[dask] Add DaskXGBRanker (#6576)

* Initial support for distributed LTR using dask.

* Support `qid` in libxgboost.
* Refactor `predict` and `n_features_in_`, `best_[score/iteration/ntree_limit]`
  to avoid duplicated code.
* Define `DaskXGBRanker`.

The dask ranker doesn't support group structure, instead it uses query id and
convert to group ptr internally.

tests/python/test_dmatrix.py

@@ -239,6 +239,19 @@ class TestDMatrix:
         dtrain.get_float_info('base_margin')
         dtrain.get_uint_info('group_ptr')
 
+    def test_qid(self):
+        rows = 100
+        cols = 10
+        X, y = rng.randn(rows, cols), rng.randn(rows)
+        qid = rng.randint(low=0, high=10, size=rows, dtype=np.uint32)
+        qid = np.sort(qid)
+
+        Xy = xgb.DMatrix(X, y)
+        Xy.set_info(qid=qid)
+        group_ptr = Xy.get_uint_info('group_ptr')
+        assert group_ptr[0] == 0
+        assert group_ptr[-1] == rows
+
     def test_feature_weights(self):
         kRows = 10
         kCols = 50

tests/python/test_ranking.py

@@ -1,5 +1,6 @@
 import numpy as np
 from scipy.sparse import csr_matrix
+import testing as tm
 import xgboost
 import os
 import itertools
@@ -79,22 +80,10 @@ class TestRanking:
         """
         Download and setup the test fixtures
         """
-        from sklearn.datasets import load_svmlight_files
-        # download the test data
         cls.dpath = 'demo/rank/'
-        src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
-        target = cls.dpath + '/MQ2008.zip'
-        urllib.request.urlretrieve(url=src, filename=target)
-
-        with zipfile.ZipFile(target, 'r') as f:
-            f.extractall(path=cls.dpath)
-
         (x_train, y_train, qid_train, x_test, y_test, qid_test,
-         x_valid, y_valid, qid_valid) = load_svmlight_files(
-            (cls.dpath + "MQ2008/Fold1/train.txt",
-             cls.dpath + "MQ2008/Fold1/test.txt",
-             cls.dpath + "MQ2008/Fold1/vali.txt"),
-            query_id=True, zero_based=False)
+         x_valid, y_valid, qid_valid) = tm.get_mq2008(cls.dpath)
+
         # instantiate the matrices
         cls.dtrain = xgboost.DMatrix(x_train, y_train)
         cls.dvalid = xgboost.DMatrix(x_valid, y_valid)

tests/python/test_with_dask.py

@@ -5,6 +5,7 @@ import pytest
 import xgboost as xgb
 import sys
 import numpy as np
+import scipy
 import json
 from typing import List, Tuple, Dict, Optional, Type, Any
 import asyncio
@@ -670,12 +671,56 @@ def run_aft_survival(client: "Client", dmatrix_t: Type) -> None:
     assert nloglik_rec['extreme'][-1] > 4.9
 
 
-def test_aft_survival() -> None:
+def test_dask_aft_survival() -> None:
     with LocalCluster(n_workers=kWorkers) as cluster:
         with Client(cluster) as client:
             run_aft_survival(client, DaskDMatrix)
 
 
+def test_dask_ranking(client: "Client") -> None:
+    dpath = "demo/rank/"
+    mq2008 = tm.get_mq2008(dpath)
+    data = []
+    for d in mq2008:
+        if isinstance(d, scipy.sparse.csr_matrix):
+            d[d == 0] = np.inf
+            d = d.toarray()
+            d[d == 0] = np.nan
+            d[np.isinf(d)] = 0
+            data.append(da.from_array(d))
+        else:
+            data.append(da.from_array(d))
+
+    (
+        x_train,
+        y_train,
+        qid_train,
+        x_test,
+        y_test,
+        qid_test,
+        x_valid,
+        y_valid,
+        qid_valid,
+    ) = data
+    qid_train = qid_train.astype(np.uint32)
+    qid_valid = qid_valid.astype(np.uint32)
+    qid_test = qid_test.astype(np.uint32)
+
+    rank = xgb.dask.DaskXGBRanker(n_estimators=2500)
+    rank.fit(
+        x_train,
+        y_train,
+        qid=qid_train,
+        eval_set=[(x_test, y_test), (x_train, y_train)],
+        eval_qid=[qid_test, qid_train],
+        eval_metric=["ndcg"],
+        verbose=True,
+        early_stopping_rounds=10,
+    )
+    assert rank.n_features_in_ == 46
+    assert rank.best_score > 0.98
+
+
 class TestWithDask:
     def test_global_config(self, client: "Client") -> None:
         X, y, _ = generate_array()
@@ -981,7 +1026,7 @@ class TestWithDask:
     def test_shap(self, client: "Client") -> None:
         from sklearn.datasets import load_boston, load_digits
         X, y = load_boston(return_X_y=True)
-        params = {'objective': 'reg:squarederror'}
+        params: Dict[str, Any] = {'objective': 'reg:squarederror'}
         self.run_shap(X, y, params, client)
 
         X, y = load_digits(return_X_y=True)

tests/python/test_with_sklearn.py

@@ -125,9 +125,11 @@ def test_ranking():
     x_train = np.random.rand(1000, 10)
     y_train = np.random.randint(5, size=1000)
     train_group = np.repeat(50, 20)
+
     x_valid = np.random.rand(200, 10)
     y_valid = np.random.randint(5, size=200)
     valid_group = np.repeat(50, 4)
+
     x_test = np.random.rand(100, 10)
 
     params = {'tree_method': 'exact', 'objective': 'rank:pairwise',
@@ -136,6 +138,7 @@ def test_ranking():
     model = xgb.sklearn.XGBRanker(**params)
     model.fit(x_train, y_train, group=train_group,
               eval_set=[(x_valid, y_valid)], eval_group=[valid_group])
+
     pred = model.predict(x_test)
 
     train_data = xgb.DMatrix(x_train, y_train)

tests/python/testing.py

@@ -1,5 +1,7 @@
 # coding: utf-8
 import os
+import urllib
+import zipfile
 import sys
 from contextlib import contextmanager
 from io import StringIO
@@ -209,6 +211,29 @@ def get_sparse():
     return X, y
 
 
+@memory.cache
+def get_mq2008(dpath):
+    from sklearn.datasets import load_svmlight_files
+
+    src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
+    target = dpath + '/MQ2008.zip'
+    if not os.path.exists(target):
+        urllib.request.urlretrieve(url=src, filename=target)
+
+    with zipfile.ZipFile(target, 'r') as f:
+        f.extractall(path=dpath)
+
+    (x_train, y_train, qid_train, x_test, y_test, qid_test,
+     x_valid, y_valid, qid_valid) = load_svmlight_files(
+         (dpath + "MQ2008/Fold1/train.txt",
+          dpath + "MQ2008/Fold1/test.txt",
+          dpath + "MQ2008/Fold1/vali.txt"),
+         query_id=True, zero_based=False)
+
+    return (x_train, y_train, qid_train, x_test, y_test, qid_test,
+            x_valid, y_valid, qid_valid)
+
+
 _unweighted_datasets_strategy = strategies.sampled_from(
     [TestDataset('boston', get_boston, 'reg:squarederror', 'rmse'),
      TestDataset('digits', get_digits, 'multi:softmax', 'mlogloss'),