How to get original string values of encoded categorical columns in Lime graph

I am trying to work on local explainability using Lime graph. Before building the model, I encode some of the categorical variables.

Sample Data and code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

df = pd.DataFrame({'customer_id' : np.arange(1,21),
                  'gender' : np.random.choice(['male','female'], 20),
                  'age' : np.random.randint(19,50, 20),
                  'salary' : np.random.randint(20000,95000, 20),
                  'purchased' : np.random.choice([0,1], 20, p = [.8,.2])})

Preprocessing:

df['gender'] = df['gender'].map({'female' : 0, 'male' : 1})

df['age'] = df['age'].map(lambda x : 'young' if x<=35 else 'middle aged')

df['age'] = df['age'].map({'young' : 0, 'middle aged' : 1})

bins = [0, df['salary'].quantile(q=.33),df['salary'].quantile(q=.66),df['salary'].quantile(q=1)+1]
labels = ['low salary', 'medium salary', 'high salary']
df['salary'] = pd.cut(df['salary'], bins = bins, labels=labels)

from sklearn import preprocessing
l_encoder={}
label_encoder = preprocessing.LabelEncoder()
df['salary']= label_encoder.fit_transform(df['salary'])
df

	customer_id	gender	age	salary	purchased
0	1	        0	    0	1	    0
1	2	        0	    0	0	    0
2	3	        0	    1	2	    0
3	4	        1	    0	0	    0
4	5	        1	    1	2	    0
5	6	        0	    1	1	    0
6	7	        1	    0	2	    0
7	8	        1	    1	0	    0
8	9	        1	    1	1	    0
9	10	        1	    0	0	    0
10	11	        0	    1	0	    0
11	12	        0	    0	1	    0
12	13	        1	    1	1	    0
13	14	        1	    1	1	    0
14	15	        1	    1	2	    1
15	16	        1   	1	0	    0
16	17	        1	    1	1	    0
17	18	        0	    0	0   	0
18	19       	0	    0	2   	0
19	20	        0	    0	2   	0


# input
x = df.iloc[:, :-1]
  
# output
y = df.iloc[:, 4]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state = 0)

Separating the customer_id column:

X_train_cust = X_train.pop('customer_id')
X_test_cust = X_test.pop('customer_id')

Fitting a logistic regression model:

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

Building a lime chart:

import lime
import lime.lime_tabular

explainer = lime.lime_tabular.LimeTabularExplainer(np.array(X_train),
                                feature_names=X_train.columns,
                                                  verbose=True, mode = 'classification')

exp = explainer.explain_instance(X_test.iloc[0], classifier.predict_proba)

exp.as_pyplot_figure()

The lime chart displays the encoded features/columns values. But I need the original value. For example, if the lime chart says 0, I need to display it as female.
Could someone please let me know how fix it.

You can use:

# Your direct mapping dictionary
dmap = {'gender': {'female' : 0, 'male' : 1},
        'age': {'young' : 0, 'middle aged' : 1},
        'salary': {'low salary': 0, 'medium salary': 1, 'high salary': 2}}

# Reverse mapping dictionary (not used hear)
rmap = {col: {v: k for k, v in dm.items()} for col, dm in dmap.items()}

# Categorical names, col0->gender, col1->age, col2->salary
cmap = {c: list(d.keys()) for c, d in enumerate(dmap.values())}


# Now use
explainer = lime.lime_tabular.LimeTabularExplainer(np.array(X_train),
                                feature_names=X_train.columns,
                                categorical_features=[0, 1, 2],  # <- 3 first columns
                                categorical_names=cmap,  # <- int to string
                                verbose=True, mode = 'classification')

exp = explainer.explain_instance(X_test.iloc[0], classifier.predict_proba)

exp.as_pyplot_figure()

Reat this tutorial