integrations/ml/databricks/benchmark/benchmark_sklearn.ipynb

{"cells":[{"cell_type":"code","source":["from platform import python_version\nimport sklearn\nimport pandas\nimport tensorflow as tf\n\nprint(\"Python version: \" +python_version())\nprint(\"sklearn version: \" +sklearn.__version__)\nprint(\"pandas version: \" +pandas.__version__)\nprint(\"tensorflow version: \" +tf.__version__)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"356ab222-f93f-4b3e-ada3-31027ba5ec5f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":{"text/plain":"","application/vnd.databricks.v1+bamboolib_hint":"{\"pd.DataFrames\": [], \"version\": \"0.0.1\"}"},"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"mimeBundle","arguments":{}}},"output_type":"display_data","data":{"text/plain":"","application/vnd.databricks.v1+bamboolib_hint":"{\"pd.DataFrames\": [], \"version\": \"0.0.1\"}"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"Python version: 3.9.5\nsklearn version: 0.24.2\npandas version: 1.3.4\ntensorflow version: 2.8.0\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["Python version: 3.9.5\nsklearn version: 0.24.2\npandas version: 1.3.4\ntensorflow version: 2.8.0\n"]}}],"execution_count":0},{"cell_type":"code","source":["%sh\napt-get update \napt-get -y --force-yes install git\nmkdir -p /home/intel/\ncd /home/intel\ngit clone https://github.com/IntelPython/scikit-learn_bench.git \ncd /home/intel/scikit-learn_bench\ngit reset --hard  7e0fc7f06b767d9090d766ce8a9202a292b7dc91"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"164bad4a-9195-4dd4-9da0-9438317a757b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"Hit:1 https://repos.azul.com/zulu/deb stable InRelease\nHit:2 http://security.ubuntu.com/ubuntu focal-security InRelease\nHit:3 http://archive.ubuntu.com/ubuntu focal InRelease\nHit:4 http://archive.ubuntu.com/ubuntu focal-updates InRelease\nHit:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease\nReading package lists...\nReading package lists...\nBuilding dependency tree...\nReading state information...\ngit is already the newest version (1:2.25.1-1ubuntu3.4).\n0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\nW: --force-yes is deprecated, use one of the options starting with --allow instead.\nCloning into 'scikit-learn_bench'...\nHEAD is now at 8c9e528 Update copyright for 2021 year (#47)\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["Hit:1 https://repos.azul.com/zulu/deb stable InRelease\nHit:2 http://security.ubuntu.com/ubuntu focal-security InRelease\nHit:3 http://archive.ubuntu.com/ubuntu focal InRelease\nHit:4 http://archive.ubuntu.com/ubuntu focal-updates InRelease\nHit:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease\nReading package lists...\nReading package lists...\nBuilding dependency tree...\nReading state information...\ngit is already the newest version (1:2.25.1-1ubuntu3.4).\n0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\nW: --force-yes is deprecated, use one of the options starting with --allow instead.\nCloning into 'scikit-learn_bench'...\nHEAD is now at 8c9e528 Update copyright for 2021 year (#47)\n"]}}],"execution_count":0},{"cell_type":"code","source":["%sh\ncd /home/intel/scikit-learn_bench/configs/\nwget https://raw.githubusercontent.com/oap-project/oap-tools/master/integrations/ml/databricks/benchmark/skl_config_databricks.json "],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8cb703b0-0e9a-403b-be3f-32f8676b5568"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"--2022-06-22 11:28:54--  https://raw.githubusercontent.com/oap-project/oap-tools/master/integrations/ml/databricks/benchmark/skl_config_databricks.json\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 12573 (12K) [text/plain]\nSaving to: ‘skl_config_databricks.json’\n\n     0K .......... ..                                         100% 59.6M=0s\n\n2022-06-22 11:28:54 (59.6 MB/s) - ‘skl_config_databricks.json’ saved [12573/12573]\n\nREADME.md\nconfig_example.json\ncuml_config.json\nlgbm_mb_cpu_config.json\nskl_config.json\nskl_config_databricks.json\ntesting\nxgb_cpu_config.json\nxgb_gpu_config.json\nxgb_mb_cpu_config.json\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["--2022-06-22 11:28:54--  https://raw.githubusercontent.com/oap-project/oap-tools/master/integrations/ml/databricks/benchmark/skl_config_databricks.json\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 12573 (12K) [text/plain]\nSaving to: ‘skl_config_databricks.json’\n\n     0K .......... ..                                         100% 59.6M=0s\n\n2022-06-22 11:28:54 (59.6 MB/s) - ‘skl_config_databricks.json’ saved [12573/12573]\n\nREADME.md\nconfig_example.json\ncuml_config.json\nlgbm_mb_cpu_config.json\nskl_config.json\nskl_config_databricks.json\ntesting\nxgb_cpu_config.json\nxgb_gpu_config.json\nxgb_mb_cpu_config.json\n"]}}],"execution_count":0},{"cell_type":"code","source":["%sh\ncd /home/intel/scikit-learn_bench\nexport USE_DAAL4PY_SKLEARN=YES\nsed -i.bak 's/params.n_jobs/1/' sklearn_bench/log_reg.py\npython runner.py --configs /dbfs/FileStore/skl_config_databricks.json  --verbose INFO |tee -a /dbfs/FileStore/sklearn_bench_log.txt"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"559eafe5-47da-4aa1-aa32-621a0547ca55"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"INFO: Config: /dbfs/FileStore/skl_post_config.json\ntee: /dbfs/FileStore/skl_bench_res/stock_sklearn.txt: Operation not supported\nINFO: kmeans algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/kmeans.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --time-method box_filter --time-limit 50 --n-clusters 1000 --maxiter 50 --tol 0.0 --file-X-train data/synthetic-blobs-1000-X-train-1000000x20.npy --dataset-name synthetic_blobs\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 117.50371591600015,\n        \"davies_bouldin_score\": 2.143728296757521\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 4.0138183451665554,\n        \"davies_bouldin_score\": 2.143728296757521\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: ridge algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/ridge.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --alpha 5 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 1.402384320124952,\n        \"rmse\": 0.00014827376681711014\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 0.06697629573328515,\n        \"rmse\": 0.00014827376681711014\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: linear algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/linear.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 4.647106784000243,\n        \"rmse\": 7.186717743060917e-13\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 0.06566072438944455,\n        \"rmse\": 7.186717743060917e-13\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: log_reg algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/log_reg.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --maxiter 100 --tol 0 --file-X-train data/synthetic-classification-5-X-train-10000000x20.npy --file-y-train data/synthetic-classification-5-y-train-10000000x20.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 66.04821622099917,\n        \"accuracy[%]\": 52.18992\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 0.6672714395997901,\n        \"accuracy[%]\": 52.18992\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: svm algorithm: 1 case(s), 1 dataset(s)\n\nINFO: a9a dataset is downloaded\nINFO: reading CSV file...\nINFO: dataset a9a ready.\nINFO: python sklearn_bench/svm.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 -C 500.0 --kernel rbf --file-X-train data/a9a_x_train.csv --file-y-train data/a9a_y_train.csv --file-X-test data/a9a_x_test.csv --file-y-test data/a9a_y_test.csv --dataset-name a9a\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 191.9776796469996,\n        \"accuracy[%]\": 86.681340055793\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 63.232861702000264,\n        \"accuracy[%]\": 84.90121813901116\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: knn_clsf algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/knn_clsf.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float32 --method kd_tree --file-X-train data/synthetic-classification-10-X-train-250000x16.npy --file-y-train data/synthetic-classification-10-y-train-250000x16.npy --file-X-test data/synthetic-classification-10-X-test-250000x16.npy --file-y-test data/synthetic-classification-10-y-test-250000x16.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 0.8790563570000813,\n        \"accuracy[%]\": 93.77640000000001\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 1499.7215393650004,\n        \"accuracy[%]\": 90.11479999999999\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nWARNING: benchmark running had runtime errors\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["INFO: Config: /dbfs/FileStore/skl_post_config.json\ntee: /dbfs/FileStore/skl_bench_res/stock_sklearn.txt: Operation not supported\nINFO: kmeans algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/kmeans.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --time-method box_filter --time-limit 50 --n-clusters 1000 --maxiter 50 --tol 0.0 --file-X-train data/synthetic-blobs-1000-X-train-1000000x20.npy --dataset-name synthetic_blobs\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 117.50371591600015,\n        \"davies_bouldin_score\": 2.143728296757521\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 4.0138183451665554,\n        \"davies_bouldin_score\": 2.143728296757521\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: ridge algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/ridge.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --alpha 5 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 1.402384320124952,\n        \"rmse\": 0.00014827376681711014\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 0.06697629573328515,\n        \"rmse\": 0.00014827376681711014\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: linear algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/linear.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 4.647106784000243,\n        \"rmse\": 7.186717743060917e-13\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 0.06566072438944455,\n        \"rmse\": 7.186717743060917e-13\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: log_reg algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/log_reg.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 --maxiter 100 --tol 0 --file-X-train data/synthetic-classification-5-X-train-10000000x20.npy --file-y-train data/synthetic-classification-5-y-train-10000000x20.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 66.04821622099917,\n        \"accuracy[%]\": 52.18992\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 0.6672714395997901,\n        \"accuracy[%]\": 52.18992\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: svm algorithm: 1 case(s), 1 dataset(s)\n\nINFO: a9a dataset is downloaded\nINFO: reading CSV file...\nINFO: dataset a9a ready.\nINFO: python sklearn_bench/svm.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float64 -C 500.0 --kernel rbf --file-X-train data/a9a_x_train.csv --file-y-train data/a9a_y_train.csv --file-X-test data/a9a_x_test.csv --file-y-test data/a9a_y_test.csv --dataset-name a9a\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 191.9776796469996,\n        \"accuracy[%]\": 86.681340055793\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 63.232861702000264,\n        \"accuracy[%]\": 84.90121813901116\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nINFO: knn_clsf algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/knn_clsf.py --arch 0621-131916-grcesc2u-10-139-64-4 --data-format pandas --data-order F --dtype float32 --method kd_tree --file-X-train data/synthetic-classification-10-X-train-250000x16.npy --file-y-train data/synthetic-classification-10-y-train-250000x16.npy --file-X-test data/synthetic-classification-10-X-test-250000x16.npy --file-y-test data/synthetic-classification-10-y-test-250000x16.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 0.8790563570000813,\n        \"accuracy[%]\": 93.77640000000001\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 1499.7215393650004,\n        \"accuracy[%]\": 90.11479999999999\n    }\n]\n\nWARNING: Error in benchmark: \nFailed to import daal4py.sklearn.patch_sklearn.Use stock version scikit-learn\nWARNING: benchmark running had runtime errors\n"]}}],"execution_count":0},{"cell_type":"code","source":["%sh \ncat  /dbfs/FileStore/sklearn_bench_log.txt"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c608875a-ba43-4fef-8dbc-fe15fe0987f7"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"INFO: Config: /dbfs/FileStore/skl_post_config.json\nINFO: kmeans algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/kmeans.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --time-method box_filter --time-limit 50 --n-clusters 1000 --maxiter 50 --tol 0.0 --file-X-train data/synthetic-blobs-1000-X-train-1000000x20.npy --dataset-name synthetic_blobs\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 131.5083111819995,\n        \"davies_bouldin_score\": 2.143728296757521\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 4.141849043307677,\n        \"davies_bouldin_score\": 2.143728296757521\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: ridge algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/ridge.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --alpha 5 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 1.475802742857013,\n        \"rmse\": 0.00014827376681711014\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 0.0751042173932843,\n        \"rmse\": 0.00014827376681711014\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: linear algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/linear.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 5.402836543000376,\n        \"rmse\": 5.387648090191121e-13\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 0.07181622460645118,\n        \"rmse\": 5.387648090191121e-13\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: log_reg algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/log_reg.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --maxiter 100 --tol 0 --file-X-train data/synthetic-classification-5-X-train-10000000x20.npy --file-y-train data/synthetic-classification-5-y-train-10000000x20.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 6.215999904499768,\n        \"accuracy[%]\": 52.18992\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 0.70448997328575,\n        \"accuracy[%]\": 52.18992\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: svm algorithm: 1 case(s), 1 dataset(s)\n\nINFO: a9a dataset is downloaded\nINFO: reading CSV file...\nINFO: dataset a9a ready.\nINFO: python sklearn_bench/svm.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 -C 500.0 --kernel rbf --file-X-train data/a9a_x_train.csv --file-y-train data/a9a_y_train.csv --file-X-test data/a9a_x_test.csv --file-y-test data/a9a_y_test.csv --dataset-name a9a\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 212.39110929100025,\n        \"accuracy[%]\": 86.681340055793\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 56.57654099800038,\n        \"accuracy[%]\": 84.90121813901116\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: knn_clsf algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/knn_clsf.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float32 --method kd_tree --file-X-train data/synthetic-classification-10-X-train-250000x16.npy --file-y-train data/synthetic-classification-10-y-train-250000x16.npy --file-X-test data/synthetic-classification-10-X-test-250000x16.npy --file-y-test data/synthetic-classification-10-y-test-250000x16.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 0.91992382290912,\n        \"accuracy[%]\": 93.77640000000001\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 1766.5564730670003,\n        \"accuracy[%]\": 90.11479999999999\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nWARNING: benchmark running had runtime errors\n","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"ansi","arguments":{}}},"output_type":"display_data","data":{"text/plain":["INFO: Config: /dbfs/FileStore/skl_post_config.json\nINFO: kmeans algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/kmeans.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --time-method box_filter --time-limit 50 --n-clusters 1000 --maxiter 50 --tol 0.0 --file-X-train data/synthetic-blobs-1000-X-train-1000000x20.npy --dataset-name synthetic_blobs\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 131.5083111819995,\n        \"davies_bouldin_score\": 2.143728296757521\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kmeans\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_blobs\",\n            \"rows\": 1000000,\n            \"columns\": 20,\n            \"n_clusters\": 1000\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"auto\",\n            \"copy_x\": true,\n            \"init\": \"random\",\n            \"max_iter\": 50,\n            \"n_clusters\": 1000,\n            \"n_init\": 1,\n            \"n_jobs\": \"deprecated\",\n            \"precompute_distances\": \"deprecated\",\n            \"random_state\": null,\n            \"tol\": 0.0,\n            \"verbose\": 0\n        },\n        \"time[s]\": 4.141849043307677,\n        \"davies_bouldin_score\": 2.143728296757521\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: ridge algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/ridge.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --alpha 5 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 1.475802742857013,\n        \"rmse\": 0.00014827376681711014\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"ridge_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"alpha\": 5.0,\n            \"copy_X\": true,\n            \"fit_intercept\": true,\n            \"max_iter\": null,\n            \"normalize\": false,\n            \"random_state\": null,\n            \"solver\": \"auto\",\n            \"tol\": 0.001\n        },\n        \"time[s]\": 0.0751042173932843,\n        \"rmse\": 0.00014827376681711014\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: linear algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/linear.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --file-X-train data/synthetic-regression-X-train-10000000x20.npy --file-y-train data/synthetic-regression-y-train-10000000x20.npy --dataset-name synthetic_regression\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 5.402836543000376,\n        \"rmse\": 5.387648090191121e-13\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"linear_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_regression\",\n            \"rows\": 10000000,\n            \"columns\": 20\n        },\n        \"algorithm_parameters\": {\n            \"copy_X\": false,\n            \"fit_intercept\": true,\n            \"n_jobs\": null,\n            \"normalize\": false,\n            \"positive\": false\n        },\n        \"time[s]\": 0.07181622460645118,\n        \"rmse\": 5.387648090191121e-13\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: log_reg algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/log_reg.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 --maxiter 100 --tol 0 --file-X-train data/synthetic-classification-5-X-train-10000000x20.npy --file-y-train data/synthetic-classification-5-y-train-10000000x20.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 6.215999904499768,\n        \"accuracy[%]\": 52.18992\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"logistic_regression\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 10000000,\n            \"columns\": 20,\n            \"classes\": 5\n        },\n        \"algorithm_parameters\": {\n            \"C\": 1.0,\n            \"class_weight\": null,\n            \"dual\": false,\n            \"fit_intercept\": true,\n            \"intercept_scaling\": 1,\n            \"l1_ratio\": null,\n            \"max_iter\": 100,\n            \"multi_class\": \"multinomial\",\n            \"n_jobs\": 1,\n            \"penalty\": \"l2\",\n            \"random_state\": null,\n            \"solver\": \"lbfgs\",\n            \"tol\": 1e-10,\n            \"verbose\": false,\n            \"warm_start\": false\n        },\n        \"time[s]\": 0.70448997328575,\n        \"accuracy[%]\": 52.18992\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: svm algorithm: 1 case(s), 1 dataset(s)\n\nINFO: a9a dataset is downloaded\nINFO: reading CSV file...\nINFO: dataset a9a ready.\nINFO: python sklearn_bench/svm.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float64 -C 500.0 --kernel rbf --file-X-train data/a9a_x_train.csv --file-y-train data/a9a_y_train.csv --file-X-test data/a9a_x_test.csv --file-y-test data/a9a_y_test.csv --dataset-name a9a\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 212.39110929100025,\n        \"accuracy[%]\": 86.681340055793\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"svc\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float64\",\n            \"dataset_name\": \"a9a\",\n            \"rows\": 39073,\n            \"columns\": 123,\n            \"classes\": 2\n        },\n        \"algorithm_parameters\": {\n            \"C\": 500.0,\n            \"break_ties\": false,\n            \"cache_size\": 8192.0,\n            \"class_weight\": null,\n            \"coef0\": 0.0,\n            \"decision_function_shape\": \"ovr\",\n            \"degree\": 3,\n            \"gamma\": 0.008130081300813009,\n            \"kernel\": \"rbf\",\n            \"max_iter\": -1,\n            \"probability\": false,\n            \"random_state\": null,\n            \"shrinking\": true,\n            \"tol\": 0.001,\n            \"verbose\": false\n        },\n        \"time[s]\": 56.57654099800038,\n        \"accuracy[%]\": 84.90121813901116\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nINFO: knn_clsf algorithm: 1 case(s), 1 dataset(s)\n\nINFO: python sklearn_bench/knn_clsf.py --arch 0621-131009-b9ah2jcs-10-139-64-6 --data-format pandas --data-order F --dtype float32 --method kd_tree --file-X-train data/synthetic-classification-10-X-train-250000x16.npy --file-y-train data/synthetic-classification-10-y-train-250000x16.npy --file-X-test data/synthetic-classification-10-X-test-250000x16.npy --file-y-test data/synthetic-classification-10-y-test-250000x16.npy --dataset-name synthetic_classification\n[\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"training\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 0.91992382290912,\n        \"accuracy[%]\": 93.77640000000001\n    },\n    {\n        \"library\": \"sklearn\",\n        \"algorithm\": \"kd_tree_knn_classification\",\n        \"stage\": \"prediction\",\n        \"input_data\": {\n            \"data_format\": \"pandas\",\n            \"data_order\": \"F\",\n            \"data_type\": \"float32\",\n            \"dataset_name\": \"synthetic_classification\",\n            \"rows\": 250000,\n            \"columns\": 16,\n            \"classes\": 10\n        },\n        \"algorithm_parameters\": {\n            \"algorithm\": \"kd_tree\",\n            \"leaf_size\": 30,\n            \"metric\": \"euclidean\",\n            \"metric_params\": null,\n            \"n_jobs\": null,\n            \"n_neighbors\": 5,\n            \"p\": 2,\n            \"weights\": \"uniform\"\n        },\n        \"time[s]\": 1766.5564730670003,\n        \"accuracy[%]\": 90.11479999999999\n    }\n]\n\nWARNING: Error in benchmark: \ndispatcher.py:151: FutureWarning: \nScikit-learn patching with daal4py is deprecated and will be removed in the future.\nUse Intel(R) Extension for Scikit-learn* module instead (pip install scikit-learn-intelex).\nTo enable patching, please use one of the following options:\n1) From the command line:\n    python -m sklearnex <your_script>\n2) From your script:\n    from sklearnex import patch_sklearn\n    patch_sklearn()\n\nWARNING: benchmark running had runtime errors\n"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"benchmark_sklearn","dashboards":[],"notebookMetadata":{"pythonIndentUnit":2},"language":"python","widgets":{},"notebookOrigID":2084008678105324}},"nbformat":4,"nbformat_minor":0}