EvalTask(
*,
dataset: typing.Union[pd.DataFrame, str, typing.Dict[str, typing.Any]],
metrics: typing.List[
typing.Union[
typing.Literal[
"exact_match",
"bleu",
"rouge_1",
"rouge_2",
"rouge_l",
"rouge_l_sum",
"tool_call_valid",
"tool_name_match",
"tool_parameter_key_match",
"tool_parameter_kv_match",
],
vertexai.evaluation.CustomMetric,
vertexai.evaluation.metrics._base._AutomaticMetric,
vertexai.evaluation.metrics.pointwise_metric.PointwiseMetric,
vertexai.evaluation.metrics.pairwise_metric.PairwiseMetric,
]
],
experiment: typing.Optional[str] = None,
metric_column_mapping: typing.Optional[typing.Dict[str, str]] = None,
output_uri_prefix: typing.Optional[str] = ""
)A class representing an EvalTask.
An Evaluation Tasks is defined to measure the model's ability to perform a certain task in response to specific prompts or inputs. Evaluation tasks must contain an evaluation dataset, and a list of metrics to evaluate. Evaluation tasks help developers compare propmpt templates, track experiments, compare models and their settings, and assess the quality of the model's generated text.
Dataset Details:
Default dataset column names:
* prompt_column_name: "prompt"
* reference_column_name: "reference"
* response_column_name: "response"
* baseline_model_response_column_name: "baseline_model_response"
Requirement for different use cases:
* Bring-your-own-response (BYOR): You already have the data that you
want to evaluate stored in the dataset. Response column name can be
customized by providing `response_column_name` parameter, or in the
`metric_column_mapping`. For BYOR pairwise evaluation, the baseline
model response column name can be customized by providing
`baseline_model_response_column_name` parameter, or
in the `metric_column_mapping`. If the `response` column or
`baseline_model_response` column is present while the
corresponding model is specified, an error will be raised.
* Perform model inference without a prompt template: You have a dataset
containing the input prompts to the model and want to perform model
inference before evaluation. A column named `prompt` is required
in the evaluation dataset and is used directly as input to the model.
* Perform model inference with a prompt template: You have a dataset
containing the input variables to the prompt template and want to
assemble the prompts for model inference. Evaluation dataset
must contain column names corresponding to the variable names in
the prompt template. For example, if prompt template is
"Instruction: {instruction}, context: {context}", the dataset must
contain `instruction` and `context` columns.
Metrics Details:
The supported metrics descriptions, rating rubrics, and the required
input variables can be found on the Vertex AI public documentation page.
[Evaluation methods and metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval).
Usage Examples:
1. To perform bring-your-own-response(BYOR) evaluation, provide the model
responses in the `response` column in the dataset. If a pairwise metric is
used for BYOR evaluation, provide the baseline model responses in the
`baseline_model_response` column.
```
eval_dataset = pd.DataFrame({
"prompt" : [...],
"reference": [...],
"response" : [...],
"baseline_model_response": [...],
})
eval_task = EvalTask(
dataset=eval_dataset,
metrics=[
"bleu",
"rouge_l_sum",
MetricPromptTemplateExamples.Pointwise.FLUENCY,
MetricPromptTemplateExamples.Pairwise.SAFETY
],
experiment="my-experiment",
)
eval_result = eval_task.evaluate(experiment_run_name="eval-experiment-run")
```
2. To perform evaluation with Gemini model inference, specify the `model`
parameter with a `GenerativeModel` instance. The input column name to the
model is `prompt` and must be present in the dataset.
```
eval_dataset = pd.DataFrame({
"reference": [...],
"prompt" : [...],
})
result = EvalTask(
dataset=eval_dataset,
metrics=["exact_match", "bleu", "rouge_1", "rouge_l_sum"],
experiment="my-experiment",
).evaluate(
model=GenerativeModel("gemini-1.5-pro"),
experiment_run_name="gemini-eval-run"
)
```
3. If a `prompt_template` is specified, the `prompt` column is not required.
Prompts can be assembled from the evaluation dataset, and all prompt
template variable names must be present in the dataset columns.
```
eval_dataset = pd.DataFrame({
"context" : [...],
"instruction": [...],
})
result = EvalTask(
dataset=eval_dataset,
metrics=[MetricPromptTemplateExamples.Pointwise.SUMMARIZATION_QUALITY],
).evaluate(
model=GenerativeModel("gemini-1.5-pro"),
prompt_template="{instruction}. Article: {context}. Summary:",
)
```
4. To perform evaluation with custom model inference, specify the `model`
parameter with a custom inference function. The input column name to the
custom inference function is `prompt` and must be present in the dataset.
```
from openai import OpenAI
client = OpenAI()
def custom_model_fn(input: str) -> str:
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": input}
]
)
return response.choices[0].message.content
eval_dataset = pd.DataFrame({
"prompt" : [...],
"reference": [...],
})
result = EvalTask(
dataset=eval_dataset,
metrics=[MetricPromptTemplateExamples.Pointwise.SAFETY],
experiment="my-experiment",
).evaluate(
model=custom_model_fn,
experiment_run_name="gpt-eval-run"
)
```
5. To perform pairwise metric evaluation with model inference step, specify
the `baseline_model` input to a `PairwiseMetric` instance and the candidate
`model` input to the `EvalTask.evaluate()` function. The input column name
to both models is `prompt` and must be present in the dataset.
```
baseline_model = GenerativeModel("gemini-1.0-pro")
candidate_model = GenerativeModel("gemini-1.5-pro")
pairwise_groundedness = PairwiseMetric(
metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(
"pairwise_groundedness"
),
baseline_model=baseline_model,
)
eval_dataset = pd.DataFrame({
"prompt" : [...],
})
result = EvalTask(
dataset=eval_dataset,
metrics=[pairwise_groundedness],
experiment="my-pairwise-experiment",
).evaluate(
model=candidate_model,
experiment_run_name="gemini-pairwise-eval-run",
)
```
Properties
dataset
Returns evaluation dataset.
experiment
Returns experiment name.
metrics
Returns metrics.
Methods
EvalTask
EvalTask(
*,
dataset: typing.Union[pd.DataFrame, str, typing.Dict[str, typing.Any]],
metrics: typing.List[
typing.Union[
typing.Literal[
"exact_match",
"bleu",
"rouge_1",
"rouge_2",
"rouge_l",
"rouge_l_sum",
"tool_call_valid",
"tool_name_match",
"tool_parameter_key_match",
"tool_parameter_kv_match",
],
vertexai.evaluation.CustomMetric,
vertexai.evaluation.metrics._base._AutomaticMetric,
vertexai.evaluation.metrics.pointwise_metric.PointwiseMetric,
vertexai.evaluation.metrics.pairwise_metric.PairwiseMetric,
]
],
experiment: typing.Optional[str] = None,
metric_column_mapping: typing.Optional[typing.Dict[str, str]] = None,
output_uri_prefix: typing.Optional[str] = ""
)Initializes an EvalTask.
display_runs
display_runs()Displays experiment runs associated with this EvalTask.
evaluate
evaluate(
*,
model: typing.Optional[
typing.Union[
vertexai.generative_models.GenerativeModel, typing.Callable[[str], str]
]
] = None,
prompt_template: typing.Optional[str] = None,
experiment_run_name: typing.Optional[str] = None,
response_column_name: typing.Optional[str] = None,
baseline_model_response_column_name: typing.Optional[str] = None,
evaluation_service_qps: typing.Optional[float] = None,
retry_timeout: float = 120.0,
output_file_name: typing.Optional[str] = None
) -> vertexai.evaluation.EvalResultRuns an evaluation for the EvalTask.