- Added test cases for the use of custom objective functions

- Made the indentation more consistent with pep8

tests/python/test_with_sklearn.py

@@ -1,6 +1,6 @@
 import xgboost as xgb
 import numpy as np
-from sklearn.cross_validation import KFold, train_test_split
+from sklearn.cross_validation import KFold
 from sklearn.metrics import mean_squared_error
 from sklearn.grid_search import GridSearchCV
 from sklearn.datasets import load_iris, load_digits, load_boston
@@ -62,3 +62,71 @@ def test_parameter_tuning():
     assert clf.best_score_ < 0.7
     assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4}
 
+def test_regression_with_custom_objective():
+    def objective_ls(y_true, y_pred):
+        grad = (y_pred - y_true)
+        hess = np.ones(len(y_true))
+        return grad, hess
+
+    boston = load_boston()
+    y = boston['target']
+    X = boston['data']
+    kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+    for train_index, test_index in kf:
+        xgb_model = xgb.XGBRegressor(objective=objective_ls).fit(
+            X[train_index], y[train_index]
+        )
+        preds = xgb_model.predict(X[test_index])
+        labels = y[test_index]
+    assert mean_squared_error(preds, labels) < 25
+
+    # Test that the custom objective function is actually used
+    class XGBCustomObjectiveException(Exception):
+        pass
+
+    def dummy_objective(y_true, y_pred):
+        raise  XGBCustomObjectiveException()
+
+    xgb_model = xgb.XGBRegressor(objective=dummy_objective)
+    np.testing.assert_raises(
+        XGBCustomObjectiveException,
+        xgb_model.fit,
+        X, y
+    )
+
+def test_classification_with_custom_objective():
+    def logregobj(y_true, y_pred):
+        y_pred = 1.0 / (1.0 + np.exp(-y_pred))
+        grad = y_pred - y_true
+        hess = y_pred * (1.0-y_pred)
+        return grad, hess
+
+    digits = load_digits(2)
+    y = digits['target']
+    X = digits['data']
+    kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+    for train_index, test_index in kf:
+        xgb_model = xgb.XGBClassifier(objective=logregobj).fit(
+            X[train_index],y[train_index]
+        )
+        preds = xgb_model.predict(X[test_index])
+        labels = y[test_index]
+        err = sum(1 for i in range(len(preds))
+                  if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
+    assert err < 0.1
+
+
+    # Test that the custom objective function is actually used
+    class XGBCustomObjectiveException(Exception):
+        pass
+
+    def dummy_objective(y_true, y_preds):
+        raise  XGBCustomObjectiveException()
+
+    xgb_model = xgb.XGBClassifier(objective=dummy_objective)
+    np.testing.assert_raises(
+        XGBCustomObjectiveException,
+        xgb_model.fit,
+        X, y
+    )
+