7.5. ParAMSResults

Important

You should not initialize ParAMSResults yourself. Instead, use the corresponding ParAMSJob’s results attribute to access the results.

To get started, see the tutorial Getting Started: Python.

class ParAMSResults(*args, **kwargs)

Use this class to access results from a finished ParAMS job (the job may have been run either with the GUI or using the ParAMSJob Python class).

You should not create an instance of this class yourself. Instead, create a ParAMSJob, which will have a results attribute of type ParAMSResults.

Example:

job = ParAMSJob.load_external("/path/to/jobname.results")
print(type(job.results)) # job.results is of type ParAMSResults
print(job.results.get_loss()) # print the best loss function value for the training set
print(job.results.path) # "/path/to/jobname.results"

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

get_loss(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → float

Get the loss function value. Works for tasks ‘optimization’ and ‘singlepoint’.

source: str

‘best’, ‘latest’, ‘validation_set_best_parameters’, etc. Ignored if the task is ‘singlepoint’.

data_set: str

‘training_set’ or ‘validation_set’. Will read results from the directory training_set_results etc.

optimizer: int or None

If integer, read from the corresponding optimizer_XXX directory. If None, read from the “overall” (global) results. Ignored if the task is ‘singlepoint’.

Returns: float

The loss function value (loss.txt)

get_evaluation_number(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → int

Get the (global) evaluation number.

Returns: int

The other arguments are described for ParAMSResults.get_loss()

get_running_loss(data_set: str = 'training_set', optimizer: int = None) → tuple

Get the running loss from running_loss.txt.

data_setstr

‘training_set’ or ‘validation_set’

optimizer: int or None

If an integer, only get the running loss for that optimizer. Otherwise, get the loss for all optimizers.

Returns: 2-tuple (evaluation_id, loss) where each item is a list of length N

get_running_active_parameters(data_set: str = 'training_set', optimizer: int = None) → tuple

Get the running active parameters from running_active_parameters.txt.

data_setstr

‘training_set’ or ‘validation_set’

optimizer: int or None

If an integer, only get the running active parameters for that optimizer. Otherwise, get the parameters for all optimizers.

Returns: 2-tuple (evaluation_id, parameters) where evaluation_id is a list of float, and parameters is a dict where the keys are the parameter names and the values are a list of float (of the same length as evaluation_id)

get_parameter_interface_path(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → scm.params.parameterinterfaces.base.BaseParameters

Returns: the path to a parameter interface (ReaxFFParameters, GFN1xTBParameters, …) yaml file.

The arguments are described for ParAMSResults.get_loss()

get_parameter_interface(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → scm.params.parameterinterfaces.base.BaseParameters

Returns: a parameter interface (ReaxFFParameters, GFN1xTBParameters, …) from a parameter_interface.yaml file.

The arguments are described for ParAMSResults.get_loss()

get_ffield(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → str

Returnsstr

Path to the ffield.ff file from ReaxFF optimizations

The arguments are described for ParAMSResults.get_loss()

get_xtb_files(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → str

Returnsstr

Path to the xtb_files directory from GFN1-xTB optimizations

The arguments are described for ParAMSResults.get_loss()

get_deployed_model_path() → str

Returnsstr

Path to the deployed.pth file from a NequIP training.

get_data_set(data_set: str = 'training_set') → scm.params.core.dataset.DataSet

Returns a DataSet based on settings_and_initial_data/data_sets/data_set.yaml.gz

get_training_set_path() → str

Returns the absolute path to settings_and_initial_data/data_sets/training_set.yaml.gz

get_training_set() → scm.params.core.dataset.DataSet

Returns a DataSet based on settings_and_initial_data/data_sets/training_set.yaml.gz

get_validation_set_path() → str

Returns the absolute path to settings_and_initial_data/data_sets/validation_set.yaml.gz

get_validation_set() → scm.params.core.dataset.DataSet

Returns a DataSet based on settings_and_initial_data/data_sets/validation_set.yaml.gz

get_training_set_ref_path() → str

Returns the absolute path to training_set.ref.yaml from a task GenerateReference job

get_training_set_ref() → scm.params.core.dataset.DataSet

Returns a DataSet containing the newly calculated reference values from a task GenerateReference job

get_reference_jobs_dir() → str

Returns the absolute path to the directory containing the reference jobs from a task GenerateReference job

get_job_collection_path() → scm.params.core.jobcollection.JobCollection

Returns the path to settings_and_initial_data/job_collection.yaml.gz

get_job_collection() → scm.params.core.jobcollection.JobCollection

Returns a JobCollection based on settings_and_initial_data/job_collection.yaml.gz

get_initial_parameter_interface() → scm.params.parameterinterfaces.base.BaseParameters

Returns a parameter interface (ReaxFFParameters, GFN1xTBParameters, …) from settings_and_initial_data/initial_parameter_interface.yaml

Note: This function will always return the initial parameter interface. For task ‘optimization’, if you performed an evaluation with the initial parameters you could also call get_parameter_interface(source='initial').

get_engine_collection_path() → str

Returns the path to settings_and_initial_data/job_collection_engines.yaml.gz

NOTE: This file does not always exist.

get_engine_collection() → scm.params.core.engines.EngineCollection

Returns an engine collection based on settings_and_initial_data/job_collection_engines.yaml.gz

get_inputfile() → str

Returns the path to the settings_and_initial_data/params.in file.

get_data_set_evaluator(source: str = 'best', data_set: str = 'training_set', optimizer: int = None) → scm.params.common.dataset_evaluator.DataSetEvaluator

Returns: a DataSetEvaluator from a data_set_predictions.yaml file.

The arguments are described for ParAMSResults.get_loss()

get_num_optimizers() → int

Returns the number of optimizers as determined by the number of files/directories starting with optimizer_ inside the optimization/ folder

get_checkpoints() → List[str]

Returns a list of all checkpoint files sorted by creation date

get_all_pes_prediction_names(source: str = 'best', data_set: str = 'training_set', optimizer: int = None)

Returns a list of all files in pes_predictions/, excluding the .txt suffix.

get_pes_prediction(name, source: str = 'best', data_set: str = 'training_set', optimizer: int = None)

name: str

The name. Example: if there’s a file in pes_predictions/my_pesscan.txt, then set name='my_pesscan'.

The other arguments are described for ParAMSResults.get_loss().

Returns: 5-tuple

x_headers (list of str), x_values (list of list of float), ref_values (list of float), pred_values (list of float), ref_and_pred_unit (str)

E.g. x_headers[0] would be a description of the first scan direction of the first scan coordinate, and x_values[0] would be the corresponding coordinate values.

get_sensitivities(average_bootstraps: bool = True) → Dict[str, numpy.ndarray]

Normalised HSIC calculation results. If average_bootstraps is True and multiple bootstraps were calculated, this represents the mean over the bootstraps. Otherwise, a matrix of sensitivities is returned.

get_sensitivity(parameter_name: str, average_bootstraps: bool = True) → numpy.ndarray

Return the sensitivity for a particular parameter (averaged or not over the repeated calculations).

get_num_bootstraps() → int

Return the number of bootstraps (repeats) of the sensitivity calculation.

get_num_samples() → int

Return the number of samples used in each sensitivity calculation.

get_ordered_parameters() → List[str]

Return a list of parameter names, ordered from most sensitive to least sensitive.

get_parameter_rankings() → Dict[str, int]

Return a dictionary of parameter names and their relative sensitivity ranking (1 being the most sensitive.)

get_suggested_weights(data_set: str = 'training_set') → Dict[str, float]

Return a dictionary of data set items and suggested weights based on the results of the reweight calculation.