optima-ml 0.4.0a2

Distributed hyperparameter optimization and input variable selection for artificial neural networks.

OPTIMA: an Optimization Platform for Tuning Input Variables and Model Parameters

OPTIMA is a framework to perform highly parallelized hyperparameter optimization and input variable selection of arbitrary Keras or Lightning neural networks for supervised learning tasks.

Table of Contents

• Documentation
• Installation
  • Preconfigured environments
    • Dresden (Barnard / Romeo / Capella)
  • Local installation
• Usage
  • Overview
  • Running an optimization
    • Local
    • Cluster
  • Run-config
    • General
    • Output
    • Inputs
    • Evaluation
    • Training
    • Hyperparameter optimization
      • General
      • Bayesian optimization (Optuna)
      • Population Based Training
    • Trial selection
    • Cross-validation
    • Input variable selection
    • Lightning
    • Custom Keras objects
    • Cluster setup
  • Built-in functionality
    • Run-config hook overview
    • Keras
    • Inputs
    • Trial selection
    • Evaluation
    • Input variable selection

Documentation

A quick overview and instructions on how to configure OPTIMA to perform an optimization is given in usage. A more detailed introduction including a description of the inner workings of OPTIMA can be found in chapter 4.2 of https://inspirehep.net/literature/2707309. Automatically generated API documentation is published at https://optima-docs.docs.cern.ch/.

Installation

Preconfigured environments

Dresden (Barnard / Romeo / Capella)

On Barnard, Romeo, and Capella, preconfigured conda environments for both Keras (OPTIMA_Keras_cpu / OPTIMA_Keras_gpu) and Lightning (OPTIMA_lightning_cpu) are available at /projects/materie-09/OPTIMA/conda_env.

1. If you are using Anaconda for the first time, load and initialize Anaconda:

   module load release/23.10 Anaconda3/2023.07-2
   conda init
   

   This appends a new code block to your .bashrc. For later, it is useful to surround this in a function. This should look similar to:

   function load_anaconda {
       # >>> conda initialize >>>
       # !! Contents within this block are managed by 'conda init' !!
       __conda_setup="$('/software/rapids/r23.10/Anaconda3/2023.07-2/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
       if [ $? -eq 0 ]; then
           eval "$__conda_setup"
       else
           if [ -f "/software/rapids/r23.10/Anaconda3/2023.07-2/etc/profile.d/conda.sh" ]; then
               . "/software/rapids/r23.10/Anaconda3/2023.07-2/etc/profile.d/conda.sh"
           else
               export PATH="/software/rapids/r23.10/Anaconda3/2023.07-2/bin:$PATH"
           fi
       fi
       unset __conda_setup
       # <<< conda initialize <<<
   }
   

   For the changes to take effect, close and re-open your shell (or source your .bashrc), and run:

   load_anaconda
   

2. Point conda to the existing environments by adding /projects/materie-09/OPTIMA/conda_env to envs_dirs in your .condarc:

   conda config --append envs_dirs /projects/materie-09/OPTIMA/conda_env
   

   You should now be able to see the environments when running:

   conda env list
   

3. (optional) To display the environment name instead of the path, run:

   conda config --set env_prompt '({name}) '
   

Local installation

Installation with pip

Each tagged version of OPTIMA is published to PyPI. To set up the python environment and install the most recent version of OPTIMA, run:

• Keras:

  pip install optima-ml[keras]
  

• Lightning (CPU only):

  pip install optima-ml[lightning] --extra-index-url https://download.pytorch.org/whl/cpu
  

• Lightning (with GPU support):

  pip install optima-ml[lightning]
  

In case a different version of OPTIMA that has not been uploaded to PyPI is needed, clone the repository and, from the repository's root, run:

• Keras:

  pip install .[keras]
  

• Lightning (CPU only):

  pip install .[lightning] --extra-index-url https://download.pytorch.org/whl/cpu
  

• Lightning (with GPU support):

  pip install .[lightning]
  

Installation with conda

To set up the python environment and install OPTIMA with conda, clone the repository, checkout the desired version of OPTIMA (e.g. the current master version or a tagged commit), and run:

Keras

• Linux:

  conda env create -n OPTIMA --file conda-requirements.yml
  conda activate OPTIMA
  pip install --no-deps .
  

• MacOS:

  conda env create -n OPTIMA --file conda-requirements-macos.yml
  conda activate OPTIMA
  pip install --no-deps .
  

Lightning

OPTIMA with Lightning has not yet been tested on MacOS.

• Linux (CPU only):

  conda env create -n OPTIMA --file conda-requirements_lightning.yml
  conda activate OPTIMA
  pip install --no-deps .
  

• Linux (with GPU support):

  conda env create -n OPTIMA --file conda-requirements_lightning_gpu.yml
  conda activate OPTIMA
  pip install --no-deps .
  

Usage

Overview

A single optimization run consists of up to three optimization steps: an input variable selection, the main hyperparameter optimization using Bayesian optimization, and a “fine-tuning” step using Population Based Training.

The input variable selection implements a backwards elimination algorithm to remove uninformative input variables. Since this approach is based on a trained model that is representative of the final result of the hyperparameter optimization, the input variable selection is preceded by an initial hyperparameter optimization using the same settings as the main hyperparameter optimization. Its results are used to perform the input variable selection whose algorithm is described here. Inputs that are found to be uninformative are removed from the dataset for the rest of the optimization.

Both the pre- and the main hyperparameter optimization perform a Bayesian optimization based on Optuna's TPESampler. After a fixed number of trials, the best hyperparameters are determined, corresponding models are retrained using cross-validation and their performance is evaluated. This evaluation after each hyperparameter optimization is documented here.

The fine-tuning step uses the implementation of Population Based Training in Tune. Hyperparameters that do not support mutation are fixed to the optimized value found during the main optimization step. The subsequent evaluation is identical to the evaluation of the previous two steps.

Each part of the optimization can be controlled in great detail using a run-config file. Additionally, OPTIMA provides a full suite of built-in functions that can be used to optimize multilayer perceptrons for classification tasks - no additional configuration required. For other use cases, some (or all) of these functions can be re-defined in the run-config to modularly replace built-in functionality where needed. The remaining built-in behavior is left unchanged, reducing the necessary configuration to a minimum.

Running an optimization

The optimization is started by loading the python environment, executing optima and providing the run-config. Run optima --help for an explanation of the available command line arguments.

Depending on the value of the cluster argument, the optimization is started locally or sent off as a cluster job.

Local

If the cluster command line argument is set to 'local' (the default), the optimization is started locally. Relevant command line arguments are:

• config: Path to the run-config file. (Required)
• defaults: Path to an alternative defaults-config. This overwrites the built-in default parameter values. (Default: None)
• cpus: Total number of CPUs to use. At least 3 CPUs are required because Ray dataset tasks need to run alongside training tasks. (Default: 3)
• gpus: Total number of GPUs to use. (Default: 0)
• mem_per_cpu: Amount of memory to allocate per CPU (in MB). (Default: 1000)
• cpus_per_trial: Number of CPUs to use per trial. (Default: 1)
• gpus_per_trial: Number of GPUs to use per trial. This can be a fractional number if multiple trials should share a GPU. (Default: 0)
• frac_cpus_reserved: Fraction of CPUs reserved for Ray dataset tasks. The number is rounded up and at least 2 CPUs are reserved. Trials are scheduled on the remaining CPUs, so at most floor((cpus - reserved_cpus) / cpus_per_trial) trials can run concurrently. (Default: 0.1)
• max_pending_trials: The maximum number of trials that are allowed to be 'pending'. (Default: cpus / cpus_per_trial)
• temp_dir: Overwrite Ray's default root temporary directory (/tmp/ray). This must be an absolute path.

Cluster

If the cluster command line argument is set to the name of a built-in cluster or to 'custom', the optimization will be launched as a cluster job. This is done by manually launching a Ray node on each node in the job in order to build a Ray cluster, to which OPTIMA in then connected. Ray then takes care of distributing all tasks across the cluster. It is possible to run multiple optimizations in parallel on the same cluster; each job will consequently build its own Ray cluster. To ensure the communication between Ray nodes is confined to be within one Ray cluster, a file will be created on a shared file system that keeps track of the running jobs. The location of this file as well as the ports used for the communication are set in the cluster class that is discussed below.

In order to setup the OPTIMA python environment in the job, a sourcable script is needed. This can, for example, look like this:

#!/usr/bin/env bash

source ~/.bashrc
load_anaconda
conda activate OPTIMA_Keras_cpu

If the optimization is to be run on an own cluster, i.e. none of the built-in options in OPTIMA.hardware_configs, cluster must be set to 'custom' and a cluster config file containing a CustomCluster class that implements the abstract cluster base class needs to be provided via the cluster_config command line argument. If you are using a Slurm-based cluster, you can subclass the built-in SLURMCluster class; see Dresden's Barnard cluster for reference. At the moment, no such class is available for HTCondor-based clusters.

Additionally, when not running on a Slurm-based cluster, it may be necessary to extend the cluster job class that contains the job configuration by defining a ClusterJob class in the cluster configuration file.

In addition to the arguments listed in the previous section, the following command line arguments are relevant when running on a cluster:

• name: Specifies the name of the job and the filename of the output log that is created in a logs folder in the execution directory. (Default: 'DNN_optimization')
• cluster: Specifies which cluster the optimization should run on. (Default: 'local')
• cluster_config: Path to a cluster configuration file. Only used if cluster is set to 'custom'. (Default: None)
• mem_per_cpu: Amount of memory to allocate per CPU (in MB). This overwrites the value provided in the cluster-class. (Default: None)
• fixed_ray_node_size: If provided, each worker node is forced to have the same number of CPUs and GPUs instead of letting the cluster management software handle the allocation. This is implicitly set when using GPUs. (Default: False)
• workers: Sets the number of nodes to allocate. Only used if fixed_ray_node_size is set. (Default: None)
• cpus_per_worker: Sets the number of CPUs to allocate per node. Only used if fixed_ray_node_size is set. (Default: None)
• gpus_per_worker: Sets the number of GPUs to allocate per node. Only used if fixed_ray_node_size is set. (Default: None)
• frac_cpus_reserved: Fraction of CPUs reserved for Ray dataset tasks. The number is rounded up and at least 2 CPUs are reserved before scheduling trials. Trials are scheduled on the remaining CPUs, so at most floor((cpus - reserved_cpus) / cpus_per_trial) trials can run concurrently. (Default: 0.1)
• min_cpus_per_ray_node: Sets the minimum number of CPUs to allocate per node. This is not used if fixed_ray_node_size is set. (Default: None)
• runtime: The runtime in hours for which the resources should be reserved. (Default: 12.0)
• exclude: Comma separated list of nodes that should be excluded from the job. (Default: None)

Run-config

The run-config file serves as the central repository for all available settings of an optimization run within OPTIMA. The following sections collect all parameters required by OPTIMA to perform a full optimization. Within the run-config, it is only necessary to specify parameters that lack default values or whose values are to be altered from their defaults.

In addition to these required parameters, optional parameters (mostly functions and classes) can be defined in the run-config to overwrite built-in functionality. These are discussed individually in the next section.

General

Especially for testing, it can be useful to only perform the training for a fixed set of hyperparameters without any optimization. This is possible when setting perform_variable_opt, perform_main_hyperopt and perform_PBT_hyperopt to False. In this case, the search_space is assumed to only contain fixed hyperparameter values, and only the model training and subsequent evaluation is performed.

Parameter	Default value	Explanation
perform_variable_opt (bool)	None	Whether to perform the initial hyperparameter optimization and subsequent input variable selection step.
perform_main_hyperopt (bool)	None	Whether to perform the main hyperparameter optimization step with Optuna.
perform_PBT_hyperopt (bool)	None	Whether to perform the hyperparameter fine-tuning step with PBT.
model_type (str)	'Keras'	The ML library used to build the model. Allowed values are 'Keras' and 'Lightning'.
random_seed (int)	42	Random seed to make the optimization reproducible. Deterministic execution is only guaranteed if model training is not parallelized, i.e. no two trainings can run at the same time.
conventional_batch_axis (bool)	True	Whether the first axis of model inputs and outputs is the batch axis. This controls how predictions and inputs are batched for custom metrics.
fixed_input_shape (bool)	True	Whether input batch structure and shape are fixed except for the batch axis. If True, OPTIMA tries to concatenate input batches before calling custom metrics.
fixed_output_shape (bool)	True	Whether model output structure and shape are fixed except for the batch axis. If True, OPTIMA tries to concatenate prediction batches before calling custom metrics.

Output

Parameter	Default value	Explanation
output_name (str)	'OPTIMA_defaults'	Name of the output directory. Unless use_exact_name is True, this is prefixed by a string describing which of the three optimization steps was performed.
output_path (str)	'DNN_storage'	Path to the directory the output folder should be created in. This path can be absolute or relative to the directory of execution. When running on a cluster, this needs to be on a shared file system.
use_exact_name (bool)	False	If True, disables the prefix of the output folder name.

Inputs

Details on how the input data is loaded and preprocessed can be found here.

Parameter	Default value	Explanation
produce_inputs_plots (bool)	True	Whether to produce plots of the input data by calling plot_input_data().
use_testing_dataset (bool)	True	If True, the input data is split into training, validation and testing datasets. If False, it is split into training and validation sets. This affects both the training during hyperparameter optimization as well as the cross-validation during the experiment evaluation.
fixed_testing_dataset (bool)	True	If True, use the same testing dataset for all folds during cross-validation. If False, the testing dataset is rotated like the training and validation sets. Only used if use_testing_dataset is True.
use_eventNums_splitting (bool)	False	If True, use the event numbers provided by get_inputs() to perform the splitting of the dataset according to (event_number + offset) % N == 0. If False, use random splitting.
eventNums_splitting_N (int)	5	The N in (event_number + offset) % N == 0. Controls the size of each dataset when using event number splitting. A value of 5 splits the dataset into 5 parts, 1 of which is used for validation, 1 for testing if use_testing_dataset is True, and the rest for training. Only used if use_eventNums_splitting is True.
eventNums_splitting_offset_val (int)	0	Controls which subset is used for validation. All events satisfying (event_number + eventNums_splitting_offset_val) % N == 0 are used for validation. Only used if use_eventNums_splitting is True.
eventNums_splitting_offset_test (int)	1	Controls which subset is used for testing. All events satisfying (event_number + eventNums_splitting_offset_test) % N == 0 are used for testing. Only applied if use_eventNums_splitting and use_testing_dataset are True.
validation_fraction (float)	0.2	Controls the fraction of events used for validation. Only used if use_eventNums_splitting is False.
test_fraction (float)	0.2	Controls the fraction of events used for testing. Only used if use_eventNums_splitting is False and use_testing_dataset is True.
max_num_events (int)	np.inf	Controls the number of events to be loaded from the dataset. np.inf corresponds to all available events.
max_event_weight (float)	np.inf	Maximum relative event weight used by the built-in get_inputs(). Events with larger normalized weights are duplicated and their weights are halved until this threshold is satisfied.

Evaluation

Parameter	Default value	Explanation
monitor_name (str)	'val_loss'	Controls which metric is used as the target of the optimization and for early stopping during training. Allowed are native, weighted native, custom and composite metrics as well as all metrics reported by the ML backend during training (e.g. "val_loss"). Make sure to add the "val_"-prefix when using native, weighted native or custom metrics.
monitor_op (str)	'min'	Is the target metric to be minimized ('min') or maximized ('max')?
native_metrics (list)	[]	A list of references to a metric class native to the used ML backend. Each entry must be a tuple of the form ("name", (ClassReference, {"kwarg1": kwarg1, ...})). Their values are calculated separately on the training and validation datasets and reported as "train_name" and "val_name". Event weights are not applied.
weighted_native_metrics (list)	[]	A list of references to a metric class native to the used ML backend. Each entry must be a tuple of the form ("name", (ClassReference, {"kwarg1": kwarg1, ...})). Their values are calculated separately on the training and validation datasets and reported as "train_name" and "val_name". Currently, weighted metrics are not supported for Lightning (see https://github.com/Lightning-AI/torchmetrics/issues/784).
custom_metrics (list)	[]	A list of custom metrics. Each entry must be a tuple of the form ("name", callable). callable needs to accept (input_batch, y_pred_batch) and return a number or a boolean. With the built-in data pipeline, input_batch is usually (inputs, targets) or (inputs, targets, weights). OPTIMA converts batches to NumPy arrays when possible and may concatenate multiple batches according to conventional_batch_axis, fixed_input_shape and fixed_output_shape. Their values are calculated at the end of each epoch separately on the training and validation datasets and reported as "train_name" and "val_name".
composite_metrics (list)	[]	A list of composite metrics. These are metrics that combine the values of already existing metrics. Each entry must be a tuple of the form ("name", (metric_name1, metric_name2, ...), callable) where callable accepts the same number of positional arguments as the number of provided metric names and returns a number or a boolean. They are calculated at the end of each epoch and allow all native, weighted native and custom metrics as well as metrics directly reported by the ML backend (e.g. validation loss) as inputs. It is possible to mix training and validation metrics, allowing e.g. to assess the generalization gap. The value of composite metrics is reported as "name" without a prefix.
overtraining_conditions (list)	[]	A list of overtraining conditions. These are special composite metrics that should return True when overtraining is detected and False otherwise. Each entry must be a tuple of the form ("name", (metric_name1, metric_name2, ...), callable) where callable accepts the same number of positional arguments as the number of provided metric names and returns a boolean. They are calculated at the end of each epoch and allow all native, weighted native, custom and composite metrics as well as metrics directly reported by the ML backend (e.g. validation loss) as inputs. Naturally, it is possible to mix training and validation metrics. The value of overtraining conditions is not reported.
predict_batch_size (int)	-1	Batch size used when evaluating custom metrics from predictions. -1 keeps the old behavior of evaluating the full dataset in one pass. Positive values let OPTIMA evaluate and average custom metric values over prediction batches. The effective batch size is rounded up to the next complete training batch, except for the final batch. Only used when conventional_batch_axis is True.

Training

Parameter	Default value	Explanation
max_epochs (int)	200	The maximal number of epochs until the training is terminated. Early stopping may terminate the training earlier.
early_stopping_patience (int)	6	The maximal number of consecutive epochs without an improvement of the target metric before early-stopping the training.
overtraining_patience (int)	6	The maximal number of consecutive epochs with overtraining detected (i.e. at least one overtraining condition detected overtraining) before early-stopping the training. This is ignored if no overtraining conditions are defined.
callbacks (list)	[]	A list of references to a callback class native to the used ML backend (i.e. a subclass of keras.callbacks.Callback for Keras or lightning.pytorch.callbacks.Callback for Lightning. Each entry must be a tuple of the form (ClassReference, {"kwarg1": kwarg1, ...}). The callbacks are only used during the training.
checkpoint_frequency (int)	1	The number of epochs to wait between consecutive checkpoints for each model. Since this is only useful to restore the training progress if the optimization is interrupted, this should be set so that, on average, a checkpoint is created every few minutes. For the initial and main hyperparameter optimization, the first checkpoint for each trial is created after checkpoint_frequency epochs. Since for Population Based Training, a checkpoint must be created after the last epoch in each perturbation interval, the checkpoint_frequency is overwritten with the perturbation_interval, unless it is divisible by the checkpoint_frequency. The first checkpoint will be created after checkpoint_frequency epochs if the burn_in_period is divisible by the checkpoint_frequency, and after burn_in_period epochs otherwise.
crossvalidation_for_fixed_hps (bool)	True	If only a fixed set of hyperparameter is to be used (e.g. for testing purposes), this parameter controls if the training of a single model (False) or a full cross-validation (True) is performed.

Hyperparameter optimization

General

Parameter	Default value	Explanation
optimize_on_best_value (bool)	False	Controls how each trial is evaluated during the hyperparameter optimization. While the target metric is calculated every epoch, Tune only accepts a single value to evaluate a trial. If True, the best epoch that passes all overtraining conditions (which is usually representative of the best performance possible with these hyperparameters, but may also be an outlier) is used. If False, the last epoch that passed all overtraining conditions (which is less likely an outlier but may be degraded due to overtraining) is used.
restore_on_best_checkpoint (bool)	False	When resuming a trial (e.g. during Population Based Training), resume from the best epoch (True) or last epoch (False)?
num_samples_variableOpt (int)	1	The number of trials (hyperparameter combinations) to try during the initial hyperparameter optimization step.
num_samples_main (int)	1	The number of trials (hyperparameter combinations) to try during the main hyperparameter optimization step.
num_samples_PBT (int)	4	The number of trials in the population for the hyperparameter fine-tuning step with Population Based Training.
search_space (dict)	see here	Defines the hyperparameter search space. Each dictionary entry corresponds to a hyperparameter, and the corresponding search space is defined using a dictionary with the following allowed key-value pairs: 'type': Specifies if a range of values ('range') or a list of allowed values ('choice') defines the search space. 'bounds': Specifies the lower and upper bounds for range search spaces. This can be given as a list or tuple of type (lower_bound, upper_bound). Alternatively, conditional bounds that depend on the values of other hyperparameters can be specified as a tuple of type (hp_depends_tuple, callable). Here, hp_depends_tuple is a tuple of hyperparameters the bounds depend on (i.e. each entry must correspond to another key in the search space). The callable will be provided with the values of the hyperparameters specified in hp_depends_tuple as positional arguments and needs to return a list or tuple of type (lower_bound, upper_bound). Conditional bounds are not supported for Population Based Training. This is only used for range search spaces. 'value_type': Specifies if an integer ('int') or float ('float') value should be suggested. Defaults to 'float'. This is only used for range search spaces. 'sampling': Specifies if the search space should be sampled uniformly ('uniform'), logarithmically ('log') or gaussian distributed ('normal'). Gaussian sampled integer search spaces are not supported. Defaults to 'uniform'. This is only used for range search spaces. 'mean': Specifies the mean of the gaussian sampling. This can be given as a float or integer. Only used for range search spaces and 'sampling' set to 'normal'. 'std': Specifies the standard deviation of the gaussian sampling. This can be given as a float or integer. Only used for range search spaces and 'sampling' set to 'normal'. 'step': Used to quantize range search spaces. For integer search spaces, this must be an integer. For float search spaces, this can be an integer or a float. For log-sampled search spaces, setting a step size is not supported. Defaults to None (i.e. unquantized) for float and 1 for integer parameters. This is only used for range search spaces. 'values': Specifies the allowed values for choice search spaces. This can be a list or a tuple. Alternatively, conditional values that depend on the values of other hyperparameters can be specified as a tuple of type (hp_depends_tuple, callable). Here, hp_depends_tuple is a tuple of hyperparameters the values depend on (i.e. each entry must correspond to another key in the search space). The callable will be provided with the values of the hyperparameters specified in hp_depends_tuple as positional arguments and needs to return a list or tuple of allowed values. Conditional values are not supported for Population Based Training. This is only used for choice search spaces. 'supports_mutation': A bool that specifies if this hyperparameter can be altered during the training without losing the training state. Only these hyperparameters can be optimized with Population Based Training. The remaining hyperparameters will be fixed to the best value found during the previous hyperparameter optimization step. If only the Population Based Training step is performed, all hyperparameters need to be fixed or mutatable. 'only': Used to build hierarchical search spaces where not all hyperparameters are always needed (e.g. the number of neurons in the fourth layer are only needed if the number of layers is at least four). If provided, this needs to be a tuple of type (hp_depends_tuple, callable). Here, hp_depends_tuple is a tuple of hyperparameters that are necessary to decide if this hyperparameter is needed (i.e. each entry must correspond to another key in the search space). The callable will be provided with the values of the hyperparameters specified in hp_depends_tuple as positional arguments and needs to return a bool that signifies if a value should be suggested for this hyperparameter. If the condition is evaluated as False, the value of the hyperparameter is explicitly set to None and provided as such to all conditions that depend on this hyperparameter. This allows defining multiple dependency layers where, e.g., B is only needed if A > 0 and C is only needed if B exists and B > 0. Rejected hyperparameters will, however, not be added to the model_config. Hierarchical search spaces are not supported for Population Based Training. Any search space entry that is not a dictionary is considered fixed and the corresponding hyperparameter will not be optimized but still added to the model_config. Being independent of the particular task, the batch size (with key 'batch_size') must always be provided as a hyperparameter, either as a fixed integer or as an integer range search space.

Bayesian optimization (Optuna)

Parameter	Default value	Explanation
use_TPESampler (bool)	True	Use Optuna's Tree-structured Parzen Estimator algorithm or random sampling?
use_multivariate_TPE (bool)	False	Use the multivariate or the independent version of the TPESampler?
use_ASHAScheduler (bool)	True	Use the Asynchronous HyperBand scheduler (ASHA) to prune trials or not?
ASHA_grace_period (int)	15	The minimum number of epochs to wait before the ASHA scheduler is allowed to prune a trial. Only used if use_ASHAScheduler is True.
ASHA_max_t (int)	max_epochs	The end point of the reduction with ASHA. Only used if use_ASHAScheduler is True.
ASHA_reduction_factor (int)	2	Controls which fraction of trials is terminated at each point by ASHA. A value of 2 corresponds to the termination of 50% of all trials.

Population Based Training

Parameter	Default value	Explanation
use_fit_results_for_PBT (bool)	True	PBT is only applied to hyperparameters that are marked with 'supports_mutation' set to True. If the initial and/or main hyperparameter optimization step was run before, all unsupported hyperparameters are fixed to their optimized values from the previous optimization step. If use_fit_results_for_PBT is True, the best-fit results are used, otherwise the best-value results are used. If only the PBT step is performed, all provided hyperparameters need to be fixed or support mutation.
perturbation_interval (int)	6	The number of epochs to train between each perturbation of a trial.
burn_in_period (int)	6	The number of epochs to wait before the first perturbation of a trial.

Trial selection

To select the best set of hyperparameters and the number of epochs to train from the pool of tested trials, two independent algorithms are used:

• best-value: the best trial is given by the best reported value of the target metric (i.e. best single epoch) while passing all overtraining conditions.
• best-fit: the evolution of all reported metrics of all trials is fitted. The best trial is given by the best-fit function value of the fitted target metric while passing all overtraining conditions.

Parameter	Default value	Explanation
check_overtraining_with_fit (bool)	True	Controls how the overtraining conditions are evaluated during the best-fit selection. If True, the fit function values of all metrics are given to the overtraining conditions. If False, the raw metric values are given to the overtraining conditions.

Cross-validation

Parameter	Default value	Explanation
use_early_stopping_for_crossvalidation (bool)	False	If True, the maximum number of epochs is kept unchanged (max_epochs) and the training can be terminated by early stopping with the same settings as used during the hyperparameter optimization (see here). If False, early stopping only monitors the overtraining conditions but ignores the evolution of the target metric. In this case, the maximum number of epochs is set to the best-value found during the trial selection.
use_OT_conditions_for_crossvalidation (bool)	False	If True, the overtraining conditions are evaluated like during the hyperparameter optimization. If False, the overtraining conditions are not evaluated.
reuse_seed_for_crossvalidation (bool)	False	If True, will use the same random seed as during the hyperparameter optimization for all folds. For the fold that was used during the hyperparameter optimization, this should reproduce the same training progression. If False, new seeds are generated.
fixed_seed_for_crossvalidation (bool)	False	If True, each fold will use the same random seed. If False, a new random seed is generated for each fold. Only used if reuse_seed_for_crossvalidation is False.

Input variable selection

Since all training during the input variable selection is done by performing a cross-validation, the corresponding settings also affect the result of the input variable selection.

Parameter	Default value	Explanation
var_metric (str)	'loss'	Which metric should be used to check the DNN performance during the input variable selection? Allowed are 'loss', which uses the model's configured loss, or the name of any native, weighted native or custom metric.
var_metric_op (str)	'min'	Is the target metric for the input variable selection to be minimized ('min') or maximized ('max')?
variable_optimization_mode (str)	'hybrid'	Which algorithm to use to evaluate the variable importance. Possible values are 'retrain', 'shuffle', 'hybrid' and 'custom'. See here for more details.
acceptance_criterion (str)	'improvement'	Which criterion to use for deciding if an iteration should be accepted. Possible values are 'threshold', 'improvement' and 'degradation'. See here for more details.
max_rel_change (float)	0.5	The threshold for how much the metric value is allowed to degrade before the input variable selection is stopped (relative difference). Positive values correspond to worse metric values. Only used if acceptance_criterion is set to 'threshold'.
var_opt_patience (int)	3	Early-stopping-like patience to terminate the input variable selection once a certain number of consecutive iterations were not accepted.
choose_best_var_set (bool)	True	If True, the set of input variables that resulted in the best target metric will be used as the result of the input variable selection. If False, the last accepted set will be used.
hybrid_revert_to_best_before_switch (bool)	True	If True, the input variable selection is reverted to the best iteration before switching to the retrain-phase. If False, only revert to the last accepted iteration. Only used if variable_optimization_mode is 'hybrid'.
num_repeats (int)	5	How often should each variable set be evaluated per iteration?
num_repeats_hybrid_retrain (int)	1	How often should each variable set be evaluated per iteration in the retrain-phase of hybrid mode? Only used if variable_optimization_mode is 'hybrid'.
reevaluate_candidate_to_drop (bool)	True	If True, the best variable set in each iteration is re-evaluated before deciding if the iteration should be accepted. If False, the metric values from the original evaluation are used.
retrain_for_reevaluation (bool)	True	If True, the retrain-method is used to evaluate the best variable set during the re-evaluation instead of the original evaluation method. Only used if reevaluate_candidate_to_drop is True.
num_repeats_for_reevaluation (int)	2	How often should the best variable set be re-evaluated? Only used if reevaluate_candidate_to_drop is True.
use_median_for_averages (bool)	False	If True, use the median and MAD to calculate averages and uncertainties across the k folds, otherwise use mean and standard deviation.
use_fit_results_for_varOpt (bool)	True	Whether to use the best-fit (True) or best-value (False) results of the trial selection of the initial hyperparameter optimization as the basis of the input variable selection.

Lightning

When using Lightning, the Lightning model, i.e. the LightningModule-subclass, must be defined with name LitModel in the run-config. Its constructor must accept the following parameters:

• model_config: a dictionary containing the values of the hyperparameters using the same keys as used in the search_space
• input_vars: the list of input variables returned by input_handler.get_vars()
• metrics: a list of initialized metrics and corresponding names. Each entry is a tuple of the form (name, metric) where name is the name given in native_metrics plus a 'train_' / 'val_' prefix and metric is the corresponding initialized metric.
• weighted_metrics: a list of initialized weighted metrics and corresponding names. Each entry is a tuple of the form (name, metric) where name is the name given in weighted_native_metrics plus a 'train_' / 'val_' prefix and metric is the corresponding initialized metric.

Within the constructor, self.save_hyperparameters() needs to be called in order to include the hyperparameters in the saved checkpoints.

In addition to the usual functions (configure_optimizers, forward, training_step, validation_step, test_step, predict_step, on_train_epoch_end, on_validation_epoch_end), LitModel must implement a prepare()-function which needs to accept the following parameters:

• input_handler: a reference to the InputHandler instance
• data_module: the Lightning DataModule built from the Ray training and validation datasets. The built-in data module is already set up for the fit stage before prepare() is called.
• first_prepare: a bool, signifying if this is the first time calling this model's prepare()-function

This function is called after creating a model (with first_prepare set to True) and after reloading a model from a checkpoint (with first_prepare set to False). It can e.g. be used to adapt a normalization layer from the training dataloader.

Whenever OPTIMA calculates predictions for Lightning models, it calls predict_step() with the full batch returned by the corresponding dataloader. With the built-in data module, this is (inputs, targets) or (inputs, targets, weights).

It is expected that the validation loss is logged with key 'val_loss' and all metrics and weighted metrics are logged with the name provided in metrics and weighted_metrics, respectively.

Custom Keras objects

If your Keras model uses custom objects, they cannot be defined directly in the run-config for technical reasons. Instead, define your custom objects in a separate module and import it into your run-config. Additionally, all custom objects need to be registered to Keras using the @tf.keras.saving.register_keras_serializable(...)-decorator in order to allow loading the saved checkpoints.

Cluster setup

These options are only used when running on a cluster.

Parameter	Default value	Explanation
path_to_setup_file (str)	None	Path to a sourceable file that sets up the python environment. This path can be absolute or relative to the directory of execution.

Built-in functionality

---

Built-in functionality can be overwritten by defining a new function or class in the run-config with the same name as the component that should be overwritten.

All functions defined in the run-config must accept the same arguments and return the same type of return value as the built-in function they are replacing unless otherwise specified.

All classes defined in the run-config must implement the same methods as the built-in class they are replacing. Like functions, the constructor and all class methods must accept the same arguments and return the same type of return value as the built-in counterpart, unless otherwise specified.

All overwritable components are collected in the following sections. If a built-in component uses parameters in the run-config, they are collected in a table in the corresponding section.

Run-config hook overview

OPTIMA checks the run-config for the following user-definable functions and classes. Defining one of these names replaces or supplies the corresponding behavior for the current run.

Hook name	Used for	Section
build_model, compile_model, update_model	Build, compile and mutate Keras models.	Keras
get_loss, get_native_metric_values	Override backend-specific loss or native metric evaluation when the default batch assumptions do not fit.	Keras
LitModel	Provide the Lightning model. This is required when model_type = "Lightning".	Lightning
InputHandler, get_experiment_inputs, get_inputs, get_training_data, plot_input_data	Control input-variable bookkeeping, full input loading/splitting, or individual pieces of the built-in input pipeline.	Inputs
build_tensorflow_dataset	Convert a Ray dataset to a TensorFlow dataset for Keras training, evaluation and metric calculation.	Inputs
LightningDataset, DataLoader, DataModule	Customize the Lightning data stack.	Inputs
fit_function	Replace the function fitted to trial metric histories during best-fit trial selection.	Trial selection
evaluate, finalize_model	Replace model evaluation or add per-model finalization after cross-validation.	Evaluation
create_variable_sets_to_try, shuffle_input_var, evaluate_variable_importance	Customize the input-variable selection algorithm.	Input variable selection

Keras

Both the built-in build_model and compile_model functions provide default values for their required hyperparameters. If a hyperparameter is omitted from the search space, it will be fixed to its default value. Additionally, for hyperparameters that support mutation (all hyperparameters of the compile_model function and those updated by the update_model function), search space entries that do not explicitly set the supports_mutation flag will be updated with this flag set to True. If either of the two functions is overwritten, this behavior is of course disabled for the corresponding hyperparameters.

build_model

A function that builds a Functional Keras multilayer perceptron for provided hyperparameter values. Supported hyperparameters and corresponding default values are:

• 'num_layers': the number of hidden layers. Default: 3
• 'units': the number of neurons per hidden layer (constant for all layers). Default: 32
• 'units_i': the number of neurons in hidden layer i, counting from 1 (has higher priority than 'units'). Default: None
• 'activation': the activation function. Default: 'swish'
• 'kernel_initializer' and 'bias_initializer': initializers of the weights and biases of the hidden layers. Defaults: 'auto'
• 'l1_lambda' and 'l2_lambda': the strength of the L1 and L2 regularizations. Defaults: 0.0
• 'dropout': the dropout rate. Default: 0.1

The input variables are normalized using a custom non-linear normalization layer, whose documentation can be found here. This layer first applies the non-linear transformations returned by the get_nonlinear_scaling-function of the InputHandler; supported are $\sqrt{\bullet}$, $\log_{10}(\bullet)$ or the identity. After that, a linear transformation to achieve zero mean and unit variance for all input features on the training dataset is applied. A batch normalization layer is added between each hidden layer unless the SELU activation function is used. The number of nodes in the output layer is inferred from the target element spec of the TensorFlow training dataset passed to build_model. For a single output node, a sigmoid activation is applied while a softmax activation is used for more than one output node. As such, this MLP is suitable for classification tasks. The returned Keras model is not yet compiled. More details can be found here.

To overwrite the built-in model, define build_model(model_config, *, input_handler, train_data) in the run-config. train_data is the TensorFlow training dataset produced by build_tensorflow_dataset() or the built-in converter. The function must return an uncompiled Keras model.

compile_model

A function that compiles the provided Keras model. It is called after creating a new model by executing the build_model-function (with parameter first_compile set to True) and after reloading a model from a checkpoint (with parameter first_compile set to False). It uses the Adam optimizer with the following tunable hyperparameters (see https://arxiv.org/abs/1412.6980v9 for the Adam update rule):

• 'learning_rate': $\alpha$. Default: 0.001
• 'Adam_beta_1': $\beta_1$. Default: 0.9
• 'one_minus_Adam_beta_2': $1 - \beta_2$. Default: 0.001
• 'Adam_epsilon': $\varepsilon$. Default: 1e-7

Supported loss functions, set using the hyperparameter 'loss_function', are binary crossentropy loss ('BinaryCrossentropy'), categorical crossentropy loss ('CategoricalCrossentropy'), and Kullback–Leibler divergence loss ('KLDivergence'). By default, binary crossentropy loss is used. For all available loss functions, the loss values can be weighted using class weights, controlled via hyperparameters of type 'loss_weight_class_N' with N corresponding to the N-th class.

If an already compiled model is provided, the loss function and the parameters of the Adam optimizer are updated without losing the training state of the model. This functionality is needed to allow mutations during the Population Based Training step.

For more details, see the documentation here

To overwrite the built-in compilation, define compile_model(model, model_config, *, metrics, weighted_metrics, input_handler, train_data, first_compile) in the run-config. metrics and weighted_metrics contain the initialized Keras metrics. first_compile is True for a new model and False when a model is reloaded from a checkpoint and needs to keep its training state.

update_model

A function that updates all updatable hyperparameters (besides the ones that are updated in the compile_model function) of a given pretrained Keras model without losing the training state. It is called only after reloading a model from a checkpoint. This is needed to allow mutations during the Population Based Training step. This function is specific to the MLP built with the built-in build_model function and, as such, allows to update the following hyperparameters:

• 'l1_lambda'
• 'l2_lambda'
• 'dropout'

After the update, the model is compiled for the changes to take effect. For more details, see the documentation here.

To overwrite this behavior, define update_model(model, model_config, *, input_handler, train_data) in the run-config. It must return the updated Keras model.

get_loss

By default, OPTIMA calculates the loss of Keras models by iterating over the TensorFlow dataset, assuming that each batch is (inputs, targets) or (inputs, targets, sample_weight), calling the model and then calling model.loss.

To overwrite this calculation, define get_loss(run_config, model_config, model, dataset, input_handler) in the run-config. It is called once per dataset and must return a scalar loss value.

get_native_metric_values

By default, OPTIMA evaluates native metrics by iterating over backend-native datasets or dataloaders, assuming that each batch contains inputs, targets and optionally sample weights. For Keras, dataset_or_dataloader is a TensorFlow dataset. For Lightning, it is the dataloader for one stage (train, val, test or predict).

To overwrite native metric evaluation, define get_native_metric_values(run_config, native_metrics, model_config, model, dataset_or_dataloader, input_handler) in the run-config. native_metrics is a list of initialized metric objects. The function is called once per dataset or dataloader and must return either one scalar value or a list/tuple of scalar values with the same order as native_metrics.

Inputs

InputHandler

A helper class that keeps track of which input variables are currently used and which non-linear scalings are being applied to each of them. Input variables can be referenced by name, or by index tuples for custom input pipelines that select inputs from structured arrays. Details can be found here.

To overwrite it, define an InputHandler class in the run-config. Its constructor is called as InputHandler(run_config), and it must provide the same methods as the built-in class, in particular get_vars(), set_vars(), get_nonlinear_scaling() and copy().

Parameter	Default value	Explanation
input_vars (list)	None	A list of strings corresponding to the input variables that should be given to the model (before the input variable selection). If provided, this is the list that is returned by the get_vars()-method of the InputHandler (before the input variable selection). If not provided, all available input variables are used. Input variable selection requires InputHandler.get_vars() to return a variable list, so input_vars or an equivalent custom InputHandler must be provided when perform_variable_opt is True.
input_scaling (dict)	None	A dictionary of scalings to be applied to each input variable. The key of each item must either be a variable name present in input_vars or encode the index of an input (e.g. '1_2_3' encodes the input variable with index (1, 2, 3)). The value must be a tuple of type (scale_type, (scale, offset)) where scale_type specifies the type of non-linear scaling to apply (e.g. $\log_{10}(\bullet)$) and scale and offset specify a linear transformation to apply before applying the non-linearity. For each variable in input_vars (if provided) without an entry in input_scaling, the value is set to ('linear', (1., 0.)). The resulting dictionary is returned when calling the InputHandler's get_nonlinear_scaling-function. Which values are supported for scale_type depends on the implementation used to perform the input scaling. When using the built-in non-linear normalization layer, refer to the documentation for details.

get_experiment_inputs

A function that controls the full input-preparation pipeline. The built-in version calls get_inputs() to load one Ray dataset, get_training_data() to split it, and optionally plot_input_data() to create input plots. If get_experiment_inputs is defined in the run-config, these three lower-level hooks are not called by OPTIMA unless the custom implementation calls them explicitly.

To overwrite the full pipeline, define get_experiment_inputs(run_config, input_handler, output_dir=None, inputs_for_crossvalidation=False, disable_printing=False) in the run-config. For normal training it must return (dataset_train, dataset_val) or (dataset_train, dataset_val, dataset_test). When inputs_for_crossvalidation=True, each entry must instead be a list of Ray datasets, one per fold, e.g. (datasets_train, datasets_val) or (datasets_train, datasets_val, datasets_test).

get_inputs

A function that loads the input dataset from a .pickle-file and returns a Ray dataset. It expects a vector of input variables per event, event weights, event numbers and binary or one-hot encoded target labels for classification to be present in the dataset. The returned Ray dataset contains Input, Target, Weight, ScaledWeight and EventNumber columns. Weight contains the normalized training weights, while ScaledWeight keeps the original event weights used for plotting and evaluation. The documentation can be found here.

Parameter	Default value	Explanation
inputs_file (str)	None	A path to the .pickle-file containing the input dataset.
max_event_weight (float)	np.inf	The maximum relative event weight. Events with a weight larger than max_event_weight times the median weight are repeatedly duplicated and the corresponding weight is halved.

To overwrite only the loading step, define get_inputs(run_config, nevts, input_vars_list, disable_printing=False) in the run-config. The returned Ray dataset must contain an Input column and a Target column. A Weight column is used for training and weighted metrics when present. Additional columns, for example EventNumber or ScaledWeight, may be used by other built-in functionality.

get_training_data

A function that splits a Ray dataset into training, validation and testing datasets. It implements random splitting or splitting based on the EventNumber column and a callable splitting condition. In both cases, a single split or k-fold splitting is possible. Documentation can be found here.

To overwrite only the splitting step, define get_training_data(dataset, splitting_cond, do_kfold=False, shuffle=False, fixed_test_dataset=True, disable_printing=False) in the run-config. It must return Ray datasets in the same structure as described for get_experiment_inputs().

plot_input_data

A function that creates histograms of the distributions of each input variable for each target class. It can only be used for classification tasks and expects a Ray dataset with Input and Target columns containing a vector of input variables and a corresponding binary or one-hot encoded target label per event. If present, ScaledWeight is used for plot weights; otherwise Weight is used. Documentation can be found here.

To overwrite only the input-plotting step, define plot_input_data(run_config, dataset, outdir, input_vars_list=None) in the run-config.

Parameter	Default value	Explanation
evaluation_class_labels (list)	["Signal", "Background"]	A list of labels for each of the classes in the order of the target label vector. This is only used to label the plots.

build_tensorflow_dataset

A function that converts one Ray dataset into a TensorFlow dataset for Keras. If it is not defined, OPTIMA expects the Ray dataset to contain Input, Target and optionally Weight columns, materializes the full dataset in memory, builds a tf.data.Dataset from them and batches it with batch size model_config["batch_size"].

To overwrite this conversion, define build_tensorflow_dataset(ray_dataset, input_handler, run_config, model_config) in the run-config. The returned TensorFlow dataset must yield batches in the structure expected by the Keras model, compile_model(), get_loss() and get_native_metric_values(). If no custom get_loss() or get_native_metric_values() is provided, batches are expected to be (inputs, targets) or (inputs, targets, sample_weight).

LightningDataset

A class that builds a Torch dataset for Lightning from a Ray dataset. By default, OPTIMA loads the Input, Target and optional Weight columns into a torch.utils.data.TensorDataset. If LightningDataset is defined in the run-config, its constructor is provided with (ray_dataset, model_config) and must return a Torch dataset. OPTIMA also provides a built-in tensor dataset with in-memory batching at OPTIMA.lightning.inputs.LightningDataset; this class is available for custom use but is not selected automatically.

DataLoader

A class that wraps a Torch dataset for Lightning. By default, PyTorch's torch.utils.data.DataLoader is used with batch_size=model_config["batch_size"]. If DataLoader is defined in the run-config, its constructor is called as DataLoader(dataset, model_config) instead.

DataModule

A subclass of Lightning's LightningDataModule that encapsulates data preparation for Lightning. The built-in DefaultDataModule receives Ray datasets via the keyword arguments dataset_train, dataset_val, dataset_test and dataset_predict, converts them to Torch datasets in setup(), and wraps them in dataloaders. If a custom DataModule is defined in the run-config, its constructor is called with run_config, model_config, input_handler and the same dataset keyword arguments. It must provide the dataloader methods needed for the requested stages (train_dataloader, val_dataloader, and, if used, test_dataloader / predict_dataloader). The documentation can be found here.

Trial selection

fit_function

To perform the best-fit trial selection, the function

$$ f(x) = \frac{a}{x^2} + \frac{b}{x} + c x^2 + d x + \mathrm{const.} $$

is fitted to the evolution of each metric as a function of epochs using least-squares as implemented by scipy's curve_fit function. All trials with less than 10 metric values (i.e. trained for less than 10 epochs) are skipped to prevent overfitting of the fit function. This behaviour can be overwritten by defining a fit_function function in the run-config that accepts the following parameters as positional arguments:

• df: A pandas dataframe of metric values. All reported metrics are contained as columns. The epoch is contained as column training_iteration.
• metric_to_fit: A string containing the name of the metric to fit.
• metric_to_fit_op: Either 'min', 'max' or None. If metric_to_fit is the target metric monitor_name, this is equal to monitor_op. For any other metric, this parameter will be None.
• overtraining_conditions: The list of overtraining conditions overtraining_conditions as defined in the run-config.

fit_function is expected to return a callable that returns the fit function value when called and provided with an epoch number as a positional argument.

Parameter	Default value	Explanation
fit_min_R_squared (float)	0.9	To evaluate the goodness of fit, the R-squared value of each fit is calculated. If any of the fits for a trial have an R-squared value lower than fit_min_R_squared, the trial is disregarded for the best-fit selection.

Evaluation

evaluate

A function that evaluates the performance of the trained models. Supported are classification tasks with binary or one-hot encoded target labels. While the model predictions are calculated using the batched inputs as during the training of the model, it is assumed that the target labels, event weights and aggregated model predictions needed for the built-in plots can fit into memory. Custom metrics are still evaluated through the abstract model API and can use predict_batch_size for batched prediction. The evaluation function draws histograms of:

• stacked output distributions: The model predictions for each output are calculated on the testing dataset (if present, on the validation dataset otherwise) and stacked for each target class. The contribution of each class is normalized to its total weight in the event weights.
• normalized output distributions: The model predictions for each output are calculated on the training, validation and (if present) testing datasets. The output distributions for each target class is normalized to area 1. For each class, a ratio of the validation and testing distributions to the training distributions is calculated.
• ROC-curves: A ROC-curve is calculated for each output in a One-vs-Rest scheme on the training, validation and testing datasets. Since negative weights can cause the ROC-curve to be non-monotonic, all events with negative event weights are ignored.

Additionally, the loss and all metric values are calculated on the training, validation and testing datasets. The evaluate function is expected to return a results string with placeholders {} in place of metric values (e.g. "Loss (training): {}") as well as a list of corresponding metric values. This allows to average the results over the individual crossvalidation folds.

To overwrite the evaluation, define an evaluate function in the run-config. OPTIMA calls it with keyword arguments run_config, model_path, model_config, dataset_split, input_handler, fig_dir, native_metrics, weighted_native_metrics, custom_metrics, composite_metrics, class_labels, cpus, print_results and return_unfilled. It must return either a formatted result string or, when return_unfilled=True, a tuple (results_string_template, metric_values).

Further documentation can be found here.

Parameter	Default value	Explanation
evaluation_class_labels (list)	["Signal", "Background"]	A list of labels for each of the classes in the order of the target label vector. This is only used to label the plots.

finalize_model

A helper function that can be used to perform finalization tasks, e.g. exporting the model to a different format. The finalize_model function is called for each crossvalidation model and is provided with the following arguments:

• run_config: A reference to the run-config.
• dataset_split: A tuple or list containing the training, validation and (if present) testing Ray datasets for this crossvalidation fold.
• results_dir: A path to the results directory for this optimization step.
• model_dir: A path to the directory the current model is saved in. This directory is shared by all crossvalidation models of the same trial (i.e. all best-value and all best-fit models).
• model_info: A dictionary containing information of the current model:
  • 'name': The file name of the current model. This must be appended with '.keras' for Keras models and '.ckpt' for lightning models.
  • 'split': An index denoting which of the k crossvalidation models this is. This ranges from 0 to k-1.
  • 'config': A dictionary containing the hyperparameter values of the current model.
• input_handler: A reference to the InputHandler instance.

Input variable selection

create_variable_sets_to_try

A function that generates the sets of input variables to evaluate for each iteration of the input variable selection. By default, each leave-one-out subset of the remaining set of input variables is tested. To overwrite this behavior, a create_variable_sets_to_try function that accepts the following parameters needs to be defined in the run-config:

• model_config: The dictionary of hyperparameters.
• models_with_inputs: A list of tuples, with one entry for each crossvalidation model. Each tuple is of the form (model_path, input_vars, validation_dataset) where
  • model_path: The full path to the saved model file.
  • input_vars: The list of input variables that are needed for this model, in the correct order.
  • validation_dataset: The Ray validation dataset for this model's crossvalidation fold.
• metric: The target metric descriptor used internally by the variable-optimization evaluators. It is either loss, a native/weighted-native metric tuple or a custom metric tuple. Custom metric callables receive (input_batch, y_pred_batch).
• all_vars: A list of all input variables that were available at the beginning of the input variable selection.
• vars_remaining: A list of input variables that is still remaining in the current iteration.

The create_variable_sets_to_try must return a dictionary containing all sets of input variables that should be tried this iteration. The keys for each variable set are used to label the obtained results.

shuffle_input_var

A helper function used by the built-in shuffle-based variable-importance evaluator. The default implementation assumes a Ray dataset with a two-dimensional NumPy array in the Input column and shuffles the selected feature columns according to the variable order in the InputHandler.

To overwrite the shuffling for structured or custom inputs, define shuffle_input_var(run_config, input_handler, model_config, dataset, vars_to_shuffle) in the run-config. It must return a Ray dataset with the requested variables shuffled and all columns needed for prediction and metric calculation still present.

evaluate_variable_importance

A function that evaluates the model performance for a given list of input variable sets. Two built-in algorithms for this are available, based on shuffling the input values (see here) and based on retraining the models with different input variables (see here). To use a different evaluation algorithm, a evaluate_variable_importance function needs to be defined in the run-config and variable_optimization_mode must be set to 'custom'. The evaluate_variable_importance is expected to accept the same parameters as OPTIMA.core.variable_optimization.evaluate_vars_shuffle and OPTIMA.core.variable_optimization.evaluate_vars_retrain. It must return a dictionary containing the values of the target metric for each tested input variable set. If the function is called with return_models_with_inputs=True, it must instead return (metrics, models_with_inputs_by_var_set), where models_with_inputs_by_var_set maps each tested variable-set key to updated models_with_inputs entries of the same form as created by OPTIMA.core.variable_optimization.get_models_with_inputs. For further details, see the documentations of the two built-in algorithms linked above.