# Writing Evaluation Script¶

Each challenge has an evaluation script, which evaluates the submission of participants and returns the scores which will populate the leaderboard. The logic for evaluating and judging a submission is customizable and varies from challenge to challenge, but the overall structure of evaluation scripts are fixed due to architectural reasons.

Evaluation scripts are required to have an evaluate() function. This is the main function, which is used by workers to evaluate the submission messages.

The syntax of evaluate function is:

def evaluate(test_annotation_file, user_annotation_file, phase_codename, **kwargs):
pass


It receives three arguments, namely:

• test_annotation_file: It represents the local path to the annotation file for the challenge. This is the file uploaded by the Challenge host while creating a challenge.
• user_annotation_file: It represents the local path of the file submitted by the user for a particular challenge phase.
• phase_codename: It is the codename of the challenge phase from the challenge configuration yaml. This is passed as an argument so that the script can take actions according to the challenge phase.

After reading the files, some custom actions can be performed. This varies per challenge.

The evaluate() method also accepts keyword arguments. By default, we provide you metadata of each submission to your challenge which you can use to send notifications to your slack channel or to some other webhook service. Following is an example code showing how to get the submission metadata in your evaluation script and send a slack notification if the accuracy is more than some value X (X being 90 in the expample given below).

def evaluate(test_annotation_file, user_annotation_file, phase_codename, **kwargs):

# Do stuff here
# Set score to 91 as an example

score = 91
if score > 90:
webhook_url = "Your slack webhook url comes here"
# To know more about slack webhook, checkout this link: https://api.slack.com/incoming-webhooks

response = requests.post(
webhook_url,
data=json.dumps({'text': "*Flag raised for submission:* \n \n" + str(slack_data)}),

# Do more stuff here


The above example can be modified and used to find if some participant team is cheating or not. There are many more ways for which you can use this metadata.

After all the processing is done, this evaluate() should return an output, which is used to populate the leaderboard. The output should be in the following format:

output = {}
output['result'] = [
{
'train_split': {
'Metric1': 123,
'Metric2': 123,
'Metric3': 123,
'Total': 123,
}
},
{
'test_split': {
'Metric1': 123,
'Metric2': 123,
'Metric3': 123,
'Total': 123,
}
}
]

return output


Let’s break down what is happening in the above code snippet.

1. output should contain a key named result, which is a list containing entries per dataset split that is available for the challenge phase in consideration (in the function definition of evaluate() shown above, the argument: phase_codename will receive the codename for the challenge phase against which the submission was made).
2. Each entry in the list should be a dict that has a key with the corresponding dataset split codename (train_split and test_split for this example).
3. Each of these dataset split dict contains various keys (Metric1, Metric2, Metric3, Total in this example), which are then displayed as columns in the leaderboard.