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BigCodeBench / BigCodeBench v0.1.0_hf 0f5bd7f8-0058-55cb-89c7-bcb664f362de

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.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error


def task_func(num_samples=1000, k=5, d=2,  random_seed=None):
    """
    Generate a dataset consisting of random numbers sampled from a gaussian
    normal distribution that are transformed by applying a linear
    transformation. Standardize it with the StandardScaler of sklearn,
    and calculate the average square error between the original dataset
    and the standardized dataset.

    Parameters:
    - num_samples (int): The number of samples to generate. Default is 1000.
    - k (float): Multiplicative Factor in linear transformation. Default is 5.
    - d (float): Offset in linear transformation. Default is 2.
    - random_seed (int): The random seed for reproducibility. Default is None.

    Returns:
    float: The mean squared error between the original and standardized data.
           This value represents the average squared difference between each
           original value and its standardized counterpart. The MSE can vary
           significantly depending on the random seed and the specified 
           parameters of the linear transformation.

    Requirements:
    - numpy
    - sklearn.preprocessing.StandardScaler
    - sklearn.metrics.mean_squared_error

    Example:
    >>> mse = task_func(num_samples=123, k=-6.4, d=12.1, random_seed=2)
    >>> print(mse)
    193.04172078372736

    >>> mse = task_func()
    >>> print(mse)
    19.03543917135251

    >>> mse = task_func(k=1, d=0)
    >>> print(mse)
    0.001113785307245742
    """

instruct prompt

Generate a dataset consisting of random numbers sampled from a gaussian normal distribution that are transformed by applying a linear transformation. Standardize it with the StandardScaler of sklearn, and calculate the average square error between the original dataset and the standardized dataset. >>> mse = task_func() >>> print(mse) 19.03543917135251 >>> mse = task_func(k=1, d=0) >>> print(mse) 0.001113785307245742
The function should output with:
    float: The mean squared error between the original and standardized data.
    This value represents the average squared difference between each
    original value and its standardized counterpart. The MSE can vary
    significantly depending on the random seed and the specified
    parameters of the linear transformation.
You should write self-contained code starting with:

Code

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
def task_func(num_samples=1000, k=5, d=2,  random_seed=None):

code prompt

Code

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
def task_func(num_samples=1000, k=5, d=2,  random_seed=None):

entry point

task_func

libs

  • numpy
  • sklearn

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Source and history

task_id
0f5bd7f8-0058-55cb-89c7-bcb664f362de
task_key
default--v0~2e1~2e0~5fhf--0f5bd7f8-0058-55cb-89c7-bcb664f362de
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import