bcb-0029
1.000
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
import numpy as np
import base64
from sklearn.preprocessing import StandardScaler
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=
"""
scaler = StandardScaler()
standardized_data = scaler.fit_transform(data)
return base64.b64encode(str(standardized_data).encode('ascii')).decode('ascii')
```Test output (stdout)
... [100%]
3 passed in 0.16s