refactor(tests): update tests to be ConfigSpace compliant

gama/configuration/test_configuration_task/__init__.py

@@ -0,0 +1,2 @@
+from .classifiers import TestClassifierConfig
+from .preprocessors import TestPreprocessorConfig

gama/configuration/test_configuration_task/classifiers.py

@@ -0,0 +1,238 @@
+import ConfigSpace as cs
+import ConfigSpace.hyperparameters as csh
+
+
+class TestClassifierConfig:
+    def __init__(
+        self,
+        config_space: cs.ConfigurationSpace,
+    ):
+        if "estimators" not in config_space.meta:
+            raise ValueError("Expected 'estimators' key in meta of config_space")
+        self.config_space = config_space
+        self.classifiers_setup_map = {
+            "BernoulliNB": self.setup_bernoulliNB,
+            "MultinomialNB": self.setup_multinomialNB,
+            "GaussianNB": self.setup_gaussianNB,
+            "DecisionTreeClassifier": self.setup_decision_tree,
+            "ExtraTreesClassifier": self.setup_extra_trees,
+            "RandomForestClassifier": self.setup_random_forest,
+            "GradientBoostingClassifier": self.setup_gradient_boosting,
+            "KNeighborsClassifier": self.setup_k_neighbors,
+            "LinearSVC": self.setup_linear_svc,
+            "LogisticRegression": self.setup_logistic_regression,
+        }
+        self.cs_estimators_name = self.config_space.meta["estimators"]
+
+    @property
+    def shared_hyperparameters(self):
+        return {
+            "alpha": [1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0],
+            "fit_prior": [True, False],
+            "criterion": ["gini", "entropy"],
+            "max_depth": {"lower": 1, "upper": 11},
+            "min_samples_split": {"lower": 2, "upper": 21},
+            "min_samples_leaf": {"lower": 1, "upper": 21},
+            "max_features": {"lower": 0.05, "upper": 1.01, "default_value": 1.0},
+            "n_estimators": [100],
+            "bootstrap": [True, False],
+            "dual": [True, False],
+            "C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
+        }
+
+    def setup_classifiers(self):
+        classifiers_choices = list(self.classifiers_setup_map.keys())
+
+        if not classifiers_choices:
+            raise ValueError("No classifiers to add to config space")
+
+        classifiers = csh.CategoricalHyperparameter(
+            name=self.cs_estimators_name,
+            choices=classifiers_choices,
+        )
+        self.config_space.add_hyperparameter(classifiers)
+
+        for classifier_name in classifiers_choices:
+            if setup_func := self.classifiers_setup_map.get(classifier_name):
+                setup_func(classifiers)
+
+    def _add_hyperparameters_and_equals_conditions(
+        self, local_vars: dict, estimator_name: str
+    ):
+        if "classifiers" not in local_vars or not isinstance(
+            local_vars["classifiers"], csh.CategoricalHyperparameter
+        ):
+            raise ValueError(
+                "Expected 'classifiers' key with a CategoricalHyperparameter in local"
+                "vars"
+            )
+
+        hyperparameters_to_add = [
+            hyperparameter
+            for hyperparameter in local_vars.values()
+            if isinstance(hyperparameter, csh.Hyperparameter)
+            and hyperparameter != local_vars["classifiers"]
+        ]
+
+        conditions_to_add = [
+            cs.EqualsCondition(
+                hyperparameter, local_vars["classifiers"], estimator_name
+            )
+            for hyperparameter in hyperparameters_to_add
+        ]
+
+        self.config_space.add_hyperparameters(hyperparameters_to_add)
+        self.config_space.add_conditions(conditions_to_add)
+
+    def setup_bernoulliNB(self, classifiers: csh.CategoricalHyperparameter):
+        alpha_NB = csh.CategoricalHyperparameter(
+            "alpha__bernoulliNB", self.shared_hyperparameters["alpha"]
+        )
+        fit_prior = csh.CategoricalHyperparameter(
+            "fit_prior__bernoulliNB", self.shared_hyperparameters["fit_prior"]
+        )
+        self._add_hyperparameters_and_equals_conditions(locals(), "BernoulliNB")
+
+    def setup_multinomialNB(self, classifiers: csh.CategoricalHyperparameter):
+        alpha_NB = csh.CategoricalHyperparameter(
+            "alpha__multinomialNB", self.shared_hyperparameters["alpha"]
+        )
+        fit_prior = csh.CategoricalHyperparameter(
+            "fit_prior__multinomialNB", self.shared_hyperparameters["fit_prior"]
+        )
+        self._add_hyperparameters_and_equals_conditions(locals(), "MultinomialNB")
+
+    def setup_gaussianNB(self, classifiers: csh.CategoricalHyperparameter):
+        # GaussianNB has no hyperparameters
+        pass
+
+    def setup_decision_tree(self, classifiers: csh.CategoricalHyperparameter):
+        criterion = csh.CategoricalHyperparameter(
+            "criterion__decision_tree", self.shared_hyperparameters["criterion"]
+        )
+        max_depth = csh.UniformIntegerHyperparameter(
+            "max_depth__decision_tree", **self.shared_hyperparameters["max_depth"]
+        )
+        min_samples_split = csh.UniformIntegerHyperparameter(
+            "min_samples_split__decision_tree",
+            **self.shared_hyperparameters["min_samples_split"],
+        )
+        min_samples_leaf = csh.UniformIntegerHyperparameter(
+            "min_samples_leaf__decision_tree",
+            **self.shared_hyperparameters["min_samples_leaf"],
+        )
+        self._add_hyperparameters_and_equals_conditions(
+            locals(), "DecisionTreeClassifier"
+        )
+
+    def setup_extra_trees(self, classifiers: csh.CategoricalHyperparameter):
+        criterion = csh.CategoricalHyperparameter(
+            "criterion__extra_trees", self.shared_hyperparameters["criterion"]
+        )
+        max_depth = csh.UniformIntegerHyperparameter(
+            "max_depth__extra_trees", **self.shared_hyperparameters["max_depth"]
+        )
+        min_samples_split = csh.UniformIntegerHyperparameter(
+            "min_samples_split__extra_trees",
+            **self.shared_hyperparameters["min_samples_split"],
+        )
+        min_samples_leaf = csh.UniformIntegerHyperparameter(
+            "min_samples_leaf__extra_trees",
+            **self.shared_hyperparameters["min_samples_leaf"],
+        )
+        max_features = csh.UniformFloatHyperparameter(
+            "max_features__extra_trees", **self.shared_hyperparameters["max_features"]
+        )
+        n_estimators = csh.CategoricalHyperparameter(
+            "n_estimators__extra_trees", self.shared_hyperparameters["n_estimators"]
+        )
+        bootstrap = csh.CategoricalHyperparameter(
+            "bootstrap__extra_trees", self.shared_hyperparameters["bootstrap"]
+        )
+        self._add_hyperparameters_and_equals_conditions(
+            locals(), "ExtraTreesClassifier"
+        )
+
+    def setup_random_forest(self, classifiers: csh.CategoricalHyperparameter):
+        criterion = csh.CategoricalHyperparameter(
+            "criterion__random_forest", self.shared_hyperparameters["criterion"]
+        )
+        max_depth = csh.UniformIntegerHyperparameter(
+            "max_depth__random_forest", **self.shared_hyperparameters["max_depth"]
+        )
+        min_samples_split = csh.UniformIntegerHyperparameter(
+            "min_samples_split", **self.shared_hyperparameters["min_samples_split"]
+        )
+        min_samples_leaf = csh.UniformIntegerHyperparameter(
+            "min_samples_leaf", **self.shared_hyperparameters["min_samples_leaf"]
+        )
+        max_features = csh.UniformFloatHyperparameter(
+            "max_features", **self.shared_hyperparameters["max_features"]
+        )
+        n_estimators = csh.CategoricalHyperparameter(
+            "n_estimators__random_forest", self.shared_hyperparameters["n_estimators"]
+        )
+        bootstrap = csh.CategoricalHyperparameter(
+            "bootstrap", self.shared_hyperparameters["bootstrap"]
+        )
+        self._add_hyperparameters_and_equals_conditions(
+            locals(), "RandomForestClassifier"
+        )
+
+    def setup_gradient_boosting(self, classifiers: csh.CategoricalHyperparameter):
+        sub_sample = csh.CategoricalHyperparameter(
+            "subsample", [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+        )
+        learning_rate = csh.CategoricalHyperparameter(
+            "learning_rate", [1e-3, 1e-2, 1e-1, 0.5, 1.0]
+        )
+        max_features = csh.UniformFloatHyperparameter(
+            "max_features__gradient_boosting",
+            **self.shared_hyperparameters["max_features"],
+        )
+        n_estimators = csh.CategoricalHyperparameter(
+            "n_estimators__gradient_boosting",
+            self.shared_hyperparameters["n_estimators"],
+        )
+        self._add_hyperparameters_and_equals_conditions(
+            locals(), "GradientBoostingClassifier"
+        )
+
+    def setup_k_neighbors(self, classifiers: csh.CategoricalHyperparameter):
+        n_neighbors = csh.UniformIntegerHyperparameter("n_neighbors", 1, 51)
+        weights = csh.CategoricalHyperparameter("weights", ["uniform", "distance"])
+        p = csh.UniformIntegerHyperparameter("p", 1, 2)
+        self._add_hyperparameters_and_equals_conditions(
+            locals(), "KNeighborsClassifier"
+        )
+
+    def setup_linear_svc(self, classifiers: csh.CategoricalHyperparameter):
+        loss = csh.CategoricalHyperparameter(
+            "loss__linear_svc", ["hinge", "squared_hinge"]
+        )
+        penalty = csh.CategoricalHyperparameter("penalty__linear_svc", ["l1", "l2"])
+        dual = csh.CategoricalHyperparameter(
+            "dual__svc", self.shared_hyperparameters["dual"]
+        )
+        tol = csh.CategoricalHyperparameter("tol__svc", [1e-5, 1e-4, 1e-3, 1e-2, 1e-1])
+        C = csh.CategoricalHyperparameter("C__svc", self.shared_hyperparameters["C"])
+        self._add_hyperparameters_and_equals_conditions(locals(), "LinearSVC")
+
+        # Forbidden clause: Penalty 'l1' cannot be used with loss 'hinge'
+        forbidden_penalty_loss = cs.ForbiddenAndConjunction(
+            cs.ForbiddenEqualsClause(self.config_space["penalty__linear_svc"], "l1"),
+            cs.ForbiddenEqualsClause(self.config_space["loss__linear_svc"], "hinge"),
+        )
+        self.config_space.add_forbidden_clause(forbidden_penalty_loss)
+
+    def setup_logistic_regression(self, classifiers: csh.CategoricalHyperparameter):
+        penalty = csh.CategoricalHyperparameter(
+            "penalty__logistic_regression", ["l1", "l2"]
+        )
+        C = csh.CategoricalHyperparameter(
+            "C__logistic_regression", self.shared_hyperparameters["C"]
+        )
+        dual = csh.CategoricalHyperparameter(
+            "dual__logistic_regression", self.shared_hyperparameters["dual"]
+        )
+        self._add_hyperparameters_and_equals_conditions(locals(), "LogisticRegression")