ray/release/golden_notebook_tests/workloads/modin_xgboost_test.py

76 lines
2 KiB
Python

import argparse
import json
import os
import time
import modin.pandas as pd
import ray
from xgboost_ray import RayDMatrix, RayParams, train
from utils.utils import is_anyscale_connect
HIGGS_S3_URI = "s3://ray-ci-higgs/HIGGS.csv"
SIMPLE_HIGGS_S3_URI = "s3://ray-ci-higgs/simpleHIGGS.csv"
def main():
print("Loading HIGGS data.")
colnames = ["label"] + ["feature-%02d" % i for i in range(1, 29)]
if args.smoke_test:
data = pd.read_csv(SIMPLE_HIGGS_S3_URI, names=colnames)
else:
data = pd.read_csv(HIGGS_S3_URI, names=colnames)
print("Loaded HIGGS data.")
# partition on a column
df_train = data[(data["feature-01"] < 0.4)]
df_validation = data[(data["feature-01"] >= 0.4)
& (data["feature-01"] < 0.8)]
dtrain = RayDMatrix(df_train, label="label", columns=colnames)
dvalidation = RayDMatrix(df_validation, label="label")
evallist = [(dvalidation, "eval")]
evals_result = {}
config = {"tree_method": "hist", "eval_metric": ["logloss", "error"]}
train(
params=config,
dtrain=dtrain,
evals_result=evals_result,
ray_params=RayParams(
max_actor_restarts=1, num_actors=4, cpus_per_actor=2),
num_boost_round=100,
evals=evallist)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test",
action="store_true",
help="Finish quickly for testing.")
args = parser.parse_args()
start = time.time()
client_builder = ray.client()
if is_anyscale_connect():
job_name = os.environ.get("RAY_JOB_NAME", "modin_xgboost_test")
client_builder.job_name(job_name)
client_builder.connect()
main()
taken = time.time() - start
result = {
"time_taken": taken,
}
test_output_json = os.environ.get("TEST_OUTPUT_JSON",
"/tmp/modin_xgboost_test.json")
with open(test_output_json, "wt") as f:
json.dump(result, f)
print("Test Successful!")