eelbrain.BoostingResult

class eelbrain.BoostingResult(y, x, tstart, tstop, scale_data, delta, mindelta, error, selective_stopping, y_mean, y_scale, x_mean, x_scale, _h, _isnan, t_run, basis, basis_window, splits=None, n_samples=None, _y_info=<factory>, _y_dims=None, i_test=None, residual=None, r=None, r_rank=None, r_l1=None, partition_results=None, version=13, algorithm_version=-1, y_pred=None, fit=None, prefit=None)

Result from boosting

Variables
  • h (NDVar | tuple of NDVar) – The temporal response function (the average of all TRFs from the different runs/partitions). Whether h is an NDVar or a tuple of NDVar depends on whether the x parameter to boosting() was an NDVar or a sequence of NDVar.

  • h_scaled (NDVar | tuple of NDVar) – h scaled such that it applies to the original input y and x. If boosting was done with scale_data=False, h_scaled is the same as h.

  • h_source (NDVar | tuple of NDVar) – If h was constructed using a basis, h_source represents the source of h before being convolved with the basis.

  • h_time (UTS) – Time dimension of the kernel.

  • r (float | NDVar) – Correlation between the measured response y and the predicted response h * x. When using cross-validation (calling boosting() with test=True), each partition of y is predicted using the h estimated from the corresponding training partitions. Otherwise, all of y is estimated using the average h. For vector data, measured and predicted responses are normalized, and r is computed as the average dot product over time. The type of r depends on the y parameter to boosting(): If y is one-dimensional, r is scalar, otherwise it is a NDVar.

  • r_rank (float | NDVar) – As r, the Spearman rank correlation.

  • t_run (float) – Time it took to run the boosting algorithm (in seconds).

  • error (str) – The error evaluation method used.

  • residual (float | NDVar) –

    The residual of the final result

    • 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.

  • delta (scalar) – Kernel modification step used.

  • mindelta (None | scalar) – Mindelta parameter used.

  • n_samples (int) – Number of samples in the input data time axis.

  • proportion_explained (float | NDVar) – The proportion of the explained variability. Variability is caculated as l1 or l2 norm, depending on the error that was used for model fitting. For l2, it corresponds to the proportion of variance explained. Calculated as 1 - (residual / variability).

  • scale_data (bool) – Scale_data parameter used.

  • y_mean (NDVar | scalar) – Mean that was subtracted from y.

  • y_scale (NDVar | scalar) – Scale by which y was divided.

  • x_mean (NDVar | scalar | tuple) – Mean that was subtracted from x.

  • x_scale (NDVar | scalar | tuple) – Scale by which x was divided.

  • splits (Splits) – Data splits used for cross-validation. Use splits.plot() to visualize the cross-validation scheme.

  • partition_results (list of BoostingResuls) – If boosting() is called with partition_results=True, this attribute contains the results for the individual test paritions.

  • algorithm_version (int) –

    Version of the algorithm with which the model was estimated

    • -1: results from before this attribute was added

    • 0: Normalize x after applying basis

    • 1: Numba implementation

    • 2: Cython multiprocessing implementation (Eelbrain 0.38)

Methods

cross_predict([x, ds, name])

Predict responses to x using complementary training data

partition_result_data()

Results from the different test partitions in a Dataset