Appearance
question:**Objective:** Create a custom SMTP server by extending the `smtpd.SMTPServer` class in Python. Your server should process incoming email messages and perform specific actions based on their contents. **Task:** 1. Create a class `CustomSMTPServer` that extends `smtpd.SMTPServer`. 2. Override the `process_message` method to process incoming emails. **Specifications:** 1. **Initialization:** - The server should bind to local address `('localhost', 1025)` and relay to remote address `None`. - It should have a `data_size_limit` of 1MB. 2. **process_message Method:** - Check if the email subject contains the keyword "ALERT". - If "ALERT" is found, print the email's sender, recipients, and body to the stdout. - Otherwise, simply print "No alert found in the email." **Input:** - Emails are sent to the server following the SMTP protocol. **Output:** - Depending on the email subject, output relevant information as described above. **Constraints:** - Handle maximum email data size of up to 1MB. - Ensure that only one of `decode_data` or `enable_SMTPUTF8` is set to True. **Performance Requirements:** - The server should handle incoming email data efficiently, providing output promptly. **Example Usage:** ```python import smtpd import asyncore class CustomSMTPServer(smtpd.SMTPServer): def process_message(self, peer, mailfrom, rcpttos, data, **kwargs): subject = self.extract_subject(data) if "ALERT" in subject: print(f"Sender: {mailfrom}") print(f"Recipients: {', '.join(rcpttos)}") print(f"Body: {data}") else: print("No alert found in the email.") def extract_subject(self, data): for line in data.split("n"): if line.startswith("Subject:"): return line[len("Subject: "):].strip() return "" if __name__ == "__main__": server = CustomSMTPServer(('localhost', 1025), None, data_size_limit=1048576) asyncore.loop() ``` In this example, the `CustomSMTPServer` class is created that extends the `smtpd.SMTPServer`. The `process_message` method is overridden to check the subject of incoming emails for the keyword "ALERT" and output relevant information if found.
answer:import smtpd import asyncore class CustomSMTPServer(smtpd.SMTPServer): def __init__(self, localaddr, remoteaddr, **kwargs): kwargs['data_size_limit'] = 1048576 # 1MB size limit super().__init__(localaddr, remoteaddr, **kwargs) def process_message(self, peer, mailfrom, rcpttos, data, **kwargs): subject = self.extract_subject(data) if "ALERT" in subject: print(f"Sender: {mailfrom}") print(f"Recipients: {', '.join(rcpttos)}") print(f"Body: {data}") else: print("No alert found in the email.") def extract_subject(self, data): for line in data.split("n"): if line.startswith("Subject:"): return line[len("Subject: "):].strip() return "" if __name__ == "__main__": server = CustomSMTPServer(('localhost', 1025), None, decode_data=True, enable_SMTPUTF8=False) asyncore.loop()
question:# Asyncio Coding Challenge You have been tasked to implement a simple echo server using Python's asyncio library. The server should be able to handle multiple client connections concurrently, echoing back any messages received from a client to the same client. # Requirements 1. **Echo Server Implementation:** - Create an `EchoServer` class. - The server should accept connections on a specified host and port. - When a client sends a message, the server should echo the message back to the same client. - The server should handle multiple clients simultaneously. 2. **Event Loop Management:** - Use the appropriate asyncio methods to create and run the event loop. 3. **Protocol Implementation:** - Use transport and protocol for managing client connections. - Implement the following protocol methods: - `connection_made` - `data_received` - `connection_lost` 4. **Asynchronous Communication:** - Ensure that the communication with each client is handled asynchronously. # Additional Constraints - You are not allowed to use higher-level abstractions from asyncio such as `asyncio.run()`, `asyncio.start_server()`, or `asyncio.StreamReader/StreamWriter`. # Expected Behavior - The server starts listening on `localhost` port `8888`. - Multiple telnet clients can connect to `localhost 8888` and send messages. - The server echoes any message received back to the respective client. - The server can handle multiple clients simultaneously without blocking. # Example Usage 1. Start your server: ```python server = EchoServer('localhost', 8888) server.start() ``` 2. In a terminal, connect to your server using telnet: ```sh telnet localhost 8888 ``` 3. Type a message and observe that it is echoed back. # Implementation ```python import asyncio class EchoServerProtocol(asyncio.Protocol): def connection_made(self, transport): self.transport = transport peername = transport.get_extra_info('peername') print(f"Connection from {peername}") def data_received(self, data): message = data.decode() print(f"Data received: {message}") # Echo back the received message self.transport.write(data) print(f"Data echoed back to the client") def connection_lost(self, exc): print("Connection closed") class EchoServer: def __init__(self, host, port): self.host = host self.port = port async def start_server(self): loop = asyncio.get_running_loop() server = await loop.create_server( lambda: EchoServerProtocol(), self.host, self.port ) async with server: await server.serve_forever() def start(self): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(self.start_server()) # To run the server, uncomment the following lines: # if __name__ == "__main__": # server = EchoServer('localhost', 8888) # server.start() ```
answer:import asyncio class EchoServerProtocol(asyncio.Protocol): def connection_made(self, transport): self.transport = transport peername = transport.get_extra_info('peername') print(f"Connection from {peername}") def data_received(self, data): message = data.decode() print(f"Data received: {message}") # Echo back the received message self.transport.write(data) print(f"Data echoed back to the client") def connection_lost(self, exc): print("Connection closed or lost") class EchoServer: def __init__(self, host, port): self.host = host self.port = port async def start_server(self): loop = asyncio.get_running_loop() server = await loop.create_server( lambda: EchoServerProtocol(), self.host, self.port ) async with server: await server.serve_forever() def start(self): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) loop.run_until_complete(self.start_server()) # To run the server, uncomment the following lines: # if __name__ == "__main__": # server = EchoServer('localhost', 8888) # server.start()
question:**Objective:** Implement a custom pipeline that demonstrates the use of multiple dataset transformers from the scikit-learn library. **Problem Description:** You are provided with a dataset containing numerical and categorical features. Your task is to design a pipeline using scikit-learn that includes the following steps: 1. **Impute Missing Values**: For numerical features, impute missing values using the mean. For categorical features, impute missing values using the most frequent value. 2. **Standardize Numerical Features**: Normalize numerical features to have zero mean and unit variance. 3. **One-Hot Encode Categorical Features**: Convert categorical features into a one-hot encoded format. 4. **Principal Component Analysis (PCA)**: Apply PCA to reduce the dimensionality of the dataset to 2 principal components. **Input:** - A dataset containing numerical and categorical features. - A list of columns indicating which features are numerical and which are categorical. **Output:** - A transformed dataset with imputation, standardization, one-hot encoding, and PCA applied. **Constraints:** - Use scikit-learn transformers and pipeline functionalities. - Handle any potential errors such as missing columns or incorrect data types. - Ensure that the resulting dataset has exactly 2 principal components. **Performance Requirements:** - The solution should be efficient and capable of handling large datasets (e.g., 10,000 samples with 100 features). **Code Template:** ```python import pandas as pd from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline def transform_dataset(dataset: pd.DataFrame, numerical_features: list, categorical_features: list) -> pd.DataFrame: # Define transformers numerical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()) ]) categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Combine transformers into a preprocessor preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer, numerical_features), ('cat', categorical_transformer, categorical_features) ] ) # Create the full pipeline pipeline = Pipeline(steps=[ ('preprocessor', preprocessor), ('pca', PCA(n_components=2)) ]) # Fit and transform the dataset transformed_dataset = pipeline.fit_transform(dataset) # Convert the result to a DataFrame return pd.DataFrame(transformed_dataset, columns=['PC1', 'PC2']) # Example usage: # dataset = pd.DataFrame({'num1': [1, 2, np.nan], 'num2': [4, 5, 6], 'cat1': ['a', 'b', 'a']}) # numerical_features = ['num1', 'num2'] # categorical_features = ['cat1'] # transformed_dataset = transform_dataset(dataset, numerical_features, categorical_features) # print(transformed_dataset) ``` **Explanation:** 1. Define pipelines for transforming numerical and categorical data. 2. Combine these pipelines using a `ColumnTransformer`. 3. Integrate the `ColumnTransformer` with PCA in a final pipeline. 4. Fit and transform the input dataset using this pipeline. 5. Return the transformed dataset as a DataFrame with 2 principal components. Given the provided template, your task is to complete and execute it, ensuring that it runs correctly on the example usage.
answer:import pandas as pd import numpy as np from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline def transform_dataset(dataset: pd.DataFrame, numerical_features: list, categorical_features: list) -> pd.DataFrame: # Define transformers numerical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()) ]) categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Combine transformers into a preprocessor preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer, numerical_features), ('cat', categorical_transformer, categorical_features) ] ) # Create the full pipeline pipeline = Pipeline(steps=[ ('preprocessor', preprocessor), ('pca', PCA(n_components=2)) ]) # Fit and transform the dataset transformed_dataset = pipeline.fit_transform(dataset) # Convert the result to a DataFrame return pd.DataFrame(transformed_dataset, columns=['PC1', 'PC2']) # Example usage: # dataset = pd.DataFrame({'num1': [1, 2, np.nan], 'num2': [4, 5, 6], 'cat1': ['a', 'b', 'a']}) # numerical_features = ['num1', 'num2'] # categorical_features = ['cat1'] # transformed_dataset = transform_dataset(dataset, numerical_features, categorical_features) # print(transformed_dataset)
question:# Custom Fixer Implementation in 2to3 Background `2to3` is an automated tool that assists in converting Python 2.x code to Python 3.x by applying a series of predefined transformations, known as fixers. While `2to3` comes with a comprehensive set of built-in fixers, it is also flexible enough to allow the creation of custom fixers tailored to specific needs. Your task is to implement a custom fixer that converts the Python 2 `print` statement into the Python 3 `print()` function. Specifically, this fixer should ensure that all instances of `print` statements are correctly changed to `print()` function calls, including handling scenarios with multiple arguments and string formatting. Task 1. Create a custom fixer named `fix_print` that converts the Python 2 `print` statement to the Python 3 `print()` function call. 2. The fixer should be able to handle different forms of `print` statements, such as: - `print "Hello, World"` - `print "Value:", x` - `print "Sum: %d" % total` - `print a, b, c` 3. Ensure your fixer maintains correct syntax and handles various edge cases, such as comments and line continuations. 4. Provide test cases in Python 2.x code format and demonstrate how your fixer transforms them to Python 3.x code. Implementation Details 1. Your implementation should use the `lib2to3` library and specifically subclass the `fix_base.FixerBase` class to define the custom fixer. 2. Use the appropriate parsing and pattern-matching utilities provided by `lib2to3` to identify and transform `print` statements. 3. Implement unit tests to verify the correctness of your fixer. Input - A Python 2.x source file containing `print` statements. Output - The transformed Python 3.x source code after applying the custom `fix_print` fixer. Constraints - The code should run within reasonable time limits for typical source files (up to a few hundreds of lines of code). - Ensure compatibility with both Python 2.7 and Python 3.x environments for the transformation process. Example **Input (Python 2.x code):** ```python print "Hello, World!" print "Value:", x print "Sum: %d" % total print a, b, c ``` **Output (Python 3.x code):** ```python print("Hello, World!") print("Value:", x) print("Sum: %d" % total) print(a, b, c) ``` Provide your implementation of the `fix_print` fixer along with a demonstration of its usage.
answer:from lib2to3 import fixer_base from lib2to3.fixer_util import Name, Call, Comma class FixPrint(fixer_base.BaseFix): Custom fixer that converts Python 2 print statements to Python 3 print() function calls. PATTERN = simple_stmt< any* > | print_stmt< 'print' (not_atom< atom='>' | atom=any* >|'print'|'print_stmt')* > def transform(self, node, results): if node.children[0].value == 'print': new_node = Call(Name('print'), node.children[1:]) node.replace(new_node)