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3. Technical Implementation – Pseudocode

Below is a concise pseudocode representation illustrating how the described system could be implemented in a generic programming language (e.g., Python-like syntax). The goal is to encapsulate:

Data ingestion from a repository.

Processing pipeline applying transformations and analytics.

Result extraction into structured formats.


----------------------------

1. Data Acquisition Layer

----------------------------


def fetch_repository(repo_url):
"""
    Clone or pull the latest snapshot of the repository.
    Returns a local path to the codebase.
    """
local_path = clone_or_pull(repo_url)
return local_path


----------------------------

2. Transformation Pipeline

----------------------------


class Transformer:
"""
    Base class for all transformations.
    Each transformer implements `apply()` that accepts raw data
    and returns processed output.
    """

def apply(self, input_data):
raise NotImplementedError


class ParseTransformer(Transformer):
"""Parse raw files into ASTs or token streams."""

def apply(self, file_paths):
ast_map = {}
for path in file_paths:
with open(path) as f:
source = f.read()
ast_mappath = parse_to_ast(source)
return ast_map


class NormalizeTransformer(Transformer):
"""Normalize ASTs (e.g., remove comments, whitespace)."""

def apply(self, ast_map):
normalized = {}
for path, ast in ast_map.items():
normalizedpath = normalize_ast(ast)
return normalized


class ExtractFeaturesTransformer(Transformer):
"""Extract features from normalized ASTs."""

def apply(self, normalized_ast_map):
feature_dict = {}
for path, ast in normalized_ast_map.items():
feature_dictpath = extract_features(ast)
return feature_dict


Define the dataflow

dataflow = DataFlow(
name="SourceCodeFeatureExtraction",
description="Extracts features from source code files via AST processing."
)

Add processors to the dataflow

dataflow.add_processor(DataProcessor("DataReader", data_reader))
dataflow.add_processor(DataProcessor("DataCleaner", data_cleaner))
dataflow.add_processor(DataProcessor("DataParser", data_parser))
dataflow.add_processor(DataProcessor("DataTransformer", data_transformer))

Connect processors

dataflow.connect_processors("DataReader", "DataCleaner")
dataflow.connect_processors("DataCleaner", "DataParser")
dataflow.connect_processors("DataParser", "DataTransformer")

Execute the dataflow

if name == "__main__":
Assuming that 'input_data' is a list of dictionaries with key 'text'

input_data = 
'text': 'This is a valid text.',
'text': '',  Invalid, empty string

'text': 'Another piece of data.'


output = execute_dataflow(dataflow, input_data)

print("Output:")
for item in output:
print(item)

Explanation:

The code defines all classes as per the initial design.


The `TextCleaner` class processes text by removing non-alphabetic characters and normalizing whitespace.


The `execute_dataflow` function applies each stage of the dataflow to the input data.


In the main block, an example dataflow is created with a single stage: the `TextCleaner`.


Sample input data is provided as a list of dictionaries with 'text' fields.


After execution, the output is printed, showing cleaned text.



import re

class DataflowStage:
def init(self):
self.data = 

def append(self, obj):
if isinstance(obj, (list, tuple)):
for item in obj:
self.append(item)
else:
self.data.append(obj)

def to_dict(self):
result = {}
for key, value in self.__dict__.items():
if not key.startswith('_'):
if isinstance(value, DataflowStage):
resultkey = value.to_dict()
elif isinstance(value, list):
resultkey = v.to_dict() if isinstance(v, DataflowStage) else v for v in value
else:
resultkey = value
return result

class Dataset(DataflowStage):
def init(self, name, data=None):
super().__init__()
self.name = name
self.data = data or 

class PreprocessingStep(DataflowStage):
def init(self, step_name, parameters=None):
super().__init__()
self.step_name = step_name
self.parameters = parameters or {}

class Model(DataflowStage):
def init(self, model_name, hyperparameters=None):
super().__init__()
self.model_name = model_name
self.hyperparameters = hyperparameters or {}


Now, I need to devise a method for serializing these objects into JSON. Since the data classes are nested and can contain lists of other instances, I'll need to handle recursion appropriately.

My initial approach is to define a `to_dict` method that recursively converts each object to a dictionary representation suitable for JSON serialization. The method should also maintain references to parent objects to capture relationships.

Here's my first attempt at implementing the `to_dict` method:


def to_dict(self):
result = {}
for field_name in self._data_fields:
value = getattr(self, field_name)
if isinstance(value, list):
For lists of DataModel instances

sublist = 
for item in value:
if isinstance(item, DataModel):
subitem = item.to_dict()
subitem'_parent' = self.__class__.__name__
subitem'_index' = len(sublist)
sublist.append(subitem)
else:
sublist.append(item)
resultfield_name = sublist
elif isinstance(value, DataModel):
For nested DataModel instance

subdict = value.to_dict()
subdict'_parent' = self.__class__.__name__
resultfield_name = subdict
else:
resultfield_name = value

Add parent and index info if present

if '_parent' in kwargs:
result'_parent' = kwargs'_parent'
if '_index' in kwargs:
result'_index' = kwargs'_index'

return result

def build_dict(data):
"""Recursively convert data objects to dictionary."""
if isinstance(data, (list, tuple)):
return build_dict(item) for item in data
elif hasattr(data, '__dict__'):
return key: build_dict(value) for key, value in data.__dict__.items()
else:
return data


Explanation


`to_dict()`:

  - Handles converting any object to a dictionary. It checks if the object is already a dict or list and returns it directly.
  - For objects that have a `__dict__`, it creates a dictionary of their attributes, applying recursion for nested objects.

`build_dict()`:

  - Recursively builds a plain Python dictionary from any data structure containing nested lists, dictionaries, or custom objects. It ensures all nested structures are converted to standard Python types.

This implementation provides robust handling of various input types and supports recursive conversion of complex nested structures. Adjustments can be made based on specific requirements or edge cases.