Merge pull request #594 from Far0n/feval

python: multiple eval_metrics changes

tests/python/test_eval_metrics.py

@@ -0,0 +1,95 @@
+import xgboost as xgb
+import numpy as np
+from sklearn.cross_validation import KFold, train_test_split
+from sklearn.metrics import mean_squared_error
+from sklearn.grid_search import GridSearchCV
+from sklearn.datasets import load_iris, load_digits, load_boston
+import unittest
+
+rng = np.random.RandomState(1337)
+
+
+class TestEvalMetrics(unittest.TestCase):
+    xgb_params_01 = {
+        'silent': 1,
+        'nthread': 1,
+        'eval_metric': 'error'
+    }
+
+    xgb_params_02 = {
+        'silent': 1,
+        'nthread': 1,
+        'eval_metric': ['error']
+    }
+
+    xgb_params_03 = {
+        'silent': 1,
+        'nthread': 1,
+        'eval_metric': ['rmse', 'error']
+    }
+
+    xgb_params_04 = {
+        'silent': 1,
+        'nthread': 1,
+        'eval_metric': ['error', 'rmse']
+    }
+
+    def evalerror_01(self, preds, dtrain):
+        labels = dtrain.get_label()
+        return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
+
+    def evalerror_02(self, preds, dtrain):
+        labels = dtrain.get_label()
+        return [('error', float(sum(labels != (preds > 0.0))) / len(labels))]
+
+    def evalerror_03(self, preds, dtrain):
+        labels = dtrain.get_label()
+        return [('rmse', mean_squared_error(labels, preds)),
+                ('error', float(sum(labels != (preds > 0.0))) / len(labels))]
+
+    def evalerror_04(self, preds, dtrain):
+        labels = dtrain.get_label()
+        return [('error', float(sum(labels != (preds > 0.0))) / len(labels)),
+                ('rmse', mean_squared_error(labels, preds))]
+
+    def test_eval_metrics(self):
+        digits = load_digits(2)
+        X = digits['data']
+        y = digits['target']
+
+        Xt, Xv, yt, yv = train_test_split(X, y, test_size=0.2, random_state=0)
+
+        dtrain = xgb.DMatrix(Xt, label=yt)
+        dvalid = xgb.DMatrix(Xv, label=yv)
+
+        watchlist = [(dtrain, 'train'), (dvalid, 'val')]
+
+        gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
+        gbdt_02 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=10)
+        gbdt_03 = xgb.train(self.xgb_params_03, dtrain, num_boost_round=10)
+        assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
+        assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
+
+        gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
+                            early_stopping_rounds=2)
+        gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
+                            early_stopping_rounds=2)
+        gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
+                            early_stopping_rounds=2)
+        gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
+                            early_stopping_rounds=2)
+        assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
+        assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
+        assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
+
+        gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
+                            early_stopping_rounds=2, feval=self.evalerror_01)
+        gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
+                            early_stopping_rounds=2, feval=self.evalerror_02)
+        gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
+                            early_stopping_rounds=2, feval=self.evalerror_03)
+        gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
+                            early_stopping_rounds=2, feval=self.evalerror_04)
+        assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
+        assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
+        assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]