New translations parallel.py (Chinese Simplified)

lang/zh/gklearn/utils/parallel.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec 11 11:39:46 2018
+Parallel aid functions.
+@author: ljia
+"""
+import multiprocessing
+from multiprocessing import Pool
+from tqdm import tqdm
+import sys
+
+def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None, 
+                glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='',
+                verbose=True):
+    '''
+    '''
+    if method == 'imap_unordered':
+        if glbv: # global varibles required.
+#            def init_worker(v_share):
+#                global G_var
+#                G_var = v_share
+            if n_jobs == None:
+                n_jobs = multiprocessing.cpu_count()
+            with Pool(processes=n_jobs, initializer=init_worker, 
+                      initargs=glbv) as pool:                
+                if chunksize is None:
+                    if len_itr < 100 * n_jobs:
+                        chunksize = int(len_itr / n_jobs) + 1
+                    else:
+                        chunksize = 100
+                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
+                    desc=itr_desc, file=sys.stdout) if verbose else 
+                    pool.imap_unordered(func, itr, chunksize)):
+                    func_assign(result, var_to_assign)
+            pool.close()
+            pool.join()
+        else:
+            if n_jobs == None:
+                n_jobs = multiprocessing.cpu_count()
+            with Pool(processes=n_jobs) as pool:
+                if chunksize is None:
+                    if len_itr < 100 * n_jobs:
+                        chunksize = int(len_itr / n_jobs) + 1
+                    else:
+                        chunksize = 100
+                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
+                    desc=itr_desc, file=sys.stdout) if verbose else 
+                    pool.imap_unordered(func, itr, chunksize)):
+                    func_assign(result, var_to_assign)
+            pool.close()
+            pool.join()
+                    
+
+def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None, 
+                method='imap_unordered', n_jobs=None, chunksize=None, 
+                verbose=True): # @todo: Gn seems not necessary.
+    from itertools import combinations_with_replacement
+    def func_assign(result, var_to_assign):
+        var_to_assign[result[0]][result[1]] = result[2]
+        var_to_assign[result[1]][result[0]] = result[2]
+    itr = combinations_with_replacement(range(0, len(Gn)), 2)
+    len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
+    parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr,
+        init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs, 
+        chunksize=chunksize, itr_desc='calculating kernels', verbose=verbose)