Relax check_X_y for DecayEstimator (#580)

* blanding check_X_y for decay estimator

* docs

* make check_X_y optional

* variable naming

---------

Co-authored-by: vincent d warmerdam <vincentwarmerdam@gmail.com>

sklego/meta/decay_estimator.py

@@ -16,15 +16,27 @@ class DecayEstimator(BaseEstimator):
     This meta estimator will only work for estimators that have a
     "sample_weights" argument in their `.fit()` method.
 
+    The `fit` method computes the weights to pass to the estimator.
+
+    .. warning:: By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator.
+
+        To change such behaviour, set `check_input` to `True`.
+
+        Remark that if the check is skipped, then `y` should have a `shape` attrbute, which is
+        used to extract the number of samples in training data, and compute the weights.
+
     The DecayEstimator will use exponential decay to weight the parameters.
 
     w_{t-1} = decay * w_{t}
     """
 
-    def __init__(self, model, decay: float = 0.999, decay_func="exponential"):
+    def __init__(
+        self, model, decay: float = 0.999, decay_func="exponential", check_input=False
+    ):
         self.model = model
         self.decay = decay
         self.decay_func = decay_func
+        self.check_input = check_input
 
     def _is_classifier(self):
         return any(
@@ -40,12 +52,17 @@ def fit(self, X, y):
         """
         Fit the data after adapting the same weight.
 
-        :param X: array-like, shape=(n_columns, n_samples,) training data.
-        :param y: array-like, shape=(n_samples,) training data.
+        :param X: array-like, shape=(n_samples, n_features,) training data.
+        :param y: array-like, shape=(n_samples,) target values.
         :return: Returns an instance of self.
         """
-        X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES)
-        self.weights_ = np.cumprod(np.ones(X.shape[0]) * self.decay)[::-1]
+
+        if self.check_input:
+            X, y = check_X_y(
+                X, y, estimator=self, dtype=FLOAT_DTYPES, ensure_min_features=0
+            )
+
+        self.weights_ = np.cumprod(np.ones(y.shape[0]) * self.decay)[::-1]
         self.estimator_ = clone(self.model)
         try:
             self.estimator_.fit(X, y, sample_weight=self.weights_)
@@ -62,7 +79,7 @@ def predict(self, X):
         """
         Predict new data.
 
-        :param X: array-like, shape=(n_columns, n_samples,) training data.
+        :param X: array-like, shape=(n_samples, n_features,) data to predict.
         :return: array, shape=(n_samples,) the predicted data
         """
         if self._is_classifier():

tests/test_meta/test_decay_estimator.py

@@ -17,13 +17,13 @@
 
 @pytest.mark.parametrize("test_fn", flatten([general_checks, regressor_checks]))
 def test_estimator_checks_regression(test_fn):
-    trf = DecayEstimator(LinearRegression())
+    trf = DecayEstimator(LinearRegression(), check_input=True)
     test_fn(DecayEstimator.__name__, trf)
 
 
 @pytest.mark.parametrize("test_fn", flatten([general_checks, classifier_checks]))
 def test_estimator_checks_classification(test_fn):
-    trf = DecayEstimator(LogisticRegression(solver="lbfgs"))
+    trf = DecayEstimator(LogisticRegression(solver="lbfgs"), check_input=True)
     test_fn(DecayEstimator.__name__, trf)