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import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import VotingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
import gradio as gr
import joblib
# Load data
df = pd.read_csv('City_Employee_Payroll__Current__20240915.csv', low_memory=False)
df = df.replace([np.inf, -np.inf], np.nan)
# Define categorical and continuous variables
cat_names = ['EMPLOYMENT_TYPE', 'JOB_STATUS', 'MOU', 'GENDER', 'ETHNICITY', 'JOB_TITLE', 'DEPARTMENT_NO']
cont_names = ['PAY_YEAR', 'REGULAR_PAY', 'OVERTIME_PAY', 'ALL_OTHER_PAY', 'PAY_RATIO', 'TOTAL_NON_REGULAR_PAY']
# Load the trained model
ensemble = joblib.load('ensemble_model.joblib')
def predict_total_pay(gender, job_title, ethnicity):
sample = pd.DataFrame({
'GENDER': [gender],
'JOB_TITLE': [job_title],
'ETHNICITY': [ethnicity],
})
group = df[(df['GENDER'] == gender) & (df['JOB_TITLE'] == job_title) & (df['ETHNICITY'] == ethnicity)]
if len(group) > 0:
sample['EMPLOYMENT_TYPE'] = [group['EMPLOYMENT_TYPE'].mode().iloc[0]]
sample['JOB_STATUS'] = [group['JOB_STATUS'].mode().iloc[0]]
sample['MOU'] = [group['MOU'].mode().iloc[0]]
sample['DEPARTMENT_NO'] = [group['DEPARTMENT_NO'].mode().iloc[0]]
sample['REGULAR_PAY'] = [group['REGULAR_PAY'].mean()]
sample['OVERTIME_PAY'] = [group['OVERTIME_PAY'].mean()]
sample['ALL_OTHER_PAY'] = [group['ALL_OTHER_PAY'].mean()]
else:
job_group = df[df['JOB_TITLE'] == job_title]
if len(job_group) > 0:
sample['EMPLOYMENT_TYPE'] = [job_group['EMPLOYMENT_TYPE'].mode().iloc[0]]
sample['JOB_STATUS'] = [job_group['JOB_STATUS'].mode().iloc[0]]
sample['MOU'] = [job_group['MOU'].mode().iloc[0]]
sample['DEPARTMENT_NO'] = [job_group['DEPARTMENT_NO'].mode().iloc[0]]
sample['REGULAR_PAY'] = [job_group['REGULAR_PAY'].mean()]
sample['OVERTIME_PAY'] = [job_group['OVERTIME_PAY'].mean()]
sample['ALL_OTHER_PAY'] = [job_group['ALL_OTHER_PAY'].mean()]
else:
sample['EMPLOYMENT_TYPE'] = [df['EMPLOYMENT_TYPE'].mode().iloc[0]]
sample['JOB_STATUS'] = [df['JOB_STATUS'].mode().iloc[0]]
sample['MOU'] = [df['MOU'].mode().iloc[0]]
sample['DEPARTMENT_NO'] = [df['DEPARTMENT_NO'].mode().iloc[0]]
sample['REGULAR_PAY'] = [df['REGULAR_PAY'].mean()]
sample['OVERTIME_PAY'] = [df['OVERTIME_PAY'].mean()]
sample['ALL_OTHER_PAY'] = [df['ALL_OTHER_PAY'].mean()]
sample['PAY_YEAR'] = [df['PAY_YEAR'].max()]
sample['PAY_RATIO'] = sample['REGULAR_PAY'] / (sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY'] + 1)
sample['TOTAL_NON_REGULAR_PAY'] = sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY']
categorical_columns = ['GENDER', 'JOB_TITLE', 'ETHNICITY', 'EMPLOYMENT_TYPE', 'JOB_STATUS', 'MOU', 'DEPARTMENT_NO']
for col in categorical_columns:
sample[col] = sample[col].astype('object')
prediction = ensemble.predict(sample)[0]
return prediction
def gradio_predict(gender, ethnicity, job_title):
predicted_pay = predict_total_pay(gender, job_title, ethnicity)
return f"${predicted_pay:.2f}"
# Prepare dropdown options
genders = df['GENDER'].dropna().unique().tolist()
ethnicities = df['ETHNICITY'].dropna().unique().tolist()
job_titles = sorted(df['JOB_TITLE'].dropna().unique().tolist())
# Create Gradio interface
iface = gr.Interface(
fn=gradio_predict,
inputs=[
gr.Dropdown(choices=genders, label="Gender"),
gr.Dropdown(choices=ethnicities, label="Ethnicity"),
gr.Dropdown(choices=job_titles, label="Job Title")
],
outputs=gr.Textbox(label="Predicted Total Pay"),
title="LA City Employee Pay Predictor",
description="Predict the total pay for LA City employees based on gender, ethnicity, and job title."
)
# Launch the interface
iface.launch() |