Sequence Explorer

Interactive Streamlit dashboard for visualizing how a sequence model's hidden state evolves over transaction sequences. Works with any PyTorch RNN (GRU, LSTM, RNN) with any number of layers.

Two Ways to Use

As a Package (pip install)

pip install seq-explorer

In Python/Notebooks:

from seq_explorer import SequenceTrace

trace = SequenceTrace.from_arrays(...)

Run dashboard:

streamlit run src/seq_explorer/app.py

As a Project (clone & run)

git clone https://github.com/chris-santiago/seq-explorer
cd seq-explorer
uv sync
uv run python src/seq_explorer/build_cache.py dataframe your_data.csv -o cache.parquet
uv run streamlit run src/seq_explorer/app.py

What it shows

• Model score timeline — running P(fraud) at every timestep, color-coded green → red
• Hidden state heatmap — per-neuron activations across the sequence (any number of layers)
• Hidden state norms — L2 norm over time for all layers, plus rate-of-change bars
• Top-k neuron drill-down — neurons most correlated with the fraud score, traced over time
• Layer similarity — cosine similarity between consecutive hidden state layers
• Raw features table — the actual transaction data, highlighted at the selected timestep
• Metadata overlays — visualize categorical/numeric metadata on timelines (e.g., risk tiers, channels)
• Timestep scrubber — linked across all panels for synchronized inspection

Quick Start

# Install dependencies
uv sync

# Build cache from CSV/Parquet (auto-detects schema)
uv run python src/seq_explorer/build_cache.py dataframe your_data.csv -o cache.parquet

# Launch dashboard
uv run streamlit run src/seq_explorer/app.py

The dashboard auto-detects hidden state columns - just use any prefix pattern like h0_*, h1_* or encoder_*, decoder_*.

Usage Options

Option 1: Construct + Plot Directly in Jupyter (Simplest!)

No need to save files or run Streamlit. Just use the plotting functions:

from seq_explorer import (
    SequenceTrace,
    fraud_score_timeline,
    hidden_state_heatmap,
    hidden_norm_plot,
    top_neuron_traces,
    layer_similarity_plot,
    raw_feature_heatmap,
    feature_fraud_correlation,
    metadata_timeline_overlay,
)

trace = SequenceTrace.from_arrays(
    sequence_id=0,
    label=1,
    raw_features=my_features,        # (seq_len, n_features)
    feature_names=['amount', ...],
    hidden_states=[h0, h1],         # list of (seq_len, hidden_dim)
    running_fraud_scores=scores,    # (seq_len,)
)

# All plotting functions return Plotly figures - show them inline!
fraud_score_timeline(trace.running_fraud_scores).show()
hidden_state_heatmap(trace.hidden_states[0]).show()
hidden_norm_plot(trace.hidden_norms).show()
top_neuron_traces(
    trace.hidden_states[0],
    trace.top_neuron_indices[0],
    trace.top_neuron_correlations[0]
).show()

Option 2: Construct + Save + Dashboard

# Save to Parquet
df = SequenceTrace.to_dataframe({0: trace})
df.write_parquet('cache.parquet')

# Launch dashboard
streamlit run src/seq_explorer/app.py

Option 3: From DataFrame

python src/seq_explorer/build_cache.py dataframe data.csv -o cache.parquet

Option 4: From Model

python src/seq_explorer/build_cache.py model \
    --checkpoint model.ckpt \
    --data transactions.pt \
    --auto-select \
    -o cache.parquet

Model Support

Works with any PyTorch sequence model:

• GRU - any number of layers
• LSTM - any number of layers
• RNN - any number of layers
• Custom architectures with different attribute names (e.g., encoder, rnn_module)

Project structure

seq-explorer/
├── seq_explorer/           # Package + CLI
│   ├── __init__.py
│   ├── app.py            # Streamlit dashboard
│   ├── plots.py          # Plotly figure builders
│   ├── extractor.py      # Model trace extraction
│   ├── trace.py          # Data models
│   └── build_cache.py   # Cache builder CLI
├── docs/                  # Documentation
├── demo/                  # Demo notebooks
└── README.md

Documentation

See the docs/ folder for full documentation:

• Quick Start
• Dashboard Guide
• Cache Format
• Architecture