Analyzing rtweet Knowledge with kerasformula

Overview

The kerasformula package deal provides a high-level interface for the R interface to Keras. It’s important interface is the kms operate, a regression-style interface to keras_model_sequential that makes use of formulation and sparse matrices.

The kerasformula package deal is offered on CRAN, and could be put in with:

# set up the kerasformula package deal
set up.packages("kerasformula")    
# or devtools::install_github("rdrr1990/kerasformula")

library(kerasformula)

# set up the core keras library (if you have not already performed so)
# see ?install_keras() for choices e.g. install_keras(tensorflow = "gpu")
install_keras()

The kms() operate

Many traditional machine studying tutorials assume that knowledge are available in a comparatively homogenous type (e.g., pixels for digit recognition or phrase counts or ranks) which might make coding considerably cumbersome when knowledge is contained in a heterogenous knowledge body. kms() takes benefit of the pliability of R formulation to easy this course of.

kms builds dense neural nets and, after becoming them, returns a single object with predictions, measures of match, and particulars in regards to the operate name. kms accepts a lot of parameters together with the loss and activation capabilities present in keras. kms additionally accepts compiled keras_model_sequential objects permitting for even additional customization. This little demo reveals how kms can assist is mannequin constructing and hyperparameter choice (e.g., batch measurement) beginning with uncooked knowledge gathered utilizing library(rtweet).

Let’s take a look at #rstats tweets (excluding retweets) for a six-day interval ending January 24, 2018 at 10:40. This occurs to offer us a pleasant cheap variety of observations to work with when it comes to runtime (and the aim of this doc is to point out syntax, not construct notably predictive fashions).

rstats <- search_tweets("#rstats", n = 10000, include_rts = FALSE)
dim(rstats)

  [1] 2840   42

Suppose our purpose is to foretell how well-liked tweets will probably be primarily based on how usually the tweet was retweeted and favorited (which correlate strongly).

cor(rstats$favorite_count, rstats$retweet_count, methodology="spearman")

    [1] 0.7051952

Since few tweeets go viral, the information are fairly skewed in direction of zero.

Getting probably the most out of formulation

Let’s suppose we’re taken with placing tweets into classes primarily based on recognition however we’re undecided how finely-grained we need to make distinctions. A few of the knowledge, like rstats$mentions_screen_name is available in an inventory of various lengths, so let’s write a helper operate to depend non-NA entries.

Let’s begin with a dense neural internet, the default of kms. We are able to use base R capabilities to assist clear the information–on this case, reduce to discretize the end result, grepl to search for key phrases, and weekdays and format to seize completely different features of the time the tweet was posted.

breaks <- c(-1, 0, 1, 10, 100, 1000, 10000)
recognition <- kms(reduce(retweet_count + favorite_count, breaks) ~ screen_name + 
                  supply + n(hashtags) + n(mentions_screen_name) + 
                  n(urls_url) + nchar(textual content) +
                  grepl('picture', media_type) +
                  weekdays(created_at) + 
                  format(created_at, '%H'), rstats)
plot(recognition$historical past) 
  + ggtitle(paste("#rstat recognition:", 
            paste0(spherical(100*recognition$evaluations$acc, 1), "%"),
            "out-of-sample accuracy")) 
  + theme_minimal()

recognition$confusion

recognition$confusion

                    (-1,0] (0,1] (1,10] (10,100] (100,1e+03] (1e+03,1e+04]
      (-1,0]            37    12     28        2           0             0
      (0,1]             14    19     72        1           0             0
      (1,10]             6    11    187       30           0             0
      (10,100]           1     3     54       68           0             0
      (100,1e+03]        0     0      4       10           0             0
      (1e+03,1e+04]      0     0      0        1           0             0

The mannequin solely classifies about 55% of the out-of-sample knowledge appropriately and that predictive accuracy doesn’t enhance after the primary ten epochs. The confusion matrix means that mannequin does finest with tweets which might be retweeted a handful of instances however overpredicts the 1-10 stage. The historical past plot additionally means that out-of-sample accuracy just isn’t very steady. We are able to simply change the breakpoints and variety of epochs.

breaks <- c(-1, 0, 1, 25, 50, 75, 100, 500, 1000, 10000)
recognition <- kms(reduce(retweet_count + favorite_count, breaks) ~  
                  n(hashtags) + n(mentions_screen_name) + n(urls_url) +
                  nchar(textual content) +
                  screen_name + supply +
                  grepl('picture', media_type) +
                  weekdays(created_at) + 
                  format(created_at, '%H'), rstats, Nepochs = 10)

plot(recognition$historical past) 
  + ggtitle(paste("#rstat recognition (new breakpoints):",
            paste0(spherical(100*recognition$evaluations$acc, 1), "%"),
            "out-of-sample accuracy")) 
  + theme_minimal()

That helped some (about 5% further predictive accuracy). Suppose we need to add a bit extra knowledge. Let’s first retailer the enter components.

pop_input <- "reduce(retweet_count + favorite_count, breaks) ~  
                          n(hashtags) + n(mentions_screen_name) + n(urls_url) +
                          nchar(textual content) +
                          screen_name + supply +
                          grepl('picture', media_type) +
                          weekdays(created_at) + 
                          format(created_at, '%H')"

Right here we use paste0 so as to add to the components by looping over consumer IDs including one thing like:

grepl("12233344455556", mentions_user_id)

mentions <- unlist(rstats$mentions_user_id)
mentions <- distinctive(mentions[which(table(mentions) > 5)]) # take away rare
mentions <- mentions[!is.na(mentions)] # drop NA

for(i in mentions)
  pop_input <- paste0(pop_input, " + ", "grepl(", i, ", mentions_user_id)")

recognition <- kms(pop_input, rstats)

That helped a contact however the predictive accuracy remains to be pretty unstable throughout epochs…

Customizing layers with kms()

We might add extra knowledge, maybe add particular person phrases from the textual content or another abstract stat (imply(textual content %in% LETTERS) to see if all caps explains recognition). However let’s alter the neural internet.

The enter.components is used to create a sparse mannequin matrix. For instance, rstats$supply (Twitter or Twitter-client utility kind) and rstats$screen_name are character vectors that will probably be dummied out. What number of columns does it have?

    [1] 1277

Say we wished to reshape the layers to transition extra regularly from the enter form to the output.

recognition <- kms(pop_input, rstats,
                  layers = record(
                    items = c(1024, 512, 256, 128, NA),
                    activation = c("relu", "relu", "relu", "relu", "softmax"), 
                    dropout = c(0.5, 0.45, 0.4, 0.35, NA)
                  ))

kms builds a keras_sequential_model(), which is a stack of linear layers. The enter form is set by the dimensionality of the mannequin matrix (recognition$P) however after that customers are free to find out the variety of layers and so forth. The kms argument layers expects an inventory, the primary entry of which is a vector items with which to name keras::layer_dense(). The primary aspect the variety of items within the first layer, the second aspect for the second layer, and so forth (NA as the ultimate aspect connotes to auto-detect the ultimate variety of items primarily based on the noticed variety of outcomes). activation can be handed to layer_dense() and should take values corresponding to softmax, relu, elu, and linear. (kms additionally has a separate parameter to manage the optimizer; by default kms(... optimizer="rms_prop").) The dropout that follows every dense layer fee prevents overfitting (however after all isn’t relevant to the ultimate layer).

Selecting a Batch Dimension

By default, kms makes use of batches of 32. Suppose we had been pleased with our mannequin however didn’t have any explicit instinct about what the scale needs to be.

Nbatch <- c(16, 32, 64)
Nruns <- 4
accuracy <- matrix(nrow = Nruns, ncol = size(Nbatch))
colnames(accuracy) <- paste0("Nbatch_", Nbatch)

est <- record()
for(i in 1:Nruns){
  for(j in 1:size(Nbatch)){
   est[[i]] <- kms(pop_input, rstats, Nepochs = 2, batch_size = Nbatch[j])
   accuracy[i,j] <- est[[i]][["evaluations"]][["acc"]]
  }
}
  
colMeans(accuracy)

    Nbatch_16 Nbatch_32 Nbatch_64 
    0.5088407 0.3820850 0.5556952 

For the sake of curbing runtime, the variety of epochs was set arbitrarily brief however, from these outcomes, 64 is the most effective batch measurement.

Making predictions for brand new knowledge

To this point, now we have been utilizing the default settings for kms which first splits knowledge into 80% coaching and 20% testing. Of the 80% coaching, a sure portion is put aside for validation and that’s what produces the epoch-by-epoch graphs of loss and accuracy. The 20% is simply used on the finish to evaluate predictive accuracy.
However suppose you wished to make predictions on a brand new knowledge set…

recognition <- kms(pop_input, rstats[1:1000,])
predictions <- predict(recognition, rstats[1001:2000,])
predictions$accuracy

    [1] 0.579

As a result of the components creates a dummy variable for every display screen title and point out, any given set of tweets is all however assured to have completely different columns. predict.kms_fit is an S3 methodology that takes the brand new knowledge and constructs a (sparse) mannequin matrix that preserves the unique construction of the coaching matrix. predict then returns the predictions together with a confusion matrix and accuracy rating.

In case your newdata has the identical noticed ranges of y and columns of x_train (the mannequin matrix), you too can use keras::predict_classes on object$mannequin.

Utilizing a compiled Keras mannequin

This part reveals how you can enter a mannequin compiled within the vogue typical to library(keras), which is helpful for extra superior fashions. Right here is an instance for lstm analogous to the imbd with Keras instance.

okay <- keras_model_sequential()
okay %>%
  layer_embedding(input_dim = recognition$P, output_dim = recognition$P) %>% 
  layer_lstm(items = 512, dropout = 0.4, recurrent_dropout = 0.2) %>% 
  layer_dense(items = 256, activation = "relu") %>%
  layer_dropout(0.3) %>%
  layer_dense(items = 8, # variety of ranges noticed on y (consequence)  
              activation = 'sigmoid')

okay %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = 'rmsprop',
  metrics = c('accuracy')
)

popularity_lstm <- kms(pop_input, rstats, okay)

Drop me a line through the venture’s Github repo. Particular due to @dfalbel and @jjallaire for useful ideas!!