Introduction

Use-Case

• A bio data scientist wants to  study the sequence of the nucleotides using the input stream of DNA sequencing to identify the bonds.
  The input Stream contains the DNA sequence eg AGCTTCGA. The output contains the bigram sequence AG, GC, CT, TT, TC, CG, GA

   

User Stories

• As a Hydrator user,I want to transfom input features data in a column from source schema into output schema which will have a single column having n-gram data.
• As a Hydrator user I want to have configuration for specifying the column name from input schema on which transformation has to be performed.
• As a Hydrator user I want to have configuration to specify the no of terms which would be used for transformation of input features into n-grams.
• As a Hydrator user I want to have configuration to specify output column name wherein ngrams will be emitted.

Conditions

• Source field ,to be transformed,can be of only type string array.
• User can transform single field only from the source schema.
• Output schema will have a single field of type string array.
• If the input sequence contains fewer than n strings, no output is produced.
  
  

Example

Input source:    

Mandatory inputs from user:NGramTransform:

• • Field to be used to transform input features into n-grams:”DNASequence”
  • Number of terms in each n-gram:”2”
  • Transformed field for sequence of n-gram:”bigram”

Output:

End to End Example pipeline:       

Input source:

Tokenizer:

Mandatory inputs from user:

• • Column on which tokenization to be done:”sentence”
  • Delimiter for tokenization:” ”
  • Output column name for tokenized data:”tokens”

NGramTransform:

Mandatory inputs from user:

• • Field to be used to transform input features into n-grams:”tokens”
  • Number of terms in each n-gram:”2”
  • Transformed field for sequence of n-gram:”ngrams”

TPFSAvro Output

Design

This is a sparkcompute type of plugin and is meant to work with Spark only.

Properties:

• **fieldToBeTransformed:** Column to be used to transform input features into n-grams.

• **numberOfTerms:** Number of terms in each n-gram.

• **outputField:** Transformed column for sequence of n-gram.

Input JSON:

         {
           "name": "NGramTransform",
           "type": "sparkcompute",
           "properties": {
                                   "fieldToBeTransformed": "tokens",
                                   "numberOfTerms": "2",
                                   "outputField": "ngrams"
                                }
          }