localgraph 0.1.1

Local graph estimation with pathwise feature selection

Local graph estimation with pathwise feature selection

Local graph estimation is a framework for discovering local graph/network structure around specific variables of interest. Pathwise feature selection (PFS) is an algorithm for performing local graph estimation with pathwise false discovery control.

Associated paper

• Local graph estimation: Interpretable network discovery for complex data
  In preparation

Installation

pip install localgraph

Usage

from localgraph import pfs, plot_graph

# Load n-by-p data matrix X (n samples, p features)

# Specify the target features (list of indices)
target_features = [0, 1]

# Specify the pathwise q-value threshold
qpath_max = 0.2

# Optional: specify the maximum radius of the local graph (default is 3)
max_radius = 3

# Optional: specify the neighborhood FDR thresholds for nodes in each radius
fdr_local = [0.2, 0.1, 0.1]

# Run PFS
Q = pfs(X, target_features, qpath_max=qpath_max, max_radius=max_radius, fdr_local=fdr_local)

# Plot the estimated subgraph
plot_graph(graph=Q, target_features=target_features, radius=max_radius)

Outputs

• Q: Dictionary mapping edges (i,j) to q-values. Edges are undirected, so (i,j) and (j,i) are included.

What PFS does

• Expands the local graph outward, layer by layer, starting from target variables.
• Performs neighborhood selection with FDR control using integrated path stability selection.
• Controls pathwise false discoveries by summing q-values along candidate paths.

Full list of pfs arguments

Required arguments:

• X: n-by-p data matrix (NumPy array). Each column is a feature/variable.
• target_features: Feature index or list of indices to center the graph around.
• qpath_max: Maximum allowed sum of q-values along any path.

Optional arguments:

• max_radius: Maximum number of expansion layers around each target (int; default 3).
• fdr_local: Neighborhood FDR threshold at each radius (list of length max_radius; default [qpath_max]*max_radius).
• custom_nbhd: Dictionary specifying custom FDR cutoffs for certain features (dict; default None).
• feature_names: List of feature names; required if custom_nbhd is provided (list of strings).
• criterion: Rule for resolving multiple edges (default 'min').
• selector: Feature importance method used by IPSS (str; default 'gb'). Options:
  • 'gb': Gradient boosting
  • 'l1': L1-regularized regression (lasso)
  • 'rf': Random forest
  • Custom function (see ipss_args)
• ipss_args: Dictionary of arguments to pass to ipss (dict; default None)
• verbose: Whether to print progress during selection (bool; default False)

Graph plotting

Use plot_graph to visualize a local graph up to the specified radius around one or more target features.

from localgraph import plot_graph

# Plot local graph around target_features using output Q from pfs
plot_graph(graph=Q, target_features=target_features, radius=3)

Features and customization

plot_graph visualizes a local graph of a user-specified radius around one or more target features. It supports:

• Flexible input formats: edge dictionary, adjacency matrix, or NetworkX graph
• Automatic subgraph extraction around the targets
• Node coloring by distance from the target(s) (default), or user-specified colors (e.g., by variable type)
• Several layout algorithms ('kamada_kawai', 'spring', 'circular', etc.)
• Customizable node size, font sizes, and edge thickness
• Optional display of q-values; edge widths can reflect q-value strength (edge_widths='q_value')
• False positives shown in red if the true graph is provided
• Integration with custom plots via ax or pos
• Optional saving of figures (save_fig) and graphs (save_graph)

For a full list of arguments, see the plot_graph docstring.

Returns

The function returns a dictionary containing:

• feature_radius_list: List of (feature name, radius) pairs for all nodes in the graph.
• graph: The NetworkX subgraph used for plotting.
• positions: Dictionary of node coordinates.
• figure: The matplotlib figure object (only if the function creates the figure).

Further customization

To manually adjust node positions for publication-quality figures, you can export graphs to Gephi, edit them interactively, and re-import the updated layout into Python. See: gephi_instructions.md for a full walkthrough.

Examples

The examples/ folder contains scripts that demonstrate end-to-end usage:

• simple_example.py: Simulate data, run PFS, and visualize the result.

Evaluation tools

The evaluation/ folder contains helper functions for measuring subgraph recovery in simulation settings.

• The eval.py script contains two functions:
  • subgraph_within_radius: Extract true subgraph around a target node (useful for identifying subgraphs within full graphs)
  • tp_and_fp: Count true and false positives compared to ground truth

These are useful for benchmarking PFS and other graph estimation methods when the true graph is known.