# graph4nlp.evaluation

## Evaluation Metrics

class `graph4nlp.evaluation.``BLEU`(n_grams, verbose=0)

  > The BLEU evaluation metric class.

  Parameters

    - n_grams (list[int]) – The BLEU’s n_gram parameter. The results will be returned according to the `n_grams` one-by-one.

    - verbose (int, default = 0) – The log indicator. If set to 0, it will output no logs.

  Methods

  `calculate_scores`(ground_truth, predict) | The BLEU calculation function. It will compute the BLEU scores.

  `calculate_scores`(ground_truth, predict)

    > The BLEU calculation function. It will compute the BLEU scores.

    Parameters

      - ground_truth (list[string]) – The ground truth (correct) target values. It is a list of strings.

      - predict (list[string]) – The predicted target values. It is a list of strings.

    Returns

      - score (list[float]) – The list contains BLEU_n results according to `n_grams`.

      - scores (list[list[float]]) – The specific results for each needed BLEU_n metric.

class `graph4nlp.evaluation.``CIDEr`(df)

  > The CIDEr evaluation metric class.

  Parameters

    df (string) – Parameter indicating document frequency.

  Methods

  `calculate_scores`(ground_truth, predict) | The CIDEr calculation function. It will compute the CIDEr scores.

  `calculate_scores`(ground_truth, predict)

    > The CIDEr calculation function. It will compute the CIDEr scores.

    Parameters

      - ground_truth (list[string]) – The ground truth (correct) target values. It is a list of strings.

      - predict (list[string]) – The predicted target values. It is a list of strings.

    Returns

      - score (float) – The CIDEr value.

      - scores (list[float]) – The specific results for CIDEr metric.

class `graph4nlp.evaluation.``METEOR`

  > The METEOR evaluation metric class.

  Parameters

    - rubric: (.) – Methods:

    - autosummary:: (.) –

      toctree

      calculate_scores

    - . – !! processed by numpydoc !!

  `calculate_scores`(ground_truth, predict)

    > The METEOR calculation function. It will compute the METEOR scores.

    Parameters

      - ground_truth (list[string]) – The ground truth (correct) target values. It is a list of strings.

      - predict (list[string]) – The predicted target values. It is a list of strings.

    Returns

      - score (float) – The METEOR value.

      - scores (list[float]) – The specific results for METEOR metric.

class `graph4nlp.evaluation.``ROUGE`

  > The METEOR evaluation metric class.

  Parameters

    - rubric: (.) – Methods:

    - autosummary:: (.) –

      toctree

      calculate_scores

    - . – !! processed by numpydoc !!

  `calculate_scores`(ground_truth, predict)

    > The METEOR calculation function. It will compute the METEOR scores.

    Parameters

      - ground_truth (list[string]) – The ground truth (correct) target values. It is a list of strings.

      - predict (list[string]) – The predicted target values. It is a list of strings.

    Returns

      - score (float) – The METEOR value.

      - scores (list[float]) – The specific results for METEOR metric.