How to use test_empty method in avocado

Best Python code snippet using avocado_python

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...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.