NtBeq2hxX78bFGDYxOwoAaCibglBRlBkzZvzlL3+5ePHiTz/9tGzZsnvvvVdEcnJyUlJSiouLReS+++5bsWLF3r17q6qqXnzxxYSEhODg4KatXURE/M0yJMLAvqMAgIax9faJp59+unXr1u3bt588efLf//73rl27ikh1dfXx48ctFouIdO3a9c0335w0aVJ0dHR6evrnn3/ehFX/J9aOAgAazKZrhCLi4+Nz+S0Tbdq02bNnT+3bKVOmTJkyxW6l2WxsjGHW1uqLVcZAj+b/cACAc3PiLdZq+ZtlaITh69PMjgIA6s0VglBE7uts+MchghAAUG8uEoS3tlGKq+WnPK4UAgDqx0WCUBFJ7WyYz6AQAFBPLhKEIvK7joaV6dbc6986CQDAv7lOELbwlNtjDB8eY1AIAKgH1wlCEZndzfDOYauFC4UAAJu5VBD2ClFa+wq7zAAAbOdSQSgi/92VJTMAgHpwtSD8rzjDwQvqkUKmRwEANnG1IPQwyO86Gd46zKAQAGATVwtCEXmwi2HRCWtRtd51AACcgQsGYaSPMizSsOgEg0IAwPW5YBCKyH93NfzjoNXKhUIAwPW4ZhAOi1ACPGTxrwwKAQDX4ZpBKCIv9zM+udNaRRQCAK7JZYNwcLjSIUA+Ysc1AMA1uWwQisgLfY3P77GW1ehdBwDAgblyEPZuqQxspfDAXgDANbhyEIrIn/sYXt1nuVCpdx0AAEfl4kHYMVAZG2N4bb9F70IAAA7KxYNQRJ6NN7x7xJrDA3sBAFfi+kEY5avc3cHwlz0MCgEAV+D6QSgiT9xg/OJX66/F7DQDAKjLLYKwpZc80s34h59YPgoAqMstglBE5vQ0HLyg8vB6AEAd7hKEnkZ5J8E4e6u1lPvrAQCXcJcgFJEhEUpiuPI8q2YAAJdwoyAUkdf6Gxccte4tYNUMAOA37hWEYd7yfB/jzB8tPKoQAKBxryAUkfs7GYyKLOCpFAAAEXHDIDQo8m6i8U87LbnsNQMAcMMgFJHuLZS7Oxj+ZwerZgAAbhmEIjI33rgtV/3iJBOkAODu3DQIfU3ydZLxke2WAxdYNgMAbs1Ng1BEegQrr99knLDecrFK71IAAPpx3yAUkSntDKOilLs3WhgVAoDbcusgFJHXbzIWVKov7+ViIQC4KXcPQrNBvhxhmn/IuiaDYSEAuCN3D0IRCfeWT4ca795Yc5oHFgKA+yEIRUSGRih/6Gkcv85SyMIZAHAzBOFvft/DkBSl3PJtDc9pAgC3QhD+2yv9jd1aKLevq6lkzxkAcBsE4b8pIu8mGgM8lHs38XgKAHAXBOF/MCqSNsx4vkKdvY1RIQC4BYKwLg+DLE0y7chVn9lNFgKA6yMIryDALN+MNn1xUn3hF260BwAXRxBeWStv2Xib6YtfrX/8mXEhALgygvCqwrxl4xjThiz1f3awGSkAuCyC8FpaeMraW0xbzqkPbWEdKQC4JoLwOoI8ZN0tpqOF6swfyUIAcEEE4fX5mWXlaNPJInXy95aSar2rAQDYFUFoE1+TfHOzKdBD+i6v4aH2AOBKCEJbeRnlvUTjk70Mw1bXvHeE2yoAwEUQhPUzrb3hx2TTPw5Z7/7BwvbcAOACCMJ66xSobBtrUhQZsKLmUCHTpADg3AjChvA1ycdDjI91NwxdVfPWIRaTAoATIwgb7t6Ohp/GmdJOWm/+V012md7VAAAahCBslFh/5YcxpgFhSu9/Vq9KZ2QIAM6HIGwsk0GeiTd+Mdw0a6tl1lZLETcaAoBTIQjtY3C48ssEU5VVui6tWfwrN1cAgNMgCO0myEPeSzQuH2l8bb912OqawywoBQBnQBDaWe+WytZk09gYw5BVNc/stpRzryEAODaC0P5MBnmsu2HP7aYjhdLxy5p3j1irmSsFAEdFEDaVKF/li+HGb0Ybv8tUOyypee+IlacaAoADIgibVo9gZckI48IhxoXHrPHLalamc/c9ADgWgrA5DI1Qto41/bmPYe4u6w1f13x2wlrDZCkAOAaCsPkkRxt23256a6Bx8a/W9ktq5h2wlrGUBgD0RhA2t8RwZeUo05IRxk05atvF1U/vsmSVMV0KALox2d50y5Yt//jHP8rLy++8884777yzzldLS0sff/zx2rdjxowZO3asfWp0Rf1Cla+SjEcvGt48aO3xVU1SlGFWV8OgcEXvugDA7dgahMeOHbvlllteffXV8PDwmTNnenp63n777Zc2qKioeP/999955x3tbWRkpJ0rdUWdApV/DDS+0Nf4yXFr6o8WT4P8d1fDHe0MAWa9KwMAt2FrEL799tuTJk1KTU0VkaysrNdff71OEGq0BqiXALPM6mr4766G9ZnqO4etf/ip+tY2hrs7GJKiFCNDRABoYrZeI9y1a1dCQoL2OiEhYefOnVds9tBDD82aNWvZsmX2qc6dKCIjo5Svkown7zAPDFPm7rZEf17zh58s+wq4gggATcjWEeG5c+eCg4O11yEhIeXl5RcvXgwMDKxtYDabH3nkkfj4+Ly8vIcffnjbtm1//etfr3iq06dPf/fdd8OHD6898vTTT/fu3buh34KrMYvc3UbubiPHipQvzhjHrjF6GmVitGVCG0unALWkpETvAl0HnWlHdKYd0Zn2YrVaFeX6E2u2BqGPj095ebn2ury83GAw+Pj4XNogICDg9ddf114PGDBg8ODBzz//vKen5+WnioqK6t69+x//+EftrdFo7NevX52zQUR6+0vvKHlloPyUpy7+1ThhkxrkKeMiTXd28urWgjlT+/D399e7BNdBZ9oRnWkXVqu1NrmuwdYgjI6OPn36tPb61KlTERERZvNVV3R07NixpqbmwoUL4eHhl3/VbDaHhYUlJSXZ+NHoF6r0CzW+2l+2nlMXHa2+ba3FqMi4GGVstCExnOuIANAotl4jTElJWbRokRatH374YUpKinY8LS3t2LFjIpKVlVVZWSkiqqrOnz8/Njb2iimIBlNEEsKUl2+sOXWH6askY5CH8vsdlvBF1dN+sHxy3Jpdpnd9AOCcbB0RpqSkLF26tEuXLtp1wXnz5mnH586d+9RTT3Xs2HHVqlX/+7//27Zt2/z8fJPJlJaW1lQlQ+SGYOWGYOWpGw0Zpeq3GeqqdPXx7dWRvsqoKCUpypAQpvhzAwYA2EZR1XosSjx58mRFRUWXLl0Mht+GkiUlJZ6ento0aX5+fnp6emBgYHR0tMl01YhNS0tbvXr1okWLGlm6eyouLr7ixQOLKrvOq+sy1XWZ1l3n1c6BSmK4MihcSQwztPJu/jKdw9U6Ew1AZ9oRnWkv2jVCX1/fazerx84yItKuXbs6R/z8/Gpfh4SEhISE1OuEsBejol1KVJ7sZai0yM956uZz6odHrTM2WZp+qboAABDASURBVMK9lQFhysBWysAwpUuQYuCaIgBcon5BCKfgaZTEcCUxXJEbDFZVDl5Qt+aqP55TX9lvzS1Xb2qldG+htA9QOgQqHQKktS/RCMCtEYQuzqBIj2ClR7Ays7OISF6FbM+1Hi6U3fnq4l+tx4skv0LtFaLc2dZwZztDGJOoANwPQeheQr0kOdqQHP3vI2U1svWc+tkJ67N7qge0Uqa1N4yPMXjz7wKA2+AxTO7OxyRJUcrCIcaMyeap7Q2fnrBGfV798XEeHAzAXfCbP37jY5Ip7QxT2hmOFKo3f2spqJTHuvN7EgDXx0861NU5SNk21rTgqPWPP1v0rgUAmhxBiCuI8JGNt5l+yFYf2mKx8vQLAC6NIMSVBXvK+ltMx4vUu36wVHPFEIDrIghxVX5mWTHSVFStTt/IHCkAl0UQ4lq8TbJ0hOnbDGvO9Z9kAgBOiSDEdXga5dY2hn+eZnoUgGsiCHF9E2KVrwlCAC6KIMT13dza8FOeer5C7zoAoAkQhLg+H5MkRRlWpTMoBOCCCELYZEKs8vVp7igE4IIIQtgkOdqwKcdaVK13HQBgbwQhbOJvloQw5RtmRwG4HIIQtpoQa2B2FIDrIQhhq/GxhjUZ1rIavesAALsiCGGrEE/pE6qsy2R2FIBLIQhRD8yOAnA9BCHqYWKcYVW6tYoxIQAXQhCiHsK9pXOQ8n0Wg0IAroMgRP1MiDWw7ygAV0IQon4mxirLz1gtjAkBuAqCEPUT669E+SpbzpGEAFwEQYh6Gxyu7MwjCAG4CIIQ9Rbtp5wtJQgBuAiCEPUW7SvpJXoXAQB2QhCi3qL9lPQSRoQAXARBiHqL9lPOEIQAXAVBiHpr5S0lNcLu2wBcA0GIelNEWvuyXgaAiyAI0RAxfqyXAeAiCEI0RLQv62UAuAiCEA3Rxk8IQgCugSBEQ0T7KemlehcBAPZAEKIhYriVEICrIAjREGwuA8BlEIRoiGg/JaNUZUgIwAUQhGgIT6MEesi5cr3rAIBGIwjRQNF+yplixoQAnB5BiAaK9lPS2VwGgPMjCNFAbC4DwDUQhGigNmw3CsAlEIRooGg/OVOsdxEA0GgEIRqIa4QAXANBiAZi320AroEgRAO18pbyGinl8bwAnBxBiIZr7aucZVAIwMkRhGi4aD/hGRQAnB1BiIZjcxkALoAgRMNxKyEAF0AQouHYXAaACyAI0XDRPJ4XgPMjCNFw0X5yhhEhACdHEKLh2vgqmWWqlTEhAGdGEKLhPI3SwkNyyklCAE6MIESjRPsprJcB4NQIQjRKDOtlADg5ghCNwuYyAJwdQYhGacMzKAA4OYIQjRLNPfUAnBxBiEbhGiEAZ0cQolHYXAaAsyMI0SgtvaTCIsXVetcBAA1FEKKx2vjxDAoATowgRGNF+7JeBoATM9nedPfu3Rs2bAgLC5s0aZKXl9fVmm3fvj03N3fs2LH2KA9O4P9fJlT0LgQAGsLWEeHSpUtHjx5dUFDwySefjBgxwmq1XrHZiRMnxowZc99999mvQji6aKZGATgzW4Pw2WefnTdv3gsvvPDNN9+cO3fu22+/vbyNqqoPPfTQY489ZtcK4eii/eRMsd5FAEBD2RSEmZmZBw4cSE5OFhGz2Tx69Oi1a9de3uytt95q167doEGD7FwjHFu0n5LOiBCA07LpGmFWVpafn5+/v7/2NiIiYt++fXXanDlz5u9///uOHTv27t177bPl5+fv2bNnzpw52luj0XjvvffGxsbWr3B3VVlZ6eHhoXcV/yHCQ84UGyorK/UupN4csDOdF51pR3SmvVitVlW9/q/pNgWhoiiXnktVVUWpuzJi5syZL730UlBQkC1n8/DwuLSlwcDiVSfWxkcuVElBlRLswbgQgPOxKQgjIyNLS0uLi4u1QWFOTk5ERMSlDU6cOLF58+aAgIDPP/88Ly+vqKgoJSVl3rx5dZppgoODu3Xr9uSTT9rlG3A3VVVVnp6eelfxHzxFEsNqtp43Toxzsl9oHLAznRedaUd0pr1Yrdby8vLrNrPpJ1dkZGSPHj1WrFghIlVVVd9+++3o0aNFpLy8/MiRI1qD5cuXp6ampqamJicne3l5paamBgYGNu5bgNMYHmnYkM1wEIBTsvU+wmeeeWbmzJn79u3buXNnVFSUFoR79uxJSEhQVdXHxycpKUlraTabPTw8at/CHQyPVN7bcOU7agDAwdk6lzVhwoTvvvsuPDz8/vvvX7dunXZVr0uXLsuXL6/TsmvXrh988IGdy4RjuyFEKahUM1g7CsAJ1WNnmZ49e/bs2fPSIy1atLh8B5nQ0FC2lXE3isiQCMP32epd7dlfBoCTcbLVDXBYwyOVDVmMCAE4H4IQ9jEiUvkukyAE4HwIQthHx0BFFTlRRBYCcDIEIexmeKTyHbOjAJwNQQi7GR6pfE8QAnA2BCHsJilS+T7bShICcC4EIewmylcJ9FD2FxCFAJwJQQh7GsFNFACcDUEIexoWQRACcDIEIexpRJRhU461hm1HATgPghD2FOIpsX7KrvMMCgE4DYIQdsbdhACcC0EIOxseafg+m7lRAE6DIISdDYlQduSq5TV61wEAtiEIYWf+ZunWQtmex+woAOdAEML+JsYZzpYQhACcQz0ezAvY6H968AsWAKfBDywAgFsjCAEAbo0gBAC4NYLQybzyyiuqyjoUO7BYLH/729/0rsJFlJSUvPXWW3pX4SJyc3M/+ugjvatwEadOnVq8ePF1mxGETuall16qqeEePTsoLi5+/fXX9a7CRWRnZ7///vt6V+EiTpw4kZaWpncVLmLfvn3//Oc/r9uMIAQAuDWCEADg1ghCAIBb0+GG+jNnzqxcubJdu3bN/9EuwGKxdO7cWe8qXIGqqiUlJfw7tIuampqcnBw60y4qKyvz8/PpTLsoKytr0aLFdZspzb8E0Wq1Hj9+3Gw2N/PnuobKykpPT0+9q3ARdKYd0Zl2RGfai6qqISEhQUFB126mQxACAOA4uEYIAHBrBCEAwK0RhAAAt0YQAgDcGs8jdFyqqu7YsWPDhg0FBQU9evSYPHmyh4eH9qX8/PwPPvjg3Llzt9xyy8iRI/Wt07lYrdaPP/44JiZm+PDh2pGioqL33nsvKytr2LBhycnJ+pbnRI4dO/bFF19cuHChZ8+e06dPNxgMInL69OmFCxeWlZWlpKT06dNH7xqdw4ULFz7++OOzZ8/GxMRMnz49MDBQO3706NFPP/3UYrFMnTq1e/fu+hbpyE6dOrVr166CgoKUlJRLF4ju2bPniy++8PLymj59etu2bbWD1dXVCxYsOHr0aM+ePe+66y6j0SiMCB3ZqVOnJk+eXFhY2KZNm/nz548ePdpqtYpIZWXlwIEDDx48GBcXN3369E8//VTvSp3Jm2+++eijjy5YsEB7a7FYhg4dun379nbt2j3yyCNvvvmmvuU5i7Vr1/br1+/ixYuxsbEbNmzQ9r/Nzs7u27dvcXFxaGhoUlLS5s2b9S7TCZSXl/fv3//nn3/u2bPnli1bBgwYUFlZKSLHjx/v37+/oih+fn4JCQn79u3Tu1IHdf78+fj4+HfeeWfmzJk5OTm1x3fs2DFkyJDg4ODKysq+ffuePXtWO3733XcvWrSoQ4cO8+fPf/jhh39rrcJRVVVV1dTUaK8vXLhgMpkOHDigqupnn312ww03WK1WVVWXLl3auXNn7TWu69SpUz169Hj00UenTp2qHVm1alXbtm21fl63bl1UVFR1dbWuNTqB6urq1q1bL168uM7xuXPnTpw4UXv94osv3nbbbc1emvPZtm1bQECAxWJRVbW6utrHx2fXrl2qqs6ePfv+++/X2syZM2f69Ok6FunIan/6icjhw4drj0+cOHHu3Lna66lTpz7xxBOqqh4/ftzLy6ugoEBV1bNnz3p5eeXk5KiqyojQcZnNZm3YLiLV1dVWq9XPz09ENm3alJSUpCiKiIwaNerIkSPnzp3Ts1Anoapqamr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eL3UVdgKdaUHoTAtCZ1qKhYOwa9euRUVF4uuCggIvL68mb4TJz8///vvv165d2/Kp4uPjP/vss8Zfuqcrf7FCcHaWmVmVAzKZTM7OzlJXYSfQmRaEzrQgdKal8DxfXV3dajNzrxEOHz780KFD4usff/xx+PDh4mu9Xs/zfwzgvvjiiyFDhkRERLSx2jsG+TBsQwEAAJ3J3BHhs88+O2DAgFdffdXb2/utt95KSkoSj/fq1evNN9989NFHxbdbtmx5+eWX213NQB92sUQw8KSwlgf9AQDAzpkbOKGhocnJyTzPZ2VlHTx4MCEhQTz+2muvxcbGiq/Ly8vnzp378MMPt7saZzl1c2OXSjEoBACATtKGlWV69uy5evXqBgeffvrputceHh53MxwUxfmyU0VCf2+sLwMAAJ3B6qYgB/rgsXoAAOg8VheEcdiYEAAAOpHVBWGMN0stE2rwLCkAAHQKqwtCtYx6e7CLJRgUAgBAZ7C6ICSiQb54mhAAADqJVQahDzuNy4QAANAprDEIE7qww7lmLA8HAABw16wxCCM9mYqjsxgUAgBAx7PGICSiad3Y7pvYgwIAADqclQbhg6HcNzcwIgQAgA5npUE42I9pDXSlDFkIAAAdy0qDkBFNCWXf3kQQAgBAx7LSICSiB7txuzNxmRAAADqW9QbhqK7shla4XYlBIQAAdCDrDUIZowdCuG8zEYQAANCBrDcIiejBUIbZUQAA6FBWHYQTgrizxUJhjdR1AACA/bLqIFTJaFwgt+8WBoUAANBRrDoIiejBbpgdBQCADmTtQTgphDuWJ1QYpK4DAADslLUHoZuChnZh/76NQSEAAHQIaw9CInowlNuNJWYAAKBj2EIQduP+fZuvMUldBwAA2CMbCEIfNcV4s4PZmB0FAADLs4EgJKI54dzmNMyOAgCA5dlGED7Sg/tPLp9XLXUdAABgd2wjCF0V9GAoty0Ds6MAAGBhthGERDSvN/fpFR7TowAAYFk2E4QJXRjH6EQBohAAACzJZoKQiJ7qxX1+FbOjAABgSbYUhE/05P6VyWux3BoAAFiOLQVhFyca4c/tuoFBIQAAWIwtBSERzevFPk9DEAIAgMXYWBBODOauV1BqGW6ZAQAAy7CxIJRzNKcn+yIdg0IAALAMGwtCIprXi/sijTcgCgEAwBJsLwh7ebBwD/YDdigEAABLsL0gJKKne3GbruIyIQAAWIBNBuHMHtyZYuFcMbIQAADulk0GobOcXonhXj2FvXoBAOBumRuERqPx1VdfjYqKGjly5P79+5tsk52d/fTTT0dGRg4dOjQxMdFyRTZhfgSXWkZHcjEoBACAuyI3s93bb7/973//e+fOnampqTNmzLhw4UK3bt3qN6ioqBg5cuTUqVMTExNramp0Op3li61HwdGKWO7V06Zjk8z9JwAAADRmboqsX79+w4YNffr06dOnz86dOz/77LNVq1bVb7Bhw4bg4OB33323A4ps2uxw7q0L/P4sYUIQ67QfCgAAdsasqdGysrLbt2/HxcWJb+Pi4lJSUhq0OXny5NChQ1944YUJEyYsW7assrLSwpU2ImO0ciC37JQJ06MAANBuZo0ICwsLicjDw0N8q9FoCgoKGrS5ffv2gQMHPvjgg8cee2zFihWPP/747t27mzxbenr6nj17unfvXndk/fr1CQkJ7ah+nDetEZRbL+unBTvKY4UdPefsUNCZFoTOtCB0pqXwPM9Y61OGZgWhGIGVlZVKpZKItFqtp6dngzbu7u4TJkx46qmniOjjjz/u2bOnTqdzdXVtfLbw8PAxY8a8//77dyqQy4OCgjiunfevrh4s/Pm4bFaEXG6TN8C2h5ubm9Ql2A90pgWhMy0InWkRPM9XV1e32sys9PDx8XFzc0tLSxPfpqWlNbhThojCwsLqDxkFQWhudpQx5urq2uN3ISEh7U5BIhoXyIJc6KsMRxkRAgCAZZmVQBzHzZ49+4MPPhAEISsra9euXXPmzCGi4uLil19+uaqqioiefPLJpKSkvLw8Itq0aVNkZGSXLl06tPQ6a+JkfzvD6/FUIQAAtJ25Q7FVq1bl5uZ26dKlX79+ixcvjo+PJ6Ly8vJPPvmkpqaGiOLj4xcvXhwVFRUSEvLPf/6zo58jrG+wL4vxZh+kYFAIAABtxgShDTddVlRUqFQqlUrVXIPa2tra2tomLw3WSUxMTEpK2rZtWxvKbM1NnTDoW+ORSfK+Gjt/lEKr1eLigaWgMy0InWlB6ExLEa8Ruri4tNysbRfn3N3dW0hBIlIqlS2nYAcJdWV/i5U9fdSEZykAAKBN7OdWy4V9OVcFfXwJE6QAANAG9hOEjOiTYbI1503p5RgVAgCAuewnCImouxv73xjZk0dNPKIQAADMY1dBSESLIjkZo/WpmCAFAACz2FsQcow2DZe9fsZ0rQKjQgAAaJ29BSER9fJgL0XL5v+MO0gBAKB1dhiERPRiFMeIXv4Ni80AAEAr7DMIZYx2jpHvvSV8egUXCwEAoCX2GYRE5KWifeNly06ZjuRihhQAAJplt0FIRL092NbR8kcOG69rkYUAANA0ew5CIhofyF6OkU3eb6owSF0KAABYJTsPQiJ6oR83zJ/N/Q+esgcAgCbYfxAS0cdDZcV64X+ScRMpAAA05BBBqORo33j5sTxkIQAANOQQQUhEHko6OFH+U66w8iweqAAAgD84ShASkUZJ/75PvvM6v/ocshAAAO5woCAkIl81/ThRvjWDf/M8shAAAIgcLQiJqIsTHbhf9ulVft1lZCEAADheEBJRkAv7caLsvYv8/57EMxUAAI7OEYOQiEJd2W/T5L8VCpMOGMtqpa4GAACk46BBSETeKvr3ffJoLxa/x3ilDANDAAAH5bhBSERyjtbGyZZGc8O/M+67hUuGAACOSC51AdJ7pjcX4cEeOWxKiaSXojkZk7ogAADoRA49IqwzzJ+dmCo7lM2PSsJWFQAAjgVBeEewCzs0UT4nnIvfY/wwBTeTAgA4CgThHxjR/Aju6CT51gz+/n8bsyuRhgAA9g9B2FAfDTs+RR7vywZ9a/wiHUNDAAA7hyBsgoKjvw2UJU2Qr7vMj/rOeBkPVwAA2C8EYbNifVjyVPnTvbkxScbnfzXpsMc9AIA9QhC2hBE90ZM7+5CiqIaivzHuzsRMKQCAvUEQts7fibaNln06XPb3c3zMN8bt13gT8hAAwF4gCM01JoCdmib/x1DZpqt8z53GD1N4Pba7BwCwfQjCthnmzw5NlG8eIUu6zff+2vhhCo9rhwAANg1B2B4ju7ID98t3jpH9ki9032H435Om3CqpawIAgHZBELbfYF+2c4wseapca6B+/zLMO2q6WIKLhwAANgZBeLd6uLGPh8rSZyrC3Nn9+01D9xo/u4r5UgAAm4EgtAwvFS3rz918VL5sgOy7W0LIdsOfjplOFGCACABg7bANkyXJGD0QzB4IluVVy75I4+ceMQlEM7qzmT24GC9s7wQAYI0wIuwQ/k70cgx3dYZ8+2iZSaCHDpoivjYuP206j4uIAABWpg0jwtra2sOHD9fU1IwaNUqj0TT4qtFoPH/+fN3brl27BgQEWKZGWxbrw2J9ZGvj6FSRsPM6/9BBk1GgB4LZ5BBudABTy6SuDwDA4ZkbhJWVlSNGjHB2dvbz81u4cOGxY8fCw8PrNygvLx80aNDAgQPFt88888yzzz5r4WJt2SAfNshH9tZgulwmfHdLWHve9OhhYXQAd18QGxfIwtwxcQoAIA1zg/DLL79UqVRHjhzhOG7x4sVr167dtGlTgzaMsVOnTlm6QnvTV8P6athL0VyJnv6dxe/PElaeNTnJ2NhANi6Q3RvAeamkLhEAwJGYG4R79uyZMWMGx3FENHPmzAcffLBxEBLRiRMnZDJZZGSks7OzJcu0R14qeiyMeyyMiGQppcKhbGFzGv/0UVOoGxvuzxK6sOH+LNgFI0UAgI5lbhBmZ2cHBQWJr4OCgoqLi2tqatRqdf02Pj4+r7zySklJSX5+fmJi4r333tvkqbRabXp6+oYNG+qOTJ061c/Pr13124k+7tTHnRb1YUaenSuhn/OFXdeFF08IKhkb6keDfSjej/X3IpWMTCaTyYRFTi0DnWlB6EwLQmdaCs/zgtD6LYrmBqHRaJTJ7tzaIZfLBUEwGAz1g1Cj0eTk5MjlciJ677335syZk5WVxVgTAxqdTldSUlJ/EjU+Pt7T09PMSuxejAfFeND/60VElFZBJ4vYyWL2ZQalVbBIjRDjLo/3N/b3FCI8SI57fu+OwWAwGLD2gWWgMy0InWkpFg5Cf3//wsJC8XVBQYGbm5ubm1v9BnUxSURPPvnkX/7yl9zc3CZvHO3atWt8fHyTM6vQQLSaov3oaSIiqjLSmSLhaJbpcL78nUtCVqXQz4vFerMB3qyfF4v0ZO4Kiau1OQ3+Zw7uBjrTgtCZlsLzfHV1davNzA3CESNGHDp0aMGCBUR06NChESNGiMdramoUCkX9FCSiixcvKpVKHx+fNtYMLXGW0zB/FuNidHNzIiKtgc4VC2eKhBMFwqdX+culgq8Ti9RQlBfr5cEiNKyXB/PGfTcAAK0xNwgXLFgQHR390ksv+fv7r169es+ePeLxqKio5cuXP/HEE5s3bz5x4kS/fv0KCwvXr1+/bNkypVLZYWUDuSlouD8b7n9n8pkX6IZWuFgqXC6lwznC+lQ+rVyQM+qtYb09WE8PFu5OPd1ZuDtzxcARAKAec4MwICDg5MmTmzdvzsnJOXDgwODBg8Xjy5cvF1+PHj26tLQ0IyNDo9F88803w4cP76iSoSkcozB3FubOpoX+cTC/mq6UCekVQnq5sP0apVfwGRWCRknBLqyLE/NzogBn8lWzbm50jx/ni5kYAHBIzJwLiZaVmJiYlJS0bdu2Tv659kGr1Ta4OtsmAlFOpZBVSQU1QkE15VRRYY2QUSEczxf8ndjQLmyYP0vownp7OMRjG3fZmVAfOtOC0JmWIl4jdHFxabkZFt12LIwo0IUFuogv/8ALdKlUOJYnHM4RVpzmIzT00RBZH41DxCEAODgEIRARcYyivFiUF3uuL/ECbc3g700yzuzBrRokw82oAGDf8CQaNMQxeqInd+lhBRH13WX85ArPY88MALBfCEJompeKPhwi+2asbNNVfsoBo9TlAAB0FAQhtGSwLzsxRZ5VSd/dwqgQAOwTghBawTFaEcutOGNCEgKAXUIQQuumdeNkjPbd5KUuBADA8hCE0DpG9Gp/7tXTuGsGAOwQghDMMiWUc5LRHgwKAcDuIAjBXK/Fyl7DoBAA7A6CEMz1QDBzUdA3mRgUAoBdQRBCG7weK3v9DAaFAGBXEITQBvcFMS8VfX0Dg0IAsB8IQmib5QNkr53m8VAhANgNBCG0zbhA5qumvbh9FADsBYIQ2mxSCHc8H0NCALATCEJosxgvdq4EQQgAdgJBCG3W35udL0YQAoCdQBBCm3V1Jo5RThWyEADsAYIQ2iPai50vlroIAABLQBBCe+AyIQDYDQQhtEcMLhMCgL1AEEJ79Pdm5zEiBAC7gCCE9ojwYLd1QpVR6joAAO4aghDaQ85Rbw1LKcWgEABsHoIQ2inGi53DZUIAsH0IQminGK/2XyasMFBulWXLAQBoJwQhtNPdrC/z8m+mN86ZLFsPAED7IAihnWK82cUSoR2b9Jbo6at0vlTfATUBALQdghDaSaMkTxW7rm1zEm5I5X3UrFSP64sAYBUQhNB+7Xis3sDT+lT+lf5cWW0HFQUA0DYIQmi//l7U1vtl/nmN76uhkf4MU6MAYCUQhNB+Md7sfEnbvuXjS/wLUTKNipXVYmoUAKyCXOoCwIbFeLFzxbz57f+TK2gNND6QGXjCiBAArARGhNB+PdxZea1gfqS9n8K/GMVxjFQy4hhVY4U2ALACCEJoP0bUz9Pcx+ozKoTj+fzs8DsfOU8VlWJ2FACsAIIQ7or5j9V/mMI/24dz+X0yXqNkuHEUACRb7DkAAB6/SURBVKwBrhHCXYnxYr8WtB6EZbW07Rp/8aE/Pm8aJZXhMiEAWAGMCOGuxHibtfT2J1f4ySFcoAurO+KpolKMCAHACiAI4a7082RXywVDi7eO8gKtu8wv6fdfHzaNkpVhcRkAsALmBmFVVdXzzz/fp0+fe++99+eff26h5eLFix9++GFL1AY2wFlOIa7sSllLkfZboeCuoAHerP5BjAgBwEqYe41w2bJlly5d2rdv3/HjxydPnpyRkeHt7d24WWJi4sGDB4uLiy1aJFi1/t7sfIkQ5cWaa7DvFj85pOFXcY0QAKyEWSPCmpqazZs3r127Njw8/Iknnhg4cODWrVsbNysqKvr73//+xhtvWLpIsGoxXq3cOPrdLWFSSMNPGhaXAQArYVYQ3r59W6vVxsbGim8HDRqUkpLSuNlzzz23fPlyHx8fSxYIVi/Gi51r/lHCWzoht0qI92tqRIipUQCwAmZNjRYWFrq5uXHcndTUaDSpqakN2uzdu1en0z366KNHjhxp+Wzp6el79uzp3r173ZH169cnJCS0pWzHpdPppC6hoXAVO1OoLCnXKpr636pd6bKx/lyVrqbBcbWJK6yUa7VSblRvhZ1pu9CZFoTOtBSe5xlr9qpNHbOCUKPRVFZWCoIgnlGn03l5edVvoNVqlyxZsnv37tLSUq1WKwhCaWmpu7u7TCZrfLbw8PCxY8e+99574luZTBYcHFyXstAqNzc3qUv4L25u1M/L+FOJy/TuTfxHPFBg/FNvzs1N3eB4V42gyzRJ/m+RvAB7gs60IHSmRfA8X11d3Wozs4IwODiYMXb9+vWwsDAiunr1alRUVP0GRUVFjLGHHnqIiGpqakpLSwcNGnTo0KH6w746jDEXF5cePXqY9e8AW7CgD7fxCt84CCuN9Gu+8PWYJgISU6MAYCXMGoe5ublNnTpVHMOlpqbu379/9uzZRHTz5s2lS5cSUffu3a/9LjEx0cvL69q1a02mINilh7tzF0qEtPKGVwoPZPHxfsxN0cS3eKqwAQUAWAVzJyTfe++9U6dO+fn5JSQkvPnmm+LQsKCgYPPmzQ1ayuVyjUZj4TLBuik5mtuT+/RKw+fq990SJje6X1SkUeKuUQCwCuY+RxgcHJycnFxRUeHs7CyX3/muuLi4oqKiBi0TEhLS0tIsWSPYggV9uHv2GFcNkql/vy7MC/TDbX5Z/6Y/Yx5KqqglXiCu9SvZAAAdqG23qLi7u9elIEB9PdxYrA/bdeOPQeFvhYKPmoW5Nx10MkYuctIaOqs+AIBm4F5NsJgFEdzGerOj393iJzVaUKY+TxUrxXKjACA1BCFYzOQQLlNLF39/uL6FC4Qi3DgKANYAQQgWI+fo6d5MHBTe0gnZlU0sKFMf1t0GAGuAIARLWhAhS7zGaw2075YwKYSTtXgjDHZiAgBrgCAES+rqTCP9ue3X+FYvEBKRRoWpUQCQHoIQLOzZPtxHl/jj+cL4oFY+XbhGCADWAEEIFjY+iNWYKN6PuTe1oEx9mBoFAGuAhwLBwhjRa7GcixmfLE8VZVR0fEEAAC1CEILlzQk3a6YBm9QDgDXA1ChIxlNFpVhuFACkhiAEyWiUDDfLAIDkEIQgGUyNAoA1QBCCZLCyDABYAwQhSAaPTwCANUAQgmRcFVTLU23D3XwBADoVghCkhMuEACA5BCFISaNiZXiCAgAkhSAEKWG5UQCQHIIQpOSppFJMjQKApBCEICVMjQKA5BCEICVMjQKA5BCEICVPFaZGAUBiCEKQkkaJqVEAkBiCEKSE5wgBQHIIQpASrhECgOQQhCAlTxUrxXKjACApBCFICSNCAJAcghCkpMFOTAAgNQQhSMkTOzEBgNQQhCAljQpTowAgMQQhSEnJkYKjSqPUdQCAA0MQgsRw4ygASAtBCBLDjaMAIC0EIUgMi8sAgLQQhCAxTxWVYrlRAJAOghAkplEyTI0CgIQQhCAx7MQEANJCEILEcLMMAEgLQQgS02BxGQCQlNz8ppWVlYcOHeJ5fuzYsW5ubg2+ajKZLl26lJaWxnHcoEGDQkJCLFon2C2NilJKpS4CAByYuSPCwsLCmJiYjRs3bt68OTo6Oi8vr0GDH3/8cfbs2bt3705MTIyKitq8ebOlSwX7hKlRAJCWuSPCdevW9evX79tvvyWiWbNmffjhh2vWrKnfYPz48RcvXhRff/XVV6+//vpTTz1l2VrBLnmqWKmel7oKAHBc5o4I9+7dO2PGDPH1zJkz9+7d20Jjo9Ho6el5t6WBY8CIEACkZe6IMDs7OygoSHwdFBSUnZ3duE1NTc0TTzxRVFRUWFi4c+fO5k5VUVGRnp6+YcOGuiNTpkzp0qVLW8p2XCaTyWQySV2FJbnLhFI9SfKPsr/OlBA604LQmZbC87wgtH4vnrlBaDQaZTKZ+Fomk9XWNvH/8AqFYv78+fn5+R999NFHH320fv36Jk9VVVVVUlJy6tSpuiP33HOPl5eXmZU4OIPBYDAYpK7Cklw4KquVGQwS7EBhf50pIXSmBaEzLcXCQejv719YWCi+LigoCAgIaNxGJpONHTuWiEaOHBkSErJy5UpfX98mTxUfH79p0yYzfzTUZzAY1Gq11FVYkp+KKo0GhUotY539o+2vMyWEzrQgdKal8DxfXV3dajNzrxGOHDny4MGD4uuDBw+OHDlSfK3T6RoP4cvKyjiOc3Z2NrtacFwcI1c5VeAyIQBIxNwR4fPPPx8fH+/r6yuXyz/77LNjx46JxwMDA3fu3DlhwoS33nrr5s2bvXv3Li4u3rJly6JFi1xcXDqsbLArnipWWit4qjp9SAgAYP6IsHfv3snJyTzP19TUHD9+PCoqSjz+7rvv9u3bl4hmzpwZERGRmZnJcdyWLVvef//9jioZ7A52YgIACbVhZZnevXuvWrWqwcFnnnlGfNGtW7dFixZZrC5wJBollWJqFAAkgrVGQXqeKiw3CgCSQRCC9PBMPQBICEEI0vNUYWoUACSDIATpeShZeS2mRgFAGghCkJ45m9TrseAUAHQMBCFIr9VrhGW11HOn8UQBRo0AYHkIQpCep4qVtnjX6F9OmEr0wq8IQgDoAAhCkF7LI8Ifbgs/5Qpvx8t+K2xbEOZW3W1hAOAIEIQgvRZWlimvpQU/mz4dLrs3gP3WlhHh0TxhxHcS7GgBADanDSvLAHQQTxWVNnPX6IsnTJNC2JgAJhCV1gqFNeRr3qL82zL4nCpMpQJA6zAiBOlplKzJqdH9WcLhXOHNwTIiYkQDfdgp82ZHDTztzuRrearEmBAAWoMgBOk5y4kXGj4gUWGg+T+bNg6TuSnuHBnsy8y8THgwW+jlwQKdWUE1BoUA0AoEIVgFjZLevMDvuclfLRdqeSKiv5ww3R/Exgf+sTdTnC87Wcibc7bt1/hHw7guTpTf+pacAODocI0QrMI/EmQ/5wmfXBHSyimrUgh0YUaeLk7/r8/nYF+24OfWR3jVRvruNv92vGJ/Fl9QLRBhm0MAaAmCEKzCQ924h7rdeV3L0/UKwVPF6iZFRQHOTMGxTK3Qza2lbPv+Nj/Qh3VxIj81K6jpqIIBwG5gahSsjpKjCA3r4tTEl8y5TPjP68KsMI6IMDUKAOZAEIItifNlJ1sMQq2BDmXz00I5IvJzws0yANA6BCHYklZHhLsz+VFdOS8VEZGfE2FqFABahSAEWzLIh50pFozN3zq6/Tr/aI87VxC7OLF8PFMPAK1BEIIt8VBSkAtLLWs63opq6Hi+MCnkzqfaT40RIQC0DkEINqaF2dFdN/iJwZzr7/ea4hohAJgDQQg2poX7ZerPixKRj5rKaqmFeVQAAEIQgs1pbkSYUyWklAgTgv74SMsYeaqoqJl9LQAARAhCsDH9vVl6hVDVaDXt7deEqaGcSvZfB/3UmB0FgFYgCMHGKDnqo2Hniv8r3kr19NEl/qleDT/Pfk5UgGfqAaBFCEKwPQ1mRwWip4+Zpndnw/wbLr3WxYnlY0QIAC1CEILtaXC/zEcp/G2dsCZO1rglnqkHgFYhCMH21B8Rni4SVp837RgjUzb1WcY1QgBoFYIQbE+EhhXrhWI96Qw0+yfTB/fIejSzHwXW3QaAVmEbJrA9jCjWm50sFLZm8GMDmbjXRJP8nAgjQgBoGYIQbFKcL3v5NxPH6NcpLX2GsSUhALQKU6Ngk+L92HWtsONembqJW2T+gKlRAGgVRoRgkyYGcyensl4eLW1VT1huFADMgBEh2CRxF/tWmznLSc5RhaETKgIAW4UgBDuHXQkBoGUIQrBz2JUQAFqGIAQ7h8uEANAyBCHYOdw4CgAtQxCCncNyowDQMgQh2DksNwoALWtDEP7000/Dhw+PiIhYvHhxdXXDyabc3NwXXnghLi4uMjLy6aefzs7OtmidAO2ELQkBoGXmBmFBQcG0adOee+65H3744fLly8uXL2/QIDMz08nJad26dV9//XVlZeW0adMsXSpAe2BLQgBombkry3z55ZdDhgyZNWsWEa1evXrChAmrV69WKpV1DYYMGTJkyBDx9Zo1a3r06KHVat3c3CxeMUCbtPsaoUD0c54wvNFmvwBgZ8wdEV66dGngwIHi69jY2PLy8hYmP5OTk0NCQpCCYA3ad42wvJamHzKN/M74awFGkwB2ztwRYWFhYZ8+fe58j1zu6upaUFDQvXv3xi1v3bq1ZMmSjRs3Nneq9PT0Xbt2ff/99+JbmUy2devWhISENlbuoHQ6ndQl2BglkdagLi7XNt65t7nOvFrBHv9FOdSX/1uM8N450+dDsERb6/DJtCB0pqXwPM9Y65M65gahRqOprKysO3VlZaWnp2fjZjk5OWPGjPnrX/86derU5k4VHh4+ZcqUTz75pP7JzakVRBhqt5WP2lAjd/V2aeIz1rgzt2bw/5Ns+uAe2aNhnNZA3bYbKjhVYFPfCw3gk2lB6EyL4Hm+8a2djZkbhD169Lhy5Yr4Oj09XaFQBAUFNWiTn58/ZsyYZ555ZsmSJS2cijGmVCqbzFGAjiDuShjo0kozvYle+s20P0s4NFHez5MRkZuCHunBfXqVfz22xd2eAMCWmXuN8PHHHxfvFxUE4d13350+fbqzszMRffbZZz/88AMRFRYWjhkzZuLEifPnzy8tLS0tLTWZTB1YOIDZzFxcZuVZ07UK4dS0Oyko+nMk98kVvpbvwPIAQFrmBmGvXr3Wrl07bNgwX1/f1NTUd955Rzz+008/nT9/noh++eWXnJyczZs3h/0uMzOzg4oGaBMzlxs9mie8GCVzVfzXwb4a1lfDdt1AEgLYrTZszPvcc88tWLCgpqbGxeWPOaatW7eKL6ZNm4ZnB8E6mTMiNPB0rlgY5NvEtcBFkdza8/xjYViGCcA+te13WyaT1U9BAJvgq2aFNa2MCM+XCD3cmLuiiS9NDuHyq+lkIZ6jALBP+J9csH/mjAhPFAhD/Jq+NZRjtLAPt+4yZkcB7BOCEOyfOdcITxQI8c0EIRE905vbe4vHdk4AdglBCPbPzBHhPc0HoaeKpnfjPruKQSGAHUIQgv1rdbnRwhoqrhF6e7T01Pzz/bh/pPIGRCGA3UEQgv3zU7PCaqGFudHkAmGwL+NaXD2mnycLc6M9N5GEAPYGQQj2TyUjZzmV6pttcKKAb2FetM6LUdyqs7wJd48C2BcEITiElu+X+bVAGNKl9d+FqaGcr5o2p2FQCGBXEITgEFq4TMgLdLpIGNzUo/SNvR0vW37KVIHtKADsCIIQHEIL+9SnlApdnZmXyqzzDPBm4wK5ty9gHV0A+4EgBIfgp6aCZp6gaPnBicbWxHHrL/O3dLhUCGAnEITgEPycqLlrhMltDMJAF7awL/fqKVwpBLATCEJwCOKWhE36tY1BSEQvR8t+zBGw+iiAfUAQgkNobnGZslrKqhQiNW0LQlcF/W0gt+QEnqQAsAcIQnAIzT0+kVwgDPJh8rb/HszrxdWYaHcmJkgBbF4b9iMEsF3NPT6RXNjSWtst4BitjZMt/MXkqmADvJmv+m4rBACpIAjBIXRxYvlVTY4I+fkR7ZwXGRfIFkRwa8+ZzhYLbgoW68NifVigMxXrqaBaKKqhohqhWE8Dfdi0UG5U1/aMOwGgEyAIwSF4KEnPU42J1LI/DgpEyQXCZyPaH1BLo7ml0ZxAdL1COFMsnCkSfs4nbxV1cWL9PMlHzXko6Xi+8OppU0a5MDGYe7AbmxDEOePXDsCa4DcSHAIj8lWzgmohxPWPidBrWuauZP5OFjh5mDsLc2czujfx1eH+7OUYLrtS2HNTWJ/KP3nENCqAmxLCJodwfnf9owHg7iEIwVGIN46GuP5x5GQx19YHJ9ot0IU915c915crq6UfbvN7bgpLfzNEeLCpodwwfxbtxdwUnVMIADSEIARH0fh+mc4MwjoaJc0K42aFUS0vO5Ir7LvFv3iCv1QqdHVm/b1Zf28W48UiPambG+vsygAcFYIQHEWgM3v1lOlwDuvvzQZ4swgP9lsR+1OkZHGj5GhcIBsXKCMik0Bp5cL5YuFcibDuMn+5jEr0Qh8N6+fJ+nqyKE8W5UUBzkhGgA6BIARH8d49sl8LhLNFQtIt4e9n+duVAiPW39sq0kXGqI+G9dGwR8PuHKkw0OVSIaVUuFQq7M/iL5QIRp5ivFg/Lxblyfp6sr4a5mneQuEA0DIEITgKNwWND2TjA+8kn85AmcU6JaeUtqrmuCvoHj9Wf+a2oJoulAgXS4XkQuHzND61THCSUV9PFqFhER6spwfr6U6hrnhIA6DNEITgoFwVFOpiS0uk+TnR2EA2NvCPaMyuFFLL6HKZcKVc2HeLz6ig3CohxJWFu1OYO+vhxnq4UQ931sON4YENgBbg9wPAVgW6sEAXqh+NehNd1wrp5cJ1LV3XCoeyhetauqEVNEoKdWOhrizUlbq5shBX1s2NglyYxkrHwwCdCkEIYD9UsjvXGusfFIjyqihTJ9zUCpk6ulgqJN3mb+rotk4QiIJdWIgrBbmwQBcKdGYBLizIhbo6MTzjCI4DQQhg5xhRV2fq6syGNHpWpMJAt3XCTR3lVAlZlcJvhULuTf52JeVVC2V68nNigS7UxYkFOJO/E/Nzoq7O5Ktmfmrq6sxc8eAj2AsEIYDjcldQpCeL9CSihhlZy1NBtZBdSfnVQk4V5VULF0voQBYV1vAFNZRbJRh58lEzXzV1cSIfNXPn5F3deG81+ajJR828VeSlIi8VLk+CDcCHFACaoOQoyIUFuVDjjBRVG6lIL+RXU2E1FdYI2eWkMwnni6mwhkr0fLGeimuoRC8QkZeKeanIS0WeKqZRkqeKNErmqSKNktyV5KFkHgrSqMhXjeV1QBoIQgBoDyc5BctZsIv4jmm1Rje3Jq4rVhupRC+U6KlET6W1QpmeSmupTC9kVFCZnioMVF7Ll9dSeS0xRukz5VbxXCc4GAQhAHQgJzkFylng73kpbTEATcLDtwAA4NAQhAAA4NAQhAAA4NAQhDbm7bffFgRbWhjMaplMpvfee0/qKuyETqf7xz/+IXUVdqKgoGDz5s1SV2Enbty4sWPHjlabIQhtzNq1a41Go9RV2AOtVvv+++9LXYWdyM3N/fTTT6Wuwk5kZGQkJiZKXYWduHDhwrfffttqMwQhAAA4NAQhAAA4NAQhAAA4NAkeqL958+a+ffvCwsJabwqNmEymiIgIqauwB4Ig6HQ6fA4twmg05uXloTMtQq/XFxcXozMtoqqqytPTs9VmrPNvQeR5Pj09XaHAqoLtodfrVSqV1FXYCXSmBaEzLQidaSmCIHh7e2s0mpabSRCEAAAA1gPXCAEAwKEhCAEAwKEhCAEAwKEhCAEAwKFhP0LrJQhCcnLy4cOHS0pKoqKiZs2apVQqxS8VFxdv2rQpPz///vvvHzdunLR12hae57/44ovQ0NB7771XPFJRUfHJJ5/k5OSMHj168uTJ0pZnQ9LS0rZv315aWhodHT137lyO44goMzNzy5YtVVVVM2fOHDRokNQ12obS0tIvvvji9u3boaGhc+fO9fDwEI9fvXr1q6++MplMs2fP7tevn7RFWrMbN26cPn26pKRk5syZ9W8QPXv27Pbt29Vq9dy5c3v06CEeNBgMn3/++dWrV6Ojo+fMmSOTyQgjQmt248aNWbNmlZWVBQcHr1u3bsKECTzPE5Ferx86dOilS5e6d+8+d+7cr776SupKbcnHH3+8ZMmSzz//XHxrMplGjRp14sSJsLCw559//uOPP5a2PFtx4MCBwYMHl5eXd+vW7fDhw+L6t7m5uXFxcVqt1tfXd+zYsceOHZO6TBtQXV0dHx9/8uTJ6OjoX375ZciQIXq9nojS09Pj4+MZY66urgkJCRcuXJC6UitVVFQUGxu7YcOGBQsW5OXl1R1PTk4eOXKkl5eXXq+Pi4u7ffu2ePyJJ57Ytm1bz549161bt3jx4jutBbBWtbW1RqNRfF1aWiqXy1NSUgRB2Lp1a0xMDM/zgiDs2rUrIiJCfA2tunHjRlRU1JIlS2bPni0e+e6773r06CH288GDBwMDAw0Gg6Q12gCDwRAUFLRjx44Gx1esWDF9+nTx9Zo1ayZNmtTppdmeX3/91d3d3WQyCYJgMBicnZ1Pnz4tCMKiRYv+9Kc/iW2WLl06d+5cCYu0ZnV//YgoNTW17vj06dNXrFghvp49e/Yrr7wiCEJ6erparS4pKREE4fbt22q1Oi8vTxAEjAitl0KhEIftRGQwGHied3V1JaKjR4+OHTuWMUZE48ePv3LlSn5+vpSF2ghBEObPn//ee++5uLjUHTxy5Mi9994r9vOoUaMKCwszMjKkq9E2XLhwoaKiYtCgQevWrdu8ebNWqxWPHz16dPz48eLrcePGHTlyRLoabUa3bt0EQUhLSyOi1NRUuVweGhpKREeOHEFnmkP8S9hYkx147NixgQMHimvNBAUF9ejR49dffyVMjdqK559/fubMmeJvSG5urq+vr3jczc3NyckpNzd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3bt2vX79XX31VesnzfFxcnFKpJKJ+YbbTBuW9vs4yl9U5+fr6yl0F94HGdCA0pgOhMR1CEASj0dhgMXuDMCoq6sKFC9Lx+fPnIyMja43ZnTt35uXl3X///fVciuf5gICAnj171vxSryC29hLmywAAQPOxt+81ceLEFStWlJWVCYKwePHiiRMnSucXLlx45syZymL//e9/J02aVOuQqT16Y38ZAABoXvYG4bhx43r16tWmTZvWrVtXVFQ8++yz0vl33333+PHj0nFJScmaNWumTZt2y7WJ8WNlFjG34Y4sAACAY9g7NMrz/BdffHH9+nWz2RweHl55Pj09vfLYz8+vvLz8dmrDiHoEsUPXxbtbYVk9AAA0h8ZNSwkKCqqagk0Bz2MCAIDm5HTzM3sHswN5WD4BAADNxDmDED1CAABoJk4XhC29GccooxxZCAAAzcHpgpCIegahUwgAAM3EGYOwdzCHIAQAgObhjEHYJ5jtyUUQAgBAc3DGILwjjB3JF69XyF0PAADwAM4YhFoFJUdw669gEQUAADQ5ZwxCIrq/NVuH3bcBAKDpOWkQjozkdmYLJRa56wEAAO7OSYPQV0mJoWzTVYyOAgBA03LSICSiB1pzeDYhAAA0NecNwtGtuS0ZgtEqdz0AAMCtOW8Q6tXUXc9SMjE6CgAATch5g5CIHojG6CgAADQtpw7C+6PY+iuCGX1CAABoMk4dhBHerJ0/25GFTiEAADQVpw5CujF3FF1CAABoKs4ehA9Gs3WXBBv6hAAA0DScPQijfVmYlu3JQRICAECTcPYgJKL7MToKAABNxgWCcEw0W3tJRJcQAACaggsEYecApubp8HVEIQAAOJ4LBCERTYzhVpzD6CgAADieawTh9Pbsm/PYdxQAABzPNYKwpTfrFcx+uIxOIQAAOJhrBCERTW/P/fcsghAAABzMZYLwvijuRKF4oQRTZgAAwJFcJghVHE2O5ZZjygwAADiUywQhET3enluWJmK7NQAAcCBXCsIOOhbpTb9kIAkBAMBhXCkICVNmAADA0VwsCMe34XZkCVkGuesBAADuwsWC0EdJ97fmvjqPTiEAADiGiwUhEU1vzy09K+A+IQAAOITrBWH/EKbh6Y9sRCEAADiA6wUhEU1txy3FlBkAAHAElwzCR9pyG68Kl0rRKQQAgNvlkkGoV9NfO3EvH0anEAAAbpe9QSiK4uLFi++5556JEycePny41jIlJSXz5s0bPnz42LFjN23a5LhK1uIfXfjNGcKxAnQKAQDgtijsLLdo0aIPPvjgk08+OX369F133XXmzJmwsLCqBUwm05AhQ2JjY5966imTyWQwNO1aPx8lPd+Vf/mQsC6Zb9I3AgAA92ZvEH744YdvvfVWcnJycnLy9u3bly1bNmfOnKoFli5dyhj79ttvm6CStXuyI/fhSeueXLF/CGu2NwUAADdj19BoaWnpuXPnEhISpJcJCQmHDh2qVmbXrl333HPPe++9N23atMWLF1utTf44eQ1P83pw/9xva+o3AgAAN2ZXjzAnJ4eIAgICpJd6vT47O7tamStXrvz666/PPPPMqFGj3nzzzYMHDy5durTWq6Wnp//yyy/du3eXXvI8/8477/Tq1esWav9AGL1zVP3jecOQUE+Jw7KyMrmr4D7QmA6ExnQgNKajCILAWMNDhnYFoY+PDxFVVFSo1WoiMhgMvr6+1cp4eXklJCTMnTuXiNq2bRsfH79gwQKtVlvzai1btuzTp89rr71WeaZTp061lrTHa72FeUe4kfcrOI8ZH63Z+HDL0JgOhMZ0IDSmQwiCYDQaGyxmVxAGBwdrNJr09PRu3boR0cWLF1u1alWtTFRUlEJx42rh4eGCIBQVFdUab0qlMjAwsGfPnva8dYMeiObePSGsuSSMjXbJpSAAACAvu8KD5/mxY8d++umnRFRYWLhmzZrx48cTUXFx8YIFCyoqKoho4sSJW7dulXr069ati4qKqjattIkwold78i8dFKxYVQgAAI1nby9q/vz5W7du7d69e8eOHe+5554hQ4YQUW5u7qxZs6TwGzJkyPDhw9u3b5+YmDhv3rwvvvjCnpFZh0iKYK28aXEqkhAAABrN3uUTrVu3Pn36dGpqqk6ni4iIkE7GxsaWlZV5e3sTEWPs448/fumllwoLC2NjY5VKZVNVuTYLE/kB6613t2Qxfh5zqxAAABzB3iAkIp7n4+Liqp5hjEkpWCksLKx5RkSrae/Pno/nH99l2zpCgSQEAAD7uc8Ek2e7cBaRFp/BACkAADSC+wQhx2jpQP5fh2wXSrABKQAA2Mt9gpCI2vuz5+L5x3fZkIQAAGAntwpCIvpHF84sYIAUAADs5W5BiAFSAABoFHcLQiLqoGP/6MLP+N2GEVIAAGiQGwYhEf0jnuMYvYAHUwAAQEPcMwh5RmuSFJuuip9iuxkAAKiXewYhEfkpaf0w/uXDtu1ZGCEFAIA6uW0QElEbX/btEMXEbdbzmDgDAAB1cOcgJKJBYey1XvzILbYis9xVAQAAp+TmQUhE09tzyRFs/FYrntMEAAA1uX8QEtH7fXnG6Mk/sJ4CAACq84ggVHC0NklxvkScgd3XAADgZh4RhETkpaANwxRpxeIze5CFAADwJ08JQiLyVtD6YYr9eeLf92KhPQAA3OBBQUhEfkraPFzxRw6yEAAAbvCsICQifxX9MlyxPUv8vwPIQgAA8LwgJKJANaXcrdiSKT78m81olbs2AAAgK08MQiIK0tDvIxUKjhLWWy+XYfYMAIDn8tAgJCINT8sG8U925Pr/ZMV+pAAAHstzg1AyowO3crBi0nbrW8ew8QwAgCfy9CAkojtasJ33Kr48Lzy601Zikbs2AADQvBCERESxfmzvKIWao65rMUwKAOBZEIQ3+Chp8QB+yQB+ym+2J363lWM2KQCAZ0AQ3mRoBDsxRkFE8Wusu7LRNQQAcH8Iwur8VbRkAP9WH27cVuvf9uKuIQCAm0MQ1u7BaO70g0pBpE6rrV+cw4RSAAC3hSCsU4CaPurP/5jMf3xaGLzReqYII6UAAG4IQdiAnkFs90jFqChu0AbrnAO2vAq5KwQAAA6FIGyYgqO/deaO3q8osVDH7y1/22vLKEfvEADATSAI7RXhzRYm8KcfVPqrqMc66yO/2c4WIw4BAFwegrBxQrT0cg8+dawyxo8N2mAdt9W2NxdxCADgwhCEtyJQTfN6cBfGKZMi2NQdtl4/WL84J1gxtxQAwAUhCG+dj5JmdOBOPaiY05VbfEbosNr6n1NCoUnuagEAQGMgCG8Xz2hMNLd7lOLLO/n9eWKb7ywTt9u2XhMFjJgCALgChdwVcB/9Q1j/EL7QxH9zQXhun63ITI+24ybHsmhfJnfVAACgTugROliAmv7aiTt8v2JNEp9rFPv/ZO39g/WtY8LFUvQQAQCcEXqETaW7ni1I4D/sz+/KFr9PFxJ/skV4szHR3IhWLD4QfUQAAGfRiCA8ePDgokWLKioqxo8fP2rUqGpfNRgM8+fPr3w5ZMiQoUOHOqaOroxndGcLdmcLfkF/fle2uPaScH+KYBVpRCt2byQ3uAXT4p8iAACysndo9OLFi3fddVeXLl1Gjhw5ffr0DRs2VCtgNBrffvvtgP/RarWOrqpr4xjd0YJ91J+/MF7xy3A+2pe9fdwW9rXlns3W908IJwpEjJwCAMjC3v7IokWLRo8ePXv2bCK6fv36+++/f++999Ys9sILLziydm6qo4511LHn4rlCE227Jvx6TVx0RiiziHdFcEnhbEAYi/XD2CkAQDOxNwj379//yCOPSMeDBg2qK/CeffZZnucHDx589913O6aCbi1ATWOiuTHRRETppeKvmeKWTHHuIcEqiAmh3IAwlhjKuuuZElOaAACajL1BmJ2dHRgYKB0HBQUZDIaSkhI/P78/L6RQPP744zExMYWFhY8++uiMGTOq3jKs6sqVK1u3bh0yZIj0UqlUvvTSS927d7+Nn8IdBDOa0JImtCQiulpOu/O4ffnc56lcehl10Ym99GIvvdBbLwSK5Yyhv+gY5eVoTIdBYzoQGtNRBEGwpyXtDUKtVmsy3dg0paKiguO4ancB/f39lyxZIh0PGjTorrvueumll1QqVc1LhYaGxsXFzZkzp/JMt27dfHx87KyJJ+joQx1DaToREZVZ6OB1cV+u+OM18f+OilZB1TOY665nPfSsexBrg0WKt0EURXzwHAWN6UBoTEcRBMFoNDZYzN4gbNWq1eXLl6XjS5cuhYaGKpXKugrHxcVZLJb8/PwWLVrU/KparQ4LC0tKSrLzrT2cj1Kad3oj8FJzStMqvA/ni1+cF2fvFcosYjc9iwtgXQJZnI51DmS6Wv7tAQAAdbI3CB988MEPPvhg9uzZarV6xYoVDz74oHR+7dq1Xbt2jYmJyc3NDQwMVCgURLR06dJWrVqFhYU1Va09WIQXdQjlRkXdeJlXQcfyxZOF4sE8cVmacLpQ9Fex9v7Uzp+192ftdaydP0X5MB79RgCAOtgbhBMmTFi5cmV8fHxgYGBhYeG2bduk8y+88MLcuXNjYmK+//77V155pUOHDnl5eeXl5V999RXGuJtBsIaSIlhSxJ9NfalUTCuhs0Xi2WJx41UhrZhyjGJrXxbrR239WKwfa+vPIrwpRMOCNDJWHADAWdgbhCqV6pdffjl58qTBYOjRo4fU8yOiPXv2SGPZf/3rX0eOHHnlypWAgIB27drVM3AKTaq1L2vtS0OrRGOFjS6UiOdKxPMldLxAXHdZuFZOuRVisZlCtCxUS1E+rF8ISwxlPYOYhpex7gAAMmjcviadO3eudiYoKKjyODIyMjIy0gGVAofS8BQXwOICqnfQLQLlGsUcI50rEXfniLP3CGeKxK56lhjKHmvPtfNHhx4APAI2+PJcSo4ivFmEN/UIYuPbEBGVW2l/nrjtmjBgvfXRdtxL3XlfdOwBwN1hqTb8yVtBg1uwV3vyZ8YqK2zUcbX101QBD1YEAPeGIIRa6NX0UX9+bRL/37PCwA3WI/kIQwBwWwhCqFOfYLZnlGJ6e27YJmuhSe7aAAA0DQQh1IdjNK0dd18U9/5Jm9x1AQBoEghCaNjc7twnp4W8CrnrAQDQBBCE0LBIHza+DffeCXQKAcANIQjBLnO78/89K+Q0vHstAICLQRCCXVp40cQY7u3j6BQCgLtBEIK95nTjl6cJmeVYSgEAbgVBCPYK09K0dtybxwS5KwIA4EgIQmiEF7ryKy8Il0rRKQQA94EghEYI0tCMDugUAoBbQRBC4/wjnl9zSUhHpxAA3AWCEBonUE0PteFWpyMIAcBNIAih0XoGsWMFCEIAcBMIQmi0bnp2FM+jAAB3gSCERusUwNJLRYNV7noAADgCghAaTcVRO392qhCdQgBwBwhCuBUYHQUAt4EghFvRNRDzZQDATSAI4VZ01SMIAcBNIAjhVnTXs+P5ooAoBADXhyCEW6FTUYCaXcT+MgDg+hCEcIswXwYA3AOCEG5R10DCbUIAcAMIQrhFt9Mj/OKc8P4JPMICAJwCghBuUVc9O5Z/K99oE2n+EeE4epMA4BwQhHCLon1ZiUXMNzX6G3+6LGSUi4XmJqgTAEDjIQjhFjGiroG3Mjr63glhRgeuyIQeIQA4BQQh3LpbuE148LqYaaCpbTn0CAHASSAI4dZ11bNjjQzCd44Ls+O4YC0VNn5MFQCgKSAI4dY1dsfRy2Xir5nCo+05nYoVmTE0CgBOAUEIt65zADtXIlbY7C3/n1PC9Pacn5J8lGSykQULKADACSAI4dapeYr1Y6ftezBhqYW+OCc81YkjIkbkr6Ii3CYEACeAIITb0i2QHbVvdPSzVGFoBBfpw6SXOhXDxFEAcAYIQrgtds6XsYn08Wnhmc5/ft4C1ISJowDgDBCEcFu62fdgwtXpQitv6hPMKs/oMDQKAM4BQQi3pWsgO5YvNpiEH54U/tblpg9bgJoVYmgUAJxAI4Jw5cqV06ZN+wOOjIIAACAASURBVL//+7+srKx6iq1atWrBggW3XTFwDUEa8lGyS/U+mPB8iXi5TBwVedOHDT1CAHAS9gbhRx99NHfu3CFDhhQWFg4YMMBkqn059L59+5566qlXX33VcTUEZ9dNT/XvL/PTZfHeSI5jN50MUGNNPQA4BbuC0Gazvf/++wsXLpw8efKiRYv8/PzWrFlTs5jJZPrLX/7yyiuvOLqS4NQavE24/oowMpJVO4k19QDgJOwKwoyMjKtXr955553Sy8GDB//xxx81i82fP3/06NGdOnVyYP3A+XUNZEfrfh5TkZkOXxfvCq/+SdOpqAg9QgBwAgp7CmVlZfn6+qrVaullcHDwgQMHqpU5duzYpk2b9u7du2fPnvqvlpGRsWvXrjFjxlSeef7557t06dKYansuo9HI87zctbhJOy0dvq40GGqPtR8usQEhPJkNhpvvCHoRu27k6/qu5uGEjem60JgOhMZ0FEEQxIYn89kXhBqNpupNQZPJpNVqqxawWq2PPfbYxx9/rFKpGrxaYGBgVFTU+PHjK8/ExsZqNBp7agIWi8XZ2ipOQz5K4WCRekBY9fFPItqcLdzXmmk01T9pIT5UYrHJ+7M4YWO6LjSmA6ExHUUQhLpmtFRlVxBGRESYTKa8vLzg4GAiunr1akRERNUCFy9ePHbs2MMPP0xEFRUVBQUFMTExv/76a3R0dM2reXl5RUZGjhs3zq6fA27GcRzHOd2il5kdaclZcVCN8U+LQFsybe/25atPlSEK1IhFFibvz+Kcjemi0JgOhMZsZna1dXBw8MCBA7/++msiKioq2rhx4wMPPEBEBQUFa9euJaLo6OjU1NSUlJSUlJT33nvP398/JSWlZcuWTVp1cB5T2nKbMoRcY/Xzu7LFWD8W4V1LTxHLJwDASdjVIySiN998c/To0du3bz99+vTw4cP79etHRKmpqWPGjBFFUalUtmnTRip59epVnucrX4In8FfRA625ZWnCC11v+qfV+ivCyMja/7GFBfUA4CTsDcKEhITU1NT9+/eHhYV169ZNOtmjR4+0tLRqJfv27VtzKg24vac6caNTbP+I5/gq3b8NV8TVSbUHodQjFIlq6S0CADQje4OQiAIDA4cPH171jEajadu2bbViGo0mKirKAVUDl9JNz0K0tDlDvKfVjWg7VSiaBYoPrD3plBypOSqzkK+yGWsJAFAD7seCwzzZkVt05s+n9K6/Io6KZPV0+ALUWFMPAPJDEILDPNSG25crVu47uv6KMDKqvg8YdlkDAGeAIASH0Spociz32VmBiPIq6FSheEdtKwsrYeIoADgDBCE40l86cUvPCiYbbbwiJEdw6no3x8DEUQBwBghCcKRYP9YlgP1wWVh/Ray50XY16BECgDNAEIKDPdmR++iksPWacHerBj5d2HcbAJwBghAc7L4o7ko5dQlkwQ3tlRigpkLMGgUAuTViHSGAPRQcPdeF821493XSqdilMgQhAMgMQQiO90xnu0YaAtR0pO4HGQIANA8MjYJsMFkGAJwBghBkg+UTAOAMEIQgG/QIAcAZIAhBNthiDQCcAYIQZKNTYdNtAJAfghBk46skk43Mgtz1AADPhiAEOfmrqBi3CQFAVghCkJNOxYowcRQAZIUgBDkFqKkQPUIAkBWCEOSkU2HiKADIDEEIcgpQY+IoAMgMQQhyCkCPEADkhiAEOenU2FwGAGSGIAQ5YU09AMgOQQhywi5rACA7BCHICftuA4DsEIQgJzyJCQBkhyAEOaFHCACyQxCCnLB8AgBkhyAEOemwoB4A5IYgBDnpVFRsJiQhAMgIQQhyUnKk5qnMInc9AMCDIQhBZlhTDwDyQhCCzLCmHgDkhSAEmWEFBQDIC0EIMsOaegCQF4IQZIYeIQDIC0EIMsM9QgCQF4IQZKZTEWaNAoCMEIQgswAVQ48QAGSksL/o6dOnv/zyS0EQJk+e3KVLl2pfLSgoWL169dmzZ3meT0hIuO+++xhjDq0quCedmory5a4EAHgwe3uEqamp/fv312q1/v7+AwYMOHHiRLUC586dO3DgQGRkZHBw8N///vfnn3/e0VUF94R9twFAXvb2CD/66KPJkyf/61//IqKCgoIPPvjg888/r1qgb9++ffv2lY7j4uKeeOKJd955x7F1BbekU7MisyB3LQDAc9nbI9y5c2dSUpJ0nJSUtHPnzrpKCoKwa9eu+Ph4B9QOPAB6hAAgL3t7hNnZ2cHBwdJxaGjotWvXapYxGAxdunTJzs5u1arV9u3b67pUVlbWvn37pk+fXnnmL3/5S8eOHRtTbc9lNBp5npe7Fo6kFqjQpDQYDM3/1u7XmDJCYzoQGtNRBEEQxYYnpdsbhEql0mq1SscWi0WtVtcso9VqDx48WFhY+Oqrr44bN27nzp21zpfx9fXV6/W9e/e+UQOFIiIiotYLQk1ms9nN2iqUoyKzIMsP5X6NKSM0pgOhMR1FEASTqeERJ3uDMCIiIjMzUzrOyMgIDw+vWYYxFhAQEBAQ8N577+n1+qysrFqL+fj4xMbGzpw50863hqp4nnezfyr682QWBBvjVc2+lsf9GlNGaEwHQmM6CmPMnvUL9v7tGTVq1KpVq6TjVatWjRo1Sjr+7bff8vPziaisrKyy8P79+728vCqHUgHq56+iYuyyBgAysbdH+Ne//jUxMXHYsGEKhSI1NfXDDz+Uzt93332rVq0aNmzYa6+9lpKS0r59+/z8/H379i1atEipVDZZtcGtSPtuB2uw8BQAZGBvEAYFBR09enTbtm2iKA4ePNjb21s6v2PHjjZt2hDRv//97wcffPDSpUu+vr49e/YMCgpqqiqD28G+2wAgo0bsLKPVakeMGFHtZLdu3aQDnud79erVq1cvh1UNPAZWUACAjLDXKMhPp2bYdxsA5IIgBPmhRwgAMkIQgvx0atwjBADZIAhBfjoVKzRhaBQA5IEgBPkF2NEj3J4lGq3NUhsA8DAIQpBfg/cIj+aLwzZZf72Gh1QAgOMhCEF+9c8aNQs0dYetk47ty8XwKQA4HoIQ5Fd/j/DfR2xRvuzfvfn9eY0IwnIrLU9DDxIAGoYgBPnVs7PM4eviklRhyQC+TzA7eN2O56n8z+p04cWDCEIAaBiCEOQn7TVa87xZoKk7bR/048O0FKwhnYqlFdsbhd+cF3IrRAGDqQDQEAQhyM9fRcUWqplZrxy2tfVjE2JufEr7BDM7R0dzjbQ/T9TyVIjliQDQEAQhyE/JkYanMstNJw9fF5elCZ8k/vlUtj7BbL9982VWpQsjI7kIb5ZjRJcQABrQiE23AZpOS2/W6wdrBx1r50dt/Vlbf/bUH7YP+/Gh2j/L9A1h312067bftxeEOd34y2W2XCN10jVVnQHAPSAIwSkcvV9xvkRMKxbTimlfrvjleWFIOBvX5qYRi+56dqpQNNlIXe+zuy+XiWnF4tAItiJN6hHiMYcAUB8EITgFNU9xASwuoL7Q8lJQO392rEDsE1xfsZUXxAejOSVHoVrKMTq6ogDgdnCPEFxJn+CGl9WvvCBI82tCtCwX9wgBoCEIQnAlfULYgXonjp4pEgtNlBjKiChES7kVzVUzAHBZCEJwJQ2uoPj6vPBQDOMYEWFoFADsgiAEV9JJx7KNYj37sa1KFyvXHYZqsXwCABqGIARXwjHqrmcHrtceb/tyRZ5Rd/2NqTQhGspFjxAAGoIgBBfTN6TOZfUrLwqV3UFCjxAA7IMgBBfTO6j224SCSKvTxYfa/LmywkdJRNU3rAEAqAZBCC6mTwjbl1fL/jLbs8QWWmrnf9MSwxAty61ApxAA6oMgBBfTypvxjC6XVY+35WnCQzHVP8+YOAoADUIQguvpE8xVGx394bLwR444vX31z3OIBmvqAaABCEJwPdUeQ3GlTJz5u+3rwbxOVb0keoQA0CAEIbieviF/zpexCPTQNtucrnz/kFo2IEUQAkCDEITgenoFscP5olUgInp+v02voVm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…), …), …)\n args: \n 1:\tSource @012 ⏎ Table{AbstractVector{Continuous}}\n 2:\tSource @753 ⏎ AbstractVector{Multiclass{3}}\n" - }, - "metadata": {}, - "execution_count": 5 - } - ], - "cell_type": "code", - "source": [ - "mach = machine(iterated_model, X, y)\n", - "fit!(mach, force=true)" - ], - "metadata": {}, - "execution_count": 5 - }, - { - "outputs": [], - "cell_type": "code", - "source": [ - "using Literate #src" - ], - "metadata": {}, - "execution_count": 6 - }, - { - "cell_type": "markdown", - "source": [ - "---\n", - "\n", - "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" - ], - "metadata": {} - } - ], - "nbformat_minor": 3, - "metadata": { - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - }, - "kernelspec": { - "name": "julia-1.10", - "display_name": "Julia 1.10.0", - "language": "julia" - } - }, - "nbformat": 4 -} diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..75a14637 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,4 @@ +The examples in this folder are kept for legacy reasons. Users will now find examples +maintained at [/docs/src/common_workflows/](/docs/src/common_workflows/) and +[/docs/src/extended_examples](/docs/src/extended_examples), which are also integrated into +the main [documentation](https://fluxml.github.io/MLJFlux.jl/dev). diff --git a/examples/mnist/README.md b/examples/mnist/README.md index af688717..4e6605d1 100644 --- a/examples/mnist/README.md +++ b/examples/mnist/README.md @@ -1,9 +1,19 @@ # Contents -- `notebook.ipynb`: Juptyer notebook -- `notebook.jl`: executable Julia script annotated with comments +**Important.** This folder was updated in June 2024 but will no longer be updated. + +For the lastest version of this example see [here](/docs/src/full\ tutorials/MNIST). + +| file | description | +|:----------------------------|:---------------------------------------------------------| +| `notebook.ipynb` | Juptyer notebook (executed) | +| `notebook.unexecuted.ipynb` | Jupyter notebook (unexecuted) | +| `notebook.md` | static markdown (included in MLJFlux.jl docs) | +| `notebook.jl` | executable Julia script annotated with comments | +| `generate.jl` | *maintainers only:* execute to generate first 3 from 4th | + # Important Scripts or notebooks in this folder cannot be reliably exectued without the accompanying -Manifest.toml and Project.toml files. +Manifest.toml and Project.toml files. diff --git a/examples/mnist/generate.jl b/examples/mnist/generate.jl index fe74dba2..a4bad2d1 100644 --- a/examples/mnist/generate.jl +++ b/examples/mnist/generate.jl @@ -2,3 +2,4 @@ joinpath(@__DIR__, "..", "generate.jl") |> include generate(@__DIR__, execute=false, pluto=false) + diff --git a/examples/mnist/notebook.ipynb b/examples/mnist/notebook.ipynb index b3589812..41bca781 100644 --- a/examples/mnist/notebook.ipynb +++ b/examples/mnist/notebook.ipynb @@ -18,8 +18,8 @@ "text": [ "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/GoogleDrive/Julia/MLJ/MLJFlux/examples/mnist`\n", "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mPlots → IJuliaExt\u001b[39m\n", - " 1 dependency successfully precompiled in 9 seconds. 380 already precompiled.\n" + "\u001b[32m ✓ \u001b[39mMLDatasets\n", + " 1 dependency successfully precompiled in 8 seconds. 380 already precompiled.\n" ] } ], @@ -1304,7 +1304,7 @@ "output_type": "stream", "text": [ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mUpdating machine(ImageClassifier(builder = MyConvBuilder(3, 16, 32, 32), …), …).\n", - "\u001b[33mOptimising neural net: 100%[=========================] Time: 0:01:54\u001b[39m\n" + "\u001b[33mOptimising neural net: 100%[=========================] Time: 0:01:44\u001b[39m\n" ] } ], @@ -1345,7 +1345,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Or, in one line:" + "Or to fit and predict, in one line:" ] }, { @@ -1725,125 +1725,125 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - 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"Or, in one line:" + "Or to fit and predict, in one line:" ], "metadata": {} }, @@ -631,7 +631,8 @@ { "cell_type": "markdown", "source": [ - "**Note.** The higher the number, the deeper the layer we are weight-averaging." + "**Note.** The higher the number in the plot legend, the deeper the layer we are\n", + "**weight-averaging." ], "metadata": {} }, diff --git a/readme_figure.png b/readme_figure.png new file mode 100644 index 00000000..c5ad0267 Binary files /dev/null and b/readme_figure.png differ diff --git a/src/types.jl b/src/types.jl index e20d152b..b6e9af9b 100644 --- a/src/types.jl +++ b/src/types.jl @@ -124,16 +124,17 @@ const Regressor = MMI.metadata_pkg.( ( - NeuralNetworkRegressor, - MultitargetNeuralNetworkRegressor, - NeuralNetworkClassifier, - ImageClassifier, + NeuralNetworkRegressor, + MultitargetNeuralNetworkRegressor, + NeuralNetworkClassifier, + ImageClassifier, + NeuralNetworkBinaryClassifier, ), - name="MLJFlux", - uuid="094fc8d1-fd35-5302-93ea-dabda2abf845", - url="https://github.com/alan-turing-institute/MLJFlux.jl", - julia=true, - license="MIT", + name="MLJFlux", + uuid="094fc8d1-fd35-5302-93ea-dabda2abf845", + url="https://github.com/alan-turing-institute/MLJFlux.jl", + julia=true, + license="MIT", ) @@ -175,7 +176,7 @@ Train the machine with `fit!(mach, rows=...)`. - `optimiser::Optimisers.Adam()`: An Optimisers.jl optimiser. The optimiser performs the updating of the weights of the network. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at `10e-3`, and tune using powers - of 10 between `1` and `1e-7`. + of `10` between `1` and `1e-7`. - `loss=Flux.crossentropy`: The loss function which the network will optimize. Should be a function which can be called in the form `loss(yhat, y)`. Possible loss functions are @@ -203,8 +204,8 @@ Train the machine with `fit!(mach, rows=...)`. one pass through the complete the training dataset. - `batch_size::int=1`: the batch size to be used for training, representing the number of - samples per update of the network weights. Typically, batch size is between 8 and - 512. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a + samples per update of the network weights.] Typically, batch size is between `8` and + `512`. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a GPU is available. - `lambda::Float64=0`: The strength of the weight regularization penalty. Can be any value @@ -263,6 +264,7 @@ examples in the MLJFlux.jl documentation. using MLJ using Flux import RDatasets +import Optimisers ``` First, we can load the data: @@ -286,7 +288,7 @@ provided `optimizer_changes_trigger_retraining` is `false` (the default). Here, change the number of (total) iterations: ```julia -clf.optimiser.eta = clf.optimiser.eta * 2 +clf.optimiser = Optimisers.Adam(clf.optimiser.eta * 2) clf.epochs = clf.epochs + 5 fit!(mach, verbosity=2) # trains 5 more epochs @@ -365,7 +367,7 @@ Train the machine with `fit!(mach, rows=...)`. updating of the weights of the network. For further reference, see [the Flux optimiser documentation](https://fluxml.ai/Flux.jl/stable/training/optimisers/). To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out - at `10e-3`, and tune using powers of 10 between `1` and `1e-7`. + at `10e-3`, and tune using powers of `10` between `1` and `1e-7`. - `loss=Flux.binarycrossentropy`: The loss function which the network will optimize. Should be a function which can be called in the form `loss(yhat, y)`. Possible loss functions are @@ -393,8 +395,8 @@ Train the machine with `fit!(mach, rows=...)`. one pass through the complete the training dataset. - `batch_size::int=1`: the batch size to be used for training, representing the number of - samples per update of the network weights. Typically, batch size is between 8 and - 512. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a + samples per update of the network weights. Typically, batch size is between `8` and + `512`. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a GPU is available. - `lambda::Float64=0`: The strength of the weight regularization penalty. Can be any value @@ -572,7 +574,7 @@ Train the machine with `fit!(mach, rows=...)`. - `optimiser::Optimisers.Adam()`: An Optimisers.jl optimiser. The optimiser performs the updating of the weights of the network. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at `10e-3`, and tune using powers - of 10 between `1` and `1e-7`. + of `10` between `1` and `1e-7`. - `loss=Flux.crossentropy`: The loss function which the network will optimize. Should be a function which can be called in the form `loss(yhat, y)`. Possible loss functions are @@ -658,6 +660,7 @@ In this example we use MLJFlux and a custom builder to classify the MNIST image using MLJ using Flux import MLJFlux +import Optimisers import MLJIteration # for `skip` control ``` @@ -815,7 +818,7 @@ Train the machine with `fit!(mach, rows=...)`. - `optimiser::Optimisers.Adam()`: An Optimisers.jl optimiser. The optimiser performs the updating of the weights of the network. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at `10e-3`, and tune using powers - of 10 between `1` and `1e-7`. + of `10` between `1` and `1e-7`. - `loss=Flux.mse`: The loss function which the network will optimize. Should be a function which can be called in the form `loss(yhat, y)`. Possible loss functions are listed in @@ -836,8 +839,8 @@ Train the machine with `fit!(mach, rows=...)`. one pass through the complete the training dataset. - `batch_size::int=1`: the batch size to be used for training, representing the number of - samples per update of the network weights. Typically, batch size is between 8 and - 512. Increasing batch size may accelerate training if `acceleration=CUDALibs()` and a + samples per update of the network weights. Typically, batch size is between `8` and + `512`. Increasing batch size may accelerate training if `acceleration=CUDALibs()` and a GPU is available. - `lambda::Float64=0`: The strength of the weight regularization penalty. Can be any value @@ -886,6 +889,7 @@ In this example we build a regression model for the Boston house price dataset. using MLJ import MLJFlux using Flux +import Optimisers ``` First, we load in the data: The `:MEDV` column becomes the target vector `y`, and all @@ -916,7 +920,8 @@ following `@builder` call, `n_in` is a proxy for the number input features (whic known at `fit!` time) and `rng` is a proxy for a RNG (which will be passed from the `rng` field of `model` defined below). We also have the parameter `n_out` which is the number of output features. As we are doing single target regression, the value passed will always be -`1`, but the builder we define will also work for [`MultitargetNeuralRegressor`](@ref). +`1`, but the builder we define will also work for +[`MultitargetNeuralNetworkRegressor`](@ref). ```julia builder = MLJFlux.@builder begin @@ -970,7 +975,7 @@ rates = rates = [5e-5, 1e-4, 0.005, 0.001, 0.05] plt=plot() foreach(rates) do η - pipe.transformed_target_model_deterministic.model.optimiser.eta = η + pipe.transformed_target_model_deterministic.model.optimiser = Optimisers.Adam(η) fit!(mach, force=true, verbosity=0) losses = report(mach).transformed_target_model_deterministic.model.training_losses[3:end] @@ -979,7 +984,7 @@ end plt -pipe.transformed_target_model_deterministic.model.optimiser.eta = 0.0001 +pipe.transformed_target_model_deterministic.model.optimiser.eta = Optimisers.Adam(0.0001) ``` With the learning rate fixed, we compute a CV estimate of the performance (using @@ -1044,7 +1049,7 @@ Here: - `optimiser::Optimisers.Adam()`: An Optimisers.jl optimiser. The optimiser performs the updating of the weights of the network. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at `10e-3`, and tune using powers - of 10 between `1` and `1e-7`. + of `10` between `1` and `1e-7`. - `loss=Flux.mse`: The loss function which the network will optimize. Should be a function which can be called in the form `loss(yhat, y)`. Possible loss functions are listed in @@ -1065,8 +1070,8 @@ Here: one pass through the complete the training dataset. - `batch_size::int=1`: the batch size to be used for training, representing the number of - samples per update of the network weights. Typically, batch size is between 8 and - 512. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a + samples per update of the network weights. Typically, batch size is between `8` and + `512`. Increassing batch size may accelerate training if `acceleration=CUDALibs()` and a GPU is available. - `lambda::Float64=0`: The strength of the weight regularization penalty. Can be any value @@ -1116,6 +1121,7 @@ In this example we apply a multi-target regression model to synthetic data: using MLJ import MLJFlux using Flux +import Optimisers ``` First, we generate some synthetic data (needs MLJBase 0.20.16 or higher): @@ -1177,7 +1183,7 @@ report(mach).transformed_target_model_deterministic.model.training_losses For experimenting with learning rate, see the [`NeuralNetworkRegressor`](@ref) example. ``` -pipe.transformed_target_model_deterministic.model.optimiser.eta = 0.0001 +pipe.transformed_target_model_deterministic.model.optimiser = Optimisers.Adam(0.0001) ``` With the learning rate fixed, we can now compute a CV estimate of the performance (using