Added tests for additional params in sklearn wrapper (+1 squashed commit)

Squashed commits:
[43892b9] Added tests for additional params in sklearn wrapper

CHANGES.md

@@ -42,6 +42,7 @@ on going at master
 * Python module now throw exception instead of crash terminal when a parameter error happens.
 * Python module now has importance plot and tree plot functions.
 * Python module now accepts different learning rates for each boosting round.
+* Additional parameters added for sklearn wrapper
 * Java api is ready for use
 * Added more test cases and continuous integration to make each build more robust
 * Improvements in sklearn compatible module

tests/python/test_with_sklearn.py

@@ -4,54 +4,65 @@ 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(1994)
 
-def test_binary_classification():
+class TestSklearn(unittest.TestCase):
-	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])
-	    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
 
-def test_multiclass_classification():
+	def test_binary_classification():
-	iris = load_iris()
+		digits = load_digits(2)
-	y = iris['target']
+		y = digits['target']
-	X = iris['data']
+		X = digits['data']
-	kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+		kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
-	for train_index, test_index in kf:
+		for train_index, test_index in kf:
-	    xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
+		    xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
-	    preds = xgb_model.predict(X[test_index])
+		    preds = xgb_model.predict(X[test_index])
-	    labels = y[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))
+		    err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
-	assert err < 0.4
+		assert err < 0.1
 
-def test_boston_housing_regression():
+	def test_multiclass_classification():
-	boston = load_boston()
+		iris = load_iris()
-	y = boston['target']
+		y = iris['target']
-	X = boston['data']
+		X = iris['data']
-	kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+		kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
-	for train_index, test_index in kf:
+		for train_index, test_index in kf:
-	    xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
+		    xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
-	    preds = xgb_model.predict(X[test_index])
+		    preds = xgb_model.predict(X[test_index])
-	    labels = y[test_index]
+			# test other params in XGBClassifier().fit
-	assert mean_squared_error(preds, labels) < 15
+		    preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
+		    preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
+		    preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
+		    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 < 0.4
 
-def test_parameter_tuning():
+	def test_boston_housing_regression():
-	boston = load_boston()
+		boston = load_boston()
-	y = boston['target']
+		y = boston['target']
-	X = boston['data']
+		X = boston['data']
-	xgb_model = xgb.XGBRegressor()
+		kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
-	clf = GridSearchCV(xgb_model,
+		for train_index, test_index in kf:
-	                   {'max_depth': [2,4,6],
+		    xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
-	                    'n_estimators': [50,100,200]}, verbose=1)
+		    preds = xgb_model.predict(X[test_index])
-	clf.fit(X,y)
+		    # test other params in XGBRegressor().fit
-	assert clf.best_score_ < 0.7
+		    preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
-	assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4}
+		    preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
+		    preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
+		    labels = y[test_index]
+		assert mean_squared_error(preds, labels) < 15
+
+	def test_parameter_tuning():
+		boston = load_boston()
+		y = boston['target']
+		X = boston['data']
+		xgb_model = xgb.XGBRegressor()
+		clf = GridSearchCV(xgb_model,
+		                   {'max_depth': [2,4,6],
+		                    'n_estimators': [50,100,200]}, verbose=1)
+		clf.fit(X,y)
+		assert clf.best_score_ < 0.7
+		assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4}