Knockout.js - Updating Single Array Element (Oracle JET)

If you implement tables and using Knockout.js to push data updates from JS to HTML - probably you experience a situation when it doesn't work to push an update for one of the columns. I mean you could replace the whole observable array element - this would cause full row refresh. But visually this doesn't look nice and why to refresh the whole row, if only one (or few) element (-s) from the row must be refreshed.

If you need to refresh a specific array element (or row column in other words) - you must define the value of that column to be observable.

Refresh will be happening much more smooth, instead of refreshing whole row. See how fast Risk column value is changed after clicking on Process button:

Table is implemented with Oracle JET table component. JET table allows to define template slots, this helps to create a better structure for table columns implementation:

Risk column - the one which is being refreshed is defined as an observable variable in the array:

A new value for Risk column is set directly - by iterating array elements. Refresh on UI happens automatically, through Knockout observable:

Sample application source code is available on my GitHub repo.

Tweet Escalation to Your Support Team — Sentiment Analysis with Machine Learning

I have published an article on Towards Data Science. I explain end-to-end technical solution which would help to streamline your company support process. With the focus on airline support requests received from Twitter. It could save a lot of time and money for the support department if they would know in advance which request is more critical and must be handled with higher priority.

Read the full article here - Solution to automate tweet sentiment processing for airline support request escalation.

Understanding Attributes Enum in ADF BC Row Class

Did you ever wonder why Attributes Enum is generated by JDeveloper in Entity or View Row class? Attributes Enum holds a collection of attribute names and there is a set of static variables with attribute indexes. These indexes are used to locate attribute in getter/setter. Attributes Enum is a structure which is required for JDeveloper on design time to generate Java code. On runtime Attributes Enum is needed only as long as you are using a static variable index in the getter/setter.

Attributes Enum and list of static indexes in View Row class:

Static index is used in the getter/setter to access attribute:

Attributes Enum is mimicking attributes order in the VO/EO. You can think about it as about attributes metadata. It is not mandatory to use index from Attributes Enum. In some use cases, you could get attribute index directly from VO/EO Def and use it to access attribute:

First name is fetched correctly using overridden getter:

Download sample code from GitHub

Off Canvas Menu in Oracle VBCS/JET Cloud

These days I'm actively working with VBCS/JET Cloud product from Oracle. The more I work with VBCS the more I like it. VBCS follows similar declarative development concepts as Oracle ADF, this makes it easy to get up to speed with VBCS development. VBCS with declarative JavaScript development approach brings unique solution for JavaScript systems implementation for enterprise.

I will share sample with off canvas menu implementation for VBCS app. Sample is based on step by step guide shared by Shay Shmeltzer. I don't describe steps how to build off canvas in VBCS from scratch, you should watch Shay's video for the instructions.

Off canvas menu rendered in VBCS app:

You should check how to build multiple flows in VBCS app in my previous post - Flow Navigation Menu Control in Oracle VBCS. I have defined three flows in my sample, this means there will be three menu items:

To render menu in off canvas block, I'm using JET navigation list component:

Sample app code which can be imported into your VBCS instance is available on GitHub.

Date Format Handling in Oracle JET

Oracle JET comes with out of the box support for date converter, check more about it in cookbook - Date Converter. This makes it very handy to format dates in JavaScript. Here is date picker field example with yyyy-MM-dd format applied:

When button Process is pressed, I take date value from date picker and add one day - result is printed in the log. This is just to test simple date operation in JavaScript.

Date picker is defined by JET tag. Format is assigned through converter property:

Current date is displayed from observable variable. This variable is initialized from current date converted to local ISO. Converter is configured with pattern. In the JS method, where tomorrow date is calculated - make sure to convert from ISO local date:

Hope this simple example helps you to work with dates in Oracle JET application. Source code is available on my GitHub directory.

API for Amazon SageMaker ML Sentiment Analysis

Assume you manage support department and want to automate some of the workload which comes from users requesting support through Twitter. Probably you already would be using chatbot to send back replies to users. Bu this is not enough - some of the support requests must be taken with special care and handled by humans. How to understand when tweet message should be escalated and when no? Machine Learning for Business book got an answer. I recommend to read this book, my today post is based on Chapter 4.

You can download source code for Chapter 4 from book website. Model is trained based on sample dataset from Kaggle - Customer Support on Twitter. Model is trained based on subset of available data, using around 500 000 Twitter messages. Book authors converted and prepared dataset to be suitable to feed into Amazon SageMaker (dataset can be downloaded together with the source code).

Model is trained in such way, that it doesn't check if tweet is simply positive or negative. Sentiment analysis is based on the fact if tweet should be escalated or not. It could be even positive tweet should be escalated.

I have followed instructions from the book and was able to train and host the model. I have created AWS Lambda function and API Gateway to be able to call model from the outside (this part is not described in the book, but you can check my previous post to get more info about it - Amazon SageMaker Model Endpoint Access from Oracle JET).

To test trained model, I took two random tweets addressed to Lufthansa account and passed them to predict function. I exposed model through AWS Lambda function and created API Gateway, this allows to initiate REST request from such tool as Postman. Response with __label__1 needs esacalation and __label__0 doesn't need. Second tweet is more direct and it refers immediate feedback, it was labeled for escalation by our model for sentiment analysis. First tweet is a bit abstract, for this tweet no escalation:

This is AWS Lambda function, it gets data from request, calls model endpoint and returns back prediction:

Let's have a quick look into training dataset. There are around 20% of tweets representing tweets marked for escalation. This shows - there is no need to have 50%/50% split in training dataset. In real life probably number of escalations is less than half of all requests, this realistic scenario is represented in the dataset:

ML model is built using Amazon SageMaker BlazingText algorithm:

Once ML model is built, we deploy it to the endpoint. Predict function is invoked through the endpoint:

Machine Learning - Date Feature Transformation Explained

Machine Learning is all about data. The way how you transform and feed data into ML algorithm - greatly depends training success. I will give you an example based on date type data. I will be using scenario described in my previous post - Machine Learning - Getting Data Into Right Shape. This scenario is focused around invoice risk, ML trains to recognize when invoice payment is at risk.

One of the key attributes in invoice data are dates - invoice date, payment due date and payment date. ML algorithm expects number as training feature, it can't operate with literals or dates. This is when data transformation comes in - out of original data we need to prepare data which can be understood by ML.

How we can transform dates into numbers? One of the ways is to split date value into multiple columns with numbers describing original date (year, quarter, month, week, day of year, day of month, day of week). This might work? To be sure - we need to run training and validate training success.

Resources:

1. Sample Jupyter notebooks and datasets are available on my GitHub repo
2. I would recommend to read this book - Machine Learning for Business

Two approaches:

1. Date feature transformation into multiple attributes

Example where date is split into multiple columns:

Correlation between decision column and features show many dependencies, but it doesn't pick up all columns for payment date feature. This is early sign training might not work well:

We need to create test (1/3 of remaining data), validation (2/3 of remaining data) and training (70% of all data) datasets to be able to train, validate and test ML model. Splitting original dataset into three parts:

Running training using XGBoost (Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes). Read more about XGBoost parameters. We have validation dataset and this allows to use XGBoost early stopping functionality, if training quality would not improve in N (10 in our case) rounds - it will stop and pick best iteration as the one to be used for training result:

Result: training accuracy 93% and validation accuracy 74%. Validation accuracy is too low, this means training wasn't successful and we should try to transform dates in another way:

2. Date feature transformation into difference between dates

Instead of splitting date into multiple attributes, we should reduce number of attributes to two. We can use date difference as such:

- Day difference between Payment Due Date and Invoice Date
- Day difference between Payment Date and Invoice Date

This should bring clear pattern, when there is payment delay - difference between payment date/invoice date will be bigger than between payment due date/invoice date. Sample data with date feature transformed into date difference:

Correlation is much better this time. Decision correlates well with date differences and total:

Test, validation and training data sets will be prepared in the same proportions as in previous test. But we will be using stratify option. This option helps to shuffle data and create test, validation and training data sets where decision attribute is well represented:

Training, validation and test datasets are prepared:

Using same XGBoost training parameters:

Result: This time we get 99% training accuracy and 97% validation accuracy. Great result. You can see how important is data preparation step for ML. It directly relates to ML training quality: