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BigCodeBench / BigCodeBench v0.1.0_hf 4fedc293-c4c6-5bda-bd9a-77751f43b38e

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import re
from sklearn.feature_extraction.text import TfidfVectorizer


def task_func(texts):
    """
    Processes a collection of text documents to compute the TF-IDF (Term Frequency-Inverse Document Frequency) scores
    for each word, excluding any URLs present in the texts. The TF-IDF scores help to identify the importance of a word
    within a document relative to a collection of documents.

    Parameters:
    texts (list of str): A list containing the text documents to be analyzed.

    Returns:
    tuple of (list of tuples, list of str):
        - The first element is a list of tuples, each tuple representing a document with its words' TF-IDF scores in a
          dense matrix format. Each score in the tuple corresponds to a word's TF-IDF score in the document.
        - The second element is a list of strings, representing the unique words (features) across all documents for
          which TF-IDF scores have been calculated. The order of words in this list matches the order of scores in the
          tuples of the first element.

    Requirements:
    - re
    - sklearn.feature_extraction.text.TfidfVectorizer

    Example:
    >>> task_func(['Visit https://www.python.org for more info.', 'Python is great.', 'I love Python.'])
    ([(0.5, 0.0, 0.5, 0.0, 0.0, 0.5, 0.0, 0.5), (0.0, 0.62276601, 0.0, 0.62276601, 0.0, 0.0, 0.4736296, 0.0), (0.0, 0.0, 0.0, 0.0, 0.79596054, 0.0, 0.60534851, 0.0)], ['for', 'great', 'info', 'is', 'love', 'more', 'python', 'visit'])

    Notes:
    - URLs in the text documents are removed before calculating TF-IDF scores to ensure they do not affect the analysis.
    - The TF-IDF scores are rounded to 8 decimal places for precision.
    """

instruct prompt

Processes a collection of text documents to compute the TF-IDF (Term Frequency-Inverse Document Frequency) scores for each word, excluding any URLs present in the texts. The TF-IDF scores help to identify the importance of a word within a document relative to a collection of documents.
Note that: Notes: URLs in the text documents are removed before calculating TF-IDF scores to ensure they do not affect the analysis. The TF-IDF scores are rounded to 8 decimal places for precision.
The function should output with:
    tuple of (list of tuples, list of str):
    The first element is a list of tuples, each tuple representing a document with its words' TF-IDF scores in a
    dense matrix format. Each score in the tuple corresponds to a word's TF-IDF score in the document.
    The second element is a list of strings, representing the unique words (features) across all documents for
    which TF-IDF scores have been calculated. The order of words in this list matches the order of scores in the
    tuples of the first element.
You should write self-contained code starting with:

Code

import re
from sklearn.feature_extraction.text import TfidfVectorizer
def task_func(texts):

code prompt

Code

import re
from sklearn.feature_extraction.text import TfidfVectorizer
def task_func(texts):

entry point

task_func

libs

  • re
  • sklearn

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Source and history

task_id
4fedc293-c4c6-5bda-bd9a-77751f43b38e
task_key
default--v0~2e1~2e0~5fhf--4fedc293-c4c6-5bda-bd9a-77751f43b38e
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import