TST: Added tests for multi-class classification

tests/python/test_with_sklearn.py

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+import pickle
+import xgboost as xgb
+
+import numpy as np
+from sklearn.cross_validation import KFold, train_test_split
+from sklearn.metrics import confusion_matrix, mean_squared_error
+from sklearn.grid_search import GridSearchCV
+from sklearn.datasets import load_iris, load_digits, load_boston
+
+rng = np.random.RandomState(1994)
+
+def test_binary_classification():
+	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().fit(X[train_index],y[train_index])
+	    predictions = xgb_model.predict(X[test_index])
+	    actuals = y[test_index]
+	    print(confusion_matrix(actuals, predictions))
+
+def test_multiclass_classification():
+	iris = load_iris()
+	y = iris['target']
+	X = iris['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().fit(X[train_index],y[train_index])
+	    predictions = xgb_model.predict(X[test_index])
+	    actuals = y[test_index]
+	    print(confusion_matrix(actuals, predictions))
+
+
+
+
+
+