R/madlib-lda.R

In PivotalR: A Fast, Easy-to-Use Tool for Manipulating Tables in Databases and a Wrapper of MADlib

setClass("lda.madlib")

madlib.lda <- function (data, topic_num, alpha, beta,
    iter_num = 20, nstart = 1, best = TRUE,...) {

    if ( ! is( data, "db.obj" ) )
        stop( "madlib.lda can only be used on a db.obj object, ",
             "and ", deparse( substitute( documents ) ), " is not!")

    conn.id <- conn.id(data)
    warnings <- .suppress.warnings(conn.id)
    db <- .get.dbms.str(conn.id)
    madlib <- schema.madlib(conn.id) # MADlib schema name

    tbl.tf <- .unique.string()
    tbl.source <- content(data)

    sql_names <- paste("select * from ",tbl.source," limit 1")

    col.names <- names(db.q(sql_names, conn.id=conn.id, verbose=FALSE))
    docid <- col.names[1]
    words <- col.names[2]

    ##query to get the data in appropriate format for lda_train
    sql_tf <- paste("select ", madlib, ".term_frequency('", tbl.source, "','",
        docid, "','", words, "','", tbl.tf, "',", TRUE,")",sep="")

    db.q(sql_tf, nrows = 1, conn.id = conn.id, verbose = FALSE)

    #compute vocabulary size
    sql_voc_size <- paste("select count(*) from ", tbl.tf, "_vocabulary",
        sep="")
    voc_size <- as.numeric(db.q(sql_voc_size, ";", conn.id = conn.id,
        verbose= FALSE))

    sql_voc <- paste("select word from ", tbl.tf, "_vocabulary", sep="")
    vocabulary <- db.q(sql_voc, nrows = -1,conn.id = conn.id, verbose = FALSE)
    vocabulary <- t(vocabulary)

    tbl.output <- vector(length=nstart)
    tbl.model <- vector(length=nstart)
    res <- list()

    for (i in seq_len(nstart)){

        tbl.output[i] <- paste("__madlib_pivotalr_lda_out",i,"__", sep="")
        tbl.model[i] <- paste("__madlib_pivotalr_lda_mod",i,"__", sep="")

        db.q(paste( "DROP TABLE IF EXISTS ", tbl.output[i],
                sep="" ), nrows = -1, conn.id = conn.id, verbose = FALSE)

        db.q(paste( "DROP TABLE IF EXISTS ", tbl.model[i],
                sep="" ), nrows = -1, conn.id = conn.id, verbose = FALSE)

        sql_i <- paste("select ", madlib, ".lda_train('",
                 tbl.tf, "', '", tbl.model[i], "', '",
                 tbl.output[i], "',", voc_size, ", ",
                 topic_num, ",", iter_num, ",", alpha,
                 ",", beta, ")", sep = "")

        res[[i]] <- db.q(sql_i, "; select topic_count, topic_assignment from ",
            tbl.output[i], nrows = -1, conn.id = conn.id, verbose=FALSE)

    }

    if (best){
        obj <- .Machine$double.xmax
        obji <- 0

        for (i in seq_len(nstart)){

            sql_perp <- paste("select ", madlib,
                 ".lda_get_perplexity('", tbl.model[i],
                 "','", tbl.output[i], "')", sep="")

            perplexity <- db.q(sql_perp, nrows = -1, conn.id = conn.id,
                verbose = FALSE)
            perplexity <- as.numeric(perplexity$lda_get_perplexity)

            if (perplexity < obj){

                obj <- perplexity
                obji <- i
            }

        }
        for (i in seq_len(nstart)){
            if (i != obji){
                tbl.output <- paste("__madlib_pivotalr_lda_out",i,"__", sep="")
                tbl.model <- paste("__madlib_pivotalr_lda_mod",i,"__", sep="")
                db.q(paste( "DROP TABLE IF EXISTS ", tbl.output,
                    sep="" ), nrows = -1, conn.id = conn.id, verbose = FALSE)

                db.q(paste( "DROP TABLE IF EXISTS ", tbl.model,
                    sep="" ), nrows = -1, conn.id = conn.id, verbose = FALSE)
            }
        }

        min <- max <- obji

    } else{
        min <- 1
        max <- nstart
    }

    rst.list <- list()

    for (i in seq(min,max)){

        tbl.output <- paste("__madlib_pivotalr_lda_out",i,"__",sep="")
        tbl.model <- paste("__madlib_pivotalr_lda_mod",i,"__",sep="")

        res_out <- res[[i]]

        sql_parse_model <- paste("select (", madlib,
            ".lda_parse_model(model, voc_size, topic_num)).* from ",
            tbl.model, sep="")

        res_model_tuple <- db.q(sql_parse_model, nrows = -1,
                     conn.id = conn.id, verbose = FALSE)

        model_table <- db.data.frame(tbl.model, conn.id = conn.id,
            verbose = FALSE)
        output_table <- db.data.frame(tbl.output, conn.id = conn.id,
            verbose = FALSE)
        tf_table <- db.data.frame(tbl.tf, conn.id = conn.id, verbose = FALSE)

        half_voc <- as.numeric(floor(voc_size/2))
        model1_flatten <- arraydb.to.arrayr(res_model_tuple[[1]],
            "integer")#, half_voc*topic_num)
        model1 <- matrix(model1_flatten, nrow=topic_num, ncol=half_voc)
        model2_flatten <- arraydb.to.arrayr(res_model_tuple[[2]], "integer")
        model2 <- matrix(model2_flatten, nrow=topic_num,
            ncol=voc_size-half_voc)
        topic_sums <- arraydb.to.arrayr(res_model_tuple[[3]], "integer",
            topic_num)
        topic_sums <- t(topic_sums) # to match lda package
        topics<- cbind(model1,model2)
        colnames(topics) <- vocabulary

        document_sums <- arraydb.to.arrayr(res_out$topic_count)
        document_sums <- t(document_sums)

        assignments <- res_out$topic_assignment
        assignments <- lapply(assignments, arraydb.to.arrayr)
        rst <- list()
        rst$assignments <- assignments
        rst$topics <- topics
        rst$topic_sums <- topic_sums
        rst$document_sums <- document_sums
        rst$model_table <- model_table
        rst$output_table <- output_table
        rst$tf_table <- tf_table

        class(rst) <- "lda.madlib"
        rst.list[[i]] <- rst
    }

    if (min == max){
        rst.list <- rst.list[[min]]
    }

    .restore.warnings(warnings)
    return (rst.list)
}

#If the user wants to compute perplexity from prediction output, he can enter it
#as a second parameter; otherwise the output table from madlib.lda_train will
#be used.
perplexity.lda.madlib <- function(object, predict_output_table = NULL,...){
    model_table <- object$model_table
    if (is.null(predict_output_table)){ output_table <- object$output_table}
    else {output_table <- predict_output_table}

    conn.id <- conn.id(model_table)
    db <- .get.dbms.str(conn.id)
    madlib <- schema.madlib(conn.id)

    tbl.model <- content(model_table)
    tbl.output <- content(output_table)

    sql <- paste("select ", madlib,
                 ".lda_get_perplexity('", tbl.model,
                 "','", tbl.output, "')", sep="")

    perplexity <- db.q(sql, conn.id = conn.id, verbose = FALSE)
    perplexity <- as.numeric(perplexity)

    return (perplexity)
}

#Object parameter is the output of the training function
predict.lda.madlib <- function(object, data, docid, words, ...){
    conn.id <- conn.id(data)
    db <- .get.dbms.str(conn.id)
    madlib <- schema.madlib(conn.id)

    model_table <- object$model_table
    tbl.source <- content(data)
    tbl.output <- .unique.string()
    tbl.model <- content(model_table)
    tbl.tf <- .unique.string()

    sql_tf <- paste("select ", madlib, ".term_frequency('",
                tbl.source, "','", docid, "','", words, "','", tbl.tf, "',",
                TRUE,")",sep="")

    db.q(sql_tf, nrows = 1, conn.id = conn.id, verbose = FALSE)
    sql <- paste("select ", madlib,
                 ".lda_predict('", tbl.tf, "', '", tbl.model,
                 "', '", tbl.output, "')", sep = "")
    .db(sql, conn.id = conn.id, verbose = FALSE)
    #if (is.temp) delete(newdata)
    db.data.frame(tbl.output, conn.id = conn.id, verbose = FALSE)
}