Merge pull request #44 from koheiw/dev-perplexity

Dev perplexity

Author: koheiw

NAMESPACE

@@ -15,6 +15,7 @@ S3method(textmodel_lsa,tokens)
 S3method(textmodel_word2vec,tokens)
 export(analogy)
 export(as.textmodel_doc2vec)
+export(perplexity)
 export(probability)
 export(similarity)
 export(textmodel_doc2vec)

R/as.doc2vec.R

@@ -65,6 +65,7 @@ as.textmodel_doc2vec.dfm <- function(x, model = NULL, normalize = FALSE,
     result <- list(
         "values" = list("word" = wov, "doc" = dov),
         "dim" = model$dim,
+        "tolower" = model$tolower,
         "concatenator" = conc, 
         "docvars" = x@docvars,
         "normalize" = normalize,

R/lsa.R

@@ -90,15 +90,16 @@ textmodel_lsa.dfm <- function(x, dim = 50L, min_count = 5L,
         rownames(wov) <- featnames(x)
     }
     result <- list(
-        values = list(word = wov),
-        dim = dim,
-        frequency = featfreq(x),
-        engine = engine,
-        weight = weight,
-        min_count = min_count,
-        concatenator = meta(x, field = "concatenator", type = "object"),
-        call = try(match.call(sys.function(-1), call = sys.call(-1)), silent = TRUE),
-        version = utils::packageVersion("wordvector")
+        "values" = list(word = wov),
+        "dim" = dim,
+        "frequency" = featfreq(x),
+        "engine" = engine,
+        "weight" = weight,
+        "min_count" = min_count,
+        "tolower" = tolower,
+        "concatenator" = meta(x, field = "concatenator", type = "object"),
+        "call" = try(match.call(sys.function(-1), call = sys.call(-1)), silent = TRUE),
+        "version" = utils::packageVersion("wordvector")
     )
     class(result) <- c("textmodel_lsa", "textmodel_wordvector")
     return(result)

R/utils.R

@@ -95,9 +95,9 @@ similarity <- function(x, targets, layer = c("words", "documents"),
 #' Compute probability of words
 #'
 #' Compute the probability of words given other words.
-#' @param x a `textmodel_wordvector` object fitted with `normalize = FALSE`.
-#' @param targets words for which probability is computed.
-#' @param layer the layer based on which probability is computed.
+#' @param x a trained `textmodel_wordvector` object.
+#' @param targets words for which probabilities are computed.
+#' @param layer the layer based on which probabilities are computed.
 #' @param mode specify the type of resulting object.
 #' @return a matrix of words or documents sorted in descending order by the probability 
 #'   scores when `mode = "character"`; a matrix of the probability scores when `mode = "numeric"`.
@@ -164,6 +164,31 @@ probability <- function(x, targets, layer = c("words", "documents"),
     return(res)
 }
 
+#' Compute perplexity of a model
+#'
+#' Compute the perplexity of a trained word2vec model with data.
+#' @param x a trained `textmodel_wordvector` object.
+#' @param targets words for which probabilities are computed.
+#' @param data a [quanteda::tokens] or [quanteda::dfm]; the probabilities of words are 
+#'    tested against occurrences of words in it.
+#' @export
+#' @keywords internal
+perplexity <- function(x, targets, data) {
+    x <- upgrade_pre06(x)
+    
+    if (!is.character(targets))
+        stop("targets must be a character vector")
+    
+    if (!is.tokens(data) && !is.dfm(data))
+        stop("data must be a tokens or dfm")
+    data <- dfm(data, remove_padding = TRUE, tolower = x$tolower)
+    
+    p <- probability(x, targets, mode = "numeric")
+    pred <- dfm_match(dfm_weight(data, "prop"), rownames(p)) %*% p
+    tri <- Matrix::mat2triplet(dfm_match(data, colnames(pred)))
+    exp(-sum(tri$x * log(pred[cbind(tri$i, tri$j)])) / sum(tri$x))
+}
+
 get_threads <- function() {
 
     # respect other settings
@@ -194,6 +219,9 @@ upgrade_pre06 <- function(x) {
     if (is.numeric(x$type)) {
         x$type <- c("cbow", "sg")[x$type]
     }
+    if (is.null(x$tolower)) {
+        x$tolower <- TRUE
+    }
     return(x)
 }
 

R/word2vec.R

@@ -160,6 +160,7 @@ wordvector <- function(x, dim = 50, type = c("cbow", "sg", "dm", "dbow"),
 
     result$type <- type
     result$min_count <- min_count
+    result$tolower <- tolower
     result$concatenator <- meta(x, field = "concatenator", type = "object")
     if (include_data) # NOTE: consider removing
         result$data <- y

man/perplexity.Rd

@@ -30,28 +30,36 @@ wdv$weights["america",]
 wdv2$weights["america",]
 wdv3$weights["america",]
 
-#dov <- textmodel_doc2vec(toks, dim = 50, type = "cbow", min_count = 5, verbose = TRUE, iter = 10)
-dov <- textmodel_doc2vec(toks, dim = 50, type = "skip-gram", min_count = 5, verbose = TRUE, iter = 10)
+dov <- textmodel_doc2vec(toks, dim = 100, type = "dm", min_count = 5, verbose = TRUE, iter = 10)
+dov2 <- textmodel_doc2vec(toks, dim = 100, type = "dm", min_count = 5, verbose = TRUE, iter = 20)
 
-similarity(dov, analogy(~ washington - america + france)) %>% 
-    head()
+sim <- proxyC::simil(
+    dov$values$doc,
+    dov$values$doc["3555430",, drop = FALSE]
+)
+tail(sort(s <- rowSums(sim)))
 
-probability(dov, c("good")) %>% 
-    head()
+sim2 <- proxyC::simil(
+    dov2$values$doc,
+    dov2$values$doc["3555430",, drop = FALSE]
+)
+tail(sort(s2 <- rowSums(sim2)))
+
+identical(s, s2)
+cor(s, s2)
 
 sim <- proxyC::simil(
-    dov$values$doc,
-    dov$values$doc["4263794",, drop = FALSE]
+    dov2$values$doc,
+    dov2$values$doc["4263794",, drop = FALSE]
 )
 sim <- proxyC::simil(
-    dov$values$doc,
-    dov$values$doc["3016236",, drop = FALSE]
+    dov2$values$doc,
+    dov2$values$doc["3016236",, drop = FALSE]
 )
 sim <- proxyC::simil(
-    dov$values$doc,
-    dov$values$doc["3555430",, drop = FALSE]
+    dov2$values$doc,
+    dov2$values$doc["3555430",, drop = FALSE]
 )
-
 tail(sort(s <- rowSums(sim)))
 print(tail(toks[order(s)]), max_ntoken = -1)
 

man/probability.Rd

@@ -20,7 +20,7 @@ test_that("textmodel_doc2vec works", {
     expect_false(dov1$normalize)
     expect_equal(
         names(dov1),
-        c("values", "dim", "concatenator", "docvars", "normalize", "call", "version")
+        c("values", "dim", "tolower", "concatenator", "docvars", "normalize", "call", "version")
     )
     expect_equal(
         dim(dov1$values$word), c(5363L, 50L)

tests/misc/test_small.R

@@ -21,7 +21,8 @@ test_that("textmodel_doc2vec works", {
     expect_equal(
         names(dov1),
         c("values", "weights", "type", "dim", "frequency", "window",  "iter", "alpha", 
-          "use_ns", "ns_size", "sample", "normalize",  "min_count", "concatenator", "docvars", "call", "version")
+          "use_ns", "ns_size", "sample", "normalize",  "min_count", "tolower",
+          "concatenator", "docvars", "call", "version")
     )
     expect_equal(
         dim(dov1$values$word), c(5363L, 50L)
@@ -64,7 +65,8 @@ test_that("textmodel_doc2vec works", {
     expect_equal(
         names(dov2),
         c("values", "weights", "type", "dim", "frequency", "window",  "iter", "alpha", 
-          "use_ns", "ns_size", "sample", "normalize",  "min_count", "concatenator", "docvars", "call", "version")
+          "use_ns", "ns_size", "sample", "normalize",  "min_count", "tolower",
+          "concatenator", "docvars", "call", "version")
     )
     expect_null(
         dov2$values$word