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# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.
import datasets
from sklearn.feature_extraction.text import CountVectorizer
import evaluate
_DESCRIPTION = """
Returns the total number of words, and the number of unique words in the input data.
"""
_KWARGS_DESCRIPTION = """
Args:
`data`: a list of `str` for which the words are counted.
`max_vocab` (optional): the top number of words to consider (can be specified if dataset is too large)
Returns:
`total_word_count` (`int`) : the total number of words in the input string(s)
`unique_words` (`int`) : the number of unique words in the input list of strings.
Examples:
>>> data = ["hello world and hello moon"]
>>> wordcount= evaluate.load("word_count")
>>> results = wordcount.compute(data=data)
>>> print(results)
{'total_word_count': 5, 'unique_words': 4}
"""
_CITATION = ""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class WordCount(evaluate.Measurement):
"""This measurement returns the total number of words and the number of unique words
in the input string(s)."""
def _info(self):
return evaluate.MeasurementInfo(
# This is the description that will appear on the modules page.
module_type="measurement",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"data": datasets.Value("string"),
}
),
)
def _compute(self, data, max_vocab=None):
"""Returns the number of unique words in the input data"""
count_vectorizer = CountVectorizer(max_features=max_vocab)
document_matrix = count_vectorizer.fit_transform(data)
word_count = document_matrix.sum()
unique_words = document_matrix.shape[1]
return {"total_word_count": word_count, "unique_words": unique_words}
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