SHAP KernelExplainer using PipeLine

Question

I have a problem where I want to use pipeline (with OHE as preprocess and simple Linear Regression as model) with SHAP tools.

As for the data, here are my data (I’m using my modified version of bike sharing dataset):

bike_data=pd.read_csv("bike_outlier_clean.csv")

bike_data['season']=bike_data.season.astype('category')
bike_data['year']=bike_data.year.astype('category')
bike_data['holiday']=bike_data.holiday.astype('category')
bike_data['workingday']=bike_data.workingday.astype('category')
bike_data['weather_condition']=bike_data.weather_condition.astype('category')

bike_data['season'] = bike_data['season'].map({1:'Spring', 2:'Summer', 3:'Fall', 4: 'Winter'})
bike_data['year'] = bike_data['year'].map({0: 2011, 1: 2012})
bike_data['holiday'] = bike_data['holiday'].map({0: False, 1: True})
bike_data['workingday'] = bike_data['workingday'].map({0: False, 1: True})
bike_data['weather_condition'] = bike_data['weather_condition'].map({1:'Clear', 2:'Mist', 3:'Light Snow/Rain', 4: 'Heavy Snow/Rain'})

bike_data = bike_data[['total_count','season','month','year','weekday','holiday','workingday','weather_condition','humidity','temp','windspeed']]

x = bike_data.drop('total_count', axis=1)
y = bike_data['total_count']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

and for my pipeline

category_columns = list(set(bike_data.columns) - set(bike_data._get_numeric_data().columns))
preprocessor = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(), category_columns)
    ],
    remainder='passthrough'  
)
model = LinearRegression()

pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])

pipeline.fit(x_train,y_train)

and finally, using the kernelSHAP explainer

explainer = shap.KernelExplainer(pipeline.predict, shap.sample(x, 5))

However, that is where the error occur.

    123             # Make a copy so that the feature names are not removed from the original model
    124             out = copy.deepcopy(out)
--> 125             out.f.__self__.feature_names_in_ = None
    126 
    127     return out

AttributeError: can't set attribute 'feature_names_in_'

I’m quite clueless as for now what should I do to fix it.

Asked By: Chrisanto

||

Source

Answer 1

Shap doesn’t behave well with Pipeline object, so I suggest the following (watch out when I start using numpy array instead of Pandas df):

import shap

from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline

print(shap.__version__)

bike_data = pd.read_csv("archive/bike_sharing_daily.csv")
bike_data['season']=bike_data.season.astype('category')
bike_data['holiday']=bike_data.holiday.astype('category')
bike_data['workingday']=bike_data.workingday.astype('category')
bike_data['season'] = bike_data['season'].map({1:'Spring', 2:'Summer', 3:'Fall', 4: 'Winter'})
bike_data['holiday'] = bike_data['holiday'].map({0: False, 1: True})
bike_data['workingday'] = bike_data['workingday'].map({0: False, 1: True})
bike_data = bike_data[['season','weekday','holiday','workingday','temp','windspeed']]

x = bike_data

y = np.random.randint(0, 10, len(bike_data))

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

category_columns = list(set(bike_data.columns) - set(bike_data._get_numeric_data().columns))

col_idx = [i for i, col in enumerate(x_train.columns) if col in category_columns]

preprocessor = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(), col_idx)
    ],
    remainder='passthrough'  
)
model = LinearRegression()

pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])

pipeline.fit(x_train.values, y_train) # <-- from here

explainer = shap.KernelExplainer(pipeline.predict, x_train.values[:10])
sv = explainer.shap_values(x_train.values)

shap.summary_plot(sv, x.columns) # <-- add column names back

0.44.1.dev4

Answered By: Sergey Bushmanov

SHAP KernelExplainer using PipeLine

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