A SHAP Waterfall Chart interpreting local differences between observations
Using pip (recommended)
pip install shapwaterfall
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.
Currently, this package only works for tree and tree ensemble classification models. Our decision to limit the use to tree methods was based on two considerations. We desired to take advantage of the tree explainer's speed. As a business practice, we tend to deploy Random Forest, XGBoost, LightGBM, and other tree ensembles more often than other classifications methods.
However, we plan to include the kernel explainer in future versions.
The package requires a tree classifier, training data, validation/test/scoring data with a column titled 'Reference', the two observations of interest, and the desired number of important features. The package produces a Waterfall Chart.
shapwaterfall(clf, X_tng, X_val, ref1, ref2, num_features)
- 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'.
- ref1 and ref2: the first and second reference, observation, client, or customer under study. Can either be a string or an integer. If the column data is a string, use 'ref1' and 'ref2'. Otherwise, use an integer, such as 4 or 107.
- num_features: the number of important features that describe the local interpretability between to the two observations.
The package users have to take care of the following with respect to the 'Reference' column. Otherwise it could result in errors.
- X_tng should only have the features used while training using model.fit() without the feature ‘Reference’.
- X_sc should have the feature named ‘Reference’ which is unique identifier for the scoring data frame and this should not be a feature used in the model training.
- The features in X_sc should be in the same order as X_tng. (This is important as I got totally wrong prediction scores when the order was not maintained)
- ‘ref1’ and ‘ref2’ are values of ‘Reference’ feature used for comparison
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
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 # IMPORTANT: add a 'Reference' column to the val/test/score data X_val = pd.DataFrame(X_val) X_val['Reference'] = X_val.index print(X_val.shape) # (188, 31) # 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)
Random Forest on UCI House Vote Data
# University of California, Irvine House Votes 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() # UCI Data df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/voting-records/house-votes-84.data') names = ['Republican', 'handicap infants', 'water project', 'budget resolution', 'physician fee freeze', 'el salvador aide', 'school religious groups', 'anti satellite', 'nicaraguan contras', 'mx missle', 'immigration', 'synfuels', 'education spending', 'superfund', 'crime', 'duty free exports', 'south africa'] df.columns = names df = df.replace(to_replace =["republican", "y"], value = 1) df = df.replace(to_replace =["democrat", "n", "?"], value = 0) label = df.iloc[:,0] features = df.iloc[:,1:17] # data splits X_tng, X_val, y_tng, y_val = train_test_split(features, label, test_size=0.33, random_state=42) print(X_tng.shape) print(X_val.shape) # 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') # 0.99238683127572 Random Forest AUC # IMPORTANT: add a 'Reference' column to the val/test/score data X_val = pd.DataFrame(X_val) X_val['Reference'] = X_val.index print(X_val.shape) # Use Case 3 shapwaterfall(clf, X_tng, X_val, 78, 387, 5) shapwaterfall(clf, X_tng, X_val, 387, 78, 7) # Use Case 4 shapwaterfall(clf, X_tng, X_val, 253, 157, 5) shapwaterfall(clf, X_tng, X_val, 157, 253, 7)
John Halstead, email@example.com
Rajesh Vikraman, firstname.lastname@example.org
Ravi Prasad K, email@example.com
Kiran R, firstname.lastname@example.org
Dua, D., Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/machine-learning-databases/voting-records/house-votes-84.data]. Irvine, CA: University of California, School of Information and Computer Science.
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