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question:# Question: Optimizing Prediction Latency and Throughput with Scikit-learn Context You are provided with a dataset consisting of predictors (features) and a target variable for a binary classification task. Your task is to: 1. Build a model using scikit-learn to predict the target variable. 2. Optimize the prediction latency and throughput of your scikit-learn model. Requirements 1. **Data Preprocessing**: - Convert the data into an appropriate format for your model. - Ensure that the data preprocessing pipeline includes handling missing values, scaling, and encoding as necessary. 2. **Model Training**: - Train a classification model of your choice from scikit-learn. - Experiment with different model complexities and configurations to balance predictive power and latency. 3. **Performance Optimization**: - Measure the prediction latency and throughput of your model. - Optimize the model for lower latency and higher throughput using techniques like bulk predictions, sparse matrix representation, and model compression. - Measure the impact of these optimizations. 4. **Evaluation**: - Report the final prediction accuracy, latency, and throughput of your optimized model. - Visualize the trade-offs between model complexity and prediction latency. Constraints - The dataset will be provided in a CSV file format: - `data.csv`: Contains the features and target variable. ```plaintext feature1, feature2, ..., featureN, target value11, value12, ..., value1N, target1 value21, value22, ..., value2N, target2 ... ``` Expected Input and Output - **Input**: A CSV file `data.csv` containing the dataset. - **Output**: - Prediction accuracy. - Average prediction latency. - Prediction throughput. - Visualizations showing the trade-offs between model complexity and prediction latency. Implementation Implement the required functions in the following steps. You can use Jupyter Notebook or any Python script to write your solution. ```python import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import SGDClassifier from sklearn.metrics import accuracy_score from sklearn.preprocessing import StandardScaler import time def load_data(file_path): Load the dataset from the given CSV file path. Args: - file_path (str): The file path to the CSV file. Returns: - X (np.ndarray): Features. - y (np.ndarray): Target variable. # Implement loading data from CSV pass def preprocess_data(X): Preprocess the dataset. Args: - X (np.ndarray): The features. Returns: - X_transformed (np.ndarray): The preprocessed features. # Implement data preprocessing steps here pass def train_model(X_train, y_train): Train the classifier model. Args: - X_train (np.ndarray): Training features. - y_train (np.ndarray): Training target variable. Returns: - model (sklearn estimator): The trained model. model = SGDClassifier(penalty='elasticnet', l1_ratio=0.25) # Train the model here pass def evaluate_performance(model, X_test, y_test): Evaluate the model's performance on the test set. Args: - model (sklearn estimator): The trained model. - X_test (np.ndarray): Test features. - y_test (np.ndarray): Test target variable. Returns: - accuracy (float): The prediction accuracy. - latency (float): Average prediction latency. - throughput (float): Number of predictions per second. # Implement performance evaluation here pass def main(): # Load the dataset X, y = load_data('data.csv') # Split the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Preprocess the data X_train = preprocess_data(X_train) X_test = preprocess_data(X_test) # Train the model model = train_model(X_train, y_train) # Evaluate performance accuracy, latency, throughput = evaluate_performance(model, X_test, y_test) print(f'Accuracy: {accuracy}') print(f'Average Prediction Latency: {latency} seconds') print(f'Prediction Throughput: {throughput} predictions/second') if __name__ == "__main__": main() ``` **Note**: Complete the `load_data`, `preprocess_data`, `train_model`, and `evaluate_performance` functions with the necessary code to achieve the desired outputs. Bonus (Optional) - Experiment with different machine learning models and compare their performance. - Implement additional optimizations like model sparsification and reshaping, and measure their impact on performance.
answer:import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import SGDClassifier from sklearn.metrics import accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline import time def load_data(file_path): Load the dataset from the given CSV file path. Args: - file_path (str): The file path to the CSV file. Returns: - X (np.ndarray): Features. - y (np.ndarray): Target variable. data = pd.read_csv(file_path) X = data.drop(columns=['target']).values y = data['target'].values return X, y def preprocess_data(X): Preprocess the dataset. Args: - X (np.ndarray): The features. Returns: - X_transformed (np.ndarray): The preprocessed features. pipeline = Pipeline([ ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()) ]) X_transformed = pipeline.fit_transform(X) return X_transformed def train_model(X_train, y_train): Train the classifier model. Args: - X_train (np.ndarray): Training features. - y_train (np.ndarray): Training target variable. Returns: - model (sklearn estimator): The trained model. model = SGDClassifier(penalty='elasticnet', l1_ratio=0.25, random_state=42) model.fit(X_train, y_train) return model def evaluate_performance(model, X_test, y_test): Evaluate the model's performance on the test set. Args: - model (sklearn estimator): The trained model. - X_test (np.ndarray): Test features. - y_test (np.ndarray): Test target variable. Returns: - accuracy (float): The prediction accuracy. - latency (float): Average prediction latency. - throughput (float): Number of predictions per second. start_time = time.time() predictions = model.predict(X_test) end_time = time.time() accuracy = accuracy_score(y_test, predictions) latency = (end_time - start_time) / len(X_test) throughput = len(X_test) / (end_time - start_time) return accuracy, latency, throughput def main(): # Load the dataset X, y = load_data('data.csv') # Split the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Preprocess the data X_train = preprocess_data(X_train) X_test = preprocess_data(X_test) # Train the model model = train_model(X_train, y_train) # Evaluate performance accuracy, latency, throughput = evaluate_performance(model, X_test, y_test) print(f'Accuracy: {accuracy}') print(f'Average Prediction Latency: {latency} seconds') print(f'Prediction Throughput: {throughput} predictions/second') if __name__ == "__main__": main()
question:Coding Assessment Question **Objective**: The `doctest` module in Python can be used to test code examples embedded in documentation strings. This question will test your ability to use this module to maintain up-to-date documentation, handle output variations, and manage exceptions within the examples. # Problem Statement You are given the following module `math_utils` with embedded interactive examples. Your task is to: 1. **Complete the functions** to match the expected outputs in the docstrings. 2. **Create a script** that uses `doctest` to verify and report the correctness of the examples. 3. **Handle variations in output** using appropriate option flags. 4. **Manage exceptions and ensure** they are correctly identified in the output. ```python # math_utils.py This is the "math_utils" module. The math_utils module supplies utility mathematical functions. For example, >>> gcd(48, 18) 6 >>> gcd(0, 0) 0 def gcd(a, b): Return the greatest common divisor of a and b. >>> gcd(54, 24) 6 >>> gcd(48, 180) 12 >>> gcd(0, 9) 9 >>> gcd(0, 0) # This should raise an exception as gcd(0, 0) is undefined Traceback (most recent call last): ... ValueError: gcd(0, 0) is undefined if a == 0 and b == 0: raise ValueError("gcd(0, 0) is undefined") while b != 0: a, b = b, a % b return a def normalize_whitespace(s): Normalize all whitespace in string `s` to single spaces. >>> normalize_whitespace('Hellotworld') 'Hello world' >>> normalize_whitespace('String withnmultiplenlines') 'String with multiple lines' >>> normalize_whitespace(' Extra spaces everywhere ') 'Extra spaces everywhere' import re return re.sub(r's+', ' ', s).strip() if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS) ``` # Requirements 1. **Function Implementations**: - Complete the `gcd` and `normalize_whitespace` functions to match the expected behavior. 2. **Testing Script**: - Add the given code into a module named `math_utils.py`. - Ensure the embedded examples in the docstrings pass using the `doctest` module. - Use appropriate option flags to handle whitespace normalization and ellipsis in output. 3. **Exception Handling**: - Ensure that exceptions are correctly captured and validated in the examples. 4. **Verification**: - Provide output of running the `doctest` to verify all examples are correct. # Constraints - Both functions should handle typical edge cases, e.g., `gcd(0, 9)`. - Use built-in Python libraries only. - Your solution should not use any additional external modules except `doctest`. # Performance Requirements - Your solution should execute the tests within reasonable time limits and handle typical input sizes efficiently. # Submission Requirements 1. **Source Code**: Provide the completed `math_utils.py` module. 2. **Test Output**: Include the output of running the `doctest` command with verbose mode. # Example Output ```sh python math_utils.py -v Trying: gcd(48, 18) Expecting: 6 ok Trying: gcd(54, 24) Expecting: 6 ok Trying: gcd(48, 180) Expecting: 12 ok Trying: gcd(0, 9) Expecting: 9 ok Trying: gcd(0, 0) Expecting: Traceback (most recent call last): ... ValueError: gcd(0, 0) is undefined ok Trying: normalize_whitespace('Hellotworld') Expecting: 'Hello world' ok Trying: normalize_whitespace('String withnmultiplenlines') Expecting: 'String with multiple lines' ok Trying: normalize_whitespace(' Extra spaces everywhere ') Expecting: 'Extra spaces everywhere' ok 1 items had no tests: __main__ 8 tests in 2 items. 8 passed and 0 failed. Test passed. ```
answer:# math_utils.py This is the "math_utils" module. The math_utils module supplies utility mathematical functions. For example, >>> gcd(48, 18) 6 >>> gcd(0, 0) 0 def gcd(a, b): Return the greatest common divisor of a and b. >>> gcd(54, 24) 6 >>> gcd(48, 180) 12 >>> gcd(0, 9) 9 >>> gcd(0, 0) # This should raise an exception as gcd(0, 0) is undefined Traceback (most recent call last): ... ValueError: gcd(0, 0) is undefined if a == 0 and b == 0: raise ValueError("gcd(0, 0) is undefined") while b != 0: a, b = b, a % b return a def normalize_whitespace(s): Normalize all whitespace in string `s` to single spaces. >>> normalize_whitespace('Hellotworld') 'Hello world' >>> normalize_whitespace('String withnmultiplenlines') 'String with multiple lines' >>> normalize_whitespace(' Extra spaces everywhere ') 'Extra spaces everywhere' import re return re.sub(r's+', ' ', s).strip() if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS)
question:# Question: Concurrent Prime Number Calculation You are tasked with implementing a concurrent prime number calculation using the low-level threading API provided by the `_thread` module. The goal is to determine the prime numbers within a given range `[start, end]` using multiple threads to speed up the computation. Synchronization should be managed using locks to ensure correct results and avoid race conditions. # Problem Statement 1. **Function Name:** `concurrent_prime_calculation` 2. **Inputs:** - An integer `start` representing the starting range (inclusive). - An integer `end` representing the ending range (inclusive). - An integer `num_threads` representing the number of threads to be used. 3. **Outputs:** - A sorted list of integers representing the prime numbers between `start` and `end`, calculated concurrently using multiple threads. # Constraints - `1 <= start <= end <= 10^6` - `1 <= num_threads <= 100` # Performance Requirements - Efficient use of threads and synchronization to minimize calculation time. - Proper handling of race conditions using locks. # Example ```python def concurrent_prime_calculation(start: int, end: int, num_threads: int) -> list: # Your implementation here # Example usage: prime_numbers = concurrent_prime_calculation(1, 100, 4) print(prime_numbers) # Expected Output: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97] ``` # Implementation Guidelines 1. **Function Definition:** Define the function `concurrent_prime_calculation` following the specified input and output signatures. 2. **Thread Management:** - Use `_thread.start_new_thread` to create and start threads. - Distribute the range `[start, end]` among the specified number of threads. 3. **Synchronization:** - Use `_thread.allocate_lock` to create lock objects. - Ensure thread-safe access to shared resources (e.g., the list of prime numbers). 4. **Prime Calculation:** Implement a method to check if a given number is prime. 5. **Collecting Results:** Gather results from all threads and return a sorted list of primes. # Notes - You must import relevant modules such as `_thread`. - Ensure your code handles edge cases and potential exceptions, such as invalid ranges. - Minimize thread creation overhead and ensure threads terminate correctly after completing their tasks.
answer:import _thread from typing import List def is_prime(n: int) -> bool: if n <= 1: return False if n <= 3: return True if n % 2 == 0 or n % 3 == 0: return False i = 5 while i * i <= n: if n % i == 0 or n % (i + 2) == 0: return False i += 6 return True def concurrent_prime_calculation(start: int, end: int, num_threads: int) -> List[int]: def worker(start: int, end: int, primes: List[int], lock) -> None: for num in range(start, end + 1): if is_prime(num): lock.acquire() primes.append(num) lock.release() primes = [] lock = _thread.allocate_lock() thread_ranges = [] range_size = (end - start + 1) // num_threads for i in range(num_threads): range_start = start + i * range_size range_end = start + (i + 1) * range_size - 1 if i == num_threads - 1: range_end = end thread_ranges.append((range_start, range_end)) for range_start, range_end in thread_ranges: _thread.start_new_thread(worker, (range_start, range_end, primes, lock)) # Busy-wait for threads to finish (in practice, we'd use condition variables or other blocking mechanisms) import time time.sleep(1 + (end - start) // 1000) return sorted(primes)
question:**Title: Advanced Data Processing with Pattern Matching in Python 3.10** **Objective:** Create a function to process a list of data entries using Python 3.10's pattern matching feature. This question assesses the ability to utilize advanced constructs introduced in Python 3.10, specifically pattern matching. **Problem Statement:** You are given a list of data entries, where each entry is a dictionary with different structures. Your task is to process these entries and extract specific information based on their structure using Python 3.10's pattern matching. The data entries can have one of the following structures: 1. A rectangle with properties `type='rectangle'`, `width`, and `height`. 2. A circle with properties `type='circle'` and `radius`. 3. A triangle with properties `type='triangle'`, `base`, and `height`. 4. Any other type will be ignored. You need to create a function `process_shapes(data: List[dict]) -> Tuple[float, float, float]` that processes the list and returns a tuple containing the sum of areas of all rectangles, circles, and triangles, respectively. Ignore any entries that do not match the specified types. # Function Signature: ```python from typing import List, Tuple def process_shapes(data: List[dict]) -> Tuple[float, float, float]: pass ``` # Constraints: - The input list `data` will contain up to 10,000 entries. - Each dictionary represents a single shape and follows the mentioned structure. - Use the following formulas for area calculations: - Rectangle: `width * height` - Circle: `π * radius^2` (use `math.pi`) - Triangle: `0.5 * base * height` # Example: ```python data = [ {"type": "rectangle", "width": 10, "height": 5}, {"type": "circle", "radius": 7}, {"type": "triangle", "base": 6, "height": 3}, {"type": "rectangle", "width": 2, "height": 8}, {"type": "circle", "radius": 3}, ] rectangles, circles, triangles = process_shapes(data) print(f"Total area of rectangles: {rectangles}") print(f"Total area of circles: {circles}") print(f"Total area of triangles: {triangles}") ``` **Expected Output:** ``` Total area of rectangles: 90.0 Total area of circles: 182.21237667601792 Total area of triangles: 9.0 ``` This question will require the students to: - Understand and implement pattern matching in Python 3.10. - Correctly utilize data extraction and processing. - Calculate areas using given formulas and aggregate results. **Hint:** Consider using Python 3.10's `match` statement to identify and process each shape type efficiently.
answer:from typing import List, Tuple import math def process_shapes(data: List[dict]) -> Tuple[float, float, float]: total_rectangle_area = 0.0 total_circle_area = 0.0 total_triangle_area = 0.0 for entry in data: match entry: case {"type": "rectangle", "width": width, "height": height}: total_rectangle_area += width * height case {"type": "circle", "radius": radius}: total_circle_area += math.pi * radius * radius case {"type": "triangle", "base": base, "height": height}: total_triangle_area += 0.5 * base * height return total_rectangle_area, total_circle_area, total_triangle_area