C4 Level 3: Component View – symbolic_features.py

This component view describes the internal structure of the symbolic_features.py module in the CIv8r mesoscope system. It is responsible for extracting symbolic features from univariate time series using cellular automata (ECA), symbolic tokenization, and motif-level analysis.

📦 Container: symbolic_features.py

Purpose: Generate symbolic representations and features from pre-break and post-break segments of a time series for downstream fusion and scoring.

Source Repositories:

  • TransformerECA (TransformerECA_ipynb.txt)
  • Intelligence_at_the_edge_of_chaos (Intelligence_at_the_edge_of_chaos_py.txt)

GitHub Traceback License:

  • Apache 2.0 (confirmed for both uploaded sources)

🧩 Internal Components

Component Description GitHub Source Reuse Type
generate_symbolic_sequence(series, rule, steps) Converts raw time series into symbolic bit string using ECA dynamics cellular_automaton() in utils.py【66†Intelligence_at_the_edge_of_chaos】 🛠️ Adapt
tokenize_symbolic_sequence(symbolic_sequence) Applies 1D tokenizer to generate motif tokens SimpleTokenizer【65†TransformerECA_ipynb】 ♻️ Reuse
compute_entropy_delta(pre_tokens, post_tokens) Measures change in symbolic entropy across boundary New logic (entropy stats) ✳️ Build
compute_compression_delta(pre_seq, post_seq) Uses LZ or dictionary compression size delta Adapted from motif stack ideas ✳️ Build
extract_motif_frequencies(tokens) Extracts motif frequency table from token list Implicit in GPT2-style prep【66†Intelligence_at_the_edge_of_chaos】 🛠️ Adapt
compute_motif_kl_divergence(pre_freq, post_freq) Computes KL divergence or JS divergence between pre/post frequency tables New logic ✳️ Build
generate_feature_vector() Combines all symbolic metrics into output dict New code ✳️ Build

🔗 Dependencies

  • numpy, collections.Counter, math.log2
  • Optional: lzma or custom LZ complexity estimator
  • tqdm, re for tokenizer sanitation (from TransformerECA)

🧪 Test Strategy

Component Test Case        
generate_symbolic_sequence() Compare output bits for known ECA rules (e.g. Rule 30, Rule 90)        
tokenize_symbolic_sequence() Validate consistent tokens from symbolic strings        
compute_entropy_delta() Assert entropy(pre) < entropy(post) for injected drift        
compute_motif_kl_divergence() KL(p   q) ≠ KL(q   p); verify symmetry using JS

📤 Output Format

{
    'entropy_delta': 0.23,
    'compression_delta': 0.18,
    'kl_divergence': 1.09,
    'js_divergence': 0.65,
    'pre_freq': {"010": 8, "110": 3},
    'post_freq': {"010": 2, "001": 6}
}

This dictionary will be passed to the fusion_score.py module for integration with latent features.

✅ Status Summary

  • Core symbolic engine fully backed by uploaded GitHub repos
  • Divergence and delta metrics need to be implemented
  • All components Colab-compatible and checkpointable

Next Steps:

  • Integrate this component view into CIv8r internal whitepaper as Appendix C
  • Begin prototyping symbolic runtime in Colab with minimal inputs
  • Validate outputs on ADIA sample test data

End of Component View

C4 Level 3: Component View – symbolic_features.py

This component view describes the internal structure of the symbolic_features.py module in the CIv8r mesoscope system. It is responsible for extracting symbolic features from univariate time series using cellular automata (ECA), symbolic tokenization, and motif-level analysis.

📦 Container: symbolic_features.py

Purpose: Generate symbolic representations and features from pre-break and post-break segments of a time series for downstream fusion and scoring.

Source Repositories:

  • TransformerECA (TransformerECA_ipynb.txt)
  • Intelligence_at_the_edge_of_chaos (Intelligence_at_the_edge_of_chaos_py.txt)

GitHub Traceback License:

  • Apache 2.0 (confirmed for both uploaded sources)

🧩 Internal Components

Component Description GitHub Source Reuse Type
generate_symbolic_sequence(series, rule, steps) Converts raw time series into symbolic bit string using ECA dynamics cellular_automaton() in utils.py【66†Intelligence_at_the_edge_of_chaos】 🛠️ Adapt
tokenize_symbolic_sequence(symbolic_sequence) Applies 1D tokenizer to generate motif tokens SimpleTokenizer【65†TransformerECA_ipynb】 ♻️ Reuse
compute_entropy_delta(pre_tokens, post_tokens) Measures change in symbolic entropy across boundary New logic (entropy stats) ✳️ Build
compute_compression_delta(pre_seq, post_seq) Uses LZ or dictionary compression size delta Adapted from motif stack ideas ✳️ Build
extract_motif_frequencies(tokens) Extracts motif frequency table from token list Implicit in GPT2-style prep【66†Intelligence_at_the_edge_of_chaos】 🛠️ Adapt
compute_motif_kl_divergence(pre_freq, post_freq) Computes KL divergence or JS divergence between pre/post frequency tables New logic ✳️ Build
generate_feature_vector() Combines all symbolic metrics into output dict New code ✳️ Build

🔗 Dependencies

  • numpy, collections.Counter, math.log2
  • Optional: lzma or custom LZ complexity estimator
  • tqdm, re for tokenizer sanitation (from TransformerECA)

🧪 Test Strategy

Component Test Case        
generate_symbolic_sequence() Compare output bits for known ECA rules (e.g. Rule 30, Rule 90)        
tokenize_symbolic_sequence() Validate consistent tokens from symbolic strings        
compute_entropy_delta() Assert entropy(pre) < entropy(post) for injected drift        
compute_motif_kl_divergence() KL(p   q) ≠ KL(q   p); verify symmetry using JS

📤 Output Format

{
    'entropy_delta': 0.23,
    'compression_delta': 0.18,
    'kl_divergence': 1.09,
    'js_divergence': 0.65,
    'pre_freq': {"010": 8, "110": 3},
    'post_freq': {"010": 2, "001": 6}
}

This dictionary will be passed to the fusion_score.py module for integration with latent features.

✅ Status Summary

  • Core symbolic engine fully backed by uploaded GitHub repos
  • Divergence and delta metrics need to be implemented
  • All components Colab-compatible and checkpointable

Next Steps:

  • Integrate this component view into CIv8r internal whitepaper as Appendix C
  • Begin prototyping symbolic runtime in Colab with minimal inputs
  • Validate outputs on ADIA sample test data

End of Component View