FIX Fixes ClassifierChain issue when a tuple is passed to order (scikit-learn#18124)

* fix error tuple passed to order

* fix linting

* Update sklearn/tests/test_multioutput.py

Co-authored-by: Roman Yurchak <[email protected]>

* updated test and whatsnew

* doc fix

* Update doc/whats_new/v0.24.rst

Co-authored-by: Roman Yurchak <[email protected]>

* Update sklearn/tests/test_multioutput.py

Co-authored-by: Thomas J. Fan <[email protected]>

* Update sklearn/tests/test_multioutput.py

Co-authored-by: Thomas J. Fan <[email protected]>

* code review fix

* code review fix

* added test

* code review fix

* Update sklearn/multioutput.py

Co-authored-by: Joel Nothman <[email protected]>

Co-authored-by: Roman Yurchak <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>
Co-authored-by: Joel Nothman <[email protected]>

Author: 4 people

doc/whats_new/v0.24.rst

@@ -260,7 +260,7 @@ Changelog
   will be removed in 0.28. :pr:`17662` by
   :user:`Joshua Newton <joshuacwnewton>`.
 
-- |Efficiency| Fixed :issue:`10493`. Improve Local Linear Embedding (LLE) 
+- |Efficiency| Fixed :issue:`10493`. Improve Local Linear Embedding (LLE)
   that raised `MemoryError` exception when used with large inputs.
   :pr:`17997` by :user:`Bertrand Maisonneuve <bmaisonn>`.
 
@@ -323,6 +323,14 @@ Changelog
   validity of the input is now delegated to the base estimator.
   :pr:`17233` by :user:`Zolisa Bleki <zoj613>`.
 
+:mod:`sklearn.multioutput`
+..........................
+
+- |Fix| A fix to accept tuples for the ``order`` parameter 
+  in :class:`multioutput.ClassifierChain`.
+  :pr:`18124` by :user:`Gus Brocchini <boldloop>` and
+  :user:`Amanda Dsouza <amy12xx>`.
+
 :mod:`sklearn.naive_bayes`
 ..........................
 

sklearn/multioutput.py

@@ -453,6 +453,9 @@ def fit(self, X, Y, **fit_params):
         random_state = check_random_state(self.random_state)
         check_array(X, accept_sparse=True)
         self.order_ = self.order
+        if isinstance(self.order_, tuple):
+            self.order_ = np.array(self.order_)
+
         if self.order_ is None:
             self.order_ = np.array(range(Y.shape[1]))
         elif isinstance(self.order_, str):

sklearn/tests/test_multioutput.py

@@ -601,3 +601,28 @@ def fit(self, X, y, **fit_params):
 
     for est in model.estimators_:
         assert est.sample_weight_ is weight
+
+
+@pytest.mark.parametrize("order_type", [list, np.array, tuple])
+def test_classifier_chain_tuple_order(order_type):
+    X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
+    y = [[3, 2], [2, 3], [3, 2]]
+    order = order_type([1, 0])
+
+    chain = ClassifierChain(RandomForestClassifier(), order=order)
+
+    chain.fit(X, y)
+    X_test = [[1.5, 2.5, 3.5]]
+    y_test = [[3, 2]]
+    assert_array_almost_equal(chain.predict(X_test), y_test)
+
+
+def test_classifier_chain_tuple_invalid_order():
+    X = [[1, 2, 3], [4, 5, 6], [1.5, 2.5, 3.5]]
+    y = [[3, 2], [2, 3], [3, 2]]
+    order = tuple([1, 2])
+
+    chain = ClassifierChain(RandomForestClassifier(), order=order)
+
+    with pytest.raises(ValueError, match='invalid order'):
+        chain.fit(X, y)