Collaborative-Filtering | Implemented Item , User and Hybrid | Recommender System library

by revantkumar Python Version: Current License: No License

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

kandi X-RAY | Collaborative-Filtering Summary

Collaborative-Filtering is a Python library typically used in Artificial Intelligence, Recommender System, Spark applications. Collaborative-Filtering has no bugs, it has no vulnerabilities and it has low support. However Collaborative-Filtering build file is not available. You can download it from GitHub.

I have written three codes, one for user-based collaborative filtering, second for item-based collaborative filtering and the third for hybrid-based collaborative filtering.

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Collaborative-Filtering has a low active ecosystem.

It has 135 star(s) with 85 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 924 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Collaborative-Filtering is current.

Quality

Collaborative-Filtering has 0 bugs and 0 code smells.

Security

Collaborative-Filtering has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Collaborative-Filtering code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Collaborative-Filtering 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

Collaborative-Filtering releases are not available. You will need to build from source code and install.

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

Collaborative-Filtering saves you 264 person hours of effort in developing the same functionality from scratch.

It has 640 lines of code, 15 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Cross validation .
Predict rating .
Compute similarity between two items
Compute the similarity between two users
Return the item data .
Get user data .
Read data from a file .

Get all kandi verified functions for this library.

Collaborative-Filtering Key Features

No Key Features are available at this moment for Collaborative-Filtering.

Collaborative-Filtering Examples and Code Snippets

No Code Snippets are available at this moment for Collaborative-Filtering.

Community Discussions

Trending Discussions on Collaborative-Filtering

TypeError: Cannot index by location index with a non-integer key

How to distribute code and dataset onto worker nodes?

How to create two columns from DataFrame in scala

Not able to understand python function of cosine similarity

Python, Cosine Similarity to Adjusted Cosine Similarity

Cannot run ALS.train, error: java.lang.IllegalArgumentException

IllegalStateException on Google Cloud Dataproc

user by item martrix pandas

Does ALS require zero entries?

In SparkALS does 'Number of features' also mean 'Number of factors'?

QUESTION

TypeError: Cannot index by location index with a non-integer key

Asked 2020-Nov-11 at 03:44

I am trying to re-write this code, which was written a while ago.

It has multiple chuncks, hence, I separated it into smaller pieces and re-write step by step. For instance, converting .ix to iloc, etc.

This chunk gives me an error:

...

ANSWER

Answered 2020-Nov-11 at 03:38

I'm not exactly sure what that piece of code is supposed to do (though it seems like there may be a more efficient way of doing it). Also, it looks like you are referencing j outside the j loop.

However, the specific error you're getting is mostly likely related to:

Source https://stackoverflow.com/questions/64780009

QUESTION

How to distribute code and dataset onto worker nodes?

Asked 2019-Jul-06 at 20:58

I've been working with dataset Movielens (20 million records) and have been using collaborative filtering in Spark MLlib.

My environment is Ubuntu 14.4 on VirtualBox. I have one master node and 2 slave nodes. I used the released Apache Hadoop, Apache Spark, Scala, sbt. The code is written in Scala.

How to distribute the code and the dataset onto worker nodes?

...

ANSWER

Answered 2019-Jun-29 at 17:32

1 - Your dataset is best placed into a distributed file system - Hadoop HDFS, S3, etc.

2 - Code is distributed via the spark-submit script, as described here https://spark.apache.org/docs/2.4.3/submitting-applications.html

Source https://stackoverflow.com/questions/56817579

QUESTION

How to create two columns from DataFrame in scala

Asked 2018-May-11 at 12:59

I am praticing this apache spark machine learning example to make a recommendation system for our users based on ratings they gave and I got this data which is top 10 recommendation product for a userId 31511 ( check out the last code of example above)

...

ANSWER

Answered 2018-May-11 at 12:59

Just explode and select

Source https://stackoverflow.com/questions/50292813

QUESTION

Not able to understand python function of cosine similarity

Asked 2018-Apr-26 at 12:40

I am working through the example in the blog to understand collaborative filter method used in recommendation system.I came across cosine similarity expressed as

In python using numpy its written as

...

ANSWER

Answered 2018-Apr-26 at 11:39

The ratings are stored in the numpy matrix ratings, where rows correspond to users (index u), while columns correspond to items (index i). Since you want to calculate sim(u, u'), i.e., the similarity between users, let's assume below that kind = 'user'.

Now, let's have a look first on r_{ui}r_{u'i} without the square-root scaling factors. This expression is summed over i which can be interpreted as matrix multiplication of r with the transpose of r, i.e.:

Source https://stackoverflow.com/questions/50038277

QUESTION

Python, Cosine Similarity to Adjusted Cosine Similarity

Asked 2018-Feb-22 at 12:46

I wish to transform a Collaborative Filtering with Python through Cosine Similarity to Adjusted Cosine Similarity.

The cosine similarity based implementation looks like this:

...

ANSWER

Answered 2017-Mar-21 at 08:29

Here's a NumPy based solution to your problem.

First we store rating data into an array:

Source https://stackoverflow.com/questions/42898466

QUESTION

Cannot run ALS.train, error: java.lang.IllegalArgumentException

Asked 2018-Feb-10 at 11:01

I am trying to run ALS of PySpark. I copied & pasted the sample code provided in the link. However, the error java.lang.IllegalArgumentException occurs at the line:

model = ALS.train(ratings, rank, numIterations)

May I ask what possible problems do I need to investigate here?

My Spark version is 2.2.1, my Java version is 9.0.4. However, I am not sure if Spark is using the right version of Java although I set the environment path and the command "java -version" does return "9.0.4".

Error:

Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.pythonToJava. : java.lang.IllegalArgumentException at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.xbean.asm5.ClassReader.(Unknown Source) at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46) at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:443) at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:426) at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733) at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230) at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40) at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103) at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732) at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:426) at org.apache.xbean.asm5.ClassReader.a(Unknown Source) at org.apache.xbean.asm5.ClassReader.b(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:257) at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:256) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:256) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156) at org.apache.spark.SparkContext.clean(SparkContext.scala:2294) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:794) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:793) at org.apache.spark.mllib.api.python.SerDeBase.pythonToJava(PythonMLLibAPI.scala:1349) at org.apache.spark.mllib.api.python.SerDe.pythonToJava(PythonMLLibAPI.scala) at jdk.internal.reflect.GeneratedMethodAccessor76.invoke(Unknown Source) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:564) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:214) at java.base/java.lang.Thread.run(Thread.java:844)

...

ANSWER

Answered 2018-Feb-10 at 11:01

I have figured out the problem. Spark 2.2.1 does not go with Java 9.0.4. Maybe I have misunderstood the part "Java 8+" of the tutorial.

If anyone meets the same error as me, just go for Java 1.8.0!

Source https://stackoverflow.com/questions/48719433

QUESTION

IllegalStateException on Google Cloud Dataproc

Asked 2017-Oct-06 at 15:55

I am trying to write a simple vanilla collaborative filtering application, running on Google Cloud Dataproc. The Data is located in BigQuery. I have implemented this according to this tutorial: https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml

Now the problem is that when running this (slightly modified) example I get an IllegalStateException. More specifically here is the stacktrace:

...

ANSWER

Answered 2017-Oct-06 at 15:55

This is a bug in BigQuery (that it returns the output file count statistics that does not include the zero-record file). The fix for this issue has been submitted, and its rollout will complete in about a week.

In the meantime, a workaround of the issue is maybe set the flag "mapred.bq.input.sharded.export.enable" (a.k.a. ENABLE_SHARDED_EXPORT_KEY) to false in your hadoop config when configuring your DataProc job.

UPDATE:
As of today Oct 6 2017, the fix is now 100% rolled out on BigQuery.

Source https://stackoverflow.com/questions/46404081

QUESTION

user by item martrix pandas

Asked 2017-Jun-20 at 06:41

I am working in recommendation system. I have followed this to make user by item matrix. However, I faced an error IndexError: index 8928358160 is out of bounds for axis 0 with size 5

The following below is example of datasets.

...

ANSWER

Answered 2017-Jun-20 at 06:41

Why dont you convert the values to string? Eventhough it's as integer, the computer might take it as a scientific value and thus becoming a float value.

Try this:

Converting the cust_id and item_number into characters from float value:

Source https://stackoverflow.com/questions/41864879

QUESTION

Does ALS require zero entries?

Asked 2017-Jun-10 at 15:19

Does the Spark MLlib implementation of alternating least squares (http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html) require that all zero entries for the training set (user-product combinations where the user has no history of interacting with the product) are manually created with a rating of 0, or will the algorithm automatically imply that all missing combinations have a zero rating?

...

ANSWER

Answered 2017-Jun-10 at 15:19

The training set can be sparse, and in fact, should be -- otherwise you'll pay a (possibly severe) performance penalty. See this discussion on the spark users mailing list for more information.

Source https://stackoverflow.com/questions/42214638

QUESTION

In SparkALS does 'Number of features' also mean 'Number of factors'?

Asked 2017-Mar-18 at 08:52

I'm working through an early version of Spark (alpha-0.1) to understand how it started and how it has evolved. I'm also trying to educate myself on how Alternating Least Squares works.

I'm looking through the SparkALS example and see the following variables:

...

ANSWER

Answered 2017-Mar-18 at 08:52

Ah, the Spark API docs refer to rank as the number of features:

rank - number of features to use

So I guess the terminology number of features and number of latent factors is interchangeable.

Also see: https://issues.apache.org/jira/browse/SPARK-20011

Source https://stackoverflow.com/questions/42837290

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

Vulnerabilities

No vulnerabilities reported

Install Collaborative-Filtering

You can download it from GitHub.
You can use Collaborative-Filtering 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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