Don't set_params at the end of set_state. (#4947)

* Don't set_params at the end of set_state.

* Also fix another issue found in dask prediction.

* Add note about prediction.

Don't support other prediction modes at the moment.

tests/python/test_pickling.py

@@ -0,0 +1,48 @@
+import pickle
+import numpy as np
+import xgboost as xgb
+import os
+
+
+kRows = 100
+kCols = 10
+
+
+def generate_data():
+    X = np.random.randn(kRows, kCols)
+    y = np.random.randn(kRows)
+    return X, y
+
+
+def test_model_pickling():
+    xgb_params = {
+        'verbosity': 0,
+        'nthread': 1,
+        'tree_method': 'hist'
+    }
+
+    X, y = generate_data()
+    dtrain = xgb.DMatrix(X, y)
+    bst = xgb.train(xgb_params, dtrain)
+
+    dump_0 = bst.get_dump(dump_format='json')
+    assert dump_0
+
+    filename = 'model.pkl'
+
+    with open(filename, 'wb') as fd:
+        pickle.dump(bst, fd)
+
+    with open(filename, 'rb') as fd:
+        bst = pickle.load(fd)
+
+    with open(filename, 'wb') as fd:
+        pickle.dump(bst, fd)
+
+    with open(filename, 'rb') as fd:
+        bst = pickle.load(fd)
+
+    assert bst.get_dump(dump_format='json') == dump_0
+
+    if os.path.exists(filename):
+        os.remove(filename)

tests/python/test_with_dask.py

@@ -43,14 +43,17 @@ def test_from_dask_dataframe(client):
 
     prediction = xgb.dask.predict(client, model=booster, data=dtrain)
 
+    assert prediction.ndim == 1
     assert isinstance(prediction, da.Array)
-    assert prediction.shape[0] == kRows and prediction.shape[1] == kCols
+    assert prediction.shape[0] == kRows
 
     with pytest.raises(ValueError):
         # evals_result is not supported in dask interface.
         xgb.dask.train(
             client, {}, dtrain, num_boost_round=2, evals_result={})
 
+    prediction = prediction.compute()  # force prediction to be computed
+
 
 def test_from_dask_array(client):
     X, y = generate_array()
@@ -59,10 +62,12 @@ def test_from_dask_array(client):
     result = xgb.dask.train(client, {}, dtrain)
 
     prediction = xgb.dask.predict(client, result, dtrain)
-    assert prediction.shape[0] == kRows and prediction.shape[1] == kCols
+    assert prediction.shape[0] == kRows
 
     assert isinstance(prediction, da.Array)
 
+    prediction = prediction.compute()  # force prediction to be computed
+
 
 def test_regressor(client):
     X, y = generate_array()
@@ -72,7 +77,8 @@ def test_regressor(client):
     regressor.fit(X, y, eval_set=[(X, y)])
     prediction = regressor.predict(X)
 
-    assert prediction.shape[0] == kRows and prediction.shape[1] == kCols
+    assert prediction.ndim == 1
+    assert prediction.shape[0] == kRows
 
     history = regressor.evals_result()
 
@@ -91,7 +97,8 @@ def test_classifier(client):
     classifier.fit(X, y,  eval_set=[(X, y)])
     prediction = classifier.predict(X)
 
-    assert prediction.shape[0] == kRows and prediction.shape[1] == kCols
+    assert prediction.ndim == 1
+    assert prediction.shape[0] == kRows
 
     history = classifier.evals_result()