wehub-resource-sync
e768098d0e
Flake8 Lint / flake8 (push) Waiting to run
Spell check CI / Spell_Check (push) Waiting to run
tools_continuous_delivery / Private PyPI non-main branch release (push) Has been skipped
tools_continuous_delivery / Private PyPI main branch release (push) Failing after 2m42s
Publish Promptflow Doc / Build (push) Has been cancelled
Publish Promptflow Doc / Deploy (push) Has been cancelled
44 lines
1.3 KiB
Markdown
44 lines
1.3 KiB
Markdown
# Basic Eval
|
||
This example shows how to create a basic evaluation flow.
|
||
|
||
Tools used in this flow：
|
||
- `python` tool
|
||
|
||
## Prerequisites
|
||
|
||
Install promptflow sdk and other dependencies in this folder:
|
||
```bash
|
||
pip install -r requirements.txt
|
||
```
|
||
|
||
## What you will learn
|
||
|
||
In this flow, you will learn
|
||
- how to compose a point based evaluation flow, where you can calculate point-wise metrics.
|
||
- the way to log metrics. use `from promptflow.core import log_metric`
|
||
- see file [aggregate](aggregate.py).
|
||
|
||
### 1. Test flow with single line data
|
||
|
||
Testing flow/node:
|
||
```bash
|
||
# test with default input value in flow.dag.yaml
|
||
pf flow test --flow .
|
||
|
||
# test with flow inputs
|
||
pf flow test --flow . --inputs groundtruth=ABC prediction=ABC
|
||
|
||
# test node with inputs
|
||
pf flow test --flow . --node line_process --inputs groundtruth=ABC prediction=ABC
|
||
```
|
||
|
||
### 2. create flow run with multi line data
|
||
There are two ways to evaluate an classification flow.
|
||
|
||
```bash
|
||
pf run create --flow . --data ./data.jsonl --column-mapping groundtruth='${data.groundtruth}' prediction='${data.prediction}' --stream
|
||
```
|
||
|
||
You can also skip providing `column-mapping` if provided data has same column name as the flow.
|
||
Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
|