UNITED KINGDOM Predicciones de cricket today

P: ¿Qué factores se consideran al hacer una predicción?

[0]: # -*- coding: utf-8 -*- [1]: import os [2]: import logging [3]: import numpy as np [4]: from math import log [5]: from scipy.stats import norm [6]: from sklearn.metrics.pairwise import cosine_similarity [7]: from util.constants import DEFAULT_MINE_PATH [8]: from util.hierarchical_label import HierarchicalLabelGenerator [9]: logger = logging.getLogger(__name__) [10]: class NdcgCalculator(object): [11]: """ [12]: Calculate NDCG of the result of MINE. [13]: """ [14]: def __init__(self, [15]: mine_path=DEFAULT_MINE_PATH, [16]: min_label_size=20, [17]: min_label_count=20, [18]: max_level=None, [19]: max_leaf_size=None, [20]: hierarchy_path=None): [21]: self.mine_path = mine_path [22]: self.min_label_size = min_label_size [23]: self.min_label_count = min_label_count [24]: self.max_level = max_level [25]: self.max_leaf_size = max_leaf_size [26]: self.hierarchy_path = hierarchy_path [27]: self.hlg = None [28]: def load(self): [29]: """ [30]: Load MINE results and calculate NDCG. [31]: :return: [32]: """ [33]: if not os.path.exists(self.mine_path): [34]: raise ValueError('Invalid path to MINE results: {}'.format(self.mine_path)) [35]: if not os.path.exists(self.hierarchy_path): [36]: raise ValueError('Invalid path to hierarchy file: {}'.format(self.hierarchy_path)) [37]: logger.info('Loading MINE results and calculating NDCG ...') [38]: # load MINE results [39]: self.data = {} [40]: for file_name in os.listdir(self.mine_path): [41]: if file_name.endswith('.txt'): [42]: label_name = file_name.split('.')[0] [43]: if len(label_name) == 0: [44]: continue data = [] with open(os.path.join(self.mine_path, file_name), 'r') as file: for line in file.readlines(): data.append([float(x) for x in line.strip().split()]) if len(data) > 0: self.data[label_name] = np.array(data) logger.info('Processing label {} ...'.format(label_name)) logger.info('Label {} is ignored because it contains no samples.'.format(label_name)) continue score_data = data[:, 1] sorted_idx = np.argsort(score_data)[::-1] sorted_data = data[:, 0][sorted_idx] self.data[label_name] = sorted_data logger.info('Generating hierarchical labels ...') self.hlg = HierarchicalLabelGenerator( hierarchy_file=self.hierarchy_path, min_label_size=self.min_label_size, min_label_count=self.min_label_count, max_level=self.max_level, max_leaf_size=self.max_leaf_size) logger.info('Done.') logger.info('Calculating NDCG ...') dcg_sum = 0. idcg_sum = 0. for root_node in self.hlg.root_nodes: dcg_sum += self._calculate_dcg(root_node) idcg_sum += self._calculate_idcg(root_node) ndcg_score = dcg_sum / idcg_sum if idcg_sum > 0 else 0. logger.info('NDCG score: {:.4f}'.format(ndcg_score)) return ndcg_score ***** Tag Data ***** ID: 3 description: Processing each label's MINE result by loading the data into arrays and sorting based on scores. start line: 39 end line: 49 dependencies: - type: Method name: load start line: 28 end line: 32 context description: This snippet loads the MINE results from text files and processes them by sorting the scores and storing the sorted data. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 4 interesting for students: 5 self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code 1. **File Handling**: The code involves reading multiple text files from a directory and processing each one individually. Handling file I/O operations efficiently is crucial, especially when dealing with large datasets or numerous files. 2. **Dynamic Data Processing**: The code dynamically processes each file based on its content. This includes parsing lines into floating-point numbers and then sorting them based on specific columns. 3. **Error Handling**: Ensuring robust error handling is essential. For instance, handling cases where files may be empty or contain malformed data. 4. **Data Integrity**: Maintaining the integrity of the data throughout the process is important. Ensuring that all numerical values are correctly parsed and stored without loss of precision. 5. **Efficient Sorting**: Sorting large datasets can be computationally expensive. The code must handle this efficiently to avoid performance bottlenecks. 6. **Memory Management**: Handling potentially large datasets requires careful memory management to prevent excessive memory usage or leaks. ### Extension 1. **Incremental File Addition**: Extend the functionality to handle files that might be added to the directory while processing is ongoing. 2. **Pointer Files**: Introduce files that contain pointers (e.g., paths) to other files located in different directories and ensure these are processed correctly. 3. **Parallel Processing**: Implement parallel processing to handle multiple files simultaneously while ensuring thread safety and consistency of the data structure. 4. **Custom Sorting Criteria**: Allow users to specify custom sorting criteria beyond just sorting by the second column's values. 5. **Data Validation**: Add more rigorous data validation steps to ensure all input data conforms to expected formats before processing. 6. **Logging and Monitoring**: Implement detailed logging and monitoring to track the progress of file processing and catch any issues early. ## Exercise ### Problem Statement You are tasked with expanding an existing script that loads MINE results from text files in a specified directory (`mine_path`), processes them by sorting the scores based on specific criteria, and stores the sorted data in a dictionary (`self.data`). The current implementation handles basic file reading and sorting but lacks several advanced features needed for robustness and scalability. Your task is to: 1. Extend the script to handle new files being added to the directory while processing is ongoing. 2. Implement functionality to handle "pointer" files that contain paths to other text files located in different directories. 3. Allow users to specify custom sorting criteria via function arguments. 4. Ensure robust error handling for empty files or malformed data. 5. Implement parallel processing to handle multiple files simultaneously while ensuring thread safety. 6. Add detailed logging to monitor the progress of file processing. Use the following snippet as your starting point: [SNIFFET] ### Requirements 1. **File Monitoring**: - Continuously monitor `mine_path` for new `.txt` files being added during processing. - Process any new files as they appear without restarting the script. 2. **Pointer Files**: - Recognize pointer files (e.g., named `*.ptr.txt`) which contain paths (one per line) to other text files. - Ensure these pointed-to files are processed as if they were directly in `mine_path`. 3. **Custom Sorting**: - Allow users to pass a custom sorting function via an argument (`sort_func`). - If no custom function is provided, default to sorting by the second column's values in descending order. 4. **Error Handling**: - Implement error handling for empty files or lines with non-numeric values. - Log errors without stopping the entire process. 5. **Parallel Processing**: - Use Python’s threading or multiprocessing modules to process multiple files concurrently. - Ensure that `self.data` remains consistent and thread-safe. 6. **Logging**: - Add detailed logging at various stages of processing (e.g., when starting/finishing reading a file, encountering an error). ## Solution python import os import time import numpy as np import threading import logging logging.basicConfig(level=logging.INFO) class MineProcessor: def __init__(self, mine_path): self.mine_path = mine_path self.data = {} self.processed_files = set() self.lock = threading.Lock() def _process_file(self, file_path): try: label_name = os.path.basename(file_path).split('.')[0] if len(label_name) == 0: return with open(file_path, 'r') as file: data = [] for line in file.readlines(): try: data.append([float(x) for x in line.strip().split()]) except ValueError: logging.error(f"Malformed line in {file_path}: {line.strip()}") continue if len(data) > 0: with self.lock: score_data = np.array(data)[:, 1] sorted_idx = np.argsort(score_data)[::-1] sorted_data = np.array(data)[:, 0][sorted_idx] self.data[label_name] = sorted_data except Exception as e: logging.error(f"Error processing {file_path}: {str(e)}") def _process_pointer_file(self, pointer_file): try: with open(pointer_file, 'r') as pf: for line in pf.readlines(): pointer_file_path = line.strip() if os.path.exists(pointer_file_path