Fix bug with interaction constraints (#3189)

* Fix bug: crashes when interaction_constraints is nonempty and not all features are used.

* Fix python lint error.

src/treelearner/col_sampler.hpp

@@ -117,8 +117,10 @@ class ColSampler {
       } else {
         for (int feat : allowed_features) {
           int inner_feat = train_data_->InnerFeatureIndex(feat);
+          if (inner_feat >= 0) {
             ret[inner_feat] = 1;
           }
+        }
         return ret;
       }
     }

tests/python_package_test/test_engine.py

@@ -2195,7 +2195,7 @@ class TestEngine(unittest.TestCase):
                   'seed': 0}
         est = lgb.train(params, train_data, num_boost_round=10)
         pred1 = est.predict(X)
-        est = lgb.train(dict(params, interation_constraints=[list(range(num_features))]), train_data,
+        est = lgb.train(dict(params, interaction_constraints=[list(range(num_features))]), train_data,
                         num_boost_round=10)
         pred2 = est.predict(X)
         np.testing.assert_allclose(pred1, pred2)
@@ -2210,3 +2210,10 @@ class TestEngine(unittest.TestCase):
                         num_boost_round=10)
         pred4 = est.predict(X)
         self.assertLess(mean_squared_error(y, pred3), mean_squared_error(y, pred4))
+        # test that interaction constraints work when not all features are used
+        X = np.concatenate([np.zeros((X.shape[0], 1)), X], axis=1)
+        num_features = X.shape[1]
+        train_data = lgb.Dataset(X, label=y)
+        est = lgb.train(dict(params, interaction_constraints=[[0] + list(range(2, num_features)),
+                                                              [1] + list(range(2, num_features))]),
+                        train_data, num_boost_round=10)