fiftyone.utils.eval.base.BaseClassificationResults

class documentation

class BaseClassificationResults(BaseEvaluationResults): (source)

Known subclasses: fiftyone.utils.eval.classification.ClassificationResults, fiftyone.utils.eval.detection.DetectionResults, fiftyone.utils.eval.segmentation.SegmentationResults

Constructor: BaseClassificationResults(samples, config, eval_key, ytrue, ...)

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Base class for evaluation results that expose classification metrics like P/R/F1 and confusion matrices.

Parameters
samples	the `fiftyone.core.collections.SampleCollection` used
config	the `fiftyone.core.evaluation.EvaluationMethodConfig` used
eval_key	the evaluation key
ytrue	a list of ground truth labels
ypred	a list of predicted labels
confs	an optional list of confidences for the predictions
weights	an optional list of sample weights
ytrue_ids	a list of IDs for the ground truth labels
ypred_ids	a list of IDs for the predicted labels
classes	the list of possible classes. If not provided, the observed ground truth/predicted labels are used
missing	a missing label string. Any None-valued labels are given this label for evaluation purposes
custom_metrics	an optional dict of custom metrics
backend	a `fiftyone.core.evaluation.EvaluationMethod` backend

Method	`__init__`	Undocumented
Method	`confusion_matrix`	Generates a confusion matrix for the results via `sklearn:sklearn.metrics.confusion_matrix`.
Method	`metrics`	Computes classification metrics for the results, including accuracy, precision, recall, and F-beta score.
Method	`plot_confusion_matrix`	Plots a confusion matrix for the evaluation results.
Method	`print_metrics`	Prints the metrics computed via `metrics`.
Method	`print_report`	Prints a classification report for the results via `sklearn:sklearn.metrics.classification_report`.
Method	`report`	Generates a classification report for the results via `sklearn:sklearn.metrics.classification_report`.
Instance Variable	`classes`	Undocumented
Instance Variable	`confs`	Undocumented
Instance Variable	`missing`	Undocumented
Instance Variable	`weights`	Undocumented
Instance Variable	`ypred`	Undocumented
Instance Variable	`ypred_ids`	Undocumented
Instance Variable	`ytrue`	Undocumented
Instance Variable	`ytrue_ids`	Undocumented
Class Method	`_from_dict`	Subclass implementation of `from_dict`.
Method	`_confusion_matrix`	Undocumented
Method	`_parse_classes`	Undocumented

Inherited from BaseEvaluationResults:

Method	`_get_custom_metrics`	Undocumented
Method	`_print_metrics`	Undocumented

Inherited from BaseRunResults (via BaseEvaluationResults, EvaluationResults):

Class Method	`from_dict`	Builds a `BaseRunResults` from a JSON dict representation of it.
Static Method	`base_results_cls`	Returns the results class for the given run type.
Method	`attributes`	Returns the list of class attributes that will be serialized by `serialize`.
Method	`save`	Saves the results to the database.
Method	`save_config`	Saves these results config to the database.
Property	`cls`	The fully-qualified name of this `BaseRunResults` class.
Property	`config`	The `BaseRunConfig` for these results.
Property	`key`	The run key for these results.
Property	`samples`	The `fiftyone.core.collections.SampleCollection` associated with these results.
Instance Variable	`_backend`	Undocumented
Instance Variable	`_config`	Undocumented
Instance Variable	`_key`	Undocumented
Instance Variable	`_samples`	Undocumented

def __init__(self, samples, config, eval_key, ytrue, ypred, confs=None, weights=None, ytrue_ids=None, ypred_ids=None, classes=None, missing=None, custom_metrics=None, backend=None): (source) ¶

overrides fiftyone.utils.eval.base.BaseEvaluationResults.__init__

overridden in fiftyone.utils.eval.classification.BinaryClassificationResults, fiftyone.utils.eval.detection.DetectionResults, fiftyone.utils.eval.segmentation.SegmentationResults

Undocumented

def confusion_matrix(self, classes=None, include_other=False, include_missing=False): (source) ¶

Generates a confusion matrix for the results via sklearn:sklearn.metrics.confusion_matrix.

The rows of the confusion matrix represent ground truth and the columns represent predictions.

Parameters
classes:`None`	an optional list of classes to include in the confusion matrix
include_other:`False`	whether to include an extra row/column at the end of the matrix for labels that do not appear in `classes`. Only applicable if `classes` are provided
include_missing:`False`	whether to include a row/column at the end of the matrix for unmatched labels. Only applicable if `self.missing` does not already appear in `classes`. If both "other" and "missing" rows/columns are requested, this one is last
Returns
a `num_classes x num_classes` confusion matrix

def metrics(self, classes=None, average='micro', beta=1.0): (source) ¶

overrides fiftyone.utils.eval.base.BaseEvaluationResults.metrics

Computes classification metrics for the results, including accuracy, precision, recall, and F-beta score.

See sklearn:sklearn.metrics.precision_recall_fscore_support for details.

Also includes any custom metrics from custom_metrics.

Parameters
classes:`None`	an optional list of classes to include in the calculations
average:"micro"	the averaging strategy to use
beta:1.0	the F-beta value to use
Returns
a dict

def plot_confusion_matrix(self, classes=None, include_other=None, include_missing=None, other_label='(other)', backend='plotly', **kwargs): (source) ¶

Plots a confusion matrix for the evaluation results.

If you are working in a notebook environment with the default plotly backend, this method returns an interactive fiftyone.core.plots.plotly.InteractiveHeatmap that you can attach to an App session via its fiftyone.core.session.Session.plots attribute, which will automatically sync the session's view with the currently selected cells in the confusion matrix.

Parameters
classes:`None`	an optional list of classes to include in the confusion matrix
include_other:`None`	whether to include a row/column for examples whose label is in `classes` but are matched to labels that do not appear in `classes`. Only applicable if `classes` are provided. The supported values are: None (default): only include a row/column for other labels if there are any True: do include a row/column for other labels False: do not include a row/column for other labels
include_missing:`None`	whether to include a row/column for missing ground truth/predictions in the confusion matrix. The supported values are: None (default): only include a row/column for missing labels if there are any True: do include a row/column for missing labels False: do not include a row/column for missing labels
other_label:"(other)"	the label to use for "other" predictions
backend:"plotly"	the plotting backend to use. Supported values are `("plotly", "matplotlib")`
**kwargs	keyword arguments for the backend plotting method: "plotly" backend: `fiftyone.core.plots.plotly.plot_confusion_matrix` "matplotlib" backend: `fiftyone.core.plots.matplotlib.plot_confusion_matrix`
Returns
`one` of the following	a `fiftyone.core.plots.plotly.InteractiveHeatmap`, if the plotly backend is used a matplotlib figure, otherwise

def print_metrics(self, classes=None, average='micro', beta=1.0, digits=2): (source) ¶

overrides fiftyone.utils.eval.base.BaseEvaluationResults.print_metrics

Prints the metrics computed via metrics.

Parameters
classes:`None`	an optional list of classes to include in the calculations
average:"micro"	the averaging strategy to use
beta:1.0	the F-beta value to use
digits:2	the number of digits of precision to print

def print_report(self, classes=None, digits=2): (source) ¶

Prints a classification report for the results via sklearn:sklearn.metrics.classification_report.

Parameters
classes:`None`	an optional list of classes to include in the report
digits:2	the number of digits of precision to print

def report(self, classes=None): (source) ¶

Generates a classification report for the results via sklearn:sklearn.metrics.classification_report.

Parameters
classes:`None`	an optional list of classes to include in the report
Returns
a dict

classes: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults, fiftyone.utils.eval.detection.DetectionResults

Undocumented

confs: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults

Undocumented

missing: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults, fiftyone.utils.eval.detection.DetectionResults, fiftyone.utils.eval.segmentation.SegmentationResults

Undocumented

weights: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults

Undocumented

ypred = (source) ¶

Undocumented

ypred_ids: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults

Undocumented

ytrue = (source) ¶

Undocumented

ytrue_ids: None = (source) ¶

overridden in fiftyone.utils.eval.classification.ClassificationResults

Undocumented

@classmethod
def _from_dict(cls, d, samples, config, eval_key, **kwargs): (source) ¶

overrides fiftyone.core.runs.BaseRunResults._from_dict

overridden in fiftyone.utils.eval.classification.BinaryClassificationResults, fiftyone.utils.eval.detection.DetectionResults, fiftyone.utils.eval.segmentation.SegmentationResults

Subclass implementation of from_dict.

Parameters
d	a JSON dict
samples	the `fiftyone.core.collections.SampleCollection` for the run
config	the `BaseRunConfig` for the run
eval_key	Undocumented
key	the run key
**kwargs	Undocumented
Returns
a `BaseRunResults`

def _confusion_matrix(self, classes=None, include_other=None, include_missing=None, other_label=None, tabulate_ids=False): (source) ¶

Undocumented

def _parse_classes(self, classes): (source) ¶

overridden in fiftyone.utils.eval.classification.BinaryClassificationResults

Undocumented