If the benchmark is not yet completed (still running or has some failed jobs, e.g. BrokenPipe Error due to multiprocessing in PIL image reading), the If the benchmark is not yet completed (still running or has some failed jobs, e.g. Out of Memory, BrokenPipe Error due to multiprocessing in PIL image reading), the The user can then obtain the aggregated partial results with plots from the partially completed benchmark by running
the following after cd into the DomainLab directory: Alternatively, one could use clean up the extra csv head generated and plot the csv using command below where rule_results is the subfolder that contains partially finished csv result files. This script will partially aggregate the csv files in a faster fashion, output latex table which summarizes the results named “output_table_perf.tex” (which also contains a text format table before the latex table), and at the end generate plots using the following functionality. Barber, David, and Felix Agakov. “The im algorithm: a variational approach to information maximization.” Advances in neural information processing systems 16, no. 320 (2004): 201. Given variational distribution of \(q(x|y)\) as decoder (i.e. \(Y\) encodes information from \(X\)) Since We have Then returns a logger
if no logger was created yet, it will create a logger with the name
specified in logger_name with the level specified in loglevel.
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index b2b66b4e9..37f6cd99b 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 946506349..827ea8dd1 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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"trainer-fishr"]], "Invariant Gradient Variances for Out-of-distribution Generalization": [[1, "invariant-gradient-variances-for-out-of-distribution-generalization"]], "Quantifying inconsistency between domains": [[1, "quantifying-inconsistency-between-domains"]], "Matching the Variances during training": [[1, "matching-the-variances-during-training"]], "Implementation": [[1, "implementation"]], "Model HDUVA": [[2, "model-hduva"]], "HDUVA: HIERARCHICAL VARIATIONAL AUTO-ENCODING FOR UNSUPERVISED DOMAIN GENERALIZATION": [[2, "hduva-hierarchical-variational-auto-encoding-for-unsupervised-domain-generalization"]], "Model Overview": [[2, "model-overview"]], "Evidence lower bound and overall loss": [[2, "evidence-lower-bound-and-overall-loss"]], "Hyperparameters loss function": [[2, "hyperparameters-loss-function"]], "Model hyperparameters": [[2, "model-hyperparameters"]], "Hyperparameter for warmup": [[2, "hyperparameter-for-warmup"]], "hduva use custom net for sandwich encoder": [[2, "hduva-use-custom-net-for-sandwich-encoder"]], "hduva use custom net for topic encoder": [[2, "hduva-use-custom-net-for-topic-encoder"]], "hduva use custom net for classification encoder": [[2, "hduva-use-custom-net-for-classification-encoder"]], "use hduva on color mnist, train on 2 domains": [[2, "use-hduva-on-color-mnist-train-on-2-domains"]], "hduva is domain-unsupervised, so it works also with a single domain": [[2, "hduva-is-domain-unsupervised-so-it-works-also-with-a-single-domain"]], "hduva with implemented neural network": [[2, "hduva-with-implemented-neural-network"]], "hduva use alex net": [[2, "hduva-use-alex-net"]], "Trainer IRM": [[3, "trainer-irm"]], "Invariant Risk Minimization": [[3, "invariant-risk-minimization"]], "IRMv1": [[3, "irmv1"]], "Model JiGen": [[4, "model-jigen"]], "Model parameters": [[4, "model-parameters"], [15, "model-parameters"]], "model jigen with implemented neural network": [[4, "model-jigen-with-implemented-neural-network"]], "sannity check with 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"module-domainlab.utils.utils_img_sav"]], "Welcome to domainlab\u2019s documentation!": [[43, "welcome-to-domainlab-s-documentation"]], "Contents:": [[43, null]], "Indices and tables": [[43, "indices-and-tables"]], "domainlab": [[44, "domainlab"]], "Introduction": [[45, "introduction"]], "DomainLab: modular python package for training domain invariant neural networks": [[45, "domainlab-modular-python-package-for-training-domain-invariant-neural-networks"]], "Distribution shifts, domain generalization and DomainLab": [[45, "distribution-shifts-domain-generalization-and-domainlab"]], "DomainLab": [[45, "domainlab"]], "Getting started": [[45, "getting-started"]], "Installation": [[45, "installation"]], "Task specification": [[45, "task-specification"]], "Example and usage": [[45, "example-and-usage"]], "Available arguments for commandline": [[45, "available-arguments-for-commandline"]], "Command line configuration file": [[45, "command-line-configuration-file"]], "or Programm against DomainLab API": [[45, "or-programm-against-domainlab-api"]], "Benchmark different methods": [[45, "benchmark-different-methods"]], "Citation": [[45, "citation"]], "recommonmark==0.5.0.dev0": [[46, "recommonmark-0-5-0-dev0"]]}, "indexentries": {"parsevaluesorkeyvaluepairs (class in domainlab.arg_parser)": [[26, "domainlab.arg_parser.ParseValuesOrKeyValuePairs"]], "apply_dict_to_args() (in module domainlab.arg_parser)": [[26, "domainlab.arg_parser.apply_dict_to_args"]], "domainlab": [[26, "module-domainlab"]], "domainlab.arg_parser": [[26, "module-domainlab.arg_parser"]], "domainlab.cli": [[26, "module-domainlab.cli"]], "domainlab.mk_exp": [[26, "module-domainlab.mk_exp"]], "domainlab_cli() (in module domainlab.cli)": [[26, "domainlab.cli.domainlab_cli"]], "g_list_model_penalized_reg_agg() (in module domainlab)": [[26, "domainlab.g_list_model_penalized_reg_agg"]], "mk_exp() (in module domainlab.mk_exp)": [[26, "domainlab.mk_exp.mk_exp"]], "mk_parser_main() (in module 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\ No newline at end of file
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"modular": 45, "shift": 45, "get": 45, "start": 45, "avail": 45, "commandlin": 45, "programm": 45, "against": 45, "differ": 45, "citat": 45, "recommonmark": 46, "0": 46, "5": 46, "dev0": 46}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "sphinx.ext.todo": 2, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"Trainer DIAL": [[0, "trainer-dial"]], "Domain Invariant Adversarial Learning": [[0, "domain-invariant-adversarial-learning"]], "generating the adversarial domain": [[0, "generating-the-adversarial-domain"]], "network structure": [[0, "network-structure"]], "loss function and gradient reversal layer": [[0, "loss-function-and-gradient-reversal-layer"]], "Examples": [[0, "examples"], [1, "examples"], [2, "examples"], [3, "examples"], [4, "examples"], [6, "examples"], [12, "examples"], [15, "examples"], [20, "examples"], [23, "examples"]], "Train DIVA model with DIAL trainer": [[0, "train-diva-model-with-dial-trainer"]], "Set hyper-parameters for trainer as well": [[0, "set-hyper-parameters-for-trainer-as-well"]], "Trainer Fishr": [[1, "trainer-fishr"]], "Invariant Gradient Variances for Out-of-distribution Generalization": [[1, "invariant-gradient-variances-for-out-of-distribution-generalization"]], "Quantifying inconsistency between domains": [[1, "quantifying-inconsistency-between-domains"]], "Matching the Variances during training": [[1, "matching-the-variances-during-training"]], "Implementation": [[1, "implementation"]], "Model HDUVA": [[2, "model-hduva"]], "HDUVA: HIERARCHICAL VARIATIONAL AUTO-ENCODING FOR UNSUPERVISED DOMAIN GENERALIZATION": [[2, "hduva-hierarchical-variational-auto-encoding-for-unsupervised-domain-generalization"]], "Model Overview": [[2, "model-overview"]], "Evidence lower bound and overall loss": [[2, "evidence-lower-bound-and-overall-loss"]], "Hyperparameters loss function": [[2, "hyperparameters-loss-function"]], "Model hyperparameters": [[2, "model-hyperparameters"]], "Hyperparameter for warmup": [[2, "hyperparameter-for-warmup"]], "hduva use custom net for sandwich encoder": [[2, "hduva-use-custom-net-for-sandwich-encoder"]], "hduva use custom net for topic encoder": [[2, "hduva-use-custom-net-for-topic-encoder"]], "hduva use custom net for classification encoder": [[2, "hduva-use-custom-net-for-classification-encoder"]], "use hduva on color mnist, train on 2 domains": [[2, "use-hduva-on-color-mnist-train-on-2-domains"]], "hduva is domain-unsupervised, so it works also with a single domain": [[2, "hduva-is-domain-unsupervised-so-it-works-also-with-a-single-domain"]], "hduva with implemented neural network": [[2, "hduva-with-implemented-neural-network"]], "hduva use alex net": [[2, "hduva-use-alex-net"]], "Trainer 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with matchdg": [[6, "training-implemented-neural-network-with-matchdg"]], "trainer matchdg with mnist": [[6, "trainer-matchdg-with-mnist"]], "colored MNIST classification": [[7, "colored-mnist-classification"]], "colored MNIST dataset": [[7, "colored-mnist-dataset"]], "domain generalisation on colored MNIST": [[7, "domain-generalisation-on-colored-mnist"]], "erm (Emperical Risk Minimization)": [[7, "erm-emperical-risk-minimization"]], "diva": [[7, "diva"]], "Results": [[7, "results"]], "Detailed prompt explanation": [[7, "detailed-prompt-explanation"]], "Benchmarking with DomainLab": [[8, "benchmarking-with-domainlab"]], "Dependencies installation": [[8, "dependencies-installation"]], "Unix installation": [[8, "unix-installation"]], "Windows installation details": [[8, "windows-installation-details"]], "Setting up a benchmark": [[8, "setting-up-a-benchmark"]], "Running a benchmark": [[8, "running-a-benchmark"]], "Benchmark on a standalone machine/computation node (with or without GPU)": [[8, "benchmark-on-a-standalone-machine-computation-node-with-or-without-gpu"]], "Benchmark on a HPC cluster with slurm": [[8, "benchmark-on-a-hpc-cluster-with-slurm"]], "Check errors for slurm runs": [[8, "check-errors-for-slurm-runs"]], "Map between slurm job id and sampled hyperparameter index": [[8, "map-between-slurm-job-id-and-sampled-hyperparameter-index"]], "Obtained results": [[8, "obtained-results"]], "Output folder structure": [[8, "output-folder-structure"]], "Aggregate obtained partial results": [[8, "aggregate-obtained-partial-results"]], "Generate plots from .csv file": [[8, "generate-plots-from-csv-file"]], "Further explanations to Benchmark Setup": [[9, "further-explanations-to-benchmark-setup"]], "Hyperparameter sampling": [[9, "hyperparameter-sampling"]], "Constraints": [[9, "constraints"]], "Let DomainLab know where your PACS data were located": [[10, "let-domainlab-know-where-your-pacs-data-were-located"]], "Benchmark yaml files": [[11, "benchmark-yaml-files"]], "General setup of the yaml file": [[11, "general-setup-of-the-yaml-file"]], "Sampling description": [[11, "sampling-description"]], "uniform and loguniform distribution": [[11, "uniform-and-loguniform-distribution"]], "normal and lognormal distribution": [[11, "normal-and-lognormal-distribution"]], "cathegorical hyperparameters": [[11, "cathegorical-hyperparameters"]], "Referenced hperparameters": [[11, "referenced-hperparameters"]], "Special Arguments in the Sampling description": [[11, "special-arguments-in-the-sampling-description"]], "Combination of Shared and Task Specific Hyperparameter Samples": [[11, "combination-of-shared-and-task-specific-hyperparameter-samples"]], "Trainer CORAL": [[12, "trainer-coral"]], "Deep CORAL: Correlation Alignment for Deep Domain Adaptation": [[12, "deep-coral-correlation-alignment-for-deep-domain-adaptation"]], "Specify neural network in command line": [[13, "specify-neural-network-in-command-line"]], "Example use case": [[13, "example-use-case"]], "model \u2018erm\u2019 with custom neural network": [[13, "model-erm-with-custom-neural-network"]], "trainer \u2018matchdg\u2019 with custom neural network": [[13, "trainer-matchdg-with-custom-neural-network"]], "model erm with custom neural network": [[13, "id1"]], "Larger images:": [[13, "larger-images"]], "model erm with implemented neural network": [[13, "model-erm-with-implemented-neural-network"]], "model dann with implemented neural network": [[13, "model-dann-with-implemented-neural-network"]], "Custom algorithm defined in external python file": [[13, "custom-algorithm-defined-in-external-python-file"]], "Model DANN": [[14, "model-dann"]], "Domain Adversarial Neural Network": [[14, "domain-adversarial-neural-network"]], "Model DIVA": [[15, "model-diva"]], "Domain Invariant Variational Autoencoders": [[15, "domain-invariant-variational-autoencoders"]], "model diva with implemented neural network": [[15, "model-diva-with-implemented-neural-network"]], "model diva with custom neural network": [[15, "model-diva-with-custom-neural-network"]], "generation of images": [[15, "generation-of-images"]], "Colored version of MNIST": [[15, "colored-version-of-mnist"]], "leave one domain out": [[15, "leave-one-domain-out"]], "choose train and test": [[15, "choose-train-and-test"]], "Software Architecture and Design": [[16, "software-architecture-and-design"]], "Code structure": [[16, "code-structure"]], "Installation of DomainLab": [[17, "installation-of-domainlab"]], "Create a virtual environment for DomainLab (strongly recommended)": [[17, "create-a-virtual-environment-for-domainlab-strongly-recommended"]], "Install Development version via github": [[17, "install-development-version-via-github"]], "Dependencies management": [[17, "dependencies-management"]], "Install Release": [[17, "install-release"]], "Trainer Causal IRL": [[18, "trainer-causal-irl"]], "Invariant Causal Mechanisms through Distribution Matching": [[18, "invariant-causal-mechanisms-through-distribution-matching"]], "Example": [[18, "example"], [25, "example"]], "MIRO: Mutual-Information Regularization": [[19, "miro-mutual-information-regularization"]], "Mutual Information Regularization with Oracle (MIRO).": [[19, "mutual-information-regularization-with-oracle-miro"]], "Pre-requisite: Variational lower bound on mutual information": [[19, "pre-requisite-variational-lower-bound-on-mutual-information"]], "Laplace approximation": [[19, "laplace-approximation"]], "Linear Gaussian": [[19, "linear-gaussian"]], "MIRO": [[19, "miro"]], "Trainer MLDG": [[20, "trainer-mldg"]], "Meta Learning Domain Generalization": [[20, "meta-learning-domain-generalization"]], "Model Specification": [[21, "model-specification"], [24, "model-specification"]], "Option 1: Extend the Custom Model Class": [[21, "option-1-extend-the-custom-model-class"]], "Option 2: Add alongside existing models": [[21, "option-2-add-alongside-existing-models"]], "Step 1: Implement Required Abstract Methods": [[21, "step-1-implement-required-abstract-methods"]], "Step 2: Add Additional Arguments": [[21, "step-2-add-additional-arguments"]], "Step 3: Create a Builder": [[21, "step-3-create-a-builder"]], "Output structure": [[22, "output-structure"]], "Task Specification": [[23, "task-specification"]], "Possibility 1: Specify train and test domain dataset directly": [[23, "possibility-1-specify-train-and-test-domain-dataset-directly"]], "Possibility 2: TaskPathList": [[23, "possibility-2-taskpathlist"]], "Possibility 3: TaskFolder": [[23, "possibility-3-taskfolder"]], "Data organization": [[23, "data-organization"]], "Specify the task with domainlab API": [[23, "specify-the-task-with-domainlab-api"]], "User defined tasks": [[23, "user-defined-tasks"]], "Folder Task": [[23, "folder-task"]], "Folder Task with implemented neural network": [[23, "folder-task-with-implemented-neural-network"]], "Folder Task with externally user defined neural network": [[23, "folder-task-with-externally-user-defined-neural-network"]], "ImagePath Task": [[23, "imagepath-task"]], "ImagePath Task with implemented algorithm": [[23, "imagepath-task-with-implemented-algorithm"]], "ImagePath Task with externally user defined neural network": [[23, "imagepath-task-with-externally-user-defined-neural-network"]], "make a sanity check for the dataset using 8 instances from each domain and from each class": [[23, "make-a-sanity-check-for-the-dataset-using-8-instances-from-each-domain-and-from-each-class"]], "sanity check on only 2 train domains and 2 test domain2": [[23, "sanity-check-on-only-2-train-domains-and-2-test-domain2"]], "Steps to Extend TrainerBasic": [[24, "steps-to-extend-trainerbasic"]], "Example Implementation": [[24, "example-implementation"]], "DomainLab Usage Guide": [[25, "domainlab-usage-guide"]], "Essential Commands": [[25, "essential-commands"]], "Example Command": [[25, "example-command"]], "Optional Commands": [[25, "optional-commands"]], 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"module-domainlab.utils.get_git_tag"]], "domainlab.utils.hyperparameter_gridsearch module": [[42, "module-domainlab.utils.hyperparameter_gridsearch"]], "domainlab.utils.hyperparameter_retrieval module": [[42, "module-domainlab.utils.hyperparameter_retrieval"]], "domainlab.utils.hyperparameter_sampling module": [[42, "module-domainlab.utils.hyperparameter_sampling"]], "domainlab.utils.logger module": [[42, "module-domainlab.utils.logger"]], "domainlab.utils.override_interface module": [[42, "module-domainlab.utils.override_interface"]], "domainlab.utils.perf module": [[42, "module-domainlab.utils.perf"]], "domainlab.utils.perf_metrics module": [[42, "module-domainlab.utils.perf_metrics"]], "domainlab.utils.sanity_check module": [[42, "module-domainlab.utils.sanity_check"]], "domainlab.utils.test_img module": [[42, "module-domainlab.utils.test_img"]], "domainlab.utils.u_import module": [[42, "module-domainlab.utils.u_import"]], "domainlab.utils.u_import_net_module module": [[42, "module-domainlab.utils.u_import_net_module"]], "domainlab.utils.utils_class module": [[42, "module-domainlab.utils.utils_class"]], "domainlab.utils.utils_classif module": [[42, "module-domainlab.utils.utils_classif"]], "domainlab.utils.utils_cuda module": [[42, "module-domainlab.utils.utils_cuda"]], "domainlab.utils.utils_img_sav module": [[42, "module-domainlab.utils.utils_img_sav"]], "Welcome to domainlab\u2019s documentation!": [[43, "welcome-to-domainlab-s-documentation"]], "Contents:": [[43, null]], "Indices and tables": [[43, "indices-and-tables"]], "domainlab": [[44, "domainlab"]], "Introduction": [[45, "introduction"]], "DomainLab: modular python package for training domain invariant neural networks": [[45, "domainlab-modular-python-package-for-training-domain-invariant-neural-networks"]], "Distribution shifts, domain generalization and DomainLab": [[45, "distribution-shifts-domain-generalization-and-domainlab"]], "DomainLab": [[45, "domainlab"]], "Getting started": [[45, "getting-started"]], "Installation": [[45, "installation"]], "Task specification": [[45, "task-specification"]], "Example and usage": [[45, "example-and-usage"]], "Available arguments for commandline": [[45, "available-arguments-for-commandline"]], "Command line configuration file": [[45, "command-line-configuration-file"]], "or Programm against DomainLab API": [[45, "or-programm-against-domainlab-api"]], "Benchmark different methods": [[45, "benchmark-different-methods"]], "Citation": [[45, "citation"]], "recommonmark==0.5.0.dev0": [[46, "recommonmark-0-5-0-dev0"]]}, "indexentries": {"parsevaluesorkeyvaluepairs (class in domainlab.arg_parser)": [[26, "domainlab.arg_parser.ParseValuesOrKeyValuePairs"]], "apply_dict_to_args() (in module domainlab.arg_parser)": [[26, "domainlab.arg_parser.apply_dict_to_args"]], "domainlab": [[26, "module-domainlab"]], "domainlab.arg_parser": [[26, "module-domainlab.arg_parser"]], "domainlab.cli": [[26, "module-domainlab.cli"]], "domainlab.mk_exp": [[26, 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"module-domainlab.tasks.task_mnist_color"]], "domainlab.tasks.task_pathlist": [[41, "module-domainlab.tasks.task_pathlist"]], "domainlab.tasks.task_utils": [[41, "module-domainlab.tasks.task_utils"]], "domainlab.tasks.utils_task": [[41, "module-domainlab.tasks.utils_task"]], "domainlab.tasks.utils_task_dset": [[41, "module-domainlab.tasks.utils_task_dset"]], "domainlab.tasks.zoo_tasks": [[41, "module-domainlab.tasks.zoo_tasks"]], "extensions (domainlab.tasks.task_folder.nodetaskfolder property)": [[41, "domainlab.tasks.task_folder.NodeTaskFolder.extensions"]], "fun_class_local_na2vec_new() (domainlab.tasks.utils_task.dsetclassvecdecorator method)": [[41, "domainlab.tasks.utils_task.DsetClassVecDecorator.fun_class_local_na2vec_new"]], "get_dset_by_domain() (domainlab.tasks.b_task.nodetaskdict method)": [[41, "domainlab.tasks.b_task.NodeTaskDict.get_dset_by_domain"]], "get_dset_by_domain() (domainlab.tasks.task_folder.nodetaskfolder method)": [[41, "domainlab.tasks.task_folder.NodeTaskFolder.get_dset_by_domain"]], "get_dset_by_domain() (domainlab.tasks.task_folder.nodetaskfolderclassnamismatch method)": [[41, "domainlab.tasks.task_folder.NodeTaskFolderClassNaMismatch.get_dset_by_domain"]], "get_dset_by_domain() (domainlab.tasks.task_mnist_color.nodetaskmnistcolor10 method)": [[41, "domainlab.tasks.task_mnist_color.NodeTaskMNISTColor10.get_dset_by_domain"]], "get_dset_by_domain() (domainlab.tasks.task_pathlist.nodetaskpathlistdummy method)": [[41, "domainlab.tasks.task_pathlist.NodeTaskPathListDummy.get_dset_by_domain"]], "get_list_domains() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.get_list_domains"]], "get_list_domains() (domainlab.tasks.task_mnist_color.nodetaskmnistcolor10 method)": [[41, "domainlab.tasks.task_mnist_color.NodeTaskMNISTColor10.get_list_domains"]], "get_list_domains_tr_te() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.get_list_domains_tr_te"]], "get_na() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.get_na"]], "get_task() (in module domainlab.tasks.zoo_tasks)": [[41, "domainlab.tasks.zoo_tasks.get_task"]], "h (domainlab.tasks.utils_task.imsize property)": [[41, "domainlab.tasks.utils_task.ImSize.h"]], "img_loader2dir() (in module domainlab.tasks.utils_task)": [[41, "domainlab.tasks.utils_task.img_loader2dir"]], "init_business() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.init_business"]], "init_business() (domainlab.tasks.b_task.nodetaskdict method)": [[41, "domainlab.tasks.b_task.NodeTaskDict.init_business"]], "init_business() (domainlab.tasks.b_task_classif.nodetaskdictclassif method)": [[41, "domainlab.tasks.b_task_classif.NodeTaskDictClassif.init_business"]], "is_myjob() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.is_myjob"]], "isize (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.isize"]], "isize (domainlab.tasks.task_mnist_color.nodetaskmnistcolor10 property)": [[41, "domainlab.tasks.task_mnist_color.NodeTaskMNISTColor10.isize"]], "list_domain_tr (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.list_domain_tr"]], "list_str_y (domainlab.tasks.a_task_classif.nodetaskdgclassif property)": [[41, "domainlab.tasks.a_task_classif.NodeTaskDGClassif.list_str_y"]], "list_str_y (domainlab.tasks.task_mnist_color.nodetaskmnistcolor10 property)": [[41, "domainlab.tasks.task_mnist_color.NodeTaskMNISTColor10.list_str_y"]], "loader_te (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.loader_te"]], "loader_tr (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.loader_tr"]], "loader_val (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.loader_val"]], "mk_loader() (in module domainlab.tasks.utils_task)": [[41, "domainlab.tasks.utils_task.mk_loader"]], "mk_node_task_path_list() (in module domainlab.tasks.task_pathlist)": [[41, "domainlab.tasks.task_pathlist.mk_node_task_path_list"]], "mk_onehot() (in module domainlab.tasks.utils_task)": [[41, "domainlab.tasks.utils_task.mk_onehot"]], "mk_task_dset() (in module domainlab.tasks.task_dset)": [[41, "domainlab.tasks.task_dset.mk_task_dset"]], "mk_task_folder() (in module domainlab.tasks.task_folder_mk)": [[41, "domainlab.tasks.task_folder_mk.mk_task_folder"]], "parse_domain_id() (in module domainlab.tasks.task_utils)": [[41, "domainlab.tasks.task_utils.parse_domain_id"]], "sample_sav() (domainlab.tasks.a_task_classif.nodetaskdgclassif method)": [[41, "domainlab.tasks.a_task_classif.NodeTaskDGClassif.sample_sav"]], "set_list_domains() (domainlab.tasks.a_task.nodetaskdg method)": [[41, "domainlab.tasks.a_task.NodeTaskDG.set_list_domains"]], "targets (domainlab.tasks.utils_task.dsetclassvecdecorator property)": [[41, "domainlab.tasks.utils_task.DsetClassVecDecorator.targets"]], "targets (domainlab.tasks.utils_task.dsetdomainvecdecorator property)": [[41, "domainlab.tasks.utils_task.DsetDomainVecDecorator.targets"]], "task_name (domainlab.tasks.a_task.nodetaskdg property)": [[41, "domainlab.tasks.a_task.NodeTaskDG.task_name"]], "tensor1hot2ind() (in module domainlab.tasks.utils_task)": [[41, "domainlab.tasks.utils_task.tensor1hot2ind"]], "w (domainlab.tasks.utils_task.imsize property)": [[41, "domainlab.tasks.utils_task.ImSize.w"]], "categoricalhyperparameter (class in domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.CategoricalHyperparameter"]], "flowgenimgs (class in domainlab.utils.flows_gen_img_model)": [[42, "domainlab.utils.flows_gen_img_model.FlowGenImgs"]], "hyperparameter (class in domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.Hyperparameter"]], "logger (class in domainlab.utils.logger)": [[42, "domainlab.utils.logger.Logger"]], "perfclassif (class in domainlab.utils.perf)": [[42, "domainlab.utils.perf.PerfClassif"]], "perfmetricclassif (class in domainlab.utils.perf_metrics)": [[42, "domainlab.utils.perf_metrics.PerfMetricClassif"]], "referencehyperparameter (class in domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.ReferenceHyperparameter"]], "sampledhyperparameter (class in domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.SampledHyperparameter"]], "sanitycheck (class in domainlab.utils.sanity_check)": [[42, "domainlab.utils.sanity_check.SanityCheck"]], "add_next_param_from_list() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.add_next_param_from_list"]], "add_references_and_check_constraints() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.add_references_and_check_constraints"]], "add_shared_params_to_param_grids() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.add_shared_params_to_param_grids"]], "boxplot() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.boxplot"]], "boxplot_stochastic() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.boxplot_stochastic"]], "boxplot_systematic() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.boxplot_systematic"]], "build_external_obj_net_module_feat_extract() (in module domainlab.utils.u_import_net_module)": [[42, "domainlab.utils.u_import_net_module.build_external_obj_net_module_feat_extract"]], "build_param_grid_of_shared_params() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.build_param_grid_of_shared_params"]], "cal_acc() (domainlab.utils.perf.perfclassif class method)": [[42, "domainlab.utils.perf.PerfClassif.cal_acc"]], "cal_metrics() (domainlab.utils.perf_metrics.perfmetricclassif method)": [[42, "domainlab.utils.perf_metrics.PerfMetricClassif.cal_metrics"]], "check_constraints() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.check_constraints"]], "create_samples_from_shared_samples() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.create_samples_from_shared_samples"]], "dataset_sanity_check() (domainlab.utils.sanity_check.sanitycheck method)": [[42, "domainlab.utils.sanity_check.SanityCheck.dataset_sanity_check"]], "datatype() (domainlab.utils.hyperparameter_sampling.categoricalhyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.CategoricalHyperparameter.datatype"]], "datatype() (domainlab.utils.hyperparameter_sampling.hyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.Hyperparameter.datatype"]], "datatype() (domainlab.utils.hyperparameter_sampling.referencehyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.ReferenceHyperparameter.datatype"]], "datatype() (domainlab.utils.hyperparameter_sampling.sampledhyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.SampledHyperparameter.datatype"]], "domainlab.utils": [[42, "module-domainlab.utils"]], "domainlab.utils.flows_gen_img_model": [[42, "module-domainlab.utils.flows_gen_img_model"]], "domainlab.utils.generate_benchmark_plots": [[42, "module-domainlab.utils.generate_benchmark_plots"]], "domainlab.utils.get_git_tag": [[42, "module-domainlab.utils.get_git_tag"]], "domainlab.utils.hyperparameter_gridsearch": [[42, "module-domainlab.utils.hyperparameter_gridsearch"]], "domainlab.utils.hyperparameter_retrieval": [[42, "module-domainlab.utils.hyperparameter_retrieval"]], "domainlab.utils.hyperparameter_sampling": [[42, "module-domainlab.utils.hyperparameter_sampling"]], "domainlab.utils.logger": [[42, "module-domainlab.utils.logger"]], "domainlab.utils.override_interface": [[42, "module-domainlab.utils.override_interface"]], "domainlab.utils.perf": [[42, "module-domainlab.utils.perf"]], "domainlab.utils.perf_metrics": [[42, "module-domainlab.utils.perf_metrics"]], "domainlab.utils.sanity_check": [[42, "module-domainlab.utils.sanity_check"]], "domainlab.utils.test_img": [[42, "module-domainlab.utils.test_img"]], "domainlab.utils.u_import": [[42, "module-domainlab.utils.u_import"]], "domainlab.utils.u_import_net_module": [[42, "module-domainlab.utils.u_import_net_module"]], "domainlab.utils.utils_class": [[42, "module-domainlab.utils.utils_class"]], "domainlab.utils.utils_classif": [[42, "module-domainlab.utils.utils_classif"]], "domainlab.utils.utils_cuda": [[42, "module-domainlab.utils.utils_cuda"]], "domainlab.utils.utils_img_sav": [[42, "module-domainlab.utils.utils_img_sav"]], "fun_gen() (in module domainlab.utils.flows_gen_img_model)": [[42, "domainlab.utils.flows_gen_img_model.fun_gen"]], "gen_benchmark_plots() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.gen_benchmark_plots"]], "gen_fun_acc() (domainlab.utils.perf.perfclassif class method)": [[42, "domainlab.utils.perf.PerfClassif.gen_fun_acc"]], "gen_img_loader() (domainlab.utils.flows_gen_img_model.flowgenimgs method)": [[42, "domainlab.utils.flows_gen_img_model.FlowGenImgs.gen_img_loader"]], "gen_img_xyd() (domainlab.utils.flows_gen_img_model.flowgenimgs method)": [[42, "domainlab.utils.flows_gen_img_model.FlowGenImgs.gen_img_xyd"]], "gen_plots() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.gen_plots"]], "get_device() (in module domainlab.utils.utils_cuda)": [[42, "domainlab.utils.utils_cuda.get_device"]], "get_gamma_reg() (in module domainlab.utils.hyperparameter_retrieval)": [[42, "domainlab.utils.hyperparameter_retrieval.get_gamma_reg"]], "get_git_tag() (in module domainlab.utils.get_git_tag)": [[42, "domainlab.utils.get_git_tag.get_git_tag"]], "get_hyperparameter() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.get_hyperparameter"]], "get_label_na() (in module domainlab.utils.utils_classif)": [[42, "domainlab.utils.utils_classif.get_label_na"]], "get_list_pred_target() (domainlab.utils.perf.perfclassif class method)": [[42, "domainlab.utils.perf.PerfClassif.get_list_pred_target"]], "get_logger() (domainlab.utils.logger.logger static method)": [[42, "domainlab.utils.logger.Logger.get_logger"]], "get_shared_samples() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.get_shared_samples"]], "get_val() (domainlab.utils.hyperparameter_sampling.hyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.Hyperparameter.get_val"]], "grid_task() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.grid_task"]], "import_path() (in module domainlab.utils.u_import)": [[42, "domainlab.utils.u_import.import_path"]], "is_dict_with_key() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.is_dict_with_key"]], "logger (domainlab.utils.logger.logger attribute)": [[42, "domainlab.utils.logger.Logger.logger"]], "logit2preds_vpic() (in module domainlab.utils.utils_classif)": [[42, "domainlab.utils.utils_classif.logit2preds_vpic"]], "lognormal_grid() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.lognormal_grid"]], "loguniform_grid() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.loguniform_grid"]], "max_0_x() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.max_0_x"]], "mk_dummy_label_list_str() (in module domainlab.utils.utils_classif)": [[42, "domainlab.utils.utils_classif.mk_dummy_label_list_str"]], "mk_fun_sav_img() (in module domainlab.utils.utils_img_sav)": [[42, "domainlab.utils.utils_img_sav.mk_fun_sav_img"]], "mk_img() (in module domainlab.utils.test_img)": [[42, "domainlab.utils.test_img.mk_img"]], "mk_rand_label_onehot() (in module domainlab.utils.test_img)": [[42, "domainlab.utils.test_img.mk_rand_label_onehot"]], "mk_rand_xyd() (in module domainlab.utils.test_img)": [[42, "domainlab.utils.test_img.mk_rand_xyd"]], "normal_grid() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.normal_grid"]], "override_interface() (in module domainlab.utils.override_interface)": [[42, "domainlab.utils.override_interface.override_interface"]], "radar_plot() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.radar_plot"]], "rais_error_if_num_not_specified() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.rais_error_if_num_not_specified"]], "round_to_discreate_grid_normal() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.round_to_discreate_grid_normal"]], "round_to_discreate_grid_uniform() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.round_to_discreate_grid_uniform"]], "round_vals_in_dict() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.round_vals_in_dict"]], "sample() (domainlab.utils.hyperparameter_sampling.categoricalhyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.CategoricalHyperparameter.sample"]], "sample() (domainlab.utils.hyperparameter_sampling.hyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.Hyperparameter.sample"]], "sample() (domainlab.utils.hyperparameter_sampling.referencehyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.ReferenceHyperparameter.sample"]], "sample() (domainlab.utils.hyperparameter_sampling.sampledhyperparameter method)": [[42, "domainlab.utils.hyperparameter_sampling.SampledHyperparameter.sample"]], "sample_grid() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.sample_grid"]], "sample_gridsearch() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.sample_gridsearch"]], "sample_hyperparameters() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.sample_hyperparameters"]], "sample_parameters() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.sample_parameters"]], "sample_task() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.sample_task"]], "sample_task_only_shared() (in module domainlab.utils.hyperparameter_sampling)": [[42, "domainlab.utils.hyperparameter_sampling.sample_task_only_shared"]], "sav_add_title() (in module domainlab.utils.utils_img_sav)": [[42, "domainlab.utils.utils_img_sav.sav_add_title"]], "save_san_check_for_domain() (domainlab.utils.sanity_check.sanitycheck method)": [[42, "domainlab.utils.sanity_check.SanityCheck.save_san_check_for_domain"]], "scatterplot() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.scatterplot"]], "scatterplot_matrix() (in module domainlab.utils.generate_benchmark_plots)": [[42, "domainlab.utils.generate_benchmark_plots.scatterplot_matrix"]], "store_args() (in module domainlab.utils.utils_class)": [[42, "domainlab.utils.utils_class.store_args"]], "uniform_grid() (in module domainlab.utils.hyperparameter_gridsearch)": [[42, "domainlab.utils.hyperparameter_gridsearch.uniform_grid"]]}})
\ No newline at end of file
results.csv file containing the aggregated results will not be created.
-The user can then obtain the aggregated partial results with plots from the partially completed benchmark by running
+Aggregate obtained partial results¶
+results.csv file containing the aggregated results will not be created.python main_out.py --agg_partial_bm OUTPUT_DIR
Obtain partial resultswhere
OUTPUT_DIR specifying the benchmark output directory containing the partially completed benchmark,
e.g. ./zoutput/benchmarks/demo_benchmark, where demo_benchmark is a name defined in the benchmark yaml file.
cat ./zoutput/benchmarks/[name of the benchmark]/rule_results/*.csv > result.csv
+
sh scripts/sh_benchmark_partial_agg.sh OUTPUT_DIR/rule_results
Generate plots from .csv file¶
diff --git a/docs/build/html/doc_benchmark.md b/docs/build/html/doc_benchmark.md
index 908e7e487..5280e3aac 100644
--- a/docs/build/html/doc_benchmark.md
+++ b/docs/build/html/doc_benchmark.md
@@ -132,8 +132,9 @@ In case that the benchmark is not entirely completed, the user can obtain partia
explained below.
-### Obtain partial results
-If the benchmark is not yet completed (still running or has some failed jobs, e.g. BrokenPipe Error due to multiprocessing in PIL image reading), the `results.csv` file containing the aggregated results will not be created.
+### Aggregate obtained partial results
+If the benchmark is not yet completed (still running or has some failed jobs, e.g. Out of Memory, BrokenPipe Error due to multiprocessing in PIL image reading), the `results.csv` file containing the aggregated results will not be created.
+
The user can then obtain the aggregated partial results with plots from the partially completed benchmark by running
the following after cd into the DomainLab directory:
```commandline
@@ -144,9 +145,9 @@ e.g. `./zoutput/benchmarks/demo_benchmark`, where `demo_benchmark` is a name def
Alternatively, one could use
```examples
-cat ./zoutput/benchmarks/[name of the benchmark]/rule_results/*.csv > result.csv
+sh scripts/sh_benchmark_partial_agg.sh OUTPUT_DIR/rule_results
```
-clean up the extra csv head generated and plot the csv using command below
+where rule_results is the subfolder that contains partially finished csv result files. This script will partially aggregate the csv files in a faster fashion, output latex table which summarizes the results named "output_table_perf.tex" (which also contains a text format table before the latex table), and at the end generate plots using the following functionality.
### Generate plots from .csv file
If the benchmark is not completed, the `graphics` subdirectory might not be created. The user can then manually
diff --git a/docs/build/html/doc_miro.html b/docs/build/html/doc_miro.html
index f2cb0a8dc..68e0af555 100644
--- a/docs/build/html/doc_miro.html
+++ b/docs/build/html/doc_miro.html
@@ -407,14 +407,16 @@ Mutual Information R
Pre-requisite: Variational lower bound on mutual information¶
Submodules