Dataset Viewer
task_id
stringclasses 10
values | prompt
stringclasses 10
values | canonical_solution
stringclasses 10
values | test
stringclasses 10
values | entry_point
stringclasses 10
values |
|---|---|---|---|---|
HumanEval/0
|
from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
True
"""
|
for idx, elem in enumerate(numbers):
for idx2, elem2 in enumerate(numbers):
if idx != idx2:
distance = abs(elem - elem2)
if distance < threshold:
return True
return False
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True
assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False
assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True
assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False
assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True
assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True
assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False
|
has_close_elements
|
HumanEval/1
|
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
|
result = []
current_string = []
current_depth = 0
for c in paren_string:
if c == '(':
current_depth += 1
current_string.append(c)
elif c == ')':
current_depth -= 1
current_string.append(c)
if current_depth == 0:
result.append(''.join(current_string))
current_string.clear()
return result
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate('(()()) ((())) () ((())()())') == [
'(()())', '((()))', '()', '((())()())'
]
assert candidate('() (()) ((())) (((())))') == [
'()', '(())', '((()))', '(((())))'
]
assert candidate('(()(())((())))') == [
'(()(())((())))'
]
assert candidate('( ) (( )) (( )( ))') == ['()', '(())', '(()())']
|
separate_paren_groups
|
HumanEval/2
|
def truncate_number(number: float) -> float:
""" Given a positive floating point number, it can be decomposed into
and integer part (largest integer smaller than given number) and decimals
(leftover part always smaller than 1).
Return the decimal part of the number.
>>> truncate_number(3.5)
0.5
"""
|
return number % 1.0
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate(3.5) == 0.5
assert abs(candidate(1.33) - 0.33) < 1e-6
assert abs(candidate(123.456) - 0.456) < 1e-6
|
truncate_number
|
HumanEval/3
|
from typing import List
def below_zero(operations: List[int]) -> bool:
""" You're given a list of deposit and withdrawal operations on a bank account that starts with
zero balance. Your task is to detect if at any point the balance of account fallls below zero, and
at that point function should return True. Otherwise it should return False.
>>> below_zero([1, 2, 3])
False
>>> below_zero([1, 2, -4, 5])
True
"""
|
balance = 0
for op in operations:
balance += op
if balance < 0:
return True
return False
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([]) == False
assert candidate([1, 2, -3, 1, 2, -3]) == False
assert candidate([1, 2, -4, 5, 6]) == True
assert candidate([1, -1, 2, -2, 5, -5, 4, -4]) == False
assert candidate([1, -1, 2, -2, 5, -5, 4, -5]) == True
assert candidate([1, -2, 2, -2, 5, -5, 4, -4]) == True
|
below_zero
|
HumanEval/4
|
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)
|
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
|
mean_absolute_deviation
|
HumanEval/5
|
from typing import List
def intersperse(numbers: List[int], delimeter: int) -> List[int]:
""" Insert a number 'delimeter' between every two consecutive elements of input list `numbers'
>>> intersperse([], 4)
[]
>>> intersperse([1, 2, 3], 4)
[1, 4, 2, 4, 3]
"""
|
if not numbers:
return []
result = []
for n in numbers[:-1]:
result.append(n)
result.append(delimeter)
result.append(numbers[-1])
return result
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([], 7) == []
assert candidate([5, 6, 3, 2], 8) == [5, 8, 6, 8, 3, 8, 2]
assert candidate([2, 2, 2], 2) == [2, 2, 2, 2, 2]
|
intersperse
|
HumanEval/6
|
from typing import List
def parse_nested_parens(paren_string: str) -> List[int]:
""" Input to this function is a string represented multiple groups for nested parentheses separated by spaces.
For each of the group, output the deepest level of nesting of parentheses.
E.g. (()()) has maximum two levels of nesting while ((())) has three.
>>> parse_nested_parens('(()()) ((())) () ((())()())')
[2, 3, 1, 3]
"""
|
def parse_paren_group(s):
depth = 0
max_depth = 0
for c in s:
if c == '(':
depth += 1
max_depth = max(depth, max_depth)
else:
depth -= 1
return max_depth
return [parse_paren_group(x) for x in paren_string.split(' ') if x]
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate('(()()) ((())) () ((())()())') == [2, 3, 1, 3]
assert candidate('() (()) ((())) (((())))') == [1, 2, 3, 4]
assert candidate('(()(())((())))') == [4]
|
parse_nested_parens
|
HumanEval/7
|
from typing import List
def filter_by_substring(strings: List[str], substring: str) -> List[str]:
""" Filter an input list of strings only for ones that contain given substring
>>> filter_by_substring([], 'a')
[]
>>> filter_by_substring(['abc', 'bacd', 'cde', 'array'], 'a')
['abc', 'bacd', 'array']
"""
|
return [x for x in strings if substring in x]
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([], 'john') == []
assert candidate(['xxx', 'asd', 'xxy', 'john doe', 'xxxAAA', 'xxx'], 'xxx') == ['xxx', 'xxxAAA', 'xxx']
assert candidate(['xxx', 'asd', 'aaaxxy', 'john doe', 'xxxAAA', 'xxx'], 'xx') == ['xxx', 'aaaxxy', 'xxxAAA', 'xxx']
assert candidate(['grunt', 'trumpet', 'prune', 'gruesome'], 'run') == ['grunt', 'prune']
|
filter_by_substring
|
HumanEval/8
|
from typing import List, Tuple
def sum_product(numbers: List[int]) -> Tuple[int, int]:
""" For a given list of integers, return a tuple consisting of a sum and a product of all the integers in a list.
Empty sum should be equal to 0 and empty product should be equal to 1.
>>> sum_product([])
(0, 1)
>>> sum_product([1, 2, 3, 4])
(10, 24)
"""
|
sum_value = 0
prod_value = 1
for n in numbers:
sum_value += n
prod_value *= n
return sum_value, prod_value
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([]) == (0, 1)
assert candidate([1, 1, 1]) == (3, 1)
assert candidate([100, 0]) == (100, 0)
assert candidate([3, 5, 7]) == (3 + 5 + 7, 3 * 5 * 7)
assert candidate([10]) == (10, 10)
|
sum_product
|
HumanEval/9
|
from typing import List, Tuple
def rolling_max(numbers: List[int]) -> List[int]:
""" From a given list of integers, generate a list of rolling maximum element found until given moment
in the sequence.
>>> rolling_max([1, 2, 3, 2, 3, 4, 2])
[1, 2, 3, 3, 3, 4, 4]
"""
|
running_max = None
result = []
for n in numbers:
if running_max is None:
running_max = n
else:
running_max = max(running_max, n)
result.append(running_max)
return result
|
METADATA = {
'author': 'jt',
'dataset': 'test'
}
def check(candidate):
assert candidate([]) == []
assert candidate([1, 2, 3, 4]) == [1, 2, 3, 4]
assert candidate([4, 3, 2, 1]) == [4, 4, 4, 4]
assert candidate([3, 2, 3, 100, 3]) == [3, 3, 3, 100, 100]
|
rolling_max
|
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