module onnx_conv.validate_scenarios
#
Short summary#
module mlprodict.onnx_conv.validate_scenarios
Scenario for additional converters.
Functions#
function |
truncated documentation |
---|---|
Defines parameters values for some operators. |
|
Defines suitables problems for additional converters. |
Documentation#
Scenario for additional converters.
- mlprodict.onnx_conv.validate_scenarios.build_custom_scenarios()#
Defines parameters values for some operators.
<<<
from mlprodict.onnx_conv.validate_scenarios import build_custom_scenarios import pprint pprint.pprint(build_custom_scenarios())
>>>
{<class 'lightgbm.sklearn.LGBMClassifier'>: [('default', {'n_estimators': 5}, {'conv_options': [{<class 'lightgbm.sklearn.LGBMClassifier'>: {'zipmap': False}}]})], <class 'lightgbm.sklearn.LGBMRegressor'>: [('default', {'n_estimators': 100})], <class 'xgboost.sklearn.XGBClassifier'>: [('default', {'n_estimators': 5}, {'conv_options': [{<class 'xgboost.sklearn.XGBClassifier'>: {'zipmap': False}}]})], <class 'xgboost.sklearn.XGBRegressor'>: [('default', {'n_estimators': 100})]}
- mlprodict.onnx_conv.validate_scenarios.find_suitable_problem(model)#
Defines suitables problems for additional converters.
<<<
from mlprodict.onnx_conv.validate_scenarios import find_suitable_problem from mlprodict.onnxrt.validate.validate_helper import sklearn_operators from pyquickhelper.pandashelper import df2rst from pandas import DataFrame res = sklearn_operators(extended=True) res = [_ for _ in res if _['package'] != 'sklearn'] rows = [] for model in res: name = model['name'] row = dict(name=name) try: prob = find_suitable_problem(model['cl']) if prob is None: continue for p in prob: row[p] = 'X' except RuntimeError: pass rows.append(row) df = DataFrame(rows).set_index('name') df = df.sort_index() print(df2rst(df, index=True))
>>>
name
b-cl
m-cl
~b-cl-64
b-reg
~b-reg-64
LGBMClassifier
X
X
X
LGBMRegressor
X
X
XGBClassifier
X
X
X
XGBRegressor
X
X