Monotone constraints for gpu_hist (#2904)

tests/python-gpu/test_gpu_updaters.py

@@ -97,7 +97,7 @@ def train_sparse(param_in, comparison_tree_method):
 
 # Enumerates all permutations of variable parameters
 def assert_updater_accuracy(tree_method, comparison_tree_method, variable_param, tolerance):
-    param = {'tree_method': tree_method }
+    param = {'tree_method': tree_method}
     names = sorted(variable_param)
     combinations = it.product(*(variable_param[Name] for Name in names))
 
@@ -109,10 +109,14 @@ def assert_updater_accuracy(tree_method, comparison_tree_method, variable_param,
             param_tmp[name] = set[i]
 
         print(param_tmp, file=sys.stderr)
-        assert_accuracy(train_boston(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance, param_tmp)
-        assert_accuracy(train_digits(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance, param_tmp)
-        assert_accuracy(train_cancer(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance, param_tmp)
-        assert_accuracy(train_sparse(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance, param_tmp)
+        assert_accuracy(train_boston(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance,
+                        param_tmp)
+        assert_accuracy(train_digits(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance,
+                        param_tmp)
+        assert_accuracy(train_cancer(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance,
+                        param_tmp)
+        assert_accuracy(train_sparse(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance,
+                        param_tmp)
 
 
 @attr('gpu')
@@ -122,5 +126,6 @@ class TestGPU(unittest.TestCase):
         assert_updater_accuracy('gpu_exact', 'exact', variable_param, 0.02)
 
     def test_gpu_hist(self):
-        variable_param = {'n_gpus': [1, -1], 'max_depth': [2, 6], 'max_leaves': [255, 4], 'max_bin': [2, 16, 1024]}
+        variable_param = {'n_gpus': [1, -1], 'max_depth': [2, 6], 'max_leaves': [255, 4], 'max_bin': [2, 16, 1024],
+                          'grow_policy': ['depthwise', 'lossguide']}
         assert_updater_accuracy('gpu_hist', 'hist', variable_param, 0.01)

tests/python-gpu/test_monotonic_constraints.py

@@ -0,0 +1,44 @@
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import xgboost as xgb
+from nose.plugins.attrib import attr
+from sklearn.datasets import make_regression
+
+rng = np.random.RandomState(1994)
+
+
+def non_decreasing(L):
+    return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
+
+
+def non_increasing(L):
+    return all((y - x) < 0.001 for x, y in zip(L, L[1:]))
+
+
+def assert_constraint(constraint, tree_method):
+    n = 1000
+    X, y = make_regression(n, random_state=rng, n_features=1, n_informative=1)
+    dtrain = xgb.DMatrix(X, y)
+    param = {}
+    param['tree_method'] = tree_method
+    param['monotone_constraints'] = "(" + str(constraint) + ")"
+    bst = xgb.train(param, dtrain)
+    dpredict = xgb.DMatrix(X[X[:, 0].argsort()])
+    pred = bst.predict(dpredict)
+    if constraint > 0:
+        assert non_decreasing(pred)
+    elif constraint < 0:
+        assert non_increasing(pred)
+
+
+@attr('gpu')
+class TestMonotonicConstraints(unittest.TestCase):
+    def test_exact(self):
+        assert_constraint(1, 'exact')
+        assert_constraint(-1, 'exact')
+
+    def test_gpu_hist(self):
+        assert_constraint(1, 'gpu_hist')
+        assert_constraint(-1, 'gpu_hist')