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Extratrees inclusion to sklearn_bench #139

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075217f
initial files for extra trees support
icfaust Sep 25, 2023
b60b68c
change readmes for et
icfaust Sep 25, 2023
4839a3d
correcting mistakes in master
icfaust Sep 25, 2023
04114e9
Update sklearn.json
icfaust Sep 25, 2023
09385d6
Update skl_config.json
icfaust Sep 25, 2023
092de2c
mse -> squared_error
icfaust Sep 25, 2023
151f9bb
Merge branch 'IntelPython:master' into extratrees
icfaust Oct 19, 2023
f8ff6b4
Merge branch 'IntelPython:master' into extratrees
icfaust Oct 30, 2023
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6 changes: 4 additions & 2 deletions README.md
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Expand Up @@ -33,7 +33,7 @@ We publish blogs on Medium, so [follow us](https://medium.com/intel-analytics-so
- [How to create conda environment for benchmarking](#how-to-create-conda-environment-for-benchmarking)
- [Running Python benchmarks with runner script](#running-python-benchmarks-with-runner-script)
- [Benchmark supported algorithms](#benchmark-supported-algorithms)
- [Scikit-learn benchmakrs](#scikit-learn-benchmakrs)
- [Scikit-learn benchmarks](#scikit-learn-benchmarks)
- [Algorithm parameters](#algorithm-parameters)

## How to create conda environment for benchmarking
Expand Down Expand Up @@ -105,6 +105,8 @@ The configuration of benchmarks allows you to select the frameworks to run, sele
|**[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)**|dbscan|:white_check_mark:|:white_check_mark:|:white_check_mark:|:white_check_mark:|:x:|
|**[RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)**|df_clfs|:white_check_mark:|:x:|:white_check_mark:|:white_check_mark:|:x:|
|**[RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)**|df_regr|:white_check_mark:|:x:|:white_check_mark:|:white_check_mark:|:x:|
|**[ExtraTreesClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)**|et_clfs|:white_check_mark:|:x:|:x:|:x:|:x:|
|**[ExtraTreesRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html)**|et_regr|:white_check_mark:|:x:|:x:|:x:|:x:|
|**[pairwise_distances](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html)**|distances|:white_check_mark:|:x:|:white_check_mark:|:x:|:x:|
|**[KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)**|kmeans|:white_check_mark:|:white_check_mark:|:white_check_mark:|:white_check_mark:|:x:|
|**[KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)**|knn_clsf|:white_check_mark:|:x:|:x:|:white_check_mark:|:x:|
Expand All @@ -118,7 +120,7 @@ The configuration of benchmarks allows you to select the frameworks to run, sele
|**[GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)**|gbt|:x:|:x:|:x:|:x:|:white_check_mark:|
|**[GradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)**|gbt|:x:|:x:|:x:|:x:|:white_check_mark:|

### Scikit-learn benchmakrs
### Scikit-learn benchmarks

When you run scikit-learn benchmarks on CPU, [Intel(R) Extension for Scikit-learn](https://github.com/intel/scikit-learn-intelex) is used by default. Use the ``--no-intel-optimized`` option to run the benchmarks without the extension.

Expand Down
72 changes: 72 additions & 0 deletions configs/skl_config.json
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Expand Up @@ -182,6 +182,78 @@
}
]
},
{
"algorithm": "et_clsf",
"dtype": "float32",
"dataset": [
{
"source": "npy",
"name": "higgs1m",
"training":
{
"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"
}
},
{
"source": "npy",
"name": "airline-ohe",
"training":
{
"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"
}
}
],
"num-trees": 50,
"max-depth": 16,
"max-leaf-nodes": 131072,
"max-features": 0.2
},
{
"algorithm": "et_regr",
"dtype": "float32",
"dataset": [
{
"source": "npy",
"name": "year_prediction_msd",
"training":
{
"x": "data/year_prediction_msd_x_train.npy",
"y": "data/year_prediction_msd_y_train.npy"
},
"testing":
{
"x": "data/year_prediction_msd_x_test.npy",
"y": "data/year_prediction_msd_y_test.npy"
}
},
{
"source": "npy",
"name": "airline_regression",
"training":
{
"x": "data/airline_regression_x_train.npy",
"y": "data/airline_regression_y_train.npy"
},
"testing":
{
"x": "data/airline_regression_x_test.npy",
"y": "data/airline_regression_y_test.npy"
}
}
]
},
{
"algorithm": "ridge",
"dataset": [
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165 changes: 165 additions & 0 deletions configs/sklearn/performance/et_clsf.json
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@@ -0,0 +1,165 @@
{
"common": {
"lib": "sklearn",
"algorithm": "et_clsf",
"data-format": "pandas",
"data-order": "F",
"dtype": ["float32", "float64"],
"max-features": "sqrt",
"device": ["host", "cpu", "gpu", "none"]
},
"cases": [
{
"dataset": [
{
"source": "npy",
"name": "higgs1m",
"training":
{
"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"
}
}
],
"workload-size": "medium",
"num-trees": 50,
"max-depth": 16,
"max-leaf-nodes": 131072,
"max-features": 0.2
},
{
"device": "none",
"dataset": [
{
"source": "npy",
"name": "airline-ohe",
"training":
{
"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"
}
}
],
"workload-size": "medium",
"num-trees": 50,
"max-depth": 16,
"max-leaf-nodes": 131072,
"max-features": 0.2
},
{
"dataset": [
{
"source": "npy",
"name": "susy",
"training":
{
"x": "data/susy_x_train.npy",
"y": "data/susy_y_train.npy"
},
"testing":
{
"x": "data/susy_x_test.npy",
"y": "data/susy_y_test.npy"
}
}
],
"workload-size": "medium",
"num-trees": 10,
"max-depth": 5
},
{
"dataset": [
{
"source": "npy",
"name": "susy",
"training":
{
"x": "data/susy_x_train.npy",
"y": "data/susy_y_train.npy"
},
"testing":
{
"x": "data/susy_x_test.npy",
"y": "data/susy_y_test.npy"
}
}
],
"workload-size": "large",
"num-trees": 100,
"max-depth": 8
},
{
"dataset": [
{
"source": "npy",
"name": "susy",
"training":
{
"x": "data/susy_x_train.npy",
"y": "data/susy_y_train.npy"
},
"testing":
{
"x": "data/susy_x_test.npy",
"y": "data/susy_y_test.npy"
}
}
],
"workload-size": "medium",
"num-trees": 20,
"max-depth": 16
},
{
"dataset": [
{
"source": "npy",
"name": "mnist",
"training":
{
"x": "data/mnist_x_train.npy",
"y": "data/mnist_y_train.npy"
},
"testing":
{
"x": "data/mnist_x_test.npy",
"y": "data/mnist_y_test.npy"
}
}
],
"workload-size": "large",
"num-trees": 100,
"max-depth": 10
},
{
"dataset": [
{
"source": "npy",
"name": "hepmass_150K",
"training":
{
"x": "data/hepmass_150K_x_train.npy",
"y": "data/hepmass_150K_y_train.npy"
},
"testing":
{
"x": "data/hepmass_150K_x_test.npy",
"y": "data/hepmass_150K_y_test.npy"
}
}
],
"workload-size": "medium",
"num-trees": 50,
"max-depth": 15
}
]
}
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