Isolation Forest (ML_ISF)

This function isolates outliers by creating binary trees to efficiently separate normal data points from anomalies based on their low-dimensional representations.

Isolation Forest is an unsupervised machine learning algorithm designed for anomaly detection. It efficiently identifies outliers in a dataset by creating random binary trees that isolate anomalies with fewer tree traversals compared to normal data points. The algorithm measures the average path length needed to isolate an observation, and anomalies are expected to have shorter path lengths due to their rarity and distinctiveness.

Isolation Forest is particularly effective for large datasets with high-dimensional features, providing a scalable and accurate solution for detecting anomalies without the need for labeled data.

Sample Request

Build an Isolation Forest model named, "IsolateForest"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_ISF",
    "args": {
        "model_name": "IsolateForest",
        "random_state": 0
    }
}

Building a Isolation Forest Anomaly Detection model

Isolation Forest

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).

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

Python

// Some code

Node

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

decisionTreeRegression = await client.isolation_forest(project_id, parent_id, block_id, {
    model_name: "IsolateForest"
});

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_ISF_P",
    "args": {
        "model_name": "IsoslateForest",
        "test_data": null
    }
}

Detecting Anomalies with Isolation Forest

Isolation Forest 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 detection
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

await client.isolation_forest_predict(project_id, parent_id, block_id, {
   model_name: "IsolateForest",
});

Sample Request

Evaluate anomaly detection model performance

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_ISF_M",
    "args": {
        "model_name": "IsoslateForest",
    }
}

Evaluating Anomaly Detection with Isolation Forest

Isolation Forest Metrics

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 detection
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 codethon

Node

// Some code