phyloModels

Python-based library with utilities for modeling and calibration, with emphasis on exploiting phylogenetic data.

Documentation

Documentation is available at https://docs.idmod.org/projects/phylomodels/en/latest/.

Files

phyloModels contains the following folders:

calibration

Algorithms and tools for model calibration.

docs

Documentation and notes.

examples

Battery of examples and use-cases of modules.

features

Summary statistics for structured (e.g., time series) and unstructured (e.g., trees, graphs) data.

models

Algorithms and tools for disease transmission models.

network

Algorithms and utilities for generation and manipulation of graphs/trees/networks.

sampling

Algorithms and utilities for sampling model generated data.

test

Main programs that test the library and demonstrate its performance.

utilities

Miscellaneous functions (read/write from/to files, data conversions, etc.)

visualization

Functions and utilities for visualization.

Data structures

Structured data is represented using Pandas dataframes. Phylogenetic trees are represented using ETE3 tree objects.

Installation

1. Download the library:

   git clone https://github.com/InstituteforDiseaseModeling/phyloModels
   

2. Install dependencies.

   Packages not on PyPI will not be installed by setuptools in step 3 below. Hence, they should be installed separately. These packages include CUDA and History Matching (at this time, CUDA and History Matching are required only for the calibration module; functions in other modules can run without these libraries).

   We recommend using a virtual environment. You can configure a virtual environment using the Conda definition file that is included in this package, or you can create a virtual environment with the required dependencies from scratch. Follow one of the steps below:

   • Create and configure virtual environment using an environment definition file.

     A Conda environment definition file is provided with this package. This file, called phylomodels_env.yml, includes definitions of all requirements, even packages not on PyPI. To use it for installing dependencies, edit the first line to set the name of the environment, and then execute the following commands for installing and activating a conda environment that satisfies all required dependencies:

     conda env create -f phylomodels_env.yml
     conda activate [environment name]`
     

     Note

     In some systems, the environment activation command is source activate [environment name] instead of conda activate [environment name].

   • Create and configure virtual environment using Conda.

     Other than CUDA libraries, which are only used for the calibration module, most of the dependencies can be installed during the installation step (see item 3 below). However, there is an exception with some of the dependencies of the ETE3 library, which is used for representing tree data structures. These dependencies and their required versions can be installed as follows:

     conda create --name [environment name] python==3.7
     conda activate [environment name]
     pip install PyQt5==5.11.3
     

   • Create and configure virtual environment using venv.

     The process is similar to creating a Conda virtual environment:

     python3 -m venv [environment name]
     source [environment name]/bin/activate
     pip install PyQt5==5.11.3
     

3. Install the package:

   python3 setup.py develop
   

   Note

   Alternatively, you could run python3 setup.py install if you plan on using the package as is, without making any changes to the code.

4. Register environment as jupyter kernel (optional).

   You may need to manually add the new environment as a jupyter kernel. With the conda environment activated, this can be done as follows:

   python -m ipykernel install --user --name [environment name] --display-name "Python ([environment name])"
   

Requirements/Dependencies

See dependencies in setup.py and phylomodels_env.yml.

Examples

• Computing summary statistics on phylogenetic trees: phylomodels/examples/features_computeTreeFeatures/features_computeTreeFeatures.ipynb

• Model calibration using History Matching: phylomodels/examples/calibration_historyMatching_featureSelectionForSIR/calibration_historyMatching_featureSelectionForSIR.ipynb

Models

The phylomodels.models module contains wrappers to models, as well as any code required for execution of models. The goal of this module is to provide a standard interface for sampling, feature extraction, and calibration functions.

File structure

This folder is organized as a collection of model wrappers. The wrappers themselves are (typically small) Python functions coded inside individual files. Additional functions (and files) that are necessary for the execution of a model must be in an individual subfolder. Optionally, there could be a test script for each individual wrapper.

For example, an SIR stochastic simulator based on Gillespie’s tao-leap method is coded as a class called sirTaoLeap. This class is coded in a file called sirTaoLeap.py. The file structure for phyloModels-standard access to sirTaoLeap is as follows (see naming conventions below):

sir_taoLeap_getIncidence.py

Wrapper function for accessing sirTaoLeap.

test_sir_taoLeap_getIncidence.py

Optional script for testing the sir_taoLeap_getIncidence wrapper.

./sir_taoLeap

sirTaoLeap class and auxiliary functions.

Note

A separate folder with model code may not be always necessary. For example, accessing models from a package that is already installed (and whose code is located in a separate folder/folder structure) may be called directly from the wrapper.

Naming conventions

Wrappers

Wrappers should be named as follows:

[type]_[descriptive name of model/library][_configuration name (optional)]

type

Indicates the type of model that is being implemented. Type can be any of the following:

• sir for SIR models (or variations of sir for other compartmental models (e.g., SEIR)

• distributions for random variables and probability distributions

descriptive name of model/library

References the model or library that is being used by this wrapper.

configuration name

Optional, describes a particular configuration mode used when calling the model.

The following are examples of wrapper names for different configurations of a model called sirTaoLeap:

sir_taoLeap
sir_taoLeap_getIncidence
sir_taoLeap_getPrevalence
sir_taoLeap_getIncidenceSampled
sir_taoLeap_getSampledIncidence

Tests

Wrapper test scripts should take the name of the wrapper with the test_ prefix.

Subfolders

As mentioned above, any other file necessary for interfacing a model (or executing it) must be placed in a separate subfolder. The subfolder should be named as follows:

[type]_[descriptive name of model/library]

where type and descriptive name of model/library follow the descriptions mentioned for the wrappers above.

For example, a subfolder containing files for the sirTaoLeap model described for wrappers above should be named sir_taoLeap.

Note that the existence of a subfolder for a given model is optional. A subfolder should only be created if there is at least one file (other than the wrapper) necessary for calling a model.

Inputs

Each wrapper receives only one input argument. This argument is a pandas dataframe. Each row of this dataframe is a set of arguments to be used as input for the model call. Columns are the arguments that will be passed to the model.

The input dataframe must not be modified by the wrapper or model.

Outputs

The output is a tuple with the following items (in order):

1. A pandas dataframe, with the same number of rows of the input argument. Each row of this dataframe is the (possibly reformatted) model output corresponding to the input dataframe row. The output dataframe could be, for example, a time series.

2. A list of NetworkX structures. Each item of this list corresponds to the output of the model to the arguments in the corresponding row of the input dataframe.

Features

The phylomodels.features module contains the definition of the features class, as well as internal methods and functions used by this class. The features class is an interface for the definition, computation, and general analyses of features. We may also refer to features as summary statistics.

The ultimate goal of the features module is to provide a standard interface that enables easy and streamlined definition, addition, deletion, and overall management of individual features. When using this module, the addition of a new feature in a given Python script should take at most 1 additional line of code. Coding and integration of new features could be also made in a few minutes.

For example, the basic usage of this module within a Python program should look as follows:

x = readTimeSeries()  # One or more time series to be analyzed
f = features(x)       # Instantiation of features object
f.compute()           # Compute features
y, s = f.get()        # Extract features and statistics as a Pandas DataFrame

Computation of features can be enabled/disabled either by groups or individually:

f.disableFeature(group="all")      # Disable computation of all available
                                   # features
f.enableFeature(name="series_sum") # Enable computation of the feature called
                                   # series_sum
f.compute()                        # Only series_sum is computed

File structure

features.py

Definition of features class

inventory.py

Methods and auxiliary functions for management of features.

testFeatures.py

Test script.

./graphs

Methods and auxiliary functions for features that are obtained from graphs.

./series`

Methods and auxiliary functions for features that are obtained from time series.

./statistics

Methods and auxiliary functions for computing statistics on computed features.

Inputs

The instantiation of a features object can receive any of the following inputs, which are attributes of the features class:

x

Set of time series to analyze. This is a pandas dataframe with m rows by n columns. m is the number of sequences (i.e., each row is a different time series) and m is the length of each sequence.

xref

Reference time series. This is a a pandas dataframe with 1 row and m columns, where m is defined as in x above.

g

Set of graphs (e.g., trees) to analyze. This is a list of NetworkX data structures (see https://networkx.github.io/).

gref

Reference graph. This is a NetworkX data structure.

Core methods

There main methods for the computation and analysis of features are:

compute

Compute all the features and statistics currently enabled (see “Management of features” below for details on enabling/disabling features).

get

Return features and statistics rendered by the compute method.

Management of features

The features class can extract and/or generate any number of features. Features are only computed when the compute method is called. Important notes regarding the management of features:

Available features

Potential features to be extracted or generated should be in an inventory of features. This inventory is an attribute called availableFeatures. availableFeatures is a list of feature cards.

Feature card

A feature card contains a brief description of a feature, as well as information regarding the actual function that computes the feature. It is defined based on the following namedtuple:

featureCard \\
  = collections.namedtuple(
         "featureCard",
          ["id", "name", "description", "subroutine"]
    )

where “id” is an integer, “name” is a string (see naming conventions below), “subroutine” is the name of the function that extracts and/or computes the feature, and “parameters” is a list of arguments for said function.

Enabled features

A feature is to be computed only if it is enabled. In general, the compute method loops over availableFeatures and checks if they are enabled. If so, then the corresponding subroutines are called. The status of a feature (enabled/disabled) is maintained in the Boolean attribute enabledFeatures.

Methods

enableFeature

Enable the computation/extraction of a feature or group of features.

disableFeature

Disable the computation/extraction of a feature or group of features.

listFeatures

Display list of available features.

listActiveFeatures

Display list of active (i.e., enabled) features.

Designing and integrating new features

• Each feature must be computed by a single function. The goal is for each of these functions to be very short. This makes it easy to code, easy to review, and easy to maintain.

• Each feature function receives two arguments, namely: input x transformed into a numpy array of size m x n (with m and n defined as indicated above), followed by input xref transformed into a numpy array. Not all the arguments have to be used by a feature function, but the order in which arguments are passed to the function must be maintained.

• Each feature function returns a Pandas DataFrame. Each row in this DataFrame is the output for the corresponding row/item in the input (e.g., row k of the DataFrame is the output corresponding to row k in x and g.

• Each feature function must be in a separate file (typically of just a few lines). The name of the file must be the same name of the feature function. It must also follow the naming conventions described below.

• A new feature function is integrated into the module by moving its corresponding file to the subfolder ./series or ./graphs, depending on which type of feature it is. In addition, the __init__.py file on the corresponding subfolder must be edited to include a line importing the function. This line looks like this:

  from [series/graphs] import [function name]
  

• Naming conventions: Features should be named as follows:

  [type of input]_[transformation of the input]_[other labels]
  

  For example:

  series_sum
  series_derivative
  graph_nodes
  graph_degree
  

Statistics

• Statistics describe the behavior of the computed features. For example, the mean statistic finds the average of every column in the DataFrame rendered by the method get.

• Management and design of statistics follow the same principles than those described for management and design of features, with a few changes:

  • Inventory management is done using the arrays availableStats and enabledStats.

  • The inventory management methods are: enableStatistic, disableStatistic, listStatistics, registerStatistic.

  • Files that contain the code for computing a statistic must be saved into the ./statistics subfolder.

Utilities

The following utility methods are available:

getArguments

Returns x, xref, g, and gref.

listFeatures

Display available features.

listActiveFeatures

Display active features.

printArguments

Display the contents of x, xref, g, and gref.

printFeatures

Display the features dataframe.

printStats

Display statistics.

Calibration

The phylomodels.calibration module contains wrappers to calibration functions and libraries, as well as any code required for their use. The goals of this module are to provide a standard interface for calibration jobs, as well as maintaining a battery of calibration methods that are well suited for our needs.

There are two types of tasks that can be done using functions in this module:

• calibration initialization, which generates one or more initial points for faster convergence of calibration algorithms; and

• calibration, which executes an actual calibration job.

Calibration algorithms could use the output of an initialization algorithm. However, the use of initialization algorithms is not required.

File structure

This directory is organized as a collection of wrappers for calibration and calibration initializers. The wrappers are Python functions. The name of the function should be the same name of the file that contains it. Additional functions (and files) that are necessary for the execution of a calibration algorithm or initializer must be in an individual subdirectory. Optionally, there could be a test script for each wrapper.

For example, a basic configuration of history matching as an initializer could require the following file structure:

init_historyMatching_basic

Wrapper function for history matching.

test_init_historyMatching_basic

Optional script for testing the basic configuration of history matching.

./init_historyMatching

Auxiliary functions for the execution of history matching.

Note

A separate folder with model code may not be always necessary. For example, accessing models from a package that is already installed (and whose code is located in a separate folder/folder structure) may be done directly from the wrapper.

Naming conventions

Wrappers

Wrappers should be named as follows:

[type]_[descriptive name of algorithm/method/library][_configuration name (optional)]

type

Indicates the type of operation that is being done. It can be:

• init, for calibration initializer; or

• cal, for calibration operations.

descriptive name of algorithm/method/library

Refers to the algorithm or library that is being used by this wrapper.

configuration name

Optional, describes a particular configuration mode used when calling the algorithm.

The following are examples of wrapper names for different configurations of a history matching initializer:

init_historyMatching
init_historyMatching_poissonBasis
init_historyMatching_gaussianBasis
init_historyMatching_rejectionRules
init_historyMatching_rejectionRules_PoissonBasis

Tests

Wrapper test scripts should take the name of the wrapper with the test_ prefix.

Subfolders

As mentioned above, any other file necessary for interfacing a method or library must be placed in a separate subfolder. The subfolder should be named as follows:

[type]_[descriptive name of algorithm/method/library]

where type and descriptive name of algorithm/method/library follow the descriptions mentioned for the wrappers above.

For example, a subfolder containing files for using history matching would be named init_historyMatching.

Note

The existence of a subfolder for a given method is optional. A subfolder should only be created if there is at least one file (other than the wrapper) necessary for calling a model.

Inputs

Initializers

Each wrapper receives the following arguments (in this order):

xInfo

Name and range of model parameters. This is a pandas dataframe with 3 columns, namely: name, min, and max. Each row of the dataframe contains the corresponding information for a given model parameter.

y

Observation or measurements. This is a pandas dataframe. Columns in this dataframe are the features that will be used for calibration. The name of the features correspond to names of features that can be computed by the features module (those names are used by the calibration algorithm for configuring calls to features methods. The first row of y contains the observations that will be used by the calibration method for fitting the model. Optionally, the second row may contain the variance of each observation. The variance may be used by initialization and/or calibration algorithms.

model

Pointer to model or model wrapper. The interface to the model (or its wrapper) should be the one defined in the phylomodels.models module.

params

Other parameters for the initializer. This argument is a Python dictionary (i.e., key-value pairs with the name of the parameters and their value).

Inputs to initializers must not be modified by the initializer (or any of the functions called internally by the initializer).

Calibration solvers

Each calibration solver receives the following arguments (in this order):

xInfo

Name and range of model parameters (see description of initializer inputs above).

xInit

Initial value(s) or guess of model parameters. This is a pandas dataframe where columns refer (and are named according) to parameters of the model, and rows are initial values for the solver.

y

Observations or measurements (see description of initializer inputs above).

model

Pointer to model or model wrapper (see description of initializer inputs above).

params

Other parameters for the calibration method. This argument is a Python dictionary.

Inputs to calibration functions must not be modified by the calibration function, or by any of the subroutines called internally by the function).

Outputs

Initializers

Each initializer returns a single output. The output is a pandas dataframe containing one or more sets of parameters (i.e., the initial values) that can be used as inputs for a calibration method. Ideally, the output of an initializer either characterizes a reduced parameter space, or provides an initial point (or set of points) for faster convergence of calibration methods. Each column of the output dataframe contains the values for a parameter (the names of the columns are indicated by xInfo["name"]). Each row contains the values of a set of parameters for initialization of a calibration method.

Calibration solvers

Each calibration solver returns a single output. The output is a pandas dataframe. Columns of this dataframe are:

• Parameters of the model that render the solution. There is one column per parameter. Names of these columns are defined by xInfo["name"].

• Distance (or cost) metric. This is a column with the distance metric obtained for each set of parameters.

• Any other additional column, as defined by the calibration method.

Rows in the output dataframe are solutions to the calibration problem. Rows are sorted in decreasing order (from best to worst). Note that, depending on the calibration method, the output can contain more than one solution.

Test

The test directory contains a battery of tests for validating numerical and/or computational performance of library components.

File structure

This directory is organized in subdirectories, with each subdirectory containing one or more stand-alone tests (one test per file in the subdirectory).

Naming conventions. Subdirectories should be named as follows:

[type of test]_[main method/algorithm][_ other labels/description],

with:

• type of test assigning a category to the example. In general, categories refer to the main operation that is being verified in the test. Possible types of tests are:

• buildTree

• calibration_historyMatching_sir

• computeDegree

• features

• sampling

  Note that a test may use components from more than one module of the library. The type of test describes the main component that is being tested. It does not list all the modules/components that are being used in the test.

• main method/algorithm describing the main method or function that is being used in the test. For example, it can be the name of the initializer of a calibration method, or the name of a model contained in the models module, or a sampling strategy contained in the sampling module.

• other labels/description being any other string that completes the description of the test.

Test functions. There must be exactly one test per file. The entry point of the file (i.e., the test function) is given by the name of the file and the suffix _test. This function should not receive any parameters and it must be self-contained. All the code relevant to a given test should be inside the corresponding test file (this requirement aids in defining tests that are short and self-contained).

Each test must return a boolean state indicating if the test was successful or if it failed.

phyloModels test script

The file phylomodels_test.py contains a script that executes all the tests necessary for validating the correct installation/operation of the library. Calls to any test that is considered important for library validation should be added to this file.

Examples

Examples of configuration and use of modules can be found in the examples directory. This directory is organized in subdirectories, with each subdirectory containing an independent (i.e, stand-alone) example. Examples aid in understanding the use of different modules and, at the same time, they serve as templates for configuring and executing modeling and calibration jobs.

Naming conventions

Example subdirectories should be named as follows:

[type of example]_[main method/algorithm][_other labels/description]

type of example

Assigns a category to the example. In general, categories refer to the main operation that is being illustrated in the example. Possible types of examples are:

• calibration

• features

• model

• network

• sampling

• visualization

Note that one example may use components from more than one module. The type of example describes the main component that is being used. It does not list all the modules that are being used in the example.

main method/algorithm

Describes the main method or function that is being used in the example. For example, it can be the name of the initializer of a calibration method, or the name of a model contained in the models module, or a sampling strategy contained in the sampling module.

other labels/description

Any other string that completes the description of the example.

File structure

All the files relevant to a given example (that are not part of a module) should be located inside the corresponding folder. The name of the main script (and notebook, if available) of an example should be the same used for naming the example subdirectory.

Data

Data that may be used in more than one example should be placed in the examples/data subdirectory.

API reference

phylomodels package

Subpackages

phylomodels.calibration package

Submodules

phylomodels.calibration.cal_parameterSweep module

phylomodels.calibration.constants module

phylomodels.calibration.init_historyMatching_base module

phylomodels.calibration.init_historyMatching_poissonGlmBasis module

phylomodels.examples package

phylomodels.features package

Subpackages

phylomodels.features.series package

Submodules

phylomodels.features.series.series module

phylomodels.features.series.series_derivative module

phylomodels.features.series.series_derivative2 module

phylomodels.features.series.series_derivative2_cauchyFit module

phylomodels.features.series.series_derivative2_gaussianFit module

phylomodels.features.series.series_derivative2_laplaceFit module

phylomodels.features.series.series_derivative_cauchyFit module

phylomodels.features.series.series_derivative_gaussianFit module

phylomodels.features.series.series_derivative_laplaceFit module

phylomodels.features.series.series_diff module

phylomodels.features.series.series_diff_L1 module

phylomodels.features.series.series_diff_L2 module

phylomodels.features.series.series_diff_Linf module

phylomodels.features.series.series_log10 module

phylomodels.features.series.series_partialSum10 module

phylomodels.features.series.series_partialSum15 module

phylomodels.features.series.series_partialSum2 module

phylomodels.features.series.series_partialSum30 module

phylomodels.features.series.series_partialSum7 module

phylomodels.features.series.series_sum module

phylomodels.features.series.series_sum_log10 module

phylomodels.features.statistics package

Submodules

phylomodels.features.statistics.fano module

phylomodels.features.statistics.mean module

phylomodels.features.statistics.qcd module

phylomodels.features.statistics.rsd module

phylomodels.features.statistics.skew module

phylomodels.features.statistics.std module

phylomodels.features.statistics.var module

phylomodels.features.trees package

Subpackages

phylomodels.features.trees.helper package

Submodules

phylomodels.features.trees.helper.calculate_branch_length_metrics module

phylomodels.features.trees.helper.check_values module

phylomodels.features.trees.helper.get_LTT module

phylomodels.features.trees.helper.get_adjacency_mat module

phylomodels.features.trees.helper.get_distance_mat module

phylomodels.features.trees.helper.get_eigenvalues_adj module

phylomodels.features.trees.helper.get_eigenvalues_dist_lap module

phylomodels.features.trees.helper.get_eigenvalues_lap module

phylomodels.features.trees.helper.get_groups module

phylomodels.features.trees.helper.get_node_properties module

phylomodels.features.trees.helper.process_optional_arguements module

phylomodels.features.trees.helper.unique_node_attr module

Submodules

phylomodels.features.trees.BL_calculate_external_max module

phylomodels.features.trees.BL_calculate_external_mean module

phylomodels.features.trees.BL_calculate_external_median module

phylomodels.features.trees.BL_calculate_external_min module

phylomodels.features.trees.BL_calculate_external_std module

phylomodels.features.trees.BL_calculate_internal_max module

phylomodels.features.trees.BL_calculate_internal_mean module

phylomodels.features.trees.BL_calculate_internal_median module

phylomodels.features.trees.BL_calculate_internal_min module

phylomodels.features.trees.BL_calculate_internal_std module

phylomodels.features.trees.BL_calculate_max module

phylomodels.features.trees.BL_calculate_mean module

phylomodels.features.trees.BL_calculate_median module

phylomodels.features.trees.BL_calculate_min module

phylomodels.features.trees.BL_calculate_ratio_max module

phylomodels.features.trees.BL_calculate_ratio_mean module

phylomodels.features.trees.BL_calculate_ratio_median module

phylomodels.features.trees.BL_calculate_ratio_min module

phylomodels.features.trees.BL_calculate_ratio_std module

phylomodels.features.trees.BL_calculate_std module

phylomodels.features.trees.LTT_calculate_max_lineages module

phylomodels.features.trees.LTT_calculate_mean_b_time module

phylomodels.features.trees.LTT_calculate_mean_s_time module

phylomodels.features.trees.LTT_calculate_slope_1 module

phylomodels.features.trees.LTT_calculate_slope_2 module

phylomodels.features.trees.LTT_calculate_slope_ratio module

phylomodels.features.trees.LTT_calculate_t_max_lineages module

phylomodels.features.trees.calculate_external_branch_length_metrics module

phylomodels.features.trees.group_calculate_max module

phylomodels.features.trees.group_calculate_mean module

phylomodels.features.trees.group_calculate_median module

phylomodels.features.trees.group_calculate_min module

phylomodels.features.trees.group_calculate_mode module

phylomodels.features.trees.group_calculate_num module

phylomodels.features.trees.group_calculate_std module

phylomodels.features.trees.local_calculate_LBI_mean module

phylomodels.features.trees.local_calculate_frac_basal module

phylomodels.features.trees.mean_NN_distance module

phylomodels.features.trees.netSci_calculate_betweenness_max module

phylomodels.features.trees.netSci_calculate_closeness_max module

phylomodels.features.trees.netSci_calculate_diameter module

phylomodels.features.trees.netSci_calculate_eigen_centrality_max module

phylomodels.features.trees.netSci_calculate_mean_path module

phylomodels.features.trees.smallConfig_calculate_cherries module

phylomodels.features.trees.smallConfig_calculate_double_cherries module

phylomodels.features.trees.smallConfig_calculate_fourprong module

phylomodels.features.trees.smallConfig_calculate_pitchforks module

phylomodels.features.trees.spectral_calculate_eigen_gap module

phylomodels.features.trees.spectral_calculate_kurtosis module

phylomodels.features.trees.spectral_calculate_max_adj_eigen module

phylomodels.features.trees.spectral_calculate_max_distLap_eigen module

phylomodels.features.trees.spectral_calculate_max_lap_eigen module

phylomodels.features.trees.spectral_calculate_min_adj_eigen module

phylomodels.features.trees.spectral_calculate_min_lap_eigen module

phylomodels.features.trees.spectral_calculate_skewness module

phylomodels.features.trees.top_calculate_B1 module

phylomodels.features.trees.top_calculate_B2 module

phylomodels.features.trees.top_calculate_FurnasR module

phylomodels.features.trees.top_calculate_WD_ratio module

phylomodels.features.trees.top_calculate_colless module

phylomodels.features.trees.top_calculate_frac_imbalance module

phylomodels.features.trees.top_calculate_frac_ladder module

phylomodels.features.trees.top_calculate_max_dW module

phylomodels.features.trees.top_calculate_max_ladder module

phylomodels.features.trees.top_calculate_mean_imbalance_ratio module

phylomodels.features.trees.top_calculate_sackin module

phylomodels.features.trees.top_calculate_sackin_var module

phylomodels.features.trees.tree_height_calculate_max module

phylomodels.features.trees.tree_height_calculate_mean module

phylomodels.features.trees.tree_height_calculate_min module

Submodules

phylomodels.features.features module

phylomodels.features.inventory module

phylomodels.features.test module

phylomodels.features.testFeatures module

phylomodels.models package

Subpackages

phylomodels.models.seir_taoLeap package

Submodules

phylomodels.models.seir_taoLeap.seirTaoLeap module

Submodules

phylomodels.models.distributions_gaussian module

phylomodels.models.seir_taoLeap_getIncidenceSampled module

phylomodels.models.sir_taoLeap_getIncidence module

phylomodels.models.sir_taoLeap_getIncidenceSampled module

phylomodels.models.test_distributions_gaussian module

phylomodels.models.test_sir_taoLeap_getIncidence module

phylomodels.models.test_sir_taoLeap_getIncidenceSampled module

phylomodels.network package

Submodules

phylomodels.network.phyloTree module

phylomodels.sampling package

Submodules

phylomodels.sampling.sample_treeIndependent module

phylomodels.utilities package

phylomodels.visualization package

Submodules

phylomodels.visualization.parallelCoordinates module

To do

The following topic lists the functionality we plan to add to phyloModels in the future.

Calibration algorithms

Features and summary statistics

• Include multiple correlation coefficients as statistics for the evaluation of dispersion of summary statistics.

• Integrate summary statistics for trees.

History matching

• Automatic selection of GLM basis. This can done by running a quick test that characterizes the data to fit as one of the basis supported by History Matching (which are the basis supported by statsmodels).

• Use history matching diagnostics information for deciding if an iteration is successful or not (and make decisions regarding the need for repeating it using a different observation or summary statistic). NOTE: This depends on history matching reporting test information.

• Add support for using a variable number of model simulations per iteration, as well as guidelines (and functions) for selecting them.

• Add calibration example calling a model in R.

• Relax the condition that the number of observations must match the number of outputs rendered by the simulation model. The number of observations to use for feature generation should be the minimum between the number of observations provided as input argument and the number of outputs delivered by the simulation model.

• Incorporate means for robustly dealing with outliers and for capturing rare events.

Parameter sweep

• Add support for additional cost metrics.

Others

• Integrate other calibration methods: ELFI, ABC

Development

General

• Document specifications for tests.

• Performance characterization for history matching.

Features

• Update conventions for the name of outputs of summary statistics. It should be the name of the summary statistic (i.e., feature) followed by underscore “_” and an integer number.

• Ensure parallelization for the computation of features. We should be able to take advantage of all available cores for computing features and statistics. Parallelization should be done on each feature or statistic independently.

• How can we optimize computations so that data can be shared/reused for the computation of multiple features or statistics? For example, dx is used in all series_derivative features.

Visualization

• Superimpose marginal distributions in parallel coordinates plots.

• Superimpose reference curve in parallel coordinates plots.

• Add support for reverse/log axis in any coordinate in parallel coordinates plots (these options are currently supported only for the rightmost axes).

Distribution

• Package in a Docker image.

• Make library available in Artifactory.