# Hierarchical Clustering (ML\_CG\_1)

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"*

```javascript
{
    "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 

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                  |
| ------------------------------------------------ | ------ | -------------------------------------------- |
| project\_id<mark style="color:red;">\*</mark>    | int    | The `id` of the current project              |
| block\_id<mark style="color:red;">\*</mark>      | int    | The `id` of the current block                |
| function\_code<mark style="color:red;">\*</mark> | string | The function code for current block          |
| parent\_id<mark style="color:red;">\*</mark>     | int    | The `id` of the previous block               |
| args<mark style="color:red;">\*</mark>           | object | Block arguments                              |
| model\_name<mark style="color:red;">\*</mark>    | String | Name of the model to be used for prediction. |

{% tabs %}
{% tab title="200 Statemanagement object" %}

```javascript
{
    "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\": ""}}"
    }
}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Python" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

```javascript
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"
});
```

{% endtab %}
{% endtabs %}

## Sample Request

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

```javascript
{
    "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

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                                            |
| ------------------------------------------------ | ------ | ---------------------------------------------------------------------- |
| model\_name<mark style="color:red;">\*</mark>    | 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<mark style="color:red;">\*</mark>    | int    | ID of the current project                                              |
| block\_id<mark style="color:red;">\*</mark>      | int    | ID of the current block                                                |
| parent\_id<mark style="color:red;">\*</mark>     | int    | ID of the previous block                                               |
| function\_code<mark style="color:red;">\*</mark> | String | Function code for the current block                                    |

{% tabs %}
{% tab title="200: OK Statemanagement object" %}

```javascript
{
    "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\": ""}"
    }
}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Python" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

```javascript
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: ""
});
```

{% endtab %}
{% endtabs %}


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