In jewelltaylor/funclustVI: Cluster Functional Data using Variational Inference
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funclustVI
The following package was created by John Jewell as a part of an Undergraduate Dissertation at Western University under the supervision of Professor Camila de Souza.
The package serves to cluster functional data using variational inference. More details in regards to the functionality of the package is available in the usage and examples section.
Installation
You can install the released version of funclustVI from CRAN with:
install.packages("funclustVI")
And the development version from GitHub with:
install.packages("devtools")
devtools::install_github("jewelltaylor/funclustVI")
Usage
The funclustVI package offers two main functions: funclustVI and simulate.
funclustVI
The funclustVI function clusters functional data using a novel variational inference based approach and returns a fitted model.
Arguments
- x: A vector of values in which the functional data is evaluated over
- Y: A matrix where each row represents a curve
- K: The number of clusters among the curves
- true_cluster_assignments: The actual cluster assignments for each curve
- init: The initialization method for the cluster assignments
- nbasis: The number of basis functions
- convergence_threshold: The threshold that determines when the model has converged
- max_iterations: The maximum amount of iterations for the algorithim
- gamma_dist_config_matrix: A matrix in which the first row is the alpha parameters for each cluster and the second row is the beta parameters for each cluster.
- verbose: Parameter that determines whether or not to log output regarding model hyperparameters
- draw: Parameter that determines whether or not to plot the real vs estimated curves after fitting the model
- plot_params: List of parameters corresponding to characteristics of the plot. Must include vectors xlim and ylim corresponding to the x and y limits of the plot. If NULL is supplied, plot function defaults are used. Must include boolean show_curves. If true the actual curves are shown with diffent colors. Must include string title. If title is NULL, the default title used is Plot.
Return Value
The funclustVI function returns a list with the following entries:
- probability_matrix: A matrix in which the columns are the probabilities that the curves belong to that cluster
- cluster_assignments: A vector of intergers that represent the cluster assignment for each curve
- init_cluster_assignments: The cluster assignments provided using the specified initlization method
simulate
The simulate function generates data, fits a model and evaluates the models predictions over a set number of iterations all using custom data generation, modelling and evaluation parameters specified by the user.
Arguments
- data_params: List object containing the parameters required for generating the functional data and its characterics. Must include function named generate_data that accepts the data params list and returns a matrix of curves in the rows. Must also include vector named true_cluster_assignments that contains the actual cluster assignments for each curve. Must also include vector named seeds that contains seeds the seed for each iteration of the simulation. Other then these requirements, anything can be included in the data_params object.
- model_params: List object containing the parameters required for modelling the data and generating the cluster assignments. Must include function named model_func that accepts Y, data_params and model_params and returns a vector containing the cluster assignment of each curve. Must also include list plot_params as with the requirements as referenced above. Other then these requirements, anything can be included in the model_params object.
- eval_func_list: List object containing the functions corresponding to the various evaluations metrics evaluation the performance of the algorithim. Each function must accept a vector cluster_assignments that is generated from the function model_func in the model_params list as well as the data_params object which contains the vector true cluster assignments as referenced above.
- number_of_simulations: The number of simulations
- save_path: The file path to save the results from the simualations. If NULL, the results are not saved.
Return Value
The simulate function returns a list with the following entries:
- result_matrix: The result matrix contains the values of each of the evaluation metric for each simulation in the simulations
- simulation_length: The length of the simuations in seconds
Examples
This is an example which shows you how to use the package to generate cluster assignments from functional data. Refer to usage section above for detailed requirements.
#Import the funclustVI package
library(funclustVI)
#Data Parameters
x = seq(from=0,to=pi/3, length = 100)
curves_per_cluster = 10
data_params = list()
data_params$x = x
data_params$curves_per_cluster = curves_per_cluster
Y = Case_7(data_params)
K = 3
true_cluster_assignments = rep(1:K,each = curves_per_cluster)
# Model Parameters
init = "km"
nbasis = 6
convergence_threshold = 1
max_iterations = 10
gamma_dist_config_matrix = matrix(0, 2, K)
gamma_dist_config_matrix[1, ] = c(78.125, 78.125, 78.125) * 100
gamma_dist_config_matrix[2, ] = c(12.5, 12.5, 12.5) * 100
verbose = FALSE
draw = FALSE
plot_params = list()
plot_params$xlim = NULL
plot_params$ylim = c(1, 6)
plot_params$show_curves = FALSE
plot_params$title = NULL
#Fit the model
model = funcslustVI(x, Y, K, true_cluster_assignments, init, nbasis, convergence_threshold, max_iterations, gamma_dist_config_matrix, verbose, draw, plot_params)
#Get the cluster assignments
cluster_assignemnts = model$cluster_assignments
print(cluster_assignemnts)
This is an example which shows how to use the funclustVI package to cluster real data including in the package. The data corresponds to the daily tempuratures in Vancouver and Toronto over the month of July over the 10 year period 1994 to 2003. Since draw = Traw a plot is generated showing the true function vs the estimated function for each cluster. Since show_curves is TRUE, the plot includes the actual curves with differing colors. Refer to above usage section for detailed requirements.
``` {r example_2_data, echo=FALSE, results='hide', message=FALSE}
res_temp = as.vector(weathercan::weather_dl(station_ids = 1776, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp)
tor_temp = as.vector(weathercan::weather_dl(station_ids = 5051, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp)
cl = matrix(0, 18, 31)
count = 1
for (i in seq(1, 3285, 365)) {
upperbound1 = i + 364
res_temp_curr = tidyr::replace_na(res_temp[i:upperbound1], 0)[187:217]
cl[count, ] = res_temp_curr
count = count + 1
}
for (j in seq(1, 3285, 365)) {
upperbound2 = j + 364
tor_temp_curr = tidyr::replace_na(tor_temp[j:upperbound2], 0)[187:217]
cl[count, ] = tor_temp_curr
count = count + 1
}
dataset = cl
``` {r example_2}
#Data Parameters
x = seq(1:31)
Y = dataset
K = 2
curves_per_cluster = 9
true_cluster_assignments = rep(1:K,each = curves_per_cluster)
#Model Parameters
init = "km"
nbasis = 10
convergence_threshold = 1
max_iterations = 10
gamma_dist_config_matrix = NULL
verbose = FALSE
draw = TRUE
plot_params = list()
plot_params$xlim = NULL
plot_params$ylim = c(3, 40)
plot_params$show_curves = TRUE
plot_params$title = NULL
#Fit the model
model = funcslustVI(x, Y, K, true_cluster_assignments, init, nbasis, convergence_threshold, max_iterations, gamma_dist_config_matrix, verbose, draw, plot_params)
This is an example which shows how to run simulations. Refer to above usage section for detailed requirements.
library(funclustVI)
#Initializationw
number_of_simulations = 1
save_path = NULL
#Data Parameters
x = seq(from=0,to=pi/3, length = 100)
K = 3
curves_per_cluster = 50
true_cluster_assignments = rep(1:K,each = curves_per_cluster)
seeds = c(1:30)
#Pack into data parameter list
data_params = list()
data_params$x = x
data_params$K = K
data_params$curves_per_cluster = curves_per_cluster
data_params$true_cluster_assignments = true_cluster_assignments
data_params$seeds = seeds
data_params$generate_data = Case_7
#Model Parameters
init = "km"
nbasis = 6
gamma_dist_config_matrix = matrix(0, 2, K)
gamma_dist_config_matrix[1, ] = c(78.125, 78.125, 78.125) * 5
gamma_dist_config_matrix[2, ] = c(12.5, 12.5, 12.5) * 5
convergence_threshold = 1
max_iterations = 10
verbose = FALSE
draw = FALSE
#Pack into model parameter list
model_params = list()
model_params$model_func = get_funclustVI_cluster_assignments
model_params$init = "km"
model_params$nbasis = 6
model_params$gamma_dist_config_matrix = gamma_dist_config_matrix
model_params$convergence_threshold = convergence_threshold
model_params$max_iterations = max_iterations
model_params$save_path = save_path
model_params$verbose = verbose
model_params$draw = draw
plot_params = list()
plot_params$xlim = NULL
plot_params$ylim = c(1, 6)
plot_params$show_curves = FALSE
plot_params$title = NULL
model_params$plot_params = plot_params
#Evaluation parameter list
eval_func_list = list()
eval_func_list$mismatch = get_mismatches
eval_func_list$vmeasure = get_v_measure
#Run the simulations
simulate(data_params, model_params, eval_func_list, number_of_simulations, save_path)
jewelltaylor/funclustVI documentation built on June 1, 2022, 12:30 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
funclustVI
The following package was created by John Jewell as a part of an Undergraduate Dissertation at Western University under the supervision of Professor Camila de Souza. The package serves to cluster functional data using variational inference. More details in regards to the functionality of the package is available in the usage and examples section.
Installation
You can install the released version of funclustVI from CRAN with:
install.packages("funclustVI")
And the development version from GitHub with:
install.packages("devtools") devtools::install_github("jewelltaylor/funclustVI")
Usage
The funclustVI package offers two main functions: funclustVI and simulate.
funclustVI
The funclustVI function clusters functional data using a novel variational inference based approach and returns a fitted model.
Arguments
- x: A vector of values in which the functional data is evaluated over
- Y: A matrix where each row represents a curve
- K: The number of clusters among the curves
- true_cluster_assignments: The actual cluster assignments for each curve
- init: The initialization method for the cluster assignments
- nbasis: The number of basis functions
- convergence_threshold: The threshold that determines when the model has converged
- max_iterations: The maximum amount of iterations for the algorithim
- gamma_dist_config_matrix: A matrix in which the first row is the alpha parameters for each cluster and the second row is the beta parameters for each cluster.
- verbose: Parameter that determines whether or not to log output regarding model hyperparameters
- draw: Parameter that determines whether or not to plot the real vs estimated curves after fitting the model
- plot_params: List of parameters corresponding to characteristics of the plot. Must include vectors xlim and ylim corresponding to the x and y limits of the plot. If NULL is supplied, plot function defaults are used. Must include boolean show_curves. If true the actual curves are shown with diffent colors. Must include string title. If title is NULL, the default title used is Plot.
Return Value
The funclustVI function returns a list with the following entries:
- probability_matrix: A matrix in which the columns are the probabilities that the curves belong to that cluster
- cluster_assignments: A vector of intergers that represent the cluster assignment for each curve
- init_cluster_assignments: The cluster assignments provided using the specified initlization method
simulate
The simulate function generates data, fits a model and evaluates the models predictions over a set number of iterations all using custom data generation, modelling and evaluation parameters specified by the user.
Arguments
- data_params: List object containing the parameters required for generating the functional data and its characterics. Must include function named generate_data that accepts the data params list and returns a matrix of curves in the rows. Must also include vector named true_cluster_assignments that contains the actual cluster assignments for each curve. Must also include vector named seeds that contains seeds the seed for each iteration of the simulation. Other then these requirements, anything can be included in the data_params object.
- model_params: List object containing the parameters required for modelling the data and generating the cluster assignments. Must include function named model_func that accepts Y, data_params and model_params and returns a vector containing the cluster assignment of each curve. Must also include list plot_params as with the requirements as referenced above. Other then these requirements, anything can be included in the model_params object.
- eval_func_list: List object containing the functions corresponding to the various evaluations metrics evaluation the performance of the algorithim. Each function must accept a vector cluster_assignments that is generated from the function model_func in the model_params list as well as the data_params object which contains the vector true cluster assignments as referenced above.
- number_of_simulations: The number of simulations
- save_path: The file path to save the results from the simualations. If NULL, the results are not saved.
Return Value
The simulate function returns a list with the following entries:
- result_matrix: The result matrix contains the values of each of the evaluation metric for each simulation in the simulations
- simulation_length: The length of the simuations in seconds
Examples
This is an example which shows you how to use the package to generate cluster assignments from functional data. Refer to usage section above for detailed requirements.
#Import the funclustVI package library(funclustVI) #Data Parameters x = seq(from=0,to=pi/3, length = 100) curves_per_cluster = 10 data_params = list() data_params$x = x data_params$curves_per_cluster = curves_per_cluster Y = Case_7(data_params) K = 3 true_cluster_assignments = rep(1:K,each = curves_per_cluster) # Model Parameters init = "km" nbasis = 6 convergence_threshold = 1 max_iterations = 10 gamma_dist_config_matrix = matrix(0, 2, K) gamma_dist_config_matrix[1, ] = c(78.125, 78.125, 78.125) * 100 gamma_dist_config_matrix[2, ] = c(12.5, 12.5, 12.5) * 100 verbose = FALSE draw = FALSE plot_params = list() plot_params$xlim = NULL plot_params$ylim = c(1, 6) plot_params$show_curves = FALSE plot_params$title = NULL #Fit the model model = funcslustVI(x, Y, K, true_cluster_assignments, init, nbasis, convergence_threshold, max_iterations, gamma_dist_config_matrix, verbose, draw, plot_params) #Get the cluster assignments cluster_assignemnts = model$cluster_assignments print(cluster_assignemnts)
This is an example which shows how to use the funclustVI package to cluster real data including in the package. The data corresponds to the daily tempuratures in Vancouver and Toronto over the month of July over the 10 year period 1994 to 2003. Since draw = Traw a plot is generated showing the true function vs the estimated function for each cluster. Since show_curves is TRUE, the plot includes the actual curves with differing colors. Refer to above usage section for detailed requirements.
``` {r example_2_data, echo=FALSE, results='hide', message=FALSE}
res_temp = as.vector(weathercan::weather_dl(station_ids = 1776, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp) tor_temp = as.vector(weathercan::weather_dl(station_ids = 5051, start = "1994-01-01", end = "2003-12-31", interval = "day")$max_temp)
cl = matrix(0, 18, 31) count = 1 for (i in seq(1, 3285, 365)) { upperbound1 = i + 364 res_temp_curr = tidyr::replace_na(res_temp[i:upperbound1], 0)[187:217] cl[count, ] = res_temp_curr
count = count + 1 }
for (j in seq(1, 3285, 365)) { upperbound2 = j + 364 tor_temp_curr = tidyr::replace_na(tor_temp[j:upperbound2], 0)[187:217] cl[count, ] = tor_temp_curr count = count + 1 }
dataset = cl
``` {r example_2}
#Data Parameters
x = seq(1:31)
Y = dataset
K = 2
curves_per_cluster = 9
true_cluster_assignments = rep(1:K,each = curves_per_cluster)
#Model Parameters
init = "km"
nbasis = 10
convergence_threshold = 1
max_iterations = 10
gamma_dist_config_matrix = NULL
verbose = FALSE
draw = TRUE
plot_params = list()
plot_params$xlim = NULL
plot_params$ylim = c(3, 40)
plot_params$show_curves = TRUE
plot_params$title = NULL
#Fit the model
model = funcslustVI(x, Y, K, true_cluster_assignments, init, nbasis, convergence_threshold, max_iterations, gamma_dist_config_matrix, verbose, draw, plot_params)
This is an example which shows how to run simulations. Refer to above usage section for detailed requirements.
library(funclustVI) #Initializationw number_of_simulations = 1 save_path = NULL #Data Parameters x = seq(from=0,to=pi/3, length = 100) K = 3 curves_per_cluster = 50 true_cluster_assignments = rep(1:K,each = curves_per_cluster) seeds = c(1:30) #Pack into data parameter list data_params = list() data_params$x = x data_params$K = K data_params$curves_per_cluster = curves_per_cluster data_params$true_cluster_assignments = true_cluster_assignments data_params$seeds = seeds data_params$generate_data = Case_7 #Model Parameters init = "km" nbasis = 6 gamma_dist_config_matrix = matrix(0, 2, K) gamma_dist_config_matrix[1, ] = c(78.125, 78.125, 78.125) * 5 gamma_dist_config_matrix[2, ] = c(12.5, 12.5, 12.5) * 5 convergence_threshold = 1 max_iterations = 10 verbose = FALSE draw = FALSE #Pack into model parameter list model_params = list() model_params$model_func = get_funclustVI_cluster_assignments model_params$init = "km" model_params$nbasis = 6 model_params$gamma_dist_config_matrix = gamma_dist_config_matrix model_params$convergence_threshold = convergence_threshold model_params$max_iterations = max_iterations model_params$save_path = save_path model_params$verbose = verbose model_params$draw = draw plot_params = list() plot_params$xlim = NULL plot_params$ylim = c(1, 6) plot_params$show_curves = FALSE plot_params$title = NULL model_params$plot_params = plot_params #Evaluation parameter list eval_func_list = list() eval_func_list$mismatch = get_mismatches eval_func_list$vmeasure = get_v_measure #Run the simulations simulate(data_params, model_params, eval_func_list, number_of_simulations, save_path)
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