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High-level description

This file contains unit tests for the tree_metrics.py module in the Cassiopeia package. These tests cover functions that calculate various tree metrics, including parsimony, log transition probability, log likelihood (both discrete and continuous), and parameter estimation for lineage tracing models.

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Code Structure

This file defines a single test class, TestCassiopeiaTree, which houses multiple test methods. Each test method focuses on a specific function or aspect of the tree_metrics.py module. The tests utilize small, predefined tree structures and character matrices to validate the correctness of the implemented algorithms.

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Symbols

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TestCassiopeiaTree

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Description

This class contains unit tests for functions in the tree_metrics.py module. It sets up test data representing small trees and character matrices, then uses these to test the calculation of various tree metrics.

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Inputs

This class does not take any explicit inputs.

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Outputs

This class does not return any explicit outputs. It is used for running tests.

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Internal Logic

The class defines several test methods, each testing a specific function or aspect of the tree_metrics.py module. These methods use assertEqual, assertTrue, assertRaises, and assertAlmostEqual to check for expected outcomes.

test_parsimony_bad_cases

Tests the calculate_parsimony function for expected error handling in cases where the tree is not properly initialized or ancestral character inference is required but not enabled.

test_parsimony_reconstruct_internal_states

Tests the calculate_parsimony function with the option to infer ancestral character states, verifying the calculated parsimony score.

test_parsimony_specify_internal_states

Tests the calculate_parsimony function with pre-specified internal character states, verifying the calculated parsimony score.

test_log_transition_probability

Tests the log_transition_probability function, which calculates the log probability of transitioning between character states, considering factors like mutation rate, missing data rate, and time.

test_log_likelihood_of_character

Tests the log_likelihood_of_character function, which calculates the log likelihood of observing a specific character on the tree, considering mutation and missing data probabilities.

test_bad_lineage_tracing_parameters

Tests for expected error handling when invalid lineage tracing parameters (mutation rate, heritable missing rate, stochastic missing probability) are provided.

test_get_lineage_tracing_parameters

Tests the get_lineage_tracing_parameters function, which retrieves or estimates lineage tracing parameters from the tree, ensuring the returned values are as expected.

test_likelihood_bad_cases

Tests the likelihood calculation functions (calculate_likelihood_discrete and calculate_likelihood_continuous) for expected error handling in cases of improper tree initialization or missing data.

test_likelihood_simple_mostly_missing

Tests the calculate_likelihood_discrete function with a simple tree and character matrix containing mostly missing data, verifying the calculated likelihood.

test_likelihood_more_complex_case

Tests the calculate_likelihood_discrete function with a more complex tree and character matrix, verifying the calculated likelihood.

test_likelihood_set_internal_states

Tests the calculate_likelihood_discrete function with pre-specified internal character states, verifying the calculated likelihood.

test_likelihood_time

Tests the calculate_likelihood_continuous function, which incorporates branch lengths (time) into the likelihood calculation, verifying the results.

test_likelihood_sum_to_one

Tests that the calculated likelihoods for all possible character state combinations sum to one, as expected for a probability distribution.

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Dependencies

  • unittest
  • itertools
  • networkx
  • numpy
  • pandas
  • cassiopeia
import unittest
import itertools
import networkx as nx
import numpy as np
import pandas as pd
import cassiopeia as cas
topology_test.pyOverview