Introduction

Use-Case

• A User in their hydrator pipeline can retrieve bigrams from the text/data which can be used for statistical analysis purpose.
  Example:User wants to tokenize the text on the basis of '.' and create bigrams.
  Text:hello my friend.how are you today bye
  Output for bigrams:
  hello my
  my friend
  how are
  are you
  you today
  today bye

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:

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”
    
    

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"
                                }
          }