module onnxrt.validate.validate_scenarios#
Short summary#
module mlprodict.onnxrt.validate.validate_scenarios
Scenarios for validation.
Functions#
function |
truncated documentation |
|---|---|
Defines parameters values for some operators. |
|
Converts a string into a dictionary. |
Documentation#
Scenarios for validation.
- mlprodict.onnxrt.validate.validate_scenarios.build_custom_scenarios()#
Defines parameters values for some operators.
<<<
from mlprodict.onnxrt.validate.validate_scenarios import build_custom_scenarios import pprint pprint.pprint(build_custom_scenarios())
>>>
{<class 'sklearn.neighbors._classification.KNeighborsClassifier'>: [('default_k3', {'algorithm': 'brute', 'n_neighbors': 3}, {'conv_options': [{<class 'sklearn.neighbors._classification.KNeighborsClassifier'>: {'optim': 'cdist', 'zipmap': False}}]}), ('weights_k3', {'algorithm': 'brute', 'n_neighbors': 3, 'weights': 'distance'}, {'conv_options': [{<class 'sklearn.neighbors._classification.KNeighborsClassifier'>: {'optim': 'cdist', 'zipmap': False}}]})], <class 'sklearn.neighbors._classification.RadiusNeighborsClassifier'>: [('default_k3', {'algorithm': 'brute'}, {'conv_options': [{<class 'sklearn.neighbors._classification.RadiusNeighborsClassifier'>: {'optim': 'cdist', 'zipmap': False}}]}), ('weights_k3', {'algorithm': 'brute', 'weights': 'distance'}, {'conv_options': [{<class 'sklearn.neighbors._classification.RadiusNeighborsClassifier'>: {'optim': 'cdist', 'zipmap': False}}]})], <class 'sklearn.neighbors._regression.KNeighborsRegressor'>: [('default_k3', {'algorithm': 'brute', 'n_neighbors': 3}, {'conv_options': [{<class 'sklearn.neighbors._regression.KNeighborsRegressor'>: {'optim': 'cdist'}}]}), ('weights_k3', {'algorithm': 'brute', 'n_neighbors': 3, 'weights': 'distance'}, {'conv_options': [{<class 'sklearn.neighbors._regression.KNeighborsRegressor'>: {'optim': 'cdist'}}]})], <class 'sklearn.neighbors._regression.RadiusNeighborsRegressor'>: [('default_k3', {'algorithm': 'brute'}, {'conv_options': [{}, {<class 'sklearn.neighbors._regression.RadiusNeighborsRegressor'>: {'optim': 'cdist'}}]}), ('weights_k3', {'algorithm': 'brute', 'weights': 'distance'}, {'conv_options': [{<class 'sklearn.neighbors._regression.RadiusNeighborsRegressor'>: {'optim': 'cdist'}}]})], <class 'sklearn.neighbors._lof.LocalOutlierFactor'>: [('novelty', {'novelty': True})], <class 'sklearn.model_selection._search.GridSearchCV'>: [('cl', {'estimator': LogisticRegression(solver='liblinear'), 'n_jobs': 1, 'param_grid': {'fit_intercept': [False, True]}}, {'conv_options': [{<class 'sklearn.model_selection._search.GridSearchCV'>: {'zipmap': False}}], 'subset_problems': ['b-cl', 'm-cl', '~b-cl-64']}), ('reg', {'estimator': LinearRegression(), 'n_jobs': 1, 'param_grid': {'fit_intercept': [False, True]}}, ['b-reg', 'm-reg', '~b-reg-64']), ('reg', {'estimator': KMeans(), 'n_jobs': 1, 'param_grid': {'n_clusters': [2, 3]}}, ['cluster'])], <class 'sklearn.model_selection._search.RandomizedSearchCV'>: [('cl', {'estimator': LogisticRegression(solver='liblinear'), 'param_distributions': {'fit_intercept': [False, True]}}), ('reg', {'estimator': LinearRegression(), 'param_distributions': {'fit_intercept': [False, True]}})], <class 'sklearn.linear_model._stochastic_gradient.SGDClassifier'>: [('log', {'loss': 'log'}, {'conv_options': [{<class 'sklearn.linear_model._stochastic_gradient.SGDClassifier'>: {'zipmap': False}}]})], <class 'sklearn.linear_model._ridge.RidgeClassifier'>: [('default', {}, {'conv_options': [{<class 'sklearn.linear_model._ridge.RidgeClassifier'>: {'zipmap': False}}]})], <class 'sklearn.linear_model._ridge.RidgeClassifierCV'>: [('default', {}, {'conv_options': [{<class 'sklearn.linear_model._ridge.RidgeClassifierCV'>: {'zipmap': False}}]})], <class 'sklearn.svm._classes.SVC'>: [('linear', {'kernel': 'linear', 'probability': True}, {'conv_options': [{<class 'sklearn.svm._classes.SVC'>: {'zipmap': False}}]}), ('poly', {'kernel': 'poly', 'probability': True}, {'conv_options': [{<class 'sklearn.svm._classes.SVC'>: {'zipmap': False}}]}), ('rbf', {'kernel': 'rbf', 'probability': True}, {'conv_options': [{<class 'sklearn.svm._classes.SVC'>: {'zipmap': False}}]}), ('sigmoid', {'kernel': 'sigmoid', 'probability': True}, {'conv_options': [{<class 'sklearn.svm._classes.SVC'>: {'zipmap': False}}]})], <class 'sklearn.svm._classes.NuSVC'>: [('prob', {'probability': True})], <class 'sklearn.svm._classes.SVR'>: [('linear', {'kernel': 'linear'}), ('poly', {'kernel': 'poly'}), ('rbf', {'kernel': 'rbf'}), ('sigmoid', {'kernel': 'sigmoid'})], <class 'sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier'>: [('logreg', {}, {'conv_options': [{<class 'sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier'>: {'zipmap': False}}]})], <class 'sklearn.linear_model._perceptron.Perceptron'>: [('logreg', {}, {'conv_options': [{<class 'sklearn.linear_model._perceptron.Perceptron'>: {'zipmap': False}}]})], <class 'sklearn.tree._classes.DecisionTreeClassifier'>: [('default', {}, {'conv_options': [{<class 'sklearn.tree._classes.DecisionTreeClassifier'>: {'zipmap': False}}]})], <class 'sklearn.tree._classes.ExtraTreeClassifier'>: [('default', {}, {'conv_options': [{<class 'sklearn.tree._classes.ExtraTreeClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._forest.RandomForestClassifier'>: [('default', {'n_estimators': 10}, {'conv_options': [{<class 'sklearn.ensemble._forest.RandomForestClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._forest.RandomForestRegressor'>: [('default', {'n_estimators': 10})], <class 'sklearn.ensemble._forest.ExtraTreesClassifier'>: [('default', {'n_estimators': 10}, {'conv_options': [{<class 'sklearn.ensemble._forest.ExtraTreesClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._forest.ExtraTreesRegressor'>: [('default', {'n_estimators': 10})], <class 'sklearn.ensemble._iforest.IsolationForest'>: [('default', {'n_estimators': 10})], <class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>: [('default', {'n_estimators': 10}, {'conv_options': [{<class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._weight_boosting.AdaBoostRegressor'>: [('default', {'n_estimators': 10})], <class 'sklearn.ensemble._gb.GradientBoostingClassifier'>: [('default', {'n_estimators': 200}, {'conv_options': [{<class 'sklearn.ensemble._gb.GradientBoostingClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._gb.GradientBoostingRegressor'>: [('default', {'n_estimators': 200})], <class 'sklearn.ensemble._voting.VotingClassifier'>: [('logreg-noflatten', {'estimators': [('lr1', LogisticRegression(solver='liblinear')), ('lr2', LogisticRegression(fit_intercept=False, solver='liblinear'))], 'flatten_transform': False, 'voting': 'soft'}, {'conv_options': [{<class 'sklearn.ensemble._voting.VotingClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._voting.VotingRegressor'>: [('linreg', {'estimators': [('lr1', LinearRegression()), ('lr2', LinearRegression(fit_intercept=False))]})], <class 'sklearn.ensemble._stacking.StackingRegressor'>: [('linreg', {'estimators': [('lr1', LinearRegression()), ('lr2', LinearRegression(fit_intercept=False))]})], <class 'sklearn.ensemble._stacking.StackingClassifier'>: [('logreg', {'estimators': [('lr1', LogisticRegression(solver='liblinear')), ('lr2', LogisticRegression(fit_intercept=False, solver='liblinear'))]}, {'conv_options': [{<class 'sklearn.ensemble._stacking.StackingClassifier'>: {'zipmap': False}}]})], <class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor'>: [('default', {'max_iter': 100})], <class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>: [('default', {'max_iter': 100}, {'conv_options': [{<class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>: {'zipmap': False}}]})], <class 'sklearn.multioutput.MultiOutputRegressor'>: [('linreg', {'estimator': LinearRegression()})], <class 'sklearn.multioutput.MultiOutputClassifier'>: [('logreg', {'estimator': LogisticRegression(solver='liblinear')}, {'conv_options': [{<class 'sklearn.multioutput.MultiOutputClassifier'>: {'zipmap': False}}]})], <class 'sklearn.multioutput.ClassifierChain'>: [('logreg', {'base_estimator': LogisticRegression(solver='liblinear')})], <class 'sklearn.multioutput.RegressorChain'>: [('linreg', {'base_estimator': LinearRegression()})], <class 'sklearn.neural_network._multilayer_perceptron.MLPClassifier'>: [('default', {}, {'conv_options': [{<class 'sklearn.neural_network._multilayer_perceptron.MLPClassifier'>: {'zipmap': False}}]})], <class 'sklearn.feature_selection._univariate_selection.SelectPercentile'>: [('p50', {'percentile': 50})], <class 'sklearn.feature_selection._univariate_selection.SelectKBest'>: [('k2', {'k': 2})], <class 'sklearn.feature_selection._univariate_selection.SelectFwe'>: [('alpha100', {'alpha': 0.5})], <class 'sklearn.feature_selection._rfe.RFE'>: [('reg', {'estimator': LinearRegression()})], <class 'sklearn.feature_selection._rfe.RFECV'>: [('reg', {'estimator': LinearRegression()})], <class 'sklearn.feature_selection._from_model.SelectFromModel'>: [('rf', {'estimator': DecisionTreeRegressor()})], <class 'sklearn.random_projection.GaussianRandomProjection'>: [('eps95', {'eps': 0.95})], <class 'sklearn.random_projection.SparseRandomProjection'>: [('eps95', {'eps': 0.95})], <class 'sklearn.preprocessing._data.Normalizer'>: [('l2', {'norm': 'l2'}), ('l1', {'norm': 'l1'}), ('max', {'norm': 'max'})], <class 'sklearn.preprocessing._data.PowerTransformer'>: [('yeo-johnson', {'method': 'yeo-johnson'}), ('box-cox', {'method': 'box-cox'})], <class 'sklearn.linear_model._logistic.LogisticRegression'>: [('liblinear', {'solver': 'liblinear'}, {'conv_options': [{}, {<class 'sklearn.linear_model._logistic.LogisticRegression'>: {'zipmap': False}}], 'optim': [None, 'onnx'], 'subset_problems': ['b-cl', '~b-cl-64', 'm-cl']}), ('liblinear-dec', {'solver': 'liblinear'}, {'conv_options': [{<class 'sklearn.linear_model._logistic.LogisticRegression'>: {'raw_scores': True, 'zipmap': False}}], 'subset_problems': ['~b-cl-dec', '~m-cl-dec']})], <class 'sklearn.linear_model._logistic.LogisticRegressionCV'>: [('default', {}, {'conv_options': [{<class 'sklearn.linear_model._logistic.LogisticRegressionCV'>: {'zipmap': False}}]})], <class 'sklearn.decomposition._dict_learning.SparseCoder'>: None, <class 'sklearn.decomposition._lda.LatentDirichletAllocation'>: [('default', {'n_components': 2})], <class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>: [('expsine', {'alpha': 20.0, 'kernel': ExpSineSquared(length_scale=1, periodicity=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>: {'optim': 'cdist'}}]}), ('dotproduct', {'alpha': 100.0, 'kernel': DotProduct(sigma_0=1)}, {'conv_options': [{}, {<class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>: {'optim': 'cdist'}}]}), ('rational', {'alpha': 100.0, 'kernel': RationalQuadratic(alpha=1, length_scale=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>: {'optim': 'cdist'}}]}), ('rbf', {'alpha': 100.0, 'kernel': RBF(length_scale=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>: {'optim': 'cdist'}}]})], <class 'sklearn.multiclass.OneVsRestClassifier'>: [('logreg', {'estimator': LogisticRegression(solver='liblinear')}, {'conv_options': [{<class 'sklearn.multiclass.OneVsOneClassifier'>: {'zipmap': False}}]})], <class 'sklearn.multiclass.OneVsOneClassifier'>: [('logreg', {'estimator': LogisticRegression(solver='liblinear')}, {'conv_options': [{<class 'sklearn.multiclass.OneVsOneClassifier'>: {'zipmap': False}}]})], <class 'sklearn.multiclass.OutputCodeClassifier'>: [('logreg', {'estimator': LogisticRegression(solver='liblinear')}, {'conv_options': [{<class 'sklearn.multiclass.OneVsOneClassifier'>: {'zipmap': False}}]})], <class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>: [('expsine', {'kernel': ExpSineSquared(length_scale=1, periodicity=1)}, {'conv_options': [{}, {<class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>: {'optim': 'cdist'}}]}), ('dotproduct', {'kernel': DotProduct(sigma_0=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>: {'optim': 'cdist'}}]}), ('rational', {'kernel': RationalQuadratic(alpha=1, length_scale=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>: {'optim': 'cdist'}}]}), ('rbf', {'kernel': RBF(length_scale=1)}, {'conv_options': [{<class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>: {'optim': 'cdist'}}]})], <class 'sklearn.calibration.CalibratedClassifierCV'>: [('sgd', {'base_estimator': SGDClassifier()}), ('default', {})], <class 'sklearn.feature_extraction._dict_vectorizer.DictVectorizer'>: [('default', {})], <class 'sklearn.feature_extraction._hash.FeatureHasher'>: [('default', {})]}
- mlprodict.onnxrt.validate.validate_scenarios.interpret_options_from_string(st)#
Converts a string into a dictionary.
- Parameters:
st – string
- Returns:
evaluated object