class Interpretation[source]

Interpretation(learn:Learner, dl:DataLoader, losses:TensorBase, act=None)

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

	Type	Default	Details
learn	Learner		No Content
dl	DataLoader		DataLoader to run inference over
losses	TensorBase		Losses calculated from dl
act		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.

Interpretation.from_learner[source]

Interpretation.from_learner(learn, ds_idx:int=1, dl: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		None	Override default or set prediction activation function

Interpretation.top_losses[source]

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.

Interpretation.plot_top_losses[source]

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			No Content

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))

Interpretation.show_results[source]

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

Show predictions and targets of idxs

	Type	Default	Details
idxs	list		Indices of predictions and targets
kwargs			No Content

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

class ClassificationInterpretation[source]

ClassificationInterpretation(learn:Learner, dl:DataLoader, losses:TensorBase, act=None) :: Interpretation

Interpretation methods for classification models.

	Type	Default	Details
learn	Learner		No Content
dl	DataLoader		DataLoader to run inference over
losses	TensorBase		Losses calculated from dl
act		None	Activation function for prediction

ClassificationInterpretation.confusion_matrix[source]

ClassificationInterpretation.confusion_matrix()

Confusion matrix as an np.ndarray.

ClassificationInterpretation.plot_confusion_matrix[source]

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			No Content

ClassificationInterpretation.most_confused[source]

ClassificationInterpretation.most_confused(min_val=1)

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

class SegmentationInterpretation[source]

SegmentationInterpretation(learn:Learner, dl:DataLoader, losses:TensorBase, act=None) :: Interpretation

Interpretation methods for segmentation models.

	Type	Default	Details
learn	Learner		No Content
dl	DataLoader		DataLoader to run inference over
losses	TensorBase		Losses calculated from dl
act		None	Activation function for prediction