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BigCodeBench / BigCodeBench v0.1.0_hf 7e99ece6-d30d-5f7e-8928-6d6bc11789e3

Problem

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

complete prompt

from scipy.spatial.distance import cdist
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt


def task_func(n_samples=200, centers=4, plot_path=None, random_seed=None):
    """
    Generate a synthetic 2D dataset using make_blobs, visualize the dataset, and then calculate
    the Euclidean distance between individual samples of the dataset.

    Parameters:
    - n_samples (int): Number of samples to generate. Default is 200.
    - centers (int): Number of centers to generate. Default is 4.
    - plot_path (str, optional): Path to save the plot. If None, the plot will be returned.
    - random_seed (int, optional): Seed for random number generation. Default is None.

    Returns:
    - tuple:
        - ndarray: A 2D array with distances between each sample.
        - Axes or None: If plot_path is None, returns the matplotlib Axes object of the plot.
                        Otherwise, saves the plot to the provided path and return None.
                        Plot shows values of the first feature dimension on the x-axis, values
                        of the second feature dimension on the y-axis, and labels of the synthetic
                        examples as color.

    Requirements:
    - scipy.spatial.distance.cdist
    - sklearn.datasets.make_blobs
    - matplotlib.pyplot

    Example:
    >>> distances, plot = task_func(random_seed=42)
    >>> distances.shape
    (200, 200)
    >>> plot
    <Axes: >
    """

instruct prompt

Generate a synthetic 2D dataset using make_blobs, visualize the dataset, and then calculate the Euclidean distance between individual samples of the dataset.
The function should output with:
    tuple:
    ndarray: A 2D array with distances between each sample.
    Axes or None: If plot_path is None, returns the matplotlib Axes object of the plot.
    Otherwise, saves the plot to the provided path and return None.
    Plot shows values of the first feature dimension on the x-axis, values
    of the second feature dimension on the y-axis, and labels of the synthetic
    examples as color.
You should write self-contained code starting with:

Code

from scipy.spatial.distance import cdist
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
def task_func(n_samples=200, centers=4, plot_path=None, random_seed=None):

code prompt

Code

from scipy.spatial.distance import cdist
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
def task_func(n_samples=200, centers=4, plot_path=None, random_seed=None):

entry point

task_func

libs

  • matplotlib
  • scipy
  • sklearn

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Source and history

task_id
7e99ece6-d30d-5f7e-8928-6d6bc11789e3
task_key
default--v0~2e1~2e0~5fhf--7e99ece6-d30d-5f7e-8928-6d6bc11789e3
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import