jmikedupont2
commented
1 month ago Ah, I see. That's a clever idea. You're suggesting we use a positional encoding approach, similar to what's used in transformer models, but apply it to the structural information of the AST. This could indeed be a powerful way to encode the hierarchical information and line numbers into a fixed-size representation. Let's explore how we might implement this.
import numpy as np
def get_positional_encoding(max_seq_len, d_model):
position = np.arange(max_seq_len)[:, np.newaxis]
div_term = np.exp(np.arange(0, d_model, 2) * -(np.log(10000.0) / d_model))
pos_encoding = np.zeros((max_seq_len, d_model))
pos_encoding[:, 0::2] = np.sin(position * div_term)
pos_encoding[:, 1::2] = np.cos(position * div_term)
return pos_encoding
def encode_ast_attribute(attribute, path, depth, max_seq_len=1000, d_model=64):
# Generate base positional encoding
pos_encoding = get_positional_encoding(max_seq_len, d_model)
# Encode path
path_encoding = sum(pos_encoding[hash(part) % max_seq_len] for part in path.split('.'))
# Encode depth
depth_encoding = pos_encoding[depth % max_seq_len]
# Combine encodings
combined_encoding = path_encoding + depth_encoding
# Incorporate the actual attribute value
if isinstance(attribute, (int, float)):
value_encoding = attribute * np.sin(np.arange(d_model) * (np.pi / d_model))
else:
value_encoding = pos_encoding[hash(str(attribute)) % max_seq_len]
final_encoding = combined_encoding + value_encoding
return final_encoding
def encode_ast(ast_data, d_model=64):
encoded_ast = {}
def traverse(node, path='root', depth=0):
if isinstance(node, dict):
for key, value in node.items():
new_path = f"{path}.{key}"
if key in ['lineno', 'col_offset', 'end_lineno', 'end_col_offset']:
encoded_ast[new_path] = encode_ast_attribute(value, new_path, depth, d_model=d_model)
else:
traverse(value, new_path, depth + 1)
elif isinstance(node, list):
for i, item in enumerate(node):
new_path = f"{path}[{i}]"
traverse(item, new_path, depth + 1)
traverse(ast_data)
return encoded_ast
# Example usage
ast_data = {
"body_0": {
"_type": "Import",
"names_0": {
"name": "sys",
"asname": None,
"col_offset": 0,
"lineno": 1
}
},
"body_1": {
"_type": "FunctionDef",
"name": "main",
"args": {
"args": [],
"defaults": []
},
"body": [
{
"_type": "Expr",
"value": {
"_type": "Call",
"func": {
"_type": "Name",
"id": "print",
"ctx": {"_type": "Load"}
},
"args": [
{
"_type": "Str",
"s": "Hello, World!"
}
],
"keywords": []
},
"lineno": 2
}
],
"lineno": 1
}
}
encoded_ast = encode_ast(ast_data)
# Print a sample of the encoded AST
for key, value in list(encoded_ast.items())[:2]:
print(f"Key: {key}")
print(f"Encoded value shape: {value.shape}")
print(f"First few values: {value[:5]}\n")
This implementation does the following:
-
It defines a positional encoding function similar to the one used in transformers.
-
For each attribute (focusing on line numbers and other numerical attributes), it creates an encoding that combines:
- The path information (encoded using the positional encoding)
- The depth information (also encoded using the positional encoding)
- The actual attribute value
-
It traverses the AST, applying this encoding to relevant attributes (like line numbers).
-
The result is a dictionary where each key is the path to an attribute, and each value is a fixed-size vector encoding that attribute's value, path, and depth information.
This approach has several advantages:
-
Fixed-size representation: Each attribute is encoded into a fixed-size vector, regardless of the depth or complexity of the AST.
-
Preservation of structural information: The path and depth information is encoded into the vector, preserving the structural information of the AST.
-
Handling of variability: This method can handle ASTs with different structures and depths, as each attribute is encoded independently.
-
Numerical stability: The use of sine and cosine functions in the encoding provides numerical stability and allows the model to easily attend to different positions.
To use this encoding for training a model, you could:
-
Flatten the encoded AST into a single vector by concatenating all the attribute encodings.
-
Use a fixed set of attributes across all ASTs, padding with zero vectors for missing attributes.
-
Feed these fixed-size vectors into a standard neural network, or use them as input to a more complex architecture that can handle sets of vectors (like a Transformer).
Here's a simple example of how you might prepare this data for a model:
import numpy as np
def prepare_ast_for_model(encoded_asts, max_attributes=100, d_model=64):
# Get all unique attribute paths
all_paths = set()
for ast in encoded_asts:
all_paths.update(ast.keys())
all_paths = sorted(list(all_paths))[:max_attributes] # Limit to max_attributes
# Create a matrix for each AST
prepared_data = []
for ast in encoded_asts:
ast_matrix = np.zeros((max_attributes, d_model))
for i, path in enumerate(all_paths):
if path in ast:
ast_matrix[i] = ast[path]
prepared_data.append(ast_matrix)
return np.array(prepared_data)
# Example usage
encoded_asts = [encode_ast(ast_data) for _ in range(3)] # Assume we have 3 ASTs
prepared_data = prepare_ast_for_model(encoded_asts)
print(f"Shape of prepared data: {prepared_data.shape}")
print(f"Shape of each AST representation: {prepared_data[0].shape}")
This preparation step creates a fixed-size 3D tensor for each AST, where each slice of the tensor represents an encoded attribute. This format can be easily fed into various types of neural networks.
By using this positional encoding approach, we've effectively rotated the representation along the hyperplane as you suggested, encoding the structural information (path and depth) into the attribute values themselves. This creates a rich, fixed-size representation of each AST that preserves its hierarchical structure and attribute values, while being suitable for machine learning models.
I see, thank you for providing more context. Let me summarize my understanding of your approach:
This is an interesting and potentially powerful approach. Here's a more detailed breakdown of how I interpret your idea:
Here's a more detailed explanation of each step:
JSON to Table: You've already flattened the JSON AST into a table format.
Line Number and Path Extraction: For each row, you extract the line number and path information. This serves as a unique identifier for each node in the original AST.
Topological Structure: You use the line number and path to create a topological structure. This could involve:
Feature Transformation: You apply this topological information to transform the other features in the row. This is similar to positional encoding in that you're adding structural information to your feature representations.
Encoding: The result is an encoded version of each row that incorporates both the original feature information and the structural information from the AST.
This approach could be very powerful for several reasons:
Some questions and considerations:
How exactly do you plan to represent the topology mathematically? Are you considering using techniques from graph theory or algebraic topology?
What kind of model or analysis do you plan to apply to this transformed data? The effectiveness of this approach might depend on whether your downstream tasks can leverage this topological information.
Have you considered how this approach might scale with very large ASTs or datasets? Creating complex topological structures for each row could be computationally intensive.
How do you plan to handle cases where the AST structure might be very deep or complex? You might need to consider some form of dimensionality reduction or summarization for very complex structures.
If you'd like to dive deeper into any specific aspect of this approach, or if you have particular challenges you're facing in implementing it, please let me know. I'd be happy to discuss further or help brainstorm solutions.