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from typing import Union
import pandas as pd
import streamlit as st
import numpy as np
from sklearn.metrics import (
classification_report,
confusion_matrix,
)
from sklearn.linear_model import LogisticRegression
from xgboost.sklearn import XGBClassifier
from common.data import SplitDataset
from common.util import (
create_cross_validation_df,
cross_validation_scores,
get_df_trueStatus_probabilityDefault_threshStatus_loanAmount,
)
from common.views import (
cross_validation_graph,
)
def make_evaluation_view(
model_name_short: str,
model_name_generic: str,
):
def view(
clf_gbt_model: Union[XGBClassifier, LogisticRegression],
split_dataset: SplitDataset,
currency: str,
prob_thresh_selected,
predicted_default_status,
):
st.header(f"Model Evaluation - {model_name_generic}")
st.subheader("Cross Validation")
st.write("Shows how our model will perform as new loans come in.")
st.write(
"If evaluation metric for test and train set improve as models \
train on each fold suggests performance will be stable."
)
st.write(f"XGBoost cross validation test:")
stcol_seed, stcol_eval_metric = st.columns(2)
with stcol_seed:
cv_seed = int(
st.number_input(
label="Random State Seed for Cross Validation:",
value=123235,
key=f"cv_seed_{model_name_short}",
)
)
with stcol_eval_metric:
eval_metric = st.selectbox(
label="Select evaluation metric",
options=[
"auc",
"aucpr",
"rmse",
"mae",
"logloss",
"error",
"merror",
"mlogloss",
],
key=f"eval_metric_{model_name_short}",
)
stcol_trees, stcol_eval_nfold, stcol_earlystoppingrounds = st.columns(
3
)
with stcol_trees:
trees = int(
st.number_input(
label="Number of trees",
value=5,
key=f"trees_{model_name_short}",
)
)
with stcol_eval_nfold:
nfolds = int(
st.number_input(
label="Number of folds",
value=5,
key=f"nfolds_{model_name_short}",
)
)
with stcol_earlystoppingrounds:
early_stopping_rounds = int(
st.number_input(
label="Early stopping rounds",
value=10,
key=f"early_stopping_rounds_{model_name_short}",
)
)
DTrain, cv_df = create_cross_validation_df(
split_dataset.X_test,
split_dataset.y_test,
eval_metric,
cv_seed,
trees,
nfolds,
early_stopping_rounds,
)
st.write(cv_df)
scoring_options = [
"roc_auc",
"accuracy",
"precision",
"recall",
"f1",
"jaccard",
]
overfit_test = st.radio(
label="Overfit test:",
options=("No", "Yes"),
key=f"overfit_test_{model_name_short}",
)
if overfit_test == "Yes":
st.write("Overfit test:")
iterations = int(
st.number_input(
label="Number of folds (iterations)",
value=500,
key=f"iterations_{model_name_short}",
)
)
DTrain, cv_df_it = create_cross_validation_df(
split_dataset.X_test,
split_dataset.y_test,
eval_metric,
cv_seed,
iterations,
nfolds,
iterations,
)
fig_it = cross_validation_graph(cv_df_it, eval_metric, iterations)
st.pyplot(fig_it)
st.write("Sklearn cross validation test:")
stcol_scoringmetric, st_nfold = st.columns(2)
with stcol_scoringmetric:
score_metric = st.selectbox(
label="Select score",
options=scoring_options,
key=f"stcol_scoringmetric_{model_name_short}",
)
with st_nfold:
nfolds_score = int(
st.number_input(
label="Number of folds",
value=5,
key=f"st_nfold_{model_name_short}",
)
)
cv_scores = cross_validation_scores(
clf_gbt_model,
split_dataset.X_test,
split_dataset.y_test,
nfolds_score,
score_metric,
cv_seed,
)
stcol_vals, stcol_mean, st_std = st.columns(3)
with stcol_vals:
st.markdown(f"{score_metric} scores:")
st.write(
pd.DataFrame(
cv_scores,
columns=[score_metric],
)
)
with stcol_mean:
st.metric(
label=f"Average {score_metric} score ",
value="{:.4f}".format(cv_scores.mean()),
delta=None,
delta_color="normal",
)
with st_std:
st.metric(
label=f"{score_metric} standard deviation (+/-)",
value="{:.4f}".format(cv_scores.std()),
delta=None,
delta_color="normal",
)
st.subheader("Classification Report")
target_names = ["Non-Default", "Default"]
classification_report_dict = classification_report(
split_dataset.y_test,
predicted_default_status,
target_names=target_names,
output_dict=True,
)
(
stcol_defaultpres,
stcol_defaultrecall,
stcol_defaultf1score,
stcol_f1score,
) = st.columns(4)
with stcol_defaultpres:
st.metric(
label="Default Precision",
value="{:.0%}".format(
classification_report_dict["Default"]["precision"]
),
delta=None,
delta_color="normal",
)
with stcol_defaultrecall:
st.metric(
label="Default Recall",
value="{:.0%}".format(
classification_report_dict["Default"]["recall"]
),
delta=None,
delta_color="normal",
)
with stcol_defaultf1score:
st.metric(
label="Default F1 Score",
value="{:.2f}".format(
classification_report_dict["Default"]["f1-score"]
),
delta=None,
delta_color="normal",
)
with stcol_f1score:
st.metric(
label="Macro avg F1 Score (Model F1 Score):",
value="{:.2f}".format(
classification_report_dict["macro avg"]["f1-score"]
),
delta=None,
delta_color="normal",
)
with st.expander("Classification Report Dictionary:"):
st.write(classification_report_dict)
st.markdown(
f'Default precision: {"{:.0%}".format(classification_report_dict["Default"]["precision"])} of loans predicted as default were actually default.'
)
st.markdown(
f'Default recall: {"{:.0%}".format(classification_report_dict["Default"]["recall"])} of true defaults predicted correctly.'
)
f1_gap = 1 - classification_report_dict["Default"]["f1-score"]
st.markdown(
f'Default F1 score: {"{:.2f}".format(classification_report_dict["Default"]["f1-score"])}\
is {"{:.2f}".format(f1_gap)} away from perfect precision and recall (no false positive rate).'
)
st.markdown(
f'macro avg F1 score: {"{:.2f}".format(classification_report_dict["macro avg"]["f1-score"])} is the models F1 score.'
)
st.subheader("Confusion Matrix")
confuctiomatrix_dict = confusion_matrix(
split_dataset.y_test, predicted_default_status
)
tn, fp, fn, tp = confusion_matrix(
split_dataset.y_test, predicted_default_status
).ravel()
with st.expander(
"Confusion matrix (column name = classification model prediction, row name = true status, values = number of loans"
):
st.write(confuctiomatrix_dict)
st.markdown(
f'{tp} ,\
{"{:.0%}".format(tp / len(predicted_default_status))} \
true positives (defaults correctly predicted as defaults).'
)
st.markdown(
f'{fp} ,\
{"{:.0%}".format(fp / len(predicted_default_status))} \
false positives (non-defaults incorrectly predicted as defaults).'
)
st.markdown(
f'{fn} ,\
{"{:.0%}".format(fn / len(predicted_default_status))} \
false negatives (defaults incorrectly predicted as non-defaults).'
)
st.markdown(
f'{tn} ,\
{"{:.0%}".format(tn / len(predicted_default_status))} \
true negatives (non-defaults correctly predicted as non-defaults).'
)
st.subheader("Bad Rate")
df_trueStatus_probabilityDefault_threshStatus_loanAmount = (
get_df_trueStatus_probabilityDefault_threshStatus_loanAmount(
clf_gbt_model,
split_dataset.X_test,
split_dataset.y_test,
prob_thresh_selected,
"loan_amnt",
)
)
with st.expander(
"Loan Status, Probability of Default, & Loan Amount DataFrame"
):
st.write(df_trueStatus_probabilityDefault_threshStatus_loanAmount)
accepted_loans = (
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
"PREDICT_DEFAULT_STATUS"
]
== 0
]
)
bad_rate = (
np.sum(accepted_loans["loan_status"])
/ accepted_loans["loan_status"].count()
)
with st.expander("Loan Amount Summary Statistics"):
st.write(
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
"loan_amnt"
].describe()
)
avg_loan = np.mean(
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
"loan_amnt"
]
)
crosstab_df = pd.crosstab(
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
"loan_status"
], # row label
df_trueStatus_probabilityDefault_threshStatus_loanAmount[
"PREDICT_DEFAULT_STATUS"
],
).apply(
lambda x: x * avg_loan, axis=0
) # column label
with st.expander(
"Cross tabulation (column name = classification model prediction, row name = true status, values = number of loans * average loan value"
):
st.write(crosstab_df)
st.write(
f'Bad rate: {"{:.2%}".format(bad_rate)} of all the loans the model accepted (classified as non-default) from the test set were actually defaults.'
)
st.write(
f'Estimated value of the bad rate is {currency} {"{:,.2f}".format(crosstab_df[0][1])}.'
)
st.write(
f'Total estimated value of actual non-default loans is {currency} {"{:,.2f}".format(crosstab_df[0][0]+crosstab_df[0][1])}'
)
st.write(
f'Estimated value of loans incorrectly predicted as default is {currency} {"{:,.2f}".format(crosstab_df[1][0])}'
)
st.write(
f'Estimated value of loans correctly predicted as defaults is {currency} {"{:,.2f}".format(crosstab_df[1][1])}'
)
return df_trueStatus_probabilityDefault_threshStatus_loanAmount
return view
decision_tree_evaluation_view = make_evaluation_view("gbt", "Decision Tree")
logistic_evaluation_view = make_evaluation_view("lg", "Logistic Regression")
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