benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf 4d1a57c1-0431-5720-a0e4-d8040fd613e1

Problem

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

complete prompt

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt


def task_func(data, save_plot=False, plot_path=None):
    """
    Unzip a list of objects and their 3D coordinates, run PCA to reduce the dimensionality to 2D, 
    and depending on the value of save_plot parameter, either save the plot to the provided path and 
    return the 2D coordinates or return the 2D coordinates and the plot's Axes.

    Parameters:
    - data (list of tuple): A list containing tuples of an object and its 3D coordinates.
    - save_plot (bool, optional): If True, the plot will be saved. Defaults to False.
    - plot_path (str, optional): The path where the plot will be saved. Required if save_plot is True.

    Returns:
    - coordinates_2d (numpy.ndarray): The 2D coordinates after applying PCA.
    - ax (matplotlib.axes._axes.Axes, optional): The plot's Axes if save_plot is True.

    Requirements:
    - numpy
    - sklearn.decomposition.PCA
    - matplotlib.pyplot

    Raises:
    - ValueError: If save_plot is True but plot_path is not provided.

    Example:
    >>> import tempfile
    >>> temp_dir = tempfile.mkdtemp()
    >>> task_func([('A', 1, 1, 1), ('B', 2, 2, 2)], save_plot=True, plot_path=f"{temp_dir}/temp_plot.png")[0]
    array([[ 8.66025404e-01,  4.09680598e-17],
           [-8.66025404e-01,  4.09680598e-17]])
    """

instruct prompt

Unzip a list of objects and their 3D coordinates, run PCA to reduce the dimensionality to 2D, and depending on the value of save_plot parameter, either save the plot to the provided path and return the 2D coordinates or return the 2D coordinates and the plot's Axes.
The function should raise the exception for: ValueError: If save_plot is True but plot_path is not provided.
The function should output with:
    coordinates_2d (numpy.ndarray): The 2D coordinates after applying PCA.
    ax (matplotlib.axes._axes.Axes, optional): The plot's Axes if save_plot is True.
You should write self-contained code starting with:

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def task_func(data, save_plot=False, plot_path=None):

code prompt

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def task_func(data, save_plot=False, plot_path=None):

entry point

task_func

libs

  • numpy
  • matplotlib
  • sklearn

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
4d1a57c1-0431-5720-a0e4-d8040fd613e1
task_key
default--v0~2e1~2e0~5fhf--4d1a57c1-0431-5720-a0e4-d8040fd613e1
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import