autoML.py

import os
from itertools import combinations

import streamlit as st
import pandas as pd
import numpy as np
from flaml import AutoML
from flaml.automl.data import get_output_from_log
import pickle
import matplotlib.pyplot as plt
import plotly.express as px
import base64
import time
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.inspection import permutation_importance
from sklearn.inspection import PartialDependenceDisplay
import shap

def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimize_reg):

    progress_text="Training in progress. Please wait."
    my_bar = st.progress(0, text=progress_text)
    time.sleep(0.5)

    df = pd.read_csv(csv)

    #msk = np.random.rand(len(df)) < 0.8
    #df_train, df_test = df[msk], df[~msk]

    df_features = df[df.columns.difference([label])]
    y = df[label]

    my_bar.progress(50, text=progress_text)


    if task == 'Classification':
        metric = metric_to_minimize_class
        log = 'classlog.log'
        automl_settings = {
            "time_budget": int(budget),
            "metric": metric,
            "task": 'classification',
            "log_file_name": log,
            "early_stop": True,
            "eval_method": "holdout"
        }

    if task == 'Regression':
        metric = metric_to_minimize_reg
        log = 'reglog.log'
        automl_settings = {
            "time_budget": int(budget),
            "metric": metric,
            "task": 'regression',
            "log_file_name": log,
            "early_stop": True,
            "eval_method": "holdout"
        }

    num_cols = df_features.select_dtypes(include=['float64', 'int64']).columns
    cat_cols = df_features.select_dtypes(include=['object']).columns

    numeric_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='mean')),
        ('scaler', StandardScaler())
    ])

    categorical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='most_frequent')),
        ('onehot', OneHotEncoder(handle_unknown='ignore'))
    ])

    preprocessor = ColumnTransformer(
        transformers=[
            ('num', numeric_transformer, num_cols),
            ('cat', categorical_transformer, cat_cols)
        ])

    automl = AutoML(**automl_settings)

    pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                                ('classifier', automl)])

    pipeline.fit(df_features, y)

    my_bar.progress(100, text=progress_text)
    time.sleep(0.5)
    my_bar.empty()

    tab1, tab2, tab3, tab4 = st.tabs(["AutoML", "Best Model", "Partial Dependence", "Shap Values"])


    with tab1:

        time_history, best_valid_loss_history, _, config_history, _ = get_output_from_log(filename=log, time_budget=120)
    
        def model(s):
            mod = s.get('Current Learner')
            return mod
        
        def hp(s):
            hparams = s.get('Current Hyper-parameters')
            return hparams

        df_res = pd.DataFrame({'time': time_history, 
                            metric: 1 - np.array(best_valid_loss_history),
                            'model': list(map(model, config_history)),
                            })
        
        fig = px.line(df_res,
                    title='evolution of best models found by AutoML',
                    x='time',
                    y=metric,
                    hover_name='model',
                    line_shape='hv',
                    range_y=[0,1])
        
        st.plotly_chart(fig, theme="streamlit")

        models = pd.DataFrame({'learner': list(map(model, config_history))})
        hps = list(map(hp, config_history))
        df_hp = pd.DataFrame(hps)
        df_models = pd.concat((models, df_hp), axis=1)

        def highlight_last_row(s):
            return ['background-color: yellow' if i == len(s) - 1 else '' for i in range(len(s))]

        st.dataframe(df_models.style.apply(highlight_last_row, axis=0))

        st.write('Estimator tested')
        st.table(automl.estimator_list)


    with tab2:
        st.header('Best Model')

        st.text(automl.model.estimator)

        col1, col2, col3 = st.columns((1,1,1))

        with col1:
            st.metric(label=metric, value=round(1 - automl.best_loss, 2))
        with col2:
            st.metric(label="Time to find", value=str(round(automl.time_to_find_best_model, 2))+' sec')
        with col3:
            st.metric(label="Time to train", value=str(round(automl.best_config_train_time, 2))+' sec')

        perm_importance = permutation_importance(
            pipeline, df_features, y, n_repeats=8
        )

        df_features_importance = pd.DataFrame({'features name': df_features.columns, 
                                                'features importance': perm_importance["importances_mean"],
                                                'std error': perm_importance["importances_std"]}).sort_values('features importance', ascending=True)
                
        fig_features = px.bar(df_features_importance, 
                              x='features importance', 
                              y='features name', 
                              error_x='std error',
                              height=50*len(df_features_importance))
    
        st.divider()
        st.plotly_chart(fig_features, theme="streamlit")

        def download_model(model):
            output_model = pickle.dumps(model)
            b64 = base64.b64encode(output_model).decode()
            href = f'<a href="data:file/output_model;base64,{b64}" download="automl.pkl">Download Trained Model File (.pkl)</a>'
            st.markdown(href, unsafe_allow_html=True)

        download_model(automl)
    
    
    with tab3:
        with st.container():
            st.subheader('1D Partial Dependance for the three most important features')
            
            l_col_1D = list(st.columns((1,1,1)))

            common_params = {
                        "subsample": 50,
                        "n_jobs": 2,
                        "grid_resolution": 20,
                        "random_state": 0
                    }

            most_important_features = list(df_features_importance.iloc[-3:]['features name'])

            for i, col in enumerate(l_col_1D): 
                with col:
                    features_info = {
                        "features": [most_important_features[i]],
                        "kind": "average",
                        "categorical_features": cat_cols
                    }

                    _, ax = plt.subplots(ncols=1, constrained_layout=True)
                    display = PartialDependenceDisplay.from_estimator(
                        pipeline,
                        df_features,
                        **features_info,
                        target=len(set(y)),
                        ax=ax,
                        **common_params,
                    )

                    st.pyplot(display.figure_)

        st.divider()
        
        with st.container():
            st.subheader('2D Partial Dependance for the three most important features')

            l_col_2D = list(st.columns((1,1,1)))

            most_important_features_comb = list(combinations(most_important_features, 2))

            for i, col in enumerate(l_col_2D): 
                with col:
                    features_info = {
                        "features": [most_important_features_comb[i]],
                        "kind": "average"
                    }

                    _, ax = plt.subplots(ncols=1, constrained_layout=True)

                    with st.spinner(f'Compute partial dependeces with {most_important_features_comb[i][0]} and {most_important_features_comb[i][1]}'):

                        display = PartialDependenceDisplay.from_estimator(
                            pipeline,
                            df_features,
                            **features_info,
                            target=len(set(y)),
                            ax=ax,
                            **common_params,
                        )
                                
                    st.pyplot(display.figure_)
    
    
    with tab4:

        #df_shap = df_features.sample(n=200, random_state=89)

        def get_only_features_names(name):
            return name[5:]
            
        df_transform = pd.DataFrame(data=preprocessor.fit_transform(df_features), 
                                         columns=list(map(get_only_features_names, 
                                                          preprocessor.get_feature_names_out())
                                                          )
                                        )

        with st.container():
            with st.spinner(f'Compute Shap Values...'):
                explainer = shap.Explainer(automl.model.predict, df_transform)
                shap_values = explainer(df_transform)

            st.subheader('Beeswarm Plot')
            plt.figure()
            st.pyplot(shap.plots.beeswarm(shap_values, show=False).figure)
            #st.divider()
            #st.pyplot(shap.plots.violin(shap_values, show=False).figure) 



    if os.path.isfile('datasets/temp_file.csv'):
        os.remove('datasets/temp_file.csv')