A newbie question on WSO2 and 'R'....
I have a customer where they are looking to build some statistical models using 'R'. These models are mostly associated with customer scoring, i.e. sucking in a table of customer data with behavioural attributes as columns, and spitting out a 'score' for each customer.
Two questions on this:
Can 'R' models by deployed like rules in a service model?
Could you deploy R models into a WSO2 middleware, and if so, how and where?
TIA
Note: I'm not familiar with wso2 but I'm with R.
The answer to your question very much depends on what type of models you would like to deploy. The easiest ones are models such as linear/logistic regression followed by decision trees.
The reason they are easy is because for linear & logistic regression you get a nice formula you can plug-in to any programming interface. An example prediction formula might be like the following:
customer_predicted_life_time_value =
17.25+2.365*num_of_products_held-16.12*time_at_address+25.36*monthly_income.
Similarly, decision trees can be easily exported as a bunch of if-then-else rules (there at least a couple of packages in R which will translate the R decision tree model into rules).
You could technically be able to deploy randomForest too in the form of rules but that will be cumbersome if you want to implenent using rules.
Related
I have a model where some of the input features are calculated from the training dataset (e.g. average or median of a value). I am trying to perform n-fold cross validation on this model, but that means that the values for these features would be different depending on the samples selected for training/validation for each fold. Is there a way in h2o (I'm using it in R) to perhaps pass a funtion that calculates those features once the training set has been determined?
It seems like a pretty intuitive feature to have, but I have not been able to find any documentation on something like it out-of-the-box. Does it exist? If so, could someone point me to a resource?
There's no way to do this while using the built-in cross-validation in H2O. If H2O were written in pure R or Python, then it would be easy to extend it to allow a user to pass in a function to create custom features within the cross-validation loop, however the core of H2O is written in Java, so automatically translating an arbitrary user-defined function from R or Python, first into a REST call and then into Java is not trivial.
Instead, what you'd have to do is write a loop to do the cross-validation yourself and compute the features within the loop.
It sounds like you may be doing target encoding (or something similar), and if that's the case, you'll be interested in this PR to add target encoding in H2O. In the discussion, we talk about the same issue that you're having.
Like in decision tree we can see or visualize the node splits , I want to do something similar . But I am using SparkR and it does not have decision trees. So I am planning to use random forest with 1 tree as parameter and run on SparkR, then save the model and use getTree to see the node splits and further visualize using ggplot.
The short answer is no.
Models built with SparkR are not compatible with ones built with the respective R packages, in this case randomForest; hence, you will not be able to use the getTree function from the latter to visualize a tree from a random forest built with SparkR.
On a different level: I am surprised that decision trees have still not found their way into SparkR - they seem to be ready since several months now in the Github repo; but even when they are, they are not expected to offer methods for visualizing trees, and you will still not be able to use functions from other R packages for that purpose.
Problem:
I have a Random Forest model trained in R. I need to deploy this model in a standalone Excel tool that will be used by 350 people across a sales network to perform real-time predictions based on data entered into the spreadsheet by users.
How can I do this?
Constraints:
It is not an option to require users to install R on their local machines.
It is not an option to have a server (physical or cloud) providing a scoring API.
What have I done so far?
1. PMML
I can export the model in PMML (XML structure). From research I can see there are libraries for loading and executing PMML inputs in Python and Java. However I haven't found anything implemented in VBA / VB.
2. Zementis
I looked into a solution called Zementis which offers an Excel add-in to deploy PMML models. However from my understanding this requires web-service calls to a cloud server (e.g. AWS) where the actual model execution happens. My IT security department will not allow this.
3. Others
The most common recommendation seems to be to call R to load the model and run the predict function. As noted above, this is not a viable option.
Detailed Context:
The Random Forest model is trained in R, with c. 30 variables. The model is used to recommend "personalised" prices for products as part of a sales process.
The model needs to be distributed to the sales network, with about 350 users. The business's preference is to integrate the model into an existing spreadsheet tool that sales teams currently use to calculate deal profitability.
This means that I need to be able to export the model in a way that it can be implemented in Excel VBA.
Given timescales, the implementation needs to be self-contained with no IT infrastructure or additional application installs. We are working with the organisation's IT team on a server based solution, however their deployment timescales are 12 months+ which means we need a tactical solution in the short-term.
Here's one approach to get the "rules" for the trees (example using the mtcars dataset)
install.packages("randomForest")
library(randomForest)
head(mtcars)
set.seed(1)
fit <- randomForest(mpg ~ ., data=mtcars, importance=TRUE, proximity=TRUE)
print(fit)
## Look at variable importance:
importance(fit)
# Print the rules for each tree in the forest
install.packages("rattle")
library(rattle)
printRandomForests(fit)
It is probably unrealistic to use the rules for 500 trees, but maybe you could implement 100 trees in your vba and then take an average of the results (for a continuous response) or predict the class with the most votes across the trees (for a categorical response).
Maybe you could recreate the model on a Worksheet.
As far as I know, Excel can import XML structures (on the Development Tools ribbon).
Edit: 1) save pmml structure in plaintext editor as .xml file.
2) Open the file in Excel 2013 (maybe other versions also do it)
3) Click through the error message and open the file anyway. Trees open as a table, a bit funny, but recognizable.
4) Create prediction calculation (generic fn in VBA) to operate on the tree.
Newbie question: I am currently writing my thesis about the impact of an analysis tool on the number of fans for a company. I created the following regression model: Fans ~ eventWeek * analysistool
The problem with this function is that it assumes that every line in the data is unrelated, which it is not. Therefore, my thesis coach advised me to use clustered standard errors within R and cluster it on Company Number. Does anyone know how to do this?
I believe this document answers your question in terms of GLMs.
I'm currently working on trust prediction in social networks - from obvious reasons I model this problem as data stream. What I want to do is to "update" my trained model using old model + new chunk of data stream. Classifiers that I am using are SVM, NB (e1071 implementation), neural network (nnet) and C5.0 decision tree.
Sidenote: I know that this solution is possible using RMOA package by defining "model" argument in trainMOA function, but I don't think I can use it with those classifiers implementations (if I am wrong please correct me).
According to strange SO rules, I can't post it as comment, so be it.
Classifiers that you've listed need full data set at the time you train a model, so whenever new data comes in, you should combine it with previous data and retrain the model. What you are probably looking for is online machine learning. One of the very popular implementations is Vowpal Wabbit, it also has bindings to R.