Interpretation of Predictions

Classes to build objects to better interpret predictions of a model

Interpretation

 Interpretation (learn:fastai.learner.Learner,
                 dl:fastai.data.load.DataLoader,
                 losses:fastai.torch_core.TensorBase, act=None)

Interpretation base class, can be inherited for task specific Interpretation classes

	Type	Default	Details
learn	Learner
dl	DataLoader		`DataLoader` to run inference over
losses	TensorBase		Losses calculated from `dl`
act	NoneType	None	Activation function for prediction

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.

Note

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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Interpretation.from_learner

 Interpretation.from_learner (learn, ds_idx:int=1,
                              dl:fastai.data.load.DataLoader=None,
                              act=None)

Construct interpretation object from a learner

	Type	Default	Details
learn			Model used to create interpretation
ds_idx	int	1	Index of `learn.dls` when `dl` is None
dl	DataLoader	None	`Dataloader` used to make predictions
act	NoneType	None	Override default or set prediction activation function

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Interpretation.top_losses

 Interpretation.top_losses (k:int|None=None, largest:bool=True,
                            items:bool=False)

k largest(/smallest) losses and indexes, defaulting to all losses.

	Type	Default	Details
k	int \| None	None	Return `k` losses, defaults to all
largest	bool	True	Sort losses by largest or smallest
items	bool	False	Whether to return input items

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.

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Interpretation.plot_top_losses

 Interpretation.plot_top_losses (k:int|list, largest:bool=True, **kwargs)

Show k largest(/smallest) preds and losses. Implementation based on type dispatch

	Type	Default	Details
k	int \| list		Number of losses to plot
largest	bool	True	Sort losses by largest or smallest
kwargs

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))

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Interpretation.show_results

 Interpretation.show_results (idxs:list, **kwargs)

Show predictions and targets of idxs

	Type	Details
idxs	list	Indices of predictions and targets
kwargs

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

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ClassificationInterpretation

 ClassificationInterpretation (learn:fastai.learner.Learner,
                               dl:fastai.data.load.DataLoader,
                               losses:fastai.torch_core.TensorBase,
                               act=None)

Interpretation methods for classification models.

	Type	Default	Details
learn	Learner
dl	DataLoader		`DataLoader` to run inference over
losses	TensorBase		Losses calculated from `dl`
act	NoneType	None	Activation function for prediction

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ClassificationInterpretation.confusion_matrix

 ClassificationInterpretation.confusion_matrix ()

Confusion matrix as an np.ndarray.

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ClassificationInterpretation.plot_confusion_matrix

 ClassificationInterpretation.plot_confusion_matrix (normalize:bool=False,
                                                     title:str='Confusion
                                                     matrix',
                                                     cmap:str='Blues',
                                                     norm_dec:int=2,
                                                     plot_txt:bool=True,
                                                     **kwargs)

Plot the confusion matrix, with title and using cmap.

	Type	Default	Details
normalize	bool	False	Whether to normalize occurrences
title	str	Confusion matrix	Title of plot
cmap	str	Blues	Colormap from matplotlib
norm_dec	int	2	Decimal places for normalized occurrences
plot_txt	bool	True	Display occurrence in matrix
kwargs

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ClassificationInterpretation.most_confused

 ClassificationInterpretation.most_confused (min_val=1)

Sorted descending largest non-diagonal entries of confusion matrix (actual, predicted, # occurrences

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SegmentationInterpretation

 SegmentationInterpretation (learn:fastai.learner.Learner,
                             dl:fastai.data.load.DataLoader,
                             losses:fastai.torch_core.TensorBase,
                             act=None)

Interpretation methods for segmentation models.

	Type	Default	Details
learn	Learner
dl	DataLoader		`DataLoader` to run inference over
losses	TensorBase		Losses calculated from `dl`
act	NoneType	None	Activation function for prediction