FIX: fix teh check that was making fail AffinityPropagation and remove the fit_intercept=False that should not be needed since scikit-learn#13279 is merged

William de Vazelhes · William de Vazelhes · commit 4163387b359f · 2019-02-27T14:51:16.000+01:00
diff --git a/sklearn/cluster/affinity_propagation_.py b/sklearn/cluster/affinity_propagation_.py
@@ -403,7 +403,7 @@ def predict(self, X):
             raise ValueError("Predict method is not supported when "
                              "affinity='precomputed'.")
 
-        if self.cluster_centers_.size > 0:
+        if self.cluster_centers_.shape[0] > 0:
             return pairwise_distances_argmin(X, self.cluster_centers_)
         else:
             warnings.warn("This model does not have any cluster centers "
diff --git a/sklearn/linear_model/tests/__pycache__/tmp1ax1d_im b/sklearn/linear_model/tests/__pycache__/tmp1ax1d_im
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -2398,10 +2398,6 @@ def check_estimator_sparse_dense(name, estimator_orig):
             estimator.__class__.__name__]
         estimator.set_params(**params)
         estimator_sp.set_params(**params)
-        # XXX should not be necessary:
-        if 'fit_intercept' in estimator.get_params():
-            estimator.set_params(fit_intercept=False)
-            estimator_sp.set_params(fit_intercept=False)
         if isinstance(estimator, NeighborsBase):
             estimator.set_params(algorithm='brute')
             estimator_sp.set_params(algorithm='brute')
