From ab66a9be6e2f000f66a3f68426a0cbb50b02dd73 Mon Sep 17 00:00:00 2001 From: pat-alt Date: Tue, 12 Mar 2024 12:53:35 +0100 Subject: [PATCH 1/5] slowly slowly --- .../paper/src/paper/execute-results/tex.json | 7 +- paper/Manifest.toml | 1708 ++++++++++++----- paper/Project.toml | 2 +- paper/paper.aux | 16 +- paper/paper.fdb_latexmk | 26 +- paper/paper.log | 41 +- paper/paper.pdf | Bin 435593 -> 436052 bytes paper/paper.synctex.gz | Bin 36076 -> 34418 bytes paper/paper.tex | 88 +- paper/src/jlcode.sty | 0 paper/src/journal_dat.tex | 2 +- paper/src/juliacon.bst | 0 paper/src/juliacon.cls | 0 paper/src/logojuliacon.pdf | Bin paper/src/paper.fdb_latexmk | 43 +- paper/src/paper.pdf | Bin 479558 -> 499773 bytes paper/src/paper.qmd | 53 +- paper/src/paper.tex | 188 +- paper/src/utils.jl | 97 + paper/www/posterior_predictive_mlp.png | Bin 233810 -> 230141 bytes paper/www/regression.png | Bin 35612 -> 36396 bytes src/baselaplace.jl | 18 +- 22 files changed, 1530 insertions(+), 759 deletions(-) mode change 100755 => 100644 paper/src/jlcode.sty mode change 100755 => 100644 paper/src/journal_dat.tex mode change 100755 => 100644 paper/src/juliacon.bst mode change 100755 => 100644 paper/src/juliacon.cls mode change 100755 => 100644 paper/src/logojuliacon.pdf create mode 100644 paper/src/utils.jl diff --git a/_freeze/paper/src/paper/execute-results/tex.json b/_freeze/paper/src/paper/execute-results/tex.json index 21fc3007..0018e07b 100644 --- a/_freeze/paper/src/paper/execute-results/tex.json +++ b/_freeze/paper/src/paper/execute-results/tex.json @@ -1,9 +1,10 @@ { - "hash": "fbf594a28da7108d38ed1482a1497238", + "hash": "be6160ad1f5b6b4ec426ef1038812677", "result": { - "markdown": "---\ntitle: Effortless Bayesian Deep Learning in Julia through Laplace\nabstract: |\n Treating deep neural networks probabilistically comes with numerous advantages including improved robustness and greater interpretability. These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a small Julia package, `LaplaceRedux.jl` that implements this new approach for deep neural networks trained in `Flux.jl`.\nkeywords:\n - Julia\n - Probabilistic Machine Learning\n - Laplace Approximation\n - Deep Learning\n - Artificial Intelligence\nauthors:\n - name: Patrick Altmeyer\n orcid: 0000-0003-4726-8613\n affiliation:\n - name: Delft University of Technology\ndate: 17 November 2023\nbibliography: ref.bib\nformat:\n juliacon-proceedings-pdf:\n keep-tex: true\nexecute:\n eval: false\n echo: false\n---\n\n\n\n# Background {#sec-intro}\n\nOver the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the tremendous success of deep neural networks, practitioners and researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other things, these pitfalls include a lack of adversarial robustness [@goodfellow2014explaining] and an inherent opaqueness of deep neural networks, often described as the black-box problem. \n\nIn deep learning, the number of parameters relative to the size of the available data is generally huge: \n\n> [...] deep neural networks are typically very underspecified by the available data, and [...] parameters [therefore] correspond to a diverse variety of compelling explanations for the data. \n> @wilson2020case\n\nA scenario like this very much calls for treating model predictions probabilistically [@wilson2020case]. It is therefore not surprising that interest in Bayesian deep learning has grown in recent years as researchers have tackled the problem from a wide range of angles including MCMC (see [`Turing`](https://turing.ml/dev/tutorials/03-bayesian-neural-network/)), Mean Field Variational Inference [@blundell2015weight], Monte Carlo Dropout [@gal2016dropout] and Deep Ensembles [@lakshminarayanan2016simple]. Laplace Redux [@immer2020improving;@daxberger2021laplace] is one of the most recent and promising approaches to Bayesian neural networks (BNN). \n\n# Laplace Approximation for Deep Learning {#sec-body}\n\nLet $\\mathcal{D}=\\{x,y\\}_{n=1}^N$ denote our feature-label pairs and let $f(x;\\theta)=y$ denote some deep neural network specified by its parameters $\\theta$. We are interested in estimating the posterior predictive distribution given by the following Bayesian model average (BMA):\n\n$$\np(y|x,\\mathcal{D}) = \\int p(y|x,\\theta)p(\\theta|\\mathcal{D})d\\theta\n$$ {#eq-bma}\n\nTo do so we first need to compute the weight posterior $p(\\theta|\\mathcal{D})$. Laplace Approximation (LA) relies on the fact that the second-order Taylor expansion of this posterior amounts to a multivariate Gaussian $q(\\theta)=\\mathcal{N}(\\hat\\mu,\\hat\\Sigma)$ centred around the maximum a posteriori (MAP) estimate $\\hat\\mu=\\hat{\\theta}=\\arg\\max_{\\theta}p(\\theta|\\mathcal{D})$ with covariance equal to the negative inverse Hessian of our loss function evaluated at the mode $\\hat{\\Sigma}=-(\\hat{\\mathcal{H}}|_{\\hat{\\theta}})^{-1}$. \n\nTo apply Laplace in the context of deep learning, we can train our network in the standard way by minimizing the negative log-likelihood $\\ell(\\theta)=-\\log p(y|x,\\mathcal{D})$. To obtain Gaussian LA weight posterior we then only need to compute the Hessian evaluated at the obtained MAP estimate. \n\nLaplace Approximation itself dates back to the 18th century, but despite its simplicity, it has not been widely used or studied by the deep learning community until recently. One reason for this may be that for large neural networks with many parameters, the exact Hessian computation is prohibitive. One can rely on linearized approximations of the Hessian, but those still scale quadratically in the number of parameters. Fortunately, recent work has shown that block-diagonal factorizations can be successfully applied in this context [@martens2015optimizing]. \n\nAnother reason why LA may have been neglected in the past is that early attempts at using it for deep learning failed: simply sampling from the Laplace posterior to compute the exact BNN posterior predictive distribution in @eq-bma does not work when using approximations for the Hessian [@lawrence2001variational]. Instead, we can use a linear expansion of the predictive around the mode as demonstrated by @immer2020improving. Formally, we locally linearize our network,\n\n$$\nf^{\\hat{\\theta}}_{\\mbox{lin}}(x;\\theta)=f(x;\\hat{\\theta}) + \\mathcal{J}_{\\theta}(\\theta-\\hat{\\theta})\n$$ {#eq-glm}\n \nwhich turns the BNN into a Bayesian generalized linear model (GLM) where $\\hat{\\theta}$ corresponds to the MAP estimate as before. The corresponding GLM predictive, \n\n$$\np(y|x,\\mathcal{D}) = \\mathbb{E} \\left[ p(y|f^{\\hat{\\theta}}_{\\mbox{lin}}(x;\\theta_n)) \\right], \\ \\ \\ \\theta_n \\sim q(\\theta)\n$$ {#eq-glm-predictive}\n\nhas a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation [@daxberger2021laplace]. \n\n@immer2020improving provide a much more detailed exposition of the above with a focus on theoretical underpinnings and intuition. @daxberger2021laplace introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: [laplace](https://aleximmer.github.io/Laplace/).\n\n# `LaplaceRedux.jl` --- a Julia implementation\n\nThe `LaplaceRedux.jl` package is intended to make this new methodological framework available to the Julia community. It is interfaced with the popular deep learning library, [`Flux.jl`](https://fluxml.ai/). \n\n\n\nUsing just a few lines of code the package enables users to compute and apply Laplace Redux to their pre-trained neural networks. A basic usage example is shown in listing \\ref{lst:laplace}: the `Laplace` function simply wraps the Flux neural network `nn`. The returned instance is then fitted to data using the generic `fit!` method. Finally, the prior precision $\\lambda$ is optimized through Empirical Bayes [@daxberger2021laplace]. Calling the generic `predict` method on the fitted instance will generate GLM predictions according to @eq-glm-predictive.\n\n\n\n\\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}]\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\noptimize_prior!(la)\n\\end{lstlisting}\n\n\n\n@fig-class shows an example involving a synthetic data set consisting of two classes. Contours indicate the predicted probabilities using the plugin estimator (left), untuned Laplace Approximation (center) and finally optimized LA (right). For the latter two, the respective choices for the prior precision parameter $\\lambda$ are indicated in the title. Relying solely on the MAP estimate, the plugin estimator produces overly confident predictions. Conversely, the GLM predictions account for predictive uncertainty as captured by the Laplace posterior.\n\n@fig-reg presents a regression example with optimized LA. Wide regions of the confidence interval (shaded area) indicate high predictive uncertainty. Intuitively, the estimated predictive uncertainty increases significantly in regions characterized by high epistemic uncertainty: epistemic uncertainty arises in regions of the domain that have not been observed by the classifier, so regions that are free of training samples. \n\n\n\n![Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.](www/posterior_predictive_mlp.png){#fig-class width=\"20pc\" height=\"6.7pc\"}\n\n![Posterior predictive distribution for regressor: wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.](www/regression.png){#fig-reg width=\"20pc\" height=\"10pc\"}\n\n# Discussion and Outlook {#sec-con}\n\nAt the time of writing, the package is still in its infancy and its functionality is limited. It currently lacks multi-class support and still works with full Hessian approximations, as opposed to the less expensive (block-) diagonal variants. That being said, choices regarding the package architecture were made with these future development opportunities in mind. This should hopefully make the package attractive to other Julia developers interested in the topic.\n\nLaplace Redux is an exciting and promising recent development in Bayesian deep learning. The goal of this project is to bring this framework to the attention of the Julia machine-learning community. The package `LaplaceRedux.jl` offers a starting ground for a full-fledged implementation in pure Julia. Future developments are planned and contributions are very much welcome.\n\n# Acknowledgements {#sec-ack}\n\nI am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. I am also grateful to the Julia community for being so kind, welcoming and helpful. \n\n", + "engine": "jupyter", + "markdown": "---\ntitle: Effortless Bayesian Deep Learning in Julia through Laplace\nabstract: |\n Treating deep neural networks probabilistically comes with numerous advantages including improved robustness and greater interpretability. These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a light-weight Julia package, `LaplaceRedux.jl` that implements this new approach for deep neural networks trained in `Flux.jl`.\nkeywords:\n - Julia\n - Probabilistic Machine Learning\n - Laplace Approximation\n - Deep Learning\n - Artificial Intelligence\nauthors:\n - name: Patrick Altmeyer\n orcid: 0000-0003-4726-8613\n affiliation:\n - name: Delft University of Technology\ndate: 17 November 2023\nbibliography: ref.bib\nformat:\n juliacon-proceedings-pdf:\n keep-tex: true\nexecute:\n eval: false\n echo: false\n---\n\n\n\n# Background {#sec-intro}\n\nOver the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the tremendous success of deep neural networks, practitioners and researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other things, these pitfalls include a lack of adversarial robustness \\cite{goodfellow2014explaining} and an inherent opaqueness of deep neural networks, often described as the black-box problem. \n\nIn deep learning, the number of parameters relative to the size of the available data is generally huge \\cite{wilson2020case}: \"[...] deep neural networks are typically very underspecified by the available data, and [...] parameters [therefore] correspond to a diverse variety of compelling explanations for the data.\" A scenario like this very much calls for treating model predictions probabilistically \\cite{wilson2020case}. It is therefore not surprising that interest in Bayesian deep learning has grown in recent years as researchers have tackled the problem from a wide range of angles including MCMC (see [`Turing`](https://turing.ml/dev/tutorials/03-bayesian-neural-network/)), Mean Field Variational Inference \\cite{blundell2015weight}, Monte Carlo Dropout \\cite{gal2016dropout} and Deep Ensembles \\cite{lakshminarayanan2016simple}. Laplace Redux \\cite{immer2020improving,daxberger2021laplace} is one of the most recent and promising approaches to Bayesian neural networks (BNN). \n\n# Laplace Approximation for Deep Learning {#sec-body}\n\nLet $\\mathcal{D}=\\{x,y\\}_{n=1}^N$ denote our feature-label pairs and let $f(x;\\theta)=y$ denote some deep neural network specified by its parameters $\\theta$. We are interested in estimating the posterior predictive distribution given by the following Bayesian model average (BMA):\n\n$$\np(y|x,\\mathcal{D}) = \\int p(y|x,\\theta)p(\\theta|\\mathcal{D})d\\theta\n$$ {#eq-bma}\n\nTo do so we first need to compute the weight posterior $p(\\theta|\\mathcal{D})$. Laplace Approximation (LA) relies on the fact that the second-order Taylor expansion of this posterior amounts to a multivariate Gaussian $q(\\theta)=\\mathcal{N}(\\hat\\mu,\\hat\\Sigma)$ centred around the maximum a posteriori (MAP) estimate $\\hat\\mu=\\hat{\\theta}=\\arg\\max_{\\theta}p(\\theta|\\mathcal{D})$ with covariance equal to the negative inverse Hessian of our loss function evaluated at the mode $\\hat{\\Sigma}=-(\\hat{\\mathcal{H}}|_{\\hat{\\theta}})^{-1}$. \n\nTo apply Laplace in the context of deep learning, we can train our network in the standard way by minimizing the negative log-likelihood $\\ell(\\theta)=-\\log p(y|x,\\mathcal{D})$. To obtain Gaussian LA weight posterior we then only need to compute the Hessian evaluated at the obtained MAP estimate. \n\nLaplace Approximation itself dates back to the 18th century, but despite its simplicity, it has not been widely used or studied by the deep learning community until recently. One reason for this may be that for large neural networks with many parameters, the exact Hessian computation is prohibitive. One can rely on linearized approximations of the Hessian, but those still scale quadratically in the number of parameters. Fortunately, recent work has shown that block-diagonal factorizations can be successfully applied in this context \\cite{martens2015optimizing}. \n\nAnother reason why LA may have been neglected in the past is that early attempts at using it for deep learning failed: simply sampling from the Laplace posterior to compute the exact BNN posterior predictive distribution in @eq-bma does not work when using approximations for the Hessian \\cite{lawrence2001variational}. Instead, we can use a linear expansion of the predictive around the mode as demonstrated by Immer et al. (2020) \\cite{immer2020improving}. Formally, we locally linearize our network,\n\n$$\nf^{\\hat{\\theta}}_{\\mbox{lin}}(x;\\theta)=f(x;\\hat{\\theta}) + \\mathcal{J}_{\\theta}(\\theta-\\hat{\\theta})\n$$ {#eq-glm}\n \nwhich turns the BNN into a Bayesian generalized linear model (GLM) where $\\hat{\\theta}$ corresponds to the MAP estimate as before. The corresponding GLM predictive, \n\n$$\np(y|x,\\mathcal{D}) = \\mathbb{E} \\left[ p(y|f^{\\hat{\\theta}}_{\\mbox{lin}}(x;\\theta_n)) \\right], \\ \\ \\ \\theta_n \\sim q(\\theta)\n$$ {#eq-glm-predictive}\n\nhas a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation \\cite{daxberger2021laplace}. \n\nImmer et al. (2020) \\cite{immer2020improving} provide a much more detailed exposition of the above with a focus on theoretical underpinnings and intuition. Daxberger et al. (2021) \\cite{daxberger2021laplace} introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: [laplace](https://aleximmer.github.io/Laplace/).\n\n# `LaplaceRedux.jl` --- a Julia implementation\n\nThe `LaplaceRedux.jl` package is intended to make this new methodological framework available to the Julia community. It is interfaced with the popular deep learning library, [`Flux.jl`](https://fluxml.ai/). \n\n\n\nUsing just a few lines of code the package enables users to compute and apply Laplace Redux to their pre-trained neural networks. A basic usage example is shown in listing \\ref{lst:laplace}: the `Laplace` function simply wraps the Flux neural network `nn`. Since the underlying model is a classifier, we need to specify the likelihood accordingly. The returned instance is then fitted to the data using the generic `fit!` method. Note that the `fit!` method also accepts a `DataLoader` as its second positional argument and mini-batch training is supported.\n\n\n\n\\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}]\nla = Laplace(nn; likelihood=:classification)\nfit!(la, data)\n\\end{lstlisting}\n\nThe `la` object is a mutable and callable struct that wraps the pre-trained neural networks along with hyperparameters relevant to the Laplace approximation. Simply calling the instance with new data will generate GLM predictions according to @eq-glm-predictive. In the classification case, softmax outputs are returned by default following the convention in the Python implementation, but this can be changed using the `predict_proba` keyword argument. It is also possible to recover the original MAP estimate directly by setting the `link_approx` keyword argument to `:plugin`. \n\n\\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}]\nla(X) # GLM predictions\nla(X; predict_proba=false) # no softmax\nla(X; link_approx=:plugin) # MAP predictions\n\\end{lstlisting}\n\nAdditional methods can be used to optimize the prior precision $\\lambda$ and to visualize the predictive distribution. The `optimize_prior!` method optimizes the prior precision $\\lambda$ through Empirical Bayes \\cite{daxberger2021laplace}. The `plot` method visualizes the predictions of the fitted instance. It is provided through the [TaijaPlotting](https://github.com/JuliaTrustworthyAI/TaijaPlotting.jl) meta package.\n\n\\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}]\noptimize_prior!(la) # optimize λ\nusing TaijaPlotting\nplot(la, X, y) # plot predictions\n\\end{lstlisting}\n\n\n\n@fig-class shows an example involving a synthetic data set consisting of two classes. Contours indicate the predicted probabilities using the plugin estimator (left), untuned Laplace Approximation (center) and finally optimized LA (right). For the latter two, the respective choices for the prior precision parameter $\\lambda$ are indicated in the title. Relying solely on the MAP estimate, the plugin estimator produces overly confident predictions. Conversely, the GLM predictions account for predictive uncertainty as captured by the Laplace posterior.\n\n@fig-reg presents a regression example with optimized LA. Wide regions of the confidence interval (shaded area) indicate high predictive uncertainty. Intuitively, the estimated predictive uncertainty increases significantly in regions characterized by high epistemic uncertainty: epistemic uncertainty arises in regions of the domain that have not been observed by the classifier, so regions that are free of training samples. \n\n\n\n![Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.](www/posterior_predictive_mlp.png){#fig-class width=\"20pc\" height=\"6.7pc\"}\n\n![Posterior predictive distribution for regressor: wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.](www/regression.png){#fig-reg width=\"20pc\" height=\"10pc\"}\n\n# Additional Features {#sec-add}\n\n\n\n# Discussion and Outlook {#sec-con}\n\nLaplace Redux is an exciting and promising recent development in Bayesian deep learning. The package `LaplaceRedux.jl` brings this framework to the Julia ecosystem. Future developments are planned and contributions are very much welcome. At the time of writing, we are particularly interested in streamlining the package's interface to the larger [Taija](https://github.com/JuliaTrustworthyAI) ecosystem and adding support for sample-based linearised Laplace to enhance scalability (see [here](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues/34) for details).\n\n# Acknowledgements {#sec-ack}\n\nI am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. Furthermore, I would like to thank the group of students who contributed to this package through a course project: Mark Ardman, Severin Bratus, Adelina Cazacu, Andrei Ionescu and Ivan Makarov.\n\n", "supporting": [ - "paper_files" + "paper_files/figure-pdf" ], "filters": [] } diff --git a/paper/Manifest.toml b/paper/Manifest.toml index 884e4d49..382da049 100644 --- a/paper/Manifest.toml +++ b/paper/Manifest.toml @@ -1,31 +1,34 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.8.3" +julia_version = "1.10.2" manifest_format = "2.0" -project_hash = "b682381dcdcd00282d133ea8f48929d3f84fb081" +project_hash = "cf1c465172bde291865fdac1a7da7ceb07c0b41b" [[deps.AbstractFFTs]] -deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +deps = ["LinearAlgebra"] +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.5.0" 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"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" @@ -76,50 +117,73 @@ git-tree-sha1 = "aebf55e6d7795e02ca500a689d326ac979aaf89e" uuid = "9718e550-a3fa-408a-8086-8db961cd8217" version = "0.1.1" -[[deps.BinaryProvider]] -deps = ["Libdl", "Logging", "SHA"] -git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058" -uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232" -version = "0.5.10" - [[deps.BitFlags]] -git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" +git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" -version = "0.1.7" +version = "0.1.8" [[deps.BufferedStreams]] -git-tree-sha1 = "bb065b14d7f941b8617bc323063dbe79f55d16ea" +git-tree-sha1 = "4ae47f9a4b1dc19897d3743ff13685925c5202ec" uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d" -version = "1.1.0" +version = "1.2.1" [[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" +git-tree-sha1 = 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[[deps.Cairo_jll]] -deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2" +deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "a4c43f59baa34011e303e76f5c8c91bf58415aaf" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" -version = "1.16.1+1" +version = "1.18.0+1" [[deps.Calculus]] deps = ["LinearAlgebra"] @@ -129,57 +193,74 @@ version = "0.5.1" [[deps.CategoricalArrays]] deps = ["DataAPI", "Future", "Missings", "Printf", "Requires", "Statistics", "Unicode"] -git-tree-sha1 = "5084cc1a28976dd1642c9f337b28a3cb03e0f7d2" +git-tree-sha1 = 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-git-tree-sha1 = "0e5c14c3bb8a61b3d53b2c0620570c332c8d0663" +git-tree-sha1 = "c0216e792f518b39b22212127d4a84dc31e4e386" uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2" -version = "1.2.0" +version = "1.3.5" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "9c209fb7536406834aa938fb149964b985de6c83" +git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.1" +version = "0.7.4" [[deps.ColorSchemes]] -deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Random", "SnoopPrecompile"] -git-tree-sha1 = "aa3edc8f8dea6cbfa176ee12f7c2fc82f0608ed3" +deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] +git-tree-sha1 = "67c1f244b991cad9b0aa4b7540fb758c2488b129" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.20.0" +version = "3.24.0" [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] @@ -188,10 +269,14 @@ uuid = 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[[deps.CommonSubexpressions]] deps = ["MacroTools", "Test"] git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7" @@ -206,32 +296,67 @@ uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" version = "0.3.0" [[deps.Compat]] -deps = ["Dates", "LinearAlgebra", "UUIDs"] -git-tree-sha1 = "00a2cccc7f098ff3b66806862d275ca3db9e6e5a" +deps = ["TOML", "UUIDs"] +git-tree-sha1 = "c955881e3c981181362ae4088b35995446298b80" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.5.0" +version = "4.14.0" +weakdeps = ["Dates", "LinearAlgebra"] + + [deps.Compat.extensions] + CompatLinearAlgebraExt = "LinearAlgebra" [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "0.5.2+0" +version = "1.1.0+0" [[deps.CompositionsBase]] -git-tree-sha1 = "455419f7e328a1a2493cabc6428d79e951349769" +git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" -version = "0.1.1" +version = "0.1.2" + + [deps.CompositionsBase.extensions] + CompositionsBaseInverseFunctionsExt = "InverseFunctions" + + [deps.CompositionsBase.weakdeps] + InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" + +[[deps.ComputationalResources]] +git-tree-sha1 = "52cb3ec90e8a8bea0e62e275ba577ad0f74821f7" +uuid = "ed09eef8-17a6-5b46-8889-db040fac31e3" +version = "0.3.2" + +[[deps.ConcurrentUtilities]] +deps = ["Serialization", "Sockets"] +git-tree-sha1 = "87944e19ea747808b73178ce5ebb74081fdf2d35" +uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" +version = "2.4.0" [[deps.Conda]] deps = ["Downloads", "JSON", "VersionParsing"] -git-tree-sha1 = "6e47d11ea2776bc5627421d59cdcc1296c058071" +git-tree-sha1 = "51cab8e982c5b598eea9c8ceaced4b58d9dd37c9" uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d" -version = "1.7.0" +version = "1.10.0" + +[[deps.ConformalPrediction]] +deps = ["CategoricalArrays", "MLJBase", "MLJModelInterface", "NaturalSort", "Plots", "StatsBase"] +git-tree-sha1 = 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"Distributions", "EvoTrees", "Flux", "LaplaceRedux", "LazyArtifacts", "LinearAlgebra", "Logging", "MLDatasets", "MLJBase", "MLJDecisionTreeInterface", "MLJModels", "MLUtils", "MultivariateStats", "NearestNeighborModels", "PackageExtensionCompat", "Parameters", "PrecompileTools", "ProgressMeter", "Random", "Serialization", "Statistics", "StatsBase", "Tables", "UUIDs", "cuDNN"] +git-tree-sha1 = "af4687806d81a3265173fad6250e3902eb659f37" +uuid = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0" +version = "0.1.31" + + [deps.CounterfactualExplanations.extensions] + MPIExt = "MPI" + PythonCallExt = "PythonCall" + RCallExt = "RCall" + + [deps.CounterfactualExplanations.weakdeps] + MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" + PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" + RCall = "6f49c342-dc21-5d91-9882-a32aef131414" + [[deps.Crayons]] git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" [[deps.DataAPI]] -git-tree-sha1 = "e8119c1a33d267e16108be441a287a6981ba1630" +git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.14.0" +version = "1.16.0" [[deps.DataDeps]] -deps = ["HTTP", "Libdl", "Reexport", "SHA", "p7zip_jll"] -git-tree-sha1 = "bc0a264d3e7b3eeb0b6fc9f6481f970697f29805" +deps = ["HTTP", "Libdl", "Reexport", "SHA", "Scratch", "p7zip_jll"] +git-tree-sha1 = "8ae085b71c462c2cb1cfedcb10c3c877ec6cf03f" uuid = "124859b0-ceae-595e-8997-d05f6a7a8dfe" -version = "0.7.10" +version = "0.7.13" [[deps.DataFrames]] -deps = ["Compat", "DataAPI", "Future", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SnoopPrecompile", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"] -git-tree-sha1 = "d4f69885afa5e6149d0cab3818491565cf41446d" +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" uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -version = "1.4.4" +version = "1.6.1" [[deps.DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "d1fff3a548102f48987a52a2e0d114fa97d730f0" +git-tree-sha1 = "0f4b5d62a88d8f59003e43c25a8a90de9eb76317" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.13" +version = "0.18.18" [[deps.DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" @@ -281,6 +422,12 @@ version = "1.0.0" deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" +[[deps.DecisionTree]] +deps = ["AbstractTrees", "DelimitedFiles", "LinearAlgebra", "Random", "ScikitLearnBase", "Statistics"] +git-tree-sha1 = "526ca14aaaf2d5a0e242f3a8a7966eb9065d7d78" +uuid = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb" +version = "0.12.4" + [[deps.DefineSingletons]] git-tree-sha1 = "0fba8b706d0178b4dc7fd44a96a92382c9065c2c" uuid = "244e2a9f-e319-4986-a169-4d1fe445cd52" @@ -288,7 +435,9 @@ version = "0.1.2" [[deps.DelimitedFiles]] deps = ["Mmap"] +git-tree-sha1 = "9e2f36d3c96a820c678f2f1f1782582fcf685bae" uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" +version = "1.9.1" [[deps.DiffResults]] deps = ["StaticArraysCore"] @@ -298,14 +447,41 @@ version = "1.1.0" [[deps.DiffRules]] deps = ["IrrationalConstants", "LogExpFunctions", "NaNMath", "Random", "SpecialFunctions"] -git-tree-sha1 = "c5b6685d53f933c11404a3ae9822afe30d522494" +git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" -version = "1.12.2" +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 = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169" +uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" +version = "0.25.107" + + [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" @@ -329,21 +505,43 @@ git-tree-sha1 = "e3290f2d49e661fbd94046d7e3726ffcb2d41053" uuid = "5ae413db-bbd1-5e63-b57d-d24a61df00f5" version = "2.2.4+0" +[[deps.EpollShim_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8e9441ee83492030ace98f9789a654a6d0b1f643" +uuid = "2702e6a9-849d-5ed8-8c21-79e8b8f9ee43" +version = "0.0.20230411+0" + +[[deps.EvoTrees]] +deps = ["BSON", "CategoricalArrays", "Distributions", "MLJModelInterface", "NetworkLayout", "Random", "RecipesBase", "Statistics", "StatsBase", "Tables"] +git-tree-sha1 = "e1107e45d7fe1a3c5dd335376bb6333b42cf9d1c" +uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5" +version = "0.16.6" +weakdeps = ["CUDA"] + + [deps.EvoTrees.extensions] + EvoTreesCUDAExt = "CUDA" + +[[deps.ExceptionUnwrapping]] +deps = ["Test"] +git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a" +uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" +version = "0.1.10" + [[deps.Expat_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "bad72f730e9e91c08d9427d5e8db95478a3c323d" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "4558ab818dcceaab612d1bb8c19cee87eda2b83c" uuid = "2e619515-83b5-522b-bb60-26c02a35a201" -version = "2.4.8+0" +version = "2.5.0+0" [[deps.ExprTools]] -git-tree-sha1 = "56559bbef6ca5ea0c0818fa5c90320398a6fbf8d" +git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec" uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04" -version = "0.1.8" +version = "0.1.10" [[deps.Extents]] -git-tree-sha1 = "5e1e4c53fa39afe63a7d356e30452249365fba99" +git-tree-sha1 = "2140cd04483da90b2da7f99b2add0750504fc39c" uuid = "411431e0-e8b7-467b-b5e0-f676ba4f2910" -version = "0.1.1" +version = "0.1.2" [[deps.FFMPEG]] deps = ["FFMPEG_jll"] @@ -352,10 +550,10 @@ uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a" version = "0.4.1" [[deps.FFMPEG_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] -git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd" +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] +git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e" uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5" -version = "4.4.2+2" +version = "4.4.4+1" [[deps.FLoops]] deps = ["BangBang", "Compat", "FLoopsBase", "InitialValues", "JuliaVariables", "MLStyle", "Serialization", "Setfield", "Transducers"] @@ -371,24 +569,30 @@ version = "0.1.1" [[deps.FileIO]] deps = ["Pkg", "Requires", "UUIDs"] -git-tree-sha1 = "7be5f99f7d15578798f338f5433b6c432ea8037b" +git-tree-sha1 = "c5c28c245101bd59154f649e19b038d15901b5dc" uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" -version = "1.16.0" +version = "1.16.2" [[deps.FilePathsBase]] deps = ["Compat", "Dates", "Mmap", "Printf", "Test", "UUIDs"] -git-tree-sha1 = "e27c4ebe80e8699540f2d6c805cc12203b614f12" +git-tree-sha1 = "9f00e42f8d99fdde64d40c8ea5d14269a2e2c1aa" uuid = "48062228-2e41-5def-b9a4-89aafe57970f" -version = "0.9.20" +version = "0.9.21" [[deps.FileWatching]] uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" [[deps.FillArrays]] -deps = ["LinearAlgebra", "Random", "SparseArrays", "Statistics"] -git-tree-sha1 = "d3ba08ab64bdfd27234d3f61956c966266757fe6" +deps = ["LinearAlgebra", "Random"] +git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "0.13.7" +version = "1.9.3" +weakdeps = ["PDMats", "SparseArrays", "Statistics"] + + [deps.FillArrays.extensions] + FillArraysPDMatsExt = "PDMats" + FillArraysSparseArraysExt = "SparseArrays" + FillArraysStatisticsExt = "Statistics" [[deps.FixedPointNumbers]] deps = ["Statistics"] @@ -397,16 +601,22 @@ uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" version = "0.8.4" [[deps.Flux]] -deps = ["Adapt", "CUDA", "ChainRulesCore", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "NNlibCUDA", "OneHotArrays", "Optimisers", "ProgressLogging", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "Zygote"] -git-tree-sha1 = "518b553ec3776dde058aebd2750c109d04ee5593" +deps = ["Adapt", "ChainRulesCore", "Compat", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "OneHotArrays", "Optimisers", "Preferences", "ProgressLogging", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "Zygote"] +git-tree-sha1 = "5a626d6ef24ae0a8590c22dc12096fb65eb66325" uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c" -version = "0.13.11" +version = "0.14.13" -[[deps.FoldsThreads]] -deps = ["Accessors", "FunctionWrappers", "InitialValues", "SplittablesBase", "Transducers"] -git-tree-sha1 = "eb8e1989b9028f7e0985b4268dabe94682249025" -uuid = "9c68100b-dfe1-47cf-94c8-95104e173443" -version = "0.1.1" + [deps.Flux.extensions] + FluxAMDGPUExt = "AMDGPU" + FluxCUDAExt = "CUDA" + FluxCUDAcuDNNExt = ["CUDA", "cuDNN"] + FluxMetalExt = "Metal" + + [deps.Flux.weakdeps] + AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e" + CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + Metal = "dde4c033-4e86-420c-a63e-0dd931031962" + cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" [[deps.Fontconfig_jll]] deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Pkg", "Zlib_jll"] @@ -414,23 +624,26 @@ git-tree-sha1 = "21efd19106a55620a188615da6d3d06cd7f6ee03" uuid = "a3f928ae-7b40-5064-980b-68af3947d34b" version = "2.13.93+0" -[[deps.Formatting]] -deps = ["Printf"] -git-tree-sha1 = "8339d61043228fdd3eb658d86c926cb282ae72a8" -uuid = "59287772-0a20-5a39-b81b-1366585eb4c0" -version = "0.4.2" +[[deps.Format]] +git-tree-sha1 = "f3cf88025f6d03c194d73f5d13fee9004a108329" +uuid = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" +version = "1.3.6" [[deps.ForwardDiff]] -deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions", "StaticArrays"] -git-tree-sha1 = "a69dd6db8a809f78846ff259298678f0d6212180" +deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions"] +git-tree-sha1 = "cf0fe81336da9fb90944683b8c41984b08793dad" uuid = "f6369f11-7733-5829-9624-2563aa707210" -version = "0.10.34" +version = "0.10.36" +weakdeps = ["StaticArrays"] + + [deps.ForwardDiff.extensions] + ForwardDiffStaticArraysExt = "StaticArrays" [[deps.FreeType2_jll]] -deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "87eb71354d8ec1a96d4a7636bd57a7347dde3ef9" +deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "d8db6a5a2fe1381c1ea4ef2cab7c69c2de7f9ea0" uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7" -version = "2.10.4+0" +version = "2.13.1+0" [[deps.FriBidi_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -438,74 +651,69 @@ git-tree-sha1 = "aa31987c2ba8704e23c6c8ba8a4f769d5d7e4f91" uuid = "559328eb-81f9-559d-9380-de523a88c83c" version = "1.0.10+0" -[[deps.FunctionWrappers]] -git-tree-sha1 = "d62485945ce5ae9c0c48f124a84998d755bae00e" -uuid = "069b7b12-0de2-55c6-9aab-29f3d0a68a2e" -version = "1.1.3" - [[deps.Functors]] deps = ["LinearAlgebra"] -git-tree-sha1 = "61fa9cf802d35fe1b5b8ea9fbaac4b8f020d19b1" +git-tree-sha1 = "8ae30e786837ce0a24f5e2186938bf3251ab94b2" uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196" -version = "0.4.2" +version = "0.4.8" [[deps.Future]] deps = ["Random"] uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820" [[deps.GLFW_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] -git-tree-sha1 = "d972031d28c8c8d9d7b41a536ad7bb0c2579caca" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] +git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb" uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89" -version = "3.3.8+0" +version = "3.3.9+0" [[deps.GPUArrays]] deps = ["Adapt", "GPUArraysCore", "LLVM", "LinearAlgebra", "Printf", "Random", "Reexport", "Serialization", "Statistics"] -git-tree-sha1 = "494f1e456000c00c93dde79b38094e023f639dac" +git-tree-sha1 = "47e4686ec18a9620850bad110b79966132f14283" uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7" -version = "8.6.1" +version = "10.0.2" [[deps.GPUArraysCore]] deps = ["Adapt"] -git-tree-sha1 = "57f7cde02d7a53c9d1d28443b9f11ac5fbe7ebc9" +git-tree-sha1 = 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"0.73.3" [[deps.GR_jll]] -deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Pkg", "Qt5Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "aa23c9f9b7c0ba6baeabe966ea1c7d2c7487ef90" +deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "a96d5c713e6aa28c242b0d25c1347e258d6541ab" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.71.5+0" +version = "0.73.3+0" [[deps.GZip]] -deps = ["Libdl"] -git-tree-sha1 = "039be665faf0b8ae36e089cd694233f5dee3f7d6" +deps = ["Libdl", "Zlib_jll"] +git-tree-sha1 = "0085ccd5ec327c077ec5b91a5f937b759810ba62" uuid = "92fee26a-97fe-5a0c-ad85-20a5f3185b63" -version = "0.5.1" +version = "0.6.2" [[deps.GeoInterface]] deps = ["Extents"] -git-tree-sha1 = "e07a1b98ed72e3cdd02c6ceaab94b8a606faca40" +git-tree-sha1 = "d4f85701f569584f2cff7ba67a137d03f0cfb7d0" uuid = "cf35fbd7-0cd7-5166-be24-54bfbe79505f" -version = "1.2.1" +version = "1.3.3" [[deps.GeometryBasics]] -deps = ["EarCut_jll", "GeoInterface", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"] -git-tree-sha1 = "fe9aea4ed3ec6afdfbeb5a4f39a2208909b162a6" +deps = ["EarCut_jll", "Extents", "GeoInterface", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"] +git-tree-sha1 = "5694b56ccf9d15addedc35e9a4ba9c317721b788" uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326" -version = "0.4.5" +version = "0.4.10" [[deps.GeometryTypes]] deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "StaticArrays"] @@ -520,15 +728,15 @@ uuid = "78b55507-aeef-58d4-861c-77aaff3498b1" version = "0.21.0+0" [[deps.Glib_jll]] -deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "d3b3624125c1474292d0d8ed0f65554ac37ddb23" +deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Zlib_jll"] +git-tree-sha1 = "359a1ba2e320790ddbe4ee8b4d54a305c0ea2aff" uuid = "7746bdde-850d-59dc-9ae8-88ece973131d" -version = "2.74.0+2" +version = "2.80.0+0" [[deps.Glob]] -git-tree-sha1 = "4df9f7e06108728ebf00a0a11edee4b29a482bb2" +git-tree-sha1 = "97285bbd5230dd766e9ef6749b80fc617126d496" uuid = "c27321d9-0574-5035-807b-f59d2c89b15c" -version = "1.3.0" +version = "1.3.1" [[deps.GraphRecipes]] deps = ["AbstractTrees", "GeometryTypes", "Graphs", "InteractiveUtils", "Interpolations", "LinearAlgebra", "NaNMath", "NetworkLayout", "PlotUtils", "RecipesBase", "SparseArrays", "Statistics"] @@ -536,12 +744,6 @@ git-tree-sha1 = "e5f13c467f99f6b348020369c519cd6c8b56f75d" uuid = "bd48cda9-67a9-57be-86fa-5b3c104eda73" version = "0.5.12" -[[deps.Graphics]] -deps = ["Colors", "LinearAlgebra", "NaNMath"] -git-tree-sha1 = "d61890399bc535850c4bf08e4e0d3a7ad0f21cbd" -uuid = "a2bd30eb-e257-5431-a919-1863eab51364" -version = "1.1.2" - [[deps.Graphite2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "344bf40dcab1073aca04aa0df4fb092f920e4011" @@ -550,9 +752,9 @@ version = "1.3.14+0" [[deps.Graphs]] deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] -git-tree-sha1 = "ba2d094a88b6b287bd25cfa86f301e7693ffae2f" +git-tree-sha1 = "899050ace26649433ef1af25bc17a815b3db52b7" uuid = "86223c79-3864-5bf0-83f7-82e725a168b6" -version = "1.7.4" +version = "1.9.0" [[deps.Grisu]] git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2" @@ -560,28 +762,28 @@ uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe" version = "1.0.2" [[deps.HDF5]] -deps = ["Compat", "HDF5_jll", "Libdl", "Mmap", "Random", "Requires", "UUIDs"] -git-tree-sha1 = "b5df7c3cab3a00c33c2e09c6bd23982a75e2fbb2" +deps = ["Compat", "HDF5_jll", "Libdl", "MPIPreferences", "Mmap", "Preferences", "Printf", "Random", "Requires", "UUIDs"] +git-tree-sha1 = "26407bd1c60129062cec9da63dc7d08251544d53" uuid = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" -version = "0.16.13" +version = "0.17.1" + + [deps.HDF5.extensions] + MPIExt = "MPI" + + [deps.HDF5.weakdeps] + MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" [[deps.HDF5_jll]] -deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "OpenSSL_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "4cc2bb72df6ff40b055295fdef6d92955f9dede8" +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "LazyArtifacts", "LibCURL_jll", "Libdl", "MPICH_jll", "MPIPreferences", "MPItrampoline_jll", "MicrosoftMPI_jll", "OpenMPI_jll", "OpenSSL_jll", "TOML", "Zlib_jll", "libaec_jll"] +git-tree-sha1 = "38c8874692d48d5440d5752d6c74b0c6b0b60739" uuid = "0234f1f7-429e-5d53-9886-15a909be8d59" -version = "1.12.2+2" - -[[deps.HTML_Entities]] -deps = ["StrTables"] -git-tree-sha1 = "c4144ed3bc5f67f595622ad03c0e39fa6c70ccc7" -uuid = "7693890a-d069-55fe-a829-b4a6d304f0ee" -version = "1.0.1" +version = "1.14.2+1" [[deps.HTTP]] -deps = ["Base64", "CodecZlib", "Dates", "IniFile", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "37e4657cd56b11abe3d10cd4a1ec5fbdb4180263" +deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] +git-tree-sha1 = "db864f2d91f68a5912937af80327d288ea1f3aee" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.7.4" +version = "1.10.3" [[deps.HarfBuzz_jll]] deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] @@ -595,51 +797,52 @@ git-tree-sha1 = "0341077e8a6b9fc1c2ea5edc1e93a956d2aec0c7" uuid = "eafb193a-b7ab-5a9e-9068-77385905fa72" version = "0.5.2" +[[deps.Hwloc_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "ca0f6bf568b4bfc807e7537f081c81e35ceca114" +uuid = "e33a78d0-f292-5ffc-b300-72abe9b543c8" +version = "2.10.0+0" + [[deps.HypergeometricFunctions]] -deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions", "Test"] -git-tree-sha1 = "709d864e3ed6e3545230601f94e11ebc65994641" +deps = ["DualNumbers", "LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] +git-tree-sha1 = "f218fe3736ddf977e0e772bc9a586b2383da2685" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" -version = "0.3.11" +version = "0.3.23" [[deps.IJulia]] deps = ["Base64", "Conda", "Dates", "InteractiveUtils", "JSON", "Libdl", "Logging", "Markdown", "MbedTLS", "Pkg", "Printf", "REPL", "Random", "SoftGlobalScope", "Test", "UUIDs", "ZMQ"] -git-tree-sha1 = "59e19713542dd9dd02f31d59edbada69530d6a14" +git-tree-sha1 = "47ac8cc196b81001a711f4b2c12c97372338f00c" uuid = "7073ff75-c697-5162-941a-fcdaad2a7d2a" -version = "1.24.0" +version = "1.24.2" [[deps.IRTools]] deps = ["InteractiveUtils", "MacroTools", "Test"] -git-tree-sha1 = "2e99184fca5eb6f075944b04c22edec29beb4778" +git-tree-sha1 = "5d8c5713f38f7bc029e26627b687710ba406d0dd" uuid = "7869d1d1-7146-5819-86e3-90919afe41df" -version = "0.4.7" +version = "0.4.12" [[deps.ImageBase]] deps = ["ImageCore", "Reexport"] -git-tree-sha1 = "b51bb8cae22c66d0f6357e3bcb6363145ef20835" +git-tree-sha1 = "eb49b82c172811fd2c86759fa0553a2221feb909" uuid = "c817782e-172a-44cc-b673-b171935fbb9e" -version = "0.1.5" +version = "0.1.7" [[deps.ImageCore]] -deps = ["AbstractFFTs", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Graphics", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "Reexport"] -git-tree-sha1 = "acf614720ef026d38400b3817614c45882d75500" +deps = ["ColorVectorSpace", "Colors", "FixedPointNumbers", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "PrecompileTools", "Reexport"] +git-tree-sha1 = "b2a7eaa169c13f5bcae8131a83bc30eff8f71be0" uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534" -version = "0.9.4" +version = "0.10.2" [[deps.ImageShow]] -deps = ["Base64", "FileIO", "ImageBase", "ImageCore", "OffsetArrays", "StackViews"] -git-tree-sha1 = "b563cf9ae75a635592fc73d3eb78b86220e55bd8" +deps = ["Base64", "ColorSchemes", "FileIO", "ImageBase", "ImageCore", "OffsetArrays", "StackViews"] +git-tree-sha1 = "3b5344bcdbdc11ad58f3b1956709b5b9345355de" uuid = "4e3cecfd-b093-5904-9786-8bbb286a6a31" -version = "0.3.6" +version = "0.3.8" [[deps.Inflate]] -git-tree-sha1 = "5cd07aab533df5170988219191dfad0519391428" +git-tree-sha1 = "ea8031dea4aff6bd41f1df8f2fdfb25b33626381" uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9" -version = "0.1.3" - -[[deps.IniFile]] -git-tree-sha1 = "f550e6e32074c939295eb5ea6de31849ac2c9625" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.1" +version = "0.1.4" [[deps.InitialValues]] git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" @@ -663,31 +866,25 @@ uuid = "7d512f48-7fb1-5a58-b986-67e6dc259f01" version = "0.7.0" [[deps.Interpolations]] -deps = ["Adapt", "AxisAlgorithms", "ChainRulesCore", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "Requires", "SharedArrays", "SparseArrays", "StaticArrays", "WoodburyMatrices"] -git-tree-sha1 = "721ec2cf720536ad005cb38f50dbba7b02419a15" +deps = ["AxisAlgorithms", "ChainRulesCore", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "Requires", "SharedArrays", "SparseArrays", "StaticArrays", "WoodburyMatrices"] +git-tree-sha1 = "00a19d6ab0cbdea2978fc23c5a6482e02c192501" uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" -version = "0.14.7" - -[[deps.InverseFunctions]] -deps = ["Test"] -git-tree-sha1 = "49510dfcb407e572524ba94aeae2fced1f3feb0f" -uuid = "3587e190-3f89-42d0-90ee-14403ec27112" -version = "0.1.8" +version = "0.14.0" [[deps.InvertedIndices]] -git-tree-sha1 = "82aec7a3dd64f4d9584659dc0b62ef7db2ef3e19" +git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f" -version = "1.2.0" +version = "1.3.0" [[deps.IrrationalConstants]] -git-tree-sha1 = "7fd44fd4ff43fc60815f8e764c0f352b83c49151" +git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" -version = "0.1.1" +version = "0.2.2" [[deps.IterTools]] -git-tree-sha1 = "fa6287a4469f5e048d763df38279ee729fbd44e5" +git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023" uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e" -version = "1.4.0" +version = "1.10.0" [[deps.IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" @@ -695,40 +892,52 @@ uuid = "82899510-4779-5014-852e-03e436cf321d" version = "1.0.0" [[deps.JLD2]] -deps = ["FileIO", "MacroTools", "Mmap", "OrderedCollections", "Pkg", "Printf", "Reexport", "TranscodingStreams", "UUIDs"] -git-tree-sha1 = "c3244ef42b7d4508c638339df1bdbf4353e144db" +deps = ["FileIO", "MacroTools", "Mmap", "OrderedCollections", "Pkg", "PrecompileTools", "Printf", "Reexport", "Requires", "TranscodingStreams", "UUIDs"] +git-tree-sha1 = "5ea6acdd53a51d897672edb694e3cc2912f3f8a7" uuid = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -version = "0.4.30" +version = "0.4.46" [[deps.JLFzf]] deps = ["Pipe", "REPL", "Random", "fzf_jll"] -git-tree-sha1 = "f377670cda23b6b7c1c0b3893e37451c5c1a2185" +git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af" uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c" -version = "0.1.5" +version = "0.1.7" [[deps.JLLWrappers]] -deps = ["Preferences"] -git-tree-sha1 = "abc9885a7ca2052a736a600f7fa66209f96506e1" +deps = ["Artifacts", "Preferences"] +git-tree-sha1 = "7e5d6779a1e09a36db2a7b6cff50942a0a7d0fca" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.4.1" +version = "1.5.0" [[deps.JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] -git-tree-sha1 = "3c837543ddb02250ef42f4738347454f95079d4e" +git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.21.3" +version = "0.21.4" [[deps.JSON3]] -deps = ["Dates", "Mmap", "Parsers", "SnoopPrecompile", "StructTypes", "UUIDs"] -git-tree-sha1 = "84b10656a41ef564c39d2d477d7236966d2b5683" +deps = ["Dates", "Mmap", "Parsers", "PrecompileTools", "StructTypes", "UUIDs"] +git-tree-sha1 = "eb3edce0ed4fa32f75a0a11217433c31d56bd48b" uuid = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" -version = "1.12.0" +version = "1.14.0" + + [deps.JSON3.extensions] + JSON3ArrowExt = ["ArrowTypes"] + + [deps.JSON3.weakdeps] + ArrowTypes = "31f734f8-188a-4ce0-8406-c8a06bd891cd" [[deps.JpegTurbo_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "b53380851c6e6664204efb2e62cd24fa5c47e4ba" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "3336abae9a713d2210bb57ab484b1e065edd7d23" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" -version = "2.1.2+0" +version = "3.0.2+0" + +[[deps.JuliaNVTXCallbacks_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "af433a10f3942e882d3c671aacb203e006a5808f" +uuid = "9c1d0b0a-7046-5b2e-a33f-ea22f176ac7e" +version = "0.2.1+0" [[deps.JuliaVariables]] deps = ["MLStyle", "NameResolution"] @@ -736,6 +945,18 @@ git-tree-sha1 = "49fb3cb53362ddadb4415e9b73926d6b40709e70" uuid = "b14d175d-62b4-44ba-8fb7-3064adc8c3ec" version = "0.2.4" +[[deps.KernelAbstractions]] +deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] +git-tree-sha1 = "ed7167240f40e62d97c1f5f7735dea6de3cc5c49" +uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" +version = "0.9.18" + + [deps.KernelAbstractions.extensions] + EnzymeExt = "EnzymeCore" + + [deps.KernelAbstractions.weakdeps] + EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869" + [[deps.LAME_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "f6250b16881adf048549549fba48b1161acdac8c" @@ -749,16 +970,31 @@ uuid = "88015f11-f218-50d7-93a8-a6af411a945d" version = "3.0.0+1" [[deps.LLVM]] -deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "b8ae281340f0d3e973aae7b96fb7502b0119b376" +deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Preferences", "Printf", "Requires", "Unicode"] +git-tree-sha1 = "ddab4d40513bce53c8e3157825e245224f74fae7" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "4.15.0" +version = "6.6.0" +weakdeps = ["BFloat16s"] + + [deps.LLVM.extensions] + BFloat16sExt = "BFloat16s" [[deps.LLVMExtra_jll]] -deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg", "TOML"] -git-tree-sha1 = "771bfe376249626d3ca12bcd58ba243d3f961576" +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] +git-tree-sha1 = "88b916503aac4fb7f701bb625cd84ca5dd1677bc" uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" -version = "0.0.16+0" +version = "0.0.29+0" + +[[deps.LLVMLoopInfo]] +git-tree-sha1 = "2e5c102cfc41f48ae4740c7eca7743cc7e7b75ea" +uuid = "8b046642-f1f6-4319-8d3c-209ddc03c586" +version = "1.0.0" + +[[deps.LLVMOpenMP_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713" +uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" +version = "15.0.7+0" [[deps.LZO_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -767,21 +1003,29 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac" version = "2.10.1+0" [[deps.LaTeXStrings]] -git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996" +git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" -version = "1.3.0" +version = "1.3.1" [[deps.LaplaceRedux]] -deps = ["Flux", "LinearAlgebra", "Parameters", "Plots", "Zygote"] +deps = ["Compat", "ComputationalResources", "Flux", "LinearAlgebra", "MLJFlux", "MLJModelInterface", "MLUtils", "Parameters", "ProgressMeter", "Random", "Statistics", "Tables", "Tullio", "Zygote", "cuDNN"] path = ".." uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478" -version = "0.1.2" +version = "0.1.4" [[deps.Latexify]] -deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"] -git-tree-sha1 = "2422f47b34d4b127720a18f86fa7b1aa2e141f29" +deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"] +git-tree-sha1 = "cad560042a7cc108f5a4c24ea1431a9221f22c1b" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" -version = "0.15.18" +version = "0.16.2" + + [deps.Latexify.extensions] + DataFramesExt = "DataFrames" + SymEngineExt = "SymEngine" + + [deps.Latexify.weakdeps] + DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" + SymEngine = "123dc426-2d89-5057-bbad-38513e3affd8" [[deps.LazyArtifacts]] deps = ["Artifacts", "Pkg"] @@ -795,21 +1039,26 @@ version = "0.3.1" [[deps.LibCURL]] deps = 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deps = ["DataAPI"] @@ -958,7 +1297,13 @@ version = "0.3.4" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2022.2.1" +version = "2023.1.10" + +[[deps.MultivariateStats]] +deps = ["Arpack", "LinearAlgebra", "SparseArrays", "Statistics", "StatsBase"] +git-tree-sha1 = "6d019f5a0465522bbfdd68ecfad7f86b535d6935" +uuid = "6f286f6a-111f-5878-ab1e-185364afe411" +version = "0.9.0" [[deps.MyterialColors]] git-tree-sha1 = "01d8466fb449436348999d7c6ad740f8f853a579" @@ -966,16 +1311,22 @@ uuid = "1c23619d-4212-4747-83aa-717207fae70f" version = "0.3.0" [[deps.NNlib]] -deps = ["Adapt", "ChainRulesCore", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] -git-tree-sha1 = "b488fc28dfae4c8ec3d61a34a0143a4245e7b13b" +deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"] +git-tree-sha1 = "877f15c331337d54cf24c797d5bcb2e48ce21221" uuid = 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"LinearAlgebra", "MLJModelInterface", "NearestNeighbors", "Statistics", "StatsBase", "Tables"] +git-tree-sha1 = "e411143a8362926e4284a54e745972e939fbab78" +uuid = "636a865e-7cf4-491e-846c-de09b730eb36" +version = "0.2.3" + +[[deps.NearestNeighbors]] +deps = ["Distances", "StaticArrays"] +git-tree-sha1 = "ded64ff6d4fdd1cb68dfcbb818c69e144a5b2e4c" +uuid = "b8a86587-4115-5ab1-83bc-aa920d37bbce" +version = "0.4.16" + [[deps.NetworkLayout]] -deps = ["GeometryBasics", "LinearAlgebra", "Random", "Requires", "SparseArrays"] -git-tree-sha1 = "cac8fc7ba64b699c678094fa630f49b80618f625" +deps = ["GeometryBasics", "LinearAlgebra", "Random", "Requires", "StaticArrays"] +git-tree-sha1 = "91bb2fedff8e43793650e7a677ccda6e6e6e166b" uuid = "46757867-2c16-5918-afeb-47bfcb05e46a" -version = "0.4.4" +version = "0.4.6" +weakdeps = ["Graphs"] + + [deps.NetworkLayout.extensions] + NetworkLayoutGraphsExt = "Graphs" [[deps.NetworkOptions]] uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" version = "1.2.0" [[deps.OffsetArrays]] -deps = ["Adapt"] -git-tree-sha1 = "f71d8950b724e9ff6110fc948dff5a329f901d64" +git-tree-sha1 = "6a731f2b5c03157418a20c12195eb4b74c8f8621" uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" -version = "1.12.8" +version = "1.13.0" +weakdeps = ["Adapt"] + + [deps.OffsetArrays.extensions] + OffsetArraysAdaptExt = "Adapt" [[deps.Ogg_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1019,31 +1406,37 @@ version = "1.3.5+1" [[deps.OneHotArrays]] deps = ["Adapt", "ChainRulesCore", "Compat", "GPUArraysCore", "LinearAlgebra", "NNlib"] -git-tree-sha1 = "f511fca956ed9e70b80cd3417bb8c2dde4b68644" +git-tree-sha1 = "963a3f28a2e65bb87a68033ea4a616002406037d" uuid = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f" -version = "0.2.3" +version = "0.2.5" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.20+0" +version = "0.3.23+4" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] uuid = "05823500-19ac-5b8b-9628-191a04bc5112" -version = "0.8.1+0" +version = "0.8.1+2" + +[[deps.OpenMPI_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "Hwloc_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "MPIPreferences", "PMIx_jll", "TOML", "Zlib_jll", "libevent_jll", "prrte_jll"] +git-tree-sha1 = "f46caf663e069027a06942d00dced37f1eb3d8ad" +uuid = "fe0851c0-eecd-5654-98d4-656369965a5c" +version = "5.0.2+0" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "6503b77492fd7fcb9379bf73cd31035670e3c509" +git-tree-sha1 = "af81a32750ebc831ee28bdaaba6e1067decef51e" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.3.3" +version = "1.4.2" [[deps.OpenSSL_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f6e9dba33f9f2c44e08a020b0caf6903be540004" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.19+0" +version = "3.0.13+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -1053,9 +1446,9 @@ version = "0.5.5+0" [[deps.Optimisers]] deps = ["ChainRulesCore", "Functors", "LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "e657acef119cc0de2a8c0762666d3b64727b053b" +git-tree-sha1 = "264b061c1903bc0fe9be77cb9050ebacff66bb63" uuid = "3bd65402-5787-11e9-1adc-39752487f4e2" -version = "0.2.14" +version = "0.3.2" [[deps.Opus_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1064,20 +1457,38 @@ uuid = "91d4177d-7536-5919-b921-800302f37372" version = "1.3.2+0" [[deps.OrderedCollections]] -git-tree-sha1 = "85f8e6578bf1f9ee0d11e7bb1b1456435479d47c" +git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.4.1" +version = "1.6.3" [[deps.PCRE2_jll]] deps = ["Artifacts", "Libdl"] uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15" -version = "10.40.0+0" +version = "10.42.0+1" + +[[deps.PDMats]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65" +uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" +version = "0.11.31" + +[[deps.PMIx_jll]] +deps = ["Artifacts", "Hwloc_jll", "JLLWrappers", "Libdl", "Zlib_jll", "libevent_jll"] +git-tree-sha1 = "8b3b19351fa24791f94d7ae85faf845ca1362541" +uuid = "32165bc3-0280-59bc-8c0b-c33b6203efab" +version = "4.2.7+0" + +[[deps.PackageExtensionCompat]] +git-tree-sha1 = "fb28e33b8a95c4cee25ce296c817d89cc2e53518" +uuid = "65ce6f38-6b18-4e1d-a461-8949797d7930" +version = "1.0.2" +weakdeps = ["Requires", "TOML"] [[deps.PaddedViews]] deps = ["OffsetArrays"] -git-tree-sha1 = "03a7a85b76381a3d04c7a1656039197e70eda03d" +git-tree-sha1 = "0fac6313486baae819364c52b4f483450a9d793f" uuid = "5432bcbf-9aad-5242-b902-cca2824c8663" -version = "0.5.11" +version = "0.5.12" [[deps.Parameters]] deps = ["OrderedCollections", "UnPack"] @@ -1086,16 +1497,28 @@ uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a" version = "0.12.3" [[deps.Parsers]] -deps = ["Dates", "SnoopPrecompile"] -git-tree-sha1 = "8175fc2b118a3755113c8e68084dc1a9e63c61ee" +deps = ["Dates", "PrecompileTools", "UUIDs"] +git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.5.3" +version = "2.8.1" + +[[deps.PartialFunctions]] +deps = ["MacroTools"] +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 = ["DataStructures", "InternedStrings", "Serialization", "SparseArrays", "Strided", "StringEncodings", "ZipFile"] -git-tree-sha1 = "e6a34eb1dc0c498f0774bbfbbbeff2de101f4235" +deps = ["BFloat16s", "DataStructures", "InternedStrings", "Serialization", "SparseArrays", "Strided", "StringEncodings", "ZipFile"] +git-tree-sha1 = "2e71d7dbcab8dc47306c0ed6ac6018fbc1a7070f" uuid = "fbb45041-c46e-462f-888f-7c521cafbc2c" -version = "0.3.2" +version = "0.3.3" [[deps.Pipe]] git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d" @@ -1103,15 +1526,15 @@ uuid = "b98c9c47-44ae-5843-9183-064241ee97a0" version = "1.3.0" [[deps.Pixman_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "b4f5d02549a10e20780a24fce72bea96b6329e29" +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"] +git-tree-sha1 = "64779bc4c9784fee475689a1752ef4d5747c5e87" uuid = "30392449-352a-5448-841d-b1acce4e97dc" -version = "0.40.1+0" +version = "0.42.2+0" [[deps.Pkg]] -deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] +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" -version = "1.8.0" +version = "1.10.0" [[deps.PlotThemes]] deps = ["PlotUtils", "Statistics"] @@ -1120,39 +1543,64 @@ uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" version = "3.1.0" [[deps.PlotUtils]] -deps = ["ColorSchemes", "Colors", "Dates", "Printf", "Random", "Reexport", "SnoopPrecompile", "Statistics"] -git-tree-sha1 = "daff8d21e3f4c596387867329be49b8c45f4f0f3" +deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] +git-tree-sha1 = "7b1a9df27f072ac4c9c7cbe5efb198489258d1f5" uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" -version = "1.3.3" +version = "1.4.1" [[deps.Plots]] -deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SnoopPrecompile", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "Unzip"] -git-tree-sha1 = "a99bbd3664bb12a775cda2eba7f3b2facf3dad94" +deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] +git-tree-sha1 = "3c403c6590dd93b36752634115e20137e79ab4df" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.38.2" +version = "1.40.2" + + [deps.Plots.extensions] + FileIOExt = "FileIO" + GeometryBasicsExt = "GeometryBasics" + IJuliaExt = "IJulia" + ImageInTerminalExt = "ImageInTerminal" + UnitfulExt = "Unitful" + + [deps.Plots.weakdeps] + FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" + GeometryBasics = "5c1252a2-5f33-56bf-86c9-59e7332b4326" + IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" + ImageInTerminal = "d8c32880-2388-543b-8c61-d9f865259254" + Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" [[deps.PooledArrays]] deps = ["DataAPI", "Future"] -git-tree-sha1 = "a6062fe4063cdafe78f4a0a81cfffb89721b30e7" +git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3" uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" -version = "1.4.2" +version = "1.4.3" + +[[deps.PrecompileTools]] +deps = ["Preferences"] +git-tree-sha1 = "5aa36f7049a63a1528fe8f7c3f2113413ffd4e1f" +uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" +version = "1.2.1" [[deps.Preferences]] deps = ["TOML"] -git-tree-sha1 = "47e5f437cc0e7ef2ce8406ce1e7e24d44915f88d" +git-tree-sha1 = "9306f6085165d270f7e3db02af26a400d580f5c6" uuid = "21216c6a-2e73-6563-6e65-726566657250" -version = "1.3.0" +version = "1.4.3" [[deps.PrettyPrint]] 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", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "96f6db03ab535bdb901300f88335257b0018689d" +deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"] +git-tree-sha1 = "88b895d13d53b5577fd53379d913b9ab9ac82660" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.2" +version = "2.3.1" [[deps.Printf]] deps = ["Unicode"] @@ -1164,31 +1612,37 @@ git-tree-sha1 = 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"Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"] +git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b" +uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56" +version = "6.5.3+1" -[[deps.RCall]] -deps = ["CategoricalArrays", "Conda", "DataFrames", "DataStructures", "Dates", "Libdl", "Missings", "REPL", "Random", "Requires", "StatsModels", "WinReg"] -git-tree-sha1 = "2c0ffd39860c9a48259a0f57214ced2024ab63bc" -uuid = "6f49c342-dc21-5d91-9882-a32aef131414" -version = "0.13.14" +[[deps.QuadGK]] +deps = ["DataStructures", "LinearAlgebra"] +git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e" +uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" +version = "2.9.4" [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA", "Serialization"] +deps = ["SHA"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Random123]] deps = ["Random", "RandomNumbers"] -git-tree-sha1 = "7a1a306b72cfa60634f03a911405f4e64d1b718b" +git-tree-sha1 = "4743b43e5a9c4a2ede372de7061eed81795b12e7" uuid = "74087812-796a-5b5d-8853-05524746bad3" -version = "1.6.0" +version = "1.7.0" [[deps.RandomNumbers]] deps = ["Random", "Requires"] @@ -1198,9 +1652,13 @@ version = "1.5.3" [[deps.Ratios]] deps = ["Requires"] -git-tree-sha1 = "dc84268fe0e3335a62e315a3a7cf2afa7178a734" +git-tree-sha1 = "1342a47bf3260ee108163042310d26f2be5ec90b" uuid = "c84ed2f1-dad5-54f0-aa8e-dbefe2724439" -version = "0.4.3" +version = "0.4.5" +weakdeps = ["FixedPointNumbers"] + + [deps.Ratios.extensions] + RatiosFixedPointNumbersExt = "FixedPointNumbers" [[deps.RealDot]] deps = ["LinearAlgebra"] @@ -1209,16 +1667,16 @@ uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9" version = "0.1.0" [[deps.RecipesBase]] -deps = ["SnoopPrecompile"] -git-tree-sha1 = "261dddd3b862bd2c940cf6ca4d1c8fe593e457c8" +deps = ["PrecompileTools"] +git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" -version = "1.3.3" +version = "1.3.4" [[deps.RecipesPipeline]] -deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase", "SnoopPrecompile"] -git-tree-sha1 = "e974477be88cb5e3040009f3767611bc6357846f" +deps = ["Dates", "NaNMath", "PlotUtils", "PrecompileTools", "RecipesBase"] +git-tree-sha1 = "45cf9fd0ca5839d06ef333c8201714e888486342" uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" -version = "0.6.11" +version = "0.6.12" [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" @@ -1227,9 +1685,9 @@ version = "1.2.2" [[deps.RelocatableFolders]] deps = ["SHA", "Scratch"] -git-tree-sha1 = "90bc7a7c96410424509e4263e277e43250c05691" +git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864" uuid = "05181044-ff0b-4ac5-8273-598c1e38db00" -version = "1.0.0" +version = "1.0.1" [[deps.Requires]] deps = ["UUIDs"] @@ -1239,31 +1697,48 @@ version = "1.3.0" [[deps.Rmath]] deps = ["Random", "Rmath_jll"] -git-tree-sha1 = "bf3188feca147ce108c76ad82c2792c57abe7b1f" +git-tree-sha1 = "f65dcb5fa46aee0cf9ed6274ccbd597adc49aa7b" uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa" -version = "0.7.0" +version = "0.7.1" [[deps.Rmath_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "68db32dff12bb6127bac73c209881191bf0efbb7" +git-tree-sha1 = "6ed52fdd3382cf21947b15e8870ac0ddbff736da" uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f" -version = "0.3.0+0" +version = "0.4.0+0" [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" +[[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" +uuid = "30f210dd-8aff-4c5f-94ba-8e64358c1161" +version = "3.0.0" + +[[deps.ScikitLearnBase]] +deps = ["LinearAlgebra", "Random", "Statistics"] +git-tree-sha1 = "7877e55c1523a4b336b433da39c8e8c08d2f221f" +uuid = "6e75b9c4-186b-50bd-896f-2d2496a4843e" +version = "0.5.0" + [[deps.Scratch]] deps = ["Dates"] -git-tree-sha1 = "f94f779c94e58bf9ea243e77a37e16d9de9126bd" +git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386" uuid = "6c6a2e73-6563-6170-7368-637461726353" -version = "1.1.1" +version = "1.2.1" [[deps.SentinelArrays]] deps = ["Dates", "Random"] -git-tree-sha1 = "c02bd3c9c3fc8463d3591a62a378f90d2d8ab0f3" +git-tree-sha1 = "0e7508ff27ba32f26cd459474ca2ede1bc10991f" uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c" -version = "1.3.17" +version = "1.4.1" [[deps.Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" @@ -1278,11 +1753,6 @@ version = "1.1.1" deps = ["Distributed", "Mmap", "Random", "Serialization"] uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" -[[deps.ShiftedArrays]] -git-tree-sha1 = "503688b59397b3307443af35cd953a13e8005c16" -uuid = "1277b4bf-5013-50f5-be3d-901d8477a67a" -version = "2.0.0" - [[deps.ShowCases]] git-tree-sha1 = "7f534ad62ab2bd48591bdeac81994ea8c445e4a5" uuid = "605ecd9f-84a6-4c9e-81e2-4798472b76a3" @@ -1305,12 +1775,6 @@ git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231" uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d" version = "0.9.4" -[[deps.SnoopPrecompile]] -deps = ["Preferences"] -git-tree-sha1 = "e760a70afdcd461cf01a575947738d359234665c" -uuid = "66db9d55-30c0-4569-8b51-7e840670fc0c" -version = "1.0.3" - [[deps.Sockets]] uuid = "6462fe0b-24de-5631-8697-dd941f90decc" @@ -1322,19 +1786,30 @@ version = "1.1.0" [[deps.SortingAlgorithms]] deps = ["DataStructures"] -git-tree-sha1 = "a4ada03f999bd01b3a25dcaa30b2d929fe537e00" +git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "1.1.0" +version = "1.2.1" [[deps.SparseArrays]] -deps = ["LinearAlgebra", "Random"] +deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" +version = "1.10.0" + +[[deps.SparseInverseSubset]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "52962839426b75b3021296f7df242e40ecfc0852" +uuid = "dc90abb0-5640-4711-901d-7e5b23a2fada" +version = "0.1.2" [[deps.SpecialFunctions]] -deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] -git-tree-sha1 = "d75bda01f8c31ebb72df80a46c88b25d1c79c56d" +deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] +git-tree-sha1 = "e2cfc4012a19088254b3950b85c3c1d8882d864d" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "2.1.7" +version = "2.3.1" +weakdeps = ["ChainRulesCore"] + + [deps.SpecialFunctions.extensions] + SpecialFunctionsChainRulesCoreExt = "ChainRulesCore" [[deps.SplittablesBase]] deps = ["Setfield", "Test"] @@ -1349,25 +1824,37 @@ uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15" version = "0.1.1" [[deps.StaticArrays]] -deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"] -git-tree-sha1 = "6954a456979f23d05085727adb17c4551c19ecd1" +deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"] +git-tree-sha1 = "bf074c045d3d5ffd956fa0a461da38a44685d6b2" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.5.12" +version = "1.9.3" +weakdeps = ["ChainRulesCore", "Statistics"] + + [deps.StaticArrays.extensions] + StaticArraysChainRulesCoreExt = "ChainRulesCore" + StaticArraysStatisticsExt = "Statistics" [[deps.StaticArraysCore]] -git-tree-sha1 = "6b7ba252635a5eff6a0b0664a41ee140a1c9e72a" +git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" -version = "1.4.0" +version = "1.4.2" + +[[deps.StatisticalTraits]] +deps = ["ScientificTypesBase"] +git-tree-sha1 = "30b9236691858e13f167ce829490a68e1a597782" +uuid = "64bff920-2084-43da-a3e6-9bb72801c0c9" +version = "3.2.0" [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +version = "1.10.0" [[deps.StatsAPI]] deps = ["LinearAlgebra"] -git-tree-sha1 = "f9af7f195fb13589dd2e2d57fdb401717d2eb1f6" +git-tree-sha1 = "1ff449ad350c9c4cbc756624d6f8a8c3ef56d3ed" uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" -version = "1.5.0" +version = "1.7.0" [[deps.StatsBase]] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] @@ -1376,22 +1863,18 @@ uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" version = "0.33.21" [[deps.StatsFuns]] -deps = ["ChainRulesCore", "HypergeometricFunctions", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] -git-tree-sha1 = "ab6083f09b3e617e34a956b43e9d51b824206932" +deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"] +git-tree-sha1 = "cef0472124fab0695b58ca35a77c6fb942fdab8a" uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c" -version = "1.1.1" +version = "1.3.1" -[[deps.StatsModels]] -deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Printf", "REPL", "ShiftedArrays", "SparseArrays", "StatsBase", "StatsFuns", "Tables"] -git-tree-sha1 = "a5e15f27abd2692ccb61a99e0854dfb7d48017db" -uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d" -version = "0.6.33" + [deps.StatsFuns.extensions] + StatsFunsChainRulesCoreExt = "ChainRulesCore" + StatsFunsInverseFunctionsExt = "InverseFunctions" -[[deps.StrTables]] -deps = ["Dates"] -git-tree-sha1 = "5998faae8c6308acc25c25896562a1e66a3bb038" -uuid = "9700d1a9-a7c8-5760-9816-a99fda30bb8f" -version = "1.0.1" + [deps.StatsFuns.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.Strided]] deps = ["LinearAlgebra", "TupleTools"] @@ -1401,20 +1884,28 @@ version = "1.2.3" [[deps.StringEncodings]] deps = ["Libiconv_jll"] -git-tree-sha1 = "33c0da881af3248dafefb939a21694b97cfece76" +git-tree-sha1 = "b765e46ba27ecf6b44faf70df40c57aa3a547dcb" uuid = "69024149-9ee7-55f6-a4c4-859efe599b68" -version = "0.3.6" +version = "0.3.7" [[deps.StringManipulation]] -git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" +deps = ["PrecompileTools"] +git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" -version = "0.3.0" +version = "0.3.4" [[deps.StructArrays]] -deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"] -git-tree-sha1 = "b03a3b745aa49b566f128977a7dd1be8711c5e71" +deps = ["ConstructionBase", "DataAPI", "Tables"] +git-tree-sha1 = "f4dc295e983502292c4c3f951dbb4e985e35b3be" uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" -version = "0.6.14" +version = "0.6.18" +weakdeps = ["Adapt", "GPUArraysCore", "SparseArrays", "StaticArrays"] + + [deps.StructArrays.extensions] + StructArraysAdaptExt = "Adapt" + StructArraysGPUArraysCoreExt = "GPUArraysCore" + StructArraysSparseArraysExt = "SparseArrays" + StructArraysStaticArraysExt = "StaticArrays" [[deps.StructTypes]] deps = ["Dates", "UUIDs"] @@ -1422,10 +1913,19 @@ 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" + +[[deps.SuiteSparse_jll]] +deps = ["Artifacts", "Libdl", "libblastrampoline_jll"] +uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" +version = "7.2.1+1" + [[deps.TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" -version = "1.0.0" +version = "1.0.3" [[deps.TableTraits]] deps = ["IteratorInterfaceExtensions"] @@ -1434,15 +1934,21 @@ uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"] -git-tree-sha1 = "c79322d36826aa2f4fd8ecfa96ddb47b174ac78d" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] +git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.10.0" +version = "1.11.1" + +[[deps.TaijaPlotting]] +deps = ["CategoricalArrays", "ConformalPrediction", "CounterfactualExplanations", "DataAPI", "Distributions", "Flux", "LaplaceRedux", "LinearAlgebra", "MLJBase", "MLUtils", "ManifoldLearning", "MultivariateStats", "NaturalSort", "NearestNeighborModels", "Plots"] +git-tree-sha1 = "a4ef915d36801127141afb726acd8b78be6abec7" +uuid = "bd7198b4-c7d6-400c-9bab-9a24614b0240" +version = "1.0.6" [[deps.Tar]] deps = ["ArgTools", "SHA"] uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" -version = "1.10.1" +version = "1.10.0" [[deps.TensorCore]] deps = ["LinearAlgebra"] @@ -1451,10 +1957,10 @@ uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50" version = "0.1.1" [[deps.Term]] -deps = ["CodeTracking", "Dates", "Highlights", "InteractiveUtils", "Logging", "Markdown", "MyterialColors", "OrderedCollections", "Parameters", "ProgressLogging", "Tables", "UUIDs", "Unicode", "UnicodeFun", "WordTokenizers"] -git-tree-sha1 = "af539c0921d43b8cf24422324a4443ff9cbc1601" +deps = ["AbstractTrees", "CodeTracking", "Dates", "Highlights", "InteractiveUtils", "Logging", "Markdown", "MyterialColors", "OrderedCollections", "Parameters", "PrecompileTools", "ProgressLogging", "REPL", "Tables", "UUIDs", "Unicode", "UnicodeFun"] +git-tree-sha1 = "ffac67f6fbcbb32027d924b93ba91b7633af9220" uuid = "22787eb5-b846-44ae-b979-8e399b8463ab" -version = "1.2.0" +version = "2.0.5" [[deps.Test]] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] @@ -1462,31 +1968,66 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.TimerOutputs]] deps = ["ExprTools", "Printf"] -git-tree-sha1 = "f2fd3f288dfc6f507b0c3a2eb3bac009251e548b" +git-tree-sha1 = "f548a9e9c490030e545f72074a41edfd0e5bcdd7" uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" -version = "0.5.22" +version = "0.5.23" [[deps.TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "94f38103c984f89cf77c402f2a68dbd870f8165f" +git-tree-sha1 = "3caa21522e7efac1ba21834a03734c57b4611c7e" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.11" +version = "0.10.4" +weakdeps = ["Random", "Test"] + + [deps.TranscodingStreams.extensions] + TestExt = ["Test", "Random"] [[deps.Transducers]] -deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] -git-tree-sha1 = "c42fa452a60f022e9e087823b47e5a5f8adc53d5" +deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"] +git-tree-sha1 = "3064e780dbb8a9296ebb3af8f440f787bb5332af" uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999" -version = "0.4.75" +version = "0.4.80" + + [deps.Transducers.extensions] + TransducersBlockArraysExt = "BlockArrays" + TransducersDataFramesExt = "DataFrames" + TransducersLazyArraysExt = "LazyArrays" + TransducersOnlineStatsBaseExt = "OnlineStatsBase" + TransducersReferenceablesExt = "Referenceables" + + [deps.Transducers.weakdeps] + BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e" + DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" + LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02" + OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338" + Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e" + +[[deps.Tullio]] +deps = ["DiffRules", "LinearAlgebra", "Requires"] +git-tree-sha1 = "6d476962ba4e435d7f4101a403b1d3d72afe72f3" +uuid = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc" +version = "0.3.7" + + [deps.Tullio.extensions] + TullioCUDAExt = "CUDA" + TullioChainRulesCoreExt = "ChainRulesCore" + TullioFillArraysExt = "FillArrays" + TullioTrackerExt = "Tracker" + + [deps.Tullio.weakdeps] + CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b" + Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" [[deps.TupleTools]] -git-tree-sha1 = "3c712976c47707ff893cf6ba4354aa14db1d8938" +git-tree-sha1 = "41d61b1c545b06279871ef1a4b5fcb2cac2191cd" uuid = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6" -version = "1.3.0" +version = "1.5.0" [[deps.URIs]] -git-tree-sha1 = "ac00576f90d8a259f2c9d823e91d1de3fd44d348" +git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b" uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" -version = "1.4.1" +version = "1.5.1" [[deps.UUIDs]] deps = ["Random", "SHA"] @@ -1506,6 +2047,43 @@ git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf" uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1" version = "0.4.1" +[[deps.Unitful]] +deps = ["Dates", "LinearAlgebra", "Random"] +git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa" +uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" +version = "1.19.0" + + [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.UnitfulLatexify]] +deps = ["LaTeXStrings", "Latexify", "Unitful"] +git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd" +uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728" +version = "1.6.3" + +[[deps.UnsafeAtomics]] +git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" +uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" +version = "0.2.1" + +[[deps.UnsafeAtomicsLLVM]] +deps = ["LLVM", "UnsafeAtomics"] +git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" +uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" +version = "0.1.3" + [[deps.Unzip]] git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78" uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d" @@ -1516,17 +2094,23 @@ git-tree-sha1 = "58d6e80b4ee071f5efd07fda82cb9fbe17200868" uuid = "81def892-9a0e-5fdd-b105-ffc91e053289" version = "1.3.0" +[[deps.Vulkan_Loader_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Wayland_jll", "Xorg_libX11_jll", "Xorg_libXrandr_jll", "xkbcommon_jll"] +git-tree-sha1 = "2f0486047a07670caad3a81a075d2e518acc5c59" +uuid = "a44049a8-05dd-5a78-86c9-5fde0876e88c" +version = "1.3.243+0" + [[deps.Wayland_jll]] -deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"] -git-tree-sha1 = "ed8d92d9774b077c53e1da50fd81a36af3744c1c" +deps = ["Artifacts", "EpollShim_jll", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"] +git-tree-sha1 = "7558e29847e99bc3f04d6569e82d0f5c54460703" uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89" -version = "1.21.0+0" +version = "1.21.0+1" [[deps.Wayland_protocols_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da" +git-tree-sha1 = "93f43ab61b16ddfb2fd3bb13b3ce241cafb0e6c9" uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" -version = "1.25.0+0" +version = "1.31.0+0" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] @@ -1534,22 +2118,10 @@ git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.2" -[[deps.WinReg]] -deps = ["Test"] -git-tree-sha1 = "808380e0a0483e134081cc54150be4177959b5f4" -uuid = "1b915085-20d7-51cf-bf83-8f477d6f5128" -version = "0.3.1" - [[deps.WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3" +git-tree-sha1 = "5f24e158cf4cee437052371455fe361f526da062" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" -version = "0.5.5" - -[[deps.WordTokenizers]] -deps = ["DataDeps", "HTML_Entities", "StrTables", "Unicode"] -git-tree-sha1 = "01dd4068c638da2431269f49a5964bf42ff6c9d2" -uuid = "796a5d58-b03d-544a-977e-18100b691f6e" version = "0.5.6" [[deps.WorkerUtilities]] @@ -1558,10 +2130,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60" version = "1.6.1" [[deps.XML2_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "93c41695bc1c08c46c5899f4fe06d6ead504bb73" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"] +git-tree-sha1 = "07e470dabc5a6a4254ffebc29a1b3fc01464e105" uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" -version = "2.10.3+0" +version = "2.12.5+0" [[deps.XSLT_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] @@ -1569,17 +2141,35 @@ git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a" uuid = "aed1982a-8fda-507f-9586-7b0439959a61" version = "1.1.34+0" +[[deps.XZ_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "31c421e5516a6248dfb22c194519e37effbf1f30" +uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800" +version = "5.6.1+0" + +[[deps.Xorg_libICE_jll]] +deps = ["Libdl", "Pkg"] +git-tree-sha1 = "e5becd4411063bdcac16be8b66fc2f9f6f1e8fe5" +uuid = "f67eecfb-183a-506d-b269-f58e52b52d7c" +version = "1.0.10+1" + +[[deps.Xorg_libSM_jll]] +deps = ["Libdl", "Pkg", "Xorg_libICE_jll"] +git-tree-sha1 = "4a9d9e4c180e1e8119b5ffc224a7b59d3a7f7e18" +uuid = "c834827a-8449-5923-a945-d239c165b7dd" +version = "1.2.3+0" + [[deps.Xorg_libX11_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] -git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] +git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495" uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc" -version = "1.6.9+4" +version = "1.8.6+0" [[deps.Xorg_libXau_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8" uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec" -version = "1.0.9+4" +version = "1.0.11+0" [[deps.Xorg_libXcursor_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"] @@ -1588,10 +2178,10 @@ uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724" version = "1.2.0+4" [[deps.Xorg_libXdmcp_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "34d526d318358a859d7de23da945578e8e8727b7" uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" -version = "1.1.3+4" +version = "1.1.4+0" [[deps.Xorg_libXext_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] @@ -1630,22 +2220,28 @@ uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" version = "0.9.10+4" [[deps.Xorg_libpthread_stubs_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9" uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" -version = "0.1.0+3" +version = "0.1.1+0" [[deps.Xorg_libxcb_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] -git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6" +deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] +git-tree-sha1 = "b4bfde5d5b652e22b9c790ad00af08b6d042b97d" uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" -version = "1.13.0+3" +version = "1.15.0+0" [[deps.Xorg_libxkbfile_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] -git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "730eeca102434283c50ccf7d1ecdadf521a765a4" uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a" -version = "1.1.0+4" +version = "1.1.2+0" + +[[deps.Xorg_xcb_util_cursor_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_jll", "Xorg_xcb_util_renderutil_jll"] +git-tree-sha1 = "04341cb870f29dcd5e39055f895c39d016e18ccd" +uuid = "e920d4aa-a673-5f3a-b3d7-f755a4d47c43" +version = "0.1.4+0" [[deps.Xorg_xcb_util_image_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] @@ -1678,22 +2274,22 @@ uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" [[deps.Xorg_xkbcomp_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] -git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"] +git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" -version = "1.4.2+4" +version = "1.4.6+0" [[deps.Xorg_xkeyboard_config_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] -git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"] +git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3" uuid = "33bec58e-1273-512f-9401-5d533626f822" -version = "2.27.0+4" +version = "2.39.0+0" [[deps.Xorg_xtrans_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" -version = "1.4.0+3" +version = "1.5.0+0" [[deps.ZMQ]] deps = ["FileWatching", "Sockets", "ZeroMQ_jll"] @@ -1702,10 +2298,10 @@ uuid = "c2297ded-f4af-51ae-bb23-16f91089e4e1" version = "1.2.2" [[deps.ZeroMQ_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "libsodium_jll"] -git-tree-sha1 = "fe5c65a526f066fb3000da137d5785d9649a8a47" +deps = ["Artifacts", "JLLWrappers", "Libdl", "libsodium_jll"] +git-tree-sha1 = "42f97fb27394378591666ab0e9cee369e6d0e1f9" uuid = "8f1865be-045e-5c20-9c9f-bfbfb0764568" -version = "4.3.4+0" +version = "4.3.5+0" [[deps.ZipFile]] deps = ["Libdl", "Printf", "Zlib_jll"] @@ -1716,31 +2312,65 @@ version = "0.10.1" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.12+3" +version = "1.2.13+1" [[deps.Zstd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.2+0" +version = "1.5.5+0" [[deps.Zygote]] -deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"] -git-tree-sha1 = "e1af683167eea952684188f5e1e29b9cabc2e5f9" +deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"] +git-tree-sha1 = "4ddb4470e47b0094c93055a3bcae799165cc68f1" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" -version = "0.6.55" +version = "0.6.69" + + [deps.Zygote.extensions] + ZygoteColorsExt = "Colors" + ZygoteDistancesExt = "Distances" + ZygoteTrackerExt = "Tracker" + + [deps.Zygote.weakdeps] + Colors = "5ae59095-9a9b-59fe-a467-6f913c188581" + Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" + Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" [[deps.ZygoteRules]] -deps = ["MacroTools"] -git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +deps = ["ChainRulesCore", "MacroTools"] +git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00" uuid = "700de1a5-db45-46bc-99cf-38207098b444" -version = "0.2.2" +version = "0.2.5" + +[[deps.cuDNN]] +deps = ["CEnum", "CUDA", "CUDA_Runtime_Discovery", "CUDNN_jll"] +git-tree-sha1 = "d433ec29756895512190cac9c96666d879f07b92" +uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" +version = "1.3.0" + +[[deps.eudev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"] +git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba" +uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06" +version = "3.2.9+0" [[deps.fzf_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "a68c9655fbe6dfcab3d972808f1aafec151ce3f8" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" -version = "0.29.0+0" +version = "0.43.0+0" + +[[deps.gperf_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033" +uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70" +version = "3.1.1+0" + +[[deps.libaec_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "46bf7be2917b59b761247be3f317ddf75e50e997" +uuid = "477f73a3-ac25-53e9-8cc3-50b2fa2566f0" +version = "1.1.2+0" [[deps.libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1755,9 +2385,21 @@ uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" [[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.1.1+0" +version = "5.8.0+1" + +[[deps.libevdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22" +uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc" +version = "1.11.0+0" + +[[deps.libevent_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenSSL_jll"] +git-tree-sha1 = "f04ec6d9a186115fb38f858f05c0c4e1b7fc9dcb" +uuid = "1080aeaf-3a6a-583e-a51c-c537b09f60ec" +version = "2.1.13+1" [[deps.libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1765,11 +2407,17 @@ git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" +[[deps.libinput_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"] +git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce" +uuid = "36db933b-70db-51c0-b978-0f229ee0e533" +version = "1.18.0+0" + [[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" -version = "1.6.38+0" +version = "1.6.43+1" [[deps.libsodium_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1783,15 +2431,27 @@ git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" +[[deps.mtdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11" +uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e" +version = "1.1.6+0" + [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.48.0+0" +version = "1.52.0+1" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+0" +version = "17.4.0+2" + +[[deps.prrte_jll]] +deps = ["Artifacts", "Hwloc_jll", "JLLWrappers", "Libdl", "PMIx_jll", "libevent_jll"] +git-tree-sha1 = "5adb2d7a18a30280feb66cad6f1a1dfdca2dc7b0" +uuid = "eb928a42-fffd-568d-ab9c-3f5d54fc65b9" +version = "3.0.2+0" [[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1807,6 +2467,6 @@ 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 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "1.4.1+0" +version = "1.4.1+1" diff --git a/paper/Project.toml b/paper/Project.toml index e13d3f87..a81afbbe 100644 --- a/paper/Project.toml +++ b/paper/Project.toml @@ -8,6 +8,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" PlotThemes = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -RCall = "6f49c342-dc21-5d91-9882-a32aef131414" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +TaijaPlotting = "bd7198b4-c7d6-400c-9bab-9a24614b0240" Term = "22787eb5-b846-44ae-b979-8e399b8463ab" diff --git a/paper/paper.aux b/paper/paper.aux index e30e6ed5..5cd5fe41 100644 --- a/paper/paper.aux +++ b/paper/paper.aux @@ -27,15 +27,17 @@ \citation{daxberger2021laplace} \citation{immer2020improving} \citation{daxberger2021laplace} +\citation{daxberger2021laplace} \newlabel{@firstpg}{{}{1}{}{Doc-Start}{}} \@writefile{toc}{\contentsline {section}{\numberline {1}Background}{1}{section.1}\protected@file@percent } \newlabel{sec-intro}{{1}{1}{Background}{section.1}{}} 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{\texttt {LaplaceRedux.jl} --- a Julia implementation}{LaplaceRedux.jl --- a Julia implementation}}{section.3}{}} \bibstyle{juliacon} \bibdata{ref.bib} \bibcite{blundell2015weight}{1} @@ -49,13 +51,11 @@ \bibcite{wilson2020case}{9} \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.}}{2}{figure.1}\protected@file@percent } \newlabel{fig-class}{{1}{2}{Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). 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zv^iNMu+5=azF2Up{q)Be6zBEdR#G79md&5JHoAGGhNB!3z7_QWh^rgZjKsEQYI^c- zTBI=k{@1EbwZj5q@-9=JYA*DK%-Q zYP5-A6B_y-?+7ZqWX;NTWvV;AA#6c%S@66O>aVdi2LAo+hokOV##`p;>ajWadR2}A=B&91<`VP7jZtnCAy{rePM{YM89_1+zp0#6v__4A zh+J?ps)`ZC<7p9AQ5o(=r0X_M3(;YeLoZ+!Gv6X-+VcBEhze>&gi}*rcU4tyG%7A<9ap9 zyr&_Rj-Xij6#TFM%C&(}S+_+BWrT#cl)8B;>oP+3g?1uqA2e%#==h0{`XlPjJNnL1 z0>(BTZ4^FoNGATeW4K?|b^%&0oaNhS5|wJcXty0zUEX-e3Lz83D6EzEY#H!p;e_^) yILJ(`9^Tx+?7ORrxv~9!j~q>G;J>>XySRF|m|MWJadL67z_P=C`zEd^0smhUcvK+( diff --git a/paper/src/paper.qmd b/paper/src/paper.qmd index 0cea3d2e..fddaaa2a 100644 --- a/paper/src/paper.qmd +++ b/paper/src/paper.qmd @@ -1,7 +1,7 @@ --- title: "Effortless Bayesian Deep Learning in Julia through Laplace" abstract: | - Treating deep neural networks probabilistically comes with numerous advantages including improved robustness and greater interpretability. These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a small Julia package, `LaplaceRedux.jl` that implements this new approach for deep neural networks trained in `Flux.jl`. + Treating deep neural networks probabilistically comes with numerous advantages including improved robustness and greater interpretability. These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a light-weight Julia package, `LaplaceRedux.jl` that implements this new approach for deep neural networks trained in `Flux.jl`. keywords: - Julia - Probabilistic Machine Learning @@ -34,23 +34,19 @@ using LinearAlgebra using Plots using Random using Statistics +using TaijaPlotting Random.seed!(2022) theme(:wong) default(size=(500, 375)) www_path = "paper/www" -include("paper/utils.jl") +include("utils.jl") ``` # Background {#sec-intro} -Over the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the tremendous success of deep neural networks, practitioners and researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other things, these pitfalls include a lack of adversarial robustness [@goodfellow2014explaining] and an inherent opaqueness of deep neural networks, often described as the black-box problem. +Over the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the tremendous success of deep neural networks, practitioners and researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other things, these pitfalls include a lack of adversarial robustness \cite{goodfellow2014explaining} and an inherent opaqueness of deep neural networks, often described as the black-box problem. -In deep learning, the number of parameters relative to the size of the available data is generally huge: - -> [...] deep neural networks are typically very underspecified by the available data, and [...] parameters [therefore] correspond to a diverse variety of compelling explanations for the data. -> @wilson2020case - -A scenario like this very much calls for treating model predictions probabilistically [@wilson2020case]. It is therefore not surprising that interest in Bayesian deep learning has grown in recent years as researchers have tackled the problem from a wide range of angles including MCMC (see [`Turing`](https://turing.ml/dev/tutorials/03-bayesian-neural-network/)), Mean Field Variational Inference [@blundell2015weight], Monte Carlo Dropout [@gal2016dropout] and Deep Ensembles [@lakshminarayanan2016simple]. Laplace Redux [@immer2020improving;@daxberger2021laplace] is one of the most recent and promising approaches to Bayesian neural networks (BNN). +In deep learning, the number of parameters relative to the size of the available data is generally huge \cite{wilson2020case}: "[...] deep neural networks are typically very underspecified by the available data, and [...] parameters [therefore] correspond to a diverse variety of compelling explanations for the data." A scenario like this very much calls for treating model predictions probabilistically \cite{wilson2020case}. It is therefore not surprising that interest in Bayesian deep learning has grown in recent years as researchers have tackled the problem from a wide range of angles including MCMC (see [`Turing`](https://turing.ml/dev/tutorials/03-bayesian-neural-network/)), Mean Field Variational Inference \cite{blundell2015weight}, Monte Carlo Dropout \cite{gal2016dropout} and Deep Ensembles \cite{lakshminarayanan2016simple}. Laplace Redux \cite{immer2020improving,daxberger2021laplace} is one of the most recent and promising approaches to Bayesian neural networks (BNN). # Laplace Approximation for Deep Learning {#sec-body} @@ -64,9 +60,9 @@ To do so we first need to compute the weight posterior $p(\theta|\mathcal{D})$. To apply Laplace in the context of deep learning, we can train our network in the standard way by minimizing the negative log-likelihood $\ell(\theta)=-\log p(y|x,\mathcal{D})$. To obtain Gaussian LA weight posterior we then only need to compute the Hessian evaluated at the obtained MAP estimate. -Laplace Approximation itself dates back to the 18th century, but despite its simplicity, it has not been widely used or studied by the deep learning community until recently. One reason for this may be that for large neural networks with many parameters, the exact Hessian computation is prohibitive. One can rely on linearized approximations of the Hessian, but those still scale quadratically in the number of parameters. Fortunately, recent work has shown that block-diagonal factorizations can be successfully applied in this context [@martens2015optimizing]. +Laplace Approximation itself dates back to the 18th century, but despite its simplicity, it has not been widely used or studied by the deep learning community until recently. One reason for this may be that for large neural networks with many parameters, the exact Hessian computation is prohibitive. One can rely on linearized approximations of the Hessian, but those still scale quadratically in the number of parameters. Fortunately, recent work has shown that block-diagonal factorizations can be successfully applied in this context \cite{martens2015optimizing}. -Another reason why LA may have been neglected in the past is that early attempts at using it for deep learning failed: simply sampling from the Laplace posterior to compute the exact BNN posterior predictive distribution in @eq-bma does not work when using approximations for the Hessian [@lawrence2001variational]. Instead, we can use a linear expansion of the predictive around the mode as demonstrated by @immer2020improving. Formally, we locally linearize our network, +Another reason why LA may have been neglected in the past is that early attempts at using it for deep learning failed: simply sampling from the Laplace posterior to compute the exact BNN posterior predictive distribution in @eq-bma does not work when using approximations for the Hessian \cite{lawrence2001variational}. Instead, we can use a linear expansion of the predictive around the mode as demonstrated by Immer et al. (2020) \cite{immer2020improving}. Formally, we locally linearize our network, $$ f^{\hat{\theta}}_{\mbox{lin}}(x;\theta)=f(x;\hat{\theta}) + \mathcal{J}_{\theta}(\theta-\hat{\theta}) @@ -78,9 +74,9 @@ $$ p(y|x,\mathcal{D}) = \mathbb{E} \left[ p(y|f^{\hat{\theta}}_{\mbox{lin}}(x;\theta_n)) \right], \ \ \ \theta_n \sim q(\theta) $$ {#eq-glm-predictive} -has a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation [@daxberger2021laplace]. +has a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation \cite{daxberger2021laplace}. -@immer2020improving provide a much more detailed exposition of the above with a focus on theoretical underpinnings and intuition. @daxberger2021laplace introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: [laplace](https://aleximmer.github.io/Laplace/). +Immer et al. (2020) \cite{immer2020improving} provide a much more detailed exposition of the above with a focus on theoretical underpinnings and intuition. Daxberger et al. (2021) \cite{daxberger2021laplace} introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: [laplace](https://aleximmer.github.io/Laplace/). # `LaplaceRedux.jl` --- a Julia implementation @@ -128,7 +124,7 @@ for epoch = 1:epochs end ``` -Using just a few lines of code the package enables users to compute and apply Laplace Redux to their pre-trained neural networks. A basic usage example is shown in listing \ref{lst:laplace}: the `Laplace` function simply wraps the Flux neural network `nn`. The returned instance is then fitted to data using the generic `fit!` method. Finally, the prior precision $\lambda$ is optimized through Empirical Bayes [@daxberger2021laplace]. Calling the generic `predict` method on the fitted instance will generate GLM predictions according to @eq-glm-predictive. +Using just a few lines of code the package enables users to compute and apply Laplace Redux to their pre-trained neural networks. A basic usage example is shown in listing \ref{lst:laplace}: the `Laplace` function simply wraps the Flux neural network `nn`. Since the underlying model is a classifier, we need to specify the likelihood accordingly. The returned instance is then fitted to the data using the generic `fit!` method. Note that the `fit!` method also accepts a `DataLoader` as its second positional argument and mini-batch training is supported. ```{julia} la = Laplace(nn; likelihood=:classification) @@ -137,10 +133,25 @@ la_untuned = deepcopy(la) # saving for plotting optimize_prior!(la) ``` -\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}] +\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:fit}, caption={}] la = Laplace(nn; likelihood=:classification) fit!(la, data) -optimize_prior!(la) +\end{lstlisting} + +The `la` object is a mutable and callable struct that wraps the pre-trained neural networks along with hyperparameters relevant to the Laplace approximation. Simply calling the instance with new data will generate GLM predictions according to @eq-glm-predictive. In the classification case, softmax outputs are returned by default following the convention in the Python implementation, but this can be changed using the `predict_proba` keyword argument. It is also possible to recover the original MAP estimate directly by setting the `link_approx` keyword argument to `:plugin`. + +\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:predict}, caption={}] +la(X) # GLM predictions +la(X; predict_proba=false) # no softmax +la(X; link_approx=:plugin) # MAP predictions +\end{lstlisting} + +Additional methods can be used to optimize the prior precision $\lambda$ and to visualize the predictive distribution. The `optimize_prior!` method optimizes the prior precision $\lambda$ through Empirical Bayes \cite{daxberger2021laplace}. The `plot` method visualizes the predictions of the fitted instance. It is provided through the [TaijaPlotting](https://github.com/JuliaTrustworthyAI/TaijaPlotting.jl) meta package. + +\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:other}, caption={}] +optimize_prior!(la) # optimize λ +using TaijaPlotting +plot(la, X, y) # plot predictions \end{lstlisting} ```{julia} @@ -206,12 +217,14 @@ savefig(plt_reg, joinpath(www_path,"regression.png")) ![Posterior predictive distribution for regressor: wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.](www/regression.png){#fig-reg width="20pc" height="10pc"} -# Discussion and Outlook {#sec-con} +# Additional Features {#sec-add} -At the time of writing, the package is still in its infancy and its functionality is limited. It currently lacks multi-class support and still works with full Hessian approximations, as opposed to the less expensive (block-) diagonal variants. That being said, choices regarding the package architecture were made with these future development opportunities in mind. This should hopefully make the package attractive to other Julia developers interested in the topic. -Laplace Redux is an exciting and promising recent development in Bayesian deep learning. The goal of this project is to bring this framework to the attention of the Julia machine-learning community. The package `LaplaceRedux.jl` offers a starting ground for a full-fledged implementation in pure Julia. Future developments are planned and contributions are very much welcome. + +# Discussion and Outlook {#sec-con} + +Laplace Redux is an exciting and promising recent development in Bayesian deep learning. The package `LaplaceRedux.jl` brings this framework to the Julia ecosystem. Future developments are planned and contributions are very much welcome. At the time of writing, we are particularly interested in streamlining the package's interface to the larger [Taija](https://github.com/JuliaTrustworthyAI) ecosystem and adding support for sample-based linearised Laplace to enhance scalability (see [here](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues/34) for details). # Acknowledgements {#sec-ack} -I am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. I am also grateful to the Julia community for being so kind, welcoming and helpful. \ No newline at end of file +I am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. Furthermore, I would like to thank the group of students who contributed to this package through a course project: Mark Ardman, Severin Bratus, Adelina Cazacu, Andrei Ionescu and Ivan Makarov. \ No newline at end of file diff --git a/paper/src/paper.tex b/paper/src/paper.tex index c0447606..8ea0129b 100644 --- a/paper/src/paper.tex +++ b/paper/src/paper.tex @@ -114,10 +114,6 @@ \usepackage{orcidlink} \definecolor{mypink}{RGB}{219, 48, 122} \makeatletter -\makeatother -\makeatletter -\makeatother -\makeatletter \@ifpackageloaded{caption}{}{\usepackage{caption}} \AtBeginDocument{% \ifdefined\contentsname @@ -153,25 +149,18 @@ \newcommand*\listoflistings{\listof{codelisting}{List of Listings}} \makeatother \makeatletter -\@ifpackageloaded{caption}{}{\usepackage{caption}} -\@ifpackageloaded{subcaption}{}{\usepackage{subcaption}} -\makeatother -\makeatletter -\@ifpackageloaded{tcolorbox}{}{\usepackage[skins,breakable]{tcolorbox}} -\makeatother -\makeatletter -\@ifundefined{shadecolor}{\definecolor{shadecolor}{rgb}{.97, .97, .97}} -\makeatother -\makeatletter \makeatother \makeatletter +\@ifpackageloaded{caption}{}{\usepackage{caption}} +\@ifpackageloaded{subcaption}{}{\usepackage{subcaption}} \makeatother \ifLuaTeX \usepackage{selnolig} % disable illegal ligatures \fi \usepackage[]{biblatex} \addbibresource{ref.bib} -\IfFileExists{bookmark.sty}{\usepackage{bookmark}}{\usepackage{hyperref}} +\usepackage{bookmark} + \IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available \urlstyle{same} % disable monospaced font for URLs \hypersetup{ @@ -203,7 +192,7 @@ trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace -approximation. We propose a small Julia package, +approximation. We propose a light-weight Julia package, \texttt{LaplaceRedux.jl} that implements this new approach for deep neural networks trained in \texttt{Flux.jl}. \end{abstract} @@ -224,11 +213,9 @@ \setcounter{page}{1} -\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[interior hidden, breakable, sharp corners, enhanced, boxrule=0pt, frame hidden, borderline west={3pt}{0pt}{shadecolor}]}{\end{tcolorbox}}\fi \setstretch{1} -\hypertarget{sec-intro}{% -\section{Background}\label{sec-intro}} +\section{Background}\label{sec-intro} Over the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the @@ -236,33 +223,27 @@ \section{Background}\label{sec-intro}} researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other things, these pitfalls include a lack of adversarial robustness -\autocite{goodfellow2014explaining} and an inherent opaqueness of deep +\cite{goodfellow2014explaining} and an inherent opaqueness of deep neural networks, often described as the black-box problem. In deep learning, the number of parameters relative to the size of the -available data is generally huge: - -\begin{quote} -{[}\ldots{]} deep neural networks are typically very underspecified by -the available data, and {[}\ldots{]} parameters {[}therefore{]} -correspond to a diverse variety of compelling explanations for the data. -\textcite{wilson2020case} -\end{quote} - -A scenario like this very much calls for treating model predictions -probabilistically \autocite{wilson2020case}. It is therefore not -surprising that interest in Bayesian deep learning has grown in recent -years as researchers have tackled the problem from a wide range of -angles including MCMC (see +available data is generally huge \cite{wilson2020case}: ``{[}\ldots{]} +deep neural networks are typically very underspecified by the available +data, and {[}\ldots{]} parameters {[}therefore{]} correspond to a +diverse variety of compelling explanations for the data.'' A scenario +like this very much calls for treating model predictions +probabilistically \cite{wilson2020case}. It is therefore not surprising +that interest in Bayesian deep learning has grown in recent years as +researchers have tackled the problem from a wide range of angles +including MCMC (see \href{https://turing.ml/dev/tutorials/03-bayesian-neural-network/}{\texttt{Turing}}), -Mean Field Variational Inference \autocite{blundell2015weight}, Monte -Carlo Dropout \autocite{gal2016dropout} and Deep Ensembles -\autocite{lakshminarayanan2016simple}. Laplace Redux -\autocite{immer2020improving,daxberger2021laplace} is one of the most -recent and promising approaches to Bayesian neural networks (BNN). +Mean Field Variational Inference \cite{blundell2015weight}, Monte Carlo +Dropout \cite{gal2016dropout} and Deep Ensembles +\cite{lakshminarayanan2016simple}. Laplace Redux +\cite{immer2020improving,daxberger2021laplace} is one of the most recent +and promising approaches to Bayesian neural networks (BNN). -\hypertarget{sec-body}{% -\section{Laplace Approximation for Deep Learning}\label{sec-body}} +\section{Laplace Approximation for Deep Learning}\label{sec-body} Let \(\mathcal{D}=\{x,y\}_{n=1}^N\) denote our feature-label pairs and let \(f(x;\theta)=y\) denote some deep neural network specified by its @@ -270,9 +251,9 @@ \section{Laplace Approximation for Deep Learning}\label{sec-body}} predictive distribution given by the following Bayesian model average (BMA): -\begin{equation}\protect\hypertarget{eq-bma}{}{ +\begin{equation}\phantomsection\label{eq-bma}{ p(y|x,\mathcal{D}) = \int p(y|x,\theta)p(\theta|\mathcal{D})d\theta -}\label{eq-bma}\end{equation} +}\end{equation} To do so we first need to compute the weight posterior \(p(\theta|\mathcal{D})\). Laplace Approximation (LA) relies on the fact @@ -298,42 +279,42 @@ \section{Laplace Approximation for Deep Learning}\label{sec-body}} the Hessian, but those still scale quadratically in the number of parameters. Fortunately, recent work has shown that block-diagonal factorizations can be successfully applied in this context -\autocite{martens2015optimizing}. +\cite{martens2015optimizing}. Another reason why LA may have been neglected in the past is that early attempts at using it for deep learning failed: simply sampling from the Laplace posterior to compute the exact BNN posterior predictive distribution in Equation~\ref{eq-bma} does not work when using -approximations for the Hessian \autocite{lawrence2001variational}. -Instead, we can use a linear expansion of the predictive around the mode -as demonstrated by \textcite{immer2020improving}. Formally, we locally -linearize our network, +approximations for the Hessian \cite{lawrence2001variational}. Instead, +we can use a linear expansion of the predictive around the mode as +demonstrated by Immer et al.~(2020) \cite{immer2020improving}. Formally, +we locally linearize our network, -\begin{equation}\protect\hypertarget{eq-glm}{}{ +\begin{equation}\phantomsection\label{eq-glm}{ f^{\hat{\theta}}_{\mbox{lin}}(x;\theta)=f(x;\hat{\theta}) + \mathcal{J}_{\theta}(\theta-\hat{\theta}) -}\label{eq-glm}\end{equation} +}\end{equation} which turns the BNN into a Bayesian generalized linear model (GLM) where \(\hat{\theta}\) corresponds to the MAP estimate as before. The corresponding GLM predictive, -\begin{equation}\protect\hypertarget{eq-glm-predictive}{}{ +\begin{equation}\phantomsection\label{eq-glm-predictive}{ p(y|x,\mathcal{D}) = \mathbb{E} \left[ p(y|f^{\hat{\theta}}_{\mbox{lin}}(x;\theta_n)) \right], \ \ \ \theta_n \sim q(\theta) -}\label{eq-glm-predictive}\end{equation} +}\end{equation} has a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation -\autocite{daxberger2021laplace}. +\cite{daxberger2021laplace}. -\textcite{immer2020improving} provide a much more detailed exposition of -the above with a focus on theoretical underpinnings and intuition. -\textcite{daxberger2021laplace} introduce Laplace Redux from more of an +Immer et al.~(2020) \cite{immer2020improving} provide a much more +detailed exposition of the above with a focus on theoretical +underpinnings and intuition. Daxberger et al.~(2021) +\cite{daxberger2021laplace} introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: \href{https://aleximmer.github.io/Laplace/}{laplace}. -\hypertarget{laplaceredux.jl-a-julia-implementation}{% \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia -implementation}{LaplaceRedux.jl --- a Julia implementation}}\label{laplaceredux.jl-a-julia-implementation}} +implementation}{LaplaceRedux.jl --- a Julia implementation}}\label{laplaceredux.jl-a-julia-implementation} The \texttt{LaplaceRedux.jl} package is intended to make this new methodological framework available to the Julia community. It is @@ -343,17 +324,47 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia Using just a few lines of code the package enables users to compute and apply Laplace Redux to their pre-trained neural networks. A basic usage example is shown in listing \ref{lst:laplace}: the \texttt{Laplace} -function simply wraps the Flux neural network \texttt{nn}. The returned -instance is then fitted to data using the generic \texttt{fit!} method. -Finally, the prior precision \(\lambda\) is optimized through Empirical -Bayes \autocite{daxberger2021laplace}. Calling the generic -\texttt{predict} method on the fitted instance will generate GLM -predictions according to Equation~\ref{eq-glm-predictive}. +function simply wraps the Flux neural network \texttt{nn}. Since the +underlying model is a classifier, we need to specify the likelihood +accordingly. The returned instance is then fitted to the data using the +generic \texttt{fit!} method. Note that the \texttt{fit!} method also +accepts a \texttt{DataLoader} as its second positional argument and +mini-batch training is supported. \begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}] la = Laplace(nn; likelihood=:classification) fit!(la, data) -optimize_prior!(la) +\end{lstlisting} + +The \texttt{la} object is a mutable and callable struct that wraps the +pre-trained neural networks along with hyperparameters relevant to the +Laplace approximation. Simply calling the instance with new data will +generate GLM predictions according to Equation~\ref{eq-glm-predictive}. +In the classification case, softmax outputs are returned by default +following the convention in the Python implementation, but this can be +changed using the \texttt{predict\_proba} keyword argument. It is also +possible to recover the original MAP estimate directly by setting the +\texttt{link\_approx} keyword argument to \texttt{:plugin}. + +\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}] +la(X) # GLM predictions +la(X; predict_proba=false) # no softmax +la(X; link_approx=:plugin) # MAP predictions +\end{lstlisting} + +Additional methods can be used to optimize the prior precision +\(\lambda\) and to visualize the predictive distribution. The +\texttt{optimize\_prior!} method optimizes the prior precision +\(\lambda\) through Empirical Bayes \cite{daxberger2021laplace}. The +\texttt{plot} method visualizes the predictions of the fitted instance. +It is provided through the +\href{https://github.com/JuliaTrustworthyAI/TaijaPlotting.jl}{TaijaPlotting} +meta package. + +\begin{lstlisting}[language=Julia, escapechar=@, numbers=left, label={lst:laplace}, caption={}] +optimize_prior!(la) # optimize λ +using TaijaPlotting +plot(la, X, y) # plot predictions \end{lstlisting} Figure~\ref{fig-class} shows an example involving a synthetic data set @@ -376,7 +387,9 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia \begin{figure} -{\centering \includegraphics[width=3.33333in,height=1.11667in]{www/posterior_predictive_mlp.png} +\centering{ + +\includegraphics[width=3.33333in,height=1.11667in]{www/posterior_predictive_mlp.png} } @@ -386,11 +399,13 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.} -\end{figure} +\end{figure}% \begin{figure} -{\centering \includegraphics[width=3.33333in,height=1.66667in]{www/regression.png} +\centering{ + +\includegraphics[width=3.33333in,height=1.66667in]{www/regression.png} } @@ -398,33 +413,30 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.} -\end{figure} +\end{figure}% -\hypertarget{sec-con}{% -\section{Discussion and Outlook}\label{sec-con}} +\section{Additional Features}\label{sec-add} -At the time of writing, the package is still in its infancy and its -functionality is limited. It currently lacks multi-class support and -still works with full Hessian approximations, as opposed to the less -expensive (block-) diagonal variants. That being said, choices regarding -the package architecture were made with these future development -opportunities in mind. This should hopefully make the package attractive -to other Julia developers interested in the topic. +\section{Discussion and Outlook}\label{sec-con} Laplace Redux is an exciting and promising recent development in -Bayesian deep learning. The goal of this project is to bring this -framework to the attention of the Julia machine-learning community. The -package \texttt{LaplaceRedux.jl} offers a starting ground for a -full-fledged implementation in pure Julia. Future developments are -planned and contributions are very much welcome. - -\hypertarget{sec-ack}{% -\section{Acknowledgements}\label{sec-ack}} +Bayesian deep learning. The package \texttt{LaplaceRedux.jl} brings this +framework to the Julia ecosystem. Future developments are planned and +contributions are very much welcome. At the time of writing, we are +particularly interested in streamlining the package's interface to the +larger \href{https://github.com/JuliaTrustworthyAI}{Taija} ecosystem and +adding support for sample-based linearised Laplace to enhance +scalability (see +\href{https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues/34}{here} +for details). + +\section{Acknowledgements}\label{sec-ack} I am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. -I am also grateful to the Julia community for being so kind, welcoming -and helpful. +Furthermore, I would like to thank the group of students who contributed +to this package through a course project: Mark Ardman, Severin Bratus, +Adelina Cazacu, Andrei Ionescu and Ivan Makarov. \printbibliography diff --git a/paper/src/utils.jl b/paper/src/utils.jl new file mode 100644 index 00000000..aba53a63 --- /dev/null +++ b/paper/src/utils.jl @@ -0,0 +1,97 @@ +# Plot data points: +using Plots +""" + plot_data!(plt,X,y) + +# Examples + +```julia-repl +using LaplaceRedux, Plots +X, y = toy_data_linear(100) +plt = plot() +plot_data!(plt, hcat(X...)', y) +``` + +""" +function plot_data!(plt, X, y) + Plots.scatter!(plt, X[y .== 1.0, 1], X[y .== 1.0, 2]; color=1, clim=(0, 1), label="y=1") + return Plots.scatter!( + plt, X[y .== 0.0, 1], X[y .== 0.0, 2]; color=0, clim=(0, 1), label="y=0" + ) +end + +# Plot contour of posterior predictive: +using Plots +""" + plot_contour(X,y,𝑴;clegend=true,title="",length_out=50,type=:laplace,zoom=0,xlim=nothing,ylim=nothing) + +Generates a contour plot for the posterior predictive surface. + +# Examples + +```julia-repl +using LaplaceRedux, Plots +import LaplaceRedux: predict +using NNlib: σ +X, y = toy_data_linear(100) +X = hcat(X...)' +β = [1,1] +𝑴 =(β=β,) +predict(𝑴, X) = σ.(𝑴.β' * X) +plot_contour(X, y, 𝑴) +``` + +""" +function plot_contour( + X, + y, + 𝑴; + clegend=true, + title="", + length_out=50, + type=:laplace, + zoom=0, + xlim=nothing, + ylim=nothing, +) + + # Surface range: + if isnothing(xlim) + xlim = (minimum(X[:, 1]), maximum(X[:, 1])) .+ (zoom, -zoom) + else + xlim = xlim .+ (zoom, -zoom) + end + if isnothing(ylim) + ylim = (minimum(X[:, 2]), maximum(X[:, 2])) .+ (zoom, -zoom) + else + ylim = ylim .+ (zoom, -zoom) + end + x_range = collect(range(xlim[1]; stop=xlim[2], length=length_out)) + y_range = collect(range(ylim[1]; stop=ylim[2], length=length_out)) + + # Predictions: + if type == :laplace + Z = [predict(𝑴, [x, y])[1] for x in x_range, y in y_range] + else + Z = [plugin(𝑴, [x, y])[1] for x in x_range, y in y_range] + end + + # Plot: + plt = contourf( + x_range, + y_range, + Z'; + legend=clegend, + title=title, + linewidth=0, + clims=(0.0, 1.0), + xlim=xlim, + ylim=ylim, + ) + return plot_data!(plt, X, y) +end + +# Helper function to predict from network trained for binary classification and producing logits as output: +import LaplaceRedux: predict +using Flux +predict(𝑴::Flux.Chain, X::AbstractArray) = Flux.σ.(𝑴(X)) diff --git a/paper/www/posterior_predictive_mlp.png b/paper/www/posterior_predictive_mlp.png index a5744565695e9c39bce15eca1f32f2ddd4dbc86b..010e5f991afc2b9045729313132593ad2b63309e 100644 GIT binary patch literal 230141 zcmYgXWmJ_xw|+rDT2dMbrMp48l`kdW@~?vgHPK|nx4x)JGaxWl*Zx9&au z&_$egX7=7s%`sF_{v8@JAuQ7|-^*|_R8^aM?A%^p6JXzYoev=+Bf?V9(NRdaN$AbYxZ$D{H+{WT 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These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a small Julia package, \texttt{LaplaceRedux.jl} that implements this new approach for deep neural networks trained in \texttt{Flux.jl}. +Treating deep neural networks probabilistically comes with numerous advantages including improved robustness and greater interpretability. These factors are key to building Artificial Intelligence (AI) that is trustworthy. A drawback commonly associated with existing Bayesian methods is that they increase computational costs. Recent work has shown that Bayesian deep learning can be effortless through Laplace approximation. We propose a light-weight Julia package, \texttt{LaplaceRedux.jl} that implements this new approach for deep neural networks trained in \texttt{Flux.jl}. \end{abstract} -\hypertarget{sec-intro}{% -\section{Background}\label{sec-intro}} +\section{Background}\label{sec-intro} Over the past decade, Deep Learning (DL) has arguably been one of the dominating subdisciplines of Artificial Intelligence. Despite the tremendous success of deep neural networks, practitioners and researchers have also pointed to a vast number of pitfalls that have so far inhibited the use of DL in safety-critical applications. Among other -things these pitfalls include a lack of adversarial robustness +things, these pitfalls include a lack of adversarial robustness \cite{goodfellow2014explaining} and an inherent opaqueness of deep neural networks, often described as the black-box problem. In deep learning, the number of parameters relative to the size of the -available data is generally huge: - -\begin{quote} -{[}\ldots{]} deep neural networks are typically very underspecified by -the available data, and {[}\ldots{]} parameters {[}therefore{]} -correspond to a diverse variety of compelling explanations for the data. -\cite{wilson2020case} -\end{quote} - -A scenario like this very much calls for treating model predictions +available data is generally huge \cite{wilson2020case}: ``{[}\ldots{]} +deep neural networks are typically very underspecified by the available +data, and {[}\ldots{]} parameters {[}therefore{]} correspond to a +diverse variety of compelling explanations for the data.'' A scenario +like this very much calls for treating model predictions probabilistically \cite{wilson2020case}. It is therefore not surprising that interest in Bayesian deep learning has grown in recent years as researchers have tackled the problem from a wide range of angles @@ -46,11 +40,10 @@ \section{Background}\label{sec-intro}} Mean Field Variational Inference \cite{blundell2015weight}, Monte Carlo Dropout \cite{gal2016dropout} and Deep Ensembles \cite{lakshminarayanan2016simple}. Laplace Redux -(\cite{immer2020improving},\cite{daxberger2021laplace}) is one of the -most recent and promising approaches to Bayesian neural networks (BNN). +\cite{immer2020improving,daxberger2021laplace} is one of the most recent +and promising approaches to Bayesian neural networks (BNN). -\hypertarget{sec-body}{% -\section{Laplace Approximation for Deep Learning}\label{sec-body}} +\section{Laplace Approximation for Deep Learning}\label{sec-body} Let \(\mathcal{D}=\{x,y\}_{n=1}^N\) denote our feature-label pairs and let \(f(x;\theta)=y\) denote some deep neural network specified by its @@ -58,9 +51,9 @@ \section{Laplace Approximation for Deep Learning}\label{sec-body}} predictive distribution given by the following Bayesian model average (BMA): -\begin{equation}\protect\hypertarget{eq-bma}{}{ +\begin{equation}\phantomsection\label{eq-bma}{ p(y|x,\mathcal{D}) = \int p(y|x,\theta)p(\theta|\mathcal{D})d\theta -}\label{eq-bma}\end{equation} +}\end{equation} To do so we first need to compute the weight posterior \(p(\theta|\mathcal{D})\). Laplace Approximation (LA) relies on the fact @@ -94,35 +87,33 @@ \section{Laplace Approximation for Deep Learning}\label{sec-body}} distribution in Equation~\ref{eq-bma} does not work when using approximations for the Hessian \cite{lawrence2001variational}. Instead, we can use a linear expansion of the predictive around the mode as -demonstrated by Immer et al.~(2020) \cite{immer2020improving}. Formally, -we locally linearize our network, +demonstrated by \cite{immer2020improving}. Formally, we locally +linearize our network, -\begin{equation}\protect\hypertarget{eq-glm}{}{ +\begin{equation}\phantomsection\label{eq-glm}{ f^{\hat{\theta}}_{\mbox{lin}}(x;\theta)=f(x;\hat{\theta}) + \mathcal{J}_{\theta}(\theta-\hat{\theta}) -}\label{eq-glm}\end{equation} +}\end{equation} which turns the BNN into a Bayesian generalized linear model (GLM) where \(\hat{\theta}\) corresponds to the MAP estimate as before. The corresponding GLM predictive, -\begin{equation}\protect\hypertarget{eq-glm-predictive}{}{ +\begin{equation}\phantomsection\label{eq-glm-predictive}{ p(y|x,\mathcal{D}) = \mathbb{E} \left[ p(y|f^{\hat{\theta}}_{\mbox{lin}}(x;\theta_n)) \right], \ \ \ \theta_n \sim q(\theta) -}\label{eq-glm-predictive}\end{equation} +}\end{equation} has a closed-form solution for regression problems. For classification problems it can be approximated using (extended) probit approximation \cite{daxberger2021laplace}. -Immer et al.~(2020) \cite{immer2020improving} provide a much more -detailed exposition of the above with a focus on theoretical -underpinnings and intuition. Daxberger et el. (2021) +\cite{immer2020improving} provide a much more detailed exposition of the +above with a focus on theoretical underpinnings and intuition. \cite{daxberger2021laplace} introduce Laplace Redux from more of an applied perspective and present a comprehensive Python implementation: \href{https://aleximmer.github.io/Laplace/}{laplace}. -\hypertarget{laplaceredux.jl-a-julia-implementation}{% \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia -implementation}{LaplaceRedux.jl --- a Julia implementation}}\label{laplaceredux.jl-a-julia-implementation}} +implementation}{LaplaceRedux.jl --- a Julia implementation}}\label{laplaceredux.jl-a-julia-implementation} The \texttt{LaplaceRedux.jl} package is intended to make this new methodological framework available to the Julia community. It is @@ -165,7 +156,9 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia \begin{figure} -{\centering \includegraphics[width=3.33333in,height=1.11667in]{www/posterior_predictive_mlp.png} +\centering{ + +\includegraphics[width=3.33333in,height=1.11667in]{www/posterior_predictive_mlp.png} } @@ -175,11 +168,13 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.} -\end{figure} +\end{figure}% \begin{figure} -{\centering \includegraphics[width=3.33333in,height=1.66667in]{www/regression.png} +\centering{ + +\includegraphics[width=3.33333in,height=1.66667in]{www/regression.png} } @@ -187,27 +182,18 @@ \section{\texorpdfstring{\texttt{LaplaceRedux.jl} --- a Julia wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.} -\end{figure} - -\hypertarget{sec-con}{% -\section{Discussion and Outlook}\label{sec-con}} +\end{figure}% -At the time of writing, the package is still in its infancy and its -functionality is limited. It currently lacks multi-class support and -still works with full Hessian approximations, as opposed to the less -expensive (block-) diagonal variants. That being said, choices regarding -the package architecture were made with these future development -opportunities in mind. This should hopefully make the package attractive -to other Julia developers interested in the topic. +\section{Discussion and Outlook}\label{sec-con} -Laplace Redux is an exciting and promising recent development in Bayesian deep learning. The goal of this project is to -bring this framework to the attention of the Julia machine-learning -community. The package \texttt{LaplaceRedux.jl} offers a starting ground -for a full-fledged implementation in pure Julia. Future developments are +Laplace Redux is an exciting and promising recent development in +Bayesian deep learning. The goal of this project is to bring this +framework to the attention of the Julia machine-learning community. The +package \texttt{LaplaceRedux.jl} offers a solid starting ground for a +full-fledged implementation in pure Julia. Future developments are planned and contributions are very much welcome. -\hypertarget{sec-ack}{% -\section{Acknowledgements}\label{sec-ack}} +\section{Acknowledgements}\label{sec-ack} I am grateful to my PhD supervisors Cynthia C. S. Liem and Arie van Deursen for being so supportive of my work on open-source developments. diff --git a/paper/src/jlcode.sty b/paper/src/jlcode.sty old mode 100755 new mode 100644 diff --git a/paper/src/journal_dat.tex b/paper/src/journal_dat.tex old mode 100755 new mode 100644 index 0592d527..ccf8e467 --- a/paper/src/journal_dat.tex +++ b/paper/src/journal_dat.tex @@ -3,4 +3,4 @@ \def\@journalName{Proceedings of JuliaCon} \def\@volume{1} \def\@issue{1} -\def\@year{2023} +\def\@year{2022} diff --git a/paper/src/juliacon.bst b/paper/src/juliacon.bst old mode 100755 new mode 100644 diff --git a/paper/src/juliacon.cls b/paper/src/juliacon.cls old mode 100755 new mode 100644 diff --git a/paper/src/logojuliacon.pdf b/paper/src/logojuliacon.pdf old mode 100755 new mode 100644 diff --git a/paper/src/paper.fdb_latexmk b/paper/src/paper.fdb_latexmk index f1464e6e..32dc31a2 100644 --- a/paper/src/paper.fdb_latexmk +++ b/paper/src/paper.fdb_latexmk @@ -1,12 +1,12 @@ # Fdb version 4 -["biber 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