Add test (#17)
* update submodule LightGBM to latest commit * fix test about valid data * upgrade version to 3.3.2 * install sklearn in ci setup
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@ -12,5 +12,6 @@ cp ./external_libs/LightGBM/lib_lightgbm.so ${lgb_python_pkg_dir}/lightgbm || ex
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# modify `basic.py` to load all libs first, or cannot find them when calling python interfaces.
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# modify `basic.py` to load all libs first, or cannot find them when calling python interfaces.
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cp ${lgb_python_pkg_dir}/lightgbm/basic.py raw && cat ./scripts/load_precompiled_libs.py ${lgb_python_pkg_dir}/lightgbm/basic.py > tmp && cp tmp ${lgb_python_pkg_dir}/lightgbm/basic.py || exit -1
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cp ${lgb_python_pkg_dir}/lightgbm/basic.py raw && cat ./scripts/load_precompiled_libs.py ${lgb_python_pkg_dir}/lightgbm/basic.py > tmp && cp tmp ${lgb_python_pkg_dir}/lightgbm/basic.py || exit -1
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# install python package
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# install python package
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pip install pytest numpy scipy pandas
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# install sklearn to avoid error "ImportError: cannot import name '_LGBMBaseCrossValidator' from 'lightgbm.compat'"
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pip install pytest numpy scipy pandas sklearn
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pip list
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pip list
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@ -1 +1 @@
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3.3.1.post1
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3.3.2
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@ -1 +1 @@
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Subproject commit b0137debe6e9cc92b65ec71b0fe8a56ea213c143
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Subproject commit 865c126a1e3ccdd77ec205b9dde46e5f3c5b6b21
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@ -1,4 +1,4 @@
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12 12 12 2 18 7 16 5 11 14 1
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:12 12 12 2 18 7 16 5 11 14 1
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3 12 10 11 10 8 5 0 7 8 0
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3 12 10 11 10 8 5 0 7 8 0
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9 3 18 4 8 18 12 14 3 4 1
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15 15 14 17 0 10 2 5 2 8 0
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15 15 14 17 0 10 2 5 2 8 0
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@ -99,10 +99,9 @@ def test_e2e(params, trained_model_path):
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np.testing.assert_allclose(pred[:5], np.array([0.83267298, 0.388454, 0.35369267, 0.60330376, -1.24218415]))
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np.testing.assert_allclose(pred[:5], np.array([0.83267298, 0.388454, 0.35369267, 0.60330376, -1.24218415]))
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def test_train_data_no_header(binary_params, simple_ds_with_header, trained_model_path):
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def test_train_data_no_header(binary_params, simple_ds_with_header):
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train_data = lgb.Dataset(simple_ds.data, params={"parser_config_file": simple_ds.parser_config})
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train_data = lgb.Dataset(simple_ds.data, params={"parser_config_file": simple_ds.parser_config})
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valid_data = lgb.Dataset(simple_ds_with_header.data, params={
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valid_data = lgb.Dataset(simple_ds_with_header.data, params={"header": True})
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"parser_config_file": simple_ds_with_header.parser_config, "header": True})
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bst = lgb.train(binary_params, train_data, valid_sets=[valid_data])
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bst = lgb.train(binary_params, train_data, valid_sets=[valid_data])
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expected_pred = 0.4894574
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expected_pred = 0.4894574
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# predict data with no header.
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# predict data with no header.
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