Class EvalTask (1.75.0)

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.

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).

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",
  )
  ```