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question:**Objective:** Demonstrate your ability to configure and manage logging in a Python application using the `logging.config` module. Problem Statement You are tasked with creating a logging configuration for a Python application. Your logging setup needs to meet the following requirements: 1. Use the `dictConfig` function to configure logging from a dictionary. 2. The configuration must include: - Two handlers: a console handler that logs messages to the console and a file handler that logs messages to a file named `app.log`. - Custom formatters for each of the handlers. - A logger named `myapp` that uses both handlers. 3. The logger `myapp` should log all messages at the INFO level or higher. 4. The configuration should handle possible errors in the logging setup gracefully. If any configuration error occurs, print out an appropriate error message. Requirements 1. Implement a function `setup_logging(config: dict) -> None` which takes a logging configuration dictionary as input and configures the logging system. 2. Implement a function `main() -> None` as the entry point that: - Calls `setup_logging` with the correct dictionary configuration. - Logs messages at different levels (INFO, WARNING, ERROR) using the `myapp` logger. 3. Ensure that your logging configuration matches the given requirements. 4. Handle configuration errors by catching exceptions and printing a descriptive error message. Input - A dictionary defining the logging configuration. Output - Correctly configured logging that outputs to both console and file. Example Here’s an example dictionary configuration to help you get started: ```python logging_config = { 'version': 1, 'formatters': { 'console': { 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' }, 'file': { 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' } }, 'handlers': { 'console': { 'class': 'logging.StreamHandler', 'formatter': 'console', 'level': 'INFO' }, 'file': { 'class': 'logging.FileHandler', 'formatter': 'file', 'level': 'INFO', 'filename': 'app.log', 'mode': 'a' } }, 'loggers': { 'myapp': { 'handlers': ['console', 'file'], 'level': 'INFO', 'propagate': False } } } def setup_logging(config): import logging.config try: logging.config.dictConfig(config) except (ValueError, TypeError, AttributeError, ImportError) as e: print(f"Error configuring logging: {e}") def main(): setup_logging(logging_config) logger = logging.getLogger('myapp') logger.info("This is an info message") logger.warning("This is a warning message") logger.error("This is an error message") if __name__ == "__main__": main() ``` Constraints - Do not hard-code paths or environment specifics. Focus on using the provided configuration schema and handling errors appropriately. - The logging should be demonstrated by logging messages in different levels as shown in the `main` function. Happy coding!
answer:import logging.config def setup_logging(config): Sets up logging configuration from a dictionary. :param config: dict, logging configuration dictionary. try: logging.config.dictConfig(config) except (ValueError, TypeError, AttributeError, ImportError) as e: print(f"Error configuring logging: {e}") def main(): logging_config = { 'version': 1, 'formatters': { 'console': { 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' }, 'file': { 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' } }, 'handlers': { 'console': { 'class': 'logging.StreamHandler', 'formatter': 'console', 'level': 'INFO' }, 'file': { 'class': 'logging.FileHandler', 'formatter': 'file', 'level': 'INFO', 'filename': 'app.log', 'mode': 'a' } }, 'loggers': { 'myapp': { 'handlers': ['console', 'file'], 'level': 'INFO', 'propagate': False } } } setup_logging(logging_config) logger = logging.getLogger('myapp') logger.info("This is an info message") logger.warning("This is a warning message") logger.error("This is an error message") if __name__ == "__main__": main()
question:**Question:** # Create a Typed Dictionary Using Python's Typing and Validate Using Doctest You are required to create a Python class that makes use of typed dictionaries and type hints. Additionally, you will use `doctest` to write tests for your class. # Requirements: 1. **TypedDict**: Create a `TypedDict` class `PersonDict` with the following fields: - `name`: a non-empty string - `age`: a non-negative integer - `email`: a string containing a valid email format 2. **Class**: Create a class `Person` that initializes an instance using a `PersonDict`. It should include methods to: - Get the person's information as a formatted string. - Update the person's email, ensuring the new email is valid. 3. **Validations**: Ensure the following when initializing and updating the instance: - The `name` should not be empty. - The `age` should be a non-negative integer. - The `email` should be in a valid email format (you can use a simple regex for validation). 4. **Testing with Doctest**: - Write a `doctest` at the end of your class file to validate the class functionality. - Ensure that the `doctest` runs without errors when executed with Python's `doctest` module. # Implementation Details: - **TypedDict**: ```python from typing import TypedDict class PersonDict(TypedDict): name: str age: int email: str ``` - **Class Definition**: ```python import re class Person: EMAIL_REGEX = r'^[a-z0-9]+[._]?[a-z0-9]+[@]w+[.]w+' def __init__(self, person_dict: PersonDict): self.name = person_dict['name'] self.age = person_dict['age'] self.email = person_dict['email'] self._validate() def _validate(self): if not self.name: raise ValueError("Name cannot be empty") if self.age < 0: raise ValueError("Age cannot be negative") if not re.match(self.EMAIL_REGEX, self.email): raise ValueError("Invalid email address") def get_info(self) -> str: return f"Name: {self.name}, Age: {self.age}, Email: {self.email}" def update_email(self, new_email: str): if not re.match(self.EMAIL_REGEX, new_email): raise ValueError("Invalid email address") self.email = new_email def __repr__(self): return self.get_info() ``` - **Doctest**: ```python if __name__ == "__main__": import doctest doctest.testmod() ``` - **Example Usage**: ```python >>> person_data = {"name": "Alice", "age": 30, "email": "[email protected]"} >>> alice = Person(person_data) >>> print(alice) Name: Alice, Age: 30, Email: [email protected] >>> alice.update_email("[email protected]") >>> print(alice) Name: Alice, Age: 30, Email: [email protected] >>> alice.update_email("invalid-email") Traceback (most recent call last): ... ValueError: Invalid email address >>> invalid_person_data = {"name": "", "age": -1, "email": "invalid-email"} >>> Person(invalid_person_data) Traceback (most recent call last): ... ValueError: Name cannot be empty >>> Person({"name": "Bob", "age": -1, "email": "[email protected]"}) Traceback (most recent call last): ... ValueError: Age cannot be negative ``` # Constraints: - Use `TypedDict` from the `typing` module. - Use regular expressions for email validation. - Use `doctest` for validating your class. # Performance Requirements: - Ensure that the class handles invalid inputs gracefully by raising appropriate exceptions. - The `doctest` should run without any errors when executed. Implement the `Person` class according to the requirements and ensure all `doctest` cases pass.
answer:from typing import TypedDict import re class PersonDict(TypedDict): name: str age: int email: str class Person: EMAIL_REGEX = r'^[a-z0-9]+[._]?[a-z0-9]+[@]w+[.]w+' def __init__(self, person_dict: PersonDict): self.name = person_dict['name'] self.age = person_dict['age'] self.email = person_dict['email'] self._validate() def _validate(self): if not self.name: raise ValueError("Name cannot be empty") if self.age < 0: raise ValueError("Age cannot be negative") if not re.match(self.EMAIL_REGEX, self.email): raise ValueError("Invalid email address") def get_info(self) -> str: return f"Name: {self.name}, Age: {self.age}, Email: {self.email}" def update_email(self, new_email: str): if not re.match(self.EMAIL_REGEX, new_email): raise ValueError("Invalid email address") self.email = new_email def __repr__(self): return self.get_info() if __name__ == "__main__": import doctest doctest.testmod()
question:Coding Assessment Question # Objective Implement a custom image type detection function and integrate it with the deprecated `imghdr` module to detect a new, hypothetical image format named "hypothetical format" (with a file extension '.hyp'). # Task You are required to: 1. Implement a function `detect_hypothetical_format(h, f)` that identifies if a given byte stream or file corresponds to the "hypothetical format". 2. Integrate this function with the `imghdr` module so that `imghdr.what()` can identify files of the hypothetical format. # Specifications 1. The 'hypothetical format' files have a unique signature: - The first 4 bytes of the file are `b'x89HYP'`. - The next 2 bytes are the width of the image. - The following 2 bytes are the height of the image. - The rest of the file contains pixel data. 2. Implement the function `detect_hypothetical_format(h, f)`: - Parameters: - `h` (bytes): A byte stream to test. If `None`, the function should read from the file. - `f` (file-like object): An open file object. If `None`, the function should rely on the byte stream. - Returns: - A string 'hypothetical' if the test is successful. - `None` if the test fails. 3. Extend `imghdr.tests` with the new `detect_hypothetical_format` function. # Constraints 1. Your function should correctly handle both file objects and byte streams. 2. You must ensure that the detection function does not raise exceptions for invalid inputs or non-hypothetical format files. # Input 1. A path to an image file. 2. A byte stream of image data. # Expected Output A string that identifies the image format (including the new 'hypothetical' type) or `None` if the image format is unrecognized. # Example Here is an example of how the function might be used: ```python import imghdr # Extend imghdr to recognize 'hypothetical' format imghdr.tests.append(detect_hypothetical_format) # Assuming 'image.hyp' is a file of the hypothetical format print(imghdr.what('image.hyp')) # Output should be 'hypothetical' # Assuming 'hypothetical_data' is a byte stream of the hypothetical format hypothetical_data = b'x89HYPx00x10x00x10' + b'x00' * 256 # Header + pixel data print(imghdr.what(None, hypothetical_data)) # Output should be 'hypothetical' ``` # Notes - Remember to handle both file objects and byte streams effectively. - Ensure your custom detection function is robust and handles edge cases gracefully. - You do not need to handle any other image formats beyond the hypothetical format in your custom function.
answer:import imghdr def detect_hypothetical_format(h, f): Detect if a given byte stream or file corresponds to the 'hypothetical format'. Parameters: h (bytes): A byte stream to test. If None, the function should read from the file. f (file-like object): An open file object. If None, the function should rely on the byte stream. Returns: str: 'hypothetical' if the test is successful. None: if the test fails. signature = b'x89HYP' try: if h: header = h[:4] elif f: header = f.read(4) else: return None if header == signature: return 'hypothetical' else: return None except Exception: return None # Integrate with the imghdr module imghdr.tests.append(detect_hypothetical_format)
question:Question: You are given a high-dimensional dataset and are tasked to perform dimensionality reduction using both Gaussian and sparse random projections. The goal is to reduce to a specified number of dimensions and validate the approximation quality. # Requirements: 1. Implement a function `dimensionality_reduction` that takes in the dataset, the method of projection (either `"gaussian"` or `"sparse"`), and the target number of dimensions. 2. The function should return the transformed dataset. 3. Implement a function `validate_inverse_transform` that checks the quality of the dimensionally-reduced data approximation by performing an inverse transform and comparing it to the original data. 4. The function should return the approximation error as the mean squared error between the original and the recovered dataset. # Expected Input and Output: ```python import numpy as np from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection from sklearn.metrics import mean_squared_error def dimensionality_reduction(X: np.ndarray, method: str, n_components: int) -> np.ndarray: Args: - X: np.ndarray of shape (n_samples, n_features), input data - method: str, either "gaussian" or "sparse" specifying the type of random projection to use - n_components: int, the target number of dimensions Returns: - X_transformed: np.ndarray of shape (n_samples, n_components), the dimensionally reduced data # Your code here def validate_inverse_transform(X: np.ndarray, X_transformed: np.ndarray, method: str, n_components: int) -> float: Args: - X: np.ndarray of shape (n_samples, n_features), original input data - X_transformed: np.ndarray of shape (n_samples, n_components), transformed data - method: str, either "gaussian" or "sparse" specifying the type of random projection to use - n_components: int, the target number of dimensions Returns: - error: float, mean squared error between the original data and the recovered data after inverse transform # Your code here # Example usage: X = np.random.rand(100, 10000) method = "sparse" n_components = 100 X_transformed = dimensionality_reduction(X, method, n_components) error = validate_inverse_transform(X, X_transformed, method, n_components) print("Approximation error:", error) ``` # Constraints: - Ensure the `compute_inverse_components` parameter is set to `True` for the transformer to enable inverse transform calculations. - Validate results using mean squared error to quantify the approximation quality. - Pay attention to the potential high memory usage for dense matrices during inverse transformations. # Evaluation: - Correct and efficient implementation of both functions. - Accurate calculation and return of the mean squared error for validation.
answer:import numpy as np from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection from sklearn.metrics import mean_squared_error def dimensionality_reduction(X: np.ndarray, method: str, n_components: int) -> np.ndarray: Perform dimensionality reduction using Gaussian or sparse random projection. Args: - X: np.ndarray of shape (n_samples, n_features), input data - method: str, either "gaussian" or "sparse" specifying the type of random projection to use - n_components: int, the target number of dimensions Returns: - X_transformed: np.ndarray of shape (n_samples, n_components), the dimensionally reduced data if method == "gaussian": transformer = GaussianRandomProjection(n_components=n_components) elif method == "sparse": transformer = SparseRandomProjection(n_components=n_components) else: raise ValueError("Method should be either 'gaussian' or 'sparse'") X_transformed = transformer.fit_transform(X) return X_transformed, transformer def validate_inverse_transform(X: np.ndarray, X_transformed: np.ndarray, method: str, transformer) -> float: Validate the quality of dimensionally reduced data by performing an inverse transform and comparing it to the original data. Args: - X: np.ndarray of shape (n_samples, n_features), original input data - X_transformed: np.ndarray of shape (n_samples, n_components), transformed data - method: str, either "gaussian" or "sparse" specifying the type of random projection to use - transformer: the transformer object used for the projection Returns: - error: float, mean squared error between the original data and the recovered data after inverse transform X_reconstructed = X_transformed @ transformer.components_ error = mean_squared_error(X, X_reconstructed) return error # Example usage: X = np.random.rand(100, 10000) method = "sparse" n_components = 100 X_transformed, transformer = dimensionality_reduction(X, method, n_components) error = validate_inverse_transform(X, X_transformed, method, transformer) print("Approximation error:", error)