Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/hypothesis/statistics.py: 15%

1# This file is part of Hypothesis, which may be found at 

2# https://github.com/HypothesisWorks/hypothesis/ 

3# 

4# Copyright the Hypothesis Authors. 

5# Individual contributors are listed in AUTHORS.rst and the git log. 

6# 

7# This Source Code Form is subject to the terms of the Mozilla Public License, 

8# v. 2.0. If a copy of the MPL was not distributed with this file, You can 

9# obtain one at https://mozilla.org/MPL/2.0/. 

10 

11import math 

12from collections import Counter 

13 

14from hypothesis._settings import Phase 

15from hypothesis.utils.dynamicvariables import DynamicVariable 

16 

17collector = DynamicVariable(None) 

18 

19 

20def note_statistics(stats_dict): 

21 callback = collector.value 

22 if callback is not None: 

23 callback(stats_dict) 

24 

25 

26def describe_targets(best_targets): 

27 """Return a list of lines describing the results of `target`, if any.""" 

28 # These lines are included in the general statistics description below, 

29 # but also printed immediately below failing examples to alleviate the 

30 # "threshold problem" where shrinking can make severe bug look trivial. 

31 # See https://github.com/HypothesisWorks/hypothesis/issues/2180 

32 if not best_targets: 

33 return [] 

34 elif len(best_targets) == 1: 

35 label, score = next(iter(best_targets.items())) 

36 return [f"Highest target score: {score:g} ({label=})"] 

37 else: 

38 lines = ["Highest target scores:"] 

39 for label, score in sorted(best_targets.items(), key=lambda x: x[::-1]): 

40 lines.append(f"{score:>16g} ({label=})") 

41 return lines 

42 

43 

44def format_ms(times): 

45 """Format `times` into a string representing approximate milliseconds. 

46 

47 `times` is a collection of durations in seconds. 

48 """ 

49 ordered = sorted(times) 

50 n = len(ordered) - 1 

51 if n < 0 or any(math.isnan(t) for t in ordered): 

52 return "NaN ms" 

53 lower = int(ordered[int(math.floor(n * 0.05))] * 1000) 

54 upper = int(ordered[int(math.ceil(n * 0.95))] * 1000) 

55 if upper == 0: 

56 return "< 1ms" 

57 elif lower == upper: 

58 return f"~ {lower}ms" 

59 else: 

60 return f"~ {lower}-{upper} ms" 

61 

62 

63def describe_statistics(stats_dict): 

64 """Return a multi-line string describing the passed run statistics. 

65 

66 `stats_dict` must be a dictionary of data in the format collected by 

67 `hypothesis.internal.conjecture.engine.ConjectureRunner.statistics`. 

68 

69 We DO NOT promise that this format will be stable or supported over 

70 time, but do aim to make it reasonably useful for downstream users. 

71 It's also meant to support benchmarking for research purposes. 

72 

73 This function is responsible for the report which is printed in the 

74 terminal for our pytest --hypothesis-show-statistics option. 

75 """ 

76 lines = [stats_dict["nodeid"] + ":\n"] if "nodeid" in stats_dict else [] 

77 prev_failures = 0 

78 for phase in (p.name for p in list(Phase)[1:]): 

79 d = stats_dict.get(phase + "-phase", {}) 

80 # Basic information we report for every phase 

81 cases = d.get("test-cases", []) 

82 if not cases: 

83 continue 

84 statuses = Counter(t["status"] for t in cases) 

85 runtime_ms = format_ms(t["runtime"] for t in cases) 

86 drawtime_ms = format_ms(t["drawtime"] for t in cases) 

87 lines.append( 

88 f" - during {phase} phase ({d['duration-seconds']:.2f} seconds):\n" 

89 f" - Typical runtimes: {runtime_ms}, of which {drawtime_ms} in data generation\n" 

90 f" - {statuses['valid']} passing examples, {statuses['interesting']} " 

91 f"failing examples, {statuses['invalid'] + statuses['overrun']} invalid examples" 

92 ) 

93 # If we've found new distinct failures in this phase, report them 

94 distinct_failures = d["distinct-failures"] - prev_failures 

95 if distinct_failures: 

96 plural = distinct_failures > 1 

97 lines.append( 

98 " - Found {}{} distinct error{} in this phase".format( 

99 distinct_failures, " more" * bool(prev_failures), "s" * plural 

100 ) 

101 ) 

102 prev_failures = d["distinct-failures"] 

103 # Report events during the generate phase, if there were any 

104 if phase == "generate": 

105 events = Counter(sum((t["events"] for t in cases), [])) 

106 if events: 

107 lines.append(" - Events:") 

108 lines += [ 

109 f" * {100 * v / len(cases):.2f}%, {k}" 

110 for k, v in sorted(events.items(), key=lambda x: (-x[1], x[0])) 

111 ] 

112 # Some additional details on the shrinking phase 

113 if phase == "shrink": 

114 lines.append( 

115 " - Tried {} shrinks of which {} were successful".format( 

116 len(cases), d["shrinks-successful"] 

117 ) 

118 ) 

119 lines.append("") 

120 

121 target_lines = describe_targets(stats_dict.get("targets", {})) 

122 if target_lines: 

123 lines.append(" - " + target_lines[0]) 

124 lines.extend(" " + l for l in target_lines[1:]) 

125 lines.append(" - Stopped because " + stats_dict["stopped-because"]) 

126 return "\n".join(lines)