shapwaterfall 0.2.4

A SHAP Waterfall Chart for interpreting local differences between observations

Install

Using pip (recommended)

pip install shapwaterfall

Introduction

Many times when VMware Data Science Teams present their Machine Learning models' propensity to buy scores (estimated probabilities) to stakeholders, stakeholders ask why a customer's propensity to buy is higher than the other customer. The stakeholder's question was our primary motivation.

We were further concerned with recent algorithm transparency language in the EU's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Although the 'right to explanation' is not necessarily clear, our desire is to act in good faith by providing local explainability and interpretability between two references, observations, clients, and customers.

This graph solution provides a local classification model interpretability between two observations, which internally we call customers. It uses each customer's estimated probability and fills the gap between the two probabilities with SHAP values that are ordered from higher to lower importance. We prefer SHAP over others (for example, LIME) because of its concrete theory and ability to fairly distribute effects.

Updated, this package works for all classification models. We added the Kernel Explainer.

The package requires a classifier, training data, validation/test/scoring data, the two observations of interest (row index), and the desired number of important features. The package produces a Waterfall Chart.

Command

shapwaterfall(clf, X_tng, X_val, index1, index2, num_features)

Required

• clf: a tree based classifier that is fitted to X_tng, training data.
• X_tng: the training Data Frame used to fit the model.
• X_val: the validation, test, or scoring Data Frame under observation. Note that the data frame must contain an extra column who's label is 'Reference'.
• index1 and index2: the first and second index numbers.
• num_features: the number of important features that describe the local interpretability between to the two observations.

Dependent Packages

The shapwaterfall package requires the following python packages:

import pandas as pd
import numpy as np
import shap
import matplotlib.pyplot as plt
import waterfall_chart

Examples

Random Forest on WI Breast Cancer Data

# Scikit-Learn WI Breast Cancer Data Example
# packages
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
import shap
import matplotlib.pyplot as plt
import waterfall_chart
from shapwaterfall import shapwaterfall

# models
rf_clf = RandomForestClassifier(n_estimators=1666, max_features="auto", min_samples_split=2, min_samples_leaf=2, max_depth=20, bootstrap=True, n_jobs=1)

# load and organize Wisconsin Breast Cancer Data
data = load_breast_cancer()
label_names = data['target_names']
labels = data['target']
feature_names = data['feature_names']
features = data['data']

# data splits
X_tng, X_val, y_tng, y_val = train_test_split(features, labels, test_size=0.33, random_state=42)

print(X_tng.shape) # (381, 30)
print(X_val.shape) # (188, 30)

X_tng = pd.DataFrame(X_tng)
X_tng.columns = feature_names
X_val = pd.DataFrame(X_val)
X_val.columns = feature_names

# fit classifiers and measure AUC
clf = rf_clf.fit(X_tng, y_tng)
pred_rf = clf.predict_proba(X_val)
score_rf = roc_auc_score(y_val,pred_rf[:,1])
print(score_rf, 'Random Forest AUC')

# 0.9951893425434809 Random Forest AUC

# Use Case 1
shapwaterfall(clf, X_tng, X_val, 5, 100, 5)
shapwaterfall(clf, X_tng, X_val, 100, 5, 7)

# Use Case 2
shapwaterfall(clf, X_tng, X_val, 36, 94, 5)
shapwaterfall(clf, X_tng, X_val, 94, 36, 7)

Authors

John Halstead, [e-mail]

Rajesh Vikraman, [e-mail]

Ravi Prasad K, [e-mail]

References

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9. Moffit, C. (2014). “Creating a Waterfall Chart in Python”, Practical Business Python, available at https://pbpython.com/waterfall-chart.html.

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12. Hulstaert, L. (2018). “Understanding model predictions with LIME”, Medium: Towards Data Science, available at https://towardsdatascience.com/understanding-model-predictions-with-lime-a582fdff3a3b.

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15. Singh, M., Kiran R, Harris, S. (2019). “Corona Impact: VMW Bookings and Propensity Models”, Vmware EDA AA & DS CoE PowerPoint Presentation, Palo Alto, CA, USA.

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17. Bowen, D., Ungar, L., (2020). “Generalized SHAP: Generating multiple types of explanations in machine learning”, Pre-print, June 15, 2020.

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