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Save model in ubj as the default. (#9947)

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This commit is contained in:
Jiaming Yuan
2024-01-05 17:53:36 +08:00
committed by GitHub
parent c03a4d5088
commit 38dd91f491
23 changed files with 598 additions and 550 deletions
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39
tests/python/test_dmatrix.py
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@@ -1,6 +1,7 @@
import csv
import os
import tempfile
import warnings
import numpy as np
import pytest
@@ -24,20 +25,18 @@ class TestDMatrix:
with pytest.warns(UserWarning):
data._warn_unused_missing("uri", 4)
with pytest.warns(None) as record:
with warnings.catch_warnings():
warnings.simplefilter("error")
data._warn_unused_missing("uri", None)
data._warn_unused_missing("uri", np.nan)
assert len(record) == 0
with pytest.warns(None) as record:
with warnings.catch_warnings():
warnings.simplefilter("error")
x = rng.randn(10, 10)
y = rng.randn(10)
xgb.DMatrix(x, y, missing=4)
assert len(record) == 0
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
dm = xgb.DMatrix(data)
@@ -264,7 +263,7 @@ class TestDMatrix:
dtrain = xgb.DMatrix(x, label=rng.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, "train")]
param = {"max_depth": 3, "objective": "binary:logistic", "verbosity": 0}
param = {"max_depth": 3, "objective": "binary:logistic"}
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)
@@ -302,7 +301,7 @@ class TestDMatrix:
dtrain = xgb.DMatrix(x, label=rng.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, "train")]
param = {"max_depth": 3, "objective": "binary:logistic", "verbosity": 0}
param = {"max_depth": 3, "objective": "binary:logistic"}
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)
@@ -475,17 +474,19 @@ class TestDMatrixColumnSplit:
def test_uri(self):
def verify_uri():
rank = xgb.collective.get_rank()
data = np.random.rand(5, 5)
filename = f"test_data_{rank}.csv"
with open(filename, mode="w", newline="") as file:
writer = csv.writer(file)
for row in data:
writer.writerow(row)
dtrain = xgb.DMatrix(
f"{filename}?format=csv", data_split_mode=DataSplitMode.COL
)
assert dtrain.num_row() == 5
assert dtrain.num_col() == 5 * xgb.collective.get_world_size()
with tempfile.TemporaryDirectory() as tmpdir:
filename = os.path.join(tmpdir, f"test_data_{rank}.csv")
data = np.random.rand(5, 5)
with open(filename, mode="w", newline="") as file:
writer = csv.writer(file)
for row in data:
writer.writerow(row)
dtrain = xgb.DMatrix(
f"{filename}?format=csv", data_split_mode=DataSplitMode.COL
)
assert dtrain.num_row() == 5
assert dtrain.num_col() == 5 * xgb.collective.get_world_size()
tm.run_with_rabit(world_size=3, test_fn=verify_uri)