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| import streamlit as st | |
| from streamlit_shap import st_shap | |
| import shap | |
| from datasets import load_dataset | |
| from sklearn.model_selection import train_test_split | |
| import lightgbm as lgb | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def load_data(selected_columns): | |
| dataset = load_dataset("ttd22/house-price", streaming = True) | |
| df = pd.DataFrame.from_dict(dataset["train"]) | |
| df = df.drop('Id', axis=1) | |
| df = df[selected_columns] | |
| X, y = df.drop("SalePrice", axis=1), df["SalePrice"] | |
| return X,y | |
| def load_model(X, y): | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| params = {'n_estimators': 378, 'num_leaves': 31, 'max_depth': 35, 'learning_rate': 0.01713200441531346, 'colsample_bytree': 0.5167187468778591, 'subsample': 0.7380799250244564, 'lambda_l1': 4.712815578969268, 'lambda_l2': 1.7427073600749474, 'min_child_weight': 9.962946763677316} | |
| model = lgb.LGBMRegressor(**params) | |
| model.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose = False) | |
| return model | |
| selected_columns = ['OverallQual', 'GrLivArea', 'TotalBsmtSF', 'YearBuilt', 'BsmtFinSF1', | |
| 'GarageCars', 'OverallCond', 'LotArea', 'YearRemodAdd', '1stFlrSF', | |
| 'Fireplaces', 'GarageArea','OpenPorchSF', '2ndFlrSF', | |
| 'TotRmsAbvGrd', 'FullBath', 'HalfBath', 'MSSubClass', 'BedroomAbvGr', 'KitchenAbvGr', 'SalePrice'] | |
| # train LightGBM model | |
| X,y = load_data(selected_columns) | |
| model = load_model(X, y) | |
| def buil_UI(X): | |
| # min_values = X.min() | |
| # max_values = X.max() | |
| OverallQual = st.sidebar.slider('What is Overall material and finish quality?', min_value = 1, max_value= 10) | |
| GrLivArea = st.sidebar.slider('What is Above grade (ground) living area square feet?', min_value = 334, max_value= 5642) | |
| TotalBsmtSF = st.sidebar.slider('What is Total square feet of basement area?', min_value = 0, max_value= 6110) | |
| YearBuilt = st.sidebar.slider('What is Original construction date?', min_value = 1872, max_value= 2010) | |
| BsmtFinSF1 = st.sidebar.slider('What is Type 1 finished square feet?', min_value = 0, max_value= 5644) | |
| GarageCars = st.sidebar.slider('What is Size of garage in car capacity?', min_value = 0, max_value= 4) | |
| OverallCond = st.sidebar.slider('What is Overall condition rating?', min_value = 1, max_value= 9) | |
| LotArea = st.sidebar.slider('What is Lot size in square feet?', min_value = 1300, max_value= 215245) | |
| YearRemodAdd = st.sidebar.slider('What is Remodel date?', min_value = 1950, max_value= 2010) | |
| FirstFlrSF = st.sidebar.slider('What is First Floor square feet?', min_value = 334, max_value= 4692) | |
| Fireplaces = st.sidebar.slider('What is Number of fireplaces?', min_value = 0, max_value= 3) | |
| GarageArea = st.sidebar.slider('What is Size of garage in square feet?', min_value = 0, max_value= 1418) | |
| OpenPorchSF = st.sidebar.slider('What is Open porch area in square feet?', min_value = 0, max_value= 547) | |
| SecondFlrSF = st.sidebar.slider('What is Second floor square feet?', min_value = 0, max_value= 2062) | |
| TotRmsAbvGrd = st.sidebar.slider('What is Total rooms above grade (does not include bathrooms)?', min_value = 2, max_value= 14) | |
| FullBath = st.sidebar.slider('What is Full bathrooms above grade?', min_value = 0, max_value= 3) | |
| HalfBath = st.sidebar.slider('What is Half baths above grade?', min_value = 0, max_value= 2) | |
| MSSubClass = st.sidebar.slider('What is The building class?', min_value = 20, max_value= 190) | |
| BedroomAbvGr = st.sidebar.slider('What is Number of bedrooms above basement level?', min_value = 0, max_value= 8) | |
| KitchenAbvGr = st.sidebar.slider('What is Number of kitchens?', min_value = 0, max_value= 3) | |
| feature_titles = ['OverallQual', 'GrLivArea', 'TotalBsmtSF', 'YearBuilt', 'BsmtFinSF1', | |
| 'GarageCars', 'OverallCond', 'LotArea', 'YearRemodAdd', '1stFlrSF', | |
| 'Fireplaces', 'GarageArea','OpenPorchSF', '2ndFlrSF', | |
| 'TotRmsAbvGrd', 'FullBath', 'HalfBath', 'MSSubClass', 'BedroomAbvGr', 'KitchenAbvGr'] | |
| feature_values = features = [OverallQual, GrLivArea, TotalBsmtSF, YearBuilt, BsmtFinSF1, GarageCars, OverallCond, LotArea, YearRemodAdd, FirstFlrSF, Fireplaces, GarageArea,OpenPorchSF, SecondFlrSF, TotRmsAbvGrd, FullBath, HalfBath, MSSubClass, BedroomAbvGr, KitchenAbvGr] | |
| df_new = pd.DataFrame([feature_values], columns=feature_titles) | |
| return df_new | |
| input_val = buil_UI(X) | |
| st.markdown(""" <style> .font {font-size:50px ; font-family: 'Cooper Black'; color: #FF9633;} </style> """, unsafe_allow_html=True) | |
| st.markdown('<p class="font">House price prediction - Apply LightGBM Hyperparameter Tuning with Optuna</p>', unsafe_allow_html=True) | |
| if st.button('Calculate House Price'): | |
| predictLGBM = model.predict(input_val) | |
| st.write('Predicted house price is: $',predictLGBM[0]) | |
| st.write('Please wait for 4 figures loading') | |
| #input_val | |
| st.write('SHAP Summary Plot and Interaction Summary Plot for this predicted house') | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| shap_values = shap.TreeExplainer(model).shap_values(input_val) | |
| fig_summary = shap.summary_plot(shap_values, input_val) | |
| st.pyplot(fig_summary) | |
| shap_interaction_values = shap.TreeExplainer(model).shap_interaction_values(input_val.iloc[:,:]) | |
| interaction_fig = shap.summary_plot(shap_interaction_values, input_val.iloc[:,:]) | |
| st.pyplot(interaction_fig) | |
| # X | |
| st.write('SHAP Summary Plot and Interaction Summary Plot for train dataset') | |
| shap_values_X = shap.TreeExplainer(model).shap_values(X) | |
| fig_summary_X = shap.summary_plot(shap_values_X, X) | |
| st.pyplot(fig_summary_X) | |
| shap_interaction_values_X = shap.TreeExplainer(model).shap_interaction_values(X.iloc[:,:]) | |
| interaction_fig_X = shap.summary_plot(shap_interaction_values_X, X.iloc[:,:]) | |
| st.pyplot(interaction_fig_X) | |