voltus 0.0.4

A Python client for the Voltus API

Voltus Python Client

This is a Python client for interacting with the Voltus feature store API. It provides a convenient way to programmatically access Voltus functionalities, such as adding datasets, applying feature functions, and retrieving results.

Installation

You can install the client using pip:

pip install voltus

Usage

Initialization

First, you need to initialize the client with your Voltus API base URL and a user authentication token:

import os
from voltus.client import VoltusClient
from dotenv import load_dotenv
load_dotenv(verbose=True)

BASE_URL = os.getenv("BASE_URL", None)
USER_TOKEN = os.getenv("USER_TOKEN", None)

# Initialize the client
client = VoltusClient(api_base_url=BASE_URL, token=USER_TOKEN, verify_requests=True)

Make sure to set the BASE_URL and USER_TOKEN environment variables before running the script.

Basic Operations

Here's how to perform common operations with the client:

Healthcheck

Check if the server is healthy

# Check server health
if client.healthcheck():
    print("Server is healthy")
else:
    print("Server is not healthy")

Current Authenticated User

Retrieve information about the currently authenticated user.

# Get the current authenticated user
user_data = client.get_current_authenticated_user()
print(f"Current User: {user_data['user']['username']}")

Get Task Status

Retrieve status of background tasks

# Get task status for a specific task id
task_status = client.get_task_status(task_id='your_task_id')
print(task_status)

# Get status of all tasks
all_tasks_status = client.get_task_status()
print(all_tasks_status)

Add Dataset

Add a Pandas dataframe as a dataset on the server.

import pandas as pd

# Create a sample pandas DataFrame
data = {
    "timestamp": pd.to_datetime(
        ["2023-01-01 00:00:00", "2023-01-01 01:00:00", "2023-01-01 02:00:00"], utc=True
    ),
    "power": [10, 12, 15],
    "unit": ["MW", "MW", "MW"],
}
df = pd.DataFrame(data)

# Add a dataset
dataset_name = "my_test_dataset"
client.add_dataset(df, dataset_name=dataset_name)
print(f"Dataset '{dataset_name}' added")

List Datasets

List all dataset names in the user's account.

# List datasets
datasets = client.list_datasets()
print(f"Datasets available: {datasets}")

Retrieve Dataset

Retrieve a dataset in JSON format.

# Retrieve a dataset
retrieved_dataset = client.retrieve_dataset(dataset_name=dataset_name)
print(f"Retrieved dataset: {retrieved_dataset['data'][:2]}")

Delete Datasets

Delete multiple datasets using a list of dataset names.

# delete dataset(s)
client.delete_datasets(dataset_names=[dataset_name, "test_kmeans_from_data"])
print("Datasets deleted")

List Available Example Datasets

Lists all available example datasets.

# List example datasets
example_dataset_names = client.list_example_datasets()
print(f"Example datasets available: {example_dataset_names}")

Retrieve Example Datasets

Retrieve the contents of a specific example dataset.

# retrieve example dataset
example_dataset = client.retrieve_example_dataset(dataset_name="Power Usage")
print(f"Example dataset: {example_dataset['data'][:2]}")

Apply Feature Function to Dataset

Apply a feature function to a dataset that already exists on the server, creating a new dataset in the process.

# Apply feature function
ff_response = client.apply_feature_function_to_dataset(
    feature_function_name="k_means_clustering",
    original_datasets=[dataset_name],
    generated_dataset_name="test_kmeans_from_data",
    kwargs={"num_clusters": 2},
    process_synchronously=False
)
print(f"Applying feature function. Response: {ff_response}")

List Available Feature Functions

Lists available feature functions on the server.

# List available functions
available_functions = client.list_feature_functions()
print(f"Available functions: {[f['name'] for f in available_functions]}")

List Available Feature Functions Tags

Lists available feature functions tags on the server.

# List available tags
available_tags = client.list_feature_functions_tags()
print(f"Available function tags: {available_tags}")

Error Handling

The client raises exceptions if there are issues with the API requests, including network errors, authentication failures, and server-side errors.

Examples

For more complete examples, see the tests/test_client.py file in the project repository.

Contributing

Feel free to submit pull requests to the repository, in order to improve this library.