381 lines
15 KiB
Python
381 lines
15 KiB
Python
import sys
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import pytest
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import numpy as np
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import xgboost as xgb
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from xgboost.compat import PANDAS_INSTALLED
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from hypothesis import given, strategies, assume, settings, note
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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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import testing as tm
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from test_predict import run_threaded_predict # noqa
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from test_predict import run_predict_leaf # noqa
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rng = np.random.RandomState(1994)
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shap_parameter_strategy = strategies.fixed_dictionaries({
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'max_depth': strategies.integers(0, 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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}).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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'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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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(np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2))
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dval = xgb.DMatrix(np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2))
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dtest = xgb.DMatrix(np.random.randn(num_rows, num_cols),
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label=[0, 1] * int(num_rows / 2))
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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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"predictor": "gpu_predictor",
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'eval_metric': 'logloss',
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'tree_method': 'gpu_hist',
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'max_depth': 1
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}
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bst = xgb.train(param, dtrain, iterations, evals=watchlist,
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evals_result=res)
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assert self.non_increasing(res["train"]["logloss"])
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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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param["predictor"] = "cpu_predictor"
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bst_cpu = xgb.train(param, dtrain, iterations, evals=watchlist)
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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,
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rtol=1e-6)
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np.testing.assert_allclose(cpu_pred_val, gpu_pred_val,
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rtol=1e-6)
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np.testing.assert_allclose(cpu_pred_test, gpu_pred_test,
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rtol=1e-6)
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def non_increasing(self, L):
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return all((y - x) < 0.001 for x, y in zip(L, L[1:]))
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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,
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random_state=123)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test)
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params = {}
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params["tree_method"] = "gpu_hist"
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params['predictor'] = "gpu_predictor"
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bst_gpu_predict = xgb.train(params, dtrain)
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params['predictor'] = "cpu_predictor"
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bst_cpu_predict = xgb.train(params, dtrain)
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predict0 = bst_gpu_predict.predict(dtest)
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predict1 = bst_gpu_predict.predict(dtest)
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cpu_predict = bst_cpu_predict.predict(dtest)
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assert np.allclose(predict0, predict1)
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assert np.allclose(predict0, cpu_predict)
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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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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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# First with cpu_predictor
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params = {'tree_method': 'gpu_hist',
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'predictor': 'cpu_predictor',
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'n_jobs': -1,
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'seed': 123}
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m = xgb.XGBRegressor(**params).fit(X_train, y_train)
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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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# Now with gpu_predictor
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params['predictor'] = 'gpu_predictor'
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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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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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def run_inplace_base_margin(self, booster, dtrain, X, base_margin):
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import cupy as cp
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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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@pytest.mark.skipif(**tm.no_cupy())
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def test_inplace_predict_cupy(self):
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import cupy as cp
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cp.cuda.runtime.setDevice(0)
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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(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({'tree_method': 'gpu_hist'}, dtrain, num_boost_round=10)
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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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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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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(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(tree_method="gpu_hist", n_estimators=8, missing=missing)
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reg.fit(X, y)
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gpu_predt = reg.predict(X)
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reg.set_params(predictor="cpu_predictor")
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cpu_predt = reg.predict(X)
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np.testing.assert_allclose(gpu_predt, cpu_predt, atol=1e-6)
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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 cupy as cp
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import cudf
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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({'tree_method': 'gpu_hist'},
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dtrain, num_boost_round=10)
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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(booster, dtrain, X, base_margin)
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@given(strategies.integers(1, 10),
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tm.dataset_strategy, shap_parameter_strategy)
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@settings(deadline=None)
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def test_shap(self, num_rounds, dataset, param):
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param.update({"predictor": "gpu_predictor", "gpu_id": 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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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(strategies.integers(1, 10),
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tm.dataset_strategy, shap_parameter_strategy)
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@settings(deadline=None, max_examples=20)
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def test_shap_interactions(self, num_rounds, dataset, param):
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param.update({"predictor": "gpu_predictor", "gpu_id": 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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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(np.sum(shap, axis=(len(shap.shape) - 1, len(shap.shape) - 2)),
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margin,
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1e-3, 1e-3)
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def test_predict_leaf_basic(self):
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gpu_leaf = run_predict_leaf('gpu_predictor')
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cpu_leaf = run_predict_leaf('cpu_predictor')
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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(param, dtrain=dataset.get_dmat(), num_boost_round=num_rounds)
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booster.set_param({'predictor': 'cpu_predictor'})
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cpu_leaf = booster.predict(m, pred_leaf=True)
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booster.set_param({'predictor': 'gpu_predictor'})
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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.dataset_strategy)
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@settings(deadline=None)
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def test_predict_leaf_gbtree(self, param, dataset):
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param['booster'] = 'gbtree'
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param['tree_method'] = 'gpu_hist'
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self.run_predict_leaf_booster(param, 10, dataset)
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@given(predict_parameter_strategy, tm.dataset_strategy)
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@settings(deadline=None)
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def test_predict_leaf_dart(self, param, dataset):
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param['booster'] = 'dart'
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param['tree_method'] = 'gpu_hist'
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self.run_predict_leaf_booster(param, 10, dataset)
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@pytest.mark.skipif(**tm.no_sklearn())
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@pytest.mark.skipif(**tm.no_pandas())
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@given(df=data_frames([column('x0', elements=strategies.integers(min_value=0, max_value=3)),
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column('x1', elements=strategies.integers(min_value=0, max_value=5))],
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index=range_indexes(min_size=20, max_size=50)))
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@settings(deadline=None)
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def test_predict_categorical_split(self, df):
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from sklearn.metrics import mean_squared_error
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df = df.astype('category')
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x0, x1 = df['x0'].to_numpy(), df['x1'].to_numpy()
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y = (x0 * 10 - 20) + (x1 - 2)
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dtrain = xgb.DMatrix(df, label=y, enable_categorical=True)
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params = {
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'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor',
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'max_depth': 3, 'learning_rate': 1.0, 'base_score': 0.0, 'eval_metric': 'rmse'
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}
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eval_history = {}
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bst = xgb.train(params, dtrain, num_boost_round=5, evals=[(dtrain, 'train')],
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verbose_eval=False, evals_result=eval_history)
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pred = bst.predict(dtrain)
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rmse = mean_squared_error(y_true=y, y_pred=pred, squared=False)
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np.testing.assert_almost_equal(rmse, eval_history['train']['rmse'][-1], decimal=5)
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@pytest.mark.parametrize("n_classes", [2, 3])
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def test_predict_dart(self, n_classes):
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from sklearn.datasets import make_classification
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import cupy as cp
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n_samples = 1000
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X_, y_ = make_classification(
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n_samples=n_samples, n_informative=5, n_classes=n_classes
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)
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X, y = cp.array(X_), cp.array(y_)
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Xy = xgb.DMatrix(X, y)
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if n_classes == 2:
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params = {
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"tree_method": "gpu_hist",
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"booster": "dart",
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"rate_drop": 0.5,
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"objective": "binary:logistic"
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}
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else:
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params = {
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"tree_method": "gpu_hist",
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"booster": "dart",
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"rate_drop": 0.5,
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"objective": "multi:softprob",
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"num_class": n_classes
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}
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booster = xgb.train(params, Xy, num_boost_round=32)
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# predictor=auto
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inplace = booster.inplace_predict(X)
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copied = booster.predict(Xy)
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cpu_inplace = booster.inplace_predict(X_)
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booster.set_param({"predictor": "cpu_predictor"})
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cpu_copied = booster.predict(Xy)
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copied = cp.array(copied)
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cp.testing.assert_allclose(cpu_inplace, copied, atol=1e-6)
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cp.testing.assert_allclose(cpu_copied, copied, atol=1e-6)
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cp.testing.assert_allclose(inplace, copied, atol=1e-6)
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booster.set_param({"predictor": "gpu_predictor"})
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inplace = booster.inplace_predict(X)
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copied = booster.predict(Xy)
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copied = cp.array(copied)
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cp.testing.assert_allclose(inplace, copied, atol=1e-6)
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