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* Adding workflow for benchmarking queries with filters Signed-off-by: Martin Gaievski <[email protected]> (cherry picked from commit 79ae6c2) Co-authored-by: Martin Gaievski <[email protected]>
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| # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # The OpenSearch Contributors require contributions made to | ||
| # this file be licensed under the Apache-2.0 license or a | ||
| # compatible open source license. | ||
| """ | ||
| Script builds complex dataset with additional attributes from exiting dataset that has only vectors. | ||
| Additional attributes are predefined in the script: color, taste, age. Only HDF5 format of vector dataset is supported. | ||
| Output dataset file will have additional dataset 'attributes' with multiple columns, each column corresponds to one attribute | ||
| from an attribute set, and value is generated at random, e.g.: | ||
| 0: green None 71 | ||
| 1: green bitter 28 | ||
| there is no explicit index reference in 'attributes' dataset, index of the row corresponds to a document id. | ||
| For instance, in example above two rows of fields mapped to documents with ids '0' and '1'. | ||
| If 'generate_filters' flag is set script generates additional dataset of neighbours (ground truth) for each filter type. | ||
| Output is a new file with several datasets, each dataset corresponds to one filter. Datasets are named 'neighbour_filter_X' | ||
| where X is 1 based index of particular filter. | ||
| Each dataset has rows with array of integers, where integer corresponds to | ||
| a document id from original dataset with additional fields. Array ca have -1 values that are treated as null, this is because | ||
| subset of filtered documents is same of smaller than original set. | ||
| For example, dataset file content may look like : | ||
| neighbour_filter_1: [[ 2, 5, -1], | ||
| [ 3, 1, -1], | ||
| [ 2 5, 7]] | ||
| neighbour_filter_2: [[-1, -1, -1], | ||
| [ 5, 6, -1], | ||
| [ 4, 2, 1]] | ||
| In this case we do have datasets for two filters, 3 query results for each. [2, 5, -1] indicates that for first query | ||
| if filter 1 is used most similar document is with id 2, next similar is 5, and the rest do not pass filter 1 criteria. | ||
| Example of script usage: | ||
| create new hdf5 file with attribute dataset | ||
| add-filters-to-dataset.py ~/dev/opensearch/k-NN/benchmarks/perf-tool/dataset/data.hdf5 ~/dev/opensearch/datasets/data-with-attr True False | ||
| create new hdf5 file with filter datasets | ||
| add-filters-to-dataset.py ~/dev/opensearch/k-NN/benchmarks/perf-tool/dataset/data-with-attr.hdf5 ~/dev/opensearch/datasets/data-with-filters False True | ||
| """ | ||
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| import getopt | ||
| import os | ||
| import random | ||
| import sys | ||
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| import h5py | ||
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| from osb.extensions.data_set import HDF5DataSet | ||
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| class _Dataset: | ||
| """Type of dataset container for data with additional attributes""" | ||
| DEFAULT_TYPE = HDF5DataSet.FORMAT_NAME | ||
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| def create_dataset(self, source_dataset_path, out_file_path, generate_attrs: bool, generate_filters: bool) -> None: | ||
| path_elements = os.path.split(os.path.abspath(source_dataset_path)) | ||
| data_set_dir = path_elements[0] | ||
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| # For HDF5, because multiple data sets can be grouped in the same file, | ||
| # we will build data sets in memory and not write to disk until | ||
| # _flush_data_sets_to_disk is called | ||
| # read existing dataset | ||
| data_hdf5 = os.path.join(os.path.dirname(os.path.realpath('/')), source_dataset_path) | ||
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| with h5py.File(data_hdf5, "r") as hf: | ||
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| if generate_attrs: | ||
| data_set_w_attr = self.create_dataset_file(out_file_path, self.DEFAULT_TYPE, data_set_dir) | ||
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| possible_colors = ['red', 'green', 'yellow', 'blue', None] | ||
| possible_tastes = ['sweet', 'salty', 'sour', 'bitter', None] | ||
| max_age = 100 | ||
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| for key in hf.keys(): | ||
| if key not in ['neighbors', 'test', 'train']: | ||
| continue | ||
| data_set_w_attr.create_dataset(key, data=hf[key][()]) | ||
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| attributes = [] | ||
| for i in range(len(hf['train'])): | ||
| attr = [random.choice(possible_colors), random.choice(possible_tastes), | ||
| random.randint(0, max_age + 1)] | ||
| attributes.append(attr) | ||
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| data_set_w_attr.create_dataset('attributes', (len(attributes), 3), 'S10', data=attributes) | ||
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| data_set_w_attr.flush() | ||
| data_set_w_attr.close() | ||
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| if generate_filters: | ||
| attributes = hf['attributes'][()] | ||
| expected_neighbors = hf['neighbors'][()] | ||
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| data_set_filters = self.create_dataset_file(out_file_path, self.DEFAULT_TYPE, data_set_dir) | ||
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| def filter1(attributes, vector_idx): | ||
| if attributes[vector_idx][0].decode() == 'red' and int(attributes[vector_idx][2].decode()) >= 20: | ||
| return True | ||
| else: | ||
| return False | ||
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| self.apply_filter(expected_neighbors, attributes, data_set_filters, 'neighbors_filter_1', filter1) | ||
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| # filter 2 - color = blue or None and taste = 'salty' | ||
| def filter2(attributes, vector_idx): | ||
| if (attributes[vector_idx][0].decode() == 'blue' or attributes[vector_idx][ | ||
| 0].decode() == 'None') and attributes[vector_idx][1].decode() == 'salty': | ||
| return True | ||
| else: | ||
| return False | ||
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| self.apply_filter(expected_neighbors, attributes, data_set_filters, 'neighbors_filter_2', filter2) | ||
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| # filter 3 - color and taste are not None and age is between 20 and 80 | ||
| def filter3(attributes, vector_idx): | ||
| if attributes[vector_idx][0].decode() != 'None' and attributes[vector_idx][ | ||
| 1].decode() != 'None' and 20 <= \ | ||
| int(attributes[vector_idx][2].decode()) <= 80: | ||
| return True | ||
| else: | ||
| return False | ||
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| self.apply_filter(expected_neighbors, attributes, data_set_filters, 'neighbors_filter_3', filter3) | ||
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| # filter 4 - color green or blue and taste is bitter and age is between (30, 60) | ||
| def filter4(attributes, vector_idx): | ||
| if (attributes[vector_idx][0].decode() == 'green' or attributes[vector_idx][0].decode() == 'blue') \ | ||
| and (attributes[vector_idx][1].decode() == 'bitter') \ | ||
| and 30 <= int(attributes[vector_idx][2].decode()) <= 60: | ||
| return True | ||
| else: | ||
| return False | ||
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| self.apply_filter(expected_neighbors, attributes, data_set_filters, 'neighbors_filter_4', filter4) | ||
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| # filter 5 color is (green or blue or yellow) or taste = sweet or age is between (30, 70) | ||
| def filter5(attributes, vector_idx): | ||
| if attributes[vector_idx][0].decode() == 'green' or attributes[vector_idx][0].decode() == 'blue' \ | ||
| or attributes[vector_idx][0].decode() == 'yellow' \ | ||
| or attributes[vector_idx][1].decode() == 'sweet' \ | ||
| or 30 <= int(attributes[vector_idx][2].decode()) <= 70: | ||
| return True | ||
| else: | ||
| return False | ||
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| self.apply_filter(expected_neighbors, attributes, data_set_filters, 'neighbors_filter_5', filter5) | ||
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| data_set_filters.flush() | ||
| data_set_filters.close() | ||
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| def apply_filter(self, expected_neighbors, attributes, data_set_w_filtering, filter_name, filter_func): | ||
| neighbors_filter = [] | ||
| filtered_count = 0 | ||
| for expected_neighbors_row in expected_neighbors: | ||
| neighbors_filter_row = [-1] * len(expected_neighbors_row) | ||
| idx = 0 | ||
| for vector_idx in expected_neighbors_row: | ||
| if filter_func(attributes, vector_idx): | ||
| neighbors_filter_row[idx] = vector_idx | ||
| idx += 1 | ||
| filtered_count += 1 | ||
| neighbors_filter.append(neighbors_filter_row) | ||
| overall_count = len(expected_neighbors) * len(expected_neighbors[0]) | ||
| perc = float(filtered_count / overall_count) * 100 | ||
| print('ground truth size for {} is {}, percentage {}'.format(filter_name, filtered_count, perc)) | ||
| data_set_w_filtering.create_dataset(filter_name, data=neighbors_filter) | ||
| return expected_neighbors | ||
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| def create_dataset_file(self, file_name, extension, data_set_dir) -> h5py.File: | ||
| data_set_file_name = "{}.{}".format(file_name, extension) | ||
| data_set_path = os.path.join(data_set_dir, data_set_file_name) | ||
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| data_set_w_filtering = h5py.File(data_set_path, 'a') | ||
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| return data_set_w_filtering | ||
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| def main(argv): | ||
| opts, args = getopt.getopt(argv, "") | ||
| in_file_path = args[0] | ||
| out_file_path = args[1] | ||
| generate_attr = str2bool(args[2]) | ||
| generate_filters = str2bool(args[3]) | ||
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| worker = _Dataset() | ||
| worker.create_dataset(in_file_path, out_file_path, generate_attr, generate_filters) | ||
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| def str2bool(v): | ||
| return v.lower() in ("yes", "true", "t", "1") | ||
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| if __name__ == "__main__": | ||
| main(sys.argv[1:]) |
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