eelbrain.boosting

eelbrain.boosting(y, x, tstart, tstop, scale_data=True, delta=0.005, mindelta=None, error='l2', basis=0, basis_window='hamming', partitions=None, model=None, validate=1, test=0, ds=None, selective_stopping=0, partition_results=False, debug=False)

Estimate a linear filter with coordinate descent

Parameters

  • y (NDVar) – Signal to predict.

  • x (NDVar | sequence of NDVar) – Signal to use to predict y. Can be sequence of NDVars to include multiple predictors. Time dimension must correspond to y.

  • tstart (scalar | sequence of scalar) – Start of the TRF in seconds. A list can be used to specify different values for each item in x.

  • tstop (scalar | sequence of scalar) – Stop of the TRF in seconds. Format must match tstart.

  • scale_data (bool | 'inplace') – Scale y and x before boosting: subtract the mean and divide by the standard deviation (when error='l2') or the mean absolute value (when error='l1'). Use 'inplace' to save memory by scaling the original objects specified as y and x instead of making a copy.

  • delta (scalar) – Step for changes in the kernel.

  • mindelta (scalar) – If the error for the training data can’t be reduced, divide delta in half until delta < mindelta. The default is mindelta = delta, i.e. delta is constant.

  • error ('l2' | 'l1') –

    Error function to use (default is l2).

    • error='l1': the sum of the absolute differences between y and h * x.

    • error='l2': the sum of the squared differences between y and h * x.

    For vector y, the error is defined based on the distance in space for each data point.

  • basis (float) – Use a basis of windows with this length for the kernel (by default, impulses are used).

  • basis_window (str | scalar | tuple) – Basis window (see scipy.signal.get_window() for options; default is 'hamming').

  • partitions (Optional[int]) – Divide the data into this many partitions for cross-validation-based early stopping. In each partition, n - 1 segments are used for training, and the remaining segment is used for validation. If data is continuous, data are divided into contiguous segments of equal length (default 10). If data has cases, cases are divided with [::partitions] slices (default min(n_cases, 10); if model is specified, n_cases is the lowest number of cases in any cell of the model).

  • model (Union[Factor, Interaction, NestedEffect, str, None]) – If data has cases, divide cases into different categories (division for crossvalidation is done separately for each cell).

  • ds (Optional[Dataset]) – If provided, other parameters can be specified as string for items in ds.

  • validate (int) – Number of segments in validation dataset (currently has to be 1).

  • test (int) – By default (test=0), the boosting algorithm uses all available data to estimate the kernel. Set test=1 to perform k-fold cross- validation instead (with k = partitions): Each partition is used as test dataset in turn, while the remaining k-1 partitions are used to estimate the kernel. The resulting model fit metrics reflect the re-combination of all partitions, each one predicted from the corresponding, independent training set.

  • selective_stopping (int) – By default, the boosting algorithm stops when the testing error stops decreasing. With selective_stopping=True, boosting continues but excludes the predictor (one time-series in x) that caused the increase in testing error, and continues until all predictors are stopped. The integer value of selective_stopping determines after how many steps with error increases each predictor is excluded.

  • partition_results (bool) – Keep results (TRFs and model evaluation) for each test-partition. This is disabled by default to reduce file size when saving results.

  • debug (bool) – Add additional attributes to the returned result.

Returns

result – Results (see BoostingResult).

Return type

BoostingResult

Notes

In order to predict data, use the convolve() function:

>>> ds = datasets.get_uts()
>>> ds['a1'] = epoch_impulse_predictor('uts', 'A=="a1"', ds=ds)
>>> ds['a0'] = epoch_impulse_predictor('uts', 'A=="a0"', ds=ds)
>>> res = boosting('uts', ['a0', 'a1'], 0, 0.5, partitions=10, model='A', ds=ds)
>>> y_pred = convolve(res.h_scaled, ['a0', 'a1'], ds=ds)

The boosting algorithm is described in 1.

References

1

David, S. V., Mesgarani, N., & Shamma, S. A. (2007). Estimating sparse spectro-temporal receptive fields with natural stimuli. Network: Computation in Neural Systems, 18(3), 191-212. 10.1080/09548980701609235.