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How to use _assert_test method in green
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lift_test.py
Source:lift_test.py 
...22from tensorflow_model_analysis.eval_saved_model import testutil23from tensorflow_model_analysis.metrics import metric_types24from tensorflow_model_analysis.metrics import metric_util25class LiftTest(testutil.TensorflowModelAnalysisTest, parameterized.TestCase):26 def _assert_test(self,27 num_buckets,28 baseline_examples,29 comparison_examples,30 lift_metric_value,31 ignore_out_of_bound_examples=False):32 eval_config = config.EvalConfig(33 cross_slicing_specs=[config.CrossSlicingSpec()])34 computations = lift.Lift(35 num_buckets=num_buckets,36 ignore_out_of_bound_examples=ignore_out_of_bound_examples).computations(37 eval_config=eval_config)38 histogram = computations[0]39 lift_metrics = computations[1]40 with beam.Pipeline() as pipeline:41 # pylint: disable=no-value-for-parameter42 baseline_result = (43 pipeline44 | 'CreateB' >> beam.Create(baseline_examples)45 | 'ProcessB' >> beam.Map(metric_util.to_standard_metric_inputs)46 | 'AddSliceB' >> beam.Map(lambda x: ((), x))47 | 'ComputeHistogramB' >> beam.CombinePerKey(histogram.combiner)48 ) # pyformat: ignore49 comparison_result = (50 pipeline51 | 'CreateC' >> beam.Create(comparison_examples)52 | 'ProcessC' >> beam.Map(metric_util.to_standard_metric_inputs)53 | 'AddSliceC' >> beam.Map(lambda x: (('slice'), x))54 | 'ComputeHistogramC' >> beam.CombinePerKey(histogram.combiner)55 ) # pyformat: ignore56 # pylint: enable=no-value-for-parameter57 merged_result = ((baseline_result, comparison_result)58 | 'MergePCollections' >> beam.Flatten())59 def check_result(got):60 try:61 self.assertLen(got, 2)62 slice_1, metric_1 = got[0]63 slice_2, metric_2 = got[1]64 lift_value = None65 if not slice_1:66 lift_value = lift_metrics.cross_slice_comparison(metric_1, metric_2)67 else:68 lift_value = lift_metrics.cross_slice_comparison(metric_2, metric_1)69 self.assertDictElementsAlmostEqual(70 lift_value, {71 metric_types.MetricKey(name=f'lift@{num_buckets}'):72 lift_metric_value,73 })74 except AssertionError as err:75 raise util.BeamAssertException(err)76 util.assert_that(merged_result, check_result, label='result')77 def testLift_continuousLabelsAndPredictions(self):78 baseline_examples = [{79 'labels': np.array([0.0]),80 'predictions': np.array([0.1]),81 'example_weights': np.array([3.0]),82 }, {83 'labels': np.array([0.3]),84 'predictions': np.array([0.5]),85 'example_weights': np.array([5.0]),86 }, {87 'labels': np.array([0.6]),88 'predictions': np.array([0.8]),89 'example_weights': np.array([2.0]),90 }, {91 'labels': np.array([0.9]),92 'predictions': np.array([0.3]),93 'example_weights': np.array([8.0]),94 }, {95 'labels': np.array([0.9]),96 'predictions': np.array([0.9]),97 'example_weights': np.array([3.0]),98 }]99 comparison_examples = [{100 'labels': np.array([0.0]),101 'predictions': np.array([0.8]),102 'example_weights': np.array([1.0]),103 }, {104 'labels': np.array([0.2]),105 'predictions': np.array([0.3]),106 'example_weights': np.array([2.0]),107 }, {108 'labels': np.array([0.5]),109 'predictions': np.array([0.5]),110 'example_weights': np.array([5.0]),111 }, {112 'labels': np.array([0.7]),113 'predictions': np.array([0.4]),114 'example_weights': np.array([2.0]),115 }, {116 'labels': np.array([0.9]),117 'predictions': np.array([0.3]),118 'example_weights': np.array([3.0]),119 }]120 self._assert_test(3, baseline_examples, comparison_examples, -0.136013986)121 def testLift_baselineAndComparisonAreSame(self):122 baseline_examples = [{123 'labels': np.array([0.0]),124 'predictions': np.array([0.1]),125 'example_weights': np.array([3.0]),126 }, {127 'labels': np.array([0.3]),128 'predictions': np.array([0.5]),129 'example_weights': np.array([5.0]),130 }, {131 'labels': np.array([0.6]),132 'predictions': np.array([0.8]),133 'example_weights': np.array([2.0]),134 }, {135 'labels': np.array([0.9]),136 'predictions': np.array([0.3]),137 'example_weights': np.array([8.0]),138 }, {139 'labels': np.array([1.0]),140 'predictions': np.array([0.9]),141 'example_weights': np.array([3.0]),142 }]143 self._assert_test(3, baseline_examples, baseline_examples, 0.0)144 def testLift_ignoringOutOfBoundExamples(self):145 baseline_examples = [146 {147 'labels': np.array([0.0]),148 'predictions': np.array([0.1]),149 'example_weights': np.array([3.0]),150 },151 {152 'labels': np.array([0.3]),153 'predictions': np.array([0.5]),154 'example_weights': np.array([5.0]),155 },156 {157 'labels': np.array([0.6]),158 'predictions': np.array([0.8]),159 'example_weights': np.array([2.0]),160 },161 {162 'labels': np.array([0.9]),163 'predictions': np.array([0.3]),164 'example_weights': np.array([8.0]),165 },166 {167 'labels': np.array([-0.9]), # Ignore this example168 'predictions': np.array([0.3]),169 'example_weights': np.array([8.0]),170 },171 {172 'labels': np.array([0.9]),173 'predictions': np.array([0.9]),174 'example_weights': np.array([3.0]),175 }176 ]177 comparison_examples = [178 {179 'labels': np.array([0.0]),180 'predictions': np.array([0.8]),181 'example_weights': np.array([1.0]),182 },183 {184 'labels': np.array([0.2]),185 'predictions': np.array([0.3]),186 'example_weights': np.array([2.0]),187 },188 {189 'labels': np.array([0.5]),190 'predictions': np.array([0.5]),191 'example_weights': np.array([5.0]),192 },193 {194 'labels': np.array([0.7]),195 'predictions': np.array([0.4]),196 'example_weights': np.array([2.0]),197 },198 {199 'labels': np.array([1.9]), # Ignore this example200 'predictions': np.array([0.3]),201 'example_weights': np.array([8.0]),202 },203 {204 'labels': np.array([0.9]),205 'predictions': np.array([0.3]),206 'example_weights': np.array([3.0]),207 }208 ]209 self._assert_test(210 3,211 baseline_examples,212 comparison_examples,213 -0.136013986,214 ignore_out_of_bound_examples=True)215 def testLift_binaryLabelsAndContinuousPredictions(self):216 baseline_examples = [{217 'labels': np.array([0.0]),218 'predictions': np.array([0.1]),219 'example_weights': np.array([3.0]),220 }, {221 'labels': np.array([0.0]),222 'predictions': np.array([0.5]),223 'example_weights': np.array([5.0]),224 }, {225 'labels': np.array([1.0]),226 'predictions': np.array([0.8]),227 'example_weights': np.array([2.0]),228 }, {229 'labels': np.array([1.0]),230 'predictions': np.array([0.3]),231 'example_weights': np.array([8.0]),232 }, {233 'labels': np.array([1.0]),234 'predictions': np.array([0.9]),235 'example_weights': np.array([3.0]),236 }]237 comparison_examples = [{238 'labels': np.array([0.0]),239 'predictions': np.array([0.8]),240 'example_weights': np.array([1.0]),241 }, {242 'labels': np.array([0.0]),243 'predictions': np.array([0.3]),244 'example_weights': np.array([2.0]),245 }, {246 'labels': np.array([0.0]),247 'predictions': np.array([0.5]),248 'example_weights': np.array([5.0]),249 }, {250 'labels': np.array([1.0]),251 'predictions': np.array([0.4]),252 'example_weights': np.array([2.0]),253 }, {254 'labels': np.array([1.0]),255 'predictions': np.array([0.3]),256 'example_weights': np.array([3.0]),257 }]258 self._assert_test(2, baseline_examples, comparison_examples, 0.01715976331)259 def testLift_binaryLabelsAndPredictions(self):260 baseline_examples = [{261 'labels': np.array([0.0]),262 'predictions': np.array([1.0]),263 'example_weights': np.array([3.0]),264 }, {265 'labels': np.array([0.0]),266 'predictions': np.array([0.0]),267 'example_weights': np.array([5.0]),268 }, {269 'labels': np.array([1.0]),270 'predictions': np.array([0.0]),271 'example_weights': np.array([2.0]),272 }, {273 'labels': np.array([1.0]),274 'predictions': np.array([1.0]),275 'example_weights': np.array([8.0]),276 }, {277 'labels': np.array([1.0]),278 'predictions': np.array([0.0]),279 'example_weights': np.array([3.0]),280 }]281 comparison_examples = [{282 'labels': np.array([0.0]),283 'predictions': np.array([1.0]),284 'example_weights': np.array([1.0]),285 }, {286 'labels': np.array([0.0]),287 'predictions': np.array([0.0]),288 'example_weights': np.array([2.0]),289 }, {290 'labels': np.array([0.0]),291 'predictions': np.array([1.0]),292 'example_weights': np.array([5.0]),293 }, {294 'labels': np.array([1.0]),295 'predictions': np.array([0.0]),296 'example_weights': np.array([2.0]),297 }, {298 'labels': np.array([1.0]),299 'predictions': np.array([1.0]),300 'example_weights': np.array([3.0]),301 }]302 self._assert_test(2, baseline_examples, comparison_examples, 0.224852071)303 def testLift_raisesExceptionWhenEvalConfigIsNone(self):304 with self.assertRaises(ValueError):305 _ = lift.Lift(num_buckets=3).computations()306 def testLift_raisesExceptionWhenCrossSlicingSpecIsAbsent(self):307 with self.assertRaises(ValueError):308 _ = lift.Lift(num_buckets=3).computations(eval_config=config.EvalConfig())309if __name__ == '__main__':...
scc.py
Source:scc.py
...85 for k in d1:86 if sorted(d1[k]) != sorted(d2[k]):87 return False88 return True89def _assert_test(g, expected_counts, expected_groups = None):90 groups = scc(g)91 if expected_groups:92 assert _assert_dict_equal(groups, expected_groups), '%s -- %s' % (expected_groups, groups)93 assert count_len(groups) == expected_counts, '%s -- %s' % (expected_counts,94 count_len(groups))95 nodes = set()96 for s in g:97 nodes.add(s)98 for t in g[s]:99 nodes.add(t)100 assert sum(count_len(groups)) == len(nodes), '%s -- %s' % (sum(count_len(groups)), len(nodes))101 def _same_group(groups, s, t):102 """Return true if s and t are in the same group."""103 for group in groups.values():104 if s in group and t in group:105 return True106 return False107 def _is_connected(g, s, t):108 explored = {}109 def _dsf(g, i):110 explored[i] = True111 if i in g:112 for j in g[i]:113 if j not in explored:114 _dsf(g, j)115 _dsf(g, s)116 return t in explored117 for s in nodes:118 for t in nodes:119 s_to_t = _is_connected(g, s, t)120 t_to_s = _is_connected(g, t, s)121 # print '%s -> %s: %s, %s -> %s: %s' % (s, t, s_to_t, s, t, t_to_s)122 # print 'is same group: %s' % (_same_group(groups, s, t))123 if _same_group(groups, s, t):124 assert s_to_t and t_to_s, '%s and %s should be in same group. %s' % (s, t, groups)125 else:126 assert not s_to_t or not t_to_s, '%s and %s should not be in the same group. %s' % (s, t, groups)127def test1():128 g ={1: [7],129 7: [4, 9],130 4: [1],131 9: [6],132 6: [3, 8],133 3: [9],134 8: [2],135 2: [5],136 5: [8],137 }138 _assert_test(g,139 [3, 3, 3],140 {1: [1, 4, 7],141 2: [2, 5, 8],142 6: [6, 3, 9]})143 g = {1: [2],144 2: [3],145 3: [1, 4]}146 _assert_test(g,147 [3, 1],148 {1: [3, 2, 1], 4: [4]})149 g = {1: [2, 2],150 2: [3, 1],151 3: [4, 2, 2]}152 _assert_test(g,153 [3, 1],154 {1: [1, 2, 3], 4: [4]})155 g = {2: [46, 15],156 46: [15, 9],157 15: [9],158 }159 _assert_test(g,160 [1, 1, 1, 1])161def tests():162 g = read_input('test1.txt')163 expected = {1: [6, 3, 5, 1],164 2: [10, 4, 2],165 11: [11],166 9: [8, 7, 9]}167 _assert_test(g,168 [4, 3, 3, 1],169 expected)170 g = read_input('test2.txt')171 expected = {1: [5, 2, 1],172 3: [8, 4, 3],173 6: [7, 6]}174 _assert_test(g,175 [3, 3, 2],176 expected)177 g = read_input('test3.txt')178 _assert_test(g,179 [6, 3, 2, 1])180 g = read_input('test4.txt')181 _assert_test(g,182 [35, 7, 1, 1, 1, 1, 1, 1, 1, 1])183 g = read_input('test5.txt')184 _assert_test(g,[36, 7, 1, 1, 1, 1, 1, 1, 1])185 g = read_input('test6.txt')186 _assert_test(g, [8, 5, 2, 1])187 g = read_input('test7.txt')188 _assert_test(g, [3, 3, 3, 1])189 g = read_input('test8.txt')190 _assert_test(g, [3, 3, 3, 1])191def random_tests(test_cnt=300):192 for _ in xrange(test_cnt):193 problem_size = random.randint(2, 500)194 g = {}195 for s in range(1, problem_size+1):196 g[s] = []197 for t in range(1, problem_size + 1):198 if s == t: continue199 if random.randint(1, 300) < 100:200 g[s].append(t)201 groups1 = scc(g, 'stack')202 groups2 = scc(g, 'rec')203 assert _assert_dict_equal(groups1, groups2), '%s \n%s\n%s' % (g, groups1, groups2)204def read_input(filename):...
code_challenge.py
Source:code_challenge.py
...20 ([9,10,19,13,19,13],19),21 ([16,0,11,4,8,16,0,11],12),22 ([5,17, 18, 11, 13, 18, 11, 13],22),23 ([5,10,19,13,10,13],24)]24 _assert_test(tests, repeats)25 26def capitalize(string):27 even_altered = "".join([ s.upper() if i % 2 == 0 else s 28 for i,s in enumerate(string)])29 return [even_altered, even_altered.swapcase()]30def test_capitalize():31 tests = [32 ("abcdef", ['AbCdEf', 'aBcDeF']),33 ("codewars", ['CoDeWaRs', 'cOdEwArS']),34 ("abracadabra", ['AbRaCaDaBrA', 'aBrAcAdAbRa'])]35 _assert_test(tests, capitalize)36def isPrime(num):37 from math import sqrt38 return num >=2 and not [n for n in range(2, int(sqrt(num))) if num % n == 0]39def maxMultiple(divisor, bound):40 for n in range(bound,0,-1):41 if n % divisor == 0:42 return n43 return max([n for n in range(bound, 0, -1) if n % divisor == 0])44def test_maxMultiple():45 tests = [46 ((2,7),6),47 ((3,10),9),48 ((7,17),14),49 ((10,50),50),50 ((37,200),185),51 ((7,100),98)]52 53 _assert_test(tests, maxMultiple)54def _assert_test(input_expect_arr, func):55 for test_input, expected in input_expect_arr:56 print(test_input,expected, func(*test_input))57 assert func(*test_input) == expected58def main():59 #test_maxMultiple()60 stringify_list([8, 11,12,13,14,15, 25,72,88])61if __name__ == '__main__':...
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