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BigCodeBench / BigCodeBench v0.1.0_hf 0565b973-94a5-5c4c-803e-89e0cbbb5e53

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
import seaborn as sns


def task_func(n_components=2, N_SAMPLES=500, N_FEATURES=50, random_seed=None):
    """
    Generate a high-dimensional dataset, run PCA to reduce its dimensionality, and then draw a heatmap of
    the covariance matrix of the transformed data.

    Parameters:
    n_components (int, optional): The number of components for PCA. Defaults to 2.
    N_SAMPLES (int, optional): Number of samples in the dataset. Defaults to 500.
    N_FEATURES (int, optional): Number of features in the dataset. Defaults to 50.
    random_seed (int, optional): Seed for the numpy and sklearn random number generator. Defaults to None.

    Returns:
    tuple:
        transformed_data (ndarray): The transformed data of shape (N_SAMPLES, n_components).
        heatmap_axes (Axes): The heatmap of the covariance matrix of the transformed data or None if n_components=1.

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

    Example:
    >>> transformed, ax = task_func(n_components=2, random_seed=42)
    >>> transformed.shape
    (500, 2)
    """

instruct prompt

Generate a high-dimensional dataset, run PCA to reduce its dimensionality, and then draw a heatmap of the covariance matrix of the transformed data.
The function should output with:
    tuple:
    transformed_data (ndarray): The transformed data of shape (N_SAMPLES, n_components).
    heatmap_axes (Axes): The heatmap of the covariance matrix of the transformed data or None if n_components=1.
You should write self-contained code starting with:

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
def task_func(n_components=2, N_SAMPLES=500, N_FEATURES=50, random_seed=None):

code prompt

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
def task_func(n_components=2, N_SAMPLES=500, N_FEATURES=50, random_seed=None):

entry point

task_func

libs

  • sklearn
  • numpy
  • matplotlib
  • seaborn

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Source and history

task_id
0565b973-94a5-5c4c-803e-89e0cbbb5e53
task_key
default--v0~2e1~2e0~5fhf--0565b973-94a5-5c4c-803e-89e0cbbb5e53
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import