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BigCodeBench / BigCodeBench v0.1.0_hf f83c4190-8a56-5682-b889-27cb6245d18d

Problem

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

complete prompt

import numpy as np
import matplotlib.pyplot as plt

# Constants
NUM_SAMPLES = 100
NUM_OUTLIERS = 5


def task_func(num_samples=NUM_SAMPLES, num_outliers=NUM_OUTLIERS):
    """
    Generate a dataset comprising both normal data and artificially introduced outliers,
    and plot a histogram of the combined data. The function detects outliers in the dataset
    using the Interquartile Range (IQR) method, but it only considers the normally distributed
    portion of the data for outlier detection. The outliers detected and the artificially
    introduced outliers might not always coincide.

    Parameters:
    - num_samples (int): Number of samples to be drawn from a normal distribution. The default 
      value is 100. If set to zero or a negative number, no normal data will be generated, 
      and the dataset will only contain artificially introduced outliers.
    - num_outliers (int): Number of outliers to be artificially introduced into the dataset. 
      These outliers are uniformly distributed between -10 and 10. The default value is 5. 
      If set to zero, no outliers will be artificially introduced.


    Returns:
    - data (numpy array): The combined dataset, including both normally distributed data and 
      the artificially introduced outliers.
    - outliers_detected (numpy array): The outliers detected using the IQR method. This 
      detection is based solely on the normally distributed portion of the data.
    - ax (matplotlib.axes._axes.Axes): The Axes object for the histogram 
      plot of the combined dataset.

    Requirements:
    - numpy
    - matplotlib

    Note:
    - The artificially introduced outliers are not necessarily the same as the outliers
    detected by the IQR method. The IQR method is applied only to the normally distributed
    data, and thus some of the artificially introduced outliers may not be detected,
    and some normal data points may be falsely identified as outliers.

    Example:
    >>> import numpy as np
    >>> np.random.seed(0)
    >>> data, outliers_detected, ax = task_func()
    >>> print(outliers_detected)
    [-9.61613603 -3.96850367  3.20347075]
    """

instruct prompt

Generate a dataset comprising both normal data and artificially introduced outliers, and plot a histogram of the combined data. The function detects outliers in the dataset using the Interquartile Range (IQR) method, but it only considers the normally distributed portion of the data for outlier detection. The outliers detected and the artificially introduced outliers might not always coincide.
Note that: The artificially introduced outliers are not necessarily the same as the outliers detected by the IQR method. The IQR method is applied only to the normally distributed data, and thus some of the artificially introduced outliers may not be detected, and some normal data points may be falsely identified as outliers.
The function should output with:
    data (numpy array): The combined dataset, including both normally distributed data and
    the artificially introduced outliers.
    outliers_detected (numpy array): The outliers detected using the IQR method. This
    detection is based solely on the normally distributed portion of the data.
    ax (matplotlib.axes._axes.Axes): The Axes object for the histogram
    plot of the combined dataset.
You should write self-contained code starting with:

Code

import numpy as np
import matplotlib.pyplot as plt
# Constants
NUM_SAMPLES = 100
NUM_OUTLIERS = 5
def task_func(num_samples=NUM_SAMPLES, num_outliers=NUM_OUTLIERS):

code prompt

Code

import numpy as np
import matplotlib.pyplot as plt
# Constants
NUM_SAMPLES = 100
NUM_OUTLIERS = 5
def task_func(num_samples=NUM_SAMPLES, num_outliers=NUM_OUTLIERS):

entry point

task_func

libs

  • numpy
  • matplotlib

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Source and history

task_id
f83c4190-8a56-5682-b889-27cb6245d18d
task_key
default--v0~2e1~2e0~5fhf--f83c4190-8a56-5682-b889-27cb6245d18d
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import