---
title: Evaluate dataset quality | Adaption
description: Poll evaluation status and inspect quality metrics for an adapted dataset with the Python SDK.
---

After an [adaptation run](/adaptive-data/configure-adaptive-data/index.md) succeeds, evaluation compares the source and adapted data on quality dimensions measured by the platform. Evaluation runs on its own schedule, so adaptation can finish before quality results are ready.

The Python SDK exposes results in two forms:

- **[`datasets.get_evaluation(dataset_id)`](/api/python/resources/datasets/methods/get_evaluation/index.md)** returns evaluation status and the full `quality` result.
- **[`datasets.get(dataset_id)`](/api/python/resources/datasets/methods/get/index.md)** returns the dataset record and its compact `evaluation_summary`.

## Fetch evaluation results

Call `get_evaluation` with the same `dataset_id` used for the adaptation run:

```
evaluation = client.datasets.get_evaluation(dataset_id)


print(evaluation.status)
if evaluation.quality:
    print(f"Score before: {evaluation.quality.score_before}")
    print(f"Score after:  {evaluation.quality.score_after}")
    print(f"Improvement:  {evaluation.quality.improvement_percent}%")
```

When evaluation succeeds, `quality` can include before-and-after scores on a 0–10 scale, letter grades, improvement percentage, and an after-adaptation percentile. While status is `pending` or `running`, `quality` can be absent.

## Poll until evaluation finishes

Poll evaluation status with a deadline so a notebook or CI job cannot wait forever:

```
import time


deadline = time.monotonic() + 600


while True:
    evaluation = client.datasets.get_evaluation(dataset_id)
    if evaluation.status in ("succeeded", "failed", "skipped"):
        break
    if time.monotonic() >= deadline:
        raise TimeoutError("Evaluation did not finish within 10 minutes")
    time.sleep(5)


if evaluation.status == "failed":
    raise RuntimeError("Dataset evaluation failed")
if evaluation.status == "skipped":
    print("Evaluation was skipped")
elif evaluation.quality:
    print(evaluation.quality.model_dump(exclude_none=True))
```

Async clients return the same response shape with `await client.datasets.get_evaluation(dataset_id)`.

## Read the summary from the dataset

If your application already [fetches the dataset](/api/python/resources/datasets/methods/get/index.md), use `evaluation_summary` for headline metrics without a separate evaluation request:

```
dataset = client.datasets.get(dataset_id)


if dataset.evaluation_summary:
    summary = dataset.evaluation_summary
    print(f"Score after: {summary.score_after}")
    print(f"Improvement: {summary.improvement_percent}%")
```

[`datasets.get_status`](/api/python/resources/datasets/methods/get_status/index.md) reports ingestion and adaptation progress; it does not include evaluation results.

## Complete example

This script reads a completed adaptation’s dataset ID from the environment, waits for evaluation, and prints both the detailed result and compact summary:

```
import os
import time


from adaption import Adaption


client = Adaption()
dataset_id = os.environ["ADAPTION_DATASET_ID"]
deadline = time.monotonic() + 600


while True:
    evaluation = client.datasets.get_evaluation(dataset_id)
    print(f"Evaluation status: {evaluation.status}")


    if evaluation.status in ("succeeded", "failed", "skipped"):
        break
    if time.monotonic() >= deadline:
        raise TimeoutError("Evaluation did not finish within 10 minutes")
    time.sleep(5)


if evaluation.status == "failed":
    raise RuntimeError("Dataset evaluation failed")
if evaluation.status == "skipped":
    print("Evaluation was skipped for this dataset")
elif not evaluation.quality:
    raise RuntimeError("Evaluation succeeded without quality metrics")
else:
    print(evaluation.quality.model_dump(exclude_none=True))


dataset = client.datasets.get(dataset_id)
if dataset.evaluation_summary:
    print(dataset.evaluation_summary.model_dump(exclude_none=True))
```

Use `get_evaluation` when you need explicit pipeline status or complete quality details. Use `evaluation_summary` when a compact result on the dataset record is enough. To iterate economically on quality, [limit rows and estimate the run](/adaptive-data/configure-adaptive-data#limit-rows-and-estimate-a-run/index.md) before scaling.