How to install jRip package for ensemble learning in Weka. As part of aggregating collective clustering results through ensemble I need to install jRip package. I couldn't find any relevant link on Web giving access to same.
Please help.
The JRip algorithm is bundled with Weka. Here is a reference to it in Weka's JavaDoc:
http://weka.sourceforge.net/doc.stable/weka/classifiers/rules/JRip.html
Here is a download link:
http://www.cs.waikato.ac.nz/ml/weka/
Related
I am using Colab: https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg?usp=sharing to train YOLOV4 custom detector. The repo used in the Colab is: https://github.com/AlexeyAB/darknet
I want the training results (Loss, mAP) to be displayed on Tensorboard. How can I do that?
I am not sure if its possible but you can use --mjpeg_port 8090 to stream the mAP plot to http. Which you can use in an iframe.
Where can i found lvq in weka dev 3.7.11 jar?
As while importing this weka.classifiers.neural.lvq, it shows me an error of not found.
In the Weka Package Manager you have the LVQ clustering method, but I guess you refer to the classifier from this site?
I am looking for using KCL on SparkStreaming using pySpark.
Any pointers would be helpful.
I tried few given by spark Kinesis Ingeration link.
But i get the error for JAVA class reference.
Seems Python is using JAVA class.
i tried linking
spark-streaming-kinesis-asl-assembly_2.10-2.0.0-preview.jar
while trying to apply the KCL app on spark.
but still having the error.
Please let me know if anyone has done it already.
if i search online i get more about Twitter and Kafka.
Not able to get much help with regard to Kinesis.
spark verision used: 1.6.3
I encountered the same problem. The kinesis-asl jar had several files missing.
To overcome this problem, I had included the following jars in my spark-submit.
amazon-kinesis-client-1.9.0.jar
aws-java-sdk-1.11.310.jar
jackson-dataformat-cbor-2.6.7.jar
Note: I am using Spark 2.3.0 so the jar versions listed might not be the same as those you should be using for your spark version.
Hope this helps.
I am running a Django App inside GCP. My idea was to call a python script from "view.py" for some machine learning algorithm and then display the result on the page.
But now I understand that running a machine learning library like Scikit-learn on GAE will not be possible (read Tim's answer here and this thread).
But suppose I need to still do this, I believe there are 2 ways possible, but I am not sure weather my guess is right or wrong
1) As the Google-Datalab provides the entire anaconda like distribution, if we have any datalab api which can be called from a python file in the Django app, I can achieve my goal ?
2) If I can install the scikit-learn library on any compute engine on GCP and somehow send it the request to run my code and then return the output back to the python file in the Django app ?
I am very new to client-server and cloud computing on the whole, so please provide examples (if possible) for any suggestion/ pointer for the help.
Regards,
I believe what you want is to use the App Engine Flex environment rather than the standard App Engine environment.
App Engine Flex uses a compute engine VM for running your code, so it does not have the library limitations that standard App Engine has.
Specifically, you'll need to add a 'requirements.txt' file to specify the version of scikit-learn that you want installed, and then add a 'vm: true' clause to your app.yaml file.
sklearn is now supported on ML Engine.
So, another alternative now is to use online prediction on Cloud ML Engine, and deploy your scikit-learn model as a web service.
Here is a fully worked out example of using fully-managed scikit-learn training, online prediction and hyperparameter tuning:
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/blogs/sklearn/babyweight_skl.ipynb
I have a very big Hbase table apData, but it was not set as compressed when it was created. Right now it's 1.5TB. So I wanna enable compression feature on this table. I did the following:
(1)disable apData
(2)alter apData,{NAME=>'cf1',COMPRESSION=>'snappy'}
(3)enable 'apData'.
But when I use "desc apData" to see the configuration, it's still showing:
COMPRESSION => 'NONE'
Why it didn't take effect? How should I compress the table please, and also make sure that the future data would be compressed automatically when it is inserted.
Thanks in advance!
HBase will only compress new HFiles - either new data you write or the results of compactions
Did you configure Snappy.
Verify first snappy is loaded in all the nodes. To verify please use this command.
hbase org.apache.hadoop.hbase.util.CompressionTest
hdfs://host/path/to/hbase snappy
Once snappy test is successful. The mentioned above compression should work.
For more detail about configuration and installation of snappy:
http://hbase.apache.org/0.94/book/snappy.compression.html
You would need to configure HBase to use Snappy.
You can follow steps mentioned in the reference link to enable snappy compression in hbase:
configure snappy compression with HBase
Hope it helps you.
We need to configure HBase to use Snappy if we installed Hadoop and HBase from tarballs; if we installed them from RPM or Debian packages, Snappy requires no HBase configuration.
Depending on the architecture of the machine we are installing on, we have to add one of the following lines to /etc/hbase/conf/hbase-env.sh:
For 32-bit platforms:
export HBASE_LIBRARY_PATH=/usr/lib/hadoop/lib/native/Linux-i386-32
For 64-bit platforms:
export HBASE_LIBRARY_PATH=/usr/lib/hadoop/lib/native/Linux-amd64-64