start adding fit_params in learning_curve

fixes scikit-learn#10252

sklearn/model_selection/_validation.py

@@ -975,7 +975,7 @@ def learning_curve(estimator, X, y, groups=None,
                    train_sizes=np.linspace(0.1, 1.0, 5), cv=None, scoring=None,
                    exploit_incremental_learning=False, n_jobs=1,
                    pre_dispatch="all", verbose=0, shuffle=False,
-                   random_state=None):
+                   random_state=None, fit_params=None):
     """Learning curve.
 
     Determines cross-validated training and test scores for different training
@@ -1062,6 +1062,9 @@ def learning_curve(estimator, X, y, groups=None,
         If None, the random number generator is the RandomState instance used
         by `np.random`. Used when ``shuffle`` is True.
 
+    fit_params : dict, optional
+        Parameters to pass to the fit method of the estimator.
+
     Returns
     -------
     train_sizes_abs : array, shape = (n_unique_ticks,), dtype int
@@ -1121,7 +1124,8 @@ def learning_curve(estimator, X, y, groups=None,
 
         out = parallel(delayed(_fit_and_score)(
             clone(estimator), X, y, scorer, train, test,
-            verbose, parameters=None, fit_params=None, return_train_score=True)
+            verbose, parameters=None, fit_params=fit_params,
+            return_train_score=True)
             for train, test in train_test_proportions)
         out = np.array(out)
         n_cv_folds = out.shape[0] // n_unique_ticks