Coverage for src/jupytalk/benchmark/mlprediction.py: 72%

1# -*- coding: utf-8 -*- 

2""" 

3@file 

4@brief 

5""" 

6import timeit 

7import pandas 

8 

9 

10def unit(x): 

11 """ 

12 Optimizes the rendering of time. 

13 

14 .. runpython:: 

15 :showcode: 

16 

17 from jupytalk.benchmark.mlprediction import unit 

18 

19 print(unit(34)) 

20 print(unit(3.4)) 

21 print(unit(0.34)) 

22 print(unit(0.034)) 

23 print(unit(0.0034)) 

24 print(unit(0.00034)) 

25 print(unit(0.000034)) 

26 print(unit(0.0000034)) 

27 print(unit(0.00000034)) 

28 """ 

29 if x >= 1: 

30 return "%1.2f s" % x 

31 elif x >= 1e-3: 

32 return "%1.2f ms" % (x * 1000) 

33 elif x >= 1e-6: 

34 return "%1.2f µs" % (x * 1000**2) 

35 elif x >= 1e-9: 

36 return "%1.2f ns" % (x * 1000**3) 

37 else: 

38 return "%1.2g s" % x 

39 

40 

41def timeexec(legend, code, number=50, repeat=200, verbose=True, context=None): 

42 """ 

43 Measures the time for a given expression. 

44 

45 @param legend name of the experiment 

46 @param code code to measure (as a string) 

47 @param number number of time to run the expression 

48 (and then divide by this number to get an average) 

49 @param repeat number of times to repeat the computation 

50 of the above average 

51 @param verbose print the time 

52 @param globals context (usuable equal to ``globals()``) 

53 @return dictionary 

54 

55 .. runpython:: 

56 :showcode: 

57 

58 from jupytalk.benchmark.mlprediction import timeexec 

59 

60 code = "3 * 45535266234653452" 

61 print(timeexec("multiplication", code)) 

62 """ 

63 if context is None: 

64 context = globals() 

65 rep = timeit.repeat(code, number=number, repeat=repeat, globals=context) 

66 ave = sum(rep) / (number * repeat) 

67 std = (sum((x / number - ave)**2 for x in rep) / repeat)**0.5 

68 fir = rep[0] / number 

69 fir3 = sum(rep[:3]) / (3 * number) 

70 las3 = sum(rep[-3:]) / (3 * number) 

71 rep.sort() 

72 mini = rep[len(rep) // 20] / number 

73 maxi = rep[-len(rep) // 20] / number 

74 if verbose: 

75 print("Average: %s deviation %s (with %d runs) in [%s, %s]" % ( 

76 unit(ave), unit(std), number, unit(mini), unit(maxi))) 

77 return dict(legend=legend, average=ave, deviation=std, first=fir, first3=fir3, 

78 last3=las3, repeat=repeat, min5=mini, max5=maxi, code=code, run=number) 

79 

80 

81def make_dataframe(labels, arrays): 

82 """ 

83 Builds a dataframe from multiple arrays. 

84 

85 @param labels list of labels 

86 @param arrays list of arrays (or one array) 

87 @return dataframes 

88 """ 

89 if labels is not None: 

90 df = [pandas.DataFrame(data={'Label': labels})] 

91 else: 

92 df = [] 

93 if isinstance(arrays, list): 

94 for i, ar in enumerate(arrays): 

95 d = pandas.DataFrame( 

96 data=ar, columns=["F%d_%d" % (i, j) for j in range(ar.shape[1])]) 

97 df.append(d) 

98 else: 

99 ar = arrays 

100 d = pandas.DataFrame( 

101 data=ar, columns=["F%d" % j for j in range(ar.shape[1])]) 

102 df.append(d) 

103 return pandas.concat(df, axis=1)