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Xgb datasets adding #60
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Merged
Alexsandruss
merged 39 commits into
IntelPython:master
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RukhovichIV:xgb-nvidia-datasets
Apr 26, 2021
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62f87c3
Applied mypy + flake8 for all files
132d73f
Sorted imports with ISort
4aa4898
Moved env change to runner
5a8db33
fixed all mypy errors and added mypy check to CI
5594efd
Yet another mypy fixes
35b55b8
Small runner refactoring
56de8f7
First attempt of adding nvidia datasets
0ee5f05
Merge branch 'master' into mypy-applying
04e7a64
removed E265 ignoring for flake8 job
8268747
Merge remote-tracking branch 'my/mypy-applying' into xgb-nvidia-datasets
b6a7eb0
NVidia benchmarks are working now
7e780bb
Added higgs, msrank and airline fetching
670c289
small fixes of env
dc0e9c9
Applying comments
f64ae68
Merge branch 'mypy-applying' into xgb-nvidia-datasets
873754b
Split dataset loading to different files
93ea32d
Merge remote-tracking branch 'origin/master' into xgb-nvidia-datasets
dcfc5b9
Why doesnt mypy work?
340402e
Added abalone + letters, updated all GB configs
6e47423
Added links and descriptions for new datasets
340a628
Merge remote-tracking branch 'origin/master' into xgb-nvidia-datasets
4be3720
handling mypy
8184016
Handled skex fake message throwing
cf5ee76
Trying to handle mypy, at. 3
9db3177
Trying to handle mypy, at. 4
5e76a0b
Trying to handle mypy, at. 5
13fcd20
Changed configs readme and made small fixes in GB testing configs
0873f97
Merge branch 'master' of https://github.com/IntelPython/scikit-learn_…
877e0fd
Applying more comments, updating readme's
8bdc7f2
Applying comments: renamed configs
f9cf09b
Changed all datasets to npy, applied Kirill's comments
41e003f
Merge branch 'master' of https://github.com/IntelPython/scikit-learn_…
523df30
Cleanup after someone's commit
59303fa
Applying mypy
b56e42c
Applied Ekaterina's suggestions
ad176e5
Applied other Ekaterina's comments
b92a27f
Merge branch 'xgb-nvidia-datasets' of https://github.com/RukhovichIV/…
11a8ffc
Final commits applying
37d5461
Alexander's final comments
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Added abalone + letters, updated all GB configs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,108 +1,120 @@ | ||
| { | ||
| "common": { | ||
| "lib": ["modelbuilders"], | ||
| "data-format": ["pandas"], | ||
| "data-order": ["F"], | ||
| "dtype": ["float32"] | ||
| "lib": "modelbuilders", | ||
| "data-format": "pandas", | ||
| "data-order": "F", | ||
| "dtype": "float32", | ||
| "algorithm": "lgbm_mb" | ||
| }, | ||
| "cases": [ | ||
| { | ||
| "algorithm": "lgbm_mb", | ||
| "dataset": [ | ||
| { | ||
| "source": "csv", | ||
| "name": "mortgage1Q", | ||
| "source": "npy", | ||
| "name": "airline-ohe", | ||
| "training": | ||
| { | ||
| "x": "data/mortgage_x.csv", | ||
| "y": "data/mortgage_y.csv" | ||
| "x": "data/airline-ohe_x_train.npy", | ||
| "y": "data/airline-ohe_y_train.npy" | ||
| }, | ||
| "testing": | ||
| { | ||
| "x": "data/airline-ohe_x_test.npy", | ||
| "y": "data/airline-ohe_y_test.npy" | ||
| } | ||
| } | ||
| ], | ||
| "n-estimators": [100], | ||
| "objective": ["regression"], | ||
| "max-depth": [8], | ||
| "scale-pos-weight": [2], | ||
| "learning-rate": [0.1], | ||
| "subsample": [1], | ||
| "reg-alpha": [0.9], | ||
| "reg-lambda": [1], | ||
| "min-child-weight": [0], | ||
| "max-leaves": [256] | ||
| "reg-alpha": 0.9, | ||
| "max-bin": 256, | ||
| "scale-pos-weight": 2, | ||
| "learning-rate": 0.1, | ||
| "subsample": 1, | ||
| "reg-lambda": 1, | ||
| "min-child-weight": 0, | ||
| "max-depth": 8, | ||
| "max-leaves": 256, | ||
| "n-estimators": 1000, | ||
| "objective": "binary" | ||
| }, | ||
| { | ||
| "algorithm": "lgbm_mb", | ||
| "dataset": [ | ||
| { | ||
| "source": "csv", | ||
| "name": "airline-ohe", | ||
| "source": "npy", | ||
| "name": "higgs1m", | ||
| "training": | ||
| { | ||
| "x": "data/airline-ohe_x_train.csv", | ||
| "y": "data/airline-ohe_y_train.csv" | ||
| "x": "data/higgs1m_x_train.npy", | ||
| "y": "data/higgs1m_y_train.npy" | ||
| }, | ||
| "testing": | ||
| { | ||
| "x": "data/higgs1m_x_test.npy", | ||
| "y": "data/higgs1m_y_test.npy" | ||
| } | ||
| } | ||
| ], | ||
| "reg-alpha": [0.9], | ||
| "max-bin": [256], | ||
| "scale-pos-weight": [2], | ||
| "learning-rate": [0.1], | ||
| "subsample": [1], | ||
| "reg-lambda": [1], | ||
| "min-child-weight": [0], | ||
| "max-depth": [8], | ||
| "max-leaves": [256], | ||
| "n-estimators": [1000], | ||
| "objective": ["binary"] | ||
| "reg-alpha": 0.9, | ||
| "max-bin": 256, | ||
| "scale-pos-weight": 2, | ||
| "learning-rate": 0.1, | ||
| "subsample": 1, | ||
| "reg-lambda": 1, | ||
| "min-child-weight": 0, | ||
| "max-depth": 8, | ||
| "max-leaves": 256, | ||
| "n-estimators": 1000, | ||
| "objective": "binary" | ||
| }, | ||
| { | ||
| "algorithm": "lgbm_mb", | ||
| "dataset": [ | ||
| { | ||
| "source": "csv", | ||
| "name": "higgs1m", | ||
| "source": "csv", | ||
| "name": "mortgage1Q", | ||
| "training": | ||
| { | ||
| "x": "data/higgs1m_x_train.csv", | ||
| "y": "data/higgs1m_y_train.csv" | ||
| "x": "data/mortgage_x.csv", | ||
| "y": "data/mortgage_y.csv" | ||
| } | ||
| } | ||
| ], | ||
| "reg-alpha": [0.9], | ||
| "max-bin": [256], | ||
| "scale-pos-weight": [2], | ||
| "learning-rate": [0.1], | ||
| "subsample": [1], | ||
| "reg-lambda": [1], | ||
| "min-child-weight": [0], | ||
| "max-depth": [8], | ||
| "max-leaves": [256], | ||
| "n-estimators": [1000], | ||
| "objective": ["binary"] | ||
| "n-estimators": 100, | ||
| "objective": "regression", | ||
| "max-depth": 8, | ||
| "scale-pos-weight": 2, | ||
| "learning-rate": 0.1, | ||
| "subsample": 1, | ||
| "reg-alpha": 0.9, | ||
| "reg-lambda": 1, | ||
| "min-child-weight": 0, | ||
| "max-leaves": 256 | ||
| }, | ||
| { | ||
| "algorithm": "lgbm_mb", | ||
| "dataset": [ | ||
| { | ||
| "source": "csv", | ||
| "name": "msrank", | ||
| "source": "npy", | ||
| "name": "msrank", | ||
| "training": | ||
| { | ||
| "x": "data/mlsr_x_train.csv", | ||
| "y": "data/mlsr_y_train.csv" | ||
| "x": "data/msrank_x_train.npy", | ||
| "y": "data/msrank_y_train.npy" | ||
| }, | ||
| "testing": | ||
| { | ||
| "x": "data/msrank_x_test.npy", | ||
| "y": "data/msrank_y_test.npy" | ||
| } | ||
| } | ||
| ], | ||
| "max-bin": [256], | ||
| "learning-rate": [0.3], | ||
| "subsample": [1], | ||
| "reg-lambda": [2], | ||
| "min-child-weight": [1], | ||
| "min-split-gain": [0.1], | ||
| "max-depth": [8], | ||
| "max-leaves": [256], | ||
| "n-estimators": [200], | ||
| "objective": ["multiclass"] | ||
| "max-bin": 256, | ||
| "learning-rate": 0.3, | ||
| "subsample": 1, | ||
| "reg-lambda": 2, | ||
| "min-child-weight": 1, | ||
| "min-split-loss": 0.1, | ||
| "max-depth": 8, | ||
| "max-leaves": 256, | ||
| "n-estimators": 200, | ||
| "objective": "multiclass" | ||
| } | ||
| ] | ||
| } | ||
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Add note to README that parameters might be set with single value or list of values
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done earlier