DataHowLab Prediction SDK

DHL_SDK is a software development kit (SDK) designed to simplify and streamline the integration of DataHowLab's Prediction capabilities into a simple Python package.

This SDK provides a convenient and efficient way to interact with DataHowLab's API, enabling access to the Projects and Models created, as well as making predictions using these models with new sets of data.

Prerequisites

• Python: Ensure that you have Python installed (version 3.9 or higher) on your system.
• API Key Make sure you have a valid DataHowLab API Key.

Installation

1. Install Poetry If you don't have Poetry installed, you can do it using pipx:

$ pip install poetry

For more detailed installation instructions, you can refer to the Poetry documentation.

2. Clone the Repository: You'll need to clone this project's repository to your local machine. You can do this using git:

# Example code for installation
$ git clone https://github.com/DataHow/DHL_SDK.git
$ cd your-repo

4. Install Project Dependencies: Use Poetry to install the project's dependencies. Poetry will read the pyproject.toml file and set up your project environment:

$ poetry install

5. Activate Virtual Environment (Optional): Poetry creates a virtual environment for your project. You can activate it using the following command:

$ poetry shell

Usage

import numpy as np
from dhl_sdk import SpectraHowClient, APIKeyAuthentication

# DHL_API_KEY env var is loaded from the .env file or added directly as an argument here 
key = APIKeyAuthentication()
your_url = "https://yourdomain.datahowlab.ch/"
client = SpectraHowClient(auth_key=key, base_url=your_url)

# `client.get_projects()` is called to retrieve a list of projects. You can filter the projects by name if you include `name=project_name`. 
projects = client.get_projects(name="project_name")

# This will result in a Iterable object. To access each project, use the `next(projects)` function.
project = next(projects)

# Once you find your project of interest, you can access all the models
models = project.get_models(name="Test model")

#If you want to check all the models inside a project, just list the models and select from there
list_of_models = list(models)
model = list_of_models[2]

# Now you just need some data. Here is an example how to load data from an example.csv file using numpy
# make sure your array only contains the values and not other information, like labels
spectra = np.genfromtxt("example.csv", delimiter=',')

# next, use the selected model to predict you outputs using the loaded spectra
predictions = model.predict(spectra)