mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-07-29 20:14:08 +00:00
Allow changepoints on dates that aren't in history, and allow for repeated observations on days. Previously we worked with changepoints via their index in the history. Now we work with them using just their value in scaled time.
This commit is contained in:
Ben Letham
parent
158f49db59
commit
443d475468
10 changed files with 98 additions and 172 deletions
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@ -101,8 +101,9 @@ prophet <- function(df = df,
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interval.width = interval.width,
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uncertainty.samples = uncertainty.samples,
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start = NULL, # This and following attributes are set during fitting
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end = NULL,
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y.scale = NULL,
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t.scale = NULL,
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changepoints.t = NULL,
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stan.fit = NULL,
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params = list(),
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history = NULL
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@ -206,12 +207,10 @@ setup_dataframe <- function(m, df, initialize_scales = FALSE) {
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if (initialize_scales) {
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m$y.scale <- max(df$y)
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m$start <- min(df$ds)
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m$end <- max(df$ds)
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m$t.scale <- as.numeric(max(df$ds) - m$start)
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}
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t.scale <- as.numeric(m$end - m$start)
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df$t <- as.numeric(df$ds - m$start) / t.scale
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df$t <- as.numeric(df$ds - m$start) / m$t.scale
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if (exists('y', where=df)) {
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df$y_scaled <- df$y / m$y.scale
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}
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@ -254,32 +253,13 @@ set_changepoints <- function(m) {
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m$changepoints <- c()
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}
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}
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return(m)
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}
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#' Gets changepoint indexes in history dataframe.
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#'
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#' @param m Prophet object.
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#'
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#' @return array of indexes.
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#'
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get_changepoint_indexes <- function(m) {
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if (length(m$changepoints) == 0) {
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return(c(1))
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if (length(m$changepoints) > 0) {
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m$changepoints <- zoo::as.Date(m$changepoints)
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m$changepoints.t <- sort(as.numeric(m$changepoints - m$start) / m$t.scale)
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} else {
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return(match(zoo::as.Date(m$changepoints), m$history$ds))
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m$changepoints.t <- c(0) # dummy changepoint
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}
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}
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#' Gets changepoint times, in scaled space.
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#'
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#' @param m Prophet object.
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#'
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#' @return array of times.
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#'
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get_changepoint_times <- function(m) {
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cpi <- get_changepoint_indexes(m)
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return(m$history$t[cpi])
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return(m)
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}
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#' Gets changepoint matrix for history dataframe.
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@ -289,11 +269,9 @@ get_changepoint_times <- function(m) {
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#' @return array of indexes.
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#'
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get_changepoint_matrix <- function(m) {
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changepoint.indexes <- get_changepoint_indexes(m)
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A <- matrix(0, nrow(m$history), length(changepoint.indexes))
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for (i in 1:length(changepoint.indexes)) {
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A[changepoint.indexes[i]:nrow(m$history), i] <- 1
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A <- matrix(0, nrow(m$history), length(m$changepoints.t))
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for (i in 1:length(m$changepoints.t)) {
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A[m$history$t >= m$changepoints.t[i], i] <- 1
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}
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return(A)
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}
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@ -470,17 +448,16 @@ fit.prophet <- function(m, df, ...) {
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m <- set_changepoints(m)
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A <- get_changepoint_matrix(m)
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changepoint.indexes <- get_changepoint_indexes(m)
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# Construct input to stan
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dat <- list(
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T = nrow(history),
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K = ncol(seasonal.features),
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S = length(changepoint.indexes),
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S = length(m$changepoints.t),
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y = history$y_scaled,
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t = history$t,
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A = A,
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s_indx = array(changepoint.indexes),
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t_change = array(m$changepoints.t),
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X = as.matrix(seasonal.features),
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sigma = m$seasonality.prior.scale,
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tau = m$changepoint.prior.scale
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@ -499,7 +476,7 @@ fit.prophet <- function(m, df, ...) {
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stan_init <- function() {
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list(k = kinit[1],
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m = kinit[2],
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delta = array(rep(0, length(changepoint.indexes))),
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delta = array(rep(0, length(m$changepoints.t))),
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beta = array(rep(0, ncol(seasonal.features))),
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sigma_obs = 1
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)
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@ -649,12 +626,12 @@ predict_trend <- function(model, df) {
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deltas <- colMeans(model$params$delta, na.rm = TRUE)
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t <- df$t
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cpts <- get_changepoint_times(model)
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if (model$growth == 'linear') {
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trend <- piecewise_linear(t, deltas, k, param.m, cpts)
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trend <- piecewise_linear(t, deltas, k, param.m, model$changepoints.t)
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} else {
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cap <- df$cap_scaled
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trend <- piecewise_logistic(t, cap, deltas, k, param.m, cpts)
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trend <- piecewise_logistic(
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t, cap, deltas, k, param.m, model$changepoints.t)
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}
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return(trend * model$y.scale)
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}
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@ -785,7 +762,6 @@ sample_predictive_trend <- function(model, df, iteration) {
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deltas <- model$params$delta[iteration,]
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t <- df$t
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changepoint.ts <- get_changepoint_times(model)
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T <- max(t)
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if (T > 1) {
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@ -794,7 +770,7 @@ sample_predictive_trend <- function(model, df, iteration) {
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dt <- min(dt[dt > 0])
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# Number of time periods in the future
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N <- ceiling((T - 1) / dt)
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S <- length(changepoint.ts)
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S <- length(model$changepoints.t)
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# The history had S split points, over t = [0, 1].
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# The forecast is on [1, T], and should have the same average frequency of
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# rate changes. Thus for N time periods in the future, we want an average
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@ -820,7 +796,7 @@ sample_predictive_trend <- function(model, df, iteration) {
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deltas.new <- extraDistr::rlaplace(n.changes, mu = 0, sigma = lambda)
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# Combine with changepoints from the history
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changepoint.ts <- c(changepoint.ts, changepoint.ts.new)
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changepoint.ts <- c(model$changepoints.t, changepoint.ts.new)
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deltas <- c(deltas, deltas.new)
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# Get the corresponding trend
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@ -3,9 +3,9 @@ data {
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int<lower=1> K; // Number of seasonal vectors
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vector[T] t; // Day
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vector[T] y; // Time-series
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int S; // Number of split points
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int S; // Number of changepoints
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matrix[T, S] A; // Split indicators
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int s_indx[S]; // Index of split points
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real t_change[S]; // Index of changepoints
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matrix[T,K] X; // season vectors
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real<lower=0> sigma; // scale on seasonality prior
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real<lower=0> tau; // scale on changepoints prior
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@ -23,7 +23,7 @@ transformed parameters {
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vector[S] gamma; // adjusted offsets, for piecewise continuity
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for (i in 1:S) {
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gamma[i] = -t[s_indx[i]] * delta[i];
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gamma[i] = -t_change[i] * delta[i];
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}
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}
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@ -4,24 +4,14 @@ data {
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vector[T] t; // Day
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vector[T] cap; // Capacities
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vector[T] y; // Time-series
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int S; // Number of split points
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int S; // Number of changepoints
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matrix[T, S] A; // Split indicators
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int s_indx[S]; // Index of split points
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real t_change[S]; // Index of changepoints
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matrix[T,K] X; // season vectors
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real<lower=0> sigma; // scale on seasonality prior
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real<lower=0> tau; // scale on changepoints prior
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}
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transformed data {
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int s_ext[S + 1]; // Segment endpoints
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for (j in 1:S) {
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s_ext[j] = s_indx[j];
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}
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s_ext[S + 1] = T + 1; // Used for the m_adj loop below.
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}
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parameters {
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real k; // Base growth rate
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real m; // offset
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@ -30,7 +20,6 @@ parameters {
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vector[K] beta; // seasonal vector
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}
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transformed parameters {
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vector[S] gamma; // adjusted offsets, for piecewise continuity
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vector[S + 1] k_s; // actual rate in each segment
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@ -45,7 +34,7 @@ transformed parameters {
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// Piecewise offsets
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m_pr = m; // The offset in the previous segment
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for (i in 1:S) {
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gamma[i] = (t[s_indx[i]] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
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gamma[i] = (t_change[i] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
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m_pr = m_pr + gamma[i]; // update for the next segment
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}
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}
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@ -1,18 +0,0 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/prophet.R
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\name{get_changepoint_indexes}
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\alias{get_changepoint_indexes}
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\title{Gets changepoint indexes in history dataframe.}
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\usage{
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get_changepoint_indexes(m)
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}
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\arguments{
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\item{m}{Prophet object.}
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}
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\value{
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array of indexes.
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}
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\description{
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Gets changepoint indexes in history dataframe.
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}
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@ -1,18 +0,0 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/prophet.R
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\name{get_changepoint_times}
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\alias{get_changepoint_times}
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\title{Gets changepoint times, in scaled space.}
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\usage{
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get_changepoint_times(m)
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}
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\arguments{
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\item{m}{Prophet object.}
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}
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\value{
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array of times.
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}
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\description{
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Gets changepoint times, in scaled space.
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}
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@ -30,6 +30,25 @@ test_that("fit_predict_no_changepoints", {
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_changepoint_not_in_history", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train_t <- dplyr::mutate(DATA, ds=zoo::as.Date(ds))
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train_t <- dplyr::filter(train_t, (ds < zoo::as.Date('2013-01-01')) |
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(ds > zoo::as.Date('2014-01-01')))
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future <- data.frame(ds=DATA$ds)
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m <- prophet(train_t, changepoints=c('2013-06-06'))
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expect_error(predict(m, future), NA)
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})
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test_that("fit_predict_duplicates", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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train2 <- train
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train2$y <- train2$y + 10
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train_t <- rbind(train, train2)
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m <- prophet(train_t)
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expect_error(predict(m, future), NA)
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})
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test_that("setup_dataframe", {
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history <- train
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m <- prophet(history, fit = FALSE)
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@ -56,7 +75,7 @@ test_that("get_changepoints", {
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m <- prophet:::set_changepoints(m)
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cp <- prophet:::get_changepoint_indexes(m)
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cp <- m$changepoints.t
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expect_equal(length(cp), m$n.changepoints)
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expect_true(min(cp) > 0)
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expect_true(max(cp) < N)
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@ -76,9 +95,9 @@ test_that("get_zero_changepoints", {
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m$history <- history
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m <- prophet:::set_changepoints(m)
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cp <- prophet:::get_changepoint_indexes(m)
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cp <- m$changepoints.t
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expect_equal(length(cp), 1)
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expect_equal(cp[1], 1)
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expect_equal(cp[1], 0)
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mat <- prophet:::get_changepoint_matrix(m)
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expect_equal(nrow(mat), floor(N / 2))
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@ -84,8 +84,9 @@ class Prophet(object):
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# Set during fitting
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self.start = None
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self.end = None
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self.y_scale = None
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self.t_scale = None
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self.changepoints_t = None
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self.stan_fit = None
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self.params = {}
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self.history = None
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@ -124,14 +125,14 @@ class Prophet(object):
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df['ds'] = pd.to_datetime(df['ds'])
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df = df.sort_values('ds')
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df.reset_index(inplace=True, drop=True)
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if initialize_scales:
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self.y_scale = df['y'].max()
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self.start, self.end = df['ds'].min(), df['ds'].max()
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self.start = df['ds'].min()
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self.t_scale = df['ds'].max() - self.start
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t_scale = self.end - self.start
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df['t'] = (df['ds'] - self.start) / t_scale
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df['t'] = (df['ds'] - self.start) / self.t_scale
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if 'y' in df:
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df['y_scaled'] = df['y'] / self.y_scale
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@ -171,31 +172,17 @@ class Prophet(object):
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else:
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# set empty changepoints
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self.changepoints = []
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def get_changepoint_indexes(self):
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if len(self.changepoints) == 0:
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return np.array([0]) # a dummy changepoint
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if len(self.changepoints) > 0:
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self.changepoints_t = np.sort(np.array(
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(self.changepoints - self.start) / self.t_scale))
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else:
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row_index = pd.DatetimeIndex(self.history['ds'])
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indexes = []
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for cp in self.changepoints:
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# In the future this may raise a KeyError, but for now we
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# should guarantee that all changepoint dates are included in
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# the historical data.
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indexes.append(row_index.get_loc(cp))
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return np.array(indexes).astype(np.int)
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self.changepoints_t = np.array([0]) # dummy changepoint
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def get_changepoint_times(self):
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cpi = self.get_changepoint_indexes()
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return np.array(self.history['t'].iloc[cpi])
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def get_changepoint_matrix(self):
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changepoint_indexes = self.get_changepoint_indexes()
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A = np.zeros((self.history.shape[0], len(changepoint_indexes)))
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for i, index in enumerate(changepoint_indexes):
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A[index:self.history.shape[0], i] = 1
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A = np.zeros((self.history.shape[0], len(self.changepoints_t)))
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for i, t_i in enumerate(self.changepoints_t):
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A[self.history['t'].values >= t_i, i] = 1
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return A
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@staticmethod
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@ -345,7 +332,6 @@ class Prophet(object):
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The fitted Prophet object.
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"""
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history = df[df['y'].notnull()].copy()
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history.reset_index(inplace=True, drop=True)
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history = self.setup_dataframe(history, initialize_scales=True)
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self.history = history
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@ -353,17 +339,15 @@ class Prophet(object):
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self.set_changepoints()
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A = self.get_changepoint_matrix()
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changepoint_indexes = self.get_changepoint_indexes()
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dat = {
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'T': history.shape[0],
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'K': seasonal_features.shape[1],
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'S': len(changepoint_indexes),
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'S': len(self.changepoints_t),
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'y': history['y_scaled'],
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't': history['t'],
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'A': A,
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# Need to add one because Stan is 1-indexed.
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's_indx': changepoint_indexes + 1,
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't_change': self.changepoints_t,
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'X': seasonal_features,
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'sigma': self.seasonality_prior_scale,
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'tau': self.changepoint_prior_scale,
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@ -381,7 +365,7 @@ class Prophet(object):
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return {
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'k': kinit[0],
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'm': kinit[1],
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'delta': np.zeros(len(changepoint_indexes)),
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'delta': np.zeros(len(self.changepoints_t)),
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'beta': np.zeros(seasonal_features.shape[1]),
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'sigma_obs': 1,
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}
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@ -419,7 +403,7 @@ class Prophet(object):
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`df` can be None, in which case we predict only on history.
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"""
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if df is None:
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df = self.history
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df = self.history.copy()
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else:
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df = self.setup_dataframe(df)
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@ -469,12 +453,12 @@ class Prophet(object):
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deltas = np.nanmean(self.params['delta'], axis=0)
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t = np.array(df['t'])
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cpts = self.get_changepoint_times()
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if self.growth == 'linear':
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trend = self.piecewise_linear(t, deltas, k, m, cpts)
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trend = self.piecewise_linear(t, deltas, k, m, self.changepoints_t)
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else:
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cap = df['cap_scaled']
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trend = self.piecewise_logistic(t, cap, deltas, k, m, cpts)
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trend = self.piecewise_logistic(
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t, cap, deltas, k, m, self.changepoints_t)
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return trend * self.y_scale
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|
|
@ -565,7 +549,6 @@ class Prophet(object):
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deltas = self.params['delta'][iteration]
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|
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t = np.array(df['t'])
|
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changepoint_ts = self.get_changepoint_times()
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T = t.max()
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|
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if T > 1:
|
||||
|
|
@ -574,7 +557,7 @@ class Prophet(object):
|
|||
dt = np.min(dt[dt > 0])
|
||||
# Number of time periods in the future
|
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N = np.ceil((T - 1) / float(dt))
|
||||
S = len(changepoint_ts)
|
||||
S = len(self.changepoints_t)
|
||||
|
||||
prob_change = min(1, (S * (T - 1)) / N)
|
||||
n_changes = np.random.binomial(N, prob_change)
|
||||
|
|
@ -593,7 +576,8 @@ class Prophet(object):
|
|||
deltas_new = np.random.laplace(0, lambda_, n_changes)
|
||||
|
||||
# Prepend the times and deltas from the history
|
||||
changepoint_ts = np.concatenate((changepoint_ts, changepoint_ts_new))
|
||||
changepoint_ts = np.concatenate((self.changepoints_t,
|
||||
changepoint_ts_new))
|
||||
deltas = np.concatenate((deltas, deltas_new))
|
||||
|
||||
if self.growth == 'linear':
|
||||
|
|
|
|||
|
|
@ -57,6 +57,25 @@ class TestProphet(TestCase):
|
|||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
|
||||
def test_fit_changepoint_not_in_history(self):
|
||||
train = DATA[(DATA['ds'] < '2013-01-01') | (DATA['ds'] > '2014-01-01')]
|
||||
train[(train['ds'] > '2014-01-01')] += 20
|
||||
future = pd.DataFrame({'ds': DATA['ds']})
|
||||
forecaster = Prophet(changepoints=['2013-06-06'])
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
|
||||
def test_fit_predict_duplicates(self):
|
||||
N = DATA.shape[0]
|
||||
train1 = DATA.head(N // 2).copy()
|
||||
train2 = DATA.head(N // 2).copy()
|
||||
train2['y'] += 10
|
||||
train = train1.append(train2)
|
||||
future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
|
||||
forecaster = Prophet()
|
||||
forecaster.fit(train)
|
||||
forecaster.predict(future)
|
||||
|
||||
def test_setup_dataframe(self):
|
||||
m = Prophet()
|
||||
N = DATA.shape[0]
|
||||
|
|
@ -81,7 +100,7 @@ class TestProphet(TestCase):
|
|||
|
||||
m.set_changepoints()
|
||||
|
||||
cp = m.get_changepoint_indexes()
|
||||
cp = m.changepoints_t
|
||||
self.assertEqual(cp.shape[0], m.n_changepoints)
|
||||
self.assertEqual(len(cp.shape), 1)
|
||||
self.assertTrue(cp.min() > 0)
|
||||
|
|
@ -100,7 +119,7 @@ class TestProphet(TestCase):
|
|||
m.history = history
|
||||
|
||||
m.set_changepoints()
|
||||
cp = m.get_changepoint_indexes()
|
||||
cp = m.changepoints_t
|
||||
self.assertEqual(cp.shape[0], 1)
|
||||
self.assertEqual(cp[0], 0)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,18 +1,11 @@
|
|||
# Copyright (c) 2017-present, Facebook, Inc.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the BSD-style license found in the
|
||||
# LICENSE file in the root directory of this source tree. An additional grant
|
||||
# of patent rights can be found in the PATENTS file in the same directory.
|
||||
|
||||
data {
|
||||
int T; // Sample size
|
||||
int<lower=1> K; // Number of seasonal vectors
|
||||
vector[T] t; // Day
|
||||
vector[T] y; // Time-series
|
||||
int S; // Number of split points
|
||||
int S; // Number of changepoints
|
||||
matrix[T, S] A; // Split indicators
|
||||
int s_indx[S]; // Index of split points
|
||||
real t_change[S]; // Index of changepoints
|
||||
matrix[T,K] X; // season vectors
|
||||
real<lower=0> sigma; // scale on seasonality prior
|
||||
real<lower=0> tau; // scale on changepoints prior
|
||||
|
|
@ -30,7 +23,7 @@ transformed parameters {
|
|||
vector[S] gamma; // adjusted offsets, for piecewise continuity
|
||||
|
||||
for (i in 1:S) {
|
||||
gamma[i] = -t[s_indx[i]] * delta[i];
|
||||
gamma[i] = -t_change[i] * delta[i];
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,34 +1,17 @@
|
|||
# Copyright (c) 2017-present, Facebook, Inc.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the BSD-style license found in the
|
||||
# LICENSE file in the root directory of this source tree. An additional grant
|
||||
# of patent rights can be found in the PATENTS file in the same directory.
|
||||
|
||||
data {
|
||||
int T; // Sample size
|
||||
int<lower=1> K; // Number of seasonal vectors
|
||||
vector[T] t; // Day
|
||||
vector[T] cap; // Capacities
|
||||
vector[T] y; // Time-series
|
||||
int S; // Number of split points
|
||||
int S; // Number of changepoints
|
||||
matrix[T, S] A; // Split indicators
|
||||
int s_indx[S]; // Index of split points
|
||||
real t_change[S]; // Index of changepoints
|
||||
matrix[T,K] X; // season vectors
|
||||
real<lower=0> sigma; // scale on seasonality prior
|
||||
real<lower=0> tau; // scale on changepoints prior
|
||||
}
|
||||
|
||||
|
||||
transformed data {
|
||||
int s_ext[S + 1]; // Segment endpoints
|
||||
for (j in 1:S) {
|
||||
s_ext[j] = s_indx[j];
|
||||
}
|
||||
s_ext[S + 1] = T + 1; // Used for the m_adj loop below.
|
||||
}
|
||||
|
||||
|
||||
parameters {
|
||||
real k; // Base growth rate
|
||||
real m; // offset
|
||||
|
|
@ -37,7 +20,6 @@ parameters {
|
|||
vector[K] beta; // seasonal vector
|
||||
}
|
||||
|
||||
|
||||
transformed parameters {
|
||||
vector[S] gamma; // adjusted offsets, for piecewise continuity
|
||||
vector[S + 1] k_s; // actual rate in each segment
|
||||
|
|
@ -52,7 +34,7 @@ transformed parameters {
|
|||
// Piecewise offsets
|
||||
m_pr = m; // The offset in the previous segment
|
||||
for (i in 1:S) {
|
||||
gamma[i] = (t[s_indx[i]] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
|
||||
gamma[i] = (t_change[i] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
|
||||
m_pr = m_pr + gamma[i]; // update for the next segment
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue