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BigCodeBench / BigCodeBench v0.1.0_hf df4334b9-3639-592f-bc4c-e963b1fded09

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, n_components=2, random_state=None):
    """
    Performs Principal Component Analysis (PCA) on the provided dataset to reduce its dimensionality,
    and visualizes the results using a scatter plot.

    This function applies PCA to the dataset, reducing its features to the specified number of principal components.
    It then visualizes the reduced data in a scatter plot. For datasets reduced to a single component, the function
    generates a 1D scatter plot along the X-axis, with all Y-values set to zero. For reductions resulting in two or more
    components, only the first two principal components are visualized.

    Parameters:
    - data (ndarray): A numpy ndarray of shape (n_samples, n_features) representing the data.
    - n_components (int, optional): Number of components to keep. Defaults to 2.
    - random_state (int, optional): Seed for reproducibility. Defaults to None.

    Returns:
    dict: A dictionary containing:
        - "transformed_data" (np.ndarray): The transformed data.
        - "ax" (plt.Axes): The scatter plot visualizing the transformed data.

    Requirements:
    - numpy
    - matplotlib
    - sklearn

    Example:
    >>> data = np.random.random((100, 5))
    >>> results = task_func(data, random_state=42)
    >>> results['transformed_data'].shape
    (100, 2)
    >>> type(results['ax'])
    <class 'matplotlib.axes._axes.Axes'>
    """

instruct prompt

Performs Principal Component Analysis (PCA) on the provided dataset to reduce its dimensionality, and visualizes the results using a scatter plot. This function applies PCA to the dataset, reducing its features to the specified number of principal components. It then visualizes the reduced data in a scatter plot. For datasets reduced to a single component, the function generates a 1D scatter plot along the X-axis, with all Y-values set to zero. For reductions resulting in two or more components, only the first two principal components are visualized.
The function should output with:
    dict: A dictionary containing:
    "transformed_data" (np.ndarray): The transformed data.
    "ax" (plt.Axes): The scatter plot visualizing the transformed data.
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, n_components=2, random_state=None):

code prompt

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def task_func(data, n_components=2, random_state=None):

entry point

task_func

libs

  • numpy
  • matplotlib
  • sklearn

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Source and history

task_id
df4334b9-3639-592f-bc4c-e963b1fded09
task_key
default--v0~2e1~2e0~5fhf--df4334b9-3639-592f-bc4c-e963b1fded09
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import