Access xgboost eval metrics by using sklearn

demo/guide-python/sklearn_evals_result.py

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+##
+#  This script demonstrate how to access the xgboost eval metrics by using sklearn
+##
+
+import xgboost as xgb
+import numpy as np
+from sklearn.datasets import make_hastie_10_2
+
+X, y = make_hastie_10_2(n_samples=2000, random_state=42)
+
+# Map labels from {-1, 1} to {0, 1}
+labels, y = np.unique(y, return_inverse=True)
+
+X_train, X_test = X[:1600], X[1600:]
+y_train, y_test = y[:1600], y[1600:]
+
+param_dist = {'objective':'binary:logistic', 'n_estimators':2}
+
+clf = xgb.XGBModel(**param_dist)
+# Or you can use: clf = xgb.XGBClassifier(**param_dist)
+
+clf.fit(X_train, y_train,
+        eval_set=[(X_train, y_train), (X_test, y_test)], 
+        eval_metric='logloss',
+        verbose=True)
+
+# Load evals result by calling the evals_result() function
+evals_result = clf.evals_result()
+
+print('Access logloss metric directly from validation_0:')
+print(evals_result['validation_0']['logloss'])
+
+print('')
+print('Access metrics through a loop:')
+for e_name, e_mtrs in evals_result.items():
+    print('- {}'.format(e_name))
+    for e_mtr_name, e_mtr_vals in e_mtrs.items():
+        print('   - {}'.format(e_mtr_name))
+        print('      - {}'.format(e_mtr_vals))
+ 
+print('')
+print('Access complete dict:')
+print(evals_result)