Merge pull request '修改map-reduce模式' (#13) from pbr4nzfkh/CodePattern:dev into dev

计算设备/map-reduce/tf-31.py

@@ -1,27 +1,42 @@
-from functools import reduce
-from cppy.cp_util import *
+# -*- coding: utf-8 -*-
 from collections import Counter
+from cppy.cp_util import *
+from functools import reduce
+
+# map - reduce
+def process_chunk(chunk):
+    # 过滤停用词
+    stop_words = get_stopwords()
+    words = [ w for w in chunk if ( not w in stop_words ) and len(w) >= 3 ]
+    return Counter(words)
+
+
+def merge_counts(count1,count2):
+    sum_counts = count1 + count2
+    return sum_counts
+
+
+@timing_decorator
+def main():
+    # 读取文件内容
+    content = re_split(read_file(testfilepath))
+
+    # 分割文件内容为多个块，每个块由一个进程处理
+    chunk_size = 1000  # 可以根据实际情况调整块大小
+    chunks = [content[i:i + chunk_size] for i in range(0, len(content), chunk_size)]
+
+    # 使用 map 方法和 process_chunk 函数处理每个分区
+    counts_list = list(map(process_chunk, chunks))
+
+    #  使用 reduce 和 merge_counts 函数统计所有分区的词频
+    total_counts = (reduce(merge_counts,counts_list))
+
+    # 输出最高频的n个词
+    print_word_freqs(total_counts.most_common(10))
+
+
+if __name__ == '__main__':
+    main()
+
+
 
-def partition(data_str, nlines):
-    lines = data_str.split('\n')
-    for i in range(0, len(lines), nlines):
-        yield '\n'.join(lines[i:i+nlines])
-
-def split_words(data_str):
-    word_list =  extract_str_words(data_str)   
-    return Counter( word_list )
-
-def count_words(pairs_list_1, pairs_list_2):
-    return pairs_list_1 + pairs_list_2   
-
-
-if __name__ == '__main__':        
-    data = read_file(testfilepath)
-    
-    # 使用 map 方法和 split_words 函数处理每个分区
-    splits = map(split_words, partition(data, 200))  
-    splits_list = list(splits)
-    
-    # 使用 reduce 和 count_words 函数统计所有分区的词频
-    word_freqs = sort_dict(reduce(count_words, splits_list, Counter()) )    
-    print_word_freqs(word_freqs)

计算设备/map-reduce/tf-32.py

@@ -1,37 +0,0 @@
-from functools import reduce
-from cppy.cp_util import *
-
-#################################################
-# Functions for map reduce
-#################################################
-def partition(data_str, nlines):
-    lines = data_str.split('\n')
-    for i in range(0, len(lines), nlines):
-        yield '\n'.join(lines[i:i+nlines])
-
-def split_words(data_str):    
-    words = extract_str_words(data_str)    
-    return [ (w, 1) for w in words ]        
-
-def regroup(pairs_list):
-    mapping = {}
-    for pairs in pairs_list:
-        for p in pairs:
-            mapping[p[0]] = mapping.get(p[0], []) + [p]
-    return mapping
-
-def count_words(mapping):
-    def add(x, y): return x+y
-    return ( mapping[0], 
-             reduce(add, (pair[1] for pair in mapping[1]))
-           )
-
-def sort (word_freq):
-    return sorted(word_freq, key=operator.itemgetter(1), reverse=True)
-
-if __name__ == '__main__':        
-    data = read_file(testfilepath)
-    splits = map(split_words, partition(data, 200))        
-    splits_per_word = regroup(splits)
-    word_freqs = sort(map(count_words, splits_per_word.items()))    
-    print_word_freqs(word_freqs)    

计算设备/map-reduce/tf-91.py

@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+from collections import Counter
+from cppy.cp_util import *
+from multiprocessing.pool import ThreadPool
+
+
+#
+# 多线程
+#
+def process_chunk(chunk):
+    # 过滤停用词
+    stop_words = get_stopwords()
+    words = [ w for w in chunk if ( not w in stop_words ) and len(w) >= 3 ]
+    return Counter(words)
+
+
+def merge_counts(counts_list):
+    # 合并多个Counter对象
+    total_counts = Counter()
+    for counts in counts_list:
+        total_counts += counts
+    return total_counts
+
+
+def thread_function(chunk, counts_list):
+    word_count = process_chunk(chunk)
+    counts_list.append(word_count)
+
+
+@timing_decorator
+def main():
+    # 读取文件内容
+    content = re_split(read_file(testfilepath))
+    chunk_size = 1000 # 可以根据实际情况调整块大小
+    chunks = [content[i:i + chunk_size] for i in range(0, len(content), chunk_size)]
+
+    # 使用多线程池，每个线程处理一个块
+    pool = ThreadPool(len(content)//chunk_size+1)
+    counts_list = pool.map(process_chunk, chunks)
+    pool.close()
+    pool.join()
+
+    # 合并计数
+    total_counts = merge_counts(counts_list)
+
+    # 输出最高频的n个词
+    print_word_freqs(total_counts.most_common(10))
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+

计算设备/map-reduce/tf-92.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+import multiprocessing
+from collections import Counter
+from cppy.cp_util import *
+
+
+#
+# 多进程
+#
+def process_chunk(chunk):
+    # 过滤停用词
+    stop_words = get_stopwords()
+    words = [ w for w in chunk if ( not w in stop_words ) and len(w) >= 3 ]
+    return Counter(words)
+
+
+def merge_counts(counts_list):
+    # 合并多个Counter对象
+    total_counts = Counter()
+    for counts in counts_list:
+        total_counts += counts
+    return total_counts
+
+
+@timing_decorator
+def main():
+    # 读取文件内容
+    content = re_split(read_file(testfilepath))
+
+    # 分割文件内容为多个块，每个块由一个进程处理
+    chunk_size = 1000  # 可以根据实际情况调整块大小
+    chunks = [content[i:i + chunk_size] for i in range(0, len(content), chunk_size)]
+
+    # 使用多进程处理每个块
+    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
+    counts_list = pool.map(process_chunk, chunks)
+    pool.close()
+    pool.join()
+
+    # 合并计数
+    total_counts = merge_counts(counts_list)
+
+    # 输出最高频的n个词
+    print_word_freqs(total_counts.most_common(10))
+
+
+if __name__ == '__main__':
+    main()
+

计算设备/map-reduce/tf_91.py

@@ -1,54 +0,0 @@
-import threading
-from collections import Counter
-from cppy.cp_util import *
-
-#
-# 多线程
-# 
-def process_chunk(start, end, text, result_index, results):   
-    # 切词并过滤停用词            
-    words = extract_str_words( text[start:end] )     
-    results[result_index] = Counter(words)
-
-def merge_counts(counts_list):  
-    # 合并多个Counter对象  
-    total_counts = Counter()  
-    for counts in counts_list:  
-        total_counts += counts  
-    return total_counts  
-
-@timing_decorator
-def main():  
-    # 读取文件内容
-    text = read_file(testfilepath)    
-
-    # 确定线程数量
-    num_threads = 4
-    text_length = len(text)
-    chunk_size = text_length // num_threads
-
-    # 存储每个线程的结果
-    results = [None] * num_threads
-    threads = []
-
-    # 创建并启动线程
-    for i in range(num_threads):
-        start = i * chunk_size
-        # 确保最后一个线程能够读取文件的末尾
-        end = text_length if i == num_threads - 1 else (i + 1) * chunk_size
-        t = threading.Thread(target=process_chunk, args=(start, end, text, i, results))
-        threads.append(t)
-        t.start()
-
-    # 等待所有线程完成
-    for t in threads: t.join()
-
-    # 合并计数  
-    total_counts = merge_counts(results)  
-
-    # 输出最高频的n个词
-    print_word_freqs( total_counts.most_common(10) )     
-
-    
-if __name__ == '__main__':
-    main()

计算设备/map-reduce/tf_92.py

@@ -1,44 +0,0 @@
-import multiprocessing  
-from collections import Counter
-from cppy.cp_util import *  
-
-#
-# 多进程
-#
-def process_chunk(chunk):  
-    # 切词并过滤停用词       
-    words = extract_str_words( chunk.lower() )
-    return Counter(words)  
-  
-def merge_counts(counts_list):  
-    # 合并多个Counter对象  
-    total_counts = Counter()  
-    for counts in counts_list:  
-        total_counts += counts  
-    return total_counts    
-
-@timing_decorator
-def main():  
-    # 读取文件内容  
-    content = read_file(testfilepath)    
-
-    # 分割文件内容为多个块，每个块由一个进程处理  
-    chunk_size = 1000  # 可以根据实际情况调整块大小  
-    chunks = [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)]  
-  
-    # 使用多进程处理每个块  
-    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())  
-    counts_list = pool.map(process_chunk, chunks)  
-    pool.close()  
-    pool.join()  
-  
-    # 合并计数  
-    total_counts = merge_counts(counts_list)  
-  
-    # 输出最高频的n个词
-    print_word_freqs( total_counts.most_common(10) )        
-
-
-if __name__ == '__main__':  
-    main()
-