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'''
Author: hibana2077 [email protected]
Date: 2024-01-02 21:43:38
LastEditors: hibana2077 [email protected]
LastEditTime: 2024-01-04 22:21:47
FilePath: \hayabusa\src\main.py
Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
'''
from operator import index
import streamlit as st
import plotly.express as px
from ydata_profiling import ProfileReport
import pandas as pd
import pickle
import time
import ydata_profiling
from streamlit_pandas_profiling import st_profile_report
import os
if os.path.exists('./dataset/') == False:
os.mkdir('./dataset/')
if os.path.exists('./model/') == False:
os.mkdir('./model/')
if os.path.exists('./pipeline/') == False:
os.mkdir('./pipeline/')
with st.sidebar:
st.title("AutoML")
choice = st.radio("Navigation", ["Upload","Profiling","Modelling", "Download"])
st.info("This project application helps you build and explore your data.")
if choice == "Upload":
st.title("Upload Your Dataset")
file = st.file_uploader("Upload Your Dataset")
if file:
df = pd.read_csv(file, index_col=None)
df.drop("Unnamed: 0", axis=1, inplace=True) if "Unnamed: 0" in df.columns else "No Unnamed: 0 Detected"
df.to_csv('dataset.csv', index=None)
st.dataframe(df)
st.session_state['df'] = df
if choice == "Profiling":
st.title("Exploratory Data Analysis")
df:pd.DataFrame = st.session_state['df']
pr = ProfileReport(df, title="Profiling Report")
st_profile_report(pr)
if choice == "Modelling":
df:pd.DataFrame = st.session_state['df']
chosen_target = st.selectbox('Choose the Target Column', df.columns)
drop_columns = st.multiselect('Choose the Columns to Drop', df.columns)
ml_task = st.selectbox('Choose the ML Task', ['Classification', 'Regression'])
if st.button('Run Modelling'):
if ml_task == 'Classification':
from pycaret.classification import setup, compare_models, pull, save_model, get_config
setup(df, target=chosen_target, ignore_features=drop_columns)
setup_df = pull()
st.dataframe(setup_df)
best_model = compare_models(exclude=['lightgbm'])
compare_df = pull()
st.dataframe(compare_df)
save_model(best_model, 'best_model')
save_model(best_model, f"./model/{chosen_target}_{time.time()}")
pipeline = get_config('pipeline')
st.write(pipeline)
# save the pipeline
with open('pipeline.pkl', 'wb') as f:
pickle.dump(pipeline, f)
else:
from pycaret.regression import setup, compare_models, pull, save_model
setup(df, target=chosen_target, ignore_features=drop_columns)
setup_df = pull()
st.dataframe(setup_df)
best_model = compare_models(exclude=['lightgbm'])
compare_df = pull()
st.dataframe(compare_df)
save_model(best_model, 'best_model')
save_model(best_model, f"./model/{chosen_target}_{time.time()}")
pipeline = get_config('pipeline')
st.write(pipeline)
# save the pipeline
with open('pipeline.pkl', 'wb') as f:
pickle.dump(pipeline, f)
if choice == "Download":
with open('best_model.pkl', 'rb') as f:
st.download_button('Download Model', f, file_name="best_model.pkl")
with open('pipeline.pkl', 'rb') as f:
st.download_button('Download Pipeline', f, file_name="pipeline.pkl") |