I want to use scala and spark to implement Graph algorithm GraphSAGE, then how to do it? Is there any source code?
I want to get the code for my question
I haven´t implemented yet this graph algorithms on top of Spark, the only available implementation, as far as I know, for using deep learning for graph analysis is this. It is a spectral graph convolution for semi-supervised learning, and it is a transductive algorithm. It can be used for node classification. I have plans to include more algorithms in the future like GraphSAGE.
Related
I want to ask is this possible to write a custom loss function for Multi class Classification in Spark using Scala. I want to code multi-class logarithmic loss in Scala. I searched Spark documentation but could not get any hint.
From the Spark 2.2.0 MLlib guide:
Currently, only binary classification is supported.. This will likely change when multiclass classification is supported.
If you are not restricted to a particular classification technique I would suggest using XGBoost. It has a Spark-compatible implementation, and it makes it possible to use any loss function provided you can compute is derivative twice.
You can find a tutorial here.
Also the explanation about why it is possible to use a custom loss function can be found here.
I am currently looking for an Algorithm in Apache Spark (Scala/Java) that is able to cluster data that has numeric and categorical features.
As far as I have seen, there is an implementation for k-medoids and k-prototypes for pyspark (https://github.com/ThinkBigAnalytics/pyspark-distributed-kmodes), but I could not identify something similar for the Scala/Java version I am currently working with.
Is there another recommend algorithm to achieve similar things for Spark running Scala? Or am I overlooking something and could actually make use of the pyspark library in my Scala project?
If you need further information or clarification feel free to ask.
I think you need first to convert your categorical variables to numbers using OneHotEncoder then, you can apply your clustering algorithm using mllib (e.g. kmeans). Also, I recommend doing scaling or normalization before applying your cluster algorithm as it is distance sensitive.
Does avyone know the reason why the Non-Linear SVM has not been implemented in Apache Spark?
I was reading this page:
https://issues.apache.org/jira/browse/SPARK-4638
Look at the last comment. It says:
"Commenting here b/c of the recent dev list thread: Non-linear kernels for SVMs in Spark would be great to have. The main barriers are:
Kernelized SVM training is hard to distribute. Naive methods require a lot of communication. To get this feature into Spark, we'd need to do proper background research and write up a good design.
Other ML algorithms are arguably more in demand and still need improvements (as of the date of this comment). Tree ensembles are first-and-foremost in my mind."
The question is: Why is the kernelized SVM hard to distribute?
Everybody knows that the non-linear SVMs exhibit better performance than the linear ones.
Wondering if there a runWithValidation feature for Gradient Boosted Trees (GBT) in Spark ml to prevent overfitting. It's there in mllib which works with RDDs. I am looking the same for dataframes.
Found a K-Fold Cross Validation support in Spark. It can be done using CrossValidation() with Estimators, Evaluators, ParamMap and number of folds. This helps in finding the best parameters for the model i.e model tuning.
Refer http://spark.apache.org/docs/latest/ml-tuning.html for more details.
I do not think gaussian mixture model is available in mllib yet. I am wondering if any good Scala/Java implementation of GMM (suitable for large data) is available elsewhere. Please let me know.
Thanks and regards,
It is available in Spark MLlib now:
http://spark.apache.org/docs/latest/mllib-clustering.html#gaussian-mixture
Have a look at https://issues.apache.org/jira/browse/SPARK-4156
It is still under progress. We can expect it soon in MLLib.