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How to use is_close_to method in assertpy

Best Python code snippet using assertpy_python

Layout.py

Source:Layout.py

...65        return count66    def valid_big_hotel(self):67        loc = self.find_first_location('big_hotel')68        if loc != None:69            return (self.is_close_to(loc, 'recreational_area')70                    and not self.is_close_to(loc, 'cemetery')71                    and not self.is_close_to(loc, 'graveyard'))72        else:73            return True74    def valid_recreational_area(self):75        loc = self.find_first_location('recreational_area')76        if loc != None:77            return self.is_close_to(loc, 'lake')78        else:79            return True80    def valid_garbage_dump(self):81        return True82    def valid_housing_complex(self):83        loc = self.find_first_location('housing_complex')84        if loc != None:85            return (self.is_close_to(loc, 'recreational_area')86                    and not self.is_close_to(loc, 'cemetery')87                    and not self.is_close_to(loc, 'graveyard'))88        else:89            return True90    def update_building_location_options(self):91        return (92            self.update_garbage_dump()93            and self.update_recreational_area()94            and self.update_housing_complex()95            and self.update_big_hotel()96        )97    def update_garbage_dump(self):98        if self.find_first_location('garbage_dump') == None:99            return True100        possibilities = []101        for loc in self.next_locations_basic():102            if not(self.is_close_to(loc, 'housing_complex') or self.is_close_to(loc, 'big_hotel')):103                possibilities.append((loc))104        if len(possibilities) > 0:105            self.building_location_options['garbage_dump'] = possibilities106            return True107        else:108            return False109    def update_recreational_area(self):110        if self.find_first_location('recreational_area') == None:111            return True112        possibilities = []113        for loc in self.next_locations_basic():114            if self.is_close_to(loc, 'lake'):115                possibilities.append(loc)116        if len(possibilities) > 0:117            self.building_location_options['recreational_area'] = possibilities118            return True119        else:120            return False121    def update_housing_complex(self):122        if self.find_first_location('housing_complex') == None:123            return True124        possibilities = []125        for loc in self.next_locations_basic():126            if self.is_close_to(loc, 'recreational_area') and not self.is_close_to(loc, 'garbage_dump') and not self.is_close_to(loc, 'cemetery'):127                possibilities.append(loc)128        if len(possibilities) > 0:129            self.building_location_options['housing_complex']130            return True131        else:132            return False133    def update_big_hotel(self):134        if self.find_first_location('big_hotel') == None:135            return True136        possibilities = []137        for loc in self.next_locations_basic():138            if self.is_close_to(loc, 'recreational_area') and not self.is_close_to(loc, 'garbage_dump') and not self.is_close_to(loc, 'cemetery'):139                possibilities.append(loc)140        if len(possibilities) > 0:141            self.building_location_options['big_hotel'] = possibilities142            return True143        else:144            return False145    def get_next_locations(self, building):146        if self.forward_checking:147            return self.next_locations_forward_checking(building)148        else:149            return self.next_locations_basic()150    def next_locations_forward_checking(self, building):151        return self.building_location_options[building]152    def next_locations_basic(self):153        locations = []154        for i in range(3):155            for j in range(3):156                if self.grid[i][j] == None:157                    locations.append((i, j))158        return locations159    def find_first_location(self, target):160        for row in range(3):161            for col in range(3):162                if self.grid[row][col] == target:163                    return (row, col)164        return None165    def is_close_to(self, loc, target):166        for pair in [(-1, 0), (1, 0), (0, -1), (0, 1)]:167            x, y = loc[0] + pair[0], loc[1] + pair[1]168            if self.in_bounds(x, y) and self.grid[x][y] == target:169                return True170        return False171    def in_bounds(self, x, y):172        return (x >= 0 and x < 3 and y >= 0 and y < 3)173    def get_building_options(self):174        if self.forward_checking:175            return self.next_building_forward_checking()176        else:177            return self.next_buildings_basic()178    def next_building_forward_checking(self):179        options = [building for building in self.building_options]...

test_confusion_matrix.py

Source:test_confusion_matrix.py

...23    assert_that(cm.FN.shape[0]).is_equal_to(nsamples + 1)24    assert_that(cm.cutpoint(0.6)).is_equal_to(300)25    TOL = 1e-726    TPR = cm.TP / cm.P27    assert_that(TPR[0]).is_close_to(0.0, TOL)28    assert_that(TPR[460]).is_close_to(0.005, TOL)29    assert_that(TPR[-1]).is_close_to(0.006, TOL)30    tmp = cm.TP / (cm.TP + cm.FN)31    for i in range(cm.tpr.shape[0]):32        assert_that(TPR[i]).is_close_to(tmp[i], TOL)33    TNR = cm.TN / cm.N34    assert_that(TNR[0]).is_close_to(1.0, TOL)35    assert_that(TNR[460]).is_close_to(0.997806880130334, TOL)36    assert_that(TNR[-1]).is_close_to(0.9976188984272197, TOL)37    tmp = cm.TN / (cm.TN + cm.FP)38    for i in range(cm.tnr.shape[0]):39        assert_that(TNR[i]).is_close_to(tmp[i], TOL)40    with errstate(divide="ignore", invalid="ignore"):41        PPV = cm.TP / (cm.TP + cm.FP)42    assert_that(PPV[0]).is_close_to(nan, TOL)43    assert_that(PPV[460]).is_close_to(0.010869565217391304, TOL)44    assert_that(PPV[-1]).is_close_to(0.012, TOL)45    NPV = cm.TN / (cm.TN + cm.FN)46    assert_that(NPV[0]).is_close_to(0.9952030777053442, TOL)47    assert_that(NPV[460]).is_close_to(0.995216507136779, TOL)48    assert_that(NPV[-1]).is_close_to(0.9952203955435237, TOL)49    FNR = cm.FN / cm.P50    assert_that(FNR[0]).is_close_to(1.0, TOL)51    assert_that(FNR[460]).is_close_to(0.995, TOL)52    assert_that(FNR[-1]).is_close_to(0.994, TOL)53    tmp = cm.FN / (cm.FN + cm.TP)54    for i in range(cm.fnr.shape[0]):55        assert_that(FNR[i]).is_close_to(tmp[i], TOL)56    FPR = cm.FP / cm.N57    assert_that(FPR[0]).is_close_to(0.0, TOL)58    assert_that(FPR[460]).is_close_to(0.002193119869666019, TOL)59    assert_that(FPR[-1]).is_close_to(0.0023811015727802495, TOL)60    tmp = cm.FP / (cm.FP + cm.TN)61    for i in range(cm.fpr.shape[0]):62        assert_that(FPR[i]).is_close_to(tmp[i], TOL)63    with errstate(divide="ignore", invalid="ignore"):64        FDR = cm.FP / (cm.FP + cm.TP)65    assert_that(FDR[0]).is_close_to(nan, TOL)66    assert_that(FDR[460]).is_close_to(0.9891304347826086, TOL)67    assert_that(FDR[-1]).is_close_to(0.988, TOL)68    FOR = cm.FN / (cm.FN + cm.TN)69    assert_that(FOR[0]).is_close_to(0.004796922294655749, TOL)70    assert_that(FOR[460]).is_close_to(0.004783492863220949, TOL)71    assert_that(FOR[-1]).is_close_to(0.004779604456476268, TOL)72    ACC = (cm.TP + cm.TN) / (cm.P + cm.N)73    assert_that(ACC[0]).is_close_to(0.9952030777053442, TOL)74    assert_that(ACC[460]).is_close_to(0.9930444626727492, TOL)75    assert_that(ACC[-1]).is_close_to(0.9928621796255522, TOL)76    for i in range(len(cm.sensitivity)):77        assert_that(TPR[i]).is_close_to(cm.sensitivity[i], TOL)78        assert_that(TPR[i]).is_close_to(cm.recall[i], TOL)79        assert_that(TPR[i]).is_close_to(cm.tpr[i], TOL)80    for i in range(len(cm.specificity)):81        assert_that(TNR[i]).is_close_to(cm.specificity[i], TOL)82        assert_that(TNR[i]).is_close_to(cm.selectivity[i], TOL)83        assert_that(TNR[i]).is_close_to(cm.tnr[i], TOL)84    for i in range(len(cm.precision)):85        assert_that(PPV[i]).is_close_to(cm.precision[i], TOL)86        assert_that(PPV[i]).is_close_to(cm.ppv[i], TOL)87        assert_that(TNR[i]).is_close_to(cm.tnr[i], TOL)88    for i in range(len(cm.npv)):89        assert_that(NPV[i]).is_close_to(cm.npv[i], TOL)90    for i in range(len(cm.fnr)):91        assert_that(FNR[i]).is_close_to(cm.miss_rate[i], TOL)92        assert_that(FNR[i]).is_close_to(cm.fnr[i], TOL)93    for i in range(len(cm.fpr)):94        assert_that(FPR[i]).is_close_to(cm.fallout[i], TOL)95        assert_that(FPR[i]).is_close_to(cm.fpr[i], TOL)96    for i in range(len(cm.fdr)):97        assert_that(FDR[i]).is_close_to(cm.fdr[i], TOL)98    for i in range(len(cm.for_)):99        assert_that(FOR[i]).is_close_to(cm.for_[i], TOL)100    for i in range(len(cm.accuracy)):101        assert_that(ACC[i]).is_close_to(cm.accuracy[i], TOL)102def test_confusion_matrix_pr_curve():103    random = RandomState(8)104    ntrues = 1000105    nfalses = 207467106    true_samples = random.choice(ntrues + nfalses, ntrues, False)107    nsamples = 500108    samples = random.choice(ntrues + nfalses, nsamples, False)109    scores = random.randn(nsamples)110    idx = argsort(scores)111    cm = ConfusionMatrix(true_samples, nfalses, samples[idx])112    pr = cm.pr_curve113    assert_that(pr.recall[420]).is_close_to(0.005, TOL)114    assert_that(pr.precision[420]).is_close_to(0.011876484560570071, TOL)115    assert_that(pr.auc).is_close_to(4.98054484430107e-05, TOL)116def test_confusion_matrix_roc_curve():117    random = RandomState(8)118    ntrues = 1000119    nfalses = 207467120    true_samples = random.choice(ntrues + nfalses, ntrues, False)121    nsamples = 500122    samples = random.choice(ntrues + nfalses, nsamples, False)123    scores = random.randn(nsamples)124    idx = argsort(scores)125    cm = ConfusionMatrix(true_samples, nfalses, samples[idx])126    roc = cm.roc_curve127    assert_that(roc.fpr[420]).is_close_to(0.00200031812288215, TOL)128    assert_that(roc.tpr[420]).is_close_to(0.005, TOL)129    assert_that(roc.auc).is_close_to(4.762203145560528e-06, TOL)130def test_confusion_matrix_write_read(tmp_path: Path):131    random = RandomState(2)132    ntrues = 100133    nfalses = 100134    true_samples = random.choice(ntrues + nfalses, ntrues, False)135    nsamples = 100136    samples = random.choice(ntrues + nfalses, nsamples, False)137    scores = random.randn(nsamples)138    idx = argsort(scores)139    pr_auc = 0.22942490919917213140    roc_auc = 0.1277141    cm = ConfusionMatrix(true_samples, nfalses, samples[idx])142    assert_that(cm.pr_curve.auc).is_close_to(pr_auc, TOL)143    assert_that(cm.roc_curve.auc).is_close_to(roc_auc, TOL)144    cm.write_pickle(tmp_path / "cm.pkl")145    cm = ConfusionMatrix.read_pickle(tmp_path / "cm.pkl")146    assert_that(cm.pr_curve.auc).is_close_to(pr_auc, TOL)...

test-application-steps.py

Source:test-application-steps.py

...32    assert_that( counts ).contains_only( count )33    assert_that( incrmts ).contains_only( 1 )34    assert_that( incrids ).contains_only( 1 )35    36    assert_that( sum( sframes ) / len( sframes ) ).is_close_to( step, 2001 )37    assert_that( min( sframes ) ).is_close_to( step, 5001 )38    assert_that( max( sframes ) ).is_close_to( step, 5002 )39    40    assert_that( sum( rframes ) / len( rframes ) ).is_close_to( step, 2002 )41    #assert_that( min( rframes ) ).is_close_to( step, 10001 )42    #assert_that( max( rframes ) ).is_close_to( step, 10002 )43    44    meandrift = sum( drifts ) / len( drifts )45    assert_that( meandrift ).is_less_than( 100003 )46    assert_that( min( drifts ) ).is_close_to( meandrift, 20001 )47    assert_that( max( drifts ) ).is_close_to( meandrift, 20002 )...

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