Class EvalTask (1.53.0)

Default dataset column names:
    * content_column_name: "content"
    * reference_column_name: "reference"
    * response_column_name: "response"
Requirement for different use cases:
  * Bring your own prediction: A `response` column is required. Response
      column name can be customized by providing `response_column_name`
      parameter.
  * Without prompt template: A column representing the input prompt to the
      model is required. If `content_column_name` is not specified, the
      eval dataset requires `content` column by default. The response
      column is not used if present and new responses from the model are
      generated with the content column and used for evaluation.
  * With prompt template: Dataset must contain column names corresponding to
      the placeholder names in the prompt template. For example, if prompt
      template is "Instruction: {instruction}, context: {context}", the
      dataset must contain `instruction` and `context` column.

The supported metrics, metric bundle descriptions, grading rubrics, and
the required input fields 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-prediction(BYOP) evaluation, provide the model
responses in the response column in the dataset. The response column name
is "response" by default, or specify `response_column_name` parameter to
customize.

  ```
  eval_dataset = pd.DataFrame({
          "reference": [...],
          "response" : [...],
  })
  eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=["bleu", "rouge_l_sum", "coherence", "fluency"],
    experiment="my-experiment",
  )
  eval_result = eval_task.evaluate(
        experiment_run_name="eval-experiment-run"
  )
  ```

2. To perform evaluation with built-in Gemini model inference, specify the
`model` parameter with a GenerativeModel instance.  The default query
column name to the model is `content`.

  ```
  eval_dataset = pd.DataFrame({
        "reference": [...],
        "content"  : [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=["exact_match", "bleu", "rouge_1", "rouge_2",
      "rouge_l_sum"],
      experiment="my-experiment",
  ).evaluate(
      model=GenerativeModel("gemini-pro"),
      experiment_run_name="gemini-pro-eval-run"
  )
  ```

3. If a `prompt_template` is specified, the `content` column is not required.
Prompts can be assembled from the evaluation dataset, and all placeholder
names must be present in the dataset columns.
  ```
  eval_dataset = pd.DataFrame({
      "context"    : [...],
      "instruction": [...],
      "reference"  : [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=["summarization_quality"],
  ).evaluate(
      model=model,
      prompt_template="{instruction}. Article: {context}. Summary:",
  )
  ```

4. To perform evaluation with custom model inference, specify the `model`
parameter with a custom prediction function. The `content` column in the
dataset is used to generate predictions with the custom model function for
evaluation.

  ```
  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({
        "content"  : [...],
        "reference": [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=["text_generation_similarity","text_generation_quality"],
      experiment="my-experiment",
  ).evaluate(
      model=custom_model_fn,
      experiment_run_name="gpt-eval-run"
  )
  ```