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question:# Task Implement a class `PrCurveDisplay` that visualizes Precision-Recall curves. The class should be similar to `RocCurveDisplay` but for Precision-Recall curves. Your implementation should include methods to create the display object from an estimator and from true/predicted values, and a method to plot the generated curve. # Requirements 1. **Class Definition**: - The class should be named `PrCurveDisplay`. - The constructor should accept and store precision, recall, average precision (AP), and estimator name. 2. **Class Methods**: - `from_estimator(cls, estimator, X, y)`: This method should compute the predicted probabilities using the estimator and call `from_predictions`. - `from_predictions(cls, y, y_pred, estimator_name=None)`: This method should compute precision, recall, and average precision using `precision_recall_curve` and `average_precision_score` from `sklearn.metrics`, then create and return an instance of `PrCurveDisplay`. 3. **Instance Methods**: - `plot(self, ax=None, name=None, **kwargs)`: This method should plot the precision-recall curve using matplotlib. If `ax` is `None`, a new matplotlib axis should be created. # Input - `from_estimator(estimator, X, y)`: - `estimator`: A trained scikit-learn estimator. - `X`: A feature matrix. - `y`: True labels. - `from_predictions(y, y_pred, estimator_name=None)`: - `y`: True labels. - `y_pred`: Predicted probabilities. - `estimator_name`: Optional name of the estimator (default is `None`). - `plot(self, ax=None, name=None, **kwargs)`: - `ax`: A matplotlib axis (default is `None`). # Output - `from_estimator`: - Returns an instance of `PrCurveDisplay`. - `from_predictions`: - Returns an instance of `PrCurveDisplay`. - `plot`: - Plots the Precision-Recall curve on the provided or a new matplotlib axis. # Example ```python from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # Create a sample dataset X, y = make_classification(n_samples=1000, n_features=20, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Train a logistic regression model model = LogisticRegression().fit(X_train, y_train) # Use PrCurveDisplay to visualize the Precision-Recall curve pr_display = PrCurveDisplay.from_estimator(model, X_test, y_test) pr_display.plot() plt.show() ``` # Constraints - You should use functions exclusively from scikit-learn and matplotlib. - The computation of precision, recall, and average precision should be done using scikit-learn functions. # Notes - Ensure your code is clean and well-documented. - Handle any potential edge cases, such as when the predictions contain only one class.
answer:import matplotlib.pyplot as plt from sklearn.metrics import precision_recall_curve, average_precision_score class PrCurveDisplay: def __init__(self, precision, recall, average_precision, estimator_name=None): self.precision = precision self.recall = recall self.average_precision = average_precision self.estimator_name = estimator_name @classmethod def from_estimator(cls, estimator, X, y): y_pred = estimator.predict_proba(X)[:, 1] return cls.from_predictions(y, y_pred, estimator_name=type(estimator).__name__) @classmethod def from_predictions(cls, y, y_pred, estimator_name=None): precision, recall, _ = precision_recall_curve(y, y_pred) average_precision = average_precision_score(y, y_pred) return cls(precision, recall, average_precision, estimator_name) def plot(self, ax=None, name=None, **kwargs): if ax is None: fig, ax = plt.subplots() name = name or self.estimator_name ax.plot(self.recall, self.precision, label=f'{name} (AP={self.average_precision:0.2f})', **kwargs) ax.set_xlabel('Recall') ax.set_ylabel('Precision') ax.set_title('Precision-Recall Curve') ax.legend() return ax
question:**Title:** Pandas with PyArrow Integration **Objective:** Create pandas data structures with PyArrow-backed data, perform various operations, and efficiently read data using PyArrow. This will test your understanding of both pandas and PyArrow functionalities. **Task:** 1. **Create Data Structures:** - Create a pandas `Series` containing the floating-point numbers `[-1.5, 0.2, None]` using the `"float32[pyarrow]"` dtype. - Create a pandas `Index` containing the boolean values `[True, None]` using the `"bool[pyarrow]"` dtype. - Create a pandas `DataFrame` containing the integer pairs `[(1, 2), (3, 4)]` using the `"uint64[pyarrow]"` dtype. 2. **Perform Operations:** - Compute the mean of the created `Series` while handling missing data appropriately. - Check if the provided boolean values in the `Index` are True. - Add the integers in the first column of the `DataFrame` to those in the second column. 3. **Efficient Data Reading:** - Use PyArrow to read a CSV data provided as a string, returning a `DataFrame` with PyArrow-backed data. The data string is: ``` a,b,c 1,2.5,True 3,4.5,False ``` **Input and Output Specifications:** - Function: `def pandas_with_pyarrow_operations() -> dict:` - The function should create the required pandas data structures, perform the specified operations, and read data using PyArrow. - The function should return a dictionary with the following keys and values: - `"mean_series"`: The mean of the Series. - `"index_is_true"`: A list of boolean values indicating whether each element in the Index is True. - `"sum_dataframe"`: A list of sums of corresponding pairs of integers from two columns of the DataFrame. - `"read_csv"`: The DataFrame obtained from reading the provided CSV string. **Constraints:** - Use PyArrow data types as specified. - Handle missing data appropriately in the series and index operations. - Ensure the CSV reading uses PyArrow for efficient data handling. **Example:** ```python def pandas_with_pyarrow_operations() -> dict: import pandas as pd import pyarrow as pa from io import StringIO # 1. Create Data Structures ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]") idx = pd.Index([True, None], dtype="bool[pyarrow]") df = pd.DataFrame([[1, 2], [3, 4]], dtype="uint64[pyarrow]") # 2. Perform Operations mean_series = ser.mean() index_is_true = idx == True sum_dataframe = (df[0] + df[1]).tolist() # 3. Efficient Data Reading csv_data = StringIO(a,b,c 1,2.5,True 3,4.5,False ) read_csv = pd.read_csv(csv_data, engine="pyarrow", dtype_backend="pyarrow") return { "mean_series": mean_series, "index_is_true": index_is_true.tolist(), "sum_dataframe": sum_dataframe, "read_csv": read_csv } ``` Your implementation should match this example's output format.
answer:def pandas_with_pyarrow_operations() -> dict: import pandas as pd import pyarrow as pa from io import StringIO # 1. Create Data Structures ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]") idx = pd.Index([True, None], dtype="bool[pyarrow]") df = pd.DataFrame({"A": [1, 3], "B": [2, 4]}, dtype="uint64[pyarrow]") # 2. Perform Operations mean_series = ser.mean() index_is_true = idx.isin([True]) sum_dataframe = (df["A"] + df["B"]).tolist() # 3. Efficient Data Reading csv_data = StringIO(a,b,c 1,2.5,True 3,4.5,False ) read_csv = pd.read_csv(csv_data, engine="pyarrow") return { "mean_series": mean_series, "index_is_true": index_is_true.tolist(), "sum_dataframe": sum_dataframe, "read_csv": read_csv }
question:Create a custom HTTP server using Python's `http.server` module to handle HTTP GET and POST requests. # Requirements: 1. Implement a subclass of `BaseHTTPRequestHandler` named `CustomHTTPRequestHandler`. 2. The `CustomHTTPRequestHandler` class should handle HTTP GET and POST requests: - For GET requests, respond with a simple HTML form that allows the user to submit data via a POST request. - For POST requests, read the data submitted from the form and respond with a confirmation message that includes the submitted data. 3. Set up the server to use the `CustomHTTPRequestHandler` class. 4. The server should run on port `8080`. # Constraints: - Ensure the server handles requests efficiently and closes connections appropriately. - Validate the POST request data and handle any potential errors gracefully. # Example Usage: When a user makes a GET request to the server (`http://localhost:8080`), they should see an HTML form similar to this: ```html <!doctype html> <html> <body> <form action="/" method="POST"> <label for="name">Name:</label> <input type="text" id="name" name="name"> <input type="submit" value="Submit"> </form> </body> </html> ``` When the form is submitted, the server should respond with a confirmation message containing the submitted data, for example: ``` Received POST request: Name: John Doe ``` # Expected Input and Output: Submitting a GET request to the server **URL:** `http://localhost:8080` **Method:** `GET` **Response:** ``` <!doctype html> <html> <body> <form action="/" method="POST"> <label for="name">Name:</label> <input type="text" id="name" name="name"> <input type="submit" value="Submit"> </form> </body> </html> ``` Submitting a POST request to the server **URL:** `http://localhost:8080` **Method:** `POST` **Form Data:** ``` name=John Doe ``` **Response:** ``` Received POST request: Name: John Doe ``` # Implementation Details: - Use the `BaseHTTPRequestHandler` class to create the custom request handler. - Use the `HTTPServer` class to set up and run the server. # Submission: Submit your implementation of the `CustomHTTPRequestHandler` class and the code to set up and run the server. ```python import http.server from urllib.parse import parse_qs from http.server import HTTPServer, BaseHTTPRequestHandler class CustomHTTPRequestHandler(BaseHTTPRequestHandler): def do_GET(self): form_html = ''' <!doctype html> <html> <body> <form action="/" method="POST"> <label for="name">Name:</label> <input type="text" id="name" name="name"> <input type="submit" value="Submit"> </form> </body> </html> ''' self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() self.wfile.write(form_html.encode('utf-8')) def do_POST(self): content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length).decode('utf-8') post_params = parse_qs(post_data) name = post_params.get('name', [''])[0] response = f''' Received POST request:<br> Name: {name} ''' self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() self.wfile.write(response.encode('utf-8')) def run(server_class=HTTPServer, handler_class=CustomHTTPRequestHandler, port=8080): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Serving at port {port}') httpd.serve_forever() if __name__ == "__main__": run() ```
answer:import http.server from urllib.parse import parse_qs from http.server import HTTPServer, BaseHTTPRequestHandler class CustomHTTPRequestHandler(BaseHTTPRequestHandler): def do_GET(self): form_html = ''' <!doctype html> <html> <body> <form action="/" method="POST"> <label for="name">Name:</label> <input type="text" id="name" name="name"> <input type="submit" value="Submit"> </form> </body> </html> ''' self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() self.wfile.write(form_html.encode('utf-8')) def do_POST(self): content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length).decode('utf-8') post_params = parse_qs(post_data) name = post_params.get('name', [''])[0] response = f''' <!doctype html> <html> <body> <p>Received POST request:</p> <p>Name: {name}</p> </body> </html> ''' self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() self.wfile.write(response.encode('utf-8')) def run(server_class=HTTPServer, handler_class=CustomHTTPRequestHandler, port=8080): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Serving at port {port}') httpd.serve_forever() if __name__ == "__main__": run()
question:You need to develop a function that reads a Sun AU audio file, extracts specific details about the audio, and then writes a new Sun AU audio file with modified parameters. # Objective 1. Read an input Sun AU audio file. 2. Extract the following details: - Number of Channels - Sample Width - Frame Rate - Number of Frames - Compression Type 3. Write a new Sun AU audio file with modified frame rate (double the original frame rate) using the extracted details and the original audio data. # Function Signature ```python def process_and_modify_sunau(input_file: str, output_file: str) -> None: pass ``` # Input - `input_file` (str): The path to the input Sun AU audio file. - `output_file` (str): The path where the new Sun AU audio file will be saved. # Output - None (The function should save the new audio file to the specified `output_file` path.) # Constraints & Requirements - Use the `sunau` module to handle reading and writing of the Sun AU files. - Only change the frame rate to be double the original frame rate; other parameters should remain the same. - Handle exceptions and ensure files are properly closed after operations. - Assume the `input_file` is a valid Sun AU file format. # Example Usage ```python # Before running the function, you have an input AU file at 'input.au' process_and_modify_sunau('input.au', 'output.au') # After running the function, the 'output.au' file should exist with the modified frame rate. ``` # Notes - Pay attention to handling the file modes correctly (`'r'` and `'w'`). - Maintain the integrity of the audio data while performing modifications.
answer:import sunau def process_and_modify_sunau(input_file: str, output_file: str) -> None: Read an input Sun AU audio file, extract its details, and write a new Sun AU audio file with modified frame rate (double the original frame rate). :param input_file: str - Path to the input Sun AU audio file :param output_file: str - Path where the new Sun AU audio file will be saved try: with sunau.open(input_file, 'r') as input_au: n_channels = input_au.getnchannels() sample_width = input_au.getsampwidth() frame_rate = input_au.getframerate() n_frames = input_au.getnframes() comp_type = input_au.getcomptype() comp_name = input_au.getcompname() audio_data = input_au.readframes(n_frames) # Update the frame rate to double the original frame rate new_frame_rate = frame_rate * 2 with sunau.open(output_file, 'w') as output_au: output_au.setnchannels(n_channels) output_au.setsampwidth(sample_width) output_au.setframerate(new_frame_rate) output_au.setnframes(n_frames) output_au.setcomptype(comp_type, comp_name) output_au.writeframes(audio_data) except Exception as e: print(f"An error occurred: {e}")