K-Nearest Neighbors - KNN (ML_CN_2)

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

In other words, it assigns a label to a new data point based on how similar it is to the existing data points, where similarity is defined by distance metric such as Euclidean or Manhattan.

This function can be used for both supervised and unsupervised learning.

Sample Request

Build a KNN model named, "ClassicModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_2",
    "args": {
        "model_name": "ClassicModel",
        "random_state": 0,
        "n_neighbors": 5,
        "distance": "minkowski",
        "p": 2
    }
}

Building a K-Nearest Neighbors model

K-Nearest Neighbors

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

Request Body

Name	Type	Description
project_id*	int	The id of the current project
block_id*	int	The id of the current block
function_code*	string	The function code for current block
parent_id*	int	The id of the previous block
args*	object	Block arguments
model_name*	String	Name of the model to be used for prediction.
random_state	int	Controls the randomness of the estimator (defaults to 0).
n_neighbors	int	Number of neighbors to use by default for kneighbors queries (defaults to 5).
distance	String	Metric to use for distance computation. Default minkowski, which results in the standard Euclidean distance when p = 2.
p	int	Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance, and euclidean_distance for p = 2 (defaults to 2).

200 Statemanagement object

{
    "status": "true",
    "message": {
        "id": 8,
        "project": 1,
        "block_id": 8,
        "parent_id": 7,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

kNearestNeighbors = await client.k_nearest_neighbors(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    random_state: 0,
    n_neighbors: 5,
    distance: "minkowski",
    p: 2
});

Sample Request

Make predictions with the pre-built model passing an optional test data.

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CN_2_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}

Predicting with K-Nearest Neighbors

K-Nearest Neighbors Predict

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

Request Body

Name	Type	Description
model_name*	String	Name of previously trained model to be used for prediction
test_data	String	Input data for prediction. Defaults to x_train_url in StateManagment
project_id*	int	ID of the current project
block_id*	int	ID of the current block
parent_id*	int	ID of the previous block
function_code*	String	Function code for the current block

200: OK Statemanagement object

{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"y_pred_url\": ""}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

kNearestNeighborsnPredict = await client.k_nearest_neighbors_predict(project_id, parent_id, block_id, {
   model_name: "ClassicModel",
    test_data: ""
});

Sample Request

Evaluate model metrics

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CN_2_M",
    "args": {
        "model_name": "ClassicModel"
    }
}

K-Nearest Neighbors Metrics

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

Request Body

Name	Type	Description
project_id*	int	ID of the current project
parent_id*	int	ID of the previous block
block_id*	int	ID of the current block
function_code*	String	Function code for the current block
model_name*	String	Name of the pre-trained model to be used for evaluation

200: OK StateManagement object

{
    "status": "true",
    "message": {
        "id": 1,
        "project": 12,
        "block_id": 10,
        "parent_id": 11,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{'confusion_matrix': '', 'accuracy': 0.9}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

kNearestNeighborsMetrics= await client.k_nearest_neighbors_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
 
});