bcb-0029
0.000
Challenge · difficulty 2/5
# BigCodeBench/29
Implement a file **`solution.py`** that completes the function below. Keep the given name and signature; define `task_func` at module level.
Allowed libraries: `base64`, `numpy`, `sklearn`.
```python
from sklearn.preprocessing import StandardScaler
import numpy as np
import base64
def task_func(data):
"""
Standardize a numeric array using sklearn's StandardScaler and encode the standardized data in base64 format as an ASCII string.
Parameters:
- data (numpy.ndarray): The numpy array to standardize and encode.
Returns:
- str: The base64-encoded ASCII string representation of the standardized data.
Requirements:
- sklearn.preprocessing.StandardScaler
- numpy
- base64
Example:
>>> data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]])
>>> encoded_data = task_func(data)
>>> print(encoded_data)
W1stMS4gLTEuXQogWy0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
"""
```
<!-- imported from BigCodeBench (BigCodeBench/29) -->
tests/test_bcb_0029.py
# Auto-generated from BigCodeBench BigCodeBench/29. Do not edit by hand.
import pathlib as _pathlib
exec(_pathlib.Path(__file__).with_name("solution.py").read_text(), globals())
import unittest
from unittest.mock import patch
import numpy as np
import base64
from sklearn.preprocessing import StandardScaler
class TestCases(unittest.TestCase):
def test_output_is_string_and_valid_base64(self):
# Check that the function returns a valid base64 string.
data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]])
encoded_data = task_func(data)
self.assertIsInstance(encoded_data, str)
try:
decoded_data = base64.b64decode(encoded_data).decode('ascii')
self.assertTrue(decoded_data.startswith('[[') and decoded_data.endswith(']]'))
except Exception as e:
self.fail(f"Decoding base64 failed with error: {e}")
def test_with_mocked_scaler(self):
# Mock StandardScaler to control the standardized output and check interaction
with patch('sklearn.preprocessing.StandardScaler.fit_transform', return_value=np.array([[0, 0], [0, 0], [1, 1], [1, 1]])) as mocked_method:
data = np.array([[10, 5], [15, 7], [12, 6]])
encoded_data = task_func(data)
mocked_method.assert_called_once()
decoded_data = base64.b64decode(encoded_data).decode('ascii')
self.assertIn('[[0 0]\n [0 0]\n [1 1]\n [1 1]]', decoded_data)
def test_varied_data_sets(self):
# This will cycle through various datasets and ensure they're processed without error
datasets = [
np.array([[10, 5], [15, 7], [12, 6]]),
np.array([[25, 30], [35, 40], [45, 50]]),
np.array([[-5, -10], [-15, -20], [-25, -30]]),
np.array([[0.5, 0.7], [0.9, 1.1], [1.3, 1.5]])
]
for data in datasets:
encoded_data = task_func(data)
self.assertIsInstance(encoded_data, str)
decoded_data = base64.b64decode(encoded_data).decode('ascii')
self.assertTrue(decoded_data.startswith('[[') and decoded_data.endswith(']]'))
Proposed solution
```python
def task_func(data):
"""
Standardize a numeric array using sklearn's StandardScaler and encode the standardized data in base64 format as an ASCII string.
Parameters:
- data (numpy.ndarray): The numpy array to standardize and encode.
Returns:
- str: The base64-encoded ASCII string representation of the standardized data.
Requirements:
- sklearn.preprocessing.StandardScaler
- numpy
- base64
Example:
>>> data = np.array([[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]])
>>> encoded_data = task_func(data)
>>> print(encoded_data)
W1stMS4gLTEuXQogWy0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
"""
```markdown
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
```markdown
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
Hogxt: 0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=
HTest output (stdout)
FFF [100%]
=================================== FAILURES ===================================
_______________ TestCases.test_output_is_string_and_valid_base64 _______________
self = <test_bcb_0029.TestCases testMethod=test_output_is_string_and_valid_base64>
def test_output_is_string_and_valid_base64(self):
# Check that the function returns a valid base64 string.
data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]])
encoded_data = task_func(data)
> self.assertIsInstance(encoded_data, str)
E AssertionError: None is not an instance of <class 'str'>
test_bcb_0029.py:15: AssertionError
_______________________ TestCases.test_varied_data_sets ________________________
self = <test_bcb_0029.TestCases testMethod=test_varied_data_sets>
def test_varied_data_sets(self):
# This will cycle through various datasets and ensure they're processed without error
datasets = [
np.array([[10, 5], [15, 7], [12, 6]]),
np.array([[25, 30], [35, 40], [45, 50]]),
np.array([[-5, -10], [-15, -20], [-25, -30]]),
np.array([[0.5, 0.7], [0.9, 1.1], [1.3, 1.5]])
]
for data in datasets:
encoded_data = task_func(data)
> self.assertIsInstance(encoded_data, str)
E AssertionError: None is not an instance of <class 'str'>
test_bcb_0029.py:39: AssertionError
______________________ TestCases.test_with_mocked_scaler _______________________
self = <test_bcb_0029.TestCases testMethod=test_with_mocked_scaler>
def test_with_mocked_scaler(self):
# Mock StandardScaler to control the standardized output and check interaction
with patch('sklearn.preprocessing.StandardScaler.fit_transform', return_value=np.array([[0, 0], [0, 0], [1, 1], [1, 1]])) as mocked_method:
data = np.array([[10, 5], [15, 7], [12, 6]])
encoded_data = task_func(data)
> mocked_method.assert_called_once()
test_bcb_0029.py:26:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <MagicMock name='fit_transform' id='132138175091792'>
def assert_called_once(self):
"""assert that the mock was called only once.
"""
if not self.call_count == 1:
msg = ("Expected '%s' to have been called once. Called %s times.%s"
% (self._mock_name or 'mock',
self.call_count,
self._calls_repr()))
> raise AssertionError(msg)
E AssertionError: Expected 'fit_transform' to have been called once. Called 0 times.
/home/psc/miniconda3/envs/peakstone-bcb/lib/python3.10/unittest/mock.py:908: AssertionError
=========================== short test summary info ============================
FAILED test_bcb_0029.py::TestCases::test_output_is_string_and_valid_base64 - ...
FAILED test_bcb_0029.py::TestCases::test_varied_data_sets - AssertionError: N...
FAILED test_bcb_0029.py::TestCases::test_with_mocked_scaler - AssertionError:...
3 failed in 0.19s