问题
I'm having some probems apllying a text search algorithm with parallelized dask insfrastructure.
I'm tryng to find the best match for 40,000 stirngs in a series object against a 4000 string list.
I could have done it using pandas.apply but it's to time expensive, so i decided try parallelization with map_partitions in dask.
I'm using this text search library with python-Levenshtein https://marcobonzanini.com/2015/02/25/fuzzy-string-matching-in-python
As you can see, it works ok on this example from a pandas dataset:
process.extractOne(df['endereco2'][1],choices=choices,scorer=fuzz.token_set_ratio,
score_cutoff=60)
Output: ('R ALVARO DUARTE DE ALMEIDA PROFESSOR', 85)
but its not working while using dask:
from dask import dataframe as dd
sd = dd.from_pandas(r13_2["endereco2"],npartitions=3).map_partitions(lambda df : df.apply(process.extractOne,choices=choices,scorer=fuzz.token_set_ratio,score_cutoff=60)).compute(scheduler='processes')
Output:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-69-f39ab0d086b5> in <module>
1 from dask import dataframe as dd
----> 2 sd = dd.from_pandas(r13_2["endereco2"],npartitions=3).map_partitions(lambda df : df.apply(process.extractOne,choices=choices,scorer=fuzz.token_set_ratio,score_cutoff=60)).compute(scheduler='processes')
~\Anaconda3\envs\mono\lib\site-packages\dask\base.py in compute(self, **kwargs)
154 dask.base.compute
155 """
--> 156 (result,) = compute(self, traverse=False, **kwargs)
157 return result
158
~\Anaconda3\envs\mono\lib\site-packages\dask\base.py in compute(*args, **kwargs)
396 keys = [x.__dask_keys__() for x in collections]
397 postcomputes = [x.__dask_postcompute__() for x in collections]
--> 398 results = schedule(dsk, keys, **kwargs)
399 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
400
~\Anaconda3\envs\mono\lib\site-packages\dask\multiprocessing.py in get(dsk, keys, num_workers, func_loads, func_dumps, optimize_graph, pool, **kwargs)
190 get_id=_process_get_id, dumps=dumps, loads=loads,
191 pack_exception=pack_exception,
--> 192 raise_exception=reraise, **kwargs)
193 finally:
194 if cleanup:
~\Anaconda3\envs\mono\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
460 _execute_task(task, data) # Re-execute locally
461 else:
--> 462 raise_exception(exc, tb)
463 res, worker_id = loads(res_info)
464 state['cache'][key] = res
~\Anaconda3\envs\mono\lib\site-packages\dask\compatibility.py in reraise(exc, tb)
109 def reraise(exc, tb=None):
110 if exc.__traceback__ is not tb:
--> 111 raise exc.with_traceback(tb)
112 raise exc
113
~\Anaconda3\envs\mono\lib\site-packages\dask\local.py in execute_task()
228 try:
229 task, data = loads(task_info)
--> 230 result = _execute_task(task, data)
231 id = get_id()
232 result = dumps((result, id))
~\Anaconda3\envs\mono\lib\site-packages\dask\core.py in _execute_task()
117 func, args = arg[0], arg[1:]
118 args2 = [_execute_task(a, cache) for a in args]
--> 119 return func(*args2)
120 elif not ishashable(arg):
121 return arg
~\Anaconda3\envs\mono\lib\site-packages\dask\optimization.py in __call__()
940 % (len(self.inkeys), len(args)))
941 return core.get(self.dsk, self.outkey,
--> 942 dict(zip(self.inkeys, args)))
943
944 def __reduce__(self):
~\Anaconda3\envs\mono\lib\site-packages\dask\core.py in get()
147 for key in toposort(dsk):
148 task = dsk[key]
--> 149 result = _execute_task(task, cache)
150 cache[key] = result
151 result = _execute_task(out, cache)
~\Anaconda3\envs\mono\lib\site-packages\dask\core.py in _execute_task()
117 func, args = arg[0], arg[1:]
118 args2 = [_execute_task(a, cache) for a in args]
--> 119 return func(*args2)
120 elif not ishashable(arg):
121 return arg
~\Anaconda3\envs\mono\lib\site-packages\dask\compatibility.py in apply()
91 def apply(func, args, kwargs=None):
92 if kwargs:
---> 93 return func(*args, **kwargs)
94 else:
95 return func(*args)
~\Anaconda3\envs\mono\lib\site-packages\dask\dataframe\core.py in apply_and_enforce()
3877 func = kwargs.pop('_func')
3878 meta = kwargs.pop('_meta')
-> 3879 df = func(*args, **kwargs)
3880 if is_dataframe_like(df) or is_series_like(df) or is_index_like(df):
3881 if not len(df):
<ipython-input-69-f39ab0d086b5> in <lambda>()
1 from dask import dataframe as dd
----> 2 sd = dd.from_pandas(r13_2["endereco2"],npartitions=3).map_partitions(lambda df : df.apply(process.extractOne,choices=choices,scorer=fuzz.token_set_ratio,score_cutoff=60)).compute(scheduler='processes')
~\Anaconda3\envs\mono\lib\site-packages\pandas\core\series.py in apply()
3589 else:
3590 values = self.astype(object).values
-> 3591 mapped = lib.map_infer(values, f, convert=convert_dtype)
3592
3593 if len(mapped) and isinstance(mapped[0], Series):
pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()
~\Anaconda3\envs\mono\lib\site-packages\pandas\core\series.py in f()
3576 if kwds or args and not isinstance(func, np.ufunc):
3577 def f(x):
-> 3578 return func(x, *args, **kwds)
3579 else:
3580 f = func
~\Anaconda3\envs\mono\lib\site-packages\fuzzywuzzy\process.py in extractOne()
218 best_list = extractWithoutOrder(query, choices, processor, scorer, score_cutoff)
219 try:
--> 220 return max(best_list, key=lambda i: i[1])
221 except ValueError:
222 return None
~\Anaconda3\envs\mono\lib\site-packages\fuzzywuzzy\process.py in extractWithoutOrder()
76
77 # Run the processor on the input query.
---> 78 processed_query = processor(query)
79
80 if len(processed_query) == 0:
~\Anaconda3\envs\mono\lib\site-packages\fuzzywuzzy\utils.py in full_process()
93 s = asciidammit(s)
94 # Keep only Letters and Numbers (see Unicode docs).
---> 95 string_out = StringProcessor.replace_non_letters_non_numbers_with_whitespace(s)
96 # Force into lowercase.
97 string_out = StringProcessor.to_lower_case(string_out)
~\Anaconda3\envs\mono\lib\site-packages\fuzzywuzzy\string_processing.py in replace_non_letters_non_numbers_with_whitespace()
24 numbers with a single white space.
25 """
---> 26 return cls.regex.sub(" ", a_string)
27
28 strip = staticmethod(string.strip)
TypeError: expected string or bytes-like object
What's happenig?
Obs: I solved my problem using the pool.apply from multplocessing lib, but i still want to know what happened with Dask
回答1:
Doing the MCVE I realized that it was a naive syntax problem: I can't use the map_partitions on a dask dataframe without specifying the column that im using even if there is only one column. So I should had used sd[0].map_partitions insted of sd.map_partitions
来源：https://stackoverflow.com/questions/56612667/having-problemns-while-using-dask-map-partitions-with-string-matching-algorithm