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"""
Learning to rank with the Dask Interface
========================================
.. versionadded:: 3.0.0
This is a demonstration of using XGBoost for learning to rank tasks using the
MSLR_10k_letor dataset. For more infomation about the dataset, please visit its
`description page <https://www.microsoft.com/en-us/research/project/mslr/>`_.
See :ref:`ltr-dist` for a general description for distributed learning to rank and
:ref:`ltr-dask` for Dask-specific features.
"""
from __future__ import annotations
import argparse
import os
from contextlib import contextmanager
from typing import Generator
import dask
import numpy as np
from dask import dataframe as dd
from distributed import Client, LocalCluster, wait
from sklearn.datasets import load_svmlight_file
from xgboost import dask as dxgb
def load_mslr_10k(
device: str, data_path: str, cache_path: str
) -> tuple[dd.DataFrame, dd.DataFrame, dd.DataFrame]:
"""Load the MSLR10k dataset from data_path and save parquet files in the cache_path."""
root_path = os.path.expanduser(args.data)
cache_path = os.path.expanduser(args.cache)
# Use only the Fold1 for demo:
# Train, Valid, Test
# {S1,S2,S3}, S4, S5
fold = 1
if not os.path.exists(cache_path):
os.mkdir(cache_path)
fold_path = os.path.join(root_path, f"Fold{fold}")
train_path = os.path.join(fold_path, "train.txt")
valid_path = os.path.join(fold_path, "vali.txt")
test_path = os.path.join(fold_path, "test.txt")
X_train, y_train, qid_train = load_svmlight_file(
train_path, query_id=True, dtype=np.float32
)
columns = [f"f{i}" for i in range(X_train.shape[1])]
X_train = dd.from_array(X_train.toarray(), columns=columns)
y_train = y_train.astype(np.int32)
qid_train = qid_train.astype(np.int32)
X_train["y"] = dd.from_array(y_train)
X_train["qid"] = dd.from_array(qid_train)
X_train.to_parquet(os.path.join(cache_path, "train"), engine="pyarrow")
X_valid, y_valid, qid_valid = load_svmlight_file(
valid_path, query_id=True, dtype=np.float32
)
X_valid = dd.from_array(X_valid.toarray(), columns=columns)
y_valid = y_valid.astype(np.int32)
qid_valid = qid_valid.astype(np.int32)
X_valid["y"] = dd.from_array(y_valid)
X_valid["qid"] = dd.from_array(qid_valid)
X_valid.to_parquet(os.path.join(cache_path, "valid"), engine="pyarrow")
X_test, y_test, qid_test = load_svmlight_file(
test_path, query_id=True, dtype=np.float32
)
X_test = dd.from_array(X_test.toarray(), columns=columns)
y_test = y_test.astype(np.int32)
qid_test = qid_test.astype(np.int32)
X_test["y"] = dd.from_array(y_test)
X_test["qid"] = dd.from_array(qid_test)
X_test.to_parquet(os.path.join(cache_path, "test"), engine="pyarrow")
df_train = dd.read_parquet(
os.path.join(cache_path, "train"), calculate_divisions=True
)
df_valid = dd.read_parquet(
os.path.join(cache_path, "valid"), calculate_divisions=True
)
df_test = dd.read_parquet(
os.path.join(cache_path, "test"), calculate_divisions=True
)
return df_train, df_valid, df_test
def ranking_demo(client: Client, args: argparse.Namespace) -> None:
"""Learning to rank with data sorted locally."""
df_tr, df_va, _ = load_mslr_10k(args.device, args.data, args.cache)
X_train: dd.DataFrame = df_tr[df_tr.columns.difference(["y", "qid"])]
y_train = df_tr[["y", "qid"]]
Xy_train = dxgb.DaskQuantileDMatrix(client, X_train, y_train.y, qid=y_train.qid)
X_valid: dd.DataFrame = df_va[df_va.columns.difference(["y", "qid"])]
y_valid = df_va[["y", "qid"]]
Xy_valid = dxgb.DaskQuantileDMatrix(
client, X_valid, y_valid.y, qid=y_valid.qid, ref=Xy_train
)
# Upon training, you will see a performance warning about sorting data based on
# query groups.
dxgb.train(
client,
{"objective": "rank:ndcg", "device": args.device},
Xy_train,
evals=[(Xy_train, "Train"), (Xy_valid, "Valid")],
num_boost_round=100,
)
def ranking_wo_split_demo(client: Client, args: argparse.Namespace) -> None:
"""Learning to rank with data partitioned according to query groups."""
df_tr, df_va, df_te = load_mslr_10k(args.device, args.data, args.cache)
X_tr = df_tr[df_tr.columns.difference(["y", "qid"])]
X_va = df_va[df_va.columns.difference(["y", "qid"])]
# `allow_group_split=False` makes sure data is partitioned according to the query
# groups.
ltr = dxgb.DaskXGBRanker(allow_group_split=False, device=args.device)
ltr.client = client
ltr = ltr.fit(
X_tr,
df_tr.y,
qid=df_tr.qid,
eval_set=[(X_tr, df_tr.y), (X_va, df_va.y)],
eval_qid=[df_tr.qid, df_va.qid],
verbose=True,
)
df_te = df_te.persist()
wait([df_te])
X_te = df_te[df_te.columns.difference(["y", "qid"])]
predt = ltr.predict(X_te)
y = client.compute(df_te.y)
wait([predt, y])
@contextmanager
def gen_client(device: str) -> Generator[Client, None, None]:
match device:
case "cuda":
from dask_cuda import LocalCUDACluster
with LocalCUDACluster() as cluster:
with Client(cluster) as client:
with dask.config.set(
{
"array.backend": "cupy",
"dataframe.backend": "cudf",
}
):
yield client
case "cpu":
with LocalCluster() as cluster:
with Client(cluster) as client:
yield client
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Demonstration of learning to rank using XGBoost."
)
parser.add_argument(
"--data",
type=str,
help="Root directory of the MSLR-WEB10K data.",
required=True,
)
parser.add_argument(
"--cache",
type=str,
help="Directory for caching processed data.",
required=True,
)
parser.add_argument("--device", choices=["cpu", "cuda"], default="cpu")
parser.add_argument(
"--no-split",
action="store_true",
help="Flag to indicate query groups should not be split.",
)
args = parser.parse_args()
with gen_client(args.device) as client:
if args.no_split:
ranking_wo_split_demo(client, args)
else:
ranking_demo(client, args)
|
