| import streamlit as st | |
| import pandas as pd | |
| from autoML import autoML | |
| st.set_page_config(layout="wide") | |
| with st.sidebar: | |
| st.subheader('Demo Datasets') | |
| demo_but_class = st.button(label="Demo Classification on Wine Rate Dataset") | |
| demo_but_reg = st.button(label="Demo Regression on California House Dataset") | |
| st.subheader('AutoML your Dataset') | |
| csv = st.file_uploader(label='CSV file', type='csv') | |
| task = st.selectbox(label='Task', options=['Classification', 'Regression']) | |
| if task == 'Classification': | |
| metric_to_minimize_class = st.selectbox(label='Metric to minimize', options=['accuracy']) | |
| metric_to_minimize_reg = None | |
| if task == 'Regression': | |
| metric_to_minimize_reg = st.selectbox(label='Metric to minimize', options=['r2']) | |
| metric_to_minimize_class = None | |
| if csv: | |
| df = pd.read_csv(csv) | |
| df.to_csv('datasets/temp_file.csv', index=False) | |
| lst_features = df.columns | |
| label = st.selectbox(label='Label', options=lst_features) | |
| budget = st.text_area(label='Budget Time', value="5") | |
| start_but = st.button(label='AutoML') | |
| if start_but: | |
| autoML('datasets/temp_file.csv', task, budget, label, metric_to_minimize_class, metric_to_minimize_reg) | |
| if demo_but_class: | |
| autoML(csv='datasets/WineRate.csv', | |
| task='Classification', | |
| budget=budget, | |
| label='quality', | |
| metric_to_minimize_class='accuracy', | |
| metric_to_minimize_reg=None) | |
| if demo_but_reg: | |
| autoML(csv='datasets/house_california.csv', | |
| task='Regression', | |
| budget=budget, | |
| label='median_house_value', | |
| metric_to_minimize_class=None, | |
| metric_to_minimize_reg='r2') | |