Interpretation of Predictions

Classes to build objects to better interpret predictions of a model

Interpretation


def Interpretation(
    learn:Learner, dl:DataLoader, # [`DataLoader`](https://docs.fast.ai/data.load.html#dataloader) to run inference over
    losses:TensorBase, # Losses calculated from `dl`
    act:NoneType=None, # Activation function for prediction
):

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

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


def from_learner(
    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
):

Construct interpretation object from a learner

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Interpretation.top_losses


def top_losses(
    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
):

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

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


def plot_top_losses(
    k:int | MutableSequence, # Number of losses to plot
    largest:bool=True, # Sort losses by largest or smallest
    kwargs:VAR_KEYWORD
):

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

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


def show_results(
    idxs:list, # Indices of predictions and targets
    kwargs:VAR_KEYWORD
):

Show predictions and targets of idxs

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

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ClassificationInterpretation


def ClassificationInterpretation(
    learn:Learner, dl:DataLoader, # [`DataLoader`](https://docs.fast.ai/data.load.html#dataloader) to run inference over
    losses:TensorBase, # Losses calculated from `dl`
    act:NoneType=None, # Activation function for prediction
):

Interpretation methods for classification models.

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ClassificationInterpretation.confusion_matrix


def confusion_matrix(
    
):

Confusion matrix as an np.ndarray.

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ClassificationInterpretation.plot_confusion_matrix


def plot_confusion_matrix(
    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:VAR_KEYWORD
):

Plot the confusion matrix, with title and using cmap.

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ClassificationInterpretation.most_confused


def most_confused(
    min_val:int=1
):

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

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SegmentationInterpretation


def SegmentationInterpretation(
    learn:Learner, dl:DataLoader, # [`DataLoader`](https://docs.fast.ai/data.load.html#dataloader) to run inference over
    losses:TensorBase, # Losses calculated from `dl`
    act:NoneType=None, # Activation function for prediction
):

Interpretation methods for segmentation models.