Fifth Round Exit </di[0]: # Copyright (c) Microsoft Corporation.
[1]: # Licensed under the MIT License.
[2]: import numpy as np
[3]: import torch
[4]: from torch.nn.utils.rnn import pad_sequence
[5]: from .base_dataset import BaseDataset
[6]: class SeqDataset(BaseDataset):
[7]: """Sequence dataset class."""
[8]: def __init__(self,
[9]: data_path,
[10]: train=True,
[11]: max_len=1000,
[12]: max_vocabulary_size=10000,
[13]: use_in_memory=False,
[14]: padding_index=0):
[15]: super(SeqDataset).__init__(data_path)
[16]: self.max_len = max_len
[17]: self.max_vocabulary_size = max_vocabulary_size
[18]: self.use_in_memory = use_in_memory
[19]: self.padding_index = padding_index
[20]: self.train = train
[21]: # Load data.
[22]: if not use_in_memory:
[23]: self.data_path = data_path
[24]: self.filenames = []
if train:
file_name_prefix = 'train'
else:
file_name_prefix = 'test'
self.file_names.append(file_name_prefix + '_inputs.txt')
self.file_names.append(file_name_prefix + '_outputs.txt')
else:
inputs_file_name = 'train_inputs.txt' if train else 'test_inputs.txt'
outputs_file_name = 'train_outputs.txt' if train else 'test_outputs.txt'
with open(inputs_file_name) as f:
lines_x = [line.rstrip() for line in f]
with open(outputs_file_name) as f:
lines_y = [line.rstrip() for line in f]
assert len(lines_x) == len(lines_y),
'The number of lines does not match between {} ({}) '
'and {} ({})'.format(inputs_file_name,
len(lines_x),
outputs_file_name,
len(lines_y))
data_x_list = []
data_x_list.append(data)
# Build vocabulary.
vocab_set = set()
vocab_count_dict = dict()
if not use_in_memory:
data_path_dirname=os.path.dirname(self.data_path)
filename=self.filenames[idx]
filename=os.path.join(data_path_dirname,filename)
lines=f.readlines()
num_lines=len(lines)
vocab_set.add(word)
vocab_count_dict[word]=vocab_count_dict.get(word,0)+1
break
vocab_list_sorted_by_count_descending=[word for word,_ in sorted(vocab_count_dict.items(),key=lambda x:x[-1],reverse=True)]
vocab_list_sorted_by_count_descending=vocab_list_sorted_by_count_descending[:max_vocabulary_size]
vocab_list_sorted_by_alphabetical_order=[word for word,_ in sorted(vocab_count_dict.items(),key=lambda x:x[-1])]
vocab_list_sorted_by_alphabetical_order=vocab_list_sorted_by_alphabetical_order[:max_vocabulary_size]
assert len(vocab_set)==len(vocab_list_sorted_by_count_descending)==len(vocab_list_sorted_by_alphabetical_order),
"the number of words must be equal"
self.vocab_word_to_index=dict(zip(vocab_list_sorted_by_alphabetical_order,list(range(len(vocab_list_sorted_by_alphabetical_order)))))
self.vocab_word_to_index['__PAD__']=self.padding_index
assert '__PAD__' not in vocab_set,'__PAD__ is reserved token'
return list(map(lambda word:self.vocab_word_to_index[word],line.split()))
return [self.vocab_word_to_index['__UNK__']]
***** Tag Data *****
ID: 4
description: Sorting vocabulary by count descending order or alphabetically then creating
word-to-index mappings including handling special tokens like '__PAD__'.
start line: 69
end line: 92
dependencies:
– type: Class
name: SeqDataset.__init__
start line: 8
end line: 20
context description: This part involves complex dictionary operations and sorting.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
interesting for students: 5
advanced coding concepts: 4
self contained: Y
*************
## Suggestions for complexity
1. **Dynamic Vocabulary Update**: Modify the code to dynamically update `vocab_set`, `vocab_count_dict`, `vocab_word_to_index` when new words are encountered during runtime without reinitializing everything.
2. **Parallel Processing**: Implement parallel processing to handle large datasets efficiently when updating `vocab_set` and `vocab_count_dict`.
3. **Custom Sorting Logic**: Introduce custom sorting logic that sorts vocabulary based on a combination of frequency count and alphabetical order.
4. **Hierarchical Vocabulary Management**: Create hierarchical management of vocabulary where words are grouped into categories based on their context or usage patterns.
5. **Persistent Storage Integration**: Integrate persistent storage solutions such as SQLite or NoSQL databases to store vocabulary information instead of keeping it all in memory.
## Conversation
I need help understanding how I can dynamically update my vocabulary during runtime without reinitializing everything.
To dynamically update your vocabulary during runtime without reinitializing everything, you can create methods that handle updates incrementally. Here’s an example:
python
def update_vocab(self, new_words):
“””
Update vocabulary with new words encountered during runtime.
“””
new_vocab_set = set(new_words)
# Update vocab_set
self.vocab_set.update(new_vocab_set)
# Update vocab_count_dict
for word in new_vocab_set:
if word not in self.vocab_count_dict:
self.vocab_count_dict[word] = 0
# Assuming you have some way to count occurrences of each new word here.
# For example purposes, we’re just adding one occurrence per word.
self.vocab_count_dict[word] += new_words.count(word)
# Example usage within your class definition:
# Assuming initial setup code already exists…
new_words_example = [‘example’, ‘words’, ‘to’, ‘update’]
self.update_vocab(new_words_example)
# Re-sorting after updating (if needed)
vocab_list_sorted_by_count_descending=[word for word,_ in sorted(self.vocab_count_dict.items(), key=lambda x:x[-1], reverse=True)]
vocab_list_sorted_by_alphabetical_order=[word for word,_ in sorted(self.vocab_count_dict.items())]
assert len(self.vocab_set)==len(vocab_list_sorted_by_count_descending)==len(vocab_list_sorted_by_alphabetical_order),
“the number of words must be equal”
self.vocab_word_to_index=dict(zip(vocab_list_sorted_by_alphabetical_order,list(range(len(vocab_list_sorted_by_alphabetical_order)))))
self.vocab_word_to_index[‘__PAD__’]=self.padding_index
assert ‘__PAD__’ not in self.vocab_set,’__PAD__ is reserved token’
This way you can keep your existing structures updated without needing to reinitialize them completely.
Can you show me how I might implement parallel processing when updating `vocab_set` and `vocab_count_dict`? My dataset is quite large.
Certainly! You can use Python’s multiprocessing module to handle large datasets more efficiently by distributing tasks across multiple processes.
Here’s an example using multiprocessing:
python
from multiprocessing import Pool, Manager
def process_chunk(chunk):
local_vocab_set = set(chunk)
local_vocab_counter = {}
for word in local_vocab_set:
local_vocab_counter[word] = chunk.count(word)
return local_vocab_set, local_vocab_counter
def merge_results(results):
global_vocab_set = set()
global_vocab_counter = {}
for local_vocab_set, local_vocab_counter in results:
global_vocab_set.update(local_vocab_set)
for word, count in local_vocab_counter.items():
if word not in global_vocab_counter:
global_vocab_counter[word] = count
else:
global_vocab_counter[word] += count
return global_vocab_set, global_vocab_counter
def update_large_dataset_parallel(dataset_chunks):
pool_size=4 # Adjust based on your CPU cores
with Pool(pool_size) as pool:
results=pool.map(process_chunk,dataset_chunks)
return merge_results(results)
# Example usage within your class definition:
dataset_chunks=[‘list’,’of’,’large’,’chunks’] # Split your dataset into manageable chunks beforehand
global_vocab_set , global_vocab_counter=update_large_dataset_parallel(dataset_chunks)
# Update existing structures
self.vocab_set.update(global_vocab_set)
for k,v in global_vocab_counter.items():
if k not in self.vocab_count_dict :
self.vocab_count_dict[k]=v
else :
self.vocab_count_dict[k]+=v
# Re-sorting after updating (if needed)
vocab_list_sorted_by_count_descending=[word for word,_
in sorted(self.vocab_count_dict.items(), key=lambda x:x[-1], reverse=True)]
vocab_list_sorted_by_alphabetical_order=[word
for word,_
in sorted(self.vocab_count_dict.items())]
assert len(self.vocab_set)==len(vocab_list_sorted_by_count_descending)==len(vocab_list_sorted_by_alphabetical_order),
“the number of words must be equal”
self.vocab_word_to_index=dict(zip(
vocab_list_sorted_by_alphabetical_order,list(range(len(vocab_list_sorted_by_alphabetical_order)))))
self.vocab_word_to_index[‘__PAD__’]=self.padding_index
assert ‘__PAD__’ not in self.vocab_set,’__PAD__ is reserved token’
This approach splits your dataset into chunks that are processed concurrently across multiple CPU cores.
That looks good! Now I want custom sorting logic that sorts vocabulary based on a combination of frequency count and alphabetical order.
To implement custom sorting logic that combines frequency count and alphabetical order, you can define a custom key function that takes both criteria into account.
Here’s how you might do it:
python
def custom_sort_key(item):
“””
Custom sort key combining frequency count (descending)
and alphabetical order (ascending).
“””
word,count=item
return (-count, word)
# Custom sorting logic applied here:
sorted_combined_criteria=[word
for word,count
in sorted(self.vocab_count_dict.items(), key=custom_sort_key)]
# Verify lengths remain consistent after custom sorting logic.
assert len(self.vocab_set)==len(sorted_combined_criteria),
“the number of words must be equal”
# Update index mapping accordingly.
self.sorted_combined_criteria_indexes=dict(zip(
sorted_combined_criteria,list(range(len(sorted_combined_criteria)))))
self.sorted_combined_criteria_indexes[‘__PAD__’]=self.padding_index
assert ‘__PAD__’ not in self.sorted_combined_criteria,’__PAD__ is reserved token’
# You may also want to maintain separate lists if necessary.
In this example:
– The custom sort key function returns a tuple where `-count` ensures descending order by frequency while `word` ensures ascending alphabetical order when counts are tied.
– We then apply this custom sort key using Python’s built-in `sorted()` function.
This allows you to prioritize frequent words but also respects alphabetical ordering among equally frequent words.
***** Tag Data *****
ID: 5
description: Mapping each sequence input/output pair through predefined transformations,
ensuring consistent length via padding/truncation techniques using PyTorch utilities.
start line: 143
end line: 167
dependencies:
– type: Method/Functionality Name/Class Contextual Reference/etc.
start line: None required directly related but contextually relevant lines include those leading up till here which describe loading sequences etc., essentially covering previous parts including lines starting around initializing variables until here which leads up directly into this transformation block segment wise specific operations performed inline or via helper functions encapsulated within this method scope specifically transforming sequences input-output pairs prior batching etc..
context description: This snippet uses PyTorch functionalities like pad_sequence which are crucial yet require understanding about tensor manipulations extensively used throughout deep learning workflows involving sequence models especially RNNs LSTMs etc..
algorithmic depth: 4 algorithmic depth external: N
obscurity: 4
interesting for students: 5
advanced coding concepts: 4
self contained:
************
## Challenging aspects
### Challenging aspects specific to above code:
#### Handling Variable-Length Sequences Efficiently:
The primary challenge lies within managing variable-length sequences effectively using PyTorch functionalities such as `pad_sequence`. Students need to ensure proper alignment while padding/truncating sequences without losing important information or causing misalignment errors.
#### Memory Management Considerations:
Given potentially large datasets being processed sequentially or batch-wise (`use_in_memory` flag), efficient memory management becomes crucial. Students must balance between loading entire datasets into memory versus processing them sequentially while ensuring optimal performance.
#### Conditional Logic Based on Dataset Type (`train` vs non-train scenarios):
The conditional branching based on whether it’s training time (`train`) introduces complexity because different filenames are used depending on this flag (`train_inputs.txt` vs `test_inputs.txt`). Understanding these nuances helps avoid logical errors related to file handling.
#### Robustness Against Inconsistent Data Lengths Between Inputs/Outputs:
Ensuring that corresponding input-output pairs have matching lengths before further processing requires careful validation checks (`assert`). This aspect tests students’ ability to handle real-world inconsistencies gracefully.
### Extensions Specific To Above Code:
#### Dynamic File Handling During Processing:
Extend functionality so that files added dynamically during processing are detected and included without restarting the process entirely.
#### Incorporating Metadata Files Pointing To Additional Data Sources:
Introduce metadata files containing pointers/references to other files/directories containing supplementary sequence information which needs integration seamlessly within current workflow.
#### Sequence Augmentation Techniques Before Padding/Truncation:
Implement various sequence augmentation techniques (e.g., noise addition) before applying padding/truncation steps which could enhance model robustness against overfitting or improve generalization capabilities.
## Exercise
### Task Description:
You will extend [SNIPPET] functionality considering additional complexities outlined below:
**Requirements**:
1. **Dynamic File Handling** – Modify code so it detects any new files added during execution time within specified directories (`data_path`) automatically incorporates them into ongoing processing seamlessly without restarting entire process flow.
2. **Metadata Pointer Integration** – Handle additional metadata files pointing towards other directories/files containing supplementary sequence information which should be merged appropriately before further processing steps such as padding/truncation occur.
3. **Sequence Augmentation Pre-Padding/Truncation** – Implement sequence augmentation techniques such as noise addition or random deletion/addition before applying padding/truncation steps ensuring these augmentations are controlled via parameters passed during initialization.
### Constraints:
– Ensure memory efficiency when toggling between loading all data into memory vs sequential loading approach (`use_in_memory` flag).
– Maintain robust validation checks ensuring input-output consistency throughout preprocessing stages.
## Solution
python
import os
import torch
from torch.nn.utils.rnn import pad_sequence
class AdvancedSequenceProcessor(object):
def __init__(self,data_path,max_len,max_vocabulary_size,padding_idx,batch_first=True,augmentation_params=None):
super().__init__()
self.data_path=data_path
self.max_len=max_len
self.max_vocabulary_size=max_vocabulary_size
self.padding_idx=padding_idx
self.batch_first=batch_first
self.augmentation_params=augmentation_params
def load_and_process_sequences(self,is_train):
inputs_filename=’train_inputs.txt’ if is_train else ‘test_inputs.txt’
outputs_filename=’train_outputs.txt’ if is_train else ‘test_outputs.txt’
def read_lines(filename):
with open(filename) as f :
return [line.rstrip()for linein f ]
lines_x=read_lines(inputs_filename)
lines_y=read_lines(outputs_filename)
assert len(lines_x)==len(lines_y),”Mismatched input/output lengths”
processed_sequences=[]
metadata_files=[f.namefor f os.scandir(os.path.join(data_path,”metadata”))if f.is_file()]
all_files=set([inputs_filename]+metadata_files+[outputs_filename])
while True :
current_files=set(os.listdir(data_path))&all_files
new_files=current_files-all_files
all_files=current_files |all_files
additional_data=[]
def process_metadata(metadata_file):
with open(os.path.join(data_path,metadata_file))as meta_f :
referenced_paths=[os.path.join(data_path,line.rstrip())for linein meta_f ]
referenced_data=[]
for pathin referenced_paths :
try :
with open(path)as ref_f :
referenced_data.extend([line.rstrip()for linein ref_f ])
except FileNotFoundError :
pass
return referenced_data
additional_data.extend([process_metadata(metafile)for metafilein metadata_files])
seq_pairs=list(zip(lines_x+additional_data,newlines_y+additional_data))
seq_pairs=[[seq[:min(len(seq),max_len)]+[”]*(max_len-len(seq))for seqin pair]for pairin seq_pairs]
augmented_seq_pairs=[]
def augment_sequence(seq,augment_params):
augmented_seq=[]
noise_level=augment_params.get(“noise_level”,0.)
delete_prob=augment_params.get(“delete_prob”,0.)
add_prob=augment_params.get(“add_prob”,0.)
aug_seq=list(seq.copy())
if noise_level >0.:
aug_seq=[”.join(c+’~’*int(noise_level*10)%10if cisalpha()else cfor cinaug_seq)]
aug_seq=[cfordel_cprobdel_delete_probandcnotin aug_seqelse ”forelse cinaug_seq]
aug_seq.extend([”]*int(max_len-len(aug_seq)))
return aug_seq[:max_len]
augmented_seq_pairs=[[augment_sequence(seq,self.augmentation_paramsor{})fork,vork,vorkpair]forkpairinkseq_pairs]
padded_seqs=torch.tensor([[ord(c)-ord(‘a’)for cinkseq]+[(padding_idx)*(max_len-len(seq))]fork,vork,vorkaugmented_seq_pair])
padded_seqs=padded_seqs.transpose(0,-1).transpose(0,-1).contiguous()if batch_firstelse padded_seqs.transpose(0,-1)
processed_sequences.append(padded_seqs)
return processed_sequences
## Follow-up exercise
### Task Description:
Enhance your implementation further by adding support for multi-threaded file reading/writing operations specifically targeting scenarios where very large datasets exist requiring concurrent access patterns significantly reducing overall preprocessing timeframes while maintaining consistency checks throughout various stages effectively leveraging concurrency paradigms available within Python ecosystem alongside PyTorch functionalities employed earlier.
### Constraints :
– Ensure thread safety measures preventing race conditions/data corruption issues occurring due concurrent access patterns especially prevalent during dynamic file detection phases incorporating additional metadata handling scenarios concurrently across multiple threads executing simultaneously.
***** Tag Data *****
ID: 6
description: Building vocabulary from loaded sequences including counting occurrences,
start line:171-end_line_212 inclusive loop body iterating over sequences generating/update counts/
set accordingly followed by assertion checks verifying consistencies post generation/collection phase.
dependencies/contextual relevance includes preceding blocks establishing variable definitions/loading text files parsing them setting up initial state pre-processing loops encompassing nested iterations conditional checks inline statements forming core logic building dictionary sets performing aggregations followed by validations/assertions post aggregation phase completing build step essential preparing final structures ready subsequent operations/data transformations manipulations inside larger dataset handling framework/class structure overall module design intended facilitating high-level sequence data handling operations specific requirements defined initially setup configurations parameters provided user inputs initializations etc…
context description/overview entire codebase designed purposeful structured logical flow from initializing reading transforming validating aggregating storing sequence data leveraging pytorch tensor utilities facilitating efficient neural network training setups specifically focused accommodating diverse sequence modeling tasks employing advanced techniques ensuring robust scalable implementations flexible adapting various settings configurations tailored specific application contexts domain requirements needs etc…
algorithmic depth/complexity/code intricacy rationale explaining extensive detailed breakdown segmented phases procedural steps involved highlighting critical components interactions dependencies interrelations demonstrating advanced proficient coding practices sophisticated methodologies employed achieving desired functional objectives aligning broader goals module architecture framework design principles underlying system conceptualization development lifecycle encompassing iterative refinements optimizations addressing challenges complexities encountered realizing effective solutions meeting performance standards expectations criteria established project specifications deliverables documentation guidelines best practices industry standards conventions adhered throughout development process resulting comprehensive well-documented high-quality reliable maintainable codebase fulfilling intended purpose serving users effectively efficiently reliably consistently delivering expected outcomes results satisfying requirements constraints imposed contextual operational environments deployment scenarios applications envisioned designed developed implemented tested verified validated thoroughly comprehensively rigorously assuring quality reliability robustness sustainability scalability adaptability flexibility extensibility future-proofing anticipating evolving changing needs demands trends advancements innovations emerging technologies developments field advancing continuously progressing enhancing improving refining optimizing capabilities functionalities features functionalities expanding extending diversifying broadening horizons pushing boundaries exploring possibilities potentials unlocking opportunities harnessing full potential power capabilities tools resources available leveraging cutting-edge state-of-the-art methodologies approaches techniques innovations breakthroughs discoveries latest research findings insights knowledge expertise accumulated gained 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