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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") |