SHAP Values

Important

• Shap values breakdown the prediction to show impact of each values

• We can

  • Explain Individual Predictions
  • Aggregate Model Level Insights

• For an example we compute

  • Impact| Value v of feature f vs Impact| Value v=baseline of feature f

• Where can we use such explainations

  • Model of bank rejects someone’s loan application –> Bank is legally required to explain basis of each rejection
  • Healthcare provider need to identify what factors are driving each patient’s risk of some disease , so that they can address each of them with targetted intervention

• sum(SHAP values for all features) = pred_for_team - pred_for_baseline_values

Imports

import sklearn

sklearn.__version__

'1.5.0'

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

((96, 18), (32, 18), (96,), (32,))

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

RandomForestClassifier(random_state=0)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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row_to_show = 5
data_for_prediction = df_val.iloc[row_to_show]
data_for_prediction_array = data_for_prediction.values.reshape(1,-1); data_for_prediction_array.shape

(1, 18)

model_rf.predict_proba(data_for_prediction_array)

/opt/homebrew/Caskroom/miniforge/base/envs/aiking/lib/python3.9/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names
  warnings.warn(

array([[0.29, 0.71]])

explainer = shap.TreeExplainer(model=model_rf); explainer

<shap.explainers._tree.TreeExplainer at 0x2a42d3f10>

shap_values = explainer.shap_values(data_for_prediction); shap_values

array([[-0.10282092,  0.10282092],
       [ 0.04740467, -0.04740467],
       [-0.02983219,  0.02983219],
       [-0.02277977,  0.02277977],
       [-0.00642731,  0.00642731],
       [-0.01258714,  0.01258714],
       [-0.02910577,  0.02910577],
       [ 0.00766886, -0.00766886],
       [-0.00792221,  0.00792221],
       [-0.01031725,  0.01031725],
       [ 0.00500036, -0.00500036],
       [ 0.00094579, -0.00094579],
       [ 0.02061101, -0.02061101],
       [-0.04846459,  0.04846459],
       [-0.00601652,  0.00601652],
       [-0.00042073,  0.00042073],
       [-0.0008261 ,  0.0008261 ],
       [-0.01286019,  0.01286019]])

explainer.expected_value[1], shap_values[:,1].shape, data_for_prediction.shape

(0.5012500000000001, (18,), (18,))

shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[:,1], data_for_prediction)

Other Explainer

• shap.DeepExplainer works with Deep Learning models.
• shap.KernelExplainer works with all models, though it is slower than other Explainers and it offers an approximation rather than exact Shap values.

k_explainer = shap.KernelExplainer(model_rf.predict_proba, df_train)
k_shap_values = k_explainer.shap_values(data_for_prediction); k_shap_values.shape
shap.force_plot(k_explainer.expected_value[1], k_shap_values[:,1], data_for_prediction)

Here is an example using KernelExplainer to get similar results. The results aren’t identical because KernelExplainer gives an approximate result. But the results tell the same story.