KappaML Python Client

Python client to interact with the KappaML platform 🐍

This SDK provides a simple interface for creating, training, and managing online machine learning models.

Platform: https://kappaml.com API Keys: https://app.kappaml.com/api-keys API Documentation: https://api.kappaml.com/docs OpenAPI Schema: https://api.kappaml.com/openapi.json

Installation

pip install kappaml

Quick Start

from kappaml import KappaML

# Initialize the client
client = KappaML(api_key="your_api_key")  # Or set KAPPAML_API_KEY env variable

# Create a new model
model_id = client.create_model(
    name="my-regression-model",
    ml_type="regression"
)

# Train the model with a single data point
client.learn(
    model_id=model_id,
    features={"x1": 1.0, "x2": 2.0},
    target=3.0
)

# Make predictions
prediction = client.predict(
    model_id=model_id,
    features={"x1": 1.5, "x2": 2.5}
)

# Get model metrics
metrics = client.get_metrics(model_id)

# Clean up
client.delete_model(model_id)

API Reference

KappaML Class

Constructor

client = KappaML(api_key: Optional[str] = None)

• api_key: Your KappaML API key. If not provided, will look for KAPPAML_API_KEY environment variable.

Methods

create_model

def create_model(
    name: str,
    ml_type: str,
    wait_for_deployment: bool = True,
    timeout: int = 60
) -> str

Creates a new model on KappaML.

• name: Name of the model
• ml_type: Type of ML task ('regression' or 'classification')
• wait_for_deployment: Whether to wait for model deployment to complete
• timeout: Maximum time to wait for deployment in seconds
• Returns: The model ID

learn

def learn(
    model_id: str,
    features: Dict[str, Any],
    target: Union[float, int, str]
) -> Dict[str, Any]

Train the model with a single data point.

• model_id: The model ID
• features: Dictionary of feature names and values
• target: The target value to learn from
• Returns: Response from the learning API

predict

def predict(
    model_id: str,
    features: Dict[str, Any]
) -> Dict[str, Any]

Make predictions using a model.

• model_id: The model ID
• features: Dictionary of feature names and values
• Returns: Model predictions

get_metrics

def get_metrics(model_id: str) -> Dict[str, Any]

Get current metrics for a model.

• model_id: The model ID
• Returns: Model metrics

delete_model

def delete_model(model_id: str) -> None

Delete a model.

• model_id: The model ID to delete

Error Handling

The SDK defines several exception classes for handling errors:

• KappaMLError: Base exception for all SDK errors
• ModelNotFoundError: Raised when a model is not found
• ModelDeploymentError: Raised when model deployment fails or times out

Example error handling:

from kappaml import KappaML, ModelNotFoundError

client = KappaML()

try:
    metrics = client.get_metrics("non-existent-model")
except ModelNotFoundError:
    print("Model not found!")