more fixes with search spaces - wrapper, make sure all supported modu…

.gitignore

@@ -13,4 +13,5 @@ dask-worker-space/
 target/
 .venv/
 build/*
-*.egg
+*.egg
+*.coverage*

tpot2/config/classifiers_sklearnex.py

@@ -1,6 +1,6 @@
 from ConfigSpace import ConfigurationSpace
 from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal
-
+from ..search_spaces.nodes.estimator_node import NONE_SPECIAL_STRING, TRUE_SPECIAL_STRING, FALSE_SPECIAL_STRING
 
 def get_RandomForestClassifier_ConfigurationSpace(random_state):
     space = {
@@ -66,10 +66,9 @@ def get_NuSVC_ConfigurationSpace(random_state):
     space = {
         'nu': Float("nu", bounds=(0.05, 1.0)),
         'kernel': Categorical("kernel", ['poly', 'rbf', 'linear', 'sigmoid']),
-        'C': Float("C", bounds=(1e-4, 25), log=True),
+        #'C': Float("C", bounds=(1e-4, 25), log=True),
         'degree': Integer("degree", bounds=(1, 4)),
-        #TODO work around for None value?
-        #'class_weight': Categorical("class_weight", [None, 'balanced']),
+        'class_weight': Categorical("class_weight", [NONE_SPECIAL_STRING, 'balanced']),
         'max_iter': 3000,
         'tol': 0.005,
         'probability': Categorical("probability", [True]), # configspace doesn't allow bools as a default value? but does allow them as a value inside a Categorical

tpot2/config/get_configspace.py

@@ -2,6 +2,7 @@
 import sys
 import numpy as np
 import warnings
+import importlib.util
 
 from ..search_spaces.nodes import EstimatorNode
 from ..search_spaces.pipelines import ChoicePipeline, WrapperPipeline
@@ -27,7 +28,7 @@
 
 from sklearn.linear_model import SGDClassifier
 from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier
-from sklearn.neural_network import MLPClassifier
+from sklearn.neural_network import MLPClassifier, MLPRegressor
 from sklearn.tree import DecisionTreeClassifier
 from xgboost import XGBClassifier
 from sklearn.neighbors import KNeighborsClassifier
@@ -101,51 +102,64 @@
 from tpot2.builtin_modules import AddTransformer, mul_neg_1_Transformer, MulTransformer, SafeReciprocalTransformer, EQTransformer, NETransformer, GETransformer, GTTransformer, LETransformer, LTTransformer, MinTransformer, MaxTransformer, ZeroTransformer, OneTransformer, NTransformer
 
 
+from tpot2.builtin_modules.genetic_encoders import DominantEncoder, RecessiveEncoder, HeterosisEncoder, UnderDominanceEncoder, OverDominanceEncoder 
+
 #MDR
 
 
 all_methods = [SGDClassifier, RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, MLPClassifier, DecisionTreeClassifier, XGBClassifier, KNeighborsClassifier, SVC, LogisticRegression, LGBMClassifier, LinearSVC, GaussianNB, BernoulliNB, MultinomialNB, ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor, BaggingRegressor, DecisionTreeRegressor, KNeighborsRegressor, XGBRegressor,  ZeroCount, OneHotEncoder, ColumnOneHotEncoder, Binarizer, FastICA, FeatureAgglomeration, MaxAbsScaler, MinMaxScaler, Normalizer, Nystroem, PCA, PolynomialFeatures, RBFSampler, RobustScaler, StandardScaler, SelectFwe, SelectPercentile, VarianceThreshold, SGDRegressor, Ridge, Lasso, ElasticNet, Lars, LassoLars, LassoLarsCV, RidgeCV, SVR, LinearSVR, AdaBoostRegressor, GradientBoostingRegressor, RandomForestRegressor, BaggingRegressor, ExtraTreesRegressor, DecisionTreeRegressor, KNeighborsRegressor, ElasticNetCV,
-               AdaBoostClassifier,
+               AdaBoostClassifier,MLPRegressor,
                GaussianProcessRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor,
                AddTransformer, mul_neg_1_Transformer, MulTransformer, SafeReciprocalTransformer, EQTransformer, NETransformer, GETransformer, GTTransformer, LETransformer, LTTransformer, MinTransformer, MaxTransformer, ZeroTransformer, OneTransformer, NTransformer,
                PowerTransformer, QuantileTransformer,ARDRegression, QuadraticDiscriminantAnalysis, PassiveAggressiveClassifier, LinearDiscriminantAnalysis,
+               DominantEncoder, RecessiveEncoder, HeterosisEncoder, UnderDominanceEncoder, OverDominanceEncoder,
                ]
 
 
 #if mdr is installed
-if 'mdr' in sys.modules:
+if importlib.util.find_spec('mdr') is not None:
     from mdr import MDR, ContinuousMDR
     all_methods.append(MDR)
     all_methods.append(ContinuousMDR)
 
-if 'skrebate' in sys.modules:
+if importlib.util.find_spec('skrebate') is not None:
     from skrebate import ReliefF, SURF, SURFstar, MultiSURF
     all_methods.append(ReliefF)
     all_methods.append(SURF)
     all_methods.append(SURFstar)
     all_methods.append(MultiSURF)
 
-if 'sklearnex' in sys.modules:
+STRING_TO_CLASS = {
+    t.__name__: t for t in all_methods
+}
+
+if importlib.util.find_spec('sklearnex') is not None:
     import sklearnex
+    import sklearnex.linear_model
+    import sklearnex.svm
+    import sklearnex.ensemble
+    import sklearnex.neighbors
 
-    all_methods.append(sklearnex.linear_model.LinearRegression)
-    all_methods.append(sklearnex.linear_model.Ridge)
-    all_methods.append(sklearnex.linear_model.Lasso)
-    all_methods.append(sklearnex.linear_model.ElasticNet)
-    all_methods.append(sklearnex.svm.SVR)
-    all_methods.append(sklearnex.svm.NuSVR)
-    all_methods.append(sklearnex.ensemble.RandomForestRegressor)
-    all_methods.append(sklearnex.neighbors.KNeighborsRegressor)
-    all_methods.append(sklearnex.ensemble.RandomForestClassifier)
-    all_methods.append(sklearnex.neighbors.KNeighborsClassifier)
-    all_methods.append(sklearnex.svm.SVC)
-    all_methods.append(sklearnex.svm.NuSVC)
-    all_methods.append(sklearnex.linear_model.LogisticRegression)
+
+    sklearnex_methods = []
+
+    sklearnex_methods.append(sklearnex.linear_model.LinearRegression)
+    sklearnex_methods.append(sklearnex.linear_model.Ridge)
+    sklearnex_methods.append(sklearnex.linear_model.Lasso)
+    sklearnex_methods.append(sklearnex.linear_model.ElasticNet)
+    sklearnex_methods.append(sklearnex.svm.SVR)
+    sklearnex_methods.append(sklearnex.svm.NuSVR)
+    sklearnex_methods.append(sklearnex.ensemble.RandomForestRegressor)
+    sklearnex_methods.append(sklearnex.neighbors.KNeighborsRegressor)
+    sklearnex_methods.append(sklearnex.ensemble.RandomForestClassifier)
+    sklearnex_methods.append(sklearnex.neighbors.KNeighborsClassifier)
+    sklearnex_methods.append(sklearnex.svm.SVC)
+    sklearnex_methods.append(sklearnex.svm.NuSVC)
+    sklearnex_methods.append(sklearnex.linear_model.LogisticRegression)
+
+    STRING_TO_CLASS.update({f"{t.__name__}_sklearnex": t for t in sklearnex_methods})
 
 
-STRING_TO_CLASS = {
-    t.__name__: t for t in all_methods
-}
 
 
 
@@ -439,15 +453,6 @@ def get_search_space(name, n_classes=3, n_samples=100, n_features=100, random_st
     if name in GROUPNAMES:
         name_list = GROUPNAMES[name]
         return get_search_space(name_list, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state)
-
-    if name is None:
-        warnings.warn(f"name is None")
-        return None
-
-    if name not in STRING_TO_CLASS:
-        print("FOOO ", name)
-        warnings.warn(f"Could not find class for {name}")
-        return None
 
     return get_node(name, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state)
 
@@ -458,21 +463,21 @@ def get_node(name, n_classes=3, n_samples=100, n_features=100, random_state=None
     # TODO Add AdaBoostRegressor, AdaBoostClassifier as wrappers? wrap a decision tree with different params?
     # TODO add other meta-estimators?
     if name == "RFE_classification":
-        rfe_sp = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
+        rfe_sp = get_configspace(name="RFE", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         ext = get_node("ExtraTreesClassifier", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
-        return WrapperPipeline(nodegen=ext, method=RFE, configspace=rfe_sp)
+        return WrapperPipeline(nodegen=ext, method=RFE, space=rfe_sp)
     if name == "RFE_regression":
-        rfe_sp = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
+        rfe_sp = get_configspace(name="RFE", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         ext = get_node("ExtraTreesRegressor", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
-        return WrapperPipeline(nodegen=ext, method=RFE, configspace=rfe_sp)
+        return WrapperPipeline(nodegen=ext, method=RFE, space=rfe_sp)
     if name == "SelectFromModel_classification":
-        sfm_sp = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
+        sfm_sp = get_configspace(name="SelectFromModel", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         ext = get_node("ExtraTreesClassifier", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
-        return WrapperPipeline(nodegen=ext, method=SelectFromModel, configspace=sfm_sp)
+        return WrapperPipeline(nodegen=ext, method=SelectFromModel, space=sfm_sp)
     if name == "SelectFromModel_regression":
-        sfm_sp = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
+        sfm_sp = get_configspace(name="SelectFromModel", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         ext = get_node("ExtraTreesRegressor", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
-        return WrapperPipeline(nodegen=ext, method=SelectFromModel, configspace=sfm_sp)
+        return WrapperPipeline(nodegen=ext, method=SelectFromModel, space=sfm_sp)
 
     #these are nodes that have special search spaces which require custom parsing of the hyperparameters
     if name == "RobustScaler":

tpot2/config/tests/test_get_configspace.py

@@ -6,7 +6,7 @@
 
 import tpot2.config
 
-from ..get_configspace import STRING_TO_CLASS
+from ..get_configspace import STRING_TO_CLASS, GROUPNAMES
 
 def test_loop_through_all_hyperparameters():
 
@@ -22,4 +22,20 @@ def test_loop_through_all_hyperparameters():
         for i in range(1):
             estnode = estnode_gen.generate()
             est = estnode.export_pipeline()
-
+
+def test_loop_through_groupnames():
+
+    n_classes=3
+    n_samples=100
+    n_features=100
+    random_state=None
+
+    for groupname, group in GROUPNAMES.items():
+        for class_name in group:
+            print(class_name)
+            estnode_gen = tpot2.config.get_search_space(class_name, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state)
+
+            #generate 10 random hyperparameters and make sure they are all valid
+            for i in range(100):
+                estnode = estnode_gen.generate()
+                est = estnode.export_pipeline()

tpot2/search_spaces/nodes/estimator_node.py

@@ -45,7 +45,7 @@ def __init__(self, method: type,
         else:
             rng = np.random.default_rng(rng)
             self.space.seed(rng.integers(0, 2**32))
-            self.hyperparameters = self.space.sample_configuration().get_dictionary()
+            self.hyperparameters = dict(self.space.sample_configuration())
 
         self.check_hyperparameters_for_None()
 
@@ -55,7 +55,7 @@ def mutate(self, rng=None):
 
         rng = np.random.default_rng(rng)
         self.space.seed(rng.integers(0, 2**32))
-        self.hyperparameters = self.space.sample_configuration().get_dictionary()
+        self.hyperparameters = dict(self.space.sample_configuration())
 
         self.check_hyperparameters_for_None()
         return True

tpot2/search_spaces/pipelines/wrapper.py

@@ -21,14 +21,14 @@ def __init__(self,
         super().__init__()
 
         self.nodegen = nodegen
-        self.node = np.random.default_rng(rng).choice(self.nodegen).generate()
+        self.node = self.nodegen.generate(rng)
 
 
         self.method = method
         self.space = space
         rng = np.random.default_rng(rng)
         self.space.seed(rng.integers(0, 2**32))
-        self.hyperparameters = self.space.sample_configuration().get_dictionary()
+        self.hyperparameters = dict(self.space.sample_configuration())
 
 
 
@@ -43,7 +43,7 @@ def mutate(self, rng=None):
     def _mutate_hyperparameters(self, rng=None):
         rng = np.random.default_rng(rng)
         self.space.seed(rng.integers(0, 2**32))
-        self.hyperparameters = self.space.sample_configuration().get_dictionary()
+        self.hyperparameters = dict(self.space.sample_configuration())
         return True
 
     def _mutate_node(self, rng=None):