:py:mod:`kwcoco.metrics` ======================== .. py:module:: kwcoco.metrics .. autoapi-nested-parse:: mkinit kwcoco.metrics -w --relative Submodules ---------- .. toctree:: :titlesonly: :maxdepth: 1 assignment/index.rst clf_report/index.rst confusion_measures/index.rst confusion_vectors/index.rst detect_metrics/index.rst drawing/index.rst functional/index.rst sklearn_alts/index.rst util/index.rst voc_metrics/index.rst Package Contents ---------------- Classes ~~~~~~~ .. autoapisummary:: kwcoco.metrics.DetectionMetrics kwcoco.metrics.BinaryConfusionVectors kwcoco.metrics.ConfusionVectors kwcoco.metrics.Measures kwcoco.metrics.OneVsRestConfusionVectors kwcoco.metrics.PerClass_Measures Functions ~~~~~~~~~ .. autoapisummary:: kwcoco.metrics.eval_detections_cli .. py:class:: DetectionMetrics(dmet, classes=None) Bases: :py:obj:`ubelt.NiceRepr` Object that computes associations between detections and can convert them into sklearn-compatible representations for scoring. :ivar gid_to_true_dets: maps image ids to truth :vartype gid_to_true_dets: Dict :ivar gid_to_pred_dets: maps image ids to predictions :vartype gid_to_pred_dets: Dict :ivar classes: category coder :vartype classes: CategoryTree .. rubric:: Example >>> dmet = DetectionMetrics.demo( >>> nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8, score_noise=0.9, hacked=False) >>> print(dmet.score_kwcoco(bias=0, compat='mutex', prioritize='iou')['mAP']) ... >>> # NOTE: IN GENERAL NETHARN AND VOC ARE NOT THE SAME >>> print(dmet.score_voc(bias=0)['mAP']) 0.8582... >>> #print(dmet.score_coco()['mAP']) .. py:attribute:: score_coco .. py:method:: clear(dmet) .. py:method:: __nice__(dmet) .. py:method:: from_coco(DetectionMetrics, true_coco, pred_coco, gids=None, verbose=0) :classmethod: Create detection metrics from two coco files representing the truth and predictions. :Parameters: * **true_coco** (*kwcoco.CocoDataset*) * **pred_coco** (*kwcoco.CocoDataset*) .. rubric:: Example >>> import kwcoco >>> from kwcoco.demo.perterb import perterb_coco >>> true_coco = kwcoco.CocoDataset.demo('shapes') >>> perterbkw = dict(box_noise=0.5, cls_noise=0.5, score_noise=0.5) >>> pred_coco = perterb_coco(true_coco, **perterbkw) >>> self = DetectionMetrics.from_coco(true_coco, pred_coco) >>> self.score_voc() .. py:method:: _register_imagename(dmet, imgname, gid=None) .. py:method:: add_predictions(dmet, pred_dets, imgname=None, gid=None) Register/Add predicted detections for an image :Parameters: * **pred_dets** (*Detections*) -- predicted detections * **imgname** (*str*) -- a unique string to identify the image * **gid** (*int, optional*) -- the integer image id if known .. py:method:: add_truth(dmet, true_dets, imgname=None, gid=None) Register/Add groundtruth detections for an image :Parameters: * **true_dets** (*Detections*) -- groundtruth * **imgname** (*str*) -- a unique string to identify the image * **gid** (*int, optional*) -- the integer image id if known .. py:method:: true_detections(dmet, gid) gets Detections representation for groundtruth in an image .. py:method:: pred_detections(dmet, gid) gets Detections representation for predictions in an image .. py:method:: confusion_vectors(dmet, iou_thresh=0.5, bias=0, gids=None, compat='mutex', prioritize='iou', ignore_classes='ignore', background_class=ub.NoParam, verbose='auto', workers=0, track_probs='try', max_dets=None) Assigns predicted boxes to the true boxes so we can transform the detection problem into a classification problem for scoring. :Parameters: * **iou_thresh** (*float | List[float], default=0.5*) -- bounding box overlap iou threshold required for assignment if a list, then return type is a dict * **bias** (*float, default=0.0*) -- for computing bounding box overlap, either 1 or 0 * **gids** (*List[int], default=None*) -- which subset of images ids to compute confusion metrics on. If not specified all images are used. * **compat** (*str, default='all'*) -- can be ('ancestors' | 'mutex' | 'all'). determines which pred boxes are allowed to match which true boxes. If 'mutex', then pred boxes can only match true boxes of the same class. If 'ancestors', then pred boxes can match true boxes that match or have a coarser label. If 'all', then any pred can match any true, regardless of its category label. * **prioritize** (*str, default='iou'*) -- can be ('iou' | 'class' | 'correct') determines which box to assign to if mutiple true boxes overlap a predicted box. if prioritize is iou, then the true box with maximum iou (above iou_thresh) will be chosen. If prioritize is class, then it will prefer matching a compatible class above a higher iou. If prioritize is correct, then ancestors of the true class are preferred over descendents of the true class, over unreleated classes. * **ignore_classes** (*set, default={'ignore'}*) -- class names indicating ignore regions * **background_class** (*str, default=ub.NoParam*) -- Name of the background class. If unspecified we try to determine it with heuristics. A value of None means there is no background class. * **verbose** (*int, default='auto'*) -- verbosity flag. In auto mode, verbose=1 if len(gids) > 1000. * **workers** (*int, default=0*) -- number of parallel assignment processes * **track_probs** (*str, default='try'*) -- can be 'try', 'force', or False. if truthy, we assume probabilities for multiple classes are available. :returns: ConfusionVectors | Dict[float, ConfusionVectors] .. rubric:: Example >>> dmet = DetectionMetrics.demo(nimgs=30, classes=3, >>> nboxes=10, n_fp=3, box_noise=10, >>> with_probs=False) >>> iou_to_cfsn = dmet.confusion_vectors(iou_thresh=[0.3, 0.5, 0.9]) >>> for t, cfsn in iou_to_cfsn.items(): >>> print('t = {!r}'.format(t)) ... print(cfsn.binarize_ovr().measures()) ... print(cfsn.binarize_classless().measures()) .. py:method:: score_kwant(dmet, iou_thresh=0.5) Scores the detections using kwant .. py:method:: score_kwcoco(dmet, iou_thresh=0.5, bias=0, gids=None, compat='all', prioritize='iou') our scoring method .. py:method:: score_voc(dmet, iou_thresh=0.5, bias=1, method='voc2012', gids=None, ignore_classes='ignore') score using voc method .. rubric:: Example >>> dmet = DetectionMetrics.demo( >>> nimgs=100, nboxes=(0, 3), n_fp=(0, 1), classes=8, >>> score_noise=.5) >>> print(dmet.score_voc()['mAP']) 0.9399... .. py:method:: _to_coco(dmet) Convert to a coco representation of truth and predictions with inverse aid mappings .. py:method:: score_pycocotools(dmet, with_evaler=False, with_confusion=False, verbose=0, iou_thresholds=None) score using ms-coco method :returns: dictionary with pct info :rtype: Dict .. rubric:: Example >>> # xdoctest: +REQUIRES(module:pycocotools) >>> from kwcoco.metrics.detect_metrics import * >>> dmet = DetectionMetrics.demo( >>> nimgs=10, nboxes=(0, 3), n_fn=(0, 1), n_fp=(0, 1), classes=8, with_probs=False) >>> pct_info = dmet.score_pycocotools(verbose=1, >>> with_evaler=True, >>> with_confusion=True, >>> iou_thresholds=[0.5, 0.9]) >>> evaler = pct_info['evaler'] >>> iou_to_cfsn_vecs = pct_info['iou_to_cfsn_vecs'] >>> for iou_thresh in iou_to_cfsn_vecs.keys(): >>> print('iou_thresh = {!r}'.format(iou_thresh)) >>> cfsn_vecs = iou_to_cfsn_vecs[iou_thresh] >>> ovr_measures = cfsn_vecs.binarize_ovr().measures() >>> print('ovr_measures = {}'.format(ub.repr2(ovr_measures, nl=1, precision=4))) .. note:: by default pycocotools computes average precision as the literal average of computed precisions at 101 uniformly spaced recall thresholds. pycocoutils seems to only allow predictions with the same category as the truth to match those truth objects. This should be the same as calling dmet.confusion_vectors with compat = mutex pycocoutils does not take into account the fact that each box often has a score for each category. pycocoutils will be incorrect if any annotation has an id of 0 a major difference in the way kwcoco scores versus pycocoutils is the calculation of AP. The assignment between truth and predicted detections produces similar enough results. Given our confusion vectors we use the scikit-learn definition of AP, whereas pycocoutils seems to compute precision and recall --- more or less correctly --- but then it resamples the precision at various specified recall thresholds (in the `accumulate` function, specifically how `pr` is resampled into the `q` array). This can lead to a large difference in reported scores. pycocoutils also smooths out the precision such that it is monotonic decreasing, which might not be the best idea. pycocotools area ranges are inclusive on both ends, that means the "small" and "medium" truth selections do overlap somewhat. .. py:method:: demo(cls, **kwargs) :classmethod: Creates random true boxes and predicted boxes that have some noisy offset from the truth. Kwargs: classes (int, default=1): class list or the number of foreground classes. nimgs (int, default=1): number of images in the coco datasts. nboxes (int, default=1): boxes per image. n_fp (int, default=0): number of false positives. n_fn (int, default=0): number of false negatives. box_noise (float, default=0): std of a normal distribution used to perterb both box location and box size. cls_noise (float, default=0): probability that a class label will change. Must be within 0 and 1. anchors (ndarray, default=None): used to create random boxes null_pred (bool, default=0): if True, predicted classes are returned as null, which means only localization scoring is suitable. with_probs (bool, default=1): if True, includes per-class probabilities with predictions .. rubric:: CommandLine .. code-block:: bash xdoctest -m kwcoco.metrics.detect_metrics DetectionMetrics.demo:2 --show .. rubric:: Example >>> kwargs = {} >>> # Seed the RNG >>> kwargs['rng'] = 0 >>> # Size parameters determine how big the data is >>> kwargs['nimgs'] = 5 >>> kwargs['nboxes'] = 7 >>> kwargs['classes'] = 11 >>> # Noise parameters perterb predictions further from the truth >>> kwargs['n_fp'] = 3 >>> kwargs['box_noise'] = 0.1 >>> kwargs['cls_noise'] = 0.5 >>> dmet = DetectionMetrics.demo(**kwargs) >>> print('dmet.classes = {}'.format(dmet.classes)) dmet.classes = >>> # Can grab kwimage.Detection object for any image >>> print(dmet.true_detections(gid=0)) >>> print(dmet.pred_detections(gid=0)) .. rubric:: Example >>> # Test case with null predicted categories >>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, classes=3, >>> nboxes=10, n_fp=3, box_noise=0.1, >>> with_probs=False) >>> dmet.gid_to_pred_dets[0].data >>> dmet.gid_to_true_dets[0].data >>> cfsn_vecs = dmet.confusion_vectors() >>> binvecs_ovr = cfsn_vecs.binarize_ovr() >>> binvecs_per = cfsn_vecs.binarize_classless() >>> measures_per = binvecs_per.measures() >>> measures_ovr = binvecs_ovr.measures() >>> print('measures_per = {!r}'.format(measures_per)) >>> print('measures_ovr = {!r}'.format(measures_ovr)) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> measures_ovr['perclass'].draw(key='pr', fnum=2) .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> from kwcoco.metrics.detect_metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> n_fp=(0, 1), n_fn=(0, 1), nimgs=32, nboxes=(0, 16), >>> classes=3, rng=0, newstyle=1, box_noise=0.5, cls_noise=0.0, score_noise=0.3, with_probs=False) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> summary = dmet.summarize(plot=True, title='DetectionMetrics summary demo', with_ovr=True, with_bin=False) >>> summary['bin_measures'] >>> kwplot.show_if_requested() .. py:method:: summarize(dmet, out_dpath=None, plot=False, title='', with_bin='auto', with_ovr='auto') .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> from kwcoco.metrics.detect_metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> n_fp=(0, 128), n_fn=(0, 4), nimgs=512, nboxes=(0, 32), >>> classes=3, rng=0) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> dmet.summarize(plot=True, title='DetectionMetrics summary demo') >>> kwplot.show_if_requested() .. py:function:: eval_detections_cli(**kw) DEPRECATED USE `kwcoco eval` instead .. rubric:: CommandLine .. code-block:: bash xdoctest -m ~/code/kwcoco/kwcoco/metrics/detect_metrics.py eval_detections_cli .. py:class:: BinaryConfusionVectors(data, cx=None, classes=None) Bases: :py:obj:`ubelt.NiceRepr` Stores information about a binary classification problem. This is always with respect to a specific class, which is given by `cx` and `classes`. The `data` DataFrameArray must contain `is_true` - if the row is an instance of class `classes[cx]` `pred_score` - the predicted probability of class `classes[cx]`, and `weight` - sample weight of the example .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> self = BinaryConfusionVectors.demo(n=10) >>> print('self = {!r}'.format(self)) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=0) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=1) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=2) >>> print('measures = {}'.format(ub.repr2(self.measures()))) .. py:method:: demo(cls, n=10, p_true=0.5, p_error=0.2, p_miss=0.0, rng=None) :classmethod: Create random data for tests :Parameters: * **n** (*int*) -- number of rows * **p_true** (*int*) -- fraction of real positive cases * **p_error** (*int*) -- probability of making a recoverable mistake * **p_miss** (*int*) -- probability of making a unrecoverable mistake * **rng** (*int | RandomState*) -- random seed / state :returns: BinaryConfusionVectors .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> cfsn = BinaryConfusionVectors.demo(n=1000, p_error=0.1, p_miss=0.1) >>> measures = cfsn.measures() >>> print('measures = {}'.format(ub.repr2(measures, nl=1))) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> kwplot.figure(fnum=1, pnum=(1, 2, 1)) >>> measures.draw('pr') >>> kwplot.figure(fnum=1, pnum=(1, 2, 2)) >>> measures.draw('roc') .. py:method:: catname(self) :property: .. py:method:: __nice__(self) .. py:method:: __len__(self) .. py:method:: measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools') Get statistics (F1, G1, MCC) versus thresholds :Parameters: * **stabalize_thresh** (*int, default=7*) -- if fewer than this many data points inserts dummy stabalization data so curves can still be drawn. * **fp_cutoff** (*int, default=None*) -- maximum number of false positives in the truncated roc curves. ``None`` is equivalent to ``float('inf')`` * **monotonic_ppv** (*bool, default=True*) -- if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I'm not sure its a good idea. .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> self = BinaryConfusionVectors.demo(n=0) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=3, p_true=0.5, p_error=0.5) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5, p_miss=0.3) >>> print('measures = {}'.format(ub.repr2(self.measures()))) >>> print('measures = {}'.format(ub.repr2(ub.odict(self.measures())))) .. rubric:: References https://en.wikipedia.org/wiki/Confusion_matrix https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Matthews_correlation_coefficient .. py:method:: _binary_clf_curves(self, stabalize_thresh=7, fp_cutoff=None) Compute TP, FP, TN, and FN counts for this binary confusion vector. Code common to ROC, PR, and threshold measures, computes the elements of the binary confusion matrix at all relevant operating point thresholds. :Parameters: * **stabalize_thresh** (*int*) -- if fewer than this many data points insert stabalization data. * **fp_cutoff** (*int*) -- maximum number of false positives .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> self = BinaryConfusionVectors.demo(n=1, p_true=0.5, p_error=0.5) >>> self._binary_clf_curves() >>> self = BinaryConfusionVectors.demo(n=0, p_true=0.5, p_error=0.5) >>> self._binary_clf_curves() >>> self = BinaryConfusionVectors.demo(n=100, p_true=0.5, p_error=0.5) >>> self._binary_clf_curves() .. py:method:: draw_distribution(self) .. py:method:: _3dplot(self) .. rubric:: Example >>> # xdoctest: +REQUIRES(module:kwplot) >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> from kwcoco.metrics.detect_metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> n_fp=(0, 1), n_fn=(0, 2), nimgs=256, nboxes=(0, 10), >>> bbox_noise=10, >>> classes=1) >>> cfsn_vecs = dmet.confusion_vectors() >>> self = bin_cfsn = cfsn_vecs.binarize_classless() >>> #dmet.summarize(plot=True) >>> import kwplot >>> kwplot.autompl() >>> kwplot.figure(fnum=3) >>> self._3dplot() .. py:class:: ConfusionVectors(cfsn_vecs, data, classes, probs=None) Bases: :py:obj:`ubelt.NiceRepr` Stores information used to construct a confusion matrix. This includes corresponding vectors of predicted labels, true labels, sample weights, etc... :ivar data: should at least have keys true, pred, weight :vartype data: kwarray.DataFrameArray :ivar classes: list of category names or category graph :vartype classes: Sequence | CategoryTree :ivar probs: probabilities for each class :vartype probs: ndarray, optional .. rubric:: Example >>> # xdoctest: IGNORE_WANT >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3) >>> cfsn_vecs = dmet.confusion_vectors() >>> print(cfsn_vecs.data._pandas()) pred true score weight iou txs pxs gid 0 2 2 10.0000 1.0000 1.0000 0 4 0 1 2 2 7.5025 1.0000 1.0000 1 3 0 2 1 1 5.0050 1.0000 1.0000 2 2 0 3 3 -1 2.5075 1.0000 -1.0000 -1 1 0 4 2 -1 0.0100 1.0000 -1.0000 -1 0 0 5 -1 2 0.0000 1.0000 -1.0000 3 -1 0 6 -1 2 0.0000 1.0000 -1.0000 4 -1 0 7 2 2 10.0000 1.0000 1.0000 0 5 1 8 2 2 8.0020 1.0000 1.0000 1 4 1 9 1 1 6.0040 1.0000 1.0000 2 3 1 .. ... ... ... ... ... ... ... ... 62 -1 2 0.0000 1.0000 -1.0000 7 -1 7 63 -1 3 0.0000 1.0000 -1.0000 8 -1 7 64 -1 1 0.0000 1.0000 -1.0000 9 -1 7 65 1 -1 10.0000 1.0000 -1.0000 -1 0 8 66 1 1 0.0100 1.0000 1.0000 0 1 8 67 3 -1 10.0000 1.0000 -1.0000 -1 3 9 68 2 2 6.6700 1.0000 1.0000 0 2 9 69 2 2 3.3400 1.0000 1.0000 1 1 9 70 3 -1 0.0100 1.0000 -1.0000 -1 0 9 71 -1 2 0.0000 1.0000 -1.0000 2 -1 9 >>> # xdoctest: +REQUIRES(--show) >>> # xdoctest: +REQUIRES(module:pandas) >>> import kwplot >>> kwplot.autompl() >>> from kwcoco.metrics.confusion_vectors import ConfusionVectors >>> cfsn_vecs = ConfusionVectors.demo( >>> nimgs=128, nboxes=(0, 10), n_fp=(0, 3), n_fn=(0, 3), classes=3) >>> cx_to_binvecs = cfsn_vecs.binarize_ovr() >>> measures = cx_to_binvecs.measures()['perclass'] >>> print('measures = {!r}'.format(measures)) measures = , 'cat_2': , 'cat_3': , }) at 0x7facf77bdfd0> >>> kwplot.figure(fnum=1, doclf=True) >>> measures.draw(key='pr', fnum=1, pnum=(1, 3, 1)) >>> measures.draw(key='roc', fnum=1, pnum=(1, 3, 2)) >>> measures.draw(key='mcc', fnum=1, pnum=(1, 3, 3)) ... .. py:method:: __nice__(cfsn_vecs) .. py:method:: __json__(self) Serialize to json .. rubric:: Example >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics import ConfusionVectors >>> self = ConfusionVectors.demo(n_imgs=1, classes=2, n_fp=0, nboxes=1) >>> state = self.__json__() >>> print('state = {}'.format(ub.repr2(state, nl=2, precision=2, align=1))) >>> recon = ConfusionVectors.from_json(state) .. py:method:: from_json(cls, state) :classmethod: .. py:method:: demo(cfsn_vecs, **kw) :classmethod: :Parameters: **\*\*kwargs** -- See :func:`kwcoco.metrics.DetectionMetrics.demo` :returns: ConfusionVectors .. rubric:: Example >>> cfsn_vecs = ConfusionVectors.demo() >>> print('cfsn_vecs = {!r}'.format(cfsn_vecs)) >>> cx_to_binvecs = cfsn_vecs.binarize_ovr() >>> print('cx_to_binvecs = {!r}'.format(cx_to_binvecs)) .. py:method:: from_arrays(ConfusionVectors, true, pred=None, score=None, weight=None, probs=None, classes=None) :classmethod: Construct confusion vector data structure from component arrays .. rubric:: Example >>> # xdoctest: +REQUIRES(module:pandas) >>> import kwarray >>> classes = ['person', 'vehicle', 'object'] >>> rng = kwarray.ensure_rng(0) >>> true = (rng.rand(10) * len(classes)).astype(int) >>> probs = rng.rand(len(true), len(classes)) >>> cfsn_vecs = ConfusionVectors.from_arrays(true=true, probs=probs, classes=classes) >>> cfsn_vecs.confusion_matrix() pred person vehicle object real person 0 0 0 vehicle 2 4 1 object 2 1 0 .. py:method:: confusion_matrix(cfsn_vecs, compress=False) Builds a confusion matrix from the confusion vectors. :Parameters: **compress** (*bool, default=False*) -- if True removes rows / columns with no entries :returns: cm : the labeled confusion matrix (Note: we should write a efficient replacement for this use case. #remove_pandas) :rtype: pd.DataFrame .. rubric:: CommandLine .. code-block:: bash xdoctest -m kwcoco.metrics.confusion_vectors ConfusionVectors.confusion_matrix .. rubric:: Example >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3, cls_noise=.2) >>> cfsn_vecs = dmet.confusion_vectors() >>> cm = cfsn_vecs.confusion_matrix() ... >>> print(cm.to_string(float_format=lambda x: '%.2f' % x)) pred background cat_1 cat_2 cat_3 real background 0.00 1.00 2.00 3.00 cat_1 3.00 12.00 0.00 0.00 cat_2 3.00 0.00 14.00 0.00 cat_3 2.00 0.00 0.00 17.00 .. py:method:: coarsen(cfsn_vecs, cxs) Creates a coarsened set of vectors :returns: ConfusionVectors .. py:method:: binarize_classless(cfsn_vecs, negative_classes=None) Creates a binary representation useful for measuring the performance of detectors. It is assumed that scores of "positive" classes should be high and "negative" clases should be low. :Parameters: **negative_classes** (*List[str | int]*) -- list of negative class names or idxs, by default chooses any class with a true class index of -1. These classes should ideally have low scores. :returns: BinaryConfusionVectors .. note:: The "classlessness" of this depends on the compat="all" argument being used when constructing confusion vectors, otherwise it becomes something like a macro-average because the class information was used in deciding which true and predicted boxes were allowed to match. .. rubric:: Example >>> from kwcoco.metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> nimgs=10, nboxes=(0, 10), n_fp=(0, 1), n_fn=(0, 1), classes=3) >>> cfsn_vecs = dmet.confusion_vectors() >>> class_idxs = list(dmet.classes.node_to_idx.values()) >>> binvecs = cfsn_vecs.binarize_classless() .. py:method:: binarize_ovr(cfsn_vecs, mode=1, keyby='name', ignore_classes={'ignore'}, approx=0) Transforms cfsn_vecs into one-vs-rest BinaryConfusionVectors for each category. :Parameters: * **mode** (*int, default=1*) -- 0 for heirarchy aware or 1 for voc like. MODE 0 IS PROBABLY BROKEN * **keyby** (*int | str*) -- can be cx or name * **ignore_classes** (*Set[str]*) -- category names to ignore * **approx** (*bool, default=0*) -- if True try and approximate missing scores otherwise assume they are irrecoverable and use -inf :returns: which behaves like Dict[int, BinaryConfusionVectors]: cx_to_binvecs :rtype: OneVsRestConfusionVectors .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> cfsn_vecs = ConfusionVectors.demo() >>> print('cfsn_vecs = {!r}'.format(cfsn_vecs)) >>> catname_to_binvecs = cfsn_vecs.binarize_ovr(keyby='name') >>> print('catname_to_binvecs = {!r}'.format(catname_to_binvecs)) cfsn_vecs.data.pandas() catname_to_binvecs.cx_to_binvecs['class_1'].data.pandas() .. note:: .. code: Consider we want to measure how well we can classify beagles. Given a multiclass confusion vector, we need to carefully select a subset. We ignore any truth that is coarser than our current label. We also ignore any background predictions on irrelevant classes y_true | y_pred | score ------------------------------- dog | dog <- ignore coarser truths dog | cat <- ignore coarser truths dog | beagle <- ignore coarser truths cat | dog cat | cat cat | background <- ignore failures to predict unrelated classes cat | maine-coon beagle | beagle beagle | dog beagle | background beagle | cat Snoopy | beagle Snoopy | cat maine-coon | background <- ignore failures to predict unrelated classes maine-coon | beagle maine-coon | cat Anything not marked as ignore is counted. We count anything marked as beagle or a finer grained class (e.g. Snoopy) as a positive case. All other cases are negative. The scores come from the predicted probability of beagle, which must be remembered outside the dataframe. .. py:method:: classification_report(cfsn_vecs, verbose=0) Build a classification report with various metrics. .. rubric:: Example >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics.confusion_vectors import * # NOQA >>> cfsn_vecs = ConfusionVectors.demo() >>> report = cfsn_vecs.classification_report(verbose=1) .. py:class:: Measures(info) Bases: :py:obj:`ubelt.NiceRepr`, :py:obj:`kwcoco.metrics.util.DictProxy` Holds accumulated confusion counts, and derived measures .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors # NOQA >>> binvecs = BinaryConfusionVectors.demo(n=100, p_error=0.5) >>> self = binvecs.measures() >>> print('self = {!r}'.format(self)) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> self.draw(doclf=True) >>> self.draw(key='pr', pnum=(1, 2, 1)) >>> self.draw(key='roc', pnum=(1, 2, 2)) >>> kwplot.show_if_requested() .. py:method:: catname(self) :property: .. py:method:: __nice__(self) .. py:method:: reconstruct(self) .. py:method:: from_json(cls, state) :classmethod: .. py:method:: __json__(self) .. rubric:: Example >>> from kwcoco.metrics.confusion_vectors import BinaryConfusionVectors # NOQA >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> binvecs = BinaryConfusionVectors.demo(n=10, p_error=0.5) >>> self = binvecs.measures() >>> info = self.__json__() >>> print('info = {}'.format(ub.repr2(info, nl=1))) >>> populate_info(info) >>> print('info = {}'.format(ub.repr2(info, nl=1))) >>> recon = Measures.from_json(info) .. py:method:: summary(self) .. py:method:: maximized_thresholds(self) Returns thresholds that maximize metrics. .. py:method:: counts(self) .. py:method:: draw(self, key=None, prefix='', **kw) .. rubric:: Example >>> # xdoctest: +REQUIRES(module:kwplot) >>> # xdoctest: +REQUIRES(module:pandas) >>> from kwcoco.metrics.confusion_vectors import ConfusionVectors # NOQA >>> cfsn_vecs = ConfusionVectors.demo() >>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name') >>> self = ovr_cfsn.measures()['perclass'] >>> self.draw('mcc', doclf=True, fnum=1) >>> self.draw('pr', doclf=1, fnum=2) >>> self.draw('roc', doclf=1, fnum=3) .. py:method:: summary_plot(self, fnum=1, title='', subplots='auto') .. rubric:: Example >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> from kwcoco.metrics.confusion_vectors import ConfusionVectors # NOQA >>> cfsn_vecs = ConfusionVectors.demo(n=3, p_error=0.5) >>> binvecs = cfsn_vecs.binarize_classless() >>> self = binvecs.measures() >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> self.summary_plot() >>> kwplot.show_if_requested() .. py:method:: demo(cls, **kwargs) :classmethod: Create a demo Measures object for testing / demos :Parameters: **\*\*kwargs** -- passed to :func:`BinaryConfusionVectors.demo`. some valid keys are: n, rng, p_rue, p_error, p_miss. .. py:method:: combine(cls, tocombine, precision=None, growth=None, thresh_bins=None) :classmethod: Combine binary confusion metrics :Parameters: * **tocombine** (*List[Measures]*) -- a list of measures to combine into one * **precision** (*int | None*) -- If specified rounds thresholds to this precision which can prevent a RAM explosion when combining a large number of measures. However, this is a lossy operation and will impact the underlying scores. NOTE: use ``growth`` instead. * **growth** (*int | None*) -- if specified this limits how much the resulting measures are allowed to grow by. If None, growth is unlimited. Otherwise, if growth is 'max', the growth is limited to the maximum length of an input. We might make this more numerical in the future. * **thresh_bins** (*int*) -- Force this many threshold bins. :returns: Measures .. rubric:: Example >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> measures1 = Measures.demo(n=15) >>> measures2 = measures1 >>> tocombine = [measures1, measures2] >>> new_measures = Measures.combine(tocombine) >>> new_measures.reconstruct() >>> print('new_measures = {!r}'.format(new_measures)) >>> print('measures1 = {!r}'.format(measures1)) >>> print('measures2 = {!r}'.format(measures2)) >>> print(ub.repr2(measures1.__json__(), nl=1, sort=0)) >>> print(ub.repr2(measures2.__json__(), nl=1, sort=0)) >>> print(ub.repr2(new_measures.__json__(), nl=1, sort=0)) >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> kwplot.figure(fnum=1) >>> new_measures.summary_plot() >>> measures1.summary_plot() >>> measures1.draw('roc') >>> measures2.draw('roc') >>> new_measures.draw('roc') .. rubric:: Example >>> # Demonstrate issues that can arrise from choosing a precision >>> # that is too low when combining metrics. Breakpoints >>> # between different metrics can get muddled, but choosing a >>> # precision that is too high can overwhelm memory. >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> base = ub.map_vals(np.asarray, { >>> 'tp_count': [ 1, 1, 2, 2, 2, 2, 3], >>> 'fp_count': [ 0, 1, 1, 2, 3, 4, 5], >>> 'fn_count': [ 1, 1, 0, 0, 0, 0, 0], >>> 'tn_count': [ 5, 4, 4, 3, 2, 1, 0], >>> 'thresholds': [.0, .0, .0, .0, .0, .0, .0], >>> }) >>> # Make tiny offsets to thresholds >>> rng = kwarray.ensure_rng(0) >>> n = len(base['thresholds']) >>> offsets = [ >>> sorted(rng.rand(n) * 10 ** -rng.randint(4, 7))[::-1] >>> for _ in range(20) >>> ] >>> tocombine = [] >>> for offset in offsets: >>> base_n = base.copy() >>> base_n['thresholds'] += offset >>> measures_n = Measures(base_n).reconstruct() >>> tocombine.append(measures_n) >>> for precision in [6, 5, 2]: >>> combo = Measures.combine(tocombine, precision=precision).reconstruct() >>> print('precision = {!r}'.format(precision)) >>> print('combo = {}'.format(ub.repr2(combo, nl=1))) >>> print('num_thresholds = {}'.format(len(combo['thresholds']))) >>> for growth in [None, 'max', 'log', 'root', 'half']: >>> combo = Measures.combine(tocombine, growth=growth).reconstruct() >>> print('growth = {!r}'.format(growth)) >>> print('combo = {}'.format(ub.repr2(combo, nl=1))) >>> print('num_thresholds = {}'.format(len(combo['thresholds']))) >>> #print(combo.counts().pandas()) .. rubric:: Example >>> # Test case: combining a single measures should leave it unchanged >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> measures = Measures.demo(n=40, p_true=0.2, p_error=0.4, p_miss=0.6) >>> df1 = measures.counts().pandas().fillna(0) >>> print(df1) >>> tocombine = [measures] >>> combo = Measures.combine(tocombine) >>> df2 = combo.counts().pandas().fillna(0) >>> print(df2) >>> assert np.allclose(df1, df2) >>> combo = Measures.combine(tocombine, thresh_bins=2) >>> df3 = combo.counts().pandas().fillna(0) >>> print(df3) >>> # I am NOT sure if this is correct or not >>> thresh_bins = 20 >>> combo = Measures.combine(tocombine, thresh_bins=thresh_bins) >>> df4 = combo.counts().pandas().fillna(0) >>> print(df4) >>> combo = Measures.combine(tocombine, thresh_bins=np.linspace(0, 1, 20)) >>> df4 = combo.counts().pandas().fillna(0) >>> print(df4) assert np.allclose(combo['thresholds'], measures['thresholds']) assert np.allclose(combo['fp_count'], measures['fp_count']) assert np.allclose(combo['tp_count'], measures['tp_count']) assert np.allclose(combo['tp_count'], measures['tp_count']) globals().update(xdev.get_func_kwargs(Measures.combine)) .. py:class:: OneVsRestConfusionVectors(cx_to_binvecs, classes) Bases: :py:obj:`ubelt.NiceRepr` Container for multiple one-vs-rest binary confusion vectors :ivar cx_to_binvecs: :ivar classes: .. rubric:: Example >>> from kwcoco.metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> nimgs=10, nboxes=(0, 10), n_fp=(0, 1), classes=3) >>> cfsn_vecs = dmet.confusion_vectors() >>> self = cfsn_vecs.binarize_ovr(keyby='name') >>> print('self = {!r}'.format(self)) .. py:method:: __nice__(self) .. py:method:: demo(cls) :classmethod: :Parameters: **\*\*kwargs** -- See :func:`kwcoco.metrics.DetectionMetrics.demo` :returns: ConfusionVectors .. py:method:: keys(self) .. py:method:: __getitem__(self, cx) .. py:method:: measures(self, stabalize_thresh=7, fp_cutoff=None, monotonic_ppv=True, ap_method='pycocotools') Creates binary confusion measures for every one-versus-rest category. :Parameters: * **stabalize_thresh** (*int, default=7*) -- if fewer than this many data points inserts dummy stabilization data so curves can still be drawn. * **fp_cutoff** (*int, default=None*) -- maximum number of false positives in the truncated roc curves. ``None`` is equivalent to ``float('inf')`` * **monotonic_ppv** (*bool, default=True*) -- if True ensures that precision is always increasing as recall decreases. This is done in pycocotools scoring, but I'm not sure its a good idea. SeeAlso: :func:`BinaryConfusionVectors.measures` .. rubric:: Example >>> self = OneVsRestConfusionVectors.demo() >>> thresh_result = self.measures()['perclass'] .. py:method:: ovr_classification_report(self) :abstractmethod: .. py:class:: PerClass_Measures(cx_to_info) Bases: :py:obj:`ubelt.NiceRepr`, :py:obj:`kwcoco.metrics.util.DictProxy` .. py:method:: __nice__(self) .. py:method:: summary(self) .. py:method:: from_json(cls, state) :classmethod: .. py:method:: __json__(self) .. py:method:: draw(self, key='mcc', prefix='', **kw) .. rubric:: Example >>> # xdoctest: +REQUIRES(module:kwplot) >>> from kwcoco.metrics.confusion_vectors import ConfusionVectors # NOQA >>> cfsn_vecs = ConfusionVectors.demo() >>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name') >>> self = ovr_cfsn.measures()['perclass'] >>> self.draw('mcc', doclf=True, fnum=1) >>> self.draw('pr', doclf=1, fnum=2) >>> self.draw('roc', doclf=1, fnum=3) .. py:method:: draw_roc(self, prefix='', **kw) .. py:method:: draw_pr(self, prefix='', **kw) .. py:method:: summary_plot(self, fnum=1, title='', subplots='auto') .. rubric:: CommandLine .. code-block:: bash python ~/code/kwcoco/kwcoco/metrics/confusion_measures.py PerClass_Measures.summary_plot --show .. rubric:: Example >>> from kwcoco.metrics.confusion_measures import * # NOQA >>> from kwcoco.metrics.detect_metrics import DetectionMetrics >>> dmet = DetectionMetrics.demo( >>> n_fp=(0, 1), n_fn=(0, 3), nimgs=32, nboxes=(0, 32), >>> classes=3, rng=0, newstyle=1, box_noise=0.7, cls_noise=0.2, score_noise=0.3, with_probs=False) >>> cfsn_vecs = dmet.confusion_vectors() >>> ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name', ignore_classes=['vector', 'raster']) >>> self = ovr_cfsn.measures()['perclass'] >>> # xdoctest: +REQUIRES(--show) >>> import kwplot >>> kwplot.autompl() >>> import seaborn as sns >>> sns.set() >>> self.summary_plot(title='demo summary_plot ovr', subplots=['pr', 'roc']) >>> kwplot.show_if_requested() >>> self.summary_plot(title='demo summary_plot ovr', subplots=['mcc', 'acc'], fnum=2)