To calculate metrics like recall, precision, and F1 score in Phoenix, you can handle the evaluation on a row-by-row basis by returning a score of 0 or 1 for each row, and then aggregate these scores to compute the final metrics. Here's how you can approach it:
1. *Row-by-Row Evaluation*: For each row, return a score of 1 if the condition for the metric is met (e.g., true positive for recall), and 0 otherwise. You can use a custom evaluator function to achieve this.
2. *Aggregate Scores*: After evaluating each row, aggregate the scores to compute the final metric. For recall, sum the true positives and divide by the total number of actual positives.
Here's a basic example of how you might implement this:
```
def recall_evaluator(output, expected):
# Return 1 for true positive, 0 otherwise
return 1 if output == expected == "positive" else 0
def precision_evaluator(output, expected):
# Return 1 for true positive, 0 otherwise
return 1 if output == "positive" and expected == "positive" else 0
def f1_score_evaluator(output, expected):
# Implement F1 score calculation based on precision and recall
pass
# Run experiment with custom evaluators
experiment = run_experiment(
dataset=your_dataset,
task=your_task,
evaluators=[recall_evaluator, precision_evaluator, f1_score_evaluator]
)
```
Would you like more detail on any specific part?
Sources:
- <https://github.com/arize-ai/phoenix/blob/main/scripts/rag/llama_index_w_evals_and_qa.py|https://github.com/arize-ai/phoenix/blob/main/scripts/rag/llama_index_w_evals_and_qa.py>
- <https://arize-ai.slack.com/archives/C04R3GXC8HK/p1725442860.605379|https://arize-ai.slack.com/archives/C04R3GXC8HK/p1725442860.605379>
