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from typing import OrderedDict
import plotly.express as px
import numpy as np
import streamlit as st
from common.util import create_strategyTable_df
from views.typing import ModelView
def strategy_table_view(
currency: str, model_views: OrderedDict[str, ModelView]
):
st.header("Strategy Table")
for (model_name, model_view) in model_views.items():
st.subheader(model_name)
strat_df = create_strategyTable_df(
0.05,
1,
20,
model_view.trueStatus_probabilityDefault_threshStatus_loanAmount_df,
"loan_status",
currency,
)
columns = strat_df.columns
with st.expander("Strategy Table:"):
st.write(strat_df)
for i in columns:
strat_df[i] = strat_df[i].astype(np.float64)
strat_df_boxPlot_data = strat_df.iloc[:, 0:3]
plot = px.box(data_frame=strat_df_boxPlot_data)
st.plotly_chart(plot)
# Plot the strategy curve
fig1 = px.line(
strat_df_boxPlot_data,
x="Acceptance Rate",
y="Bad Rate",
title="Acceptance and Bad Rates",
)
st.plotly_chart(fig1)
fig2 = px.line(
strat_df,
x="Acceptance Rate",
y=f"Estimated Value ({currency})",
title=f"Estimated Value ({currency}) by Acceptance Rate",
)
st.plotly_chart(fig2)
st.write("Row with the greatest estimated value:")
max_estimated_value = np.max(
strat_df[f"Estimated Value ({currency})"].astype(np.float64)
)
columns = strat_df.columns
max_estimated_value = np.max(strat_df[f"Estimated Value ({currency})"])
st.write(
strat_df.loc[
strat_df[f"Estimated Value ({currency})"]
== max_estimated_value
]
)
loss_given_default = 1
df_trueStatus_probabilityDefault_threshStatus_loanAmount = (
model_view.trueStatus_probabilityDefault_threshStatus_loanAmount_df[
"PROB_DEFAULT"
]
* loss_given_default
* model_view.trueStatus_probabilityDefault_threshStatus_loanAmount_df[
"loan_amnt"
]
)
tot_exp_loss = round(
np.sum(df_trueStatus_probabilityDefault_threshStatus_loanAmount),
2,
)
st.metric(
label=f"Total expected loss:",
value=f"{currency} {tot_exp_loss:,.2f}",
delta=None,
delta_color="normal",
)
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