bcb-0029

[
  {
    "total": 3,
    "answer": "```python\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\nimport base64\n\ndef task_func(data):\n    \"\"\"\n    Standardize a numeric array using sklearn's StandardScaler and encode the standardized data in base64 format as an ASCII string.\n    \n    Parameters:\n    - data (numpy.ndarray): The numpy array to standardize and encode.\n    \n    Returns:\n    - str: The base64-encoded ASCII string representation of the standardized data.\n    \n    Requirements:\n    - sklearn.preprocessing.StandardScaler\n    - numpy\n    - base64\n    \n    Example:\n    >>> data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]])\n    >>> encoded_data = task_func(data)\n    >>> print(encoded_data)\n    W1stMS4gLTEuXQogWy0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=\n    \"\"\"\n    # Standardize the data\n    scaler = StandardScaler()\n    standardized_data = scaler.fit_transform(data)\n    \n    # Convert to string representation and encode in base64\n    data_str = str(standardized_data.tolist())\n    encoded_data = base64.b64encode(data_str.encode('ascii')).decode('ascii')\n    \n    return encoded_data\n```",
    "passed": 2,
    "test_error": "..F                                                                      [100%]\n=================================== FAILURES ===================================\n______________________ TestCases.test_with_mocked_scaler _______________________\n\nself = <test_bcb_0029.TestCases testMethod=test_with_mocked_scaler>\n\n    def test_with_mocked_scaler(self):\n        # Mock StandardScaler to control the standardized output and check interaction\n        with patch('sklearn.preprocessing.StandardScaler.fit_transform', return_value=np.array([[0, 0], [0, 0], [1, 1], [1, 1]])) as mocked_method:\n            data = np.array([[10, 5], [15, 7], [12, 6]])\n            encoded_data = task_func(data)\n            mocked_method.assert_called_once()\n            decoded_data = base64.b64decode(encoded_data).decode('ascii')\n>           self.assertIn('[[0 0]\\n [0 0]\\n [1 1]\\n [1 1]]', decoded_data)\nE           AssertionError: '[[0 0]\\n [0 0]\\n [1 1]\\n [1 1]]' not found in '[[0, 0], [0, 0], [1, 1], [1, 1]]'\n\ntest_bcb_0029.py:28: AssertionError\n=========================== short test summary info ============================\nFAILED test_bcb_0029.py::TestCases::test_with_mocked_scaler - AssertionError:...\n1 failed, 2 passed in 0.18s"
  },
  {
    "total": 3,
    "answer": "Looking at the test failure, I can see the issue. The test expects the string representation to use newlines and spaces like `[[0 0]\\n [0 0]\\n [1 1]\\n [1 1]]` but my implementation is using the default numpy array string representation which uses commas and spaces like `[[0, 0], [0, 0], [1, 1], [1, 1]]`.\n\nThe problem is in how I'm converting the numpy array to string. I need to use a format that matches what the test expects.\n\nLet me fix this:\n\n```python\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\nimport base64\n\ndef task_func(data):\n    \"\"\"\n    Standardize a numeric array using sklearn's StandardScaler and encode the standardized data in base64 format as an ASCII string.\n    \n    Parameters:\n    - data (numpy.ndarray): The numpy array to standardize and encode.\n    \n    Returns:\n    - str: The base64-encoded ASCII string representation of the standardized data.\n    \n    Requirements:\n    - sklearn.preprocessing.StandardScaler\n    - numpy\n    - base64\n    \n    Example:\n    >>> data = np.array([[0, 0], [0, 0], [1, 1], [1, 1]])\n    >>> encoded_data = task_func(data)\n    >>> print(encoded_data)\n    W1stMS4gLTEuXQogWy0xLiAtMS5dCiBbIDEuICAxLl0KIFsgMS4gIDEuXV0=\n    \"\"\"\n    # Standardize the data\n    scaler = StandardScaler()\n    standardized_data = scaler.fit_transform(data)\n    \n    # Convert to string representation with proper formatting\n    # Use a format that matches what the test expects\n    data_str = np.array2string(standardized_data, separator=' ', suppress_small=True)\n    \n    # Encode in base64\n    encoded_data = base64.b64encode(data_str.encode('ascii')).decode('ascii')\n    \n    return encoded_data\n```",
    "passed": 3,
    "test_error": ""
  }
]

You are an expert programmer. Solve the task exactly as specified. Output your solution as fenced code blocks using the required file name(s) and the exact function/type signatures requested. Prefer correctness; do not include prose outside code unless asked.