4.md

Results of GPT-3.5 on HumanEval/4

Date: 18 July, 2023
Model: gpt-3.5-turbo
Temperature: 0.2
Passing runs: 10
Total runs: 10
Success rate: 100%

Prompt

Tokens: 143

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """

The above is an incomplete Python code fragment. Return the complete and correct code with no additional text.

Correct solution

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """mean = sum(numbers) / len(numbers)
    return sum(abs(x - mean) for x in numbers) / len(numbers)

Unit tests

METADATA = {
    'author': 'jt',
    'dataset': 'test'
}


def check(candidate):
    assert abs(candidate([1.0, 2.0, 3.0]) - 2.0/3.0) < 1e-6
    assert abs(candidate([1.0, 2.0, 3.0, 4.0]) - 1.0) < 1e-6
    assert abs(candidate([1.0, 2.0, 3.0, 4.0, 5.0]) - 6.0/5.0) < 1e-6

Runs

✅ Run 1

Start time: 2023-07-18 17:29:53
Duration: 5.59s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 2

Start time: 2023-07-18 17:29:47
Duration: 5.29s
Completion tokens: 142
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    mad = sum(abs(x - mean) for x in numbers) / len(numbers)
    return mad

✅ Run 3

Start time: 2023-07-18 17:29:41
Duration: 6.05s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 4

Start time: 2023-07-18 17:29:35
Duration: 5.85s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 5

Start time: 2023-07-18 17:29:31
Duration: 4.24s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 6

Start time: 2023-07-18 17:29:26
Duration: 5.38s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 7

Start time: 2023-07-18 17:29:20
Duration: 5.78s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 8

Start time: 2023-07-18 17:29:14
Duration: 5.7s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 9

Start time: 2023-07-18 17:29:09
Duration: 4.75s
Completion tokens: 146
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    deviation = [abs(x - mean) for x in numbers]
    return sum(deviation) / len(deviation)

✅ Run 10

Start time: 2023-07-18 17:29:04
Duration: 5.41s
Completion tokens: 142
Result: Pass

from typing import List


def mean_absolute_deviation(numbers: List[float]) -> float:
    """ For a given list of input numbers, calculate Mean Absolute Deviation
    around the mean of this dataset.
    Mean Absolute Deviation is the average absolute difference between each
    element and a centerpoint (mean in this case):
    MAD = average | x - x_mean |
    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
    1.0
    """
    mean = sum(numbers) / len(numbers)
    mad = sum(abs(x - mean) for x in numbers) / len(numbers)
    return mad