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dmitrii-kriukovdmitrii-kriukov
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Configs for xpu: decision forest regressor, linear regression, logistic regression (#104)
* regression xpu configs * blank line * enable devices&dtypes * regression xpu configs * blank line * enable devices&dtypes * pep8 * fix susy y shape
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‎configs/xpu/df_regr.json‎

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{
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"common": {
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"lib": "sklearn",
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"algorithm": "df_regr",
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"data-format": "pandas",
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"data-order": "F",
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"dtype": ["float32", "float64"],
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"max-features": 0.33,
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"device": ["host", "cpu", "gpu", "none"]
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},
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"cases": [
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{
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"dataset": [
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{
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"source": "npy",
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"name": "year_prediction_msd",
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"training":
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{
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"x": "data/year_prediction_msd_x_train.npy",
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"y": "data/year_prediction_msd_y_train.npy"
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},
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"testing":
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{
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"x": "data/year_prediction_msd_x_test.npy",
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"y": "data/year_prediction_msd_y_test.npy"
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}
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}
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],
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"num-trees": [10, 100],
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"max-depth": 5
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "year_prediction_msd",
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"training":
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{
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"x": "data/year_prediction_msd_x_train.npy",
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"y": "data/year_prediction_msd_y_train.npy"
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},
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"testing":
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{
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"x": "data/year_prediction_msd_x_test.npy",
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"y": "data/year_prediction_msd_y_test.npy"
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}
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}
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],
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"num-trees": [100, 20],
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"max-depth": 8
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "year_prediction_msd",
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"training":
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{
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"x": "data/year_prediction_msd_x_train.npy",
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"y": "data/year_prediction_msd_y_train.npy"
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},
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"testing":
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{
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"x": "data/year_prediction_msd_x_test.npy",
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"y": "data/year_prediction_msd_y_test.npy"
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}
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}
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],
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"num-trees": 20,
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"max-depth": 16
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "higgs1m",
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"training":
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{
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"x": "data/higgs1m_x_train.npy",
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"y": "data/higgs1m_y_train.npy"
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},
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"testing":
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{
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"x": "data/higgs1m_x_test.npy",
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"y": "data/higgs1m_y_test.npy"
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}
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}
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],
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"num-trees": [15, 20, 100],
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"max-depth": 8
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "higgs_10500K",
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"training":
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{
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"x": "data/higgs_10500K_x_train.npy",
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"y": "data/higgs_10500K_y_train.npy"
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},
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"testing":
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{
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"x": "data/higgs_10500K_x_test.npy",
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"y": "data/higgs_10500K_y_test.npy"
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}
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}
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],
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"num-trees": 100,
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"max-depth": 8
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "higgs_10500K",
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"training":
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{
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"x": "data/higgs_10500K_x_train.npy",
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"y": "data/higgs_10500K_y_train.npy"
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},
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"testing":
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{
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"x": "data/higgs_10500K_x_test.npy",
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"y": "data/higgs_10500K_y_test.npy"
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}
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}
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],
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"num-trees": 20,
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"max-depth": 16
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}
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]
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}

‎configs/xpu/linear.json‎

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{
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"common": {
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"lib": "sklearn",
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"algorithm": "linear",
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"data-format": "pandas",
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"data-order": "F",
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"dtype": ["float32", "float64"],
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"device": ["host", "cpu", "gpu", "none"]
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},
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"cases": [
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{
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"dataset": [
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{
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"source": "npy",
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"name": "year_prediction_msd",
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"training":
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{
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"x": "data/year_prediction_msd_x_train.npy",
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"y": "data/year_prediction_msd_y_train.npy"
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},
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"testing":
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{
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"x": "data/year_prediction_msd_x_test.npy",
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"y": "data/year_prediction_msd_y_test.npy"
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}
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}
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]
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "higgs1m",
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"training":
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{
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"x": "data/higgs1m_x_train.npy",
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"y": "data/higgs1m_y_train.npy"
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},
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"testing":
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{
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"x": "data/higgs1m_x_test.npy",
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"y": "data/higgs1m_y_test.npy"
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}
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}
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]
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}
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]
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}

‎configs/xpu/log_reg.json‎

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{
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"common": {
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"lib": "sklearn",
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"algorithm": "log_reg",
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"data-format": "pandas",
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"data-order": "F",
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"dtype": ["float32", "float64"],
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"device": ["host", "cpu", "gpu", "none"]
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},
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"cases": [
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{
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"dataset": [
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{
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"source": "npy",
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"name": "susy",
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"training":
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{
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"x": "data/susy_x_train.npy",
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"y": "data/susy_y_train.npy"
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},
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"testing":
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{
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"x": "data/susy_x_test.npy",
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"y": "data/susy_y_test.npy"
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}
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}
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],
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"maxiter": "20"
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "susy",
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"training":
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{
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"x": "data/susy_x_train.npy",
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"y": "data/susy_y_train.npy"
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},
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"testing":
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{
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"x": "data/susy_x_test.npy",
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"y": "data/susy_y_test.npy"
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}
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}
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],
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"maxiter": "10"
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "mnist",
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"training":
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{
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"x": "data/mnist_x_train.npy",
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"y": "data/mnist_y_train.npy"
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},
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"testing":
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{
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"x": "data/mnist_x_test.npy",
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"y": "data/mnist_y_test.npy"
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}
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}
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],
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"no-fit-intercept": "",
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"maxiter": "50"
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},
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{
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"dataset": [
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{
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"source": "npy",
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"name": "mnist",
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"training":
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{
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"x": "data/mnist_x_train.npy",
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"y": "data/mnist_y_train.npy"
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},
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"testing":
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{
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"x": "data/mnist_x_test.npy",
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"y": "data/mnist_y_test.npy"
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}
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}
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],
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"maxiter": "500"
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}
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]
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}

‎datasets/load_datasets.py‎

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klaverjas, santander, skin_segmentation, susy)
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from .loader_multiclass import (connect, covertype, covtype, letters, mlsr,
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mnist, msrank, plasticc, sensit)
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from .loader_regression import (abalone, california_housing, fried,
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from .loader_regression import (abalone, california_housing, fried, higgs_10500K,
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medical_charges_nominal, mortgage_first_q,
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twodplanes, year_prediction_msd, yolanda, airline_regression)
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"hepmass_150K": hepmass_150K,
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"higgs": higgs,
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"higgs1m": higgs_one_m,
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"higgs_10500K": higgs_10500K,
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"ijcnn": ijcnn,
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"klaverjas": klaverjas,
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"letters": letters,

‎datasets/loader_classification.py‎

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nrows=nrows_train + nrows_test)
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X = data[data.columns[1:]]
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y = data[data.columns[0:1]]
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y = data[data.columns[0:1]].values.ravel()
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x_train, x_test, y_train, y_test = train_test_split(
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X, y, train_size=nrows_train, test_size=nrows_test, shuffle=False)

‎datasets/loader_regression.py‎

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np.save(os.path.join(dataset_dir, filename), data)
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logging.info(f'dataset {dataset_name} is ready.')
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return True
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def higgs_10500K(dataset_dir: Path) -> bool:
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"""
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Higgs dataset from UCI machine learning repository
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https://archive.ics.uci.edu/ml/datasets/HIGGS
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Classification task. n_classes = 2.
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higgs_10500K X train dataset (10500000, 28)
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higgs_10500K y train dataset (10500000, 1)
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higgs_10500K X test dataset (500000, 28)
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higgs_10500K y test dataset (500000, 1)
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"""
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dataset_name = 'higgs_10500K'
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os.makedirs(dataset_dir, exist_ok=True)
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url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz'
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local_url = os.path.join(dataset_dir, os.path.basename(url))
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if not os.path.isfile(local_url):
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logging.info(f'Started loading {dataset_name}')
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retrieve(url, local_url)
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logging.info(f'{dataset_name} is loaded, started parsing...')
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nrows_train, nrows_test, dtype = 10500000, 500000, np.float32
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data: Any = pd.read_csv(local_url, delimiter=",", header=None,
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compression="gzip", dtype=dtype,
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nrows=nrows_train + nrows_test)
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X = data[data.columns[1:]]
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y = data[data.columns[0:1]]
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x_train, x_test, y_train, y_test = train_test_split(
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X, y, train_size=nrows_train, test_size=nrows_test, shuffle=False)
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for data, name in zip((x_train, x_test, y_train, y_test),
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('x_train', 'x_test', 'y_train', 'y_test')):
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filename = f'{dataset_name}_{name}.npy'
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np.save(os.path.join(dataset_dir, filename), data)
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logging.info(f'dataset {dataset_name} is ready.')
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return True

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