Interpretation of Predictions

Classes to build objects to better interpret predictions of a model

Interpretation is a helper base class for exploring predictions from trained models. It can be inherited for task specific interpretation classes, such as ClassificationInterpretation. Interpretation is memory efficient and should be able to process any sized dataset, provided the hardware could train the same model.

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Interpretation is memory efficient due to generating inputs, predictions, targets, decoded outputs, and losses for each item on the fly, using batch processing where possible.

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With the default of k=None, top_losses will return the entire dataset's losses. top_losses can optionally include the input items for each loss, which is usually a file path or Pandas DataFrame.

To plot the first 9 top losses:

interp = Interpretation.from_learner(learn)
interp.plot_top_losses(9)

Then to plot the 7th through 16th top losses:

interp.plot_top_losses(range(7,16))

Like Learner.show_results, except can pass desired index or indicies for item(s) to show results from.

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