Using Shapash with Lime explainer - Titanic

You can compute your local contributions with the Lime library and summarize them with Shapash

Contents: - Build a Binary Classifier (Random Forest) - Create Explainer using Lime - Compile Shapash SmartExplainer - Display local_plot - to_pandas export

Data from Kaggle Titanic

import numpy as np
import pandas as pd
from category_encoders import OrdinalEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import lime.lime_tabular

from shapash.data.data_loader import data_loading

titan_df, titan_dict = data_loading('titanic')
del titan_df['Name']

titan_df.head()

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked	Title
PassengerId
1	0	Third class	male	22.0	1	0	7.25	Southampton	Mr
2	1	First class	female	38.0	1	0	71.28	Cherbourg	Mrs
3	1	Third class	female	26.0	0	0	7.92	Southampton	Miss
4	1	First class	female	35.0	1	0	53.10	Southampton	Mrs
5	0	Third class	male	35.0	0	0	8.05	Southampton	Mr

Create Classification Model

y = titan_df['Survived']
X = titan_df.drop('Survived', axis=1)

varcat=['Pclass','Sex','Embarked','Title']

categ_encoding = OrdinalEncoder(cols=varcat, \
                                handle_unknown='ignore', \
                                return_df=True).fit(X)
X = categ_encoding.transform(X)

Train Test split + Random Forest fit

Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.75, random_state=1)

rf = RandomForestClassifier(n_estimators=100,min_samples_leaf=3)
rf.fit(Xtrain, ytrain)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=3, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

Create Lime Explainer

#Training Tabular Explainer
explainer = lime.lime_tabular.LimeTabularExplainer(Xtrain.values,
                                                   mode='classification',
                                                   feature_names=Xtrain.columns,
                                                   class_names=ytrain)

Apply Explainer to Test Sample And Preprocessing

# Function features_check Extract feature names from Lime Output to be used by shapash
def features_check(s):
    for w in list(Xtest.columns):
        if f' {w} ' in f' {s} ' :
            feat = w
    return feat

%%time
# Compute local Lime Explanation for each row in Test Sample
contrib_l=[]
for ind in Xtest.index:
    exp = explainer.explain_instance(Xtest.loc[ind].values, rf.predict_proba, num_features=Xtest.shape[1])
    contrib_l.append(dict([[features_check(elem[0]),elem[1]] for elem in exp.as_list()]))

CPU times: user 57.8 s, sys: 7.34 s, total: 1min 5s
Wall time: 10.9 s

contribution_df =pd.DataFrame(contrib_l,index=Xtest.index)
# sorting the columns as in the original dataset
contribution_df = contribution_df[list(Xtest.columns)]

ypred=pd.DataFrame(rf.predict(Xtest),columns=['pred'],index=Xtest.index)

Use Shapash With Lime Contributions

from shapash import SmartExplainer

xpl = SmartExplainer(
    model=rf,
    preprocessing=categ_encoding,
    features_dict=titan_dict
)

Use contributions parameter of compile method to declare Lime contributions

xpl.compile(
    contributions=contribution_df, # Lime Contribution pd.DataFrame
    y_pred=ypred,
    y_target=ytest, # Optional: allows to display True Values vs Predicted Values
    x=Xtest
)

xpl.plot.local_plot(index=3)

summary_df = xpl.to_pandas(max_contrib=3,positive=True,proba=True)
summary_df.head()

	pred	proba	feature_1	value_1	contribution_1	feature_2	value_2	contribution_2	feature_3	value_3	contribution_3
863	1	0.801675	Sex	female	0.257817	Title of passenger	Mrs	0.188714	Ticket class	First class	0.0880992
224	0	0.965208	Sex	male	0.248462	Title of passenger	Mr	0.199544	Ticket class	Third class	0.0838383
85	1	0.799397	Sex	female	0.25465	Title of passenger	Miss	0.193198	Age	17	0.0981314
681	1	0.786956	Sex	female	0.252464	Title of passenger	Miss	0.187045	Relatives such as brother or wife	0	0.0522808
536	1	0.936170	Sex	female	0.250703	Title of passenger	Miss	0.193096	Age	7	0.104632