Hierarchical Clustering (ML_CG_1)

Hierarchical Clustering groups similar data points into clusters by recursively merging the two closest clusters based on a distance metric.

Hierarchical Clustering is a machine learning algorithm used for grouping similar data points into clusters. It starts with each data point in its own cluster, and then recursively merges the two closest clusters until there is only one cluster left.

The algorithm uses a distance metric to determine the similarity between clusters and data points, and creates a dendrogram to visualize the hierarchy of the merged clusters.

Sample Request

Build a Hierarchical Clustering model named, "ClusterModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CG_1",
    "args": {
	"model_name": "ClusterModel",
        "n_clusters": 5,
        "affinity": "euclidean",
        "linkage": "ward"
    }
}

Building a Hierarchical Clustering model

Hierarchical Clustering

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.

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": "{\"ClusterModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

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

hierarchicalClustering  = await client.hierarchicalClustering(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    n_clusters: 5,
    affinity: "euclidean",
    linkage: "ward"
});

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_CG_1_P",
    "args": {
        "model_name": "ClusterModel",
        "test_data": ""
    }
}

Predicting with Hierarchical Clustering

Hierarchical Clustering 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

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