datasketch | LSH Forest , Weighted MinHash | Hashing library

 by   ekzhu Python Version: v1.5.9 License: MIT

kandi X-RAY | datasketch Summary

kandi X-RAY | datasketch Summary

datasketch is a Python library typically used in Security, Hashing applications. datasketch has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However datasketch has 85 bugs. You can download it from GitHub.

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble
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            kandi-support Support

              datasketch has a medium active ecosystem.
              It has 1991 star(s) with 267 fork(s). There are 49 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 41 open issues and 111 have been closed. On average issues are closed in 104 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of datasketch is v1.5.9

            kandi-Quality Quality

              datasketch has 85 bugs (0 blocker, 0 critical, 46 major, 39 minor) and 147 code smells.

            kandi-Security Security

              datasketch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              datasketch code analysis shows 0 unresolved vulnerabilities.
              There are 17 security hotspots that need review.

            kandi-License License

              datasketch is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              datasketch releases are available to install and integrate.
              Build file is available. You can build the component from source.
              datasketch saves you 6317 person hours of effort in developing the same functionality from scratch.
              It has 13149 lines of code, 479 functions and 88 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed datasketch and discovered the below as its top functions. This is intended to give you an instant insight into datasketch implemented functionality, and help decide if they suit your requirements.
            • Evaluate results
            • Compute the similarity between each query
            • Compute the distance between the ground truth values
            • Compute recall
            • Bootstrap sets from a set of sets
            • Updates the hash function
            • Merges two min hashes together
            • Benchmark LSHensemble
            • Insert entries into the set
            • Index a set of entries
            • Return True if all indexes are empty
            • Query the sum of keys in the pool
            • Add a key to the pool
            • Perform jaccard search
            • Adds a key to the hash
            • Compute the optimal probability for a given threshold
            • Compute the nearest neighbour between two sets
            • Query the hash values of all hashes
            • Save results to database
            • Create storages
            • Generate minhashes for a set of permutations
            • Perform LSH forest search
            • Queries the LSH algorithm
            • Reads a set of sets from a file
            • Search HNSW index using HNSW index
            • Plots a matplotlib plot
            • Create a list of objects from b
            • Calculate the minimum hash value for an input vector
            Get all kandi verified functions for this library.

            datasketch Key Features

            No Key Features are available at this moment for datasketch.

            datasketch Examples and Code Snippets

            copy iconCopy
            git clone git@github.com:xxcclong/GNN-Computing.git
            
            cd artifact
            mkdir build && cd build
            cmake ..
            make -j16
            cp fig7.out ../Figure7/
            cp fig8.out ../Figure8/
            cp fig9.out ../Figure9/
            cp fig10a.out ../Figure10/
            cp fig10b.out ../Figure10/
            cp fig11  
            OCR_POST_DE
            Pythondot img2Lines of Code : 8dot img2no licencesLicense : No License
            copy iconCopy
            @misc{lyu2021neural,
                  title={Neural OCR Post-Hoc Correction of Historical Corpora}, 
                  author={Lijun Lyu and Maria Koutraki and Martin Krickl and Besnik Fetahu},
                  year={2021},
                  eprint={2102.00583},
                  archivePrefix={arXiv},
                  
            default
            Jupyter Notebookdot img3Lines of Code : 4dot img3License : Permissive (MIT)
            copy iconCopy
            git clone https://github.com/brendano/stanford_corenlp_pywrapper
            cd stanford_corenlp_pywrapper
            pip install .
            cd ..
              

            Community Discussions

            QUESTION

            Apache Druid: count outliers
            Asked 2020-Apr-26 at 08:43

            I prepared an installation of Apache Druid that takes data from a Kafka topic. It works very smoothly and efficiently.

            I'm currently trying to implement some queries and I'm stuck in the count of rows (grouped by some fields) for which a column value is an outlier. In the normal SQL world, I will essentially compute the first and third quartiles (q1 and q3) and then use something like (I'm interested only in "right" outliers):

            SUM(IF(column_value > q3 + 1.5*(q3-q1), 1, 0))

            This approach makes use of cte and joins: I compute the quartiles in a cte with grouping and then I join it with the original table.

            I was able to easily compute the quartiles and the outlier threshold with datasketch extension using a groupBy query, but I'm not realizing how to perform a postAggregation that can perform the count.

            In theory, I may implement a second query using the thresholds obtained in the first. Unfortunately, I can get hundreds of thousands of different values. That makes this approach unfeasible.

            Do you have any suggestion on how to tackle this problem?

            ...

            ANSWER

            Answered 2020-Apr-26 at 08:43

            As of version 0.18.0, Apache Druid supports joins. This solves the problem.

            Source https://stackoverflow.com/questions/61115365

            QUESTION

            How can I authenticate when querying druid?
            Asked 2019-Dec-17 at 12:48

            I installed druid from link attached here to install druid.

            The following code has been added to the common.runtime.properties file .

            ...

            ANSWER

            Answered 2019-Dec-17 at 12:48

            You use basic authentication. You should just be able to send your query to druid with an URL like this:

            Source https://stackoverflow.com/questions/59368760

            QUESTION

            HDFS as Deep-Storage: Druid is not storing the historical data on hdfs
            Asked 2019-Dec-12 at 11:09

            I have set up a micro-server of Druid on on-prem machine. I want to use HDFS as deep-storage of druid. I have used the following Druid Docs, [druid-hdfs-storage] fully qualified deep storage path throws exceptions and imply-druid docs as references.

            I have made following changes in /apache-druid-0.16.0-incubating/conf/druid/single-server/micro-quickstart/_common/common.runtime.properties

            ...

            ANSWER

            Answered 2019-Dec-12 at 11:09

            I resolved the issue by changing the hdp.version in the mapred-site.xml manually. I was getting following exception in middleManager.log

            java.lang.IllegalArgumentException: Unable to parse '/hdp/apps/${hdp.version}/mapreduce/mapreduce.tar.gz#mr-framework' as a URI, check the setting for mapreduce.application.framework.path

            But Still the segment metadata is showing Request failed with status code 404.

            Source https://stackoverflow.com/questions/59155685

            QUESTION

            Efficiently calculate top-k elements in spark
            Asked 2019-Jul-24 at 08:35

            I have a dataframe similarly to:

            ...

            ANSWER

            Answered 2019-May-23 at 14:29

            QUESTION

            LSH Binning On-The-Fly
            Asked 2019-Jul-09 at 02:32

            I want to use MinHash LSH to bin a large number of documents into buckets of similar documents (Jaccard similarity).

            The question: Is it possible to compute the bucket of a MinHash without knowing about the MinHash of the other documents?

            As far as I understand LSH "just" computes a hash of the MinHashes. So it should be possible?

            One implementation I find quite promissing is datasketch. I can query the LSH for documents similar to a given one after knowing the MinHash of all documents. However I see no way to get the bucket of a single document before knowing the other ones. https://ekzhu.github.io/datasketch/index.html

            ...

            ANSWER

            Answered 2019-Jul-09 at 02:32

            LSH doesn't bucket entire documents, nor does it bucket individual minhashes. Rather, it buckets 'bands' of minhashes.

            LSH is a means of both reducing the number of hashes stored per document, and reducing the number of hits found when using these hashes to search for similar documents. It achieves this by combining multiple minhashes together into a single hash. So, for example, instead of storing 200 minhashes per document, you might combine them in bands of four to yield 50 locality sensitive hashes.

            The hash for each band is calculated from its constituent minhashes using a cheap hash function such as FNV-1a. This loses some information, which is why LSH is said to reduce the dimensionality of the data. The resulting hash is the bucket.

            So the bucket for each band of minhashes within a document is calculated without requiring knowledge of any other bands or any other documents.

            Using LSH hashes to find similar documents is simple: Let's say you want to find documents similar to document A. First generate the (e.g.) 50 LSH hashes for document A. Then look in your hash dictionary for all other documents that share one or more of these hashes. The more hashes they share, the higher their estimated jaccard similarity (though this is not a linear relationship, as it is when using plain minhashes).

            The fewer total hashes stored per document, the greater the error in estimated jaccard similarity, and the greater the chance of missing similar documents.

            Here's a good explanation of LSH.

            Source https://stackoverflow.com/questions/56405054

            QUESTION

            How do I enable logging/tracing in Apache Calcite using Sqlline?
            Asked 2019-Jun-18 at 08:29

            Following https://calcite.apache.org/docs/tutorial.html, I ran Apache Calcite using SqlLine. I tried activating tracing as instructed in https://calcite.apache.org/docs/howto.html#tracing. However, I don't get any logging. Here is the content of my session (hopefully containing all relevant information):

            ...

            ANSWER

            Answered 2019-Jun-18 at 08:29

            I have the impression that problem lies to the underlying implementation of the logger.

            I am not an expert on logging configurations but I think specifying the properties file through -Djava.util.logging.config.file does not have any effect since the logger that is used (according to the classpath you provided) is the Log4J implementation (slf4j-log4j12-1.7.25.jar) and not the one of the jdk (https://mvnrepository.com/artifact/org.slf4j/slf4j-jdk14/1.7.26).

            I think that the right property to use for the log4j implementation is the folowing: -Dlog4j.configuration=file:C:\Users\user0\workspaces\apache-projects\apache-calcite\core\src\test\resources\log4j.properties

            Source https://stackoverflow.com/questions/56629738

            QUESTION

            "Failed to submit supervisor: Request failed with status code 502" on submitting ingestion spec to router
            Asked 2019-May-22 at 11:36

            I am getting the error "Failed to submit supervisor: Request failed with status code 502" when I am trying to submit an ingestion spec to the druid UI (through the router). The ingestion spec works in a standalone druid server.

            I have set up the cluster using 4 machines-1 for the coordinator and overlord (master), 1 for historical and middle manager (data), 1 for broker (query), and 1 for router, with a separate instance for zookeeper. There is no error in the logs.

            The ingestion spec is as follows:

            ...

            ANSWER

            Answered 2019-May-22 at 11:36

            It happened because the druid-kafka-indexing-service extension was missing from the extension list of common.runtime.properties.

            Source https://stackoverflow.com/questions/56220800

            QUESTION

            Is it possible to store custom class object in Spark Data Frame as a column value?
            Asked 2019-Jan-16 at 08:39

            I am working on duplicate documents detection problem using LSH algorithm. To handle large-scale data, we are using spark.

            I have around 300K documents with at least 100-200 words per document. On spark cluster, these are the steps we are performing on data frame.

            1. Run Spark ML pipeline for converting text into tokens.
            ...

            ANSWER

            Answered 2019-Jan-16 at 08:39

            I don't think it might be possible to save python objects in DataFrames, but you can circumvent this in a couple of ways:

            • Store the result instead of the object (not sure about how MinHash works, but if the value is numerical/string, it should be easy to extract it from the class object).
            • If that is not feasible because you still need some properties of the object, you might want to serialize it using Pickle, saving the serialized result as an encoded string. This forces you to de-serialize every time that you want to use the object.

              final_df_limit.rdd.map(lambda x: base64.encodestring(pickle.dumps(CalculateMinHash(x),))).toDF()

            • An alternative might be to use the Spark MinHash implementation instead, but that might not suit all your requirements.

            Source https://stackoverflow.com/questions/54155341

            QUESTION

            Python: IOError 110 Connection timed out when reading from disk
            Asked 2018-May-22 at 02:00

            I'm running a Python script on a Sun Grid Engine supercompute cluster that reads in a list of file ids, sends each to a worker process for analysis, and writes one output per input file to disk.

            The trouble is I'm getting IOError(110, 'Connection timed out') somewhere inside the worker function, and I'm not sure why. I've received this error in the past when making network requests that were severely delayed, but in this case the worker is only trying to read data from disk.

            My question is: What would cause a Connection timed out error when reading from disk, and how can one resolve this error? Any help others can offer would be very appreciated.

            Full script (the IOError crops up in minhash_text()):

            ...

            ANSWER

            Answered 2018-May-22 at 02:00

            It turned out I was hammering the filesystem too hard, making too many concurrent read requests for files on the same server. That server could only allow a fixed number of reads in a given period, so any requests over that limit received a Connection Timed Out response.

            The solution was to wrap each file read request in a while loop. Inside that while loop, try to read the appropriate file from disk. If the Connection timed out error springs, sleep for a second and try again. Only once the file has been read may the while loop be broken.

            Source https://stackoverflow.com/questions/50448220

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install datasketch

            You can download it from GitHub.
            You can use datasketch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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