chore: import upstream snapshot with attribution

examples/python-guide/dask/ranking.py

@@ -0,0 +1,50 @@
+from pathlib import Path
+
+import dask.array as da
+import numpy as np
+from distributed import Client, LocalCluster
+from sklearn.datasets import load_svmlight_file
+
+import lightgbm as lgb
+
+if __name__ == "__main__":
+    print("loading data")
+
+    rank_example_dir = Path(__file__).absolute().parents[2] / "lambdarank"
+    X, y = load_svmlight_file(str(rank_example_dir / "rank.train"))
+    group = np.loadtxt(str(rank_example_dir / "rank.train.query"))
+
+    print("initializing a Dask cluster")
+
+    cluster = LocalCluster(n_workers=2)
+    client = Client(cluster)
+
+    print("created a Dask LocalCluster")
+
+    print("distributing training data on the Dask cluster")
+
+    # split training data into two partitions
+    rows_in_part1 = int(np.sum(group[:100]))
+    rows_in_part2 = X.shape[0] - rows_in_part1
+    num_features = X.shape[1]
+
+    # make this array dense because we're splitting across
+    # a sparse boundary to partition the data
+    X = X.toarray()
+
+    dX = da.from_array(x=X, chunks=[(rows_in_part1, rows_in_part2), (num_features,)])
+    dy = da.from_array(
+        x=y,
+        chunks=[
+            (rows_in_part1, rows_in_part2),
+        ],
+    )
+    dg = da.from_array(x=group, chunks=[(100, group.size - 100)])
+
+    print("beginning training")
+
+    dask_model = lgb.DaskLGBMRanker(n_estimators=10)
+    dask_model.fit(dX, dy, group=dg)
+    assert dask_model.fitted_
+
+    print("done training")