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JSON configuration IO. (#5111)

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* Add saving/loading JSON configuration.
* Implement Python pickle interface with new IO routines.
* Basic tests for training continuation.
This commit is contained in:
Jiaming Yuan
2019-12-15 17:31:53 +08:00
committed by GitHub
parent 5aa007d7b2
commit 3136185bc5
24 changed files with 761 additions and 390 deletions
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2
tests/python/test_basic_models.py
View File

@@ -203,7 +203,7 @@ class TestModels(unittest.TestCase):
self.assertRaises(ValueError, bst.predict, dm1)
bst.predict(dm2) # success
def test_json_model_io(self):
def test_model_json_io(self):
X = np.random.random((10, 3))
y = np.random.randint(2, size=(10,))

57
tests/python/test_pickling.py
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@@ -2,6 +2,7 @@ import pickle
import numpy as np
import xgboost as xgb
import os
import unittest
kRows = 100
@@ -14,35 +15,45 @@ def generate_data():
return X, y
def test_model_pickling():
xgb_params = {
'verbosity': 0,
'nthread': 1,
'tree_method': 'hist'
}
class TestPickling(unittest.TestCase):
def run_model_pickling(self, xgb_params):
X, y = generate_data()
dtrain = xgb.DMatrix(X, y)
bst = xgb.train(xgb_params, dtrain)
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
dump_0 = bst.get_dump(dump_format='json')
assert dump_0
filename = 'model.pkl'
filename = 'model.pkl'
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'wb') as fd:
pickle.dump(bst, fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
with open(filename, 'rb') as fd:
bst = pickle.load(fd)
assert bst.get_dump(dump_format='json') == dump_0
assert bst.get_dump(dump_format='json') == dump_0
if os.path.exists(filename):
os.remove(filename)
if os.path.exists(filename):
os.remove(filename)
def test_model_pickling_binary(self):
params = {
'nthread': 1,
'tree_method': 'hist'
}
self.run_model_pickling(params)
def test_model_pickling_json(self):
params = {
'nthread': 1,
'tree_method': 'hist',
'enable_experimental_json_serialization': True
}
self.run_model_pickling(params)

94
tests/python/test_training_continuation.py
View File

@@ -10,26 +10,35 @@ rng = np.random.RandomState(1337)
class TestTrainingContinuation(unittest.TestCase):
num_parallel_tree = 3
xgb_params_01 = {
'verbosity': 0,
'nthread': 1,
}
def generate_parameters(self, use_json):
xgb_params_01_binary = {
'nthread': 1,
}
xgb_params_02 = {
'verbosity': 0,
'nthread': 1,
'num_parallel_tree': num_parallel_tree
}
xgb_params_02_binary = {
'nthread': 1,
'num_parallel_tree': self.num_parallel_tree
}
xgb_params_03 = {
'verbosity': 0,
'nthread': 1,
'num_class': 5,
'num_parallel_tree': num_parallel_tree
}
xgb_params_03_binary = {
'nthread': 1,
'num_class': 5,
'num_parallel_tree': self.num_parallel_tree
}
if use_json:
xgb_params_01_binary[
'enable_experimental_json_serialization'] = True
xgb_params_02_binary[
'enable_experimental_json_serialization'] = True
xgb_params_03_binary[
'enable_experimental_json_serialization'] = True
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation(self):
return [
xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary
]
def run_training_continuation(self, xgb_params_01, xgb_params_02,
xgb_params_03):
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
@@ -45,18 +54,18 @@ class TestTrainingContinuation(unittest.TestCase):
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
gbdt_01 = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_01 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
gbdt_02 = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_02 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=0)
gbdt_02.save_model('xgb_tc.model')
gbdt_02a = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_02a = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10, xgb_model=gbdt_02)
gbdt_02b = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_02b = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10, xgb_model="xgb_tc.model")
ntrees_02a = len(gbdt_02a.get_dump())
ntrees_02b = len(gbdt_02b.get_dump())
@@ -71,13 +80,13 @@ class TestTrainingContinuation(unittest.TestCase):
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
gbdt_03 = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_03 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=3)
gbdt_03.save_model('xgb_tc.model')
gbdt_03a = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_03a = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=7, xgb_model=gbdt_03)
gbdt_03b = xgb.train(self.xgb_params_01, dtrain_2class,
gbdt_03b = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=7, xgb_model="xgb_tc.model")
ntrees_03a = len(gbdt_03a.get_dump())
ntrees_03b = len(gbdt_03b.get_dump())
@@ -88,7 +97,7 @@ class TestTrainingContinuation(unittest.TestCase):
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
assert res1 == res2
gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class,
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
num_boost_round=3)
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration +
1) * self.num_parallel_tree
@@ -100,7 +109,7 @@ class TestTrainingContinuation(unittest.TestCase):
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class,
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
num_boost_round=7, xgb_model=gbdt_04)
assert gbdt_04.best_ntree_limit == (
gbdt_04.best_iteration + 1) * self.num_parallel_tree
@@ -112,11 +121,11 @@ class TestTrainingContinuation(unittest.TestCase):
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class,
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
num_boost_round=7)
assert gbdt_05.best_ntree_limit == (
gbdt_05.best_iteration + 1) * self.num_parallel_tree
gbdt_05 = xgb.train(self.xgb_params_03,
gbdt_05 = xgb.train(xgb_params_03,
dtrain_5class,
num_boost_round=3,
xgb_model=gbdt_05)
@@ -127,3 +136,32 @@ class TestTrainingContinuation(unittest.TestCase):
res2 = gbdt_05.predict(dtrain_5class,
ntree_limit=gbdt_05.best_ntree_limit)
np.testing.assert_almost_equal(res1, res2)
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_binary(self):
params = self.generate_parameters(False)
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_json(self):
params = self.generate_parameters(True)
for p in params:
p['enable_experimental_json_serialization'] = True
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_updaters_binary(self):
updaters = 'grow_colmaker,prune,refresh'
params = self.generate_parameters(False)
for p in params:
p['updater'] = updaters
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_updaters_json(self):
# Picked up from R tests.
updaters = 'grow_colmaker,prune,refresh'
params = self.generate_parameters(True)
for p in params:
p['updater'] = updaters
self.run_training_continuation(params[0], params[1], params[2])