Thresholded Results#
When to Use Thresholded Results#
Thresholded results give you the flexibility to enable threshold dependent metrics and plots. Below are the supported use cases:
- When you want to calculate and compare metrics at different thresholds on Kolena. A common task for semantic segmentation or object detection tasks.
- Plotting the Precision-Recall (PR) Curve dynamically based on your test cases.
- Computing Average Precision dynamically based on your test cases.
Format and upload thresholded results#
A thresholded result consist of a list of dictionaries. Each dictionary contains:
- A required numeric field named
threshold(number). - An optional string field
label(string). - A true positive field
- A false positive field
- A false negative field
For instance, in a semantic segmentation problem:
[
{
"threshold": 0.3,
"label": "train",
"tp": 30, # True Positives
"fp": 20, # False Positives
"fn": 10, # False Negatives
},
{
"threshold": 0.4,
"label": "train",
"tp": 40,
"fp": 20,
"fn": 0,
},
...
]
Note
The thresholded object should be added to a model result dataframe with at least one column other
than the thresholded column that contains the thresholded object.
Uploading Thresholded Results#
After generating your thresholded object, you can upload it to Kolena using the upload_results method:
from kolena.dataset import upload_results
def generate_thresholded_object():
# Generate thresholded object based on your raw evaluation data and logic
...
df_results["thresholded"] = generate_thresholded_object()
upload_results("dataset_name", "model_name", df_results, thresholded_fields=["thresholded"])
Note
You can upload multiple thresholded object fields if needed.
Plots for Thresholded Object metrics#
You can find Kolena's plotting tools under the Debugger tab.
Task Plots#
Once on the Debugger tab, navigate to the "Task Plots" tab to view out-of-the-box plots. You are able to configure each plot based on the format of your Thresholded Results and your objectives.
