MNT compatability with sklearn 1.3.0 (close #31)

imbens/pipeline.py

@@ -18,7 +18,7 @@
 from sklearn import pipeline
 from sklearn.base import clone
 from sklearn.utils import _print_elapsed_time
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.validation import check_memory
 
 __all__ = ["Pipeline", "make_pipeline"]
@@ -183,7 +183,6 @@ def _iter(self, with_final=True, filter_passthrough=True, filter_resample=True):
     # Estimator interface
 
     def _fit(self, X, y=None, sample_weight=None, **fit_params_steps):
-
         self.steps = list(self.steps)
         self._validate_steps()
         # Setup the memory
@@ -192,7 +191,7 @@ def _fit(self, X, y=None, sample_weight=None, **fit_params_steps):
         fit_transform_one_cached = memory.cache(pipeline._fit_transform_one)
         fit_resample_one_cached = memory.cache(_fit_resample_one)
 
-        for (step_idx, name, transformer) in self._iter(
+        for step_idx, name, transformer in self._iter(
             with_final=False, filter_passthrough=False, filter_resample=False
         ):
             if transformer is None or transformer == "passthrough":
@@ -373,7 +372,7 @@ def fit_resample(self, X, y=None, sample_weight=None, **fit_params):
             if hasattr(last_step, "fit_resample"):
                 return last_step.fit_resample(Xt, yt, **fit_params_last_step)
 
-    @if_delegate_has_method(delegate="_final_estimator")
+    @available_if(pipeline._final_estimator_has("fit_predict"))
     def fit_predict(self, X, y=None, **fit_params):
         """Apply `fit_predict` of last step in pipeline after transforms.
 
@@ -414,7 +413,6 @@ def _fit_resample_one(
     sampler, X, y, sample_weight=None, message_clsname="", message=None, **fit_params
 ):
     with _print_elapsed_time(message_clsname, message):
-
         out = sampler.fit_resample(X, y, sample_weight=sample_weight, **fit_params)
 
         if sample_weight is None: