dataFitting module¶

Author:	Dominic Hunt

exception dataFitting.LengthError[source]¶: Bases: Exception

exception dataFitting.OrderError[source]¶: Bases: Exception

dataFitting.fit_record(participant_fits, file_name_generator)[source]¶

Returns the participant fits summary as a csv file

Parameters:	participant_fits (dict) – A summary of the recovered parameters file_name_generator (function) – Creates a new file with the name <handle> and the extension <extension>. It takes two string parameters: (`handle`, `extension`) and returns one `fileName` string

dataFitting.log_fitting_parameters(fit_info)[source]¶

Records and outputs to the log the parameters associated with the fitting algorithms

Parameters:	fit_info (dict) – The details of the fitting

dataFitting.log_model_fitted_parameters(model_fit_variables, model_parameters, fit_quality, participant_name)[source]¶

Logs the model and task parameters that used as initial fitting conditions

Parameters:	model_fit_variables (dict) – The model parameters that have been fitted over and varied. model_parameters (dict) – The model parameters for the fitted model fit_quality (float) – The value of goodness of fit participant_name (int or string) – The identifier for each participant

dataFitting.log_model_fitting_parameters(model, model_fit_variables, model_other_args)[source]¶

Logs the model and task parameters that used as initial fitting conditions

Parameters:	model (string) – The name of the model model_fit_variables (dict) – The model parameters that will be fitted over and varied. model_other_args (dict) – The other parameters used in the model whose attributes have been modified by the user

dataFitting.record_fitting(fitting_data, label, participant, participant_model_variables, participant_fits, file_name_generator, save_fitting_progress=False)[source]¶

Records formatted versions of the fitting data

Parameters:	fitting_data (dict, optional) – Dictionary of details of the different fits, including an ordered dictionary containing the parameter values tested, in the order they were tested, and a list of the fit qualities of these parameters. label (str) – The label used to identify the fit in the file names participant (dict) – The participant data participant_model_variables (dict of string) – A dictionary of model settings whose values should vary from participant to participant based on the values found in the imported participant data files. The key is the label given in the participant data file, as a string, and the value is the associated label in the model, also as a string. participant_fits (defaultdict of lists) – A dictionary to be filled with the summary of the participant fits file_name_generator (function) – Creates a new file with the name <handle> and the extension <extension>. It takes two string parameters: (`handle`, `extension`) and returns one `fileName` string save_fitting_progress (bool, optional) – Specifies if the results from each iteration of the fitting process should be returned. Default `False`
Returns:	participant_fits – A dictionary to be filled with the summary of the previous and current participant fits
Return type:	defaultdict of lists

dataFitting.record_participant_fit(participant, part_name, model_data, model_name, fitting_data, partModelVars, participantFits, fileNameGen=None, pickleData=False, saveFittingProgress=False, expData=None)[source]¶

Record the data relevant to the participant fitting

Parameters:	participant (dict) – The participant data part_name (int or string) – The identifier for each participant model_data (dict) – The data from the model model_name (str) – The label given to the model fitting_data (dict) – Dictionary of details of the different fits, including an ordered dictionary containing the parameter values tested, in the order they were tested, and a list of the fit qualities of these parameters partModelVars (dict of string) – A dictionary of model settings whose values should vary from participant to participant based on the values found in the imported participant data files. The key is the label given in the participant data file, as a string, and the value is the associated label in the model, also as a string. participantFits (defaultdict of lists) – A dictionary to be filled with the summary of the participant fits fileNameGen (function or None) – Creates a new file with the name <handle> and the extension <extension>. It takes two string parameters: (`handle`, `extension`) and returns one `fileName` string. Default `None` pickleData (bool, optional) – If true the data for each model, task and participant is recorded. Default is `False` saveFittingProgress (bool, optional) – Specifies if the results from each iteration of the fitting process should be returned. Default `False` expData (dict, optional) – The data from the task. Default `None`
Returns:	participantFits – A dictionary to be filled with the summary of the previous and current participant fits
Return type:	defaultdict of lists

See also

outputting.pickleLog(): records the picked data

dataFitting.run(data_folder='./', data_format='csv', data_file_filter=None, data_file_terminal_ID=True, data_read_options=None, data_split_by=None, data_group_by=None, data_extra_processing=None, model_name='QLearn', model_changing_properties=None, model_constant_properties=None, participantID='Name', participant_choices='Actions', participant_rewards='Rewards', model_fit_value='ActionProb', fit_subset=None, task_stimuli=None, participant_action_options=None, fit_method='Evolutionary', fit_method_args=None, fit_measure='-loge', fit_measure_args=None, fit_extra_measures=None, participant_varying_model_parameters=None, label=None, save_fitting_progress=False, config_file=None, output_path=None, pickle=False, boundary_excess_cost_function=None, min_log_level='INFO', numpy_error_level='log', fit_float_error_response_value=1e-100, calculate_covariance=False)[source]¶

A framework for fitting models to data for tasks, along with recording the data associated with the fits.

Parameters:

data_folder (string or list of strings, optional) – The folder where the data can be found. Default is the current folder.
data_format (string, optional) – The file type of the data, from mat, csv, xlsx and pkl. Default is csv
data_file_filter (callable, string, list of strings or None, optional) – A function to process the file names or a list of possible prefixes as strings or a single string. Default None, no file names removed
data_file_terminal_ID (bool, optional) – Is there an ID number at the end of the filename? If not then a more general search will be performed. Default True
data_read_options (dict, optional) – The keyword arguments for the data importing method chosen
data_split_by (string or list of strings, optional) – If multiple participant datasets are in one file sheet, this specifies the column or columns that can distinguish and identify the rows for each participant. Default None
data_group_by (list of strings, optional) – A list of parts of filenames that are repeated across participants, identifying all the files that should be grouped together to form one participants data. The rest of the filename is assumed to identify the participant. Default is None
data_extra_processing (callable, optional) – A function that modifies the dictionary of data read for each participant in such that it is appropriate for fitting. Default is None
model_name (string, optional) – The name of the file where a model.modelTemplate.Model class can be found. Default QLearn
model_changing_properties (dictionary with values of tuple of two floats, optional) – Parameters are the options that you allow to vary across model fits. Each model parameter is specified as a dict key. The value is a tuple containing the upper and lower search bounds, e.g. alpha has the bounds (0, 1). Default None
model_constant_properties (dictionary of float, string or binary valued elements, optional) – These contain all the the model options that define the version of the model being studied. Default None
participantID (str, optional) – The key (label) used to identify each participant. Default Name
participant_choices (string, optional) – The participant data key of their action choices. Default 'Actions'
participant_rewards (string, optional) – The participant data key of the participant reward data. Default 'Rewards'
model_fit_value (string, optional) – The key to be compared in the model data. Default 'ActionProb'
fit_subset (float('Nan'), None, "rewarded", "unrewarded", "all" or list of int, optional) – Describes which, if any, subset of trials will be used to evaluate the performance of the model. This can either be described as a list of trial numbers or, by passing - "all" for fitting all trials - float('Nan') or "unrewarded" for all those trials whose feedback was float('Nan') - "rewarded" for those who had feedback that was not float('Nan') Default None, which means all trials will be used.
task_stimuli (list of strings or None, optional) – The keys containing the observational parameters seen by the participant before taking a decision on an action. Default None
participant_action_options (string or list of strings or None or one element list with a list, optional) – If a string or list of strings these are treated as dict keys where the valid actions for each trial can be found. If None then all trials will use all available actions. If the list contains one list then it will be treated as a list of valid actions for each trialstep. Default 'None'
fit_method (string, optional) – The fitting method to be used. The names accepted are those of the modules in the folder fitAlgs containing a FitAlg class. Default 'evolutionary'
fit_method_args (dict, optional) – A dictionary of arguments specific to the fitting method. Default None
fit_measure (string, optional) – The name of the function used to calculate the quality of the fit. The value it returns provides the fitter with its fitting guide. Default -loge
fit_measure_args (dict, optional) – The parameters used to initialise fitMeasure and extraFitMeasures. Default None
fit_extra_measures (list of strings, optional) – List of fit measures not used to fit the model, but to provide more information. Any arguments needed for these measures should be placed in fitMeasureArgs. Default None
participant_varying_model_parameters (dict of string, optional) – A dictionary of model settings whose values should vary from participant to participant based on the values found in the imported participant data files. The key is the label given in the participant data file, as a string, and the value is the associated label in the model, also as a string. Default {}
label (string, optional) – The label for the data fitting. Default None will mean no data is saved to files.
save_fitting_progress (bool, optional) – Specifies if the results from each iteration of the fitting process should be returned. Default False
config_file (string, optional) – The file name and path of a .yaml configuration file. Overrides all other parameters if found. Default None
output_path (string, optional) – The path that will be used for the run output. Default None
pickle (bool, optional) – If true the data for each model, and participant is recorded. Default is False
boundary_excess_cost_function (str or callable returning a function, optional) – The function is used to calculate the penalty for exceeding the boundaries. Default is boundFunc.scalarBound()
min_log_level (str, optional) – Defines the level of the log from (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default INFO
numpy_error_level ({'log', 'raise'}) – Defines the response to numpy errors. Default log. See numpy.seterr
fit_float_error_response_value (float, optional) – If a floating point error occurs when running a fit the fitter function will return a value for each element of fpRespVal. Default is ``1/1e100`
calculate_covariance (bool, optional) – Is the covariance calculated. Default False

dataFitting module¶

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