Unable to find a model above threshold 0.5. Returning None. what is a recommended score_threshold

[zoezhang106]

Hi,

Thank for the amazing package.

I tried the advanced survival analysis demo code in the homepage.

There seem to be two small bugs:
1.
eval_time_horizons = np.linspace(T.min(), T.max(), 5)[1:-1]
should be:
eval_time_horizons = list(np.linspace(T.min(), T.max(), 5)[1:-1])
otherwise, you will get an error message saying:
eval_time_horizons is not a list.

2. get an error message saying:

[2024-09-12T12:32:21.809928+0200][25888][CRITICAL] Unable to find a model above threshold 0.5. Returning None

Inside RiskEstimationStudy, I saw that score is based on, and the default threshold is 0.65.
score = metrics["raw"]["c_index"][0] - metrics["raw"]["brier_score"][0]

From, my understanding 0.5 is already a bit low, by seting a low cutoff like 0.2 can make it work, I am just curious what the lowest cutoff for this score is acceptable.

The complete code from homepage showed below:

# stdlib
import os
from pathlib import Path

# third party
import numpy as np
from pycox import datasets

# autoprognosis absolute
from autoprognosis.studies.risk_estimation import RiskEstimationStudy
from autoprognosis.utils.serialization import load_model_from_file
from autoprognosis.utils.tester import evaluate_survival_estimator

df = datasets.gbsg.read_df()
df = df[df["duration"] > 0]

X = df.drop(columns = ["duration"])
T = df["duration"]
Y = df["event"]

eval_time_horizons = np.linspace(T.min(), T.max(), 5)[1:-1]

workspace = Path("workspace")
study_name = "example_risks"

study = RiskEstimationStudy(
    study_name=study_name,
    dataset=df,
    target="event",
    time_to_event="duration",
    time_horizons=eval_time_horizons,
    num_iter=10,
    num_study_iter=1,
    timeout=10,
    risk_estimators=["cox_ph", "survival_xgboost"],
    score_threshold=0.5,
    workspace=workspace,
)

study.run()

output = workspace / study_name / "model.p"

model = load_model_from_file(output)
# <model> contains the optimal architecture, but the model is not trained yet. You need to call fit() to use it.
# This way, we can further benchmark the selected model on the training set.

metrics = evaluate_survival_estimator(model, X, T, Y, eval_time_horizons)

print(f"Model {model.name()} score: {metrics['str']}")

# Train the model
model.fit(X, T, Y)

# Predict using the model
model.predict(X, eval_time_horizons)