Add support for RANSACRegressor (#162)

README.md

@@ -41,7 +41,7 @@ pip install m2cgen
 
 |  | Classification | Regression |
 | --- | --- | --- |
-| **Linear** | <ul><li>LogisticRegression</li><li>LogisticRegressionCV</li><li>PassiveAggressiveClassifier</li><li>Perceptron</li><li>RidgeClassifier</li><li>RidgeClassifierCV</li><li>SGDClassifier</li></ul> | <ul><li>ARDRegression</li><li>BayesianRidge</li><li>ElasticNet</li><li>ElasticNetCV</li><li>HuberRegressor</li><li>Lars</li><li>LarsCV</li><li>Lasso</li><li>LassoCV</li><li>LassoLars</li><li>LassoLarsCV</li><li>LassoLarsIC</li><li>LinearRegression</li><li>OrthogonalMatchingPursuit</li><li>OrthogonalMatchingPursuitCV</li><li>PassiveAggressiveRegressor</li><li>Ridge</li><li>RidgeCV</li><li>SGDRegressor</li><li>TheilSenRegressor</li></ul> |
+| **Linear** | <ul><li>LogisticRegression</li><li>LogisticRegressionCV</li><li>PassiveAggressiveClassifier</li><li>Perceptron</li><li>RidgeClassifier</li><li>RidgeClassifierCV</li><li>SGDClassifier</li></ul> | <ul><li>ARDRegression</li><li>BayesianRidge</li><li>ElasticNet</li><li>ElasticNetCV</li><li>HuberRegressor</li><li>Lars</li><li>LarsCV</li><li>Lasso</li><li>LassoCV</li><li>LassoLars</li><li>LassoLarsCV</li><li>LassoLarsIC</li><li>LinearRegression</li><li>OrthogonalMatchingPursuit</li><li>OrthogonalMatchingPursuitCV</li><li>PassiveAggressiveRegressor</li><li>RANSACRegressor(only supported regression estimators can be used as a base estimator)</li><li>Ridge</li><li>RidgeCV</li><li>SGDRegressor</li><li>TheilSenRegressor</li></ul> |
 | **SVM** | <ul><li>LinearSVC</li><li>NuSVC</li><li>SVC</li></ul> | <ul><li>LinearSVR</li><li>NuSVR</li><li>SVR</li></ul> |
 | **Tree** | <ul><li>DecisionTreeClassifier</li><li>ExtraTreeClassifier</li></ul> | <ul><li>DecisionTreeRegressor</li><li>ExtraTreeRegressor</li></ul> |
 | **Random Forest** | <ul><li>ExtraTreesClassifier</li><li>LGBMClassifier(rf booster only)</li><li>RandomForestClassifier</li><li>XGBRFClassifier(binary only, multiclass is not supported yet)</li></ul> | <ul><li>ExtraTreesRegressor</li><li>LGBMRegressor(rf booster only)</li><li>RandomForestRegressor</li><li>XGBRFRegressor</li></ul> |

m2cgen/assemblers/__init__.py

@@ -6,9 +6,11 @@
                        XGBoostLinearModelAssembler,
                        LightGBMModelAssembler)
 from .svm import SVMModelAssembler
+from .meta import RANSACModelAssembler
 
 __all__ = [
     LinearModelAssembler,
+    RANSACModelAssembler,
     TreeModelAssembler,
     RandomForestModelAssembler,
     XGBoostModelAssemblerSelector,
@@ -55,6 +57,7 @@
     "OrthogonalMatchingPursuit": LinearModelAssembler,
     "OrthogonalMatchingPursuitCV": LinearModelAssembler,
     "PassiveAggressiveRegressor": LinearModelAssembler,
+    "RANSACRegressor": RANSACModelAssembler,
     "Ridge": LinearModelAssembler,
     "RidgeCV": LinearModelAssembler,
     "SGDRegressor": LinearModelAssembler,

m2cgen/assemblers/meta.py

@@ -0,0 +1,19 @@
+from m2cgen.assemblers.base import ModelAssembler
+
+
+class BaseMetaAssembler(ModelAssembler):
+
+    def assemble(self):
+        # import here to avoid circular import error
+        from m2cgen.assemblers import get_assembler_cls
+        base_model = self._get_base_model()
+        return get_assembler_cls(base_model)(base_model).assemble()
+
+    def _get_base_model(self):
+        raise NotImplementedError
+
+
+class RANSACModelAssembler(BaseMetaAssembler):
+
+    def _get_base_model(self):
+        return self.model.estimator_

tests/assemblers/test_linear.py

@@ -1,5 +1,8 @@
+import pytest
 import numpy as np
 from sklearn import linear_model
+from sklearn.dummy import DummyRegressor
+from sklearn.tree import DecisionTreeRegressor
 
 from m2cgen import assemblers, ast
 from tests import utils
@@ -127,3 +130,36 @@ def test_binary_class():
         ast.BinNumOpType.ADD)
 
     assert utils.cmp_exprs(actual, expected)
+
+
+def test_ransac_custom_base_estimator():
+    base_estimator = DecisionTreeRegressor()
+    estimator = linear_model.RANSACRegressor(
+        base_estimator=base_estimator,
+        random_state=1)
+    estimator.fit([[1], [2], [3]], [1, 2, 3])
+
+    assembler = assemblers.RANSACModelAssembler(estimator)
+    actual = assembler.assemble()
+
+    expected = ast.IfExpr(
+        ast.CompExpr(
+            ast.FeatureRef(0),
+            ast.NumVal(2.5),
+            ast.CompOpType.LTE),
+        ast.NumVal(2.0),
+        ast.NumVal(3.0))
+
+    assert utils.cmp_exprs(actual, expected)
+
+
+@pytest.mark.xfail(raises=NotImplementedError, strict=True)
+def test_ransac_unknown_base_estimator():
+    base_estimator = DummyRegressor()
+    estimator = linear_model.RANSACRegressor(
+        base_estimator=base_estimator,
+        random_state=1)
+    estimator.fit([[1], [2], [3]], [1, 2, 3])
+
+    assembler = assemblers.RANSACModelAssembler(estimator)
+    assembler.assemble()

tests/e2e/test_e2e.py

@@ -213,6 +213,9 @@ def classification_binary_random(model):
         regression(linear_model.OrthogonalMatchingPursuitCV()),
         regression(linear_model.PassiveAggressiveRegressor(
             random_state=RANDOM_SEED)),
+        regression(linear_model.RANSACRegressor(
+            base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS),
+            random_state=RANDOM_SEED)),
         regression(linear_model.Ridge(random_state=RANDOM_SEED)),
         regression(linear_model.RidgeCV()),
         regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)),