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How to use test_empty method in yandex-tank

Best Python code snippet using yandex-tank
metrics.py
Source:metrics.py
1#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany2#3#    Licensed under the Apache License, Version 2.0 (the "License");4#    you may not use this file except in compliance with the License.5#    You may obtain a copy of the License at6#7#        http://www.apache.org/licenses/LICENSE-2.08#9#    Unless required by applicable law or agreed to in writing, software10#    distributed under the License is distributed on an "AS IS" BASIS,11#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12#    See the License for the specific language governing permissions and13#    limitations under the License.14import numpy as np15from medpy import metric16def assert_shape(test, reference):17    assert test.shape == reference.shape, "Shape mismatch: {} and {}".format(18        test.shape, reference.shape)19class ConfusionMatrix:20    def __init__(self, test=None, reference=None):21        self.tp = None22        self.fp = None23        self.tn = None24        self.fn = None25        self.size = None26        self.reference_empty = None27        self.reference_full = None28        self.test_empty = None29        self.test_full = None30        self.set_reference(reference)31        self.set_test(test)32    def set_test(self, test):33        self.test = test34        self.reset()35    def set_reference(self, reference):36        self.reference = reference37        self.reset()38    def reset(self):39        self.tp = None40        self.fp = None41        self.tn = None42        self.fn = None43        self.size = None44        self.test_empty = None45        self.test_full = None46        self.reference_empty = None47        self.reference_full = None48    def compute(self):49        if self.test is None or self.reference is None:50            raise ValueError("'test' and 'reference' must both be set to compute confusion matrix.")51        assert_shape(self.test, self.reference)52        self.tp = int(((self.test != 0) * (self.reference != 0)).sum())53        self.fp = int(((self.test != 0) * (self.reference == 0)).sum())54        self.tn = int(((self.test == 0) * (self.reference == 0)).sum())55        self.fn = int(((self.test == 0) * (self.reference != 0)).sum())56        self.size = int(np.prod(self.reference.shape, dtype=np.int64))57        self.test_empty = not np.any(self.test)58        self.test_full = np.all(self.test)59        self.reference_empty = not np.any(self.reference)60        self.reference_full = np.all(self.reference)61    def get_matrix(self):62        for entry in (self.tp, self.fp, self.tn, self.fn):63            if entry is None:64                self.compute()65                break66        return self.tp, self.fp, self.tn, self.fn67    def get_size(self):68        if self.size is None:69            self.compute()70        return self.size71    def get_existence(self):72        for case in (self.test_empty, self.test_full, self.reference_empty, self.reference_full):73            if case is None:74                self.compute()75                break76        return self.test_empty, self.test_full, self.reference_empty, self.reference_full77def dice(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):78    """2TP / (2TP + FP + FN)"""79    if confusion_matrix is None:80        confusion_matrix = ConfusionMatrix(test, reference)81    tp, fp, tn, fn = confusion_matrix.get_matrix()82    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()83    if test_empty and reference_empty:84        if nan_for_nonexisting:85            return float("NaN")86        else:87            return 0.88    return float(2. * tp / (2 * tp + fp + fn))89def jaccard(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):90    """TP / (TP + FP + FN)"""91    if confusion_matrix is None:92        confusion_matrix = ConfusionMatrix(test, reference)93    tp, fp, tn, fn = confusion_matrix.get_matrix()94    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()95    if test_empty and reference_empty:96        if nan_for_nonexisting:97            return float("NaN")98        else:99            return 0.100    return float(tp / (tp + fp + fn))101def precision(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):102    """TP / (TP + FP)"""103    if confusion_matrix is None:104        confusion_matrix = ConfusionMatrix(test, reference)105    tp, fp, tn, fn = confusion_matrix.get_matrix()106    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()107    if test_empty:108        if nan_for_nonexisting:109            return float("NaN")110        else:111            return 0.112    return float(tp / (tp + fp))113def sensitivity(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):114    """TP / (TP + FN)"""115    if confusion_matrix is None:116        confusion_matrix = ConfusionMatrix(test, reference)117    tp, fp, tn, fn = confusion_matrix.get_matrix()118    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()119    if reference_empty:120        if nan_for_nonexisting:121            return float("NaN")122        else:123            return 0.124    return float(tp / (tp + fn))125def recall(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):126    """TP / (TP + FN)"""127    return sensitivity(test, reference, confusion_matrix, nan_for_nonexisting, **kwargs)128def specificity(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):129    """TN / (TN + FP)"""130    if confusion_matrix is None:131        confusion_matrix = ConfusionMatrix(test, reference)132    tp, fp, tn, fn = confusion_matrix.get_matrix()133    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()134    if reference_full:135        if nan_for_nonexisting:136            return float("NaN")137        else:138            return 0.139    return float(tn / (tn + fp))140def accuracy(test=None, reference=None, confusion_matrix=None, **kwargs):141    """(TP + TN) / (TP + FP + FN + TN)"""142    if confusion_matrix is None:143        confusion_matrix = ConfusionMatrix(test, reference)144    tp, fp, tn, fn = confusion_matrix.get_matrix()145    return float((tp + tn) / (tp + fp + tn + fn))146def fscore(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, beta=1., **kwargs):147    """(1 + b^2) * TP / ((1 + b^2) * TP + b^2 * FN + FP)"""148    precision_ = precision(test, reference, confusion_matrix, nan_for_nonexisting)149    recall_ = recall(test, reference, confusion_matrix, nan_for_nonexisting)150    return (1 + beta*beta) * precision_ * recall_ /\151        ((beta*beta * precision_) + recall_)152def false_positive_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):153    """FP / (FP + TN)"""154    return 1 - specificity(test, reference, confusion_matrix, nan_for_nonexisting)155def false_omission_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):156    """FN / (TN + FN)"""157    if confusion_matrix is None:158        confusion_matrix = ConfusionMatrix(test, reference)159    tp, fp, tn, fn = confusion_matrix.get_matrix()160    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()161    if test_full:162        if nan_for_nonexisting:163            return float("NaN")164        else:165            return 0.166    return float(fn / (fn + tn))167def false_negative_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):168    """FN / (TP + FN)"""169    return 1 - sensitivity(test, reference, confusion_matrix, nan_for_nonexisting)170def true_negative_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):171    """TN / (TN + FP)"""172    return specificity(test, reference, confusion_matrix, nan_for_nonexisting)173def false_discovery_rate(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):174    """FP / (TP + FP)"""175    return 1 - precision(test, reference, confusion_matrix, nan_for_nonexisting)176def negative_predictive_value(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, **kwargs):177    """TN / (TN + FN)"""178    return 1 - false_omission_rate(test, reference, confusion_matrix, nan_for_nonexisting)179def total_positives_test(test=None, reference=None, confusion_matrix=None, **kwargs):180    """TP + FP"""181    if confusion_matrix is None:182        confusion_matrix = ConfusionMatrix(test, reference)183    tp, fp, tn, fn = confusion_matrix.get_matrix()184    return tp + fp185def total_negatives_test(test=None, reference=None, confusion_matrix=None, **kwargs):186    """TN + FN"""187    if confusion_matrix is None:188        confusion_matrix = ConfusionMatrix(test, reference)189    tp, fp, tn, fn = confusion_matrix.get_matrix()190    return tn + fn191def total_positives_reference(test=None, reference=None, confusion_matrix=None, **kwargs):192    """TP + FN"""193    if confusion_matrix is None:194        confusion_matrix = ConfusionMatrix(test, reference)195    tp, fp, tn, fn = confusion_matrix.get_matrix()196    return tp + fn197def total_negatives_reference(test=None, reference=None, confusion_matrix=None, **kwargs):198    """TN + FP"""199    if confusion_matrix is None:200        confusion_matrix = ConfusionMatrix(test, reference)201    tp, fp, tn, fn = confusion_matrix.get_matrix()202    return tn + fp203def hausdorff_distance(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):204    if confusion_matrix is None:205        confusion_matrix = ConfusionMatrix(test, reference)206    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()207    if test_empty or test_full or reference_empty or reference_full:208        if nan_for_nonexisting:209            return float("NaN")210        else:211            return 0212    test, reference = confusion_matrix.test, confusion_matrix.reference213    return metric.hd(test, reference, voxel_spacing, connectivity)214def hausdorff_distance_95(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):215    if confusion_matrix is None:216        confusion_matrix = ConfusionMatrix(test, reference)217    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()218    if test_empty or test_full or reference_empty or reference_full:219        if nan_for_nonexisting:220            return float("NaN")221        else:222            return 0223    test, reference = confusion_matrix.test, confusion_matrix.reference224    return metric.hd95(test, reference, voxel_spacing, connectivity)225def avg_surface_distance(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):226    if confusion_matrix is None:227        confusion_matrix = ConfusionMatrix(test, reference)228    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()229    if test_empty or test_full or reference_empty or reference_full:230        if nan_for_nonexisting:231            return float("NaN")232        else:233            return 0234    test, reference = confusion_matrix.test, confusion_matrix.reference235    return metric.asd(test, reference, voxel_spacing, connectivity)236def avg_surface_distance_symmetric(test=None, reference=None, confusion_matrix=None, nan_for_nonexisting=True, voxel_spacing=None, connectivity=1, **kwargs):237    if confusion_matrix is None:238        confusion_matrix = ConfusionMatrix(test, reference)239    test_empty, test_full, reference_empty, reference_full = confusion_matrix.get_existence()240    if test_empty or test_full or reference_empty or reference_full:241        if nan_for_nonexisting:242            return float("NaN")243        else:244            return 0245    test, reference = confusion_matrix.test, confusion_matrix.reference246    return metric.assd(test, reference, voxel_spacing, connectivity)247ALL_METRICS = {248    "False Positive Rate": false_positive_rate,249    "Dice": dice,250    "Jaccard": jaccard,251    "Hausdorff Distance": hausdorff_distance,252    "Hausdorff Distance 95": hausdorff_distance_95,253    "Precision": precision,254    "Recall": recall,255    "Avg. Symmetric Surface Distance": avg_surface_distance_symmetric,256    "Avg. Surface Distance": avg_surface_distance,257    "Accuracy": accuracy,258    "False Omission Rate": false_omission_rate,259    "Negative Predictive Value": negative_predictive_value,260    "False Negative Rate": false_negative_rate,261    "True Negative Rate": true_negative_rate,262    "False Discovery Rate": false_discovery_rate,263    "Total Positives Test": total_positives_test,264    "Total Negatives Test": total_negatives_test,265    "Total Positives Reference": total_positives_reference,266    "total Negatives Reference": total_negatives_reference...
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