e6088366df
* Add Python iterator interface. * Add tests. * Add demo. * Add documents. * Handle empty dataset.
136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
import xgboost as xgb
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from xgboost.data import SingleBatchInternalIter as SingleBatch
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import numpy as np
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from testing import IteratorForTest
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from typing import Tuple, List
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import pytest
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from hypothesis import given, strategies, settings
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from scipy.sparse import csr_matrix
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def make_batches(
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n_samples_per_batch: int, n_features: int, n_batches: int, use_cupy: bool = False
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) -> Tuple[List[np.ndarray], List[np.ndarray]]:
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X = []
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y = []
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if use_cupy:
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import cupy
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rng = cupy.random.RandomState(1994)
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else:
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rng = np.random.RandomState(1994)
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for i in range(n_batches):
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_X = rng.randn(n_samples_per_batch, n_features)
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_y = rng.randn(n_samples_per_batch)
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X.append(_X)
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y.append(_y)
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return X, y
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def test_single_batch(tree_method: str = "approx") -> None:
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from sklearn.datasets import load_breast_cancer
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n_rounds = 10
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X, y = load_breast_cancer(return_X_y=True)
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X = X.astype(np.float32)
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y = y.astype(np.float32)
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Xy = xgb.DMatrix(SingleBatch(data=X, label=y))
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from_it = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=n_rounds)
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Xy = xgb.DMatrix(X, y)
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from_dmat = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=n_rounds)
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assert from_it.get_dump() == from_dmat.get_dump()
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X, y = load_breast_cancer(return_X_y=True, as_frame=True)
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X = X.astype(np.float32)
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Xy = xgb.DMatrix(SingleBatch(data=X, label=y))
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from_pd = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=n_rounds)
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# remove feature info to generate exact same text representation.
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from_pd.feature_names = None
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from_pd.feature_types = None
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assert from_pd.get_dump() == from_it.get_dump()
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X, y = load_breast_cancer(return_X_y=True)
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X = csr_matrix(X)
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Xy = xgb.DMatrix(SingleBatch(data=X, label=y))
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from_it = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=n_rounds)
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X, y = load_breast_cancer(return_X_y=True)
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Xy = xgb.DMatrix(SingleBatch(data=X, label=y), missing=0.0)
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from_np = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=n_rounds)
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assert from_np.get_dump() == from_it.get_dump()
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def run_data_iterator(
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n_samples_per_batch: int,
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n_features: int,
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n_batches: int,
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tree_method: str,
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use_cupy: bool,
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) -> None:
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n_rounds = 2
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it = IteratorForTest(
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*make_batches(n_samples_per_batch, n_features, n_batches, use_cupy)
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)
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if n_batches == 0:
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with pytest.raises(ValueError, match="1 batch"):
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Xy = xgb.DMatrix(it)
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return
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Xy = xgb.DMatrix(it)
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assert Xy.num_row() == n_samples_per_batch * n_batches
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assert Xy.num_col() == n_features
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results_from_it: xgb.callback.EvaluationMonitor.EvalsLog = {}
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from_it = xgb.train(
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{"tree_method": tree_method, "max_depth": 2},
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Xy,
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num_boost_round=n_rounds,
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evals=[(Xy, "Train")],
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evals_result=results_from_it,
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verbose_eval=False,
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)
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it_predt = from_it.predict(Xy)
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X, y = it.as_arrays()
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Xy = xgb.DMatrix(X, y)
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assert Xy.num_row() == n_samples_per_batch * n_batches
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assert Xy.num_col() == n_features
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results_from_arrays: xgb.callback.EvaluationMonitor.EvalsLog = {}
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from_arrays = xgb.train(
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{"tree_method": tree_method, "max_depth": 2},
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Xy,
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num_boost_round=n_rounds,
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evals=[(Xy, "Train")],
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evals_result=results_from_arrays,
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verbose_eval=False,
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)
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arr_predt = from_arrays.predict(Xy)
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if tree_method != "gpu_hist":
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rtol = 1e-1 # flaky
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else:
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np.testing.assert_allclose(it_predt, arr_predt, rtol=1e-3)
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rtol = 1e-6
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np.testing.assert_allclose(
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results_from_it["Train"]["rmse"],
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results_from_arrays["Train"]["rmse"],
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rtol=rtol,
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)
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@given(
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strategies.integers(0, 1024), strategies.integers(1, 7), strategies.integers(0, 13)
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)
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@settings(deadline=None)
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def test_data_iterator(
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n_samples_per_batch: int, n_features: int, n_batches: int
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) -> None:
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run_data_iterator(n_samples_per_batch, n_features, n_batches, "approx", False)
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run_data_iterator(n_samples_per_batch, n_features, n_batches, "hist", False)
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