ie-package 0.0.1
Insight Extractor Package
Keywords: insight extractor

# TakeBlipInsightExtractor Package
_Data & Analytics Research_

## Overview

Here is presented these content:

* [Intro](#intro)
* [Run](#run)
* [Example of initialization e usage](#Example of initialization e usage)


## Intro

The Insight Extractor offers a way to analyze huge volumes of textual data in order to identify, cluster and detail subjects. 
This project achieves this results by way of applying a proprietary Named Entity Recognition (NER) algorithm followed by a clustering algorithm. 
The IE Cloud also allows any person to use this tool without having too many computational resources available to themselves.

The package outputs four types of files:

- **Wordcloud**: It's an image file containing a wordcloud describing the most frequent subjects on the text. The colours represent the groups of similar subjects.
- **Wordtree**: It's an html file which contains the graphic relationship between the subjects and the examples of uses in sentences. It's an interactive graphic where the user can navigate along the tree.
- **Hierarchy**: It's a json file which contains the hierarchical relationship between subjects.
- **Table**: It's a csv file containing the following columns:

       
        Message                   |  Entities                                                                                    | Groups     | Structured Message
        sobre cobranca inexistente|[{'value': 'cobranÃ§a', 'lowercase_value': 'cobranÃ§a', 'postags': 'SUBS', 'type': 'financial'}]|['cobranÃ§a']|sobre cobranÃ§a inexistente



### Parameters

The following parameters need to be set by the user on the command line:
- **embedding_path**: path to the embedding model, the file should end with .kv;
- **postagging_model_path**: path to the postagging model, the file should end with .pkl;
- **postagging_label_path**: path to the postagging label file, the file should end with .pkl;
- **ner_model_path**: path to the ner model, the file should end with .pkl;
- **ner_label_path**: path to the ner label file, the file should end with .pkl;
- **file**: path to the csv file the user wants to analyze;
- **user_email**: user's Take Blip email where they want to receive the analysis;
- **bot_name**: bot ID.


The following parameters have default settings, but can be customized by the user;
- **node_messages_examples**: it is an int representing the number of examples outputed for each subject on the Wordtree file. The default value is 100;
- **similarity_threshold**: it is a float representing the similarity threshold between the subject groups. The default value is 0.65, we recommend that this parameter not be modified;
- **percentage_threshold**: it is a float representing the frequency percentile of subject from which they are not removed from the analysis. The default value is 0.9;
- **batch_size**: it is an int representing the batch size. The default value is 50;
- **chunk_size**: it is an int representing chunk file size for upload in storaged. The default value is 1024;
- **separator**: it is a str for the csv file delimiter character. The default value is '|'.
          

## Example of initialization e usage:
1) Import main packages;
2) Initialize main variables;   
3) Initialize eventhub logger;
4) Initialize Insight Extractor;
5) Insight Extractor usage.


An example of the above steps could be found in the python code below:

1) Import main packages
```
import uuid
from TakeBlipInsightExtractor.insight_extractor import InsightExtractor
from TakeBlipInsightExtractor.outputs.eventhub_log_sender import EventHubLogSender
``` 
2) Initialize main variables
```
embedding_path = '*.kv'
postag_model_path = '*.pkl'
postag_label_path = '*.pkl'
ner_model_path = '*.pkl'
ner_label_path = '*.pkl'

user_email = 'your_email@host.com'
bot_name = 'my_bot_for_insight_extractor'
application_name = 'your application'

eventhub_name = '*'
eventhub_connection_string = '*'

file_name = '*'
input_data = '*.csv'
separator = '|'

similarity_threshold = 0.65
node_messages_examples = 100
batch_size = 1024
percentage_threshold = 0.7
```
 
3) Initialize eventhub logger
```
correlation_id = str(uuid.uuid3(uuid.NAMESPACE_DNS, user_email + bot_name))
logger = EventHubLogSender(application_name=application_name,
                           user_email=user_email,
                           bot_name=bot_name,
                           file_name=file_name,
                           correlation_id=correlation_id,
                           connection_string=eventhub_connection_string,
                           eventhub_name=eventhub_name)
```
4) Initialize Insight Extractor
```
insight_extractor = InsightExtractor(input_data,
                                     separator=separator,
                                     similarity_threshold=similarity_threshold,
                                     embedding_path=embedding_path,
                                     postagging_model_path=postag_model_path,
                                     postagging_label_path=postag_label_path,
                                     ner_model_path=ner_model_path,
                                     ner_label_path=ner_label_path,
                                     user_email=user_email,
                                     bot_name=bot_name,
                                     logger=logger)
```   
5) Insight Extractor usage
```
insight_extractor.predict(percentage_threshold=percentage_threshold,
                          node_messages_examples=node_messages_examples,
                          batch_size=batch_size)
```