:py:mod:`kwcoco.metrics.drawing`
================================

.. py:module:: kwcoco.metrics.drawing


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.metrics.drawing.draw_perclass_roc
   kwcoco.metrics.drawing.inty_display
   kwcoco.metrics.drawing._realpos_label_suffix
   kwcoco.metrics.drawing.draw_perclass_prcurve
   kwcoco.metrics.drawing.draw_perclass_thresholds
   kwcoco.metrics.drawing.draw_roc
   kwcoco.metrics.drawing.draw_prcurve
   kwcoco.metrics.drawing.draw_threshold_curves



.. py:function:: 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


.. py:function:: inty_display(val, eps=1e-08, ndigits=2)

   Make a number as inty as possible


.. py:function:: _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

   .. rubric:: 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


.. py:function:: draw_perclass_prcurve(cx_to_info, classes=None, prefix='', fnum=1, **kw)

   :Parameters: **cx_to_info** (*PerClass_Measures | Dict*)

   .. rubric:: 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()


.. py:function:: 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.

   .. rubric:: 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()


.. py:function:: 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!

   .. rubric:: 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()


.. py:function:: draw_prcurve(info, prefix='', fnum=1, **kw)

   Draws a single pr curve.

   :Parameters: **info** (*Measures | Dict*)

   .. rubric:: 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()


.. py:function:: draw_threshold_curves(info, keys=None, prefix='', fnum=1, **kw)

   :Parameters: **info** (*Measures | Dict*)

   .. rubric:: 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()