xvalidation¶
Cross-validation analysis of population receptive field models
coeff_of_determination(data, model[, axis]) |
Calculate the coefficient of determination for a model prediction, relative to data. |
kfold_xval(models, data, Fit, folds, ...) |
Perform k-fold cross-validation to generate out-of-sample predictions for each measurement. |
parallel_xval(args) |
This is a convenience function for parallelizing the fitting procedure. |
recast_xval_results(output, grid_parent, folds) |
coeff_of_determination¶
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popeye.xvalidation.coeff_of_determination(data, model, axis=-1)¶ Calculate the coefficient of determination for a model prediction, relative to data.
- data : ndarray
- The data
- model : ndarray
- The predictions of a model for this data. Same shape as the data.
- axis: int, optional
- The axis along which different samples are laid out (default: -1).
- COD : ndarray
- The coefficient of determination.
deepcopy¶
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popeye.xvalidation.deepcopy(x, memo=None, _nil=[])¶ Deep copy operation on arbitrary Python objects.
See the module’s __doc__ string for more info.
kfold_xval¶
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popeye.xvalidation.kfold_xval(models, data, Fit, folds, fit_args, fit_kwargs)¶ Perform k-fold cross-validation to generate out-of-sample predictions for each measurement.
- models : list of instances of Model
- A list containing the Model instances to be handed to Fit. If the length of models is 1, then it is assumed that data is composed of either a single run of data or of multiple runs with the same, repeated stimulus presented.
- data : ndarray
- An m x n array representing a single voxel time-series, where m is the number of time-points and n is the number of runs
- Fit : Fit class object instance
- The Fit class that will be instantiated with the left-in and left-out datasets.
- folds : int
- The number of divisions to apply to the data
- fit_args :
- Additional arguments to the model initialization
- fit_kwargs :
- Additional key-word arguments to the model initialization
This function assumes that a prediction API is implemented in the Fit class from which a prediction is conducted. That is, the Fit object that gets generated upon fitting the model needs to have a prediction method, which receives a functional time-series and a Model class instance as input and produces a predicted signal as output.
[1] Rokem, A., Chan, K.L. Yeatman, J.D., Pestilli, F., Mezer, A., Wandell, B.A., 2014. Evaluating the accuracy of diffusion models at multiple b-values with cross-validation. ISMRM 2014.
parallel_xval¶
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popeye.xvalidation.parallel_xval(args)¶ This is a convenience function for parallelizing the fitting procedure. Each call is handed a tuple or list containing all the necessary inputs for instantiaing a GaussianFit class object and estimating the model parameters.
- args : list/tuple
- A list or tuple containing all the necessary inputs for fitting the kfold_xval method.
fits : PopulationFit class objects that have been cross-validated.