SHAP Advanced

Important

• Aggregating shap values can give more detailed alternatives to permutation importance and partial dependence

• A note on SHAP Values

  • Shap values show how much a given feature changed our prediction (compared to if we made that prediction at some baseline value of that feature)
  • Harder to calculate with the sophisticated models we use in practice
  • Through some algorithmic cleverness, Shap values allow us to decompose any prediction into the sum of effects of each feature value

• Visualizations

  • These visualizations have conceptual similarities to permutation importance and partial dependence plots
    • Summary plot – permutation importance
      • Permutation importance helps us get some insights and graphics on which feature matter, but they don’t provide information on how each individual feature matter. If a feature has medium permutation importance, that could mean it has
        • a large effect for a few predictions, but no effect in general, or
        • a medium effect for all predictions.
      • Here SHAP can give a bird’s eye view of which feature are important and what is driving it. Summary plot is made of many dots. Each dot has three characteristics:
        • Vertical location shows what feature it is depicting
        • Color shows whether that feature was high or low for that row of the dataset
        • Horizontal location shows whether the effect of that value caused a higher or lower prediction.
    • Contribution plot – partial dependence plot
      • We’ve previously used Partial Dependence Plots to show how a single feature impacts predictions. These are insightful and relevant for many real-world use case.Plus, with a little effort, they can be explained to a non-technical audience.But there’s a lot they don’t show. For instance, what is the distribution of effects? Is the effect of having a certain value pretty constant, or does it vary a lot depending on the values of other feaures. SHAP dependence contribution plots provide a similar insight to PDP’s, but they add a lot more detail.

import sklearn

from aiking.data.external import *
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.inspection import PartialDependenceDisplay
import seaborn as sns
import matplotlib.pyplot as plt
import pdpbox
import graphviz
import panel as pn
from ipywidgets import interact
import shap

path = get_ds('fifa-2018-match-statistics'); path.ls()[0]

Path('/Users/rahul1.saraf/rahuketu/programming/AIKING_HOME/data/fifa-2018-match-statistics/FIFA 2018 Statistics.csv')

df = pd.read_csv(path/"FIFA 2018 Statistics.csv"); df
y = (df['Man of the Match'] == "Yes"); y
X = df.select_dtypes(np.int64); X
df_train, df_val, y_train, y_val = train_test_split(X, y, random_state=1)

df_train.shape, df_val.shape, y_train.shape, y_val.shape
model_rf = RandomForestClassifier(random_state=0).fit(df_train, y_train); model_rf

RandomForestClassifier(random_state=0)

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explainer=shap.TreeExplainer(model_rf); explainer

<shap.explainers._tree.TreeExplainer at 0x2aa49ff10>

Summary Plot

shap_values = explainer.shap_values(df_val); shap_values.shape

(32, 18, 2)

shap_values[1].shape

(18, 2)

shap.summary_plot(shap_values[:,:,1], df_val)

Dependence Contribution Plot

shap.dependence_plot('Ball Possession %', shap_values[:,:,1], df_val, interaction_index="Goal Scored")

The spread suggests that other features must interact with Ball Possession %.

shap.dependence_plot('Ball Possession %', shap_values[:,:,1], df_val)