827d0e8edb
- Bump the Python requirement. - Fix type hints. - Use loky to avoid deadlock. - Workaround cupy-numpy compatibility issue on Windows caused by the `safe` casting rule. - Simplify the repartitioning logic to avoid dask errors.
592 lines
21 KiB
Python
592 lines
21 KiB
Python
import sys
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from copy import copy
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import numpy as np
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import pytest
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from hypothesis import assume, given, settings, strategies
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import xgboost as xgb
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from xgboost import testing as tm
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from xgboost.compat import PANDAS_INSTALLED
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if PANDAS_INSTALLED:
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from hypothesis.extra.pandas import column, data_frames, range_indexes
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else:
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def noop(*args, **kwargs):
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pass
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column, data_frames, range_indexes = noop, noop, noop
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sys.path.append("tests/python")
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from test_predict import run_predict_leaf # noqa
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from test_predict import run_threaded_predict # noqa
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rng = np.random.RandomState(1994)
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shap_parameter_strategy = strategies.fixed_dictionaries(
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{
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"max_depth": strategies.integers(1, 11),
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"max_leaves": strategies.integers(0, 256),
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"num_parallel_tree": strategies.sampled_from([1, 10]),
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}
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).filter(lambda x: x["max_depth"] > 0 or x["max_leaves"] > 0)
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predict_parameter_strategy = strategies.fixed_dictionaries(
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{
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"max_depth": strategies.integers(1, 8),
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"num_parallel_tree": strategies.sampled_from([1, 4]),
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}
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)
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# cupy nvrtc compilation can take a long time for the first run
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pytestmark = tm.timeout(30)
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class TestGPUPredict:
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def test_predict(self):
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iterations = 10
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np.random.seed(1)
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test_num_rows = [10, 1000, 5000]
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test_num_cols = [10, 50, 500]
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# This test passes for tree_method=gpu_hist and tree_method=exact. but
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# for `hist` and `approx` the floating point error accumulates faster
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# and fails even tol is set to 1e-4. For `hist`, the mismatching rate
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# with 5000 rows is 0.04.
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for num_rows in test_num_rows:
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for num_cols in test_num_cols:
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dtrain = xgb.DMatrix(
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np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2),
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)
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dval = xgb.DMatrix(
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np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2),
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)
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dtest = xgb.DMatrix(
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np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2),
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)
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watchlist = [(dtrain, "train"), (dval, "validation")]
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res = {}
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param = {
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"objective": "binary:logistic",
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"eval_metric": "logloss",
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"tree_method": "hist",
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"device": "gpu:0",
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"max_depth": 1,
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}
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bst = xgb.train(
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param, dtrain, iterations, evals=watchlist, evals_result=res
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)
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assert tm.non_increasing(res["train"]["logloss"], tolerance=0.001)
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gpu_pred_train = bst.predict(dtrain, output_margin=True)
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gpu_pred_test = bst.predict(dtest, output_margin=True)
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gpu_pred_val = bst.predict(dval, output_margin=True)
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bst.set_param({"device": "cpu", "tree_method": "hist"})
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bst_cpu = copy(bst)
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cpu_pred_train = bst_cpu.predict(dtrain, output_margin=True)
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cpu_pred_test = bst_cpu.predict(dtest, output_margin=True)
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cpu_pred_val = bst_cpu.predict(dval, output_margin=True)
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np.testing.assert_allclose(cpu_pred_train, gpu_pred_train, rtol=1e-6)
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np.testing.assert_allclose(cpu_pred_val, gpu_pred_val, rtol=1e-6)
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np.testing.assert_allclose(cpu_pred_test, gpu_pred_test, rtol=1e-6)
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# Test case for a bug where multiple batch predictions made on a
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# test set produce incorrect results
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_multi_predict(self):
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from sklearn.datasets import make_regression
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from sklearn.model_selection import train_test_split
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n = 1000
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X, y = make_regression(n, random_state=rng)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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params = {}
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params["tree_method"] = "hist"
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params["device"] = "cuda:0"
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bst = xgb.train(params, dtrain)
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bst.set_param({"device": "cuda:0"})
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# Don't reuse the DMatrix for prediction, otherwise the result is cached.
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predict_gpu_0 = bst.predict(xgb.DMatrix(X_test))
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predict_gpu_1 = bst.predict(xgb.DMatrix(X_test))
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bst.set_param({"device": "cpu"})
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predict_cpu = bst.predict(xgb.DMatrix(X_test))
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assert np.allclose(predict_gpu_0, predict_gpu_1)
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assert np.allclose(predict_gpu_0, predict_cpu)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_sklearn(self):
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m, n = 15000, 14
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tr_size = 2500
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X = np.random.rand(m, n)
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y = 200 * np.matmul(X, np.arange(-3, -3 + n))
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y = y.reshape(y.size)
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X_train, y_train = X[:tr_size, :], y[:tr_size]
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X_test, y_test = X[tr_size:, :], y[tr_size:]
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params = {
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"tree_method": "hist",
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"device": "cuda:0",
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"n_jobs": -1,
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"seed": 123,
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}
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m = xgb.XGBRegressor(**params).fit(X_train, y_train)
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gpu_train_score = m.score(X_train, y_train)
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gpu_test_score = m.score(X_test, y_test)
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# Now with cpu
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m.set_params(device="cpu")
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cpu_train_score = m.score(X_train, y_train)
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cpu_test_score = m.score(X_test, y_test)
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assert np.allclose(cpu_train_score, gpu_train_score)
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assert np.allclose(cpu_test_score, gpu_test_score)
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@pytest.mark.parametrize("device", ["cpu", "cuda"])
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_cudf())
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def test_inplace_predict_device_type(self, device: str) -> None:
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"""Test inplace predict with different device and data types.
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The sklearn interface uses inplace predict by default and gbtree fallbacks to
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DMatrix whenever device doesn't match. This test checks that XGBoost can handle
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different combinations of device and input data type.
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"""
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import cudf
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import cupy as cp
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import pandas as pd
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from scipy.sparse import csr_matrix
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reg = xgb.XGBRegressor(tree_method="hist", device=device)
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n_samples = 4096
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n_features = 13
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X, y, w = tm.make_regression(n_samples, n_features, use_cupy=True)
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X[X == 0.0] = 1.0
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reg.fit(X, y, sample_weight=w)
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predt_0 = reg.predict(X)
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X = cp.asnumpy(X)
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predt_1 = reg.predict(X)
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df = pd.DataFrame(X)
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predt_2 = reg.predict(df)
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df = cudf.DataFrame(X)
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predt_3 = reg.predict(df)
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X_csr = csr_matrix(X)
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predt_4 = reg.predict(X_csr)
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np.testing.assert_allclose(predt_0, predt_1)
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np.testing.assert_allclose(predt_0, predt_2)
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np.testing.assert_allclose(predt_0, predt_3)
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np.testing.assert_allclose(predt_0, predt_4)
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def run_inplace_base_margin(
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self, device: int, booster: xgb.Booster, dtrain: xgb.DMatrix, X, base_margin
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) -> None:
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import cupy as cp
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booster.set_param({"device": f"cuda:{device}"})
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dtrain.set_info(base_margin=base_margin)
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from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
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from_dmatrix = booster.predict(dtrain)
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cp.testing.assert_allclose(from_inplace, from_dmatrix)
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booster = booster.copy() # clear prediction cache.
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booster.set_param({"device": "cpu"})
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from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
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from_dmatrix = booster.predict(dtrain)
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cp.testing.assert_allclose(from_inplace, from_dmatrix)
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booster = booster.copy() # clear prediction cache.
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base_margin = cp.asnumpy(base_margin)
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if hasattr(X, "values"):
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X = cp.asnumpy(X.values)
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booster.set_param({"device": f"cuda:{device}"})
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from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
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from_dmatrix = booster.predict(dtrain)
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cp.testing.assert_allclose(from_inplace, from_dmatrix, rtol=1e-6)
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def run_inplace_predict_cupy(self, device: int) -> None:
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import cupy as cp
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cp.cuda.runtime.setDevice(device)
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rows = 1000
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cols = 10
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missing = 11 # set to integer for testing
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cp_rng = cp.random.RandomState(np.uint64(1994))
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cp.random.set_random_state(cp_rng)
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X = cp.random.randn(rows, cols)
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missing_idx = [i for i in range(0, cols, 4)]
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X[:, missing_idx] = missing # set to be missing
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y = cp.random.randn(rows)
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train(
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{"tree_method": "hist", "device": f"cuda:{device}"},
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dtrain,
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num_boost_round=10,
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)
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test = xgb.DMatrix(X[:10, ...], missing=missing)
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predt_from_array = booster.inplace_predict(X[:10, ...], missing=missing)
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predt_from_dmatrix = booster.predict(test)
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cp.testing.assert_allclose(predt_from_array, predt_from_dmatrix)
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def predict_dense(x):
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cp.cuda.runtime.setDevice(device)
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inplace_predt = booster.inplace_predict(x)
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d = xgb.DMatrix(x)
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copied_predt = cp.array(booster.predict(d))
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return cp.all(copied_predt == inplace_predt)
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# Don't do this on Windows, see issue #5793
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if sys.platform.startswith("win"):
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pytest.skip(
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"Multi-threaded in-place prediction with cuPy is not working on Windows"
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)
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for i in range(10):
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run_threaded_predict(X, rows, predict_dense)
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base_margin = cp_rng.randn(rows)
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self.run_inplace_base_margin(device, booster, dtrain, X, base_margin)
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# Create a wide dataset
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X = cp_rng.randn(100, 10000)
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y = cp_rng.randn(100)
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missing_idx = [i for i in range(0, X.shape[1], 16)]
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X[:, missing_idx] = missing
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reg = xgb.XGBRegressor(
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tree_method="hist", n_estimators=8, missing=missing, device=f"cuda:{device}"
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)
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reg.fit(X, y)
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reg.set_params(device=f"cuda:{device}")
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gpu_predt = reg.predict(X)
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reg = reg.set_params(device="cpu")
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cpu_predt = reg.predict(cp.asnumpy(X))
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np.testing.assert_allclose(gpu_predt, cpu_predt, atol=1e-6)
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cp.cuda.runtime.setDevice(0)
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@pytest.mark.skipif(**tm.no_cupy())
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def test_inplace_predict_cupy(self):
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self.run_inplace_predict_cupy(0)
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.mgpu
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def test_inplace_predict_cupy_specified_device(self):
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import cupy as cp
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n_devices = cp.cuda.runtime.getDeviceCount()
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for d in range(n_devices):
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self.run_inplace_predict_cupy(d)
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_cudf())
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def test_inplace_predict_cudf(self):
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import cudf
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import cupy as cp
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import pandas as pd
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rows = 1000
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cols = 10
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rng = np.random.RandomState(1994)
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cp.cuda.runtime.setDevice(0)
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X = rng.randn(rows, cols)
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X = pd.DataFrame(X)
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y = rng.randn(rows)
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X = cudf.from_pandas(X)
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train(
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{"tree_method": "hist", "device": "cuda:0"}, dtrain, num_boost_round=10
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)
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test = xgb.DMatrix(X)
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predt_from_array = booster.inplace_predict(X)
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predt_from_dmatrix = booster.predict(test)
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cp.testing.assert_allclose(predt_from_array, predt_from_dmatrix)
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def predict_df(x):
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# column major array
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inplace_predt = booster.inplace_predict(x.values)
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d = xgb.DMatrix(x)
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copied_predt = cp.array(booster.predict(d))
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assert cp.all(copied_predt == inplace_predt)
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inplace_predt = booster.inplace_predict(x)
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return cp.all(copied_predt == inplace_predt)
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for i in range(10):
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run_threaded_predict(X, rows, predict_df)
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base_margin = cudf.Series(rng.randn(rows))
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self.run_inplace_base_margin(0, booster, dtrain, X, base_margin)
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@given(
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strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy
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)
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_shap(self, num_rounds, dataset, param):
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if dataset.name.endswith("-l1"): # not supported by the exact tree method
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return
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param.update({"tree_method": "hist", "device": "gpu:0"})
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param = dataset.set_params(param)
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dmat = dataset.get_dmat()
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bst = xgb.train(param, dmat, num_rounds)
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test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
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bst.set_param({"device": "gpu:0"})
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shap = bst.predict(test_dmat, pred_contribs=True)
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margin = bst.predict(test_dmat, output_margin=True)
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assume(len(dataset.y) > 0)
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assert np.allclose(np.sum(shap, axis=len(shap.shape) - 1), margin, 1e-3, 1e-3)
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@given(
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strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy
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)
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@settings(deadline=None, max_examples=10, print_blob=True)
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def test_shap_interactions(self, num_rounds, dataset, param):
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if dataset.name.endswith("-l1"): # not supported by the exact tree method
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return
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param.update({"tree_method": "hist", "device": "cuda:0"})
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param = dataset.set_params(param)
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dmat = dataset.get_dmat()
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bst = xgb.train(param, dmat, num_rounds)
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test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
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bst.set_param({"device": "cuda:0"})
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shap = bst.predict(test_dmat, pred_interactions=True)
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margin = bst.predict(test_dmat, output_margin=True)
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assume(len(dataset.y) > 0)
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assert np.allclose(
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np.sum(shap, axis=(len(shap.shape) - 1, len(shap.shape) - 2)),
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margin,
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1e-3,
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1e-3,
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)
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def test_shap_categorical(self):
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X, y = tm.make_categorical(100, 20, 7, False)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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booster = xgb.train(
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{"tree_method": "hist", "device": "gpu:0"}, Xy, num_boost_round=10
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)
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booster.set_param({"device": "cuda:0"})
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shap = booster.predict(Xy, pred_contribs=True)
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margin = booster.predict(Xy, output_margin=True)
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np.testing.assert_allclose(
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np.sum(shap, axis=len(shap.shape) - 1), margin, rtol=1e-3
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)
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booster.set_param({"device": "cpu"})
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shap = booster.predict(Xy, pred_contribs=True)
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margin = booster.predict(Xy, output_margin=True)
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np.testing.assert_allclose(
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np.sum(shap, axis=len(shap.shape) - 1), margin, rtol=1e-3
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)
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def test_predict_leaf_basic(self):
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gpu_leaf = run_predict_leaf("gpu:0")
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cpu_leaf = run_predict_leaf("cpu")
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np.testing.assert_equal(gpu_leaf, cpu_leaf)
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def run_predict_leaf_booster(self, param, num_rounds, dataset):
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param = dataset.set_params(param)
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m = dataset.get_dmat()
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booster = xgb.train(
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param, dtrain=dataset.get_dmat(), num_boost_round=num_rounds
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)
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booster.set_param({"device": "cpu"})
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cpu_leaf = booster.predict(m, pred_leaf=True)
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booster.set_param({"device": "cuda:0"})
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gpu_leaf = booster.predict(m, pred_leaf=True)
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np.testing.assert_equal(cpu_leaf, gpu_leaf)
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@given(predict_parameter_strategy, tm.make_dataset_strategy())
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_predict_leaf_gbtree(self, param: dict, dataset: tm.TestDataset) -> None:
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# Unsupported for random forest
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if param.get("num_parallel_tree", 1) > 1 and dataset.name.endswith("-l1"):
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return
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param.update({"booster": "gbtree", "tree_method": "hist", "device": "cuda:0"})
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self.run_predict_leaf_booster(param, 10, dataset)
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@given(predict_parameter_strategy, tm.make_dataset_strategy())
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_predict_leaf_dart(self, param: dict, dataset: tm.TestDataset) -> None:
|
|
# Unsupported for random forest
|
|
if param.get("num_parallel_tree", 1) > 1 and dataset.name.endswith("-l1"):
|
|
return
|
|
|
|
param.update({"booster": "dart", "tree_method": "hist", "device": "cuda:0"})
|
|
self.run_predict_leaf_booster(param, 10, dataset)
|
|
|
|
@pytest.mark.skipif(**tm.no_sklearn())
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|
@pytest.mark.skipif(**tm.no_pandas())
|
|
@given(
|
|
df=data_frames(
|
|
[
|
|
column("x0", elements=strategies.integers(min_value=0, max_value=3)),
|
|
column("x1", elements=strategies.integers(min_value=0, max_value=5)),
|
|
],
|
|
index=range_indexes(min_size=20, max_size=50),
|
|
)
|
|
)
|
|
@settings(deadline=None, max_examples=20, print_blob=True)
|
|
def test_predict_categorical_split(self, df):
|
|
from sklearn.metrics import mean_squared_error
|
|
|
|
df = df.astype("category")
|
|
x0, x1 = df["x0"].to_numpy(), df["x1"].to_numpy()
|
|
y = (x0 * 10 - 20) + (x1 - 2)
|
|
dtrain = xgb.DMatrix(df, label=y, enable_categorical=True)
|
|
|
|
params = {
|
|
"tree_method": "hist",
|
|
"max_depth": 3,
|
|
"learning_rate": 1.0,
|
|
"base_score": 0.0,
|
|
"eval_metric": "rmse",
|
|
"device": "cuda:0",
|
|
}
|
|
|
|
eval_history = {}
|
|
bst = xgb.train(
|
|
params,
|
|
dtrain,
|
|
num_boost_round=5,
|
|
evals=[(dtrain, "train")],
|
|
verbose_eval=False,
|
|
evals_result=eval_history,
|
|
)
|
|
bst.set_param({"device": "cuda:0"})
|
|
pred = bst.predict(dtrain)
|
|
rmse = mean_squared_error(y_true=y, y_pred=pred, squared=False)
|
|
np.testing.assert_almost_equal(
|
|
rmse, eval_history["train"]["rmse"][-1], decimal=5
|
|
)
|
|
|
|
@pytest.mark.skipif(**tm.no_cupy())
|
|
@pytest.mark.parametrize("n_classes", [2, 3])
|
|
def test_predict_dart(self, n_classes):
|
|
import cupy as cp
|
|
from sklearn.datasets import make_classification
|
|
|
|
n_samples = 1000
|
|
X_, y_ = make_classification(
|
|
n_samples=n_samples, n_informative=5, n_classes=n_classes
|
|
)
|
|
X, y = cp.array(X_), cp.array(y_)
|
|
|
|
Xy = xgb.DMatrix(X, y)
|
|
if n_classes == 2:
|
|
params = {
|
|
"tree_method": "hist",
|
|
"device": "cuda:0",
|
|
"booster": "dart",
|
|
"rate_drop": 0.5,
|
|
"objective": "binary:logistic",
|
|
}
|
|
else:
|
|
params = {
|
|
"tree_method": "hist",
|
|
"device": "cuda:0",
|
|
"booster": "dart",
|
|
"rate_drop": 0.5,
|
|
"objective": "multi:softprob",
|
|
"num_class": n_classes,
|
|
}
|
|
|
|
booster = xgb.train(params, Xy, num_boost_round=32)
|
|
|
|
# auto (GPU)
|
|
inplace = booster.inplace_predict(X)
|
|
copied = booster.predict(Xy)
|
|
|
|
# CPU
|
|
booster.set_param({"device": "cpu"})
|
|
cpu_inplace = booster.inplace_predict(X_)
|
|
cpu_copied = booster.predict(Xy)
|
|
|
|
copied = cp.array(copied)
|
|
cp.testing.assert_allclose(cpu_inplace, copied, atol=1e-6)
|
|
cp.testing.assert_allclose(cpu_copied, copied, atol=1e-6)
|
|
cp.testing.assert_allclose(inplace, copied, atol=1e-6)
|
|
|
|
# GPU
|
|
booster.set_param({"device": "cuda:0"})
|
|
inplace = booster.inplace_predict(X)
|
|
copied = booster.predict(Xy)
|
|
|
|
copied = cp.array(copied)
|
|
cp.testing.assert_allclose(inplace, copied, atol=1e-6)
|
|
|
|
@pytest.mark.skipif(**tm.no_cupy())
|
|
def test_dtypes(self):
|
|
import cupy as cp
|
|
|
|
rows = 1000
|
|
cols = 10
|
|
rng = cp.random.RandomState(np.uint64(1994))
|
|
orig = rng.randint(low=0, high=127, size=rows * cols).reshape(rows, cols)
|
|
y = rng.randint(low=0, high=127, size=rows)
|
|
dtrain = xgb.DMatrix(orig, label=y)
|
|
booster = xgb.train({"tree_method": "hist", "device": "cuda:0"}, dtrain)
|
|
|
|
predt_orig = booster.inplace_predict(orig)
|
|
# all primitive types in numpy
|
|
for dtype in [
|
|
cp.byte,
|
|
cp.short,
|
|
cp.intc,
|
|
cp.int_,
|
|
cp.longlong,
|
|
cp.ubyte,
|
|
cp.ushort,
|
|
cp.uintc,
|
|
cp.uint,
|
|
cp.ulonglong,
|
|
cp.half,
|
|
cp.single,
|
|
cp.double,
|
|
]:
|
|
X = cp.array(orig, dtype=dtype)
|
|
predt = booster.inplace_predict(X)
|
|
cp.testing.assert_allclose(predt, predt_orig)
|
|
|
|
# boolean
|
|
orig = cp.random.binomial(1, 0.5, size=rows * cols).reshape(rows, cols)
|
|
predt_orig = booster.inplace_predict(orig)
|
|
|
|
X = cp.array(orig, dtype=cp.bool_)
|
|
predt = booster.inplace_predict(X)
|
|
cp.testing.assert_allclose(predt, predt_orig)
|
|
|
|
# unsupported types
|
|
for dtype in [
|
|
cp.complex64,
|
|
cp.complex128,
|
|
]:
|
|
X = cp.array(orig, dtype=dtype)
|
|
with pytest.raises(ValueError):
|
|
booster.inplace_predict(X)
|