Failed Model Fits

Example of model fit failures and how to debug them.

# Import the FOOOFGroup object
from fooof import FOOOFGroup

# Import simulation code to create test power spectra
from fooof.sim.gen import gen_group_power_spectra

# Import FitError, which we will use to help debug model fit errors
from fooof.core.errors import FitError

Model Fit Failures

The power spectrum model is not guaranteed to fit - sometimes the fit procedure can fail.

Model fit failures are rare, and they typically only happen on spectra that are particular noisy, and/or are some kind of outlier for which the fitting procedure fails to find a good model solution.

In general, model fit failures should lead to a clean exit, meaning that a failed model fit does not lead to a code error. The failed fit will be encoded in the results as a null model, and the code can continue onwards.

In this example, we will look through what it looks like when model fits fail.

# Simulate some example power spectra to use for the example
freqs, powers = gen_group_power_spectra(25, [1, 50], [1, 1], [10, 0.25, 3],
                                        nlvs=0.1, freq_res=0.25)

# Initialize a FOOOFGroup object, with some desired settings
fg = FOOOFGroup(min_peak_height=0.1, max_n_peaks=6)

# Fit power spectra
fg.fit(freqs, powers)

Running FOOOFGroup across 25 power spectra.

If there are failed fits, these are stored as null models.

Let’s check if there were any null models, from model failures, in the models that we have fit so far. To do so, the FOOOFGroup object has some attributes that provide information on any null model fits.

These attributes are:

• n_null_ : the number of model results that are null

• null_inds_ : the indices of any null model results

# Check for failed model fits
print('Number of Null models  : \t', fg.n_null_)
print('Indices of Null models : \t', fg.null_inds_)

Number of Null models  :         0
Indices of Null models :         []

Inducing Model Fit Failures

So far, we have no model failures (as is typical).

For this example, to induce some model fits, we will use a trick to change the number of iterations the model uses to fit parameters (_maxfev), making it much more likely to fail.

Note that in normal usage, you would likely never want to change the value of _maxfev, and this here is a ‘hack’ of the code in order to induce reproducible failure modes in simulated data.

# Hack the object to induce model failures
fg._maxfev = 50

# Try fitting again
fg.fit(freqs, powers)

Running FOOOFGroup across 25 power spectra.
Model fitting was unsuccessful.

As we can see, there are now some model fit failures! Note that, as above, it will be printed out if there is as model fit failure when in verbose mode.

# Check how many model fit failures we have failed model fits
print('Number of Null models  : \t', fg.n_null_)
print('Indices of Null models : \t', fg.null_inds_)

Number of Null models  :         1
Indices of Null models :         [2]

Debug Mode

There are multiple possible reasons why a model fit failure can occur, or at least multiple possible steps in the algorithm at which the fit failure can occur.

If you have a small number of fit failures, you can likely just exclude them.

However, if you have multiple fit failures, and/or you want to investigate why the model is failing, you can use the debug mode to get a bit more information about where the model is failing.

The debug mode will stop the FOOOF object catching and continuing any model fit errors, allowing you to see where the error is happening, and get more information about where it is failing.

Note that here we will run the fitting in a try / except to catch the error and print it out, without the error actually being raised (for website purposes). If you just want to see the error, you can run the fit call without the try/except.

# Set FOOOFGroup into debug mode
fg.set_debug_mode(True)

# Refit in debug mode, in which failed fits will raise an error
try:
    fg.fit(freqs, powers)
except FitError as fooof_error:
    print(fooof_error)

Running FOOOFGroup across 25 power spectra.
Model fitting failed due to not finding parameters in the peak component fit.

Debugging Model Fit Errors

This debug mode should indicate in which step the model is failing, which might indicate what aspects of the data to look into, and/or which settings to try and tweak.

Also, all known model fit failures should be caught by the object, and not raise an error (when not in debug mode). If you are finding examples in which the model is failing to fit, and raising an error (outside of debug mode), then this might be an unanticipated issue with the model fit.

If you are unsure about why or how the model is failing to fit, consider opening an issue on the project repository, and we will try to look into what seems to be happening.