d6d72de339
The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
'''Tests for running inplace prediction.'''
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
import numpy as np
|
|
from scipy import sparse
|
|
|
|
import xgboost as xgb
|
|
|
|
|
|
def run_threaded_predict(X, rows, predict_func):
|
|
results = []
|
|
per_thread = 20
|
|
with ThreadPoolExecutor(max_workers=10) as e:
|
|
for i in range(0, rows, int(rows / per_thread)):
|
|
if hasattr(X, 'iloc'):
|
|
predictor = X.iloc[i:i+per_thread, :]
|
|
else:
|
|
predictor = X[i:i+per_thread, ...]
|
|
f = e.submit(predict_func, predictor)
|
|
results.append(f)
|
|
|
|
for f in results:
|
|
assert f.result()
|
|
|
|
|
|
def run_predict_leaf(predictor):
|
|
rows = 100
|
|
cols = 4
|
|
classes = 5
|
|
num_parallel_tree = 4
|
|
num_boost_round = 10
|
|
rng = np.random.RandomState(1994)
|
|
X = rng.randn(rows, cols)
|
|
y = rng.randint(low=0, high=classes, size=rows)
|
|
m = xgb.DMatrix(X, y)
|
|
booster = xgb.train(
|
|
{
|
|
"num_parallel_tree": num_parallel_tree,
|
|
"num_class": classes,
|
|
"predictor": predictor,
|
|
"tree_method": "hist",
|
|
},
|
|
m,
|
|
num_boost_round=num_boost_round,
|
|
)
|
|
|
|
empty = xgb.DMatrix(np.ones(shape=(0, cols)))
|
|
empty_leaf = booster.predict(empty, pred_leaf=True)
|
|
assert empty_leaf.shape[0] == 0
|
|
|
|
leaf = booster.predict(m, pred_leaf=True)
|
|
assert leaf.shape[0] == rows
|
|
assert leaf.shape[1] == classes * num_parallel_tree * num_boost_round
|
|
|
|
for i in range(rows):
|
|
row = leaf[i, ...]
|
|
for j in range(num_boost_round):
|
|
start = classes * num_parallel_tree * j
|
|
end = classes * num_parallel_tree * (j + 1)
|
|
layer = row[start: end]
|
|
for c in range(classes):
|
|
tree_group = layer[c * num_parallel_tree: (c + 1) * num_parallel_tree]
|
|
assert tree_group.shape[0] == num_parallel_tree
|
|
# no subsampling so tree in same forest should output same
|
|
# leaf.
|
|
assert np.all(tree_group == tree_group[0])
|
|
|
|
ntree_limit = 2
|
|
sliced = booster.predict(
|
|
m, pred_leaf=True, ntree_limit=num_parallel_tree * ntree_limit
|
|
)
|
|
first = sliced[0, ...]
|
|
|
|
assert first.shape[0] == classes * num_parallel_tree * ntree_limit
|
|
return leaf
|
|
|
|
|
|
def test_predict_leaf():
|
|
run_predict_leaf('cpu_predictor')
|
|
|
|
|
|
class TestInplacePredict:
|
|
'''Tests for running inplace prediction'''
|
|
def test_predict(self):
|
|
rows = 1000
|
|
cols = 10
|
|
|
|
np.random.seed(1994)
|
|
|
|
X = np.random.randn(rows, cols)
|
|
y = np.random.randn(rows)
|
|
dtrain = xgb.DMatrix(X, y)
|
|
|
|
booster = xgb.train({'tree_method': 'hist'},
|
|
dtrain, num_boost_round=10)
|
|
|
|
test = xgb.DMatrix(X[:10, ...])
|
|
predt_from_array = booster.inplace_predict(X[:10, ...])
|
|
predt_from_dmatrix = booster.predict(test)
|
|
|
|
np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
|
|
|
|
predt_from_array = booster.inplace_predict(X[:10, ...], iteration_range=(0, 4))
|
|
predt_from_dmatrix = booster.predict(test, ntree_limit=4)
|
|
|
|
np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
|
|
|
|
def predict_dense(x):
|
|
inplace_predt = booster.inplace_predict(x)
|
|
d = xgb.DMatrix(x)
|
|
copied_predt = booster.predict(d)
|
|
return np.all(copied_predt == inplace_predt)
|
|
|
|
for i in range(10):
|
|
run_threaded_predict(X, rows, predict_dense)
|
|
|
|
def predict_csr(x):
|
|
inplace_predt = booster.inplace_predict(sparse.csr_matrix(x))
|
|
d = xgb.DMatrix(x)
|
|
copied_predt = booster.predict(d)
|
|
return np.all(copied_predt == inplace_predt)
|
|
|
|
for i in range(10):
|
|
run_threaded_predict(X, rows, predict_csr)
|