Model Governance: Interpretability
This article describes how ModelOp Center allows for the execution of model interpretability and model bias monitoring.
Table of Contents
Introduction
Enterprises need visibility into how models are forming predictions. Mission-critical decisions cannot be made using a black box. Teams need to understand and explain how their model is making predictions.
ModelOp Center provides a framework for calculating, tracking, and visualizing both Model Interpretability metrics. You can define how each of these is determined on a model by model case. You can also enforce a standard using an MLC Process as needed. The subsequent sections provide more detail on how to use ModelOp Center to implement Interpretability into your ModelOps program.
Interpretability
While model interpretability is a complex and rapidly-changing subject, especially for AI models, ModelOp Center can assist you in understanding and calculating how much each feature contributes to the output of each individual prediction. In other words, these metrics show the impact of each feature on each prediction. ModelOp Center does this by leveraging SHAP to automatically compute the feature importance metrics. The SHAP results are persisted with the Test Results for the model for auditability. Note: not all models are easily interpretable, and as a result, model interpretability can be used as a factor for deciding the approach and choice of a model.
The following example uses the SHAP library to calculate the impact of the features on the prediction. It emits the average SHAP value for each feature and yields it as a dictionary.
import pandas as pd
import numpy as np
import shap
import pickle
# modelop.init
def begin():
global explainer, lr_model, threshold, features
model_artifacts = pickle.load(open("model_artifacts.pkl", "rb"))
explainer = model_artifacts['explainer']
lr_model = model_artifacts['lr_model']
threshold = model_artifacts['threshold']
features = model_artifacts['features']
pass
def preprocess(data):
prep_data = pd.DataFrame(index=data.index)
prep_data["logit_int_rate"] = data.int_rate.apply(logit)
prep_data["log_annual_inc"] = data.annual_inc.apply(np.log)
prep_data["log_credit_age"] = data.credit_age.apply(np.log)
prep_data["log_loan_amnt"] = data.loan_amnt.apply(np.log)
prep_data["rent_indicator"] = data.home_ownership.isin(['RENT']).astype(int)
return prep_data
def prediction(data):
return lr_model.predict_proba(data.loc[:, features])[:,1]
# modelop.metrics
def metrics(data):
metrics = {}
prep_data = preprocess(data)
data = pd.concat([data, prep_data], axis=1)
data.loc[:, 'probabilities'] = prediction(data)
data.loc[:, 'predictions'] = data.probabilities \
.apply(lambda x: threshold > x) \
.astype(int)
metrics['shap'] = get_shap_values(data)
yield metrics
def get_shap_values(data):
shap_values = explainer.shap_values(data.loc[:, features])
shap_values = np.mean(abs(shap_values), axis=0).tolist()
shap_values = dict(zip(features, shap_values))
sorted_shap_values = {
k: v for k, v in sorted(shap_values.items(), key=lambda x: x[1])
}
return sorted_shap_valuesThe following image shows the corresponding visualization for the SHAP values of the sample model to the Test Results in ModelOp Center.
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