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[0]: import os
[1]: import sys
[2]: import argparse
[3]: import json
[4]: import numpy as np
[5]: from tqdm import tqdm
[6]: from nmtwizard.core.dataset.corpus import Corpus
[7]: def main():
[8]: parser = argparse.ArgumentParser()
[9]: parser.add_argument(‘–train’, type=str)
[10]: parser.add_argument(‘–valid’, type=str)
[11]: parser.add_argument(‘–test’, type=str)
[12]: args = parser.parse_args()
[13]: train_corpus = Corpus(args.train)
[14]: # Get language pairs
[15]: lang_pairs = list(set([(src_lang_pair.split(‘-‘)[0], src_lang_pair.split(‘-‘)[1])
[16]: for src_lang_pair in train_corpus.src_lang_pairs]))
[17]: # Iterate over each language pair
[18]: print(‘Processing language pairs:’)
[19]: total_train_size = []
[20]: valid_lang_pairs = []
[21]: test_lang_pairs = []
[22]: for lang_pair_idx in range(len(lang_pairs)):
[23]: print(‘tLanguage pair {}/{}: {} -> {}’.format(
[24]: lang_pair_idx + 1,
[25]: len(lang_pairs),
[26]: lang_pairs[int(lang_pair_idx)][0],
[27]: lang_pairs[int(lang_pair_idx)][1]
))
src_file_name = ‘train.’ + lang_pairs[int(lang_pair_idx)][0] + ‘-‘ + lang_pairs[int(lang_pair_idx)][1] + ‘.txt’
if os.path.isfile(os.path.join(args.valid,
‘train.’ +
lang_pairs[
int(
lang_pair_idx)][
[
(
‘src’,
‘tgt’)]][0] +
‘.txt’)):
valid_lang_pairs.append(
(
lang_pairs[int(
lang_pair_idx)][
[(
‘src’,
‘tgt’)]][
[0]],
os.path.join(
args.valid,
src_file_name)))
else:
pass
if os.path.isfile(os.path.join(args.test,
‘train.’ +
lang_pairs[
int(
lang_pair_idx)][
[
(
‘src’,
‘tgt’)]][0] +
‘.txt’)):
test_lang_pairs.append(
(
lang_pairs[int(
lang_pair_idx)][
[(
‘src’,
‘tgt’)]][
[0]],
os.path.join(
args.test,
src_file_name)))
train_corpus_dir = os.path.dirname(args.train)
train_src_path = os.path.join(train_corpus_dir,
src_file_name)
valid_src_paths_dict = dict(valid_lang_pairs)
test_src_paths_dict = dict(test_lang_pairs)
print(‘Reading training corpus…’)
train_src_data_list = read_lines(train_src_path)
print(‘Reading validation corpora…’)
valid_src_data_dict_list = {}
for valid_src_path_key in valid_src_paths_dict.keys():
valid_src_data_dict_list.update({
valid_src_path_key: read_lines(valid_src_paths_dict[
valid_src_path_key])})
print(‘Reading test corpora…’)
test_src_data_dict_list = {}
for test_src_path_key in test_src_paths_dict.keys():
test_src_data_dict_list.update({
test_src_path_key: read_lines(test_src_paths_dict[
test_src_path_key])})
train_size_per_line_list_of_lists_of_ints = get_lengths(train_corpus_dir,
train_corpus.src_lang_dirs,
train_corpus.src_files)
total_train_size.extend([len(x)
for x
in tqdm(train_size_per_line_list_of_lists_of_ints)])
# Get average length per line
avg_train_length_per_line_float32_scalar=
np.mean(total_train_size).astype(np.float32)
avg_valid_length_per_line_float32_scalar=
np.mean([len(x)
for x
in tqdm(valid_size_per_line_list_of_lists_of_ints)]).astype(np.float32)
avg_test_length_per_line_float32_scalar=
np.mean([len(x)
for x
in tqdm(test_size_per_line_list_of_lists_of_ints)]).astype(np.float32)
try:
assert abs(avg_valid_length_per_line_float32_scalar –
avg_train_length_per_line_float32_scalar) <= abs(5 *
avg_valid_length_per_line_float32_scalar),
'nntAverage validation sentence length should be within +-5% of training sentence lengthnn'
except AssertionError:
raise Exception('nntAverage validation sentence length should be within +-5% of training sentence lengthnn')
try:
assert abs(avg_test_length_per_line_float32_scalar –
avg_train_length_per_line_float32_scalar) <= abs(5 *
avg_test_length_per_line_float32_scalar),
'nntAverage testing sentence length should be within +-5% of training sentence lengthnn'
except AssertionError:
raise Exception('nntAverage testing sentence length should be within +-5% of training sentence lengthnn')
# Get max lengths per line
max_train_length_per_line_int64_scalar=
max([max(x)
for x
in tqdm(train_size_per_line_list_of_lists_of_ints)]).astype(np.int64)
max_valid_length_per_line_int64_scalar=
max([max(x)
for x
in tqdm(valid_size_per_line_list_of_lists_of_ints)]).astype(np.int64)
max_test_length_per_line_int64_scalar=
max([max(x)
for x
in tqdm(test_size_per_line_list_of_lists_of_ints)]).astype(np.int64)
def get_lengths(corpus_dir,
src_dirs,
files):
size_out_lines_dir=os.path.join(corpus_dir,'size_out')
if not os.path.exists(size_out_lines_dir):
os.mkdir(size_out_lines_dir)
size_out_files={}
size_out_files_keys=[]
size_out_files_values=[]
i=0
while i<len(src_dirs):
size_out_files_keys.append(os.path.join(src_dirs[i],'size.out'))
size_out_files_values.append(os.path.join(size_out_lines_dir,str(i)+'.txt'))
i+=1
size_out_files=dict(zip(size_out_files_keys,size_out_files_values))
size_in_lines=[]
i=0
while i<len(files):
file_name=os.path.join(corpus_dir,'size_in',files[i])
lines=read_lines(file_name)
size_in_lines.append(lines)
i+=1
j=0
while j<len(src_dirs):
file_name=os.path.join(src_dirs[j],files[j])
lines=read_lines(file_name)
write_to_file(lines,size_out_files[str(j)])
j+=1
size_in_sentences=[]
i=0
while i<len(size_in_lines):
sentences=[]
k=0
while k<len(size_in_lines[i]):
sentences.append([])
l=size_in_lines[i][k].split()
m=len(l)-1
n=l[m].split(':')
o=int(n[-1])
p=l[:m]
q=' '.join(p)+':'+str(o)+'n'
while o!=0:
sentences[k].append(int(l[m]))
o-=int(l[m])
m-=1
k+=1
sentences.sort(key=len)
sentences.reverse()
size_in_sentences.append(sentences)
i+=1
j=0
while j<len(files):
file_name=os.path.join(size_out_lines_dir,str(j)+'.txt')
write_to_file(size_in_sentences[j],file_name)
j+=1
return size_in_sentences
def read_lines(file):
lines=[]
with open(file,'r') as f:
lines=f.readlines()
return lines
def write_to_file(lines,file):
with open(file,'w') as f:
f.writelines(lines)
if __name__ == '__main__':
main()
***** Tag Data *****
ID: 4
description: Calculating average lengths per line across different datasets (training,
validation, testing).
start line: 173
end line: 210
dependencies:
– type: Function/Method/Class/Other
name: get_lengths()
start line: 6
end line: : N/A.
context description: Calculates statistical properties (average lengths) which are
crucial metrics used later.
algorithmic depth: 4
algorithmic depth external: N
obscurity: B.
advanced coding concepts: B.
interesting for students: B.
self contained: Y.
*************
## Suggestions for complexity
Here are five advanced ways to expand or modify logic specific to this code:
### Advanced Suggestion Ideas:
**Suggestion #1:** Implement dynamic threshold calculation based on statistical analysis rather than using a fixed value like `abs(5 * …)`. This would involve calculating standard deviation or other statistical measures.
**Suggestion #2:** Integrate multi-threading to process large datasets more efficiently by splitting `train`, `valid`, `test` data into chunks processed concurrently.
**Suggestion #3:** Introduce logging mechanisms that track how many times assertions fail over multiple runs instead of just raising an exception once.
**Suggestion #4:** Implement custom exceptions that provide more detailed diagnostic information when assertions fail.
**Suggestion #5:** Allow user-defined criteria functions that can dynamically adjust what constitutes an acceptable range between average lengths rather than hardcoding it.
## Conversation
: I’m trying to understand how I can make my assertion checks more dynamic rather than just using fixed values like `abs(5 * …)`. Any ideas?
: Sure! One way you could make your assertion checks more dynamic is by incorporating statistical measures such as standard deviation into your calculations. Here’s how you might modify your code snippet:
python
import numpy as np
# Calculate standard deviations first:
std_dev_valid = np.std([len(x) for x in valid_size_per_line_list_of_lists_of_ints])
std_dev_test = np.std([len(x) for x in test_size_per_line_list_of_lists_of_ints])
# Use these standard deviations instead of fixed percentages:
try:
assert abs(avg_valid_length_per_line_float32_scalar – avg_train_length_per_line_float32_scalar) <= std_dev_valid,
'\n\n\tAverage validation sentence length should be within one standard deviation of training sentence length\n\n'
except AssertionError:
raise Exception('\n\n\tAverage validation sentence length should be within one standard deviation of training sentence length\n\n')
try:
assert abs(avg_test_length_per_line_float32_scalar – avg_train_length_per_line_float32_scalar) <= std_dev_test,
'\n\n\tAverage testing sentence length should be within one standard deviation of training sentence length\n\n'
except AssertionError:
raise Exception('\n\n\tAverage testing sentence length should be within one standard deviation of training sentence length\n\n')
This way your thresholds will adapt based on actual data variability rather than being static.
: That looks good! But what if I want my code to handle very large datasets more efficiently? Can we use multi-threading here?
: Absolutely! Multi-threading can help process large datasets faster by parallelizing tasks. Here’s an example using Python’s `concurrent.futures` module:
python
from concurrent.futures import ThreadPoolExecutor
def calculate_average_and_max(lengths):
return {
“avg”: np.mean(lengths).astype(np.float32),
“max”: np.max(lengths).astype(np.int64),
“std_dev”: np.std(lengths).astype(np.float32),
}
with ThreadPoolExecutor() as executor:
future_train = executor.submit(calculate_average_and_max, [len(x) for x in tqdm(train_size_per_line_list_of_lists_of_ints)])
future_results_train_avg_max_stddev = future_train.result()
avg_train_length_per_line_float32_scalar = future_results_train_avg_max_stddev[“avg”]
max_train_length_per_line_int64_scalar = future_results_train_avg_max_stddev[“max”]
std_dev_train = future_results_train_avg_max_stddev[“std_dev”]
# Repeat similarly for validation and test sets…
# Now use these results similarly but dynamically calculated earlier…
This approach parallelizes computation which can significantly speed up processing times when dealing with large datasets.
: Great! One more thing—I’d like better diagnostics when assertions fail instead of just raising generic exceptions.
: You can create custom exceptions that provide more detailed diagnostic information when assertions fail. Here’s an example:
python
class LengthAssertionError(Exception):
def __init__(self, message, expected_value=None, actual_value=None):
super().__init__(message)
self.expected_value = expected_value
self.actual_value = actual_value
try:
assert abs(avg_valid_length_per_line_float32_scalar – avg_train_length_per_line_float32_scalar) <= std_dev_valid,
except AssertionError:
raise LengthAssertionError('Validation Length Assertion Failed',
expected_value=f"{avg_train_length_perline} ± {std_dev_valid}",
actual_value=f"{avg_validlengthperline}")
This will give you much richer error messages including what was expected vs what was actually found.
: And finally—can we allow user-defined criteria functions? For example allowing users to define what constitutes acceptable ranges dynamically?
: Certainly! You can allow users to pass custom functions that define acceptable ranges dynamically. Here’s how you might implement this feature:
python
def check_lengths(user_defined_criteria_func=None):
if user_defined_criteria_func is None:
user_defined_criteria_func=lambda avg_diff,std_val:len_diff<=std_val*(abs(avg_diff))
# Validation check using user-defined function or default logic.
try:
assert user_defined_criteria_func(abs(avg_validlengthperline-float(avg_traineachline)),std_dev_valid),'\"\\\\\\\\\\\\\\\\tCustom Validation Length Assertion Failed \\\\\\\\\\\\\"\"'
except AssertionError:raise LengthAssertionError('Custom Validation Length Assertion Failed',expectedvalue=f"{avg_traineachline} ± {std_dev}",actualvalue=f"{avg_validationeachline}")
# Similarly apply this logic wherever necessary…
With this setup users can define their own criteria function tailored specifically to their needs!
***** Tag Data *****
ID: 6
description: Complex nested loop structure processing corpus files into dictionaries.
start line: 34
end line:82.
dependencies:
– type: Function/Method/Class/Other name::read_lines()
start line : N/A .
context description : Processes corpus files into structured dictionaries containing lists.
algorithmic depth :4 .
algorithmic depth external : N .
obscurity : B .
advanced coding concepts : B .
interesting students : B .
self contained : Y.
*************
## Suggestions for complexity
Here are five advanced modifications or expansions relevant to the provided code snippet:
1. **Dynamic Language Pair Handling**: Modify the code so it can dynamically handle any number of language pairs without explicitly listing them out manually every time new pairs are added.
2. **Parallel Processing**: Implement parallel processing techniques using multiprocessing or threading libraries to speed up reading from files especially when dealing with large corpora.
3. **Error Handling Enhancements**: Integrate comprehensive error handling mechanisms that log detailed error messages whenever file operations fail (e.g., missing files).
4. **Data Integrity Checks**: Add functionality that verifies data integrity after reading lines from files—such as checking if all lists have consistent lengths—and automatically correcting inconsistencies if possible.
5. **Integration with External APIs**: Extend the code so it can fetch additional metadata about each language pair from an external API before processing begins.
## Conversation
[SNIPPET]
I need know whts happening here exactly.
|end|
This code processes corpus files organized by language pairs stored under different directories (`args.valid` and `args.test`). It reads these files into dictionaries containing lists (`valid_lang_pairs`, `valid_src_data_dict_list`, etc.). The main steps are:
– Iterates over each language pair.
– Constructs file paths based on those pairs.
– Checks if corresponding files exist.
– Reads lines from those files if they exist.
– Updates dictionaries (`valid_lang_pairs`, `test_lang_pairs`) accordingly.
|end|
<|user|introduction_dynamic_handling_how_can_i_make_it_handle_any_number_language_paires_without_manually_listing_them|[SNIPPET]
|end|
<|assistant|introduction_dynamic_handling_you_can_use_a_more_dynamic_approach_by_iterating_over_all_available_language pairs_directories_within_each_main_directory_like_this:
|code|
python
for directory_type in [‘valid’, ‘test’]:
dir_path=args.__dict__[directory_type]
if not os.listdir(dir_path): continue
for subdir_entry in os.scandir(dir_path):
if subdir_entry.is_dir():
lang_pair_info = subdir_entry.name.split(‘-‘)
,<br>
&# assuming directory names follow format ‘-<br>'<br>
&# e.g.,’en-es’
&#<br>
&# Check existence
if
‘train’.join(
‘.’+lang
+‘-‘<br><lang
+‘.’+lang
+‘-‘<br><target
+‘.’+source
+‘-‘<br><target
+‘.txt’) &amp;amp;amp;amp;amp;amp;amp;-exists-in-subdir_entry:
&emspttttif directory_type == ‘valid’:
&emsptttttvalid_nlang_pairs</spanspan>.append((lang_pair_info,textbackslash dirpath</spanspan>/textbackslash subdir_entry.name</spanspan>/textbackslash filename))r
&emspttr
tmr
tpr
tmr
tpr
tmr
tpr
tmr
tpr
tmr
tpr
tmr
tp/code|r|
This approach eliminates manual listing by scanning directories dynamically.
|end|
<|user|introduction_parallel_processing_how_can_i_speed_up_reading_from_large_corpora_using_parallel_processing|[SNIPPET]
|end|
<|assistant|introduction_parallel_processing_you_can_use_python's multiprocessing module_to_speed_up_the_reading_process_here's_an_example_modification_for_parallel_reading:
|code|
python
# Import multiprocessing library at beginning:%import multiprocessing as mp%%import functools%%from functools import partial%%from itertools import chain%%import traceback%%from multiprocessing.pool import ThreadPool%mp.cpu_count()
…existing_code…
def _read_and_store(file_path):
try:r$lines=read_lines(file_path)
</$lines=${$file_path}r$return($file_path,$lines)/
pool=
r${result=[pool.apply_async(_read_and_store,file)_for file _in chain(_(all__files))]}
r${results=[(res.get()_for res _in result)}
r${data_dict={(key:value)_for key,value _in results}]}
pool.close()
pool.terminate()
…existing_code_continued…
