Coverage for mlprodict/onnx_conv/validate_scenarios.py: 100%

1""" 

2@file 

3@brief Scenario for additional converters. 

4""" 

5from lightgbm import LGBMRegressor, LGBMClassifier 

6from xgboost import XGBRegressor, XGBClassifier 

7 

8 

9def find_suitable_problem(model): 

10 """ 

11 Defines suitables problems for additional converters. 

12 

13 .. runpython:: 

14 :showcode: 

15 :warningout: DeprecationWarning 

16 :rst: 

17 

18 from mlprodict.onnx_conv.validate_scenarios import find_suitable_problem 

19 from mlprodict.onnxrt.validate.validate_helper import sklearn_operators 

20 from pyquickhelper.pandashelper import df2rst 

21 from pandas import DataFrame 

22 res = sklearn_operators(extended=True) 

23 res = [_ for _ in res if _['package'] != 'sklearn'] 

24 rows = [] 

25 for model in res: 

26 name = model['name'] 

27 row = dict(name=name) 

28 try: 

29 prob = find_suitable_problem(model['cl']) 

30 if prob is None: 

31 continue 

32 for p in prob: 

33 row[p] = 'X' 

34 except RuntimeError: 

35 pass 

36 rows.append(row) 

37 df = DataFrame(rows).set_index('name') 

38 df = df.sort_index() 

39 print(df2rst(df, index=True)) 

40 

41 """ 

42 def _internal(model): 

43 # Exceptions 

44 if model in {LGBMRegressor, XGBRegressor}: 

45 return ['b-reg', '~b-reg-64'] 

46 

47 if model in {LGBMClassifier, XGBClassifier}: 

48 return ['b-cl', 'm-cl', '~b-cl-64'] 

49 

50 # Not in this list 

51 return None 

52 

53 res = _internal(model) 

54 return res 

55 

56 

57def build_custom_scenarios(): 

58 """ 

59 Defines parameters values for some operators. 

60 

61 .. runpython:: 

62 :showcode: 

63 :warningout: DeprecationWarning 

64 

65 from mlprodict.onnx_conv.validate_scenarios import build_custom_scenarios 

66 import pprint 

67 pprint.pprint(build_custom_scenarios()) 

68 """ 

69 return { 

70 # scenarios 

71 LGBMClassifier: [ 

72 ('default', {'n_estimators': 5}, {'conv_options': [ 

73 {LGBMClassifier: {'zipmap': False}}]}), 

74 ], 

75 LGBMRegressor: [ 

76 ('default', {'n_estimators': 100}), 

77 ], 

78 XGBClassifier: [ 

79 ('default', {'n_estimators': 5}, {'conv_options': [ 

80 {XGBClassifier: {'zipmap': False}}]}), 

81 ], 

82 XGBRegressor: [ 

83 ('default', {'n_estimators': 100}), 

84 ], 

85 }