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$sigmoid - Amazon DocumentDB
Example (MongoDB Shell)Code examples

$sigmoid

New from version 8.0.1.

The $sigmoid operator in Amazon DocumentDB applies the logistic sigmoid function, 1 / (1 + e^(-x)), to a numeric input. The function maps any real number to a value between 0 and 1, producing an S-shaped curve. This is useful when you need to normalize values into the 0–1 range, such as converting raw scores into probabilities or relevance scores.

Parameters

  • expression: An expression that resolves to a numeric value.

Example (MongoDB Shell)

The following example shows how to use the $sigmoid operator to convert logit values into probabilities.

Create sample documents

db.predictions.insertMany([
  {_id: 1, logit: -2},
  {_id: 2, logit: 0},
  {_id: 3, logit: 2}
]);

Query example

db.predictions.aggregate([
  { $project: { probability: { $sigmoid: "$logit" } } }
]);

Output

[
  {_id: 1, probability: 0.11920292202211755},
  {_id: 2, probability: 0.5},
  {_id: 3, probability: 0.8807970779778823}
]

Code examples

To view a code example for using the $sigmoid operator, choose the tab for the language that you want to use:

Node.js
const { MongoClient } = require('mongodb');

async function example() {
  const client = new MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false');
  try {
    await client.connect();
    const db = client.db('test');
    const collection = db.collection('predictions');
    const result = await collection.aggregate([
      { $project: { probability: { $sigmoid: "$logit" } } }
    ]).toArray();
    console.log(result);
  } finally {
    await client.close();
  }
}
example();
Python
from pymongo import MongoClient

def example():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')
    try:
        db = client['test']
        collection = db['predictions']
        result = list(collection.aggregate([
            {'$project': {'probability': {'$sigmoid': '$logit'}}}
        ]))
        print(result)
    finally:
        client.close()

example()