kaushiks_criteria.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""

import evaluate
import datasets


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
Evaluate structured output formatting for generated text.
- considers header / column / tag / key names
- DOES NOT consider the cell / row values specifically

Formats:
    - [] Custom
    - [] Markdown tables
    - [] HTML tables
    - [] JSON
    - [] XML
    - [] CSV / TSV 
"""


# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how well the `structure` of the predictions matches the `structure` of the references.
Args:
    predictions: list of strings to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    invariance: bool, whether to consider the order of the columns / tags / keys.
Returns:
    kaushiks_criteria: kaushiks_criteria score.
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("DoctorSlimm/kaushiks_criteria")
    >>> results = my_new_module.compute(
            references=['<table><tr><td>1</td><td>2</td></tr></table>'],
            predictions=['<table><tr><td>1</td><td>2</td></tr></table>']
        )
    >>> print(results)
    {'accuracy': 1.0}
"""

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class kaushiks_criteria(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features({
                'predictions': datasets.Value('string'),
                'references': datasets.Value('string'),
            }),
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        import evaluate
        evaluate.load('exact_match')
        pass

    def normalize_fn(self, example, text_field='text'):
        """
        parse output text into headers, rows, and records
        - parse row by row (incomplete rows)
        :param example:
        :return:
        Note: this does not handle special tokens
        expected input format:

        | col1 | col2 | col3 |      <- start and trailing pipes required
        | ---- | ---- | ---- |      <- exactly 3x '-' per column
        | val1 | val2 | val3 |
        | ... | ... | ... |
        """
        headers, sep_row, row_counts = "", "", []

        rows = dict(example)[text_field].strip().split('\n')

        # parse headers
        if len(rows) > 0:
            headers = rows[0].strip()

        # parse separator row
        if len(rows) > 1:
            sep_row = rows[1].strip()

        # parse row cell counts
        if len(rows) > 2:
            data_rows = rows[2:]
            for row in data_rows:
                cell_counts = len(row.strip('|').split('|'))
                row_counts.append(str(int(cell_counts)))
        return {
            'headers': headers,
            'sep_row': sep_row,
            'row_counts': ''.join(row_counts)
        }

    def _compute(self, predictions, references, num_proc=None):
        """
        compute the quality of the output format with respect to the reference format
        * column names match
        * column order matches
        * total row count
        * number of cells in each row
        :param predictions:
        :param references:
        :return:
        """
        from datasets import Dataset, DatasetDict

        pred_ds = Dataset.from_dict({'text': predictions})
        refs_ds = Dataset.from_dict({'text': references})
        proc_ds = DatasetDict({'predictions': pred_ds, 'references': refs_ds})
        proc_ds = proc_ds.map(
            self.normalize_fn,
            num_proc=num_proc,
            load_from_cache_file=False
        )

        # compare headers
        exact_match = evaluate.load('exact_match')
        exact_match_headers = exact_match.compute(
            predictions=proc_ds['predictions']['headers'],
            references=proc_ds['references']['headers']
        )['exact_match']

        # compare separator row
        exact_match_sep_row = exact_match.compute(
            predictions=proc_ds['predictions']['sep_row'],
            references=proc_ds['references']['sep_row']
        )['exact_match']

        # compare row counts
        exact_match_row_counts = exact_match.compute(
            predictions=proc_ds['predictions']['row_counts'],
            references=proc_ds['references']['row_counts']
        )['exact_match']

        # compute kaushiks_criteria
        score = (exact_match_headers + exact_match_sep_row + exact_match_row_counts) / 3.0

        # round and return
        metrics = {
            'kaushiks_criteria': score,
            'exact_match_headers': exact_match_headers,
            'exact_match_sep_row': exact_match_sep_row,
            'exact_match_row_counts': exact_match_row_counts,
        }
        for key, value in metrics.items():
            metrics[key] = round(value, 2)
        return metrics