97 lines
2.7 KiB
Python
97 lines
2.7 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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if sys.platform.startswith("win"):
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pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
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try:
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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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pass
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pytestmark = pytest.mark.skipif(**tm.no_dask())
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kRows = 1000
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def generate_array():
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n = 10
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partition_size = 20
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X = da.random.random((kRows, n), 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(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 isinstance(prediction, da.Array)
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assert prediction.shape[0] == kRows, prediction
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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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def test_from_dask_array(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 isinstance(prediction, da.Array)
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def test_regressor(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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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_classifier(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(verbosity=1, n_estimators=2)
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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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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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