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51 lines
2.0 KiB
Markdown
51 lines
2.0 KiB
Markdown
# Classification Accuracy Evaluation
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This is a flow illustrating how to evaluate the performance of a classification system. It involves comparing each prediction to the groundtruth and assigns a "Correct" or "Incorrect" grade, and aggregating the results to produce metrics such as accuracy, which reflects how good the system is at classifying the data.
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Tools used in this flow：
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- `python` tool
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## What you will learn
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In this flow, you will learn
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- how to compose a point based evaluation flow, where you can calculate point-wise metrics.
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- the way to log metrics. use `from promptflow.core import log_metric`
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- see file [calculate_accuracy.py](calculate_accuracy.py)
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### 0. Setup connection
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Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
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```bash
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# Override keys with --set to avoid yaml file changes
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pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
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```
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### 1. Test flow/node
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```bash
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# test with default input value in flow.dag.yaml
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pf flow test --flow .
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# test with flow inputs
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pf flow test --flow . --inputs groundtruth=APP prediction=APP
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# test node with inputs
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pf flow test --flow . --node grade --inputs groundtruth=groundtruth prediction=prediction
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```
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### 2. create flow run with multi line data
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There are two ways to evaluate an classification flow.
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```bash
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pf run create --flow . --data ./data.jsonl --column-mapping groundtruth='${data.groundtruth}' prediction='${data.prediction}' --stream
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```
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You can also skip providing `column-mapping` if provided data has same column name as the flow.
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Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
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### 3. create run against other flow run
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Learn more in [web-classification](../../standard/web-classification/README.md)
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