286 lines
9.9 KiB
Python
286 lines
9.9 KiB
Python
from typing import Any, Dict, List
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import numpy as np
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import pytest
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from hypothesis import given, settings, strategies
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from scipy import sparse
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from xgboost.testing import (
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IteratorForTest,
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make_batches,
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make_batches_sparse,
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make_categorical,
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make_ltr,
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make_sparse_regression,
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predictor_equal,
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)
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from xgboost.testing.data import np_dtypes
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import xgboost as xgb
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class TestQuantileDMatrix:
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def test_basic(self) -> None:
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"""Checks for np array, list, tuple."""
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n_samples = 234
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n_features = 8
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rng = np.random.default_rng()
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X = rng.normal(loc=0, scale=3, size=n_samples * n_features).reshape(
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n_samples, n_features
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)
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y = rng.normal(0, 3, size=n_samples)
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Xy = xgb.QuantileDMatrix(X, y)
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assert Xy.num_row() == n_samples
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assert Xy.num_col() == n_features
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X = sparse.random(n_samples, n_features, density=0.1, format="csr")
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Xy = xgb.QuantileDMatrix(X, y)
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assert Xy.num_row() == n_samples
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assert Xy.num_col() == n_features
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X = sparse.random(n_samples, n_features, density=0.8, format="csr")
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Xy = xgb.QuantileDMatrix(X, y)
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assert Xy.num_row() == n_samples
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assert Xy.num_col() == n_features
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n_samples = 64
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data = []
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for f in range(n_samples):
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row = [f] * n_features
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data.append(row)
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assert np.array(data).shape == (n_samples, n_features)
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Xy = xgb.QuantileDMatrix(data, max_bin=256)
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assert Xy.num_row() == n_samples
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assert Xy.num_col() == n_features
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r = np.arange(1.0, n_samples)
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np.testing.assert_allclose(Xy.get_data().toarray()[1:, 0], r)
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@pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.8, 0.9])
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def test_with_iterator(self, sparsity: float) -> None:
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n_samples_per_batch = 317
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n_features = 8
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n_batches = 7
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if sparsity == 0.0:
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it = IteratorForTest(
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*make_batches(n_samples_per_batch, n_features, n_batches, False), None
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)
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else:
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it = IteratorForTest(
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*make_batches_sparse(
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n_samples_per_batch, n_features, n_batches, sparsity
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),
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None
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)
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Xy = xgb.QuantileDMatrix(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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@pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.5, 0.8, 0.9])
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def test_training(self, sparsity: float) -> None:
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n_samples_per_batch = 317
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n_features = 8
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n_batches = 7
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if sparsity == 0.0:
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it = IteratorForTest(
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*make_batches(n_samples_per_batch, n_features, n_batches, False), None
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)
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else:
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it = IteratorForTest(
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*make_batches_sparse(
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n_samples_per_batch, n_features, n_batches, sparsity
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),
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None
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)
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parameters = {"tree_method": "hist", "max_bin": 256}
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Xy_it = xgb.QuantileDMatrix(it, max_bin=parameters["max_bin"])
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from_it = xgb.train(parameters, Xy_it)
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X, y, w = it.as_arrays()
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w_it = Xy_it.get_weight()
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np.testing.assert_allclose(w_it, w)
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Xy_arr = xgb.DMatrix(X, y, weight=w)
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from_arr = xgb.train(parameters, Xy_arr)
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np.testing.assert_allclose(from_arr.predict(Xy_it), from_it.predict(Xy_arr))
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y -= y.min()
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y += 0.01
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Xy = xgb.QuantileDMatrix(X, y, weight=w)
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with pytest.raises(ValueError, match=r"Only.*hist.*"):
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parameters = {
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"tree_method": "approx",
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"max_bin": 256,
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"objective": "reg:gamma",
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}
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xgb.train(parameters, Xy)
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def run_ref_dmatrix(self, rng: Any, tree_method: str, enable_cat: bool) -> None:
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n_samples, n_features = 2048, 17
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if enable_cat:
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X, y = make_categorical(
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n_samples, n_features, n_categories=13, onehot=False
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)
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if tree_method == "gpu_hist":
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import cudf
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X = cudf.from_pandas(X)
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y = cudf.from_pandas(y)
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else:
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X = rng.normal(loc=0, scale=3, size=n_samples * n_features).reshape(
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n_samples, n_features
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)
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y = rng.normal(0, 3, size=n_samples)
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# Use ref
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Xy = xgb.QuantileDMatrix(X, y, enable_categorical=enable_cat)
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Xy_valid = xgb.QuantileDMatrix(X, y, ref=Xy, enable_categorical=enable_cat)
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qdm_results: Dict[str, Dict[str, List[float]]] = {}
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xgb.train(
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{"tree_method": tree_method},
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Xy,
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evals=[(Xy, "Train"), (Xy_valid, "valid")],
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evals_result=qdm_results,
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)
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np.testing.assert_allclose(
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qdm_results["Train"]["rmse"], qdm_results["valid"]["rmse"]
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)
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# No ref
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Xy_valid = xgb.QuantileDMatrix(X, y, enable_categorical=enable_cat)
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qdm_results = {}
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xgb.train(
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{"tree_method": tree_method},
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Xy,
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evals=[(Xy, "Train"), (Xy_valid, "valid")],
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evals_result=qdm_results,
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)
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np.testing.assert_allclose(
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qdm_results["Train"]["rmse"], qdm_results["valid"]["rmse"]
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)
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# Different number of features
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Xy = xgb.QuantileDMatrix(X, y, enable_categorical=enable_cat)
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dXy = xgb.DMatrix(X, y, enable_categorical=enable_cat)
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n_samples, n_features = 256, 15
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X = rng.normal(loc=0, scale=3, size=n_samples * n_features).reshape(
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n_samples, n_features
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)
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y = rng.normal(0, 3, size=n_samples)
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with pytest.raises(ValueError, match=r".*features\."):
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xgb.QuantileDMatrix(X, y, ref=Xy, enable_categorical=enable_cat)
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# Compare training results
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n_samples, n_features = 256, 17
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if enable_cat:
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X, y = make_categorical(n_samples, n_features, 13, onehot=False)
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if tree_method == "gpu_hist":
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import cudf
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X = cudf.from_pandas(X)
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y = cudf.from_pandas(y)
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else:
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X = rng.normal(loc=0, scale=3, size=n_samples * n_features).reshape(
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n_samples, n_features
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)
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y = rng.normal(0, 3, size=n_samples)
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Xy_valid = xgb.QuantileDMatrix(X, y, ref=Xy, enable_categorical=enable_cat)
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# use DMatrix as ref
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Xy_valid_d = xgb.QuantileDMatrix(X, y, ref=dXy, enable_categorical=enable_cat)
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dXy_valid = xgb.DMatrix(X, y, enable_categorical=enable_cat)
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qdm_results = {}
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xgb.train(
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{"tree_method": tree_method},
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Xy,
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evals=[(Xy, "Train"), (Xy_valid, "valid")],
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evals_result=qdm_results,
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)
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dm_results: Dict[str, Dict[str, List[float]]] = {}
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xgb.train(
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{"tree_method": tree_method},
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dXy,
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evals=[(dXy, "Train"), (dXy_valid, "valid"), (Xy_valid_d, "dvalid")],
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evals_result=dm_results,
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)
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np.testing.assert_allclose(
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dm_results["Train"]["rmse"], qdm_results["Train"]["rmse"]
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)
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np.testing.assert_allclose(
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dm_results["valid"]["rmse"], qdm_results["valid"]["rmse"]
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)
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np.testing.assert_allclose(
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dm_results["dvalid"]["rmse"], qdm_results["valid"]["rmse"]
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)
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def test_ref_dmatrix(self) -> None:
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rng = np.random.RandomState(1994)
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self.run_ref_dmatrix(rng, "hist", True)
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self.run_ref_dmatrix(rng, "hist", False)
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def test_predict(self) -> None:
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n_samples, n_features = 16, 2
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X, y = make_categorical(n_samples, n_features, n_categories=13, onehot=False)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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booster = xgb.train({"tree_method": "hist"}, Xy)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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a = booster.predict(Xy)
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qXy = xgb.QuantileDMatrix(X, y, enable_categorical=True)
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b = booster.predict(qXy)
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np.testing.assert_allclose(a, b)
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def test_ltr(self) -> None:
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X, y, qid, w = make_ltr(100, 3, 3, 5)
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Xy_qdm = xgb.QuantileDMatrix(X, y, qid=qid, weight=w)
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Xy = xgb.DMatrix(X, y, qid=qid, weight=w)
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xgb.train({"tree_method": "hist", "objective": "rank:ndcg"}, Xy)
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from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)
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from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
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assert predictor_equal(from_qdm, from_dm)
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# we don't test empty Quantile DMatrix in single node construction.
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@given(
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strategies.integers(1, 1000),
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strategies.integers(1, 100),
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strategies.fractions(0, 0.99),
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)
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@settings(deadline=None, print_blob=True)
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def test_to_csr(self, n_samples: int, n_features: int, sparsity: float) -> None:
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csr, y = make_sparse_regression(n_samples, n_features, sparsity, False)
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csr = csr.astype(np.float32)
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qdm = xgb.QuantileDMatrix(data=csr, label=y)
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ret = qdm.get_data()
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np.testing.assert_equal(csr.indptr, ret.indptr)
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np.testing.assert_equal(csr.indices, ret.indices)
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booster = xgb.train({"tree_method": "hist"}, dtrain=qdm)
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np.testing.assert_allclose(
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booster.predict(qdm), booster.predict(xgb.DMatrix(qdm.get_data()))
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)
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def test_dtypes(self) -> None:
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"""Checks for both np array and pd DataFrame."""
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n_samples = 128
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n_features = 16
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for orig, x in np_dtypes(n_samples, n_features):
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m0 = xgb.QuantileDMatrix(orig)
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m1 = xgb.QuantileDMatrix(x)
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assert predictor_equal(m0, m1)
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# unsupported types
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for dtype in [
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np.string_,
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np.complex64,
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np.complex128,
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]:
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X: np.ndarray = np.array(orig, dtype=dtype)
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with pytest.raises(ValueError):
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xgb.QuantileDMatrix(X)
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