Fix CLI ranking demo. (#6439)

Save model at final round.

demo/rank/mq2008.conf

@@ -5,9 +5,9 @@ objective="rank:pairwise"
 
 # Tree Booster Parameters
 # step size shrinkage
-eta = 0.1 
+eta = 0.1
 # minimum loss reduction required to make a further partition
-gamma = 1.0 
+gamma = 1.0
 # minimum sum of instance weight(hessian) needed in a child
 min_child_weight = 0.1
 # maximum depth of a tree
@@ -17,12 +17,10 @@ max_depth = 6
 # the number of round to do boosting
 num_round = 4
 # 0 means do not save any model except the final round model
-save_period = 0 
+save_period = 0
 # The path of training data
-data = "mq2008.train" 
+data = "mq2008.train"
 # The path of validation data, used to monitor training process, here [test] sets name of the validation set
-eval[test] = "mq2008.vali" 
+eval[test] = "mq2008.vali"
-# The path of test data 
+# The path of test data
-test:data = "mq2008.test"      
+test:data = "mq2008.test"
-
-

src/cli_main.cc

@@ -268,7 +268,7 @@ class CLI {
     // always save final round
     if ((param_.save_period == 0 ||
          param_.num_round % param_.save_period != 0) &&
-        param_.model_out != CLIParam::kNull && rabit::GetRank() == 0) {
+        rabit::GetRank() == 0) {
       std::ostringstream os;
       if (param_.model_out == CLIParam::kNull) {
         os << param_.model_dir << '/' << std::setfill('0') << std::setw(4)

tests/python/test_cli.py

@@ -22,6 +22,7 @@ model_in = {model_in}
 model_out = {model_out}
 test_path = {test_path}
 name_pred = {name_pred}
+model_dir = {model_dir}
 
 num_round = 10
 data = {data_path}
@@ -59,7 +60,8 @@ eval[test] = {data_path}
                                               model_in='NULL',
                                               model_out=model_out_cli,
                                               test_path='NULL',
-                                              name_pred='NULL')
+                                              name_pred='NULL',
+                                              model_dir='NULL')
             with open(config_path, 'w') as fd:
                 fd.write(train_conf)
 
@@ -73,7 +75,8 @@ eval[test] = {data_path}
                                                 model_in=model_out_cli,
                                                 model_out='NULL',
                                                 test_path=data_path,
-                                                name_pred=predict_out)
+                                                name_pred=predict_out,
+                                                model_dir='NULL')
             with open(config_path, 'w') as fd:
                 fd.write(predict_conf)
 
@@ -145,7 +148,8 @@ eval[test] = {data_path}
                                               model_in='NULL',
                                               model_out=model_out_cli,
                                               test_path='NULL',
-                                              name_pred='NULL')
+                                              name_pred='NULL',
+                                              model_dir='NULL')
             with open(config_path, 'w') as fd:
                 fd.write(train_conf)
 
@@ -154,3 +158,28 @@ eval[test] = {data_path}
                 model = json.load(fd)
 
             assert model['learner']['gradient_booster']['name'] == 'gbtree'
+
+    def test_cli_save_model(self):
+        '''Test save on final round'''
+        exe = self.get_exe()
+        data_path = "{root}/demo/data/agaricus.txt.train?format=libsvm".format(
+            root=self.PROJECT_ROOT)
+        seed = 1994
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model_out_cli = os.path.join(tmpdir, '0010.model')
+            config_path = os.path.join(tmpdir, 'test_load_cli_model.conf')
+
+            train_conf = self.template.format(data_path=data_path,
+                                              seed=seed,
+                                              task='train',
+                                              model_in='NULL',
+                                              model_out='NULL',
+                                              test_path='NULL',
+                                              name_pred='NULL',
+                                              model_dir=tmpdir)
+            with open(config_path, 'w') as fd:
+                fd.write(train_conf)
+
+            subprocess.run([exe, config_path])
+            assert os.path.exists(model_out_cli)