xplainable

Real-time explainable machine learning for business optimisation

Xplainable makes tabular machine learning transparent, fair, and actionable.

Why Was Xplainable Created?

In machine learning, there has long been a trade-off between accuracy and explainability. This drawback has led to the creation of explainable ML libraries such as Shap and Lime which make estimations of model decision processes. These can be incredibly time-expensive and often present steep learning curves making them challenging to implement effectively in production environments.

To solve this problem, we created xplainable. xplainable presents a suite of novel machine learning algorithms specifically designed to match the performance of popular black box models like XGBoost and LightGBM while providing complete transparency, all in real-time.

Xplainable Cloud

This Python package is free and open-source. To add more value to data teams within organisations, we also created Xplainable Cloud that brings your models to a collaborative environment.

Preprocessing with Xplainable Cloud

Before modeling, it's essential to preprocess your data. Xplainable Cloud facilitates this process by allowing you to create and manage preprocessors in the cloud.

import xplainable as xp
import os
from xplainable_client import Client

#Initialising the client
XClient = Client(api_key=os.environ['XP_API_KEY'])

#Creating a Preprocessor ID
preprocessor_id = XClient.create_preprocessor_id(
    preprocessor_name="Preprocessor Name",
    preprocessor_description="Preprocessor Description",
)

#Creating a Preprocessor Version
preprocessor_version = XClient.create_preprocessor_version(
    preprocessor_id, #preprocessor_id,
    pipeline, # <-- Pass the pipeline
    df # <-- Pass the raw dataframe
)

#Loading the Preprocessor Client
pp_cloud = XClient.load_preprocessor(
    preprocessor_id,
    preprocessor_version["version_id"],
    gui_object=False # Set to true to load the GUI object, keep as False for pipeline
    )

Modelling with Xplainable Cloud

After preprocessing, the next step is to create and train your model. Xplainable Cloud supports model versioning and ID creation to streamline this process.

#Creating a Model Id
model_id = XClient.create_model_id(
    model,
    model_name="Model Name",
    model_description='Model Description'
)

#Creating a Model Version
version_id = XClient.create_model_version(
    model,
    model_id,
    X_train,
    y_train
)

Deployments with Xplainable Cloud

Once your model is ready, deploying it is straightforward with Xplainable Cloud. You can deploy, activate, and manage API keys for your model deployment keys within your IDE or environment.

#Creating a Model Deployment
deployment = XClient.deploy(
    hostname="https://inference.xplainable.io", 
    model_id=model_id, 
    version_id=version_id 
)

#Activating the Deployment
XClient.activate_deployment(deployment['deployment_id'])

#Generating an API Key
deploy_key = XClient.generate_deploy_key(
    'API Key Name', 
    deployment['deployment_id'], 
    7 #-> Days until expiration
    )

#Hitting the endpoint
response = requests.post(
    url="https://inference.xplainable.io/v1/predict",
    headers={'api_key': deploy_key['deploy_key']},
    json=body
)

#Obtaining the value response
value = response.json()