Text Vectorizer (DP_VEC)

Transform textual data into numerical representations that are compatible with machine learning models, enabling efficient processing of text-based tasks.

Text Vectorizers are tools used to convert textual data into numerical representations suitable for machine learning models. They process text inputs and transform them into feature vectors, enabling the API to perform natural language processing and text-based tasks efficiently.

Supported Vectorizers:

1. TF-IDF Vectorizer: Assigns weights to words based on their importance in a document and rarity across the dataset, capturing their significance for modeling.

2. Count Vectorizer: Counts the occurrences of each word in a document, representing it as a sparse matrix with word frequencies.

3. Hashing Vectorizer: Converts words into numerical indices using a hashing trick, providing memory-efficient representations.

These vectorizers are crucial for handling text data in the API, facilitating tasks like text classification, sentiment analysis, and other natural language processing tasks.

Sample Request

The request performs text vectorization using the TF-IDF vectorizer with specified boundaries to scale the data on the specified variables.

{
    "project_id": 1,
    "parent_id": 5,
    "block_id": 6,
    "function_code": "DP_VEC",
    "args": {
        "vectorizer": "tfidf",
        "boundariestoscale": ":, :",
        "dataset": false,
        "xtrain": true,
        "xtest": true,
        "x": true,
        "ytrain": false,
        "ytest": false,
        "y": false
    }
}

Parameter Details

Principal Component Analysis

POST https://autogon.ai/api/v1/engine/start

Request Body

Name	Type	Description
project_id*	int	current project ID
parent_id*	int	parent block ID
block_id*	int	current block ID
function_code*	String	block's function code
args*	object	block arguments
boundariestoscale	String	boundaries to vectorize
dataset/x/y/xtrain/ytrain/xtest/ytest	bool	variables to apply vectorizer
vectorizer	String	Type of vectorizer to apply: tfidf : Converts text data into numerical features based on term frequency-inverse document frequency, capturing word importance in documents and across the corpus. count: Transforms text data into numerical features by counting the occurrences of words hashing: Uses a hashing trick to map words into fixed-size feature vectors

200: OK Text Vectorization Successful

{
    "status": "true",
    "message": {
        "id": 3,
        "project": 1,
        "block_id": 7,
        "parent_id": 6,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": ""
    }
}

Python

// Some code

Node

projectId = 1
parentId = 6
blockId = 7

client.array_reshaping(projectId, parentId, blockId, {