fiftyone.utils.eval.classification.BinaryClassificationResults

class documentation

class BinaryClassificationResults(ClassificationResults): (source)

Class that stores the results of a binary classification evaluation.

Any missing ground truth or prediction labels are assumed to be examples of the negative class (with zero confidence, for predictions).

Parameters
samples	the `fiftyone.core.collections.SampleCollection` used
config	the `ClassificationEvaluationConfig` used
eval_key	the evaluation key
ytrue	a list of ground truth labels
ypred	a list of predicted labels
confs	a list of confidences for the predictions
classes	the `(neg_label, pos_label)` label strings for the task
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
custom_metrics	an optional dict of custom metrics
backend	a `ClassificationEvaluation` backend

Method	`__init__`	Undocumented
Method	`average_precision`	Computes the average precision for the results via `sklearn:sklearn.metrics.average_precision_score`.
Method	`plot_pr_curve`	Plots a precision-recall (PR) curve for the results.
Method	`plot_roc_curve`	Plots a receiver operating characteristic (ROC) curve for the results.
Instance Variable	`scores`	Undocumented
Class Method	`_from_dict`	Subclass implementation of `from_dict`.
Method	`_parse_classes`	Undocumented
Instance Variable	`_pos_label`	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	`ypred`	Undocumented
Instance Variable	`ytrue`	Undocumented
Method	`_confusion_matrix`	Undocumented

Method	`_get_custom_metrics`	Undocumented
Method	`_print_metrics`	Undocumented

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, classes, weights=None, ytrue_ids=None, ypred_ids=None, custom_metrics=None, backend=None): (source) ¶

overrides fiftyone.utils.eval.base.BaseClassificationResults.__init__

Undocumented

def average_precision(self, average='micro'): (source) ¶

Computes the average precision for the results via sklearn:sklearn.metrics.average_precision_score.

Parameters
average:"micro"	the averaging strategy to use
Returns
the average precision

def plot_pr_curve(self, average='micro', backend='plotly', **kwargs): (source) ¶

Plots a precision-recall (PR) curve for the results.

Parameters
average:"micro"	the averaging strategy to use when computing average precision
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_pr_curve` "matplotlib" backend: `fiftyone.core.plots.matplotlib.plot_pr_curve`
Returns
`one` of the following	a `fiftyone.core.plots.plotly.PlotlyNotebookPlot`, if you are working in a notebook context and the plotly backend is used a plotly or matplotlib figure, otherwise

def plot_roc_curve(self, backend='plotly', **kwargs): (source) ¶

Plots a receiver operating characteristic (ROC) curve for the results.

Parameters
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_roc_curve` "matplotlib" backend: `fiftyone.core.plots.matplotlib.plot_roc_curve`
Returns
`one` of the following	a `fiftyone.core.plots.plotly.PlotlyNotebookPlot`, if you are working in a notebook context and the plotly backend is used a plotly or matplotlib figure, otherwise

Undocumented

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

overrides fiftyone.utils.eval.base.BaseClassificationResults._from_dict

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 _parse_classes(self, classes): (source) ¶

overrides fiftyone.utils.eval.base.BaseClassificationResults._parse_classes

Undocumented

Undocumented