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PetrovKPPetrovKP
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Add support proba svc; add pypi info; fix errors (#62)
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‎README.md‎

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@@ -39,25 +39,29 @@ Create a suitable conda environment for each framework to test. Each item in the
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* [**scikit-learn**](sklearn_bench#how-to-create-conda-environment-for-benchmarking)
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```bash
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conda create -n bench -c intel python=3.7 scikit-learn scikit-learn-intelex pandas
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pip install -r sklearn_bench/requirements.txt
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# or
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conda install -c conda-forge scikit-learn scikit-learn-intelex pandas
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```
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* [**daal4py**](daal4py_bench#how-to-create-conda-environment-for-benchmarking)
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```bash
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conda create -n bench -c intel python=3.7 scikit-learn daal4py pandas
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conda install -c conda-forge scikit-learn daal4py pandas
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```
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* [**cuml**](cuml_bench#how-to-create-conda-environment-for-benchmarking)
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```bash
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conda create -n bench -c rapidsai -c conda-forge python=3.7 cuml pandas cudf
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conda install -c rapidsai -c conda-forge cuml pandas cudf
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```
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* [**xgboost**](xgboost_bench#how-to-create-conda-environment-for-benchmarking)
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```bash
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conda create -n bench -c conda-forge python=3.7 xgboost pandas
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pip install -r xgboost_bench/requirements.txt
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# or
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conda install -c conda-forge xgboost pandas
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```
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## Running Python benchmarks with runner script
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## Intel(R) Extension for Scikit-learn support
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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.
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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.
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The following benchmarks have a GPU support:
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* dbscan

‎bench.py‎

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return columnwise_score(y, yp, lambda y1, y2: np.mean(y1 == y2))
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def log_loss(y, yp):
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from sklearn.metrics import log_loss as sklearn_log_loss
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y = convert_to_numpy(y)
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yp = convert_to_numpy(yp)
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return sklearn_log_loss(y, yp)
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def rmse_score(y, yp):
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return columnwise_score(
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y, yp, lambda y1, y2: float(np.sqrt(np.mean((y1 - y2)**2))))

‎configs/svm/svc_proba_cuml.json‎

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{
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"common": {
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"lib": ["cuml"],
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"data-format": ["cudf"],
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"data-order": ["F"],
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"dtype": ["float64"],
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"max-cache-size": [2],
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"probability": [""]
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},
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"cases": [
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "ijcnn",
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"training":
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{
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"x": "data/ijcnn_x_train.csv",
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"y": "data/ijcnn_y_train.csv"
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},
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"testing":
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{
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"x": "data/ijcnn_x_test.csv",
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"y": "data/ijcnn_y_test.csv"
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}
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}
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],
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"C": [1000.0],
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"kernel": ["linear"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "a9a",
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"training":
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{
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"x": "data/a9a_x_train.csv",
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"y": "data/a9a_y_train.csv"
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},
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"testing":
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{
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"x": "data/a9a_x_test.csv",
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"y": "data/a9a_y_test.csv"
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}
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}
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],
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"C": [500.0],
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"kernel": ["rbf"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "gisette",
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"training":
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{
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"x": "data/gisette_x_train.csv",
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"y": "data/gisette_y_train.csv"
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},
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"testing":
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{
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"x": "data/gisette_x_test.csv",
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"y": "data/gisette_y_test.csv"
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}
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}
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],
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"C": [1.5e-3],
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"kernel": ["linear"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "klaverjas",
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"training":
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{
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"x": "data/klaverjas_x_train.csv",
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"y": "data/klaverjas_y_train.csv"
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},
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"testing":
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{
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"x": "data/klaverjas_x_test.csv",
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"y": "data/klaverjas_y_test.csv"
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}
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}
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],
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"C": [1.0],
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"kernel": ["rbf"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "connect",
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"training":
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{
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"x": "data/connect_x_train.csv",
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"y": "data/connect_y_train.csv"
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},
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"testing":
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{
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"x": "data/connect_x_test.csv",
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"y": "data/connect_y_test.csv"
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}
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}
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],
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"C": [100.0],
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"kernel": ["linear"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "mnist",
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"training":
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{
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"x": "data/mnist_x_train.csv",
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"y": "data/mnist_y_train.csv"
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},
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"testing":
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{
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"x": "data/mnist_x_test.csv",
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"y": "data/mnist_y_test.csv"
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}
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}
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],
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"C": [50.0],
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"kernel": ["rbf"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "sensit",
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"training":
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{
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"x": "data/sensit_x_train.csv",
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"y": "data/sensit_y_train.csv"
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},
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"testing":
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{
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"x": "data/sensit_x_test.csv",
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"y": "data/sensit_y_test.csv"
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}
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}
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],
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"C": [500.0],
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"kernel": ["linear"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "skin_segmentation",
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"training":
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{
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"x": "data/skin_segmentation_x_train.csv",
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"y": "data/skin_segmentation_y_train.csv"
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},
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"testing":
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{
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"x": "data/skin_segmentation_x_test.csv",
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"y": "data/skin_segmentation_y_test.csv"
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}
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}
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],
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"C": [1.0],
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"kernel": ["rbf"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "covertype",
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"training":
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{
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"x": "data/covertype_x_train.csv",
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"y": "data/covertype_y_train.csv"
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},
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"testing":
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{
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"x": "data/covertype_x_test.csv",
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"y": "data/covertype_y_test.csv"
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}
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}
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],
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"C": [100.0],
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"kernel": ["rbf"]
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},
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{
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"algorithm": "svm",
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"dataset": [
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{
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"source": "csv",
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"name": "codrnanorm",
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"training":
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{
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"x": "data/codrnanorm_x_train.csv",
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"y": "data/codrnanorm_y_train.csv"
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},
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"testing":
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{
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"x": "data/codrnanorm_x_test.csv",
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"y": "data/codrnanorm_y_test.csv"
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}
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}
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],
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"C": [1000.0],
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"kernel": ["linear"]
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}
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]
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}

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