Enable estimator checks 🚀 (#42)

* Enable sklearn estimator checks

* Fix docstring

src/tclf/classical_classifier.py

@@ -58,7 +58,8 @@ def __init__(
                 str,
                 str,
             ]
-        ],
+        ]
+        | None = None,
         *,
         features: list[str] | None = None,
         random_state: float | None = 42,
@@ -84,7 +85,7 @@ def __init__(
             >>> pred = clf.predict_proba(X)
 
         Args:
-            layers (List[ tuple[str, str] ]): Layers of classical rule.
+            layers (List[tuple[str, str]]): Layers of classical rule. Defaults to None, which results in classification by 'strategy' parameter.
             features (List[str] | None, optional): List of feature names in order of columns. Required to match columns in feature matrix with label. Can be `None`, if `pd.DataFrame` is passed. Defaults to None.
             random_state (float | None, optional): random seed. Defaults to 42.
             strategy (Literal["random", "const"], optional): Strategy to fill unclassfied. Randomly with uniform probability or with constant 0. Defaults to "random".
@@ -94,18 +95,26 @@ def __init__(
         self.features = features
         self.strategy = strategy
 
-    def _more_tags(self) -> dict[str, bool]:
+    def _more_tags(self) -> dict[str, bool | dict[str, str]]:
         """Set tags for sklearn.
 
         See: https://scikit-learn.org/stable/developers/develop.html#estimator-tags
         """
-        # FIXME: Try enabling _skip_test again. Skip tests, as prediction is not
-        # invariant and parameters immutable.
         return {
             "allow_nan": True,
             "binary_only": True,
-            "_skip_test": True,
+            "requires_y": False,
             "poor_score": True,
+            "_xfail_checks": {
+                "check_classifiers_classes": "Disabled due to partly random classification.",
+                "check_classifiers_train": "No check, as unsupervised classifier.",
+                "check_classifiers_one_label": "Disabled due to partly random classification.",
+                "check_methods_subset_invariance": "No check, as unsupervised classifier.",
+                "check_methods_sample_order_invariance": "No check, as unsupervised classifier.",
+                "check_supervised_y_no_nan": "No check, as unsupervised classifier.",
+                "check_supervised_y_2d": "No check, as unsupervised classifier.",
+                "check_classifiers_regression_target": "No check, as unsupervised classifier.",
+            },
         }
 
     def _tick(self, subset: str) -> npt.NDArray:
@@ -429,6 +438,7 @@ def fit(
 
         X = self._validate_data(
             X,
+            y="no_validation",
             dtype=[np.float64, np.float32],
             accept_sparse=False,
             force_all_finite=False,
@@ -445,7 +455,8 @@ def fit(
                 f"Expected {len(self.columns_)} columns, got {X.shape[1]}."
             )
 
-        for func_str, _ in self.layers:
+        self._layers = self.layers if self.layers is not None else []
+        for func_str, _ in self._layers:
             if func_str not in allowed_func_str:
                 raise ValueError(
                     f"Unknown function string: {func_str},"
@@ -476,7 +487,7 @@ def predict(self, X: MatrixLike) -> npt.NDArray:
         self.X_ = pd.DataFrame(data=X, columns=self.columns_)
         pred = np.full(shape=(X.shape[0],), fill_value=np.nan)
 
-        for func_str, subset in self.layers:
+        for func_str, subset in self._layers:
             func = self.func_mapping_[func_str]
             pred = np.where(
                 np.isnan(pred),

tests/test_classical_classifier.py

@@ -1,10 +1,13 @@
 """Tests for the classical classifier."""
 
+from typing import Callable
+
 import numpy as np
 import pandas as pd
 import pytest
 from numpy.testing import assert_allclose
-from sklearn.utils.estimator_checks import check_estimator
+from sklearn.base import BaseEstimator
+from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 
 from tclf.classical_classifier import ClassicalClassifier
@@ -74,14 +77,14 @@ def clf(self, x_train: pd.DataFrame) -> ClassicalClassifier:
         Returns:
             ClassicalClassifier: fitted clf
         """
-        return ClassicalClassifier(
-            layers=[("nan", "ex")],
-            random_state=7,
-        ).fit(x_train[["ask_best", "bid_best"]])
+        return ClassicalClassifier().fit(x_train[["ask_best", "bid_best"]])
 
-    def test_sklearn_compatibility(self, clf: ClassicalClassifier) -> None:
+    @parametrize_with_checks([ClassicalClassifier()])
+    def test_sklearn_compatibility(
+        self, estimator: BaseEstimator, check: Callable
+    ) -> None:
         """Test, if classifier is compatible with sklearn."""
-        check_estimator(clf)
+        check(estimator)
 
     def test_shapes(
         self, clf: ClassicalClassifier, x_test: pd.DataFrame, y_test: pd.Series