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https://github.com/saymrwulf/prophet.git
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Change cross validation performance metrics to first aggregate over horizon before computing rolling average (#839, #863)
This commit is contained in:
Ben Letham
parent
e9c017ba01
commit
269133c133
8 changed files with 393 additions and 205 deletions
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@ -207,14 +207,18 @@ prophet_copy <- function(m, cutoff = NULL) {
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#' `metrics` argument.
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#'
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#' Metrics are calculated over a rolling window of cross validation
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#' predictions, after sorting by horizon. The size of that window (number of
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#' simulated forecast points) is determined by the rolling_window argument,
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#' which specifies a proportion of simulated forecast points to include in
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#' each window. rolling_window=0 will compute it separately for each simulated
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#' forecast point (i.e., 'mse' will actually be squared error with no mean).
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#' The default of rolling_window=0.1 will use 10% of the rows in df in each
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#' window. rolling_window=1 will compute the metric across all simulated
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#' forecast points. The results are set to the right edge of the window.
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#' predictions, after sorting by horizon. Averaging is first done within each
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#' value of the horizon, and then across horizons as needed to reach the
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#' window size. The size of that window (number of simulated forecast points)
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#' is determined by the rolling_window argument, which specifies a proportion
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#' of simulated forecast points to include in each window. rolling_window=0
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#' will compute it separately for each horizon. The default of
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#' rolling_window=0.1 will use 10% of the rows in df in each window.
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#' rolling_window=1 will compute the metric across all simulated forecast
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#' points. The results are set to the right edge of the window.
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#'
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#' If rolling_window < 0, then metrics are computed at each datapoint with no
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#' averaging (i.e., 'mse' will actually be squared error with no mean).
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#'
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#' The output is a dataframe containing column 'horizon' along with columns
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#' for each of the metrics computed.
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@ -223,7 +227,7 @@ prophet_copy <- function(m, cutoff = NULL) {
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#' @param metrics An array of performance metrics to compute. If not provided,
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#' will use c('mse', 'rmse', 'mae', 'mape', 'coverage').
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#' @param rolling_window Proportion of data to use in each rolling window for
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#' computing the metrics. Should be in [0, 1].
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#' computing the metrics. Should be in [0, 1] to average.
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#'
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#' @return A dataframe with a column for each metric, and column 'horizon'.
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#'
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@ -244,39 +248,89 @@ performance_metrics <- function(df, metrics = NULL, rolling_window = 0.1) {
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df_m <- df
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df_m$horizon <- df_m$ds - df_m$cutoff
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df_m <- df_m[order(df_m$horizon),]
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# Window size
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w <- as.integer(rolling_window * nrow(df_m))
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w <- max(w, 1)
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w <- min(w, nrow(df_m))
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cols <- c('horizon')
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for (metric in metrics) {
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df_m[[metric]] <- get(metric)(df_m, w)
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cols <- c(cols, metric)
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if (('mape' %in% metrics) & (min(abs(df_m$y)) < 1e-8)) {
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message('Skipping MAPE because y close to 0')
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metrics <- metrics[metrics != 'mape']
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}
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df_m <- df_m[cols]
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return(stats::na.omit(df_m))
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if (length(metrics) == 0) {
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return(NULL)
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}
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w <- as.integer(rolling_window * nrow(df_m))
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if (w >= 0) {
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w <- max(w, 1)
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w <- min(w, nrow(df_m))
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}
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# Compute all metrics
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dfs = list()
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for (metric in metrics) {
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dfs[[metric]] <- get(metric)(df_m, w)
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}
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res <- dfs[[metrics[1]]]
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for (i in 2:length(metrics)) {
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res_m <- dfs[[metrics[i]]]
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stopifnot(res$horizon == res_m$horizon)
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res[[metrics[i]]] = res_m[[metrics[i]]]
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}
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return(res)
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}
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#' Compute a rolling mean of x
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#' Compute a rolling mean of x, after first aggregating by h
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#'
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#' Right-aligned. Padded with NAs on the front so the output is the same
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#' size as x.
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#' Right-aligned. Computes a single mean for each unique value of h. Each mean
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#' is over at least w samples.
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#'
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#' @param x Array.
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#' @param h Array of horizon for each value in x.
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#' @param w Integer window size (number of elements).
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#' @param name String name for metric in result dataframe.
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#'
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#' @return Rolling mean of x with window size w.
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#' @return Dataframe with columns horizon and name, the rolling mean of x.
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#'
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#' @importFrom dplyr "%>%"
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#' @keywords internal
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rolling_mean <- function(x, w) {
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s <- cumsum(c(0, x))
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prefix <- rep(NA, w - 1)
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return(c(prefix, (s[(w + 1):length(s)] - s[1:(length(s) - w)]) / w))
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rolling_mean_by_h <- function(x, h, w, name) {
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# Aggregate over h
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df <- data.frame(x=x, h=h)
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df2 <- df %>%
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dplyr::group_by(h) %>%
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dplyr::summarise(mean = mean(x), n = dplyr::n())
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xm <- df2$mean
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ns <- df2$n
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hs <- df2$h
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res <- data.frame(horizon=c())
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res[[name]] <- c()
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# Start from the right and work backwards
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i <- length(hs)
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while (i > 0) {
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# Construct a mean of at least w samples
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n <- ns[i]
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xbar <- xm[i]
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j <- i - 1
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while ((n < w) & (j > 0)) {
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# Include points from the previous horizon. All of them if still less
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# than w, otherwise just enough to get to w.
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n2 <- min(w - n, ns[j])
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xbar <- xbar * (n / (n + n2)) + xm[j] * (n2 / (n + n2))
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n <- n + n2
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j <- j - 1
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}
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if (n < w) {
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# Ran out of horizons before enough points.
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break
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}
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res.i <- data.frame(horizon=hs[i])
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res.i[[name]] <- xbar
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res <- rbind(res.i, res)
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i <- i - 1
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}
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return(res)
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}
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# The functions below specify performance metrics for cross-validation results.
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# Each takes as input the output of cross_validation, and returns the statistic
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# as an array, given a window size for rolling aggregation.
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# as a dataframe, given a window size for rolling aggregation.
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#' Mean squared error
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#'
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@ -288,7 +342,10 @@ rolling_mean <- function(x, w) {
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#' @keywords internal
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mse <- function(df, w) {
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se <- (df$y - df$yhat) ** 2
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return(rolling_mean(se, w))
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if (w < 0) {
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return(data.frame(horizon = df$horizon, mse = se))
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}
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return(rolling_mean_by_h(x = se, h = df$horizon, w = w, name = 'mse'))
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}
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#' Root mean squared error
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@ -300,7 +357,10 @@ mse <- function(df, w) {
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#'
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#' @keywords internal
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rmse <- function(df, w) {
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return(sqrt(mse(df, w)))
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res <- mse(df, w)
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res$mse <- sqrt(res$mse)
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names(res)[names(res) == 'mse'] <- 'rmse'
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return(res)
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}
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#' Mean absolute error
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@ -313,7 +373,10 @@ rmse <- function(df, w) {
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#' @keywords internal
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mae <- function(df, w) {
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ae <- abs(df$y - df$yhat)
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return(rolling_mean(ae, w))
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if (w < 0) {
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return(data.frame(horizon = df$horizon, mae = ae))
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}
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return(rolling_mean_by_h(x = ae, h = df$horizon, w = w, name = 'mae'))
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}
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#' Mean absolute percent error
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@ -326,7 +389,10 @@ mae <- function(df, w) {
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#' @keywords internal
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mape <- function(df, w) {
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ape <- abs((df$y - df$yhat) / df$y)
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return(rolling_mean(ape, w))
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if (w < 0) {
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return(data.frame(horizon = df$horizon, mape = ape))
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}
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return(rolling_mean_by_h(x = ape, h = df$horizon, w = w, name = 'mape'))
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}
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#' Coverage
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@ -339,5 +405,10 @@ mape <- function(df, w) {
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#' @keywords internal
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coverage <- function(df, w) {
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is_covered <- (df$y >= df$yhat_lower) & (df$y <= df$yhat_upper)
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return(rolling_mean(is_covered, w))
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if (w < 0) {
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return(data.frame(horizon = df$horizon, coverage = is_covered))
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}
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return(
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rolling_mean_by_h(x = is_covered, h = df$horizon, w = w, name = 'coverage')
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)
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}
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@ -489,7 +489,7 @@ dyplot.prophet <- function(x, fcst, uncertainty=TRUE,
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#'
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#' @export
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plot_cross_validation_metric <- function(df_cv, metric, rolling_window=0.1) {
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df_none <- performance_metrics(df_cv, metrics = metric, rolling_window = 0)
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df_none <- performance_metrics(df_cv, metrics = metric, rolling_window = -1)
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df_h <- performance_metrics(
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df_cv, metrics = metric, rolling_window = rolling_window
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)
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@ -91,16 +91,20 @@ test_that("performance_metrics", {
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df_cv <- cross_validation(
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m, horizon = 4, units = "days", period = 10, initial = 90)
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# Aggregation level none
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df_none <- performance_metrics(df_cv, rolling_window = 0)
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df_none <- performance_metrics(df_cv, rolling_window = -1)
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expect_true(all(
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sort(colnames(df_none))
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== sort(c('horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'))
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))
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expect_equal(nrow(df_none), 16)
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# Aggregation level 0
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df_0 <- performance_metrics(df_cv, rolling_window = 0)
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expect_equal(nrow(df_0), 4)
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expect_equal(length(unique(df_0$h)), 4)
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# Aggregation level 0.2
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df_horizon <- performance_metrics(df_cv, rolling_window = 0.2)
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expect_equal(nrow(df_horizon), 4)
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expect_equal(length(unique(df_horizon$horizon)), 4)
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expect_equal(nrow(df_horizon), 14)
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# Aggregation level all
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df_all <- performance_metrics(df_cv, rolling_window = 1)
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expect_equal(nrow(df_all), 1)
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@ -112,6 +116,38 @@ test_that("performance_metrics", {
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expect_true(all(
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sort(colnames(df_horizon)) == sort(c('coverage', 'mse', 'horizon'))
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))
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# Skip MAPE
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df_cv$y[1] <- 0.
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df_horizon <- performance_metrics(df_cv, metrics = c('coverage', 'mape'))
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expect_true(all(
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sort(colnames(df_horizon)) == sort(c('coverage', 'horizon'))
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))
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df_horizon <- performance_metrics(df_cv, metrics = c('mape'))
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expect_null(df_horizon)
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})
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test_that("rolling_mean", {
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skip_if_not(Sys.getenv('R_ARCH') != '/i386')
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x <- 0:9
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h <- 0:9
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df <- prophet:::rolling_mean_by_h(x=x, h=h, w=1, name='x')
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expect_equal(x, df$x)
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expect_equal(h, df$horizon)
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df <- prophet:::rolling_mean_by_h(x=x, h=h, w=4, name='x')
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expect_equal(x[4:10] - 1.5, df$x)
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expect_equal(3:9, df$horizon)
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h <- c(1., 2., 3., 4., 4., 4., 4., 4., 7., 7.)
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x.true <- c(1., 5., 22/3)
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h.true <- c(3., 4., 7.)
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df <- prophet:::rolling_mean_by_h(x=x, h=h, w=3, name='x')
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expect_equal(x.true, df$x)
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expect_equal(h.true, df$horizon)
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df <- prophet:::rolling_mean_by_h(x=x, h=h, w=10, name='x')
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expect_equal(c(7.), df$horizon)
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expect_equal(c(4.5), df$x)
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})
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test_that("copy", {
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@ -425,11 +425,9 @@ test_that("auto_weekly_seasonality", {
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train.w <- DATA[1:N.w, ]
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m <- prophet(train.w)
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expect_false('weekly' %in% names(m$seasonalities))
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expect_warning({
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# prophet warning: non-zero return code in optimizing
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m <- prophet(train.w, weekly.seasonality = TRUE)
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expect_true('weekly' %in% names(m$seasonalities))
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})
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# prophet warning: non-zero return code in optimizing
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m <- prophet(train.w, weekly.seasonality = TRUE)
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expect_true('weekly' %in% names(m$seasonalities))
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# Should be False due to weekly spacing
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train.w <- DATA[seq(1, nrow(DATA), 7), ]
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m <- prophet(train.w)
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File diff suppressed because one or more lines are too long
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@ -205,14 +205,18 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
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`metrics` argument.
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Metrics are calculated over a rolling window of cross validation
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predictions, after sorting by horizon. The size of that window (number of
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simulated forecast points) is determined by the rolling_window argument,
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which specifies a proportion of simulated forecast points to include in
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each window. rolling_window=0 will compute it separately for each simulated
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forecast point (i.e., 'mse' will actually be squared error with no mean).
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The default of rolling_window=0.1 will use 10% of the rows in df in each
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window. rolling_window=1 will compute the metric across all simulated forecast
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points. The results are set to the right edge of the window.
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predictions, after sorting by horizon. Averaging is first done within each
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value of horizon, and then across horizons as needed to reach the window
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size. The size of that window (number of simulated forecast points) is
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determined by the rolling_window argument, which specifies a proportion of
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simulated forecast points to include in each window. rolling_window=0 will
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compute it separately for each horizon. The default of rolling_window=0.1
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will use 10% of the rows in df in each window. rolling_window=1 will
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compute the metric across all simulated forecast points. The results are
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set to the right edge of the window.
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If rolling_window < 0, then metrics are computed at each datapoint with no
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averaging (i.e., 'mse' will actually be squared error with no mean).
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The output is a dataframe containing column 'horizon' along with columns
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for each of the metrics computed.
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@ -223,7 +227,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
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metrics: A list of performance metrics to compute. If not provided, will
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use ['mse', 'rmse', 'mae', 'mape', 'coverage'].
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rolling_window: Proportion of data to use in each rolling window for
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computing the metrics. Should be in [0, 1].
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computing the metrics. Should be in [0, 1] to average
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Returns
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-------
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@ -241,42 +245,84 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
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df_m = df.copy()
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df_m['horizon'] = df_m['ds'] - df_m['cutoff']
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df_m.sort_values('horizon', inplace=True)
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# Window size
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if 'mape' in metrics and df_m['y'].abs().min() < 1e-8:
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logger.info('Skipping MAPE because y close to 0')
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metrics.remove('mape')
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if len(metrics) == 0:
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return None
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w = int(rolling_window * df_m.shape[0])
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w = max(w, 1)
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w = min(w, df_m.shape[0])
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cols = ['horizon']
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if w >= 0:
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w = max(w, 1)
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w = min(w, df_m.shape[0])
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# Compute all metrics
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dfs = {}
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for metric in metrics:
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df_m[metric] = eval(metric)(df_m, w)
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cols.append(metric)
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df_m = df_m[cols]
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return df_m.dropna()
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dfs[metric] = eval(metric)(df_m, w)
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res = dfs[metrics[0]]
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for i in range(1, len(metrics)):
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res_m = dfs[metrics[i]]
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assert np.array_equal(res['horizon'].values, res_m['horizon'].values)
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res[metrics[i]] = res_m[metrics[i]]
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return res
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def rolling_mean(x, w):
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"""Compute a rolling mean of x
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def rolling_mean_by_h(x, h, w, name):
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"""Compute a rolling mean of x, after first aggregating by h.
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Right-aligned. Padded with NaNs on the front so the output is the same
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size as x.
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Right-aligned. Computes a single mean for each unique value of h. Each
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mean is over at least w samples.
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Parameters
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----------
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x: Array.
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h: Array of horizon for each value in x.
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w: Integer window size (number of elements).
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name: Name for metric in result dataframe
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Returns
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-------
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Rolling mean of x with window size w.
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Dataframe with columns horizon and name, the rolling mean of x.
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"""
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s = np.cumsum(np.insert(x, 0, 0))
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prefix = np.empty(w - 1)
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prefix.fill(np.nan)
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return np.hstack((prefix, (s[w:] - s[:-w]) / float(w))) # right-aligned
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# Aggregate over h
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df = pd.DataFrame({'x': x, 'h': h})
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df2 = (
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df.groupby('h').agg(['mean', 'count']).reset_index().sort_values('h')
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)
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xm = df2['x']['mean'].values
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ns = df2['x']['count'].values
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hs = df2['h'].values
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res_h = []
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res_x = []
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# Start from the right and work backwards
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i = len(hs) - 1
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while i >= 0:
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# Construct a mean of at least w samples.
|
||||
n = int(ns[i])
|
||||
xbar = float(xm[i])
|
||||
j = i - 1
|
||||
while ((n < w) and j >= 0):
|
||||
# Include points from the previous horizon. All of them if still
|
||||
# less than w, otherwise just enough to get to w.
|
||||
n2 = min(w - n, ns[j])
|
||||
xbar = xbar * (n / (n + n2)) + xm[j] * (n2 / (n + n2))
|
||||
n += n2
|
||||
j -= 1
|
||||
if n < w:
|
||||
# Ran out of horizons before enough points.
|
||||
break
|
||||
res_h.append(hs[i])
|
||||
res_x.append(xbar)
|
||||
i -= 1
|
||||
res_h.reverse()
|
||||
res_x.reverse()
|
||||
return pd.DataFrame({'horizon': res_h, name: res_x})
|
||||
|
||||
|
||||
|
||||
# The functions below specify performance metrics for cross-validation results.
|
||||
# Each takes as input the output of cross_validation, and returns the statistic
|
||||
# as an array, given a window size for rolling aggregation.
|
||||
# as a dataframe, given a window size for rolling aggregation.
|
||||
|
||||
|
||||
def mse(df, w):
|
||||
|
|
@ -289,10 +335,14 @@ def mse(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of mean squared errors.
|
||||
Dataframe with columns horizon and mse.
|
||||
"""
|
||||
se = (df['y'] - df['yhat']) ** 2
|
||||
return rolling_mean(se.values, w)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mse': se})
|
||||
return rolling_mean_by_h(
|
||||
x=se.values, h=df['horizon'].values, w=w, name='mse'
|
||||
)
|
||||
|
||||
|
||||
def rmse(df, w):
|
||||
|
|
@ -305,9 +355,12 @@ def rmse(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of root mean squared errors.
|
||||
Dataframe with columns horizon and rmse.
|
||||
"""
|
||||
return np.sqrt(mse(df, w))
|
||||
res = mse(df, w)
|
||||
res['mse'] = np.sqrt(res['mse'])
|
||||
res.rename({'mse': 'rmse'}, axis='columns', inplace=True)
|
||||
return res
|
||||
|
||||
|
||||
def mae(df, w):
|
||||
|
|
@ -320,10 +373,14 @@ def mae(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of mean absolute errors.
|
||||
Dataframe with columns horizon and mae.
|
||||
"""
|
||||
ae = np.abs(df['y'] - df['yhat'])
|
||||
return rolling_mean(ae.values, w)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mae': ae})
|
||||
return rolling_mean_by_h(
|
||||
x=ae.values, h=df['horizon'].values, w=w, name='mae'
|
||||
)
|
||||
|
||||
|
||||
def mape(df, w):
|
||||
|
|
@ -336,10 +393,14 @@ def mape(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of mean absolute percent errors.
|
||||
Dataframe with columns horizon and mape.
|
||||
"""
|
||||
ape = np.abs((df['y'] - df['yhat']) / df['y'])
|
||||
return rolling_mean(ape.values, w)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'mape': ape})
|
||||
return rolling_mean_by_h(
|
||||
x=ape.values, h=df['horizon'].values, w=w, name='mape'
|
||||
)
|
||||
|
||||
|
||||
def smape(df, w):
|
||||
|
|
@ -352,10 +413,14 @@ def smape(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of symmetric mean absolute percent errors.
|
||||
Dataframe with columns horizon and smape.
|
||||
"""
|
||||
sape = np.abs(df['yhat']-df['y']) / ((np.abs(df['y']) + np.abs(df['yhat'])) /2)
|
||||
return rolling_mean(sape.values, w)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})
|
||||
return rolling_mean_by_h(
|
||||
x=sape.values, h=df['horizon'].values, w=w, name='smape'
|
||||
)
|
||||
|
||||
|
||||
def coverage(df, w):
|
||||
|
|
@ -368,7 +433,11 @@ def coverage(df, w):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Array of coverages.
|
||||
Dataframe with columns horizon and coverage.
|
||||
"""
|
||||
is_covered = (df['y'] >= df['yhat_lower']) & (df['y'] <= df['yhat_upper'])
|
||||
return rolling_mean(is_covered.values, w)
|
||||
if w < 0:
|
||||
return pd.DataFrame({'horizon': df['horizon'], 'coverage': is_covered})
|
||||
return rolling_mean_by_h(
|
||||
x=is_covered.values, h=df['horizon'].values, w=w, name='coverage'
|
||||
)
|
||||
|
|
|
|||
|
|
@ -465,7 +465,7 @@ def plot_cross_validation_metric(
|
|||
else:
|
||||
fig = ax.get_figure()
|
||||
# Get the metric at the level of individual predictions, and with the rolling window.
|
||||
df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=0)
|
||||
df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=-1)
|
||||
df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)
|
||||
|
||||
# Some work because matplotlib does not handle timedelta
|
||||
|
|
|
|||
|
|
@ -113,21 +113,25 @@ class TestDiagnostics(TestCase):
|
|||
df_cv = diagnostics.cross_validation(
|
||||
m, horizon='4 days', period='10 days', initial='90 days')
|
||||
# Aggregation level none
|
||||
df_none = diagnostics.performance_metrics(df_cv, rolling_window=0)
|
||||
df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
|
||||
self.assertEqual(
|
||||
set(df_none.columns),
|
||||
{'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
|
||||
)
|
||||
self.assertEqual(df_none.shape[0], 16)
|
||||
# Aggregation level 0
|
||||
df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)
|
||||
self.assertEqual(len(df_0), 4)
|
||||
self.assertEqual(len(df_0['horizon'].unique()), 4)
|
||||
# Aggregation level 0.2
|
||||
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
|
||||
self.assertEqual(len(df_horizon), 4)
|
||||
self.assertEqual(len(df_horizon['horizon'].unique()), 4)
|
||||
self.assertEqual(df_horizon.shape[0], 14)
|
||||
# Aggregation level all
|
||||
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
|
||||
self.assertEqual(df_all.shape[0], 1)
|
||||
for metric in ['mse', 'mape', 'mae', 'coverage']:
|
||||
self.assertEqual(df_all[metric].values[0], df_none[metric].mean())
|
||||
self.assertAlmostEqual(df_all[metric].values[0], df_none[metric].mean())
|
||||
# Custom list of metrics
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv, metrics=['coverage', 'mse'],
|
||||
|
|
@ -136,6 +140,41 @@ class TestDiagnostics(TestCase):
|
|||
set(df_horizon.columns),
|
||||
{'coverage', 'mse', 'horizon'},
|
||||
)
|
||||
# Skip MAPE
|
||||
df_cv.loc[0, 'y'] = 0.
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv, metrics=['coverage', 'mape'],
|
||||
)
|
||||
self.assertEqual(
|
||||
set(df_horizon.columns),
|
||||
{'coverage', 'horizon'},
|
||||
)
|
||||
df_horizon = diagnostics.performance_metrics(
|
||||
df_cv, metrics=['mape'],
|
||||
)
|
||||
self.assertIsNone(df_horizon)
|
||||
|
||||
def test_rolling_mean(self):
|
||||
x = np.arange(10)
|
||||
h = np.arange(10)
|
||||
df = diagnostics.rolling_mean_by_h(x=x, h=h, w=1, name='x')
|
||||
self.assertTrue(np.array_equal(x, df['x'].values))
|
||||
self.assertTrue(np.array_equal(h, df['horizon'].values))
|
||||
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=4, name='x')
|
||||
self.assertTrue(np.allclose(x[3:] - 1.5, df['x'].values))
|
||||
self.assertTrue(np.array_equal(np.arange(3, 10), df['horizon'].values))
|
||||
|
||||
h = np.array([1., 2., 3., 4., 4., 4., 4., 4., 7., 7.])
|
||||
x_true = np.array([1.0, 5.0 , 22. / 3])
|
||||
h_true = np.array([3., 4., 7.])
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=3, name='x')
|
||||
self.assertTrue(np.allclose(x_true, df['x'].values))
|
||||
self.assertTrue(np.array_equal(h_true, df['horizon'].values))
|
||||
|
||||
df = diagnostics.rolling_mean_by_h(x, h, w=10, name='x')
|
||||
self.assertTrue(np.allclose(np.array([7.]), df['horizon'].values))
|
||||
self.assertTrue(np.allclose(np.array([4.5]), df['x'].values))
|
||||
|
||||
def test_copy(self):
|
||||
df = DATA_all.copy()
|
||||
|
|
|
|||
Loading…
Reference in a new issue