Add minimalistic processing

r-spatial · Oct 20, 2019 · 3c1e3e7 · 3c1e3e7
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diff --git a/R/dtw.R b/R/dtw.R
@@ -103,7 +103,7 @@
                        X  = as.integer(match(x[[1]]$label, levels)),
                        IM = matrix(as.integer(0), nrow = n, ncol = 3),
                        A  = as.integer(x[[1]]$Alig.N),
-                       K  = as.integer(length(x)),
+                       K  = as.integer(length(x[[1]]$Alig.N)),
                        P  = as.integer(length(breaks)),
                        L  = as.integer(length(levels)),
                        OV = as.double(overlap))) 

diff --git a/R/twdtw_reduce_time.R b/R/twdtw_reduce_time.R
@@ -0,0 +1,131 @@
+#' @rdname twdtwApply 
+#' @aliases twdtwApply-twdtwTimeSeries 
+#' 
+#' @examples 
+#' 
+#' # Read time series to be labelled from csv
+#' ts_file <- system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat")
+#' ts <- read.csv(ts_file, stringsAsFactors = FALSE)
+#' 
+#' # Read labelled temporal patterns from csv
+#' pattern_files <- dir(system.file("reduce_time/patterns", package = "dtwSat"), full.names = TRUE)
+#' patterns <- lapply(pattern_files, read.csv, stringsAsFactors = FALSE)
+#' 
+#' # Label time series 
+#' ts_class <- .twdtw_reduce_time(x = ts, y = patterns, weight.fun = logisticWeight(-0.1, 50), 
+#'                                from = "2009-09-01", to = "2017-09-01", by = "6 month")
+#' ts_class
+#' 
+#' ts_class$pattern_name <- basename(pattern_files)[ts_class$label]
+#' ts_class
+#' 
+#' @export
+.twdtw_reduce_time = function(x, 
+                              y, 
+                              weight.fun = NULL,
+                              dist.method = "Euclidean",
+                              step.matrix = symmetric1,
+                              n = NULL,
+                              span = NULL, 
+                              min.length = 0, 
+                              from = NULL, 
+                              to = NULL, 
+                              by = NULL, 
+                              overlap = .5, 
+                              fill = 255){
+
+  # Split time series from dates 
+  px <- x[,names(x)!="date",drop=FALSE] 
+  tx <- as.Date(x$date) 
+
+  # Comput TWDTW alignments for all patterns   
+  aligs <- do.call("rbind", lapply(seq_along(y), function(l){
+
+    # Split pattern time series from dates 
+    py <- y[[l]][,names(y[[l]])!="date",drop=FALSE] 
+    ty <- as.Date(y[[l]]$date)
+
+    # Compute local cost matrix 
+    cm <- proxy::dist(py, px, method = dist.method)
+
+    if(!is.null(weight.fun)){
+
+      # Get day of the year for pattern and time series 
+      doyy <- as.numeric(format(ty, "%j")) 
+      doyx <- as.numeric(format(tx, "%j")) 
+
+      # Compute time-weght matrix 
+      w <- weight.fun(.g(proxy::dist(doyy, doyx, method = dist.method)))
+
+      # Apply time-weight to local cost matrix 
+      cm <- w * cm
+
+    }
+
+    # Compute accumulated DTW cost matrix 
+    internals <- .computecost(cm = cm, step.matrix = step.matrix)
+
+    # Find all low cost candidates 
+    d <- internals$costMatrix[internals$N,1:internals$M]
+    a <- internals$startingMatrix[internals$N,1:internals$M]
+
+    # Remove overlapping matches within a span from local minimum
+    if(is.null(span)){
+      candidates   <- data.frame(a, d)
+      candidates   <- candidates[ candidates$d==ave(candidates$d, candidates$a, FUN=min), ]
+      candidates$b <- as.numeric(row.names(candidates))
+    } else {
+      b <- .findMin(d, tx, span = span)
+      candidates <- data.frame(a[b], d[b], b)
+    }
+
+    # Order maches by minimum TWDTW distance 
+    I <- order(candidates$d)
+    if(length(I)<1) return(NULL)
+
+    # Select alignments 
+    if(is.null(n)) n <- length(I)
+    if(length(I) > n) I <- I[1:n]
+
+    # Remove matches that overstretch 
+    I <- I[diff(range(ty))*min.length <= tx[candidates$b[I]] - tx[candidates$a[I]]]
+
+    # Return null if there are no alignments left
+    if(length(I)<1) return(NULL)
+
+    # Build alignments table table 
+    res <- data.frame(
+      Alig.N     = I,
+      from       = tx[candidates$a[I]],
+      to         = tx[candidates$b[I]],
+      distance   = candidates$d[I],
+      label      = l
+    )
+
+    return(res)
+
+  }))
+
+  # Create classification intervals 
+  breaks <- seq(as.Date(from), as.Date(to), by = by)
+  # Find best macthes for the intervals 
+  best_matches <- .bestmatches(
+    x = list(aligs[order(aligs$from, aligs$from),]), 
+    m = length(y), 
+    n = length(breaks) - 1, 
+    levels = seq_along(y), 
+    breaks = breaks, 
+    overlap = overlap,
+    fill = 99999)$IM
+
+  # Build output 
+  out <- as.data.frame(best_matches[,c(1,3)])
+  names(out) <- c("label", "Alig.N")
+  out$from <- breaks[-length(breaks)]
+  out$to <- breaks[-1]
+  out <- merge(out, aligs[, c("label", "Alig.N", "distance")], by.x = c("label", "Alig.N"), by.y = c("label", "Alig.N"), all.x = TRUE)
+  out <- out[order(out$from), names(out)!="Alig.N"]
+  if(any(out$label==0)) out[out$label==0,]$label <- fill
+  return(out)
+}
+
diff --git a/examples/benchmark_minimalist.R b/examples/benchmark_minimalist.R
@@ -0,0 +1,20 @@
+library(dtwSat)
+log_fun = logisticWeight(-0.1, 50)
+from = "2009-09-01"
+to = "2017-09-01"
+by = "6 month"
+
+# S4 objects for original implementation 
+tw_patt = twdtwTimeSeries(MOD13Q1.patterns.list)
+tw_ts = twdtwTimeSeries(MOD13Q1.ts) 
+
+# Table from csv for minimalist version 
+mn_patt <- lapply(dir(system.file("reduce_time/patterns", package = "dtwSat"), full.names = TRUE), read.csv, stringsAsFactors = FALSE)
+mn_ts <- read.csv(system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat"), stringsAsFactors = FALSE)
+
+# Benchtmark 
+rbenchmark::benchmark(
+  original = twdtwClassify(twdtwApply(x = tw_ts, y = tw_patt, weight.fun = log_fun), from = from, to = to, by = by)[[1]],
+  minimalist = .twdtw_reduce_time(x = mn_ts, y = mn_patt, weight.fun = log_fun, from = from, to = to, by = by)  
+)
+
diff --git a/inst/reduce_time/patterns/cotton_MODIS13Q1.csv b/inst/reduce_time/patterns/cotton_MODIS13Q1.csv
@@ -0,0 +1,24 @@
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diff --git a/inst/reduce_time/patterns/maize_MODIS13Q1.csv b/inst/reduce_time/patterns/maize_MODIS13Q1.csv
@@ -0,0 +1,24 @@
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diff --git a/inst/reduce_time/patterns/soybean_MODIS13Q1.csv b/inst/reduce_time/patterns/soybean_MODIS13Q1.csv
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diff --git a/inst/reduce_time/ts_MODIS13Q1.csv b/inst/reduce_time/ts_MODIS13Q1.csv
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