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# greedy_variants_test.py

## High-level description

This file contains unit tests for the `SpectralGreedySolver` and `MaxCutGreedySolver` classes, which are variants of the greedy solver algorithm used in the Cassiopeia project for phylogenetic tree reconstruction. The tests verify the correctness of the solvers' behavior in various scenarios, including handling of sparse data, base cases, and different weighting schemes.

## Code Structure

The main class `GreedyVariantsTest` contains multiple test methods, each focusing on a specific aspect of the solvers' functionality. The tests create sample character matrices, instantiate solver objects, and compare the resulting tree structures against expected outcomes.

## Symbols

### `find_triplet_structure`

#### Description

This function determines the structure of a triplet of nodes in a given tree.

#### Inputs

| Name    | Type             | Description                  |
| :------ | :--------------- | :--------------------------- |
| triplet | tuple            | A tuple of three node labels |
| T       | networkx.DiGraph | The tree graph               |

#### Outputs

| Name      | Type | Description                                             |
| :-------- | :--- | :------------------------------------------------------ |
| structure | str  | The structure of the triplet ("ab", "ac", "bc", or "-") |

#### Internal Logic

1. Find ancestors for each node in the triplet
2. Calculate the number of common ancestors for each pair
3. Determine the structure based on which pair has the most common ancestors

### `GreedyVariantsTest`

#### Description

A test class containing multiple test methods for the `SpectralGreedySolver` and `MaxCutGreedySolver` classes.

#### Test Methods

1. `test_raises_error_on_ambiguous`: Verifies that the solver raises an error for ambiguous data.
2. `test_spectral_sparse_case`: Tests the `SpectralGreedySolver` with sparse data.
3. `test_spectral_base_case`: Tests the `SpectralGreedySolver` with a base case scenario.
4. `test_spectral_base_case_weights_almost_one`: Tests the `SpectralGreedySolver` with weights close to 1.
5. `test_maxcut_base_case`: Tests the `MaxCutGreedySolver` with a base case scenario.
6. `test_maxcut_base_case_weights_trivial`: Tests the `MaxCutGreedySolver` with trivial weights.

#### Internal Logic

Each test method follows a similar pattern:

1. Create a character matrix
2. Instantiate a solver object
3. Solve the tree
4. Compare the resulting tree structure against expected outcomes using triplet structures

## Dependencies

* unittest
* itertools
* networkx
* pandas
* cassiopeia

## Error Handling

The tests use `assertRaises` to check for expected errors and `assertEqual` to compare results against expected values.

## Notes

* The tests focus on verifying the correctness of the solvers' output rather than performance optimization.
* The use of triplet structures for comparison allows for flexible testing of tree topologies.
* The tests cover various scenarios, including sparse data and different weighting schemes, to ensure robust solver behavior.