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from typing import Tuple, cast
import pandas as pd
import streamlit as st
from common.data import Dataset, SplitDataset
from common.util import (
undersample_training_data,
)
from common.views import (
streamlit_2columns_metrics_df_shape,
streamlit_2columns_metrics_series,
streamlit_2columns_metrics_pct_series,
streamlit_2columns_metrics_df,
streamlit_2columns_metrics_pct_df,
)
# Initialize dataframe session state
def initialise_data() -> Tuple[Dataset, SplitDataset]:
if "input_data_frame" not in st.session_state:
st.session_state.input_data_frame = pd.read_csv(
r"./data/processed/cr_loan_w2.csv"
)
if "dataset" not in st.session_state:
df = cast(pd.DataFrame, st.session_state.input_data_frame)
dataset = Dataset(
df=df,
random_state=123235,
test_size=40,
)
st.session_state.dataset = dataset
else:
dataset = st.session_state.dataset
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)
st.header("Predictors")
possible_columns = dataset.x_values_column_names
selected_columns = st.sidebar.multiselect(
label="Select Predictors",
options=possible_columns,
default=possible_columns,
)
selected_x_values = dataset.x_values_filtered_columns(selected_columns)
st.sidebar.metric(
label="# of Predictors Selected",
value=selected_x_values.shape[1],
delta=None,
delta_color="normal",
)
with st.expander("Predictors Dataframe (X)"):
st.dataframe(selected_x_values)
streamlit_2columns_metrics_df_shape(selected_x_values)
# 40% of data used for training
# 14321 as random seed for reproducability
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)
# Series
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
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