Experimental support for external memory

This is similar to the one in quantile_data_iterator.py, but for external memory instead of Quantile DMatrix. The feature is not ready for production use yet.

New in version 1.5.0.

See the tutorial for more details.

import os
import tempfile
from typing import Callable, List, Tuple

import numpy as np
from sklearn.datasets import make_regression

import xgboost

def make_batches(
    n_samples_per_batch: int,
    n_features: int,
    n_batches: int,
    tmpdir: str,
) -> List[Tuple[str, str]]:
    files: List[Tuple[str, str]] = []
    rng = np.random.RandomState(1994)
    for i in range(n_batches):
        X, y = make_regression(n_samples_per_batch, n_features, random_state=rng)
        X_path = os.path.join(tmpdir, "X-" + str(i) + ".npy")
        y_path = os.path.join(tmpdir, "y-" + str(i) + ".npy")
        np.save(X_path, X)
        np.save(y_path, y)
        files.append((X_path, y_path))
    return files

class Iterator(xgboost.DataIter):
    """A custom iterator for loading files in batches."""

    def __init__(self, file_paths: List[Tuple[str, str]]):
        self._file_paths = file_paths
        self._it = 0
        # XGBoost will generate some cache files under current directory with the prefix
        # "cache"
        super().__init__(cache_prefix=os.path.join(".", "cache"))

    def load_file(self) -> Tuple[np.ndarray, np.ndarray]:
        X_path, y_path = self._file_paths[self._it]
        X = np.load(X_path)
        y = np.load(y_path)
        assert X.shape[0] == y.shape[0]
        return X, y

    def next(self, input_data: Callable) -> int:
        """Advance the iterator by 1 step and pass the data to XGBoost.  This function is
        called by XGBoost during the construction of ``DMatrix``

        if self._it == len(self._file_paths):
            # return 0 to let XGBoost know this is the end of iteration
            return 0

        # input_data is a function passed in by XGBoost who has the similar signature to
        # the ``DMatrix`` constructor.
        X, y = self.load_file()
        input_data(data=X, label=y)
        self._it += 1
        return 1

    def reset(self) -> None:
        """Reset the iterator to its beginning"""
        self._it = 0

def main(tmpdir: str) -> xgboost.Booster:
    # generate some random data for demo
    files = make_batches(1024, 17, 31, tmpdir)
    it = Iterator(files)
    # For non-data arguments, specify it here once instead of passing them by the `next`
    # method.
    missing = np.NaN
    Xy = xgboost.DMatrix(it, missing=missing, enable_categorical=False)

    # ``approx`` is also supported, but less efficient due to sketching. GPU behaves
    # differently than CPU tree methods as it uses a hybrid approach. See tutorial in
    # doc for details.
    booster = xgboost.train(
        {"tree_method": "hist", "max_depth": 4},
        evals=[(Xy, "Train")],
    return booster

if __name__ == "__main__":
    with tempfile.TemporaryDirectory() as tmpdir:

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