5374f52531
* Handles missing value. * Accept all floating point and integer types. * Move to cudf 9.0 API. * Remove requirement on `null_count`. * Arbitrary column types support.
94 lines
2.7 KiB
Python
94 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 numpy as np
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import sys
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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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except ImportError:
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pass
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pytestmark = pytest.mark.skipif(**tm.no_dask())
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def run_train():
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# Contains one label equal to rank
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dmat = xgb.DMatrix(np.array([[0]]), label=[xgb.rabit.get_rank()])
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bst = xgb.train({"eta": 1.0, "lambda": 0.0}, dmat, 1)
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pred = bst.predict(dmat)
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expected_result = np.average(range(xgb.rabit.get_world_size()))
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assert all(p == expected_result for p in pred)
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def test_train(client):
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# Train two workers, the first has label 0, the second has label 1
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# If they build the model together the output should be 0.5
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xgb.dask.run(client, run_train)
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# Run again to check we can have multiple sessions
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xgb.dask.run(client, run_train)
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def run_create_dmatrix(X, y, weights):
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dmat = xgb.dask.create_worker_dmatrix(X, y, weight=weights)
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# Expect this worker to get two partitions and concatenate them
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assert dmat.num_row() == 50
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def test_dask_dataframe(client):
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n = 10
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m = 100
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partition_size = 25
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X = dd.from_array(np.random.random((m, n)), partition_size)
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y = dd.from_array(np.random.random(m), partition_size)
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weights = dd.from_array(np.random.random(m), partition_size)
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xgb.dask.run(client, run_create_dmatrix, X, y, weights)
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def test_dask_array(client):
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n = 10
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m = 100
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partition_size = 25
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X = da.random.random((m, n), partition_size)
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y = da.random.random(m, partition_size)
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weights = da.random.random(m, partition_size)
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xgb.dask.run(client, run_create_dmatrix, X, y, weights)
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def run_get_local_data(X, y):
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X_local = xgb.dask.get_local_data(X)
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y_local = xgb.dask.get_local_data(y)
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assert (X_local.shape == (50, 10))
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assert (y_local.shape == (50,))
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def test_get_local_data(client):
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n = 10
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m = 100
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partition_size = 25
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X = da.random.random((m, n), partition_size)
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y = da.random.random(m, partition_size)
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xgb.dask.run(client, run_get_local_data, X, y)
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def run_sklearn():
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# Contains one label equal to rank
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X = np.array([[0]])
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y = [xgb.rabit.get_rank()]
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model = xgb.XGBRegressor(learning_rate=1.0)
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model.fit(X, y)
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pred = model.predict(X)
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expected_result = np.average(range(xgb.rabit.get_world_size()))
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assert all(p == expected_result for p in pred)
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return pred
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def test_sklearn(client):
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result = xgb.dask.run(client, run_sklearn)
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print(result)
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