Recommend | MapReduce implements an item-based collaborative filtering | Learning library

by ParadeTo Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Recommend Summary

Recommend is a Java library typically used in Tutorial, Learning, Spring Boot, Spring, Hibernate applications. Recommend has no bugs, it has no vulnerabilities and it has low support. However Recommend build file is not available. You can download it from GitHub.

MapReduce implements an item-based collaborative filtering algorithm, that is, a movie recommendation system

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Quality

Security

License

Reuse

Support

Recommend has a low active ecosystem.

It has 145 star(s) with 66 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

Recommend has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Recommend is current.

Quality

Recommend has 0 bugs and 107 code smells.

Security

Recommend has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Recommend code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Recommend does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Recommend releases are not available. You will need to build from source code and install.

Recommend has no build file. You will be need to create the build yourself to build the component from source.

Recommend saves you 255 person hours of effort in developing the same functionality from scratch.

It has 620 lines of code, 34 functions and 9 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Recommend and discovered the below as its top functions. This is intended to give you an instant insight into Recommend implemented functionality, and help decide if they suit your requirements.

Main command line
Run step 5
Read a file
Runs a job1
Copies a local file to HDFS
Create directory
Runs the job4j
Get the configuration
Delete a file
The main method
Sort map
Run the job4
Run 2
Get modification time
Write content to hdfs file

Get all kandi verified functions for this library.

Recommend Key Features

No Key Features are available at this moment for Recommend.

Recommend Examples and Code Snippets

No Code Snippets are available at this moment for Recommend.

Community Discussions

Trending Discussions on Recommend

AWS DynamoDB Partition Key Design

Python Flask Import file could not be resolved

disable in Angular Material custom field component not working

How to disable ESLint during build phase in React

Accessing Aurora Postgres Materialized Views from Glue data catalog for Glue Jobs

Picture changing when it shouldn't

Recommended items limit for a FlatList

ng update @angular/core@10 @angular/cli@10 isn't working

How can I set response headers in live-server to avoid CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource?

QUESTION

AWS DynamoDB Partition Key Design

Asked 2021-Jun-15 at 18:09

I read this answer, which clarified a lot of things, but I'm still confused about how I should go about designing my primary key.

First off I want to clarify the idea of WCUs. I get that WCU is the write capacity of max 1kb per second. Does it mean that if writing a piece of data takes 0.25 seconds, I would need 4 of those to be billed 1 WCU? Or each time I write something it consumes 1 WCU, but I could also write X times within 1 second and still be billed 1 WCU?

Usage

I want to create a table that stores the form data for a set of gyms (95% will be waivers, the rest will be incidents reports). Most of the time, each forms will be accessed directly via its unique ID. I also want to query the forms by date, form, userId, etc..

We can assume an average of 50k forms per gym

Options

First option is straight forward: having the formId be the partition key. What I don't like about this option is that scan operations will always filter out 90% of the data (i.e. the forms from other gyms), which isn't good for RCUs.
Second option is that I would make the gymId the partition key, and add a sort key for the date, formId, userId. To implement this option I would need to know more about the implications of having 50k records on one partition key.
Third option is to have one table per gyms and have the formId as partition key. This seems to be like the best option for now, but I don't really like the idea of having a a large number of tables doing the same thing in my account.

Is there another option? Which one of the three is better?

Edit: I'm assuming another option would be SimpleDB?

...

ANSWER

Answered 2021-May-21 at 20:26

For your PK design. What data does the app have when a user is going to look for a form? Does it have the GymID, userID, and formID? If so, make a compound key out of that for the PK perhaps? So your PK might look like:

Source https://stackoverflow.com/questions/67628589

QUESTION

Python Flask Import file could not be resolved

Asked 2021-Jun-15 at 16:25

I'm trying to import 'greek' to 'api' file in same directory

This is my directory

...

ANSWER

Answered 2021-Jun-15 at 16:13

If the parent folder is api and a child is greek, then what you need is

An __init__.py file in the api folder
Then you can do from api import greek or from api.greek import *

Updating my response since original post has been updated and a directory structure (different from earlier post) provided

Based on your updated directory structure, I do not believe you need the __init___.py.
It seems you need a function or class called alphabet which is in greek.py. You should just do import greek and then to use alphabet, you do greek.alphabet

Source https://stackoverflow.com/questions/67989019

QUESTION

disable in Angular Material custom field component not working

Asked 2021-Jun-15 at 15:14

I have a custom slide toggle component created using Angular Material. I followed this guide: https://material.angular.io/guide/creating-a-custom-form-field-control

Everything seems to be working fine except when I dynamically disable the custom component like this:

...

ANSWER

Answered 2021-Jun-11 at 19:49

You need to add a formGroup binding to your custom component,

Source https://stackoverflow.com/questions/67942009

QUESTION

How to disable ESLint during build phase in React

Asked 2021-Jun-15 at 14:34

I'm using create-react-app and have configured my project for eslint. Below is my .eslintrc file.

...

ANSWER

Answered 2021-Jun-15 at 12:54

You can do it by adding DISABLE_ESLINT_PLUGIN=true to the "build" in the "scripts" part in your package.json:

Source https://stackoverflow.com/questions/67986657

QUESTION

Accessing Aurora Postgres Materialized Views from Glue data catalog for Glue Jobs

Asked 2021-Jun-15 at 13:51

I have an Aurora Serverless instance which has data loaded across 3 tables (mixture of standard and jsonb data types). We currently use traditional views where some of the deeply nested elements are surfaced along with other columns for aggregations and such.

We have two materialized views that we'd like to send to Redshift. Both the Aurora Postgres and Redshift are in Glue Catalog and while I can see Postgres views as a selectable table, the crawler does not pick up the materialized views.

Currently exploring two options to get the data to redshift.

Output to parquet and use copy to load
Point the Materialized view to jdbc sink specifying redshift.

Wanted recommendations on what might be most efficient approach if anyone has done a similar use case.

Questions:

In option 1, would I be able to handle incremental loads?
Is bookmarking supported for JDBC (Aurora Postgres) to JDBC (Redshift) transactions even if through Glue?
Is there a better way (other than the options I am considering) to move the data from Aurora Postgres Serverless (10.14) to Redshift.

Thanks in advance for any guidance provided.

...

ANSWER

Answered 2021-Jun-15 at 13:51

Went with option 2. The Redshift Copy/Load process writes csv with manifest to S3 in any case so duplicating that is pointless.

Regarding the Questions:

N/A
Job Bookmarking does work. There is some gotchas though - ensure Connections both to RDS and Redshift are present in Glue Pyspark job, IAM self ref rules are in place and to identify a row that is unique [I chose the primary key of underlying table as an additional column in my materialized view] to use as the bookmark.
Using the primary key of core table may buy efficiencies in pruning materialized views during maintenance cycles. Just retrieve latest bookmark from cli using aws glue get-job-bookmark --job-name yourjobname and then just that in the where clause of the mv as where id >= idinbookmark

conn = glueContext.extract_jdbc_conf("yourGlueCatalogdBConnection") connection_options_source = { "url": conn['url'] + "/yourdB", "dbtable": "table in dB", "user": conn['user'], "password": conn['password'], "jobBookmarkKeys":["unique identifier from source table"], "jobBookmarkKeysSortOrder":"asc"}

datasource0 = glueContext.create_dynamic_frame.from_options(connection_type="postgresql", connection_options=connection_options_source, transformation_ctx="datasource0")

That's all, folks

Source https://stackoverflow.com/questions/67928401

QUESTION

Recommended way of measuring execution time in Tensorflow Federated

Asked 2021-Jun-15 at 13:49

I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

Thanks to anyone can help me on that.

...

ANSWER

Answered 2021-Jun-15 at 12:01

There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.
Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.
Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to trainer.next() in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).

Source https://stackoverflow.com/questions/67982276

QUESTION

Picture changing when it shouldn't

Asked 2021-Jun-15 at 11:53

I am trying to build a cinema app with flutter. The structure is as follows:

in each city there are a bunch of cinemas
in a cinema there are a bunch of showrooms(salle in french)
in a showroom(salle in french) there are five display sessions or projections, these projections are of the same film.

because the projections are of the same movie (a showroom displays the same movie in different time(e.g projections) by design), when I click on any of the projections in a showroom I should have the same posture of the same film, not a different posture in each projection.

However I get a different film posture in each projection, and I don't know what is causing this.

I am using a rest api that I created with Spring, and I am certain that the problem is not from my back-end because I am using it in an angular web app and it's working perfectly.

This is a layout of my application

this is what happened when I click on two projection of the same showroom( notice that the posture changes when it shouldn't.

and here is the code of the showroom page (salles-page.dart)

...

ANSWER

Answered 2021-Jun-15 at 11:53

Problem related to back-end and have nothing to do with Flutter.

Source https://stackoverflow.com/questions/67953351

QUESTION

Vulnerabilities

No vulnerabilities reported

Install Recommend

You can download it from GitHub.
You can use Recommend like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Recommend component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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