kwcoco.metrics.drawing

Module Contents

Functions

draw_perclass_roc(cx_to_info, classes=None, prefix='', fnum=1, fp_axis='count', **kw)	Parameters cx_to_info (PerClass_Measures | Dict)
inty_display(val, eps=1e-08, ndigits=2)	Make a number as inty as possible
_realpos_label_suffix(info)	Creates a label suffix that indicates the number of real positive cases
draw_perclass_prcurve(cx_to_info, classes=None, prefix='', fnum=1, **kw)	Parameters cx_to_info (PerClass_Measures | Dict)
draw_perclass_thresholds(cx_to_info, key='mcc', classes=None, prefix='', fnum=1, **kw)	Parameters cx_to_info (PerClass_Measures | Dict)
draw_roc(info, prefix='', fnum=1, **kw)	Parameters info (Measures | Dict)
draw_prcurve(info, prefix='', fnum=1, **kw)	Draws a single pr curve.
draw_threshold_curves(info, keys=None, prefix='', fnum=1, **kw)	Parameters info (Measures | Dict)

kwcoco.metrics.drawing.draw_perclass_roc(cx_to_info, classes=None, prefix='', fnum=1, fp_axis='count', **kw)

Parameters

• cx_to_info (PerClass_Measures | Dict)

• fp_axis (str) – can be count or rate

kwcoco.metrics.drawing.inty_display(val, eps=1e-08, ndigits=2)

Make a number as inty as possible

kwcoco.metrics.drawing._realpos_label_suffix(info)

Creates a label suffix that indicates the number of real positive cases versus the total amount of cases considered for an evaluation curve.

Parameters

info (Dict) – with keys, nsuppert, realpos_total

Example

>>> info = {'nsupport': 10, 'realpos_total': 10}
>>> _realpos_label_suffix(info)
10/10
>>> info = {'nsupport': 10.0, 'realpos_total': 10.0}
>>> _realpos_label_suffix(info)
10/10
>>> info = {'nsupport': 10.3333, 'realpos_total': 10.22222}
>>> _realpos_label_suffix(info)
10.22/10.33
>>> info = {'nsupport': 10.000000001, 'realpos_total': None}
>>> _realpos_label_suffix(info)
10
>>> info = {'nsupport': 10.009}
>>> _realpos_label_suffix(info)
10.01

kwcoco.metrics.drawing.draw_perclass_prcurve(cx_to_info, classes=None, prefix='', fnum=1, **kw)

Parameters

cx_to_info (PerClass_Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=3, nboxes=(0, 10), n_fp=(0, 3), n_fn=(0, 2), classes=3, score_noise=0.1, box_noise=0.1, with_probs=False)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> print(cfsn_vecs.data.pandas())
>>> classes = cfsn_vecs.classes
>>> cx_to_info = cfsn_vecs.binarize_ovr().measures()['perclass']
>>> print('cx_to_info = {}'.format(ub.repr2(cx_to_info, nl=1)))
>>> import kwplot
>>> kwplot.autompl()
>>> draw_perclass_prcurve(cx_to_info, classes)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_perclass_thresholds(cx_to_info, key='mcc', classes=None, prefix='', fnum=1, **kw)

Parameters

cx_to_info (PerClass_Measures | Dict)

Note

Each category is inspected independently of one another, there is no notion of confusion.

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import ConfusionVectors
>>> cfsn_vecs = ConfusionVectors.demo()
>>> classes = cfsn_vecs.classes
>>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
>>> cx_to_info = ovr_cfsn.measures()['perclass']
>>> import kwplot
>>> kwplot.autompl()
>>> key = 'mcc'
>>> draw_perclass_thresholds(cx_to_info, key, classes)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_roc(info, prefix='', fnum=1, **kw)

Parameters

info (Measures | Dict)

Note

There needs to be enough negative examples for using ROC to make any sense!

Example

>>> # xdoctest: +REQUIRES(module:kwplot, module:seaborn)
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, classes=3,
>>>                              nboxes=10, n_fp=10, box_noise=0.3,
>>>                              with_probs=False)
>>> dmet.true_detections(0).data
>>> cfsn_vecs = dmet.confusion_vectors(compat='mutex', prioritize='iou', bias=0)
>>> print(cfsn_vecs.data._pandas().sort_values('score'))
>>> classes = cfsn_vecs.classes
>>> info = ub.peek(cfsn_vecs.binarize_ovr().measures()['perclass'].values())
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> draw_roc(info)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_prcurve(info, prefix='', fnum=1, **kw)

Draws a single pr curve.

Parameters

info (Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()

>>> classes = cfsn_vecs.classes
>>> info = cfsn_vecs.binarize_classless().measures()
>>> import kwplot
>>> kwplot.autompl()
>>> draw_prcurve(info)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()

kwcoco.metrics.drawing.draw_threshold_curves(info, keys=None, prefix='', fnum=1, **kw)

Parameters

info (Measures | Dict)

Example

>>> # xdoctest: +REQUIRES(module:kwplot)
>>> import sys, ubelt
>>> sys.path.append(ubelt.expandpath('~/code/kwcoco'))
>>> from kwcoco.metrics.drawing import *  # NOQA
>>> from kwcoco.metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3)
>>> cfsn_vecs = dmet.confusion_vectors()
>>> info = cfsn_vecs.binarize_classless().measures()
>>> keys = None
>>> import kwplot
>>> kwplot.autompl()
>>> draw_threshold_curves(info, keys)
>>> # xdoctest: +REQUIRES(--show)
>>> kwplot.show_if_requested()