refactor rewritten sklearn operators

mlprodict/__init__.py

@@ -4,7 +4,7 @@
 @brief Ways to speed up predictions for a machine learned model.
 """
 
-__version__ = "0.3.1097"
+__version__ = "0.3.1099"
 __author__ = "Xavier Dupré"
 
 

mlprodict/onnx_conv/register_rewritten_converters.py

@@ -19,10 +19,24 @@
     new_convert_sklearn_random_forest_classifier,
     new_convert_sklearn_random_forest_regressor,
 )
+from .sklconv.svm_converters import (
+    new_convert_sklearn_svm_classifier,
+    new_convert_sklearn_svm_regressor,
+)
 
 
 _overwritten_operators = {
     #
+    'SklearnOneClassSVM': RegisteredConverter(
+        new_convert_sklearn_svm_regressor,
+        _converter_pool['SklearnOneClassSVM'].get_allowed_options()),
+    'SklearnSVR': RegisteredConverter(
+        new_convert_sklearn_svm_regressor,
+        _converter_pool['SklearnSVR'].get_allowed_options()),
+    'SklearnSVC': RegisteredConverter(
+        new_convert_sklearn_svm_classifier,
+        _converter_pool['SklearnSVC'].get_allowed_options()),
+    #
     'SklearnDecisionTreeRegressor': RegisteredConverter(
         new_convert_sklearn_decision_tree_regressor,
         _converter_pool['SklearnDecisionTreeRegressor'].get_allowed_options()),

mlprodict/onnx_conv/sklconv/svm_converters.py

@@ -0,0 +1,57 @@
+"""
+@file
+@brief Rewrites some of the converters implemented in
+:epkg:`sklearn-onnx`.
+"""
+import numpy
+from skl2onnx.operator_converters.support_vector_machines import (
+    convert_sklearn_svm_regressor,
+    convert_sklearn_svm_classifier)
+
+
+def _op_type_domain_regressor(container):
+    """
+    Defines *op_type* and *op_domain* based on
+    `container.dtype`.
+    """
+    if container.dtype == numpy.float32:
+        return 'SVMRegressor', 'ai.onnx.ml', 1
+    if container.dtype == numpy.float64:
+        return 'SVMRegressorDouble', 'mlprodict', 1
+    raise RuntimeError("Unsupported dtype {}.".format(container.dtype))
+
+
+def _op_type_domain_classifier(container):
+    """
+    Defines *op_type* and *op_domain* based on
+    `container.dtype`.
+    """
+    if container.dtype == numpy.float32:
+        return 'SVMClassifier', 'ai.onnx.ml', 1
+    if container.dtype == numpy.float64:
+        return 'SVMClassifierDouble', 'mlprodict', 1
+    raise RuntimeError("Unsupported dtype {}.".format(container.dtype))
+
+
+def new_convert_sklearn_svm_regressor(scope, operator, container):
+    """
+    Rewrites the converters implemented in
+    :epkg:`sklearn-onnx` to support an operator supporting
+    doubles.
+    """
+    op_type, op_domain, op_version = _op_type_domain_regressor(container)
+    convert_sklearn_svm_regressor(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
+
+
+def new_convert_sklearn_svm_classifier(scope, operator, container):
+    """
+    Rewrites the converters implemented in
+    :epkg:`sklearn-onnx` to support an operator supporting
+    doubles.
+    """
+    op_type, op_domain, op_version = _op_type_domain_classifier(container)
+    convert_sklearn_svm_classifier(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)

mlprodict/onnx_conv/sklconv/tree_converters.py

@@ -15,38 +15,52 @@
     convert_sklearn_random_forest_regressor_converter)
 
 
-def _op_type_domain(container):
+def _op_type_domain_regressor(container):
     """
     Defines *op_type* and *op_domain* based on
     `container.dtype`.
     """
     if container.dtype == numpy.float32:
-        return 'TreeEnsembleRegressor', 'ai.onnx.ml'
+        return 'TreeEnsembleRegressor', 'ai.onnx.ml', 1
     if container.dtype == numpy.float64:
-        return 'TreeEnsembleRegressorDouble', 'mlprodict'
+        return 'TreeEnsembleRegressorDouble', 'mlprodict', 1
     raise RuntimeError("Unsupported dtype {}.".format(container.dtype))
 
 
-def new_convert_sklearn_decision_tree_regressor(scope, operator, container):
+def _op_type_domain_classifier(container):
+    """
+    Defines *op_type* and *op_domain* based on
+    `container.dtype`.
+    """
+    if container.dtype == numpy.float32:
+        return 'TreeEnsembleClassifier', 'ai.onnx.ml', 1
+    if container.dtype == numpy.float64:
+        return 'TreeEnsembleClassifierDouble', 'mlprodict', 1
+    raise RuntimeError("Unsupported dtype {}.".format(container.dtype))
+
+
+def new_convert_sklearn_decision_tree_classifier(scope, operator, container):
     """
     Rewrites the converters implemented in
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
-    convert_sklearn_decision_tree_regressor(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+    op_type, op_domain, op_version = _op_type_domain_classifier(container)
+    convert_sklearn_decision_tree_classifier(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
 
 
-def new_convert_sklearn_decision_tree_classifier(scope, operator, container):
+def new_convert_sklearn_decision_tree_regressor(scope, operator, container):
     """
     Rewrites the converters implemented in
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
-    convert_sklearn_decision_tree_classifier(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+    op_type, op_domain, op_version = _op_type_domain_regressor(container)
+    convert_sklearn_decision_tree_regressor(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
 
 
 def new_convert_sklearn_gradient_boosting_classifier(scope, operator, container):
@@ -55,9 +69,10 @@ def new_convert_sklearn_gradient_boosting_classifier(scope, operator, container)
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
+    op_type, op_domain, op_version = _op_type_domain_classifier(container)
     convert_sklearn_gradient_boosting_classifier(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
 
 
 def new_convert_sklearn_gradient_boosting_regressor(scope, operator, container):
@@ -66,28 +81,31 @@ def new_convert_sklearn_gradient_boosting_regressor(scope, operator, container):
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
+    op_type, op_domain, op_version = _op_type_domain_regressor(container)
     convert_sklearn_gradient_boosting_regressor(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
 
 
-def new_convert_sklearn_random_forest_regressor(scope, operator, container):
+def new_convert_sklearn_random_forest_classifier(scope, operator, container):
     """
     Rewrites the converters implemented in
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
-    convert_sklearn_random_forest_regressor_converter(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+    op_type, op_domain, op_version = _op_type_domain_classifier(container)
+    convert_sklearn_random_forest_classifier(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)
 
 
-def new_convert_sklearn_random_forest_classifier(scope, operator, container):
+def new_convert_sklearn_random_forest_regressor(scope, operator, container):
     """
     Rewrites the converters implemented in
     :epkg:`sklearn-onnx` to support an operator supporting
     doubles.
     """
-    op_type, op_domain = _op_type_domain(container)
-    convert_sklearn_random_forest_classifier(
-        scope, operator, container, op_type=op_type, op_domain=op_domain)
+    op_type, op_domain, op_version = _op_type_domain_regressor(container)
+    convert_sklearn_random_forest_regressor_converter(
+        scope, operator, container, op_type=op_type, op_domain=op_domain,
+        op_version=op_version)

mlprodict/onnxrt/validate/validate_scenarios.py

@@ -232,11 +232,11 @@ def build_custom_scenarios():
             ('box-cox', {'method': 'box-cox'}),
         ],
         RandomForestClassifier: [
-            ('default', {'n_estimators': 10},
+            ('default', {'n_estimators': 100},
              {'conv_options': [{}, {RandomForestClassifier: {'zipmap': False}}]}),
         ],
         RandomForestRegressor: [
-            ('default', {'n_estimators': 10}),
+            ('default', {'n_estimators': 100}),
         ],
         RandomizedSearchCV: [
             ('cl', {