Spaces:
Build error
Build error
File size: 9,666 Bytes
7f0977b 7592386 7f0977b 7592386 7f0977b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
import streamlit as st
from sklearn.metrics import classification_report, roc_curve
import numpy as np
import plotly.express as px
import pandas as pd
from numpy import argmax
from visualization.metrics import streamlit_2columns_metrics_df, streamlit_2columns_metrics_pct_df
from visualization.graphs_threshold import acceptance_rate_driven_threshold_graph
def model_probability_values_df(model, X):
return pd.DataFrame(model.predict_proba(X)[:, 1], columns=["PROB_DEFAULT"])
def find_best_threshold_J_statistic(y, clf_prediction_prob_df):
fpr, tpr, thresholds = roc_curve(y, clf_prediction_prob_df)
# get the best threshold
# Youden’s J statistic tpr-fpr
# Argmax to get the index in
# thresholds
return thresholds[argmax(tpr - fpr)]
# Function that makes dataframe with probability of default, predicted default status based on threshold
# and actual default status
def classification_report_per_threshold(
threshold_list, threshold_default_status_list, y_test
):
target_names = ["Non-Default", "Default"]
classification_report_list = []
for threshold_default_status in threshold_default_status_list:
thresh_classification_report = classification_report(
y_test,
threshold_default_status,
target_names=target_names,
output_dict=True,
zero_division=0,
)
classification_report_list.append(thresh_classification_report)
# Return threshold classification report dict
return dict(zip(threshold_list, classification_report_list))
def thresh_classification_report_recall_accuracy(
thresh_classification_report_dict,
):
thresh_def_recalls_list = []
thresh_nondef_recalls_list = []
thresh_accs_list = []
for x in [*thresh_classification_report_dict]:
thresh_def_recall = thresh_classification_report_dict[x]["Default"][
"recall"
]
thresh_def_recalls_list.append(thresh_def_recall)
thresh_nondef_recall = thresh_classification_report_dict[x][
"Non-Default"
]["recall"]
thresh_nondef_recalls_list.append(thresh_nondef_recall)
thresh_accs = thresh_classification_report_dict[x]["accuracy"]
thresh_accs_list.append(thresh_accs)
return [
thresh_def_recalls_list,
thresh_nondef_recalls_list,
thresh_accs_list,
]
def apply_threshold_to_probability_values(probability_values, threshold):
return (
probability_values["PROB_DEFAULT"]
.apply(lambda x: 1 if x > threshold else 0)
.rename("PREDICT_DEFAULT_STATUS")
)
@st.cache(suppress_st_warning=True)
def find_best_threshold_J_statistic(y, clf_prediction_prob_df):
fpr, tpr, thresholds = roc_curve(y, clf_prediction_prob_df)
# get the best threshold
J = tpr - fpr # Youden’s J statistic
ix = argmax(J)
return thresholds[ix]
def default_status_per_threshold(threshold_list, prob_default):
threshold_default_status_list = []
for threshold in threshold_list:
threshold_default_status = prob_default.apply(
lambda x: 1 if x > threshold else 0
)
threshold_default_status_list.append(threshold_default_status)
return threshold_default_status_list
def threshold_and_predictions(clf_xgbt_model, split_dataset, threshold):
clf_prediction_prob_df_gbt = model_probability_values_df(
clf_xgbt_model,
split_dataset.X_test,
)
clf_thresh_predicted_default_status = (
apply_threshold_to_probability_values(
clf_prediction_prob_df_gbt,
threshold,
)
)
streamlit_2columns_metrics_df(
"# of Predicted Defaults",
"# of Predicted Non-Default",
clf_thresh_predicted_default_status,
)
streamlit_2columns_metrics_pct_df(
"% of Loans Predicted to Default",
"% of Loans Predicted not to Default",
clf_thresh_predicted_default_status,
)
return clf_thresh_predicted_default_status
def user_defined_probability_threshold(model_name_short, clf_xgbt_model, split_dataset):
st.subheader("Classification Probability Threshold - User Defined")
user_defined_threshold = st.slider(
label="Default Probability Threshold:",
min_value=0.0,
max_value=1.0,
value=0.8,
key=f"threshold_{model_name_short}_default",
)
clf_thresh_predicted_default_status = threshold_and_predictions(
clf_xgbt_model, split_dataset, user_defined_threshold)
return clf_thresh_predicted_default_status, user_defined_threshold
def J_statistic_driven_probability_threshold(clf_prediction_prob_df_gbt, clf_xgbt_model, split_dataset):
st.subheader("J Statistic Driven Classification Probability Threshold")
J_statistic_best_threshold = find_best_threshold_J_statistic(
split_dataset.y_test, clf_prediction_prob_df_gbt
)
st.metric(
label="Youden's J statistic calculated best threshold",
value=J_statistic_best_threshold,
)
clf_thresh_predicted_default_status = threshold_and_predictions(
clf_xgbt_model, split_dataset, J_statistic_best_threshold)
return clf_thresh_predicted_default_status, J_statistic_best_threshold
def create_tradeoff_graph(df):
fig2 = px.line(
data_frame=df,
y=["Default Recall", "Non Default Recall", "Accuracy"],
x="Threshold",
)
fig2.update_layout(
title="Recall and Accuracy score Trade-off with Probability Threshold",
xaxis_title="Probability Threshold",
yaxis_title="Score",
)
fig2.update_yaxes(range=[0.0, 1.0])
st.plotly_chart(fig2)
def tradeoff_threshold(clf_prediction_prob_df_gbt, split_dataset):
st.subheader(
"Recall and Accuracy Tradeoff with given Probability Threshold"
)
threshold_list = np.arange(
0, 1, 0.025).round(decimals=3).tolist()
threshold_default_status_list = default_status_per_threshold(
threshold_list, clf_prediction_prob_df_gbt["PROB_DEFAULT"]
)
thresh_classification_report_dict = (
classification_report_per_threshold(
threshold_list,
threshold_default_status_list,
split_dataset.y_test,
)
)
(
thresh_def_recalls_list,
thresh_nondef_recalls_list,
thresh_accs_list,
) = thresh_classification_report_recall_accuracy(
thresh_classification_report_dict
)
namelist = [
"Default Recall",
"Non Default Recall",
"Accuracy",
"Threshold",
]
df = pd.DataFrame(
[
thresh_def_recalls_list,
thresh_nondef_recalls_list,
thresh_accs_list,
threshold_list,
],
index=namelist,
)
df = df.T
create_tradeoff_graph(df)
def select_probability_threshold(model_name_short,
user_defined_threshold,
clf_thresh_predicted_default_status_user_gbt,
J_statistic_best_threshold,
clf_thresh_predicted_default_status_Jstatistic_gbt,
acc_rate_thresh_gbt,
clf_thresh_predicted_default_status_acceptance_gbt):
st.subheader("Selected Probability Threshold")
options = [
"User Defined",
"J Statistic Driven",
"Acceptance Rate Driven",
]
prob_thresh_option = st.radio(
label="Selected Probability Threshold",
options=options,
key=f"{model_name_short}_radio_thresh",
)
if prob_thresh_option == "User Defined":
prob_thresh_selected_gbt = user_defined_threshold
predicted_default_status_gbt = (
clf_thresh_predicted_default_status_user_gbt
)
elif prob_thresh_option == "J Statistic Driven":
prob_thresh_selected_gbt = J_statistic_best_threshold
predicted_default_status_gbt = (
clf_thresh_predicted_default_status_Jstatistic_gbt
)
else:
prob_thresh_selected_gbt = acc_rate_thresh_gbt
predicted_default_status_gbt = (
clf_thresh_predicted_default_status_acceptance_gbt
)
st.write(
f"Selected probability threshold is {prob_thresh_selected_gbt}"
)
return prob_thresh_selected_gbt, predicted_default_status_gbt
def acceptance_rate_driven_threshold(model_name_short, clf_prediction_prob_df_gbt):
st.subheader("Acceptance Rate Driven Probability Threshold")
# Steps
# Set acceptance rate
# Get default status per threshold
# Get classification report per threshold
# Get recall, nondef recall, and accuracy per threshold
acceptance_rate = (
st.slider(
label="% of loans accepted (acceptance rate):",
min_value=0,
max_value=100,
value=85,
key=f"acceptance_rate_{model_name_short}",
format="%f%%",
)
/ 100
)
acc_rate_thresh_gbt = np.quantile(
clf_prediction_prob_df_gbt["PROB_DEFAULT"], acceptance_rate
)
st.write(
f"An acceptance rate of {acceptance_rate} results in probability threshold of {acc_rate_thresh_gbt}"
)
acceptance_rate_driven_threshold_graph(
clf_prediction_prob_df_gbt, acc_rate_thresh_gbt)
clf_thresh_predicted_default_status_acceptance_gbt = apply_threshold_to_probability_values(
clf_prediction_prob_df_gbt,
acc_rate_thresh_gbt,
)
return acc_rate_thresh_gbt, clf_thresh_predicted_default_status_acceptance_gbt
|