Baunsgaard commented on a change in pull request #993: URL: https://github.com/apache/systemds/pull/993#discussion_r457212235
########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution + +## Pipeline design and primitives + +We provide two example scripts, `entity-clustering.dml` and `binary-entity-resolution.dml`. These handle reading input +files and writing output files and call functions provided in `primitives/pipeline.dml`. + +### Input files + +The provided scripts can read two types of input files. The token file is mandatory since it contains the row identifiers, +but the embedding file is optional. The actual use of tokens and/or embeddings can be configured via command line parameters +to the scripts. + +##### Token files + +This file type is a CSV file with 3 columns. The first column is the string or integer row identifier, the second is the +string token, and the third is the number of occurences. This simple format is used as a bag-of-words representation. + +##### Embedding files + +This file type is a CSV matrix file with each row containing arbitrary-dimensional embeddings. The order of row identifiers +is assumed to be the same as in the token file. This saves some computation and storage time, but could be changed with +some modifications to the example scripts. + +### Primitives + +While the example scripts may be sufficient for many simple use cases, we aim to provide a toolkit of composable functions +to facilitate more complex tasks. The top-level pipelines are defined as a couple of functions in `primitives/pipeline.dml`. +The goal is that it should be relatively easy to copy one of these pipelines and swap out the primitive functions used +to create a custom pipeline. + +To convert the input token file into a bag-of-words contingency table representation, we provide the functions +`convert_frame_tokens_to_matrix_bow` and `convert_frame_tokens_to_matrix_bow_2` in `primitives/preprocessing.dml`. +The latter is used to compute a compatible contigency table with matching vocabulary for binary entity resolution. + +We provide naive, constant-size blocking and locality-sensitive hashing (LSH) as functions in `primitives/blocking.dml`. + +For entity clustering, we only provide a simple clustering approach which makes all connected components in an adjacency +matrix fully connected. This function is located in `primitives/clustering.dml`. + +To restore an adjacency matrix to a list of pairs, we provide the functions `untable` and `untable_offset` in +`primitives/postprocessing.dml`. + +Finally, `primitives/evaluation.dml` defines some metrics that can be used to evaluate the performance of the entity +resolution pipelines. They are used in the script `eval-entity-resolution.dml`. + +## Testing and Examples + +There is a test data repository that was used to develop these scripts at +[repo](https://github.com/skogler/systemds-amls-project-data). In the examples below, it is assumed that this repo is +cloned as `data` in the SystemDS root folder. The data in that repository is sourced from the Uni Leipzig entity resolution +[benchmark](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution). + +### Preprocessing + +Since there is no tokenization functionality in SystemDS yet, we provide a Python preprocessing script in the data repository +that tokenizes the text columns and performs some simple embedding lookup using Glove embeddings. + +The tokens are written as CSV files to enable Bag-of-Words representations as well as matrices with combined embeddings. D +epending on the type of data, one or the other or a combination of both may be better. The SystemDS DML scripts can be +called with different parameters to experiment with this. Review comment: I believe it should be possible to do in systemds, but currently it is a bit of a hassle. ########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution + +## Pipeline design and primitives + +We provide two example scripts, `entity-clustering.dml` and `binary-entity-resolution.dml`. These handle reading input +files and writing output files and call functions provided in `primitives/pipeline.dml`. + +### Input files + +The provided scripts can read two types of input files. The token file is mandatory since it contains the row identifiers, +but the embedding file is optional. The actual use of tokens and/or embeddings can be configured via command line parameters +to the scripts. + +##### Token files + +This file type is a CSV file with 3 columns. The first column is the string or integer row identifier, the second is the +string token, and the third is the number of occurences. This simple format is used as a bag-of-words representation. + +##### Embedding files + +This file type is a CSV matrix file with each row containing arbitrary-dimensional embeddings. The order of row identifiers +is assumed to be the same as in the token file. This saves some computation and storage time, but could be changed with +some modifications to the example scripts. + +### Primitives + +While the example scripts may be sufficient for many simple use cases, we aim to provide a toolkit of composable functions +to facilitate more complex tasks. The top-level pipelines are defined as a couple of functions in `primitives/pipeline.dml`. +The goal is that it should be relatively easy to copy one of these pipelines and swap out the primitive functions used +to create a custom pipeline. + +To convert the input token file into a bag-of-words contingency table representation, we provide the functions +`convert_frame_tokens_to_matrix_bow` and `convert_frame_tokens_to_matrix_bow_2` in `primitives/preprocessing.dml`. +The latter is used to compute a compatible contigency table with matching vocabulary for binary entity resolution. + +We provide naive, constant-size blocking and locality-sensitive hashing (LSH) as functions in `primitives/blocking.dml`. + +For entity clustering, we only provide a simple clustering approach which makes all connected components in an adjacency +matrix fully connected. This function is located in `primitives/clustering.dml`. + +To restore an adjacency matrix to a list of pairs, we provide the functions `untable` and `untable_offset` in +`primitives/postprocessing.dml`. + +Finally, `primitives/evaluation.dml` defines some metrics that can be used to evaluate the performance of the entity +resolution pipelines. They are used in the script `eval-entity-resolution.dml`. + +## Testing and Examples + +There is a test data repository that was used to develop these scripts at +[repo](https://github.com/skogler/systemds-amls-project-data). In the examples below, it is assumed that this repo is +cloned as `data` in the SystemDS root folder. The data in that repository is sourced from the Uni Leipzig entity resolution +[benchmark](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution). Review comment: How did this implementation compare to this benchmark? ########## File path: pom.xml ########## @@ -257,12 +257,6 @@ <version>3.0.0-M4</version><!--$NO-MVN-MAN-VER$ --> <configuration> <skipTests>${skipTests}</skipTests> - <parallel>classes</parallel> - <!-- <useUnlimitedThreads>true</useUnlimitedThreads> --> - <threadCount>12</threadCount> - <!-- 1C means the number of threads times 1 possible maximum forks for testing--> - <forkCount>1C</forkCount> - <reuseForks>false</reuseForks> Review comment: I would really appreciate if this was switched back. It is a major time saver when executing the tests to run in parallel. (going from ~3 hours to 20 minutes on my laptop). In your fix you combine the `notThreadsafe` flag and `parameterized tests`. which should be sufficient for executing the tests. Most of the time, it crashes for me because out test setup is not very nice, with TestName and TestDir getting removed and from other tests if the name is equal. This means since we parallelize on classes, the parameterized test launches multiple `classes` that then delete the other tests folders. Another case is if the parallel tests are not in their own forks therefore sharing static resources, resulting in equal temporary directories. ########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution Review comment: I Like this README, but i would suggest moving it to our docs folder under `docs/site/.` and add a link to it from the `/docs/index.md` Once you do that you need to add a header: ```code --- layout: site title: Entity Resolution --- <!-- {% comment %} Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %} --> ``` ########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution + +## Pipeline design and primitives + +We provide two example scripts, `entity-clustering.dml` and `binary-entity-resolution.dml`. These handle reading input +files and writing output files and call functions provided in `primitives/pipeline.dml`. + +### Input files + +The provided scripts can read two types of input files. The token file is mandatory since it contains the row identifiers, +but the embedding file is optional. The actual use of tokens and/or embeddings can be configured via command line parameters +to the scripts. + +##### Token files + +This file type is a CSV file with 3 columns. The first column is the string or integer row identifier, the second is the +string token, and the third is the number of occurences. This simple format is used as a bag-of-words representation. + +##### Embedding files + +This file type is a CSV matrix file with each row containing arbitrary-dimensional embeddings. The order of row identifiers +is assumed to be the same as in the token file. This saves some computation and storage time, but could be changed with +some modifications to the example scripts. + +### Primitives + +While the example scripts may be sufficient for many simple use cases, we aim to provide a toolkit of composable functions +to facilitate more complex tasks. The top-level pipelines are defined as a couple of functions in `primitives/pipeline.dml`. +The goal is that it should be relatively easy to copy one of these pipelines and swap out the primitive functions used +to create a custom pipeline. + +To convert the input token file into a bag-of-words contingency table representation, we provide the functions +`convert_frame_tokens_to_matrix_bow` and `convert_frame_tokens_to_matrix_bow_2` in `primitives/preprocessing.dml`. +The latter is used to compute a compatible contigency table with matching vocabulary for binary entity resolution. + +We provide naive, constant-size blocking and locality-sensitive hashing (LSH) as functions in `primitives/blocking.dml`. + +For entity clustering, we only provide a simple clustering approach which makes all connected components in an adjacency +matrix fully connected. This function is located in `primitives/clustering.dml`. + +To restore an adjacency matrix to a list of pairs, we provide the functions `untable` and `untable_offset` in +`primitives/postprocessing.dml`. + +Finally, `primitives/evaluation.dml` defines some metrics that can be used to evaluate the performance of the entity +resolution pipelines. They are used in the script `eval-entity-resolution.dml`. + +## Testing and Examples + +There is a test data repository that was used to develop these scripts at +[repo](https://github.com/skogler/systemds-amls-project-data). In the examples below, it is assumed that this repo is Review comment: I'm not a huge fan of the data being located in this repository, but then again i don't know where we would store such a thing in Apache or if it is even possible. opinions @mboehm7 ########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution Review comment: Also somewhere here in the documentation mention the paper it is based on, i noticed it in `pipeline.dml` only: ``` Distributed representations of tuples for entity resolution. Proceedings of the VLDB Endowment ``` Also no LSTM has been used here. ########## File path: src/test/java/org/apache/sysds/test/applications/EntityResolutionBinaryTest.java ########## @@ -0,0 +1,95 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ +package org.apache.sysds.test.applications; + +import org.apache.sysds.test.AutomatedTestBase; +import org.apache.sysds.test.TestConfiguration; +import org.junit.Assert; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import java.nio.file.Files; +import java.nio.file.Paths; +import java.util.Arrays; +import java.util.Collection; + +@RunWith(value = Parameterized.class) [email protected] Review comment: If you want to avoid the NotThreadSafe, i suggest to make multiple tests that call like the bellow. I Know it is not a beautiful and concise as the previous, but would make the test non blocking for the other tests. ```java ... @Test public void test1(){ testScriptEndToEnd(1,1); } public void testScriptEndToEnd(int numLshHashTables, int numLshHyperplanes) ... ``` ########## File path: scripts/staging/entity-resolution/README.md ########## @@ -0,0 +1,99 @@ +# Entity Resolution + +## Pipeline design and primitives + +We provide two example scripts, `entity-clustering.dml` and `binary-entity-resolution.dml`. These handle reading input +files and writing output files and call functions provided in `primitives/pipeline.dml`. + +### Input files + +The provided scripts can read two types of input files. The token file is mandatory since it contains the row identifiers, +but the embedding file is optional. The actual use of tokens and/or embeddings can be configured via command line parameters +to the scripts. + +##### Token files + +This file type is a CSV file with 3 columns. The first column is the string or integer row identifier, the second is the +string token, and the third is the number of occurences. This simple format is used as a bag-of-words representation. + +##### Embedding files + +This file type is a CSV matrix file with each row containing arbitrary-dimensional embeddings. The order of row identifiers +is assumed to be the same as in the token file. This saves some computation and storage time, but could be changed with +some modifications to the example scripts. + +### Primitives + +While the example scripts may be sufficient for many simple use cases, we aim to provide a toolkit of composable functions +to facilitate more complex tasks. The top-level pipelines are defined as a couple of functions in `primitives/pipeline.dml`. +The goal is that it should be relatively easy to copy one of these pipelines and swap out the primitive functions used +to create a custom pipeline. + +To convert the input token file into a bag-of-words contingency table representation, we provide the functions +`convert_frame_tokens_to_matrix_bow` and `convert_frame_tokens_to_matrix_bow_2` in `primitives/preprocessing.dml`. +The latter is used to compute a compatible contigency table with matching vocabulary for binary entity resolution. + +We provide naive, constant-size blocking and locality-sensitive hashing (LSH) as functions in `primitives/blocking.dml`. + +For entity clustering, we only provide a simple clustering approach which makes all connected components in an adjacency +matrix fully connected. This function is located in `primitives/clustering.dml`. + +To restore an adjacency matrix to a list of pairs, we provide the functions `untable` and `untable_offset` in +`primitives/postprocessing.dml`. + +Finally, `primitives/evaluation.dml` defines some metrics that can be used to evaluate the performance of the entity +resolution pipelines. They are used in the script `eval-entity-resolution.dml`. + +## Testing and Examples + +There is a test data repository that was used to develop these scripts at +[repo](https://github.com/skogler/systemds-amls-project-data). In the examples below, it is assumed that this repo is +cloned as `data` in the SystemDS root folder. The data in that repository is sourced from the Uni Leipzig entity resolution +[benchmark](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution). + +### Preprocessing + +Since there is no tokenization functionality in SystemDS yet, we provide a Python preprocessing script in the data repository +that tokenizes the text columns and performs some simple embedding lookup using Glove embeddings. + +The tokens are written as CSV files to enable Bag-of-Words representations as well as matrices with combined embeddings. D +epending on the type of data, one or the other or a combination of both may be better. The SystemDS DML scripts can be +called with different parameters to experiment with this. + +### Entity clustering + +In this case we detect duplicates within one database. As an example, we use the benchmark dataset Affiliations from Uni Leipzig. +For this dataset, embeddings do not work well since the data is mostly just names. Therefore, we encode it as Bag-of-Words vectors +in the example below. This dataset would benefit from more preprocessing, as simply matching words for all the different kinds of +abbreviations does not work particularly well. + +Example command to run on Affiliations dataset: +``` +./bin/systemds ./scripts/algorithms/entity-resolution/entity-clustering.dml -nvargs FX=data/affiliationstrings/affiliationstrings_tokens.csv OUT=data/affiliationstrings/affiliationstrings_res.csv store_mapping=FALSE MX=data/affiliationstrings/affiliationstrings_MX.csv use_embeddings=FALSE XE=data/affiliationstrings/affiliationstrings_embeddings.csv +``` +Evaluation: +``` +./bin/systemds ./scripts/algorithms/entity-resolution/eval-entity-resolution.dml -nvargs FX=data/affiliationstrings/affiliationstrings_res.csv FY=data/affiliationstrings/affiliationstrings_mapping_fixed.csv +``` + +### Binary entity resolution + +In this case we detect duplicate pairs of rows between two databases. As an example, we use the benchmark dataset DBLP-ACM from Uni Leipzig. +Embeddings work really well for this dataset, so the results are quite good with an F1 score of 0.89. + +Example command to run on DBLP-ACM dataset with embeddings: +``` +./bin/systemds ./scripts/algorithms/entity-resolution/binary-entity-resolution.dml -nvargs FY=data/DBLP-ACM/ACM_tokens.csv FX=data/DBLP-ACM/DBLP2_tokens.csv MX=data/DBLP-ACM_MX.csv OUT=data/DBLP-ACM/DBLP-ACM_res.csv XE=data/DBLP-ACM/DBLP2_embeddings.csv YE=data/DBLP-ACM/ACM_embeddings.csv use_embeddings=TRUE +``` +Evaluation: +``` +./bin/systemds ./scripts/algorithms/entity-resolution/eval-entity-resolution.dml -nvargs FX=data/DBLP-ACM/DBLP-ACM_res.csv FY=data/DBLP-ACM/DBLP-ACM_perfectMapping.csv +``` + +## Further Work + +1. Better clustering algorithms. +2. Multi-Probe LSH. +3. Classifier-based matching. +4. Better/built-in tokenization +5. Better/built-in embeddings. Review comment: I think it is great that you include this further work. :star: Could you clarify for me: 1. Better clustering algorithms? are you thinking of something specific? 2. Multi-Probe LSH, could be cool, i think the direction should be making a dedicated operator inside the system, since currently in this implementation there is an overhead in the conversion to double all the time. Then different LSH techniques could easily be compared. 3. I don't know what you intend here could you elaborate? 4. & 5. Are you referring to the glove embedding or something else in specific? ########## File path: src/test/java/org/apache/sysds/test/TestUtils.java ########## @@ -62,9 +62,9 @@ * <li>clean up</li> * </ul> */ -public class TestUtils +public class TestUtils Review comment: I think you might have accidentally reformatted TestUtils. This needs to be done, but i would rather do it in a single PR/Commit. ########## File path: scripts/staging/entity-resolution/entity-clustering.dml ########## @@ -0,0 +1,119 @@ +#------------------------------------------------------------- +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +#------------------------------------------------------------- + +# +# THIS SCRIPT PERFORMS AN ENTITY RESOLUTION PIPELINE FOR CLUSTERING ON A SINGLE FILE +# CONSISTS OF BLOCKING, MATCHING, AND CLUSTERING +# +# INPUT PARAMETERS: +# --------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# --------------------------------------------------------------------------------------------- +# FX String --- Location to read the frame of tokens in bow format +# Each line contains comma separated list of id, token and value +# OUT String --- Location to save the output of maching pairs +# Each line contains comma separated ids of one matched pair +# Third column provides the similarity score +# threshold Double 0.9 Threshold to be considered as a match +# blocking_method String naive Possible values: ["naive", "lsh"]. +# num_blocks Int 1 Number of blocks for naive blocking +# num_hashtables Int 6 Number of hashtables for LSH blocking. +# num_hyperplanes Int 4 Number of hyperplanes for LSH blocking. + +# use_tokens Boolean TRUE Whether to use the tokens of FX to generate predictions +# use_embeddings Boolean FALSE Whether to use the embeddings of XE to generate predictions +# XE String --- Location to read the frame of embedding matrix +# Required if use_embeddings is set to TRUE +# store_mapping Boolean FALSE Whether to store the mapping of transformencode +# MX String --- Location to write the frame of mapping +# Required if store_mapping is set to TRUE +# --------------------------------------------------------------------------------------------- +# OUTPUT: frame of maching pairs +# --------------------------------------------------------------------------------------------- + +source("./scripts/staging/entity-resolution/primitives/preprocessing.dml") as pre; +source("./scripts/staging/entity-resolution/primitives/postprocessing.dml") as post; +source("./scripts/staging/entity-resolution/primitives/pipeline.dml") as pipe; + +# Command Line Arguments +fileFX = $FX; +fileOUT = $OUT; + +threshold = ifdef($threshold, 0.9); +blocking_method = ifdef($blocking_method, "lsh"); +num_blocks = ifdef($num_blocks, 1); +num_hyperplanes = ifdef($num_hyperplanes, 4); +num_hashtables = ifdef($num_hashtables, 6); +use_tokens = ifdef($use_tokens, TRUE); +use_embeddings = ifdef($use_embeddings, FALSE); +# file XE is only required if using embeddings +fileXE = ifdef($XE, ""); +# mapping file is required for evaluation +store_mapping = ifdef($store_mapping, FALSE); +fileMX = ifdef($MX, ""); + +if (!(blocking_method == "naive" | blocking_method == "lsh")) { + print("ERROR: blocking method must be in ['naive', 'lsh']"); +} + +# Read data +FX = read(fileFX); +if (use_embeddings) { + if (fileXE == "") { + print("You need to specify file XE when use_embeddings is set to TRUE"); + } else { + X_embeddings = read(fileXE); + } +} + +# Convert data +[X, MX] = pre::convert_frame_tokens_to_matrix_bow(FX); +if (use_tokens & use_embeddings) { + X = cbind(X, X_embeddings); +} else if (use_tokens) { + # Nothing to do in this case, since X already contains tokens +} else if (use_embeddings) { + X = X_embeddings; +} else { + print("Either use_tokens or use_embeddings needs to be TRUE, using tokens only as default."); +} + +if (store_mapping) { + if (fileMX == "") { + print("You need to specify file MX when store_mapping is set to TRUE."); + } else { + write(MX, fileMX); + } +} + +# Perform clustering +if (blocking_method == "naive") { + CLUSTER = pipe::entity_clustering_pipeline(X, num_blocks, threshold); +} else if (blocking_method == "lsh") { + CLUSTER = pipe::entity_clustering_pipeline_lsh(X, num_hashtables, num_hyperplanes, threshold); +} Review comment: Can you maybe elaborate on performance for these two techniques? If possible could you try adding another case using kmeans (only if applicable and you don't have to change much ) ########## File path: scripts/staging/entity-resolution/primitives/blocking.dml ########## @@ -0,0 +1,292 @@ +#------------------------------------------------------------- +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +#------------------------------------------------------------- + +# Splits the rows of X into num_blocks non-overlapping regions. +# May produce less than num_blocks regions. +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# X matrix --- A dataset with rows to split into blocks. +# num_blocks Integer --- How many blocks to produce. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# BLOCKS Double A column vector with start indices for each block. Has one more row +# than blocks such that the last row is the end index of the last block. +# -------------------------------------------------------------------------------------------- +naive_blocking = function(Matrix[Double] X, Integer num_blocks) return (Matrix[Double] BLOCKS) { + block_size_flt = nrow(X) / num_blocks; + if (block_size_flt < 1.0) { + BLOCKS= seq(1, nrow(X) + 1); + } else { + block_size = ceil(block_size_flt); + BLOCKS = rbind(as.matrix(1), 1 + block_size * seq(1, num_blocks)); + BLOCKS[num_blocks+1,] = nrow(X) + 1 + } +} + +# Sorts the rows in dataset X by the vector v. The order is defined by the ascending order of v. +# Can return the vector v as first column of X, depending on parameter prepend_v. +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# X matrix --- Any matrix with rows to be sorted. +# v matrix --- A vector with the same number of rows as X. Defines ordering. +# prepend_v boolean --- Whether to return v as first column of X. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# X_index matrix A vector with the original row number for each row in X. +# X_sorted matrix The sorted input matrix X. +# -------------------------------------------------------------------------------------------- +sort_by_vector = function(Matrix[Double] X, Matrix[Double] v, Boolean prepend_v) return (Matrix[Double] X_index, Matrix[Double] X_sorted) { + X_index = order(target=v, by=1, decreasing = FALSE, index.return=TRUE); + X_sorted = order(target=cbind(v, X), by=1, decreasing = FALSE, index.return=FALSE); + if (!prepend_v) { + X_sorted = X_sorted[,2:ncol(X_sorted)]; + } +} + +# Sorts the rows in dataset X by their sum. +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# X matrix --- Any matrix with rows to be sorted. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# X_index matrix A vector with the original row number for each row in X. +# X_sorted matrix The sorted input matrix X. +# -------------------------------------------------------------------------------------------- +row_sum_sorting = function(Matrix[Double] X) return (Matrix[Double] X_index, Matrix[Double] X_sorted) { + X_rowSum = rowSums(X); + [X_index, X_sorted] = sort_by_vector(X, X_rowSum, FALSE); +} + +# Sorts the rows in dataset X by their original row numbers (index). +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# X matrix --- Any matrix with rows to be sorted. +# X_index matrix --- The orginal row numbers to be restored. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# X_reindex matrix The reindexed matrix X. +# -------------------------------------------------------------------------------------------- +reindex_rowwise = function(Matrix[Double] X, Matrix[Double] X_index) return (Matrix[Double] X_reindex) { + X_conc = cbind(X_index, X); + X_reindex = order(target=X_conc, by=1, decreasing = FALSE, index.return=FALSE); + # Remove index column + X_reindex = X_reindex[,2:ncol(X_reindex)]; +} + +# Sorts both rows and columns in dataset X by their original row numbers (index). +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# X matrix --- Any matrix that should be resorted. +# X_index matrix --- The orginal row numbers to be restored. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# X_reindex matrix The reindexed matrix X. +# -------------------------------------------------------------------------------------------- +reindex_rows_and_cols = function(Matrix[Double] X, Matrix[Double] X_index) return (Matrix[Double] X_reindex) { + # First reindex rows + X_reindex = X; + X_reindex = reindex_rowwise(X_reindex, X_index); + # Then transpose and repeat + X_reindex = t(X_reindex); + X_reindex = reindex_rowwise(X_reindex, X_index); +} + +# Generates a random matrix of hyperplane parameters. +# +# INPUT PARAMETERS: +# -------------------------------------------------------------------------------------------- +# NAME TYPE DEFAULT MEANING +# -------------------------------------------------------------------------------------------- +# num_hyperplanes Integer --- How many hyperplanes to generate. +# dimension Integer --- The number of parameters per hyperplane. +# +# Output: +# -------------------------------------------------------------------------------------------- +# NAME TYPE MEANING +# -------------------------------------------------------------------------------------------- +# H matrix A num_hyperplanes x dimension matrix of hyperplane parameters. +# -------------------------------------------------------------------------------------------- +gen_rand_hyperplanes = function(Integer num_hyperplanes, Integer dimension) return (Matrix[Double] H) { + H = rand(rows=num_hyperplanes, cols=dimension, min=-1, max=1); +} + +# Creates a scalar hash code for each row in X_plane by interpreting positive values as a binary +# binary number. 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