cf4f019ed6
* Change DefaultEvalMetric of classification from error to logloss * Change default binary metric in plugin/example/custom_obj.cc * Set old error metric in python tests * Set old error metric in R tests * Fix missed eval metrics and typos in R tests * Fix setting eval_metric twice in R tests * Add warning for empty eval_metric for classification * Fix Dask tests Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
708 lines
26 KiB
Python
708 lines
26 KiB
Python
import testing as tm
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import pytest
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import xgboost as xgb
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import sys
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import numpy as np
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import json
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import asyncio
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from sklearn.datasets import make_classification
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import os
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import subprocess
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from hypothesis import given, strategies, settings, note
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from test_updaters import hist_parameter_strategy, exact_parameter_strategy
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if sys.platform.startswith("win"):
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pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
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pytestmark = pytest.mark.skipif(**tm.no_dask())
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try:
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from distributed import LocalCluster, Client
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from distributed.utils_test import client, loop, cluster_fixture
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import dask.dataframe as dd
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import dask.array as da
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from xgboost.dask import DaskDMatrix
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except ImportError:
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LocalCluster = None
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Client = None
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client = None
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loop = None
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cluster_fixture = None
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dd = None
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da = None
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DaskDMatrix = None
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kRows = 1000
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kCols = 10
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kWorkers = 5
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def generate_array():
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partition_size = 20
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X = da.random.random((kRows, kCols), partition_size)
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y = da.random.random(kRows, partition_size)
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return X, y
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def test_from_dask_dataframe():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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X = dd.from_dask_array(X)
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y = dd.from_dask_array(y)
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dtrain = DaskDMatrix(client, X, y)
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booster = xgb.dask.train(
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client, {}, dtrain, num_boost_round=2)['booster']
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prediction = xgb.dask.predict(client, model=booster, data=dtrain)
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assert prediction.ndim == 1
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assert isinstance(prediction, da.Array)
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assert prediction.shape[0] == kRows
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with pytest.raises(ValueError):
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# evals_result is not supported in dask interface.
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xgb.dask.train(
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client, {}, dtrain, num_boost_round=2, evals_result={})
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# force prediction to be computed
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from_dmatrix = prediction.compute()
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prediction = xgb.dask.predict(client, model=booster, data=X)
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from_df = prediction.compute()
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assert isinstance(prediction, dd.Series)
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assert np.all(prediction.compute().values == from_dmatrix)
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assert np.all(from_dmatrix == from_df.to_numpy())
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series_predictions = xgb.dask.inplace_predict(client, booster, X)
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assert isinstance(series_predictions, dd.Series)
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np.testing.assert_allclose(series_predictions.compute().values,
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from_dmatrix)
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def test_from_dask_array():
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with LocalCluster(n_workers=kWorkers, threads_per_worker=5) as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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dtrain = DaskDMatrix(client, X, y)
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# results is {'booster': Booster, 'history': {...}}
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result = xgb.dask.train(client, {}, dtrain)
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prediction = xgb.dask.predict(client, result, dtrain)
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assert prediction.shape[0] == kRows
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assert isinstance(prediction, da.Array)
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# force prediction to be computed
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prediction = prediction.compute()
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booster = result['booster']
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single_node_predt = booster.predict(
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xgb.DMatrix(X.compute())
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)
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np.testing.assert_allclose(prediction, single_node_predt)
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config = json.loads(booster.save_config())
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assert int(config['learner']['generic_param']['nthread']) == 5
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from_arr = xgb.dask.predict(
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client, model=booster, data=X)
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assert isinstance(from_arr, da.Array)
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assert np.all(single_node_predt == from_arr.compute())
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def test_dask_predict_shape_infer():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X, y = make_classification(n_samples=1000, n_informative=5,
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n_classes=3)
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X_ = dd.from_array(X, chunksize=100)
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y_ = dd.from_array(y, chunksize=100)
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dtrain = xgb.dask.DaskDMatrix(client, data=X_, label=y_)
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model = xgb.dask.train(
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client,
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{"objective": "multi:softprob", "num_class": 3},
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dtrain=dtrain
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)
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preds = xgb.dask.predict(client, model, dtrain)
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assert preds.shape[0] == preds.compute().shape[0]
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assert preds.shape[1] == preds.compute().shape[1]
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@pytest.mark.parametrize("tree_method", ["hist", "approx"])
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def test_boost_from_prediction(tree_method):
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from sklearn.datasets import load_breast_cancer
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X, y = load_breast_cancer(return_X_y=True)
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X_ = dd.from_array(X, chunksize=100)
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y_ = dd.from_array(y, chunksize=100)
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with LocalCluster(n_workers=4) as cluster:
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with Client(cluster) as client:
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model_0 = xgb.dask.DaskXGBClassifier(
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learning_rate=0.3,
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random_state=123,
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n_estimators=4,
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tree_method=tree_method,
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)
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model_0.fit(X=X_, y=y_)
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margin = model_0.predict_proba(X_, output_margin=True)
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model_1 = xgb.dask.DaskXGBClassifier(
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learning_rate=0.3,
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random_state=123,
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n_estimators=4,
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tree_method=tree_method,
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)
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model_1.fit(X=X_, y=y_, base_margin=margin)
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predictions_1 = model_1.predict(X_, base_margin=margin)
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proba_1 = model_1.predict_proba(X_, base_margin=margin)
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cls_2 = xgb.dask.DaskXGBClassifier(
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learning_rate=0.3,
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random_state=123,
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n_estimators=8,
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tree_method=tree_method,
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)
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cls_2.fit(X=X_, y=y_)
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predictions_2 = cls_2.predict(X_)
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proba_2 = cls_2.predict_proba(X_)
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cls_3 = xgb.dask.DaskXGBClassifier(
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learning_rate=0.3,
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random_state=123,
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n_estimators=8,
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tree_method=tree_method,
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)
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cls_3.fit(X=X_, y=y_)
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proba_3 = cls_3.predict_proba(X_)
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# compute variance of probability percentages between two of the
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# same model, use this to check to make sure approx is functioning
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# within normal parameters
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expected_variance = np.max(np.abs(proba_3 - proba_2)).compute()
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if expected_variance > 0:
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margin_variance = np.max(np.abs(proba_1 - proba_2)).compute()
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# Ensure the margin variance is less than the expected variance + 10%
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assert np.all(margin_variance <= expected_variance + .1)
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else:
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np.testing.assert_equal(predictions_1.compute(), predictions_2.compute())
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np.testing.assert_almost_equal(proba_1.compute(), proba_2.compute())
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def test_dask_missing_value_reg():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X_0 = np.ones((20 // 2, kCols))
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X_1 = np.zeros((20 // 2, kCols))
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X = np.concatenate([X_0, X_1], axis=0)
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np.random.shuffle(X)
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X = da.from_array(X)
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X = X.rechunk(20, 1)
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y = da.random.randint(0, 3, size=20)
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y.rechunk(20)
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regressor = xgb.dask.DaskXGBRegressor(verbosity=1, n_estimators=2,
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missing=0.0)
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regressor.client = client
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regressor.set_params(tree_method='hist')
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regressor.fit(X, y, eval_set=[(X, y)])
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dd_predt = regressor.predict(X).compute()
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np_X = X.compute()
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np_predt = regressor.get_booster().predict(
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xgb.DMatrix(np_X, missing=0.0))
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np.testing.assert_allclose(np_predt, dd_predt)
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def test_dask_missing_value_cls():
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with LocalCluster() as cluster:
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with Client(cluster) as client:
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X_0 = np.ones((kRows // 2, kCols))
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X_1 = np.zeros((kRows // 2, kCols))
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X = np.concatenate([X_0, X_1], axis=0)
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np.random.shuffle(X)
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X = da.from_array(X)
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X = X.rechunk(20, None)
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y = da.random.randint(0, 3, size=kRows)
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y = y.rechunk(20, 1)
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cls = xgb.dask.DaskXGBClassifier(verbosity=1, n_estimators=2,
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tree_method='hist',
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missing=0.0)
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cls.client = client
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cls.fit(X, y, eval_set=[(X, y)])
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dd_pred_proba = cls.predict_proba(X).compute()
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np_X = X.compute()
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np_pred_proba = cls.get_booster().predict(
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xgb.DMatrix(np_X, missing=0.0))
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np.testing.assert_allclose(np_pred_proba, dd_pred_proba)
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cls = xgb.dask.DaskXGBClassifier()
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assert hasattr(cls, 'missing')
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def test_dask_regressor():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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regressor = xgb.dask.DaskXGBRegressor(verbosity=1, n_estimators=2)
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regressor.set_params(tree_method='hist')
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regressor.client = client
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regressor.fit(X, y, eval_set=[(X, y)])
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prediction = regressor.predict(X)
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assert prediction.ndim == 1
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assert prediction.shape[0] == kRows
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history = regressor.evals_result()
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assert isinstance(prediction, da.Array)
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assert isinstance(history, dict)
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assert list(history['validation_0'].keys())[0] == 'rmse'
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assert len(history['validation_0']['rmse']) == 2
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def test_dask_classifier():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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y = (y * 10).astype(np.int32)
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classifier = xgb.dask.DaskXGBClassifier(
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verbosity=1, n_estimators=2, eval_metric='merror')
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classifier.client = client
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classifier.fit(X, y, eval_set=[(X, y)])
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prediction = classifier.predict(X)
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assert prediction.ndim == 1
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assert prediction.shape[0] == kRows
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history = classifier.evals_result()
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assert isinstance(prediction, da.Array)
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assert isinstance(history, dict)
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assert list(history.keys())[0] == 'validation_0'
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assert list(history['validation_0'].keys())[0] == 'merror'
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assert len(list(history['validation_0'])) == 1
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assert len(history['validation_0']['merror']) == 2
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# Test .predict_proba()
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probas = classifier.predict_proba(X)
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assert classifier.n_classes_ == 10
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assert probas.ndim == 2
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assert probas.shape[0] == kRows
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assert probas.shape[1] == 10
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cls_booster = classifier.get_booster()
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single_node_proba = cls_booster.inplace_predict(X.compute())
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np.testing.assert_allclose(single_node_proba,
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probas.compute())
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# Test with dataframe.
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X_d = dd.from_dask_array(X)
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y_d = dd.from_dask_array(y)
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classifier.fit(X_d, y_d)
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assert classifier.n_classes_ == 10
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prediction = classifier.predict(X_d)
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assert prediction.ndim == 1
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assert prediction.shape[0] == kRows
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_sklearn_grid_search():
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from sklearn.model_selection import GridSearchCV
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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reg = xgb.dask.DaskXGBRegressor(learning_rate=0.1,
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tree_method='hist')
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reg.client = client
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model = GridSearchCV(reg, {'max_depth': [2, 4],
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'n_estimators': [5, 10]},
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cv=2, verbose=1, iid=True)
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model.fit(X, y)
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# Expect unique results for each parameter value This confirms
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# sklearn is able to successfully update the parameter
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means = model.cv_results_['mean_test_score']
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assert len(means) == len(set(means))
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def run_empty_dmatrix_reg(client, parameters):
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def _check_outputs(out, predictions):
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assert isinstance(out['booster'], xgb.dask.Booster)
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assert len(out['history']['validation']['rmse']) == 2
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assert isinstance(predictions, np.ndarray)
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assert predictions.shape[0] == 1
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kRows, kCols = 1, 97
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.rand(kRows))
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dtrain = xgb.dask.DaskDMatrix(client, X, y)
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out = xgb.dask.train(client, parameters,
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dtrain=dtrain,
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evals=[(dtrain, 'validation')],
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num_boost_round=2)
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predictions = xgb.dask.predict(client=client, model=out,
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data=dtrain).compute()
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_check_outputs(out, predictions)
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# train has more rows than evals
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valid = dtrain
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kRows += 1
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.rand(kRows))
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dtrain = xgb.dask.DaskDMatrix(client, X, y)
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out = xgb.dask.train(client, parameters,
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dtrain=dtrain,
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evals=[(valid, 'validation')],
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num_boost_round=2)
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predictions = xgb.dask.predict(client=client, model=out,
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data=valid).compute()
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_check_outputs(out, predictions)
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def run_empty_dmatrix_cls(client, parameters):
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n_classes = 4
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def _check_outputs(out, predictions):
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assert isinstance(out['booster'], xgb.dask.Booster)
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assert len(out['history']['validation']['merror']) == 2
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assert isinstance(predictions, np.ndarray)
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assert predictions.shape[1] == n_classes, predictions.shape
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kRows, kCols = 1, 97
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.randint(low=0, high=n_classes, size=kRows))
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dtrain = xgb.dask.DaskDMatrix(client, X, y)
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parameters['objective'] = 'multi:softprob'
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parameters['eval_metric'] = 'merror'
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parameters['num_class'] = n_classes
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out = xgb.dask.train(client, parameters,
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dtrain=dtrain,
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evals=[(dtrain, 'validation')],
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num_boost_round=2)
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predictions = xgb.dask.predict(client=client, model=out,
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data=dtrain)
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assert predictions.shape[1] == n_classes
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predictions = predictions.compute()
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_check_outputs(out, predictions)
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# train has more rows than evals
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valid = dtrain
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kRows += 1
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.randint(low=0, high=n_classes, size=kRows))
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dtrain = xgb.dask.DaskDMatrix(client, X, y)
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out = xgb.dask.train(client, parameters,
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dtrain=dtrain,
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evals=[(valid, 'validation')],
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num_boost_round=2)
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predictions = xgb.dask.predict(client=client, model=out,
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data=valid).compute()
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_check_outputs(out, predictions)
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# No test for Exact, as empty DMatrix handling are mostly for distributed
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# environment and Exact doesn't support it.
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def test_empty_dmatrix_hist():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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parameters = {'tree_method': 'hist'}
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run_empty_dmatrix_reg(client, parameters)
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run_empty_dmatrix_cls(client, parameters)
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def test_empty_dmatrix_approx():
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with LocalCluster(n_workers=kWorkers) as cluster:
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with Client(cluster) as client:
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parameters = {'tree_method': 'approx'}
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run_empty_dmatrix_reg(client, parameters)
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run_empty_dmatrix_cls(client, parameters)
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async def run_from_dask_array_asyncio(scheduler_address):
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async with Client(scheduler_address, asynchronous=True) as client:
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X, y = generate_array()
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m = await DaskDMatrix(client, X, y)
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output = await xgb.dask.train(client, {}, dtrain=m)
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with_m = await xgb.dask.predict(client, output, m)
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with_X = await xgb.dask.predict(client, output, X)
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inplace = await xgb.dask.inplace_predict(client, output, X)
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assert isinstance(with_m, da.Array)
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assert isinstance(with_X, da.Array)
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assert isinstance(inplace, da.Array)
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np.testing.assert_allclose(await client.compute(with_m),
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await client.compute(with_X))
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np.testing.assert_allclose(await client.compute(with_m),
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await client.compute(inplace))
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client.shutdown()
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return output
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async def run_dask_regressor_asyncio(scheduler_address):
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async with Client(scheduler_address, asynchronous=True) as client:
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X, y = generate_array()
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regressor = await xgb.dask.DaskXGBRegressor(verbosity=1,
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n_estimators=2)
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regressor.set_params(tree_method='hist')
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regressor.client = client
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await regressor.fit(X, y, eval_set=[(X, y)])
|
|
prediction = await regressor.predict(X)
|
|
|
|
assert prediction.ndim == 1
|
|
assert prediction.shape[0] == kRows
|
|
|
|
history = regressor.evals_result()
|
|
|
|
assert isinstance(prediction, da.Array)
|
|
assert isinstance(history, dict)
|
|
|
|
assert list(history['validation_0'].keys())[0] == 'rmse'
|
|
assert len(history['validation_0']['rmse']) == 2
|
|
|
|
|
|
async def run_dask_classifier_asyncio(scheduler_address):
|
|
async with Client(scheduler_address, asynchronous=True) as client:
|
|
X, y = generate_array()
|
|
y = (y * 10).astype(np.int32)
|
|
classifier = await xgb.dask.DaskXGBClassifier(
|
|
verbosity=1, n_estimators=2, eval_metric='merror')
|
|
classifier.client = client
|
|
await classifier.fit(X, y, eval_set=[(X, y)])
|
|
prediction = await classifier.predict(X)
|
|
|
|
assert prediction.ndim == 1
|
|
assert prediction.shape[0] == kRows
|
|
|
|
history = classifier.evals_result()
|
|
|
|
assert isinstance(prediction, da.Array)
|
|
assert isinstance(history, dict)
|
|
|
|
assert list(history.keys())[0] == 'validation_0'
|
|
assert list(history['validation_0'].keys())[0] == 'merror'
|
|
assert len(list(history['validation_0'])) == 1
|
|
assert len(history['validation_0']['merror']) == 2
|
|
|
|
# Test .predict_proba()
|
|
probas = await classifier.predict_proba(X)
|
|
assert classifier.n_classes_ == 10
|
|
assert probas.ndim == 2
|
|
assert probas.shape[0] == kRows
|
|
assert probas.shape[1] == 10
|
|
|
|
# Test with dataframe.
|
|
X_d = dd.from_dask_array(X)
|
|
y_d = dd.from_dask_array(y)
|
|
await classifier.fit(X_d, y_d)
|
|
|
|
assert classifier.n_classes_ == 10
|
|
prediction = await classifier.predict(X_d)
|
|
|
|
assert prediction.ndim == 1
|
|
assert prediction.shape[0] == kRows
|
|
|
|
|
|
def test_with_asyncio():
|
|
with LocalCluster() as cluster:
|
|
with Client(cluster) as client:
|
|
address = client.scheduler.address
|
|
output = asyncio.run(run_from_dask_array_asyncio(address))
|
|
assert isinstance(output['booster'], xgb.Booster)
|
|
assert isinstance(output['history'], dict)
|
|
|
|
asyncio.run(run_dask_regressor_asyncio(address))
|
|
asyncio.run(run_dask_classifier_asyncio(address))
|
|
|
|
|
|
def test_predict():
|
|
with LocalCluster(n_workers=kWorkers) as cluster:
|
|
with Client(cluster) as client:
|
|
X, y = generate_array()
|
|
dtrain = DaskDMatrix(client, X, y)
|
|
booster = xgb.dask.train(
|
|
client, {}, dtrain, num_boost_round=2)['booster']
|
|
|
|
pred = xgb.dask.predict(client, model=booster, data=dtrain)
|
|
assert pred.ndim == 1
|
|
assert pred.shape[0] == kRows
|
|
|
|
margin = xgb.dask.predict(client, model=booster, data=dtrain,
|
|
output_margin=True)
|
|
assert margin.ndim == 1
|
|
assert margin.shape[0] == kRows
|
|
|
|
shap = xgb.dask.predict(client, model=booster, data=dtrain,
|
|
pred_contribs=True)
|
|
assert shap.ndim == 2
|
|
assert shap.shape[0] == kRows
|
|
assert shap.shape[1] == kCols + 1
|
|
|
|
|
|
def run_aft_survival(client, dmatrix_t):
|
|
# survival doesn't handle empty dataset well.
|
|
df = dd.read_csv(os.path.join(tm.PROJECT_ROOT, 'demo', 'data',
|
|
'veterans_lung_cancer.csv'))
|
|
y_lower_bound = df['Survival_label_lower_bound']
|
|
y_upper_bound = df['Survival_label_upper_bound']
|
|
X = df.drop(['Survival_label_lower_bound',
|
|
'Survival_label_upper_bound'], axis=1)
|
|
m = dmatrix_t(client, X, label_lower_bound=y_lower_bound,
|
|
label_upper_bound=y_upper_bound)
|
|
base_params = {'verbosity': 0,
|
|
'objective': 'survival:aft',
|
|
'eval_metric': 'aft-nloglik',
|
|
'learning_rate': 0.05,
|
|
'aft_loss_distribution_scale': 1.20,
|
|
'max_depth': 6,
|
|
'lambda': 0.01,
|
|
'alpha': 0.02}
|
|
|
|
nloglik_rec = {}
|
|
dists = ['normal', 'logistic', 'extreme']
|
|
for dist in dists:
|
|
params = base_params
|
|
params.update({'aft_loss_distribution': dist})
|
|
evals_result = {}
|
|
out = xgb.dask.train(client, params, m, num_boost_round=100,
|
|
evals=[(m, 'train')])
|
|
evals_result = out['history']
|
|
nloglik_rec[dist] = evals_result['train']['aft-nloglik']
|
|
# AFT metric (negative log likelihood) improve monotonically
|
|
assert all(p >= q for p, q in zip(nloglik_rec[dist],
|
|
nloglik_rec[dist][:1]))
|
|
# For this data, normal distribution works the best
|
|
assert nloglik_rec['normal'][-1] < 4.9
|
|
assert nloglik_rec['logistic'][-1] > 4.9
|
|
assert nloglik_rec['extreme'][-1] > 4.9
|
|
|
|
|
|
def test_aft_survival():
|
|
with LocalCluster(n_workers=1) as cluster:
|
|
with Client(cluster) as client:
|
|
run_aft_survival(client, DaskDMatrix)
|
|
|
|
|
|
class TestWithDask:
|
|
def run_updater_test(self, client, params, num_rounds, dataset,
|
|
tree_method):
|
|
params['tree_method'] = tree_method
|
|
params = dataset.set_params(params)
|
|
# multi class doesn't handle empty dataset well (empty
|
|
# means at least 1 worker has data).
|
|
if params['objective'] == "multi:softmax":
|
|
return
|
|
# It doesn't make sense to distribute a completely
|
|
# empty dataset.
|
|
if dataset.X.shape[0] == 0:
|
|
return
|
|
|
|
chunk = 128
|
|
X = da.from_array(dataset.X,
|
|
chunks=(chunk, dataset.X.shape[1]))
|
|
y = da.from_array(dataset.y, chunks=(chunk,))
|
|
if dataset.w is not None:
|
|
w = da.from_array(dataset.w, chunks=(chunk,))
|
|
else:
|
|
w = None
|
|
|
|
m = xgb.dask.DaskDMatrix(
|
|
client, data=X, label=y, weight=w)
|
|
history = xgb.dask.train(client, params=params, dtrain=m,
|
|
num_boost_round=num_rounds,
|
|
evals=[(m, 'train')])['history']
|
|
note(history)
|
|
history = history['train'][dataset.metric]
|
|
assert tm.non_increasing(history)
|
|
# Make sure that it's decreasing
|
|
assert history[-1] < history[0]
|
|
|
|
@given(params=hist_parameter_strategy,
|
|
num_rounds=strategies.integers(20, 30),
|
|
dataset=tm.dataset_strategy)
|
|
@settings(deadline=None)
|
|
def test_hist(self, params, num_rounds, dataset, client):
|
|
self.run_updater_test(client, params, num_rounds, dataset, 'hist')
|
|
|
|
@given(params=exact_parameter_strategy,
|
|
num_rounds=strategies.integers(20, 30),
|
|
dataset=tm.dataset_strategy)
|
|
@settings(deadline=None)
|
|
def test_approx(self, client, params, num_rounds, dataset):
|
|
self.run_updater_test(client, params, num_rounds, dataset, 'approx')
|
|
|
|
def run_quantile(self, name):
|
|
if sys.platform.startswith("win"):
|
|
pytest.skip("Skipping dask tests on Windows")
|
|
|
|
exe = None
|
|
for possible_path in {'./testxgboost', './build/testxgboost',
|
|
'../build/testxgboost',
|
|
'../cpu-build/testxgboost'}:
|
|
if os.path.exists(possible_path):
|
|
exe = possible_path
|
|
if exe is None:
|
|
return
|
|
|
|
test = "--gtest_filter=Quantile." + name
|
|
|
|
def runit(worker_addr, rabit_args):
|
|
port = None
|
|
# setup environment for running the c++ part.
|
|
for arg in rabit_args:
|
|
if arg.decode('utf-8').startswith('DMLC_TRACKER_PORT'):
|
|
port = arg.decode('utf-8')
|
|
port = port.split('=')
|
|
env = os.environ.copy()
|
|
env[port[0]] = port[1]
|
|
return subprocess.run([exe, test], env=env, capture_output=True)
|
|
|
|
with LocalCluster(n_workers=4) as cluster:
|
|
with Client(cluster) as client:
|
|
workers = list(xgb.dask._get_client_workers(client).keys())
|
|
rabit_args = client.sync(
|
|
xgb.dask._get_rabit_args, workers, client)
|
|
futures = client.map(runit,
|
|
workers,
|
|
pure=False,
|
|
workers=workers,
|
|
rabit_args=rabit_args)
|
|
results = client.gather(futures)
|
|
|
|
for ret in results:
|
|
msg = ret.stdout.decode('utf-8')
|
|
assert msg.find('1 test from Quantile') != -1, msg
|
|
assert ret.returncode == 0, msg
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.gtest
|
|
def test_quantile_basic(self):
|
|
self.run_quantile('DistributedBasic')
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.gtest
|
|
def test_quantile(self):
|
|
self.run_quantile('Distributed')
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.gtest
|
|
def test_quantile_same_on_all_workers(self):
|
|
self.run_quantile('SameOnAllWorkers')
|