src/features/build_features.py

from typing import List, Union, cast, Tuple
from dataclasses import dataclass
from sklearn.model_selection import train_test_split
import pandas as pd

import streamlit as st


from  features.util_build_features import (
    Dataset,
    SplitDataset,
    undersample_training_data,
    select_predictors,
    import_data)

from  visualization.metrics import (
    streamlit_2columns_metrics_df_shape,
    streamlit_2columns_metrics_series,
    streamlit_2columns_metrics_pct_series,
    streamlit_2columns_metrics_df,
    streamlit_2columns_metrics_pct_df,
)


def initialise_data() -> Tuple[Dataset, SplitDataset]:

    dataset = import_data()

    st.write(
        "Assuming data is already cleaned and relevant features (predictors) added."
    )

    with st.expander("Input Dataframe (X and y)"):
        st.dataframe(dataset.df)
        streamlit_2columns_metrics_df_shape(dataset.df)

    selected_x_values = select_predictors(dataset)

    with st.expander("Predictors Dataframe (X)"):
        st.dataframe(selected_x_values)
        streamlit_2columns_metrics_df_shape(selected_x_values)

    st.header("Split Testing and Training Data")

    test_size_slider_col, seed_col = st.columns(2)

    with test_size_slider_col:
        # Initialize test size
        dataset.test_size = st.slider(
            label="Test Size Percentage of Input Dataframe:",
            min_value=0,
            max_value=100,
            value=dataset.test_size,
            key="init_test_size",
            format="%f%%",
        )

    with seed_col:
        dataset.random_state = int(
            st.number_input(label="Random State:", value=dataset.random_state)
        )

    split_dataset = dataset.train_test_split(selected_x_values)

    true_status = split_dataset.y_test.to_frame().value_counts()

    st.sidebar.metric(
        label="Testing Data # of Actual Default (=1)",
        value=true_status.get(1),
    )

    st.sidebar.metric(
        label="Testing Data % of Actual Default",
        value="{:.0%}".format(true_status.get(1) / true_status.sum()),
    )

    st.sidebar.metric(
        label="Testing Data # of Actual Non-Default (=0)",
        value=true_status.get(0),
    )

    st.sidebar.metric(
        label="Testing Data % of Actual Non-Default",
        value="{:.0%}".format(true_status.get(0) / true_status.sum()),
    )

    # Concat the testing sets
    X_y_test = split_dataset.X_y_test
    X_y_train = split_dataset.X_y_train

    with st.expander("Testing Dataframe (X and y)"):
        st.dataframe(X_y_test)
        streamlit_2columns_metrics_df_shape(X_y_test)

    streamlit_2columns_metrics_series(
        "# Defaults(=1) (Testing Data)",
        "# Non-Defaults(=0) (Testing Data)",
        true_status,
    )

    streamlit_2columns_metrics_pct_series(
        "% Defaults (Testing Data)",
        "% Non-Defaults (Testing Data)",
        true_status,
    )

    st.header("Training Data")

    with st.expander("Training Dataframe (X and y)"):
        st.dataframe(X_y_train)
        streamlit_2columns_metrics_df_shape(X_y_train)

    st.subheader("Class Count")

    streamlit_2columns_metrics_df(
        "# Defaults (Training Data Class Balance Check)",
        "# Non-Defaults (Training Data Class Balance Check)",
        split_dataset.y_train,
    )

    streamlit_2columns_metrics_pct_df(
        "% Defaults (Training Data Class Balance Check)",
        "% Non-Defaults (Training Data Class Balance Check)",
        split_dataset.y_train,
    )

    balance_the_classes = st.radio(
        label="Balance the Classes:", options=("Yes", "No")
    )

    if balance_the_classes == "Yes":
        st.subheader("Balanced Classes (by Undersampling)")

        (
            split_dataset.X_train,
            split_dataset.y_train,
            _X_y_train,
            class_balance_default,
        ) = undersample_training_data(X_y_train, "loan_status", split_dataset)

        streamlit_2columns_metrics_series(
            "# Defaults (Training Data with Class Balance)",
            "# Non-Defaults (Training Data with Class Balance)",
            class_balance_default,
        )

        streamlit_2columns_metrics_pct_series(
            "% of Defaults (Training Data with Class Balance)",
            "% of Non-Defaults (Training Data with Class Balance)",
            class_balance_default,
        )

    return dataset, split_dataset