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| from operator import index | |
| import streamlit as st | |
| import plotly.express as px | |
| import numpy as np | |
| from pycaret.regression import setup, compare_models, pull, save_model, load_model, plot_model | |
| # from lazypredict.Supervised import LazyRegressor | |
| # from sklearn.model_selection import train_test_split | |
| from pandas_profiling import ProfileReport | |
| import pandas as pd | |
| from streamlit_pandas_profiling import st_profile_report | |
| import os | |
| def load_data(): | |
| return pd.read_csv('dataset.csv', index_col=None) | |
| # Rest of your code... | |
| if os.path.exists('./dataset.csv'): | |
| df = load_data() | |
| with st.sidebar: | |
| st.image("https://michael-fuchs-python.netlify.app/post/2022-01-01-automl-using-pycaret-classification_files/p133s1.png") | |
| 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.to_csv('dataset.csv', index=None) | |
| st.dataframe(df) | |
| if choice == "Profiling": | |
| st.title("Exploratory Data Analysis") | |
| profile_df = df.profile_report() | |
| st_profile_report(profile_df) | |
| if choice == "Modelling": | |
| chosen_target = st.selectbox('Choose the Target Column', df.columns) | |
| if st.button('Run Modelling'): | |
| print("WIP") | |
| # h2o.init() | |
| # df = h2o.import_file(df) | |
| # df.describe(chunk_summary=True) | |
| # train, test = df.split_frame(ratios=[0.8], seed = 1) | |
| # aml = H2OAutoML(max_models =25, | |
| # balance_classes=True, | |
| # seed =16548846) | |
| # aml.train(training_frame = train, y = 'y') | |
| # lb = aml.leaderboard | |
| # lb.head(rows=lb.nrows) | |
| # aml.train(training_frame = train, y = 'y', leaderboard_frame = my_leaderboard_frame) | |
| # best_model = aml.get_best_model() | |
| # model_path = h2o.save_model(model=best_model,force=True) | |
| setup(df.dropna(subset=chosen_target), target=chosen_target, session_id = 2774764,imputation_type = 'simple',numeric_imputation='mean',categorical_imputation='mode') | |
| setup_df = pull() | |
| st.dataframe(setup_df) | |
| best_model = compare_models(n_select = 5) | |
| compare_df = pull() | |
| st.dataframe(compare_df) | |
| plot_model(best_model, plot='residuals', display_format='streamlit') | |
| plot_model(best_model, plot='feature', display_format='streamlit') | |
| plot_model(best_model, plot='error', display_format='streamlit') | |
| save_model(best_model, 'best_model') | |
| # y = df[chosen_target] | |
| # X = df.loc[:, df.columns!=chosen_target] | |
| # X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =65481254) | |
| # reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None ) | |
| # models,predictions = reg.fit(X_train, X_test, y_train, y_test) | |
| # st.dataframe(models) | |
| # model_dictionary = reg.provide_models(X_train,X_test,y_train,y_test) | |
| if choice == "Download": | |
| print("Working") | |
| with open('best_model.pkl', 'rb') as f: | |
| st.download_button('Download Model', f, file_name="best_model.pkl") | |