Fix deprecated use of xgboost early_stopping_rounds (#3306)

* add test clustering

* run test only if xgboost is present

---------

Co-authored-by: connortann <71127464+connortann@users.noreply.github.com>

shap/utils/_clustering.py

@@ -109,8 +109,8 @@ def xgboost_distances_r2(X, y, learning_rate=0.6, early_stopping_rounds=2, subsa
     test_preds = []
     train_preds = []
     for i in range(X.shape[1]):
-        model = xgboost.XGBRegressor(subsample=subsample, n_estimators=max_estimators, learning_rate=learning_rate, max_depth=1)
-        model.fit(X_train[:,i:i+1], y_train, eval_set=[(X_test[:,i:i+1], y_test)], early_stopping_rounds=early_stopping_rounds, verbose=False)
+        model = xgboost.XGBRegressor(subsample=subsample, n_estimators=max_estimators, learning_rate=learning_rate, max_depth=1, early_stopping_rounds=early_stopping_rounds)
+        model.fit(X_train[:,i:i+1], y_train, eval_set=[(X_test[:,i:i+1], y_test)], verbose=False)
         train_preds.append(model.predict(X_train[:,i:i+1]))
         test_preds.append(model.predict(X_test[:,i:i+1]))
     train_preds = np.vstack(train_preds).T
@@ -132,8 +132,8 @@ def xgboost_distances_r2(X, y, learning_rate=0.6, early_stopping_rounds=2, subsa
 
             # fit the model
             else:
-                model = xgboost.XGBRegressor(subsample=subsample, n_estimators=max_estimators, learning_rate=learning_rate, max_depth=1)
-                model.fit(X_train[:,j:j+1], train_preds[:,i], eval_set=[(X_test[:,j:j+1], test_preds[:,i])], early_stopping_rounds=early_stopping_rounds, verbose=False)
+                model = xgboost.XGBRegressor(subsample=subsample, n_estimators=max_estimators, learning_rate=learning_rate, max_depth=1, early_stopping_rounds=early_stopping_rounds)
+                model.fit(X_train[:,j:j+1], train_preds[:,i], eval_set=[(X_test[:,j:j+1], test_preds[:,i])], verbose=False)
                 r2 = max(0, 1 - np.mean((test_preds[:,i] - model.predict(X_test[:,j:j+1]))**2) / preds_var)
             dist[i,j] = 1 - r2
 

tests/utils/test_clustering.py

@@ -0,0 +1,15 @@
+import numpy as np
+import pytest
+
+from shap.utils import hclust
+
+
+def test_hclust_runs():
+    # GH 3290
+    pytest.importorskip('xgboost')
+    X = np.column_stack((np.arange(1, 10), np.arange(100, 1000, step=100)))
+    y = np.where(X[:, 0] > 5, 1, 0)
+
+    clustered = hclust(X, y)
+    # just check if clustered ran successfully
+    assert isinstance(clustered, np.ndarray)