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from common.data import SplitDataset
import streamlit as st
from common.util import (
test_variables_gbt,
)
from common.views import (
streamlit_chart_setting_height_width,
plot_importance_gbt,
plot_tree_gbt,
download_importance_gbt,
download_tree_gbt,
)
from views.typing import ModelView
from views.threshold import decision_tree_threshold_view
from views.evaluation import decision_tree_evaluation_view
def decisiontree_view(split_dataset: SplitDataset, currency: str):
st.header("Decision Trees")
clf_gbt_model = test_variables_gbt(
split_dataset.X_train, split_dataset.y_train
)
st.subheader("Decision Tree Feature Importance")
(barxsize, barysize,) = streamlit_chart_setting_height_width(
"Chart Settings", 10, 15, "barxsize", "barysize"
)
fig1 = plot_importance_gbt(clf_gbt_model, barxsize, barysize)
st.pyplot(fig1)
download_importance_gbt(fig1, barxsize, barysize)
st.subheader("Decision Tree Structure")
(treexsize, treeysize,) = streamlit_chart_setting_height_width(
"Chart Settings", 15, 10, "treexsize", "treeysize"
)
fig2 = plot_tree_gbt(treexsize, treeysize, clf_gbt_model)
st.pyplot(fig2)
download_tree_gbt(treexsize, treeysize)
st.markdown(
"Note: The downloaded decision tree plot chart in png has higher resolution than that displayed here."
)
threshold = decision_tree_threshold_view(clf_gbt_model, split_dataset)
df_trueStatus_probabilityDefault_threshStatus_loanAmount = (
decision_tree_evaluation_view(
clf_gbt_model,
split_dataset,
currency,
threshold.probability_threshold_selected,
threshold.predicted_default_status,
)
)
return ModelView(
model=clf_gbt_model,
trueStatus_probabilityDefault_threshStatus_loanAmount_df=df_trueStatus_probabilityDefault_threshStatus_loanAmount,
probability_threshold_selected=threshold.probability_threshold_selected,
predicted_default_status=threshold.predicted_default_status,
prediction_probability_df=threshold.prediction_probability_df,
)
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