After successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it:
Open up your jupyter notebook and simply follow the code.
The goal is to be able to explain and debug the machine learning model we are building. The main goal will be to provide business-level explanations.
Make sure you have Sklearn & explainX installed. In case you don't, follow the following command to install the libraries.
Import required module.
from explainx import *from sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_split
Load and split your dataset into x_data and y_data
#Load Dataset: X_Data, Y_Data#X_Data = Pandas DataFrame#Y_Data = Numpy Array or ListX_data,Y_data = explainx.dataset_heloc()
Split dataset into training & testing.
X_train, X_test, Y_train, Y_test = train_test_split(X_data,Y_data, test_size=0.3, random_state=0)
Train your model.
# Train a RandomForest Modelmodel = RandomForestClassifier()model.fit(X_train, Y_train)
After you're done training the model, you can either access the complete explainability dashboard or access individual techniques.
To access the entire dashboard with all the explainability techniques under one roof, follow the code down below. It is great for sharing your work with your peers and managers in an interactive and easy to understand way.
5.1. Pass your model and dataset into the explainX function:
explainx.ai(X_test, Y_test, model, model_name="randomforest")
5.2. Click on the dashboard link to start exploring model behavior:
App running on https://0.0.0.0:8080
In this latest release, we have also given the option to use explainability techniques individually. This will allow the user to choose technique that fits their personal AI use case.
6.1. Pass your model, X_Data and Y_Data into the explainx_modules function.
explainx_modules.ai(X_test, Y_test, model)
As an upgrade, we have eliminated the need to pass in the model name as explainX is smart enough to identify the model type and problem type i.e. classification or regression, by itself.
You can access multiple modules:
Module 1: Dataframe with Predictions
Module 2: Model Metrics
Module 3: Global Level SHAP Values
Module 4: What-If Scenario Analysis (Local Level Explanations)
Module 5: Partial Dependence Plot & Summary Plot
Module 6: Model Performance Comparison (Cohort Analysis)
To access the modules within your jupyter notebook as IFrames, just pass the mode='inline' argument in each of the function.