Add incremental algorithms support

Author: olegkkruglov

configs/incremental.json

@@ -0,0 +1,144 @@
+{
+	"PARAMETERS_SETS": {
+		"common": {
+			"bench": { "n_runs": 10, "time_limit": 60 }
+		},
+
+		"covariance data": {
+			"data": [
+				{
+					"source": "make_blobs",
+					"generation_kwargs": {
+						"centers": 1,
+						"n_samples": 1000,
+						"n_features": [16, 64]
+					},
+					"split_kwargs": { "ignore": true }
+				},
+				{
+					"dataset_sequence": [
+						{
+							"source": "make_blobs",
+							"generation_kwargs": {
+								"centers": 1,
+								"n_samples": 1000,
+								"n_features": 16
+							},
+							"split_kwargs": { "ignore": true }
+						},
+						{
+							"source": "make_blobs",
+							"generation_kwargs": {
+								"centers": 1,
+								"n_samples": 2000,
+								"n_features": 16
+							},
+							"split_kwargs": { "ignore": true }
+						}
+					]
+				}
+			]
+		},
+
+		"basic_statistics data": {
+			"data": {
+				"source": "make_blobs",
+				"generation_kwargs": {
+					"centers": 1,
+					"n_samples": 10000,
+					"n_features": [16, 64]
+				},
+				"split_kwargs": { "ignore": true }
+			}
+		},
+		"linear_regression data": {
+			"data": {
+				"source": "make_regression",
+				"split_kwargs": { "train_size": 0.2, "test_size": 0.8 },
+				"generation_kwargs": {
+					"n_samples": 5000,
+					"n_features": [40, 100],
+					"n_informative": 5,
+					"noise": 2.0
+				}
+			}
+		},
+
+		"pca data": {
+			"data": {
+				"source": "make_blobs",
+				"generation_kwargs": {
+					"centers": 1,
+					"n_samples": 1000,
+					"n_features": [16, 64]
+				},
+				"split_kwargs": { "ignore": true }
+			}
+		},
+
+		"covariance": {
+			"algorithm":
+			[
+				{
+					"estimator": "IncrementalEmpiricalCovariance",
+					"library": "sklearnex.covariance",
+					"estimator_methods": {"training": "partial_fit"},
+					"num_batches": {
+						"training": 2
+					}
+				}
+			]
+		},
+		"basic_statistics": {
+			"algorithm":
+			[
+				{
+					"estimator": "IncrementalBasicStatistics",
+					"library": "sklearnex.basic_statistics",
+					"num_batches": {
+						"training": 2
+					}
+				}
+			]
+		},
+		"linear_regression": {
+			"algorithm":
+			[
+				{
+					"estimator": "IncrementalLinearRegression",
+					"library": "sklearnex.linear_model",
+					"num_batches": {
+						"training": 2
+					}
+				}
+			]
+		},
+		"pca": {
+			"algorithm":
+			[
+				{
+					"estimator": "IncrementalPCA",
+					"library": "sklearnex.preview.decomposition",
+					"num_batches": {
+						"training": 2
+					}
+				}
+			]
+		}	
+
+	},
+	"TEMPLATES": {
+		"covariance": {
+			"SETS": ["common", "covariance", "covariance data"]
+		},
+		"basic_statistics": {
+			"SETS": ["common", "basic_statistics", "basic_statistics data"]
+		},
+		"linear_regression": {
+			"SETS": ["common", "linear_regression", "linear_regression data"]
+		},
+		"pca": {
+			"SETS": ["common", "pca", "pca data"]
+		}				
+	}		
+}

sklbench/benchmarks/custom_function.py

@@ -16,15 +16,15 @@
 
 from typing import Dict, List, Tuple
 
-from ..datasets import load_data
+from ..datasets import load_dataset
 from ..datasets.transformer import split_and_transform_data
 from ..utils.bench_case import get_bench_case_value
 from ..utils.common import get_module_members
 from ..utils.config import bench_case_filter
 from ..utils.custom_types import BenchCase
 from ..utils.logger import logger
 from ..utils.measurement import measure_case
-from ..utils.special_params import assign_case_special_values_on_run
+from ..datasets.special_params import assign_case_special_values_on_run
 from .common import enrich_result, main_template
 
 
@@ -77,7 +77,7 @@ def main(bench_case: BenchCase, filters: List[BenchCase]):
     function_instance = get_function_instance(library_name, function_name)
 
     # load and transform data
-    data, data_description = load_data(bench_case)
+    data, data_description = load_dataset(bench_case)
     (x_train, x_test, y_train, y_test), data_description = split_and_transform_data(
         bench_case, data, data_description
     )