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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
@st.experimental_memo
def load_data():
dataset = load_dataset("ttd22/house-price", streaming = True)
df = pd.DataFrame.from_dict(dataset["train"])
df = df.drop('Id', axis=1)
drop_columns = (df.isnull().sum().sort_values(ascending=False).loc[lambda x : x > .90*1460]).index.to_list()
df = df.drop(drop_columns, axis = 'columns', errors = 'ignore')
cols_with_missing_values = df.columns[df.isnull().sum() > 0]
# Iterate through each column with missing values
for col in cols_with_missing_values:
# Check if the column is numeric
if df[col].dtype in ['int64', 'float64']:
# Impute missing values with median
median = df[col].median()
df[col].fillna(median, inplace=True)
else:
# Impute missing values with mode
mode = df[col].mode()[0]
df[col].fillna(mode, inplace=True)
X, y = df.drop("SalePrice", axis=1), df["SalePrice"]
# Extract categoricals and their indices
cat_features = X.select_dtypes(exclude=np.number).columns.to_list()
cat_idx = [X.columns.get_loc(col) for col in cat_features]
# Convert cat_features to pd.Categorical dtype
for col in cat_features:
X[col] = pd.Categorical(X[col])
return X,y,cat_idx
@st.experimental_memo
def load_model(X, y, cat_idx):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
params = {'n_estimators': 569, 'num_leaves': 62, 'max_depth': 10, 'learning_rate': 0.010786783375710743, 'colsample_bytree': 0.5065493231651268, 'subsample': 0.7900705177300663, 'lambda_l1': 4.998785478697207, 'lambda_l2': 2.1857959934319657, 'min_child_weight': 11.187719709451862}
model = lgb.LGBMRegressor(**params)
model.fit(X_train, y_train, eval_set=[(X_test, y_test)], categorical_feature=cat_idx, verbose = False)
return model
# train LightGBM model
X,y,cat_idx = load_data()
model = load_model(X, y, cat_idx)
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