SpectralSolver.py
High-level description
The SpectralSolver class in SpectralSolver.py provides a method for reconstructing phylogenetic trees using a spectral algorithm. It leverages the similarity between samples based on shared mutations to partition them recursively, ultimately building a tree structure. This approach aims to minimize the separation of samples with shared mutations while accounting for the size of each partition.
Code Structure
The SpectralSolver class inherits from the GreedySolver class. It primarily overrides the perform_split method to implement its specific partitioning logic using a spectral algorithm. The SpectralSolver utilizes functions from graph_utilities to construct and manipulate similarity graphs.
References
This code references the following symbols:
GreedySolver(fromcassiopeia.solver.GreedySolver)dissimilarity_functions(fromcassiopeia.solver)graph_utilities(fromcassiopeia.solver)
Symbols
SpectralSolver
Description
This class implements a spectral algorithm for phylogenetic tree reconstruction. It inherits from GreedySolver and overrides the perform_split method to partition samples based on a similarity graph.
Inputs
| Name | Type | Description |
|---|---|---|
| similarity_function | Optional[Callable[[int, int, pd.DataFrame, int, Optional[Dict[int, Dict[str, float]]]], float]] | A function to calculate similarity between samples (default: hamming_similarity_without_missing). |
| threshold | Optional[int] | Minimum similarity threshold for graph edges (default: 0). |
| prior_transformation | str | Transformation function for priors (default: “negative_log”). |
Outputs
This class doesn’t directly return any output. It modifies the input CassiopeiaTree object during the solve method.
Internal Logic
- Initialization: Sets the similarity threshold, similarity function, and prior transformation.
perform_splitMethod:- Constructs a similarity graph using
graph_utilities.construct_similarity_graph. - Computes the normalized Laplacian matrix of the graph.
- Calculates the second eigenvector of the Laplacian matrix (Fiedler vector).
- Orders nodes based on their corresponding values in the Fiedler vector.
- Finds the optimal partition index that minimizes the normalized cut.
- Improves the initial partition using
graph_utilities.spectral_improve_cut. - Returns the left and right partitions.
- Constructs a similarity graph using
solveMethod (inherited fromGreedySolver):- Prepares data and weights.
- Recursively partitions the samples using
perform_splituntil individual samples are reached. - Constructs the tree by connecting the partitions with ancestral nodes.
- Populates the input
CassiopeiaTreewith the reconstructed tree.
Side Effects
The solve method modifies the input CassiopeiaTree object by populating its tree structure.
Performance Considerations
The spectral algorithm’s performance depends on the size and structure of the similarity graph. Larger datasets might require more computational resources. The spectral_improve_cut function performs a greedy hill-climbing optimization, which could be computationally intensive for complex graphs.
SpectralSolver.py
High-level description
The SpectralSolver class in SpectralSolver.py provides a method for reconstructing phylogenetic trees using a spectral algorithm. It leverages the similarity between samples based on shared mutations to partition them recursively, ultimately building a tree structure. This approach aims to minimize the separation of samples with shared mutations while accounting for the size of each partition.
Code Structure
The SpectralSolver class inherits from the GreedySolver class. It primarily overrides the perform_split method to implement its specific partitioning logic using a spectral algorithm. The SpectralSolver utilizes functions from graph_utilities to construct and manipulate similarity graphs.
References
This code references the following symbols:
GreedySolver(fromcassiopeia.solver.GreedySolver)dissimilarity_functions(fromcassiopeia.solver)graph_utilities(fromcassiopeia.solver)
Symbols
SpectralSolver
Description
This class implements a spectral algorithm for phylogenetic tree reconstruction. It inherits from GreedySolver and overrides the perform_split method to partition samples based on a similarity graph.
Inputs
| Name | Type | Description |
|---|---|---|
| similarity_function | Optional[Callable[[int, int, pd.DataFrame, int, Optional[Dict[int, Dict[str, float]]]], float]] | A function to calculate similarity between samples (default: hamming_similarity_without_missing). |
| threshold | Optional[int] | Minimum similarity threshold for graph edges (default: 0). |
| prior_transformation | str | Transformation function for priors (default: “negative_log”). |
Outputs
This class doesn’t directly return any output. It modifies the input CassiopeiaTree object during the solve method.
Internal Logic
- Initialization: Sets the similarity threshold, similarity function, and prior transformation.
perform_splitMethod:- Constructs a similarity graph using
graph_utilities.construct_similarity_graph. - Computes the normalized Laplacian matrix of the graph.
- Calculates the second eigenvector of the Laplacian matrix (Fiedler vector).
- Orders nodes based on their corresponding values in the Fiedler vector.
- Finds the optimal partition index that minimizes the normalized cut.
- Improves the initial partition using
graph_utilities.spectral_improve_cut. - Returns the left and right partitions.
- Constructs a similarity graph using
solveMethod (inherited fromGreedySolver):- Prepares data and weights.
- Recursively partitions the samples using
perform_splituntil individual samples are reached. - Constructs the tree by connecting the partitions with ancestral nodes.
- Populates the input
CassiopeiaTreewith the reconstructed tree.
Side Effects
The solve method modifies the input CassiopeiaTree object by populating its tree structure.
Performance Considerations
The spectral algorithm’s performance depends on the size and structure of the similarity graph. Larger datasets might require more computational resources. The spectral_improve_cut function performs a greedy hill-climbing optimization, which could be computationally intensive for complex graphs.