Example JSON model parser and Schema. (#5137)

tests/ci_build/Dockerfile.cpu

@@ -22,7 +22,7 @@ ENV GOSU_VERSION 1.10
 # Install Python packages
 RUN \
     pip install pyyaml cpplint pylint astroid sphinx numpy scipy pandas matplotlib sh recommonmark guzzle_sphinx_theme mock \
-                breathe matplotlib graphviz pytest scikit-learn wheel kubernetes urllib3 && \
+                breathe matplotlib graphviz pytest scikit-learn wheel kubernetes urllib3 jsonschema && \
     pip install https://h2o-release.s3.amazonaws.com/datatable/stable/datatable-0.7.0/datatable-0.7.0-cp37-cp37m-linux_x86_64.whl && \
     pip install "dask[complete]"
 

tests/python/test_basic_models.py

@@ -3,6 +3,8 @@ import xgboost as xgb
 import unittest
 import os
 import json
+import testing as tm
+import pytest
 
 dpath = 'demo/data/'
 dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
@@ -11,6 +13,20 @@ dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
 rng = np.random.RandomState(1994)
 
 
+def json_model(model_path):
+    X = np.random.random((10, 3))
+    y = np.random.randint(2, size=(10,))
+
+    dm1 = xgb.DMatrix(X, y)
+
+    bst = xgb.train({'tree_method': 'hist'}, dm1)
+    bst.save_model(model_path)
+
+    with open(model_path, 'r') as fd:
+        model = json.load(fd)
+    return model
+
+
 class TestModels(unittest.TestCase):
     def test_glm(self):
         param = {'verbosity': 0, 'objective': 'binary:logistic',
@@ -42,8 +58,9 @@ class TestModels(unittest.TestCase):
 
         # save dmatrix into binary buffer
         dtest.save_binary('dtest.buffer')
+        model_path = 'xgb.model.dart'
         # save model
-        bst.save_model('xgb.model.dart')
+        bst.save_model(model_path)
         # load model and data in
         bst2 = xgb.Booster(params=param, model_file='xgb.model.dart')
         dtest2 = xgb.DMatrix('dtest.buffer')
@@ -69,6 +86,7 @@ class TestModels(unittest.TestCase):
         for ii in range(len(preds_list)):
             for jj in range(ii + 1, len(preds_list)):
                 assert np.sum(np.abs(preds_list[ii] - preds_list[jj])) > 0
+        os.remove(model_path)
 
     def test_eta_decay(self):
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
@@ -204,21 +222,27 @@ class TestModels(unittest.TestCase):
         bst.predict(dm2)  # success
 
     def test_model_json_io(self):
-        X = np.random.random((10, 3))
-        y = np.random.randint(2, size=(10,))
-
-        dm1 = xgb.DMatrix(X, y)
-        bst = xgb.train({'tree_method': 'hist'}, dm1)
-        bst.save_model('./model.json')
-
-        with open('./model.json', 'r') as fd:
-            j_model = json.load(fd)
+        model_path = './model.json'
+        j_model = json_model(model_path)
         assert isinstance(j_model['learner'], dict)
 
         bst = xgb.Booster(model_file='./model.json')
 
+        bst.save_model(fname=model_path)
         with open('./model.json', 'r') as fd:
             j_model = json.load(fd)
         assert isinstance(j_model['learner'], dict)
 
-        os.remove('model.json')
+        os.remove(model_path)
+
+    @pytest.mark.skipif(**tm.no_json_schema())
+    def test_json_schema(self):
+        import jsonschema
+        model_path = './model.json'
+        path = os.path.dirname(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        doc = os.path.join(path, 'doc', 'model.schema')
+        with open(doc, 'r') as fd:
+            schema = json.load(fd)
+        jsonschema.validate(instance=json_model(model_path), schema=schema)
+        os.remove(model_path)

tests/python/testing.py

@@ -55,3 +55,12 @@ def no_dask_cudf():
         return {'condition': False, 'reason': reason}
     except ImportError:
         return {'condition': True, 'reason': reason}
+
+
+def no_json_schema():
+    reason = 'jsonschema is not installed'
+    try:
+        import jsonschema       # noqa
+        return {'condition': False, 'reason': reason}
+    except ImportError:
+        return {'condition': True, 'reason': reason}