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import
unittest
import
lightgbm
as
lgb
import
pandas
as
pd
from
common
import
gpu_test
class
TestLightgbm
(
unittest
.
TestCase
):
# Based on the "simple_example" from their documentation:
# https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py
def
test_cpu
(
self
):
lgb_train
,
lgb_eval
=
self
.
load_datasets
()
params
=
{
'task'
:
'train'
,
'boosting_type'
:
'gbdt'
,
'objective'
:
'regression'
,
'metric'
: {
'l2'
,
'auc'
},
'num_leaves'
:
31
,
'learning_rate'
:
0.05
,
'feature_fraction'
:
0.9
,
'bagging_fraction'
:
0.8
,
'bagging_freq'
:
5
,
'verbose'
:
0
}
# Run only one round for faster test
gbm
=
lgb
.
train
(
params
,
lgb_train
,
num_boost_round
=
1
,
valid_sets
=
lgb_eval
,
early_stopping_rounds
=
1
)
self
.
assertEqual
(
1
,
gbm
.
best_iteration
)
@
gpu_test
def
test_gpu
(
self
):
lgb_train
,
lgb_eval
=
self
.
load_datasets
()
params
=
{
'boosting_type'
:
'gbdt'
,
'objective'
:
'regression'
,
'metric'
:
'auc'
,
'num_leaves'
:
31
,
'learning_rate'
:
0.05
,
'feature_fraction'
:
0.9
,
'bagging_fraction'
:
0.8
,
'bagging_freq'
:
5
,
'verbose'
:
1
,
'device'
:
'gpu'
}
# Run only one round for faster test
gbm
=
lgb
.
train
(
params
,
lgb_train
,
num_boost_round
=
1
,
valid_sets
=
lgb_eval
,
early_stopping_rounds
=
1
)
self
.
assertEqual
(
1
,
gbm
.
best_iteration
)
def
load_datasets
(
self
):
df_train
=
pd
.
read_csv
(
'/input/tests/data/lgb_train.csv'
,
header
=
None
,
sep
=
'
\t
'
)
df_test
=
pd
.
read_csv
(
'/input/tests/data/lgb_test.csv'
,
header
=
None
,
sep
=
'
\t
'
)
y_train
=
df_train
[
0
]
y_test
=
df_test
[
0
]
X_train
=
df_train
.
drop
(
0
,
axis
=
1
)
X_test
=
df_test
.
drop
(
0
,
axis
=
1
)
lgb_train
=
lgb
.
Dataset
(
X_train
,
y_train
)
lgb_eval
=
lgb
.
Dataset
(
X_test
,
y_test
,
reference
=
lgb_train
)
return
(
lgb_train
,
lgb_eval
)
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