Analyzing rtweet Knowledge with kerasformula

Overview

The kerasformula package deal presents a high-level interface for the R interface to Keras. It’s predominant interface is the kms perform, 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 will 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 achieved so)
# see ?install_keras() for choices e.g. install_keras(tensorflow = "gpu")
install_keras()

The kms() perform

Many basic machine studying tutorials assume that information are available a comparatively homogenous kind (e.g., pixels for digit recognition or phrase counts or ranks) which may make coding considerably cumbersome when information is contained in a heterogenous information 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 perform name. kms accepts a lot of parameters together with the loss and activation features present in keras. kms additionally accepts compiled keras_model_sequential objects permitting for even additional customization. This little demo reveals how kms can help is mannequin constructing and hyperparameter choice (e.g., batch measurement) beginning with uncooked information gathered utilizing library(rtweet).

Let’s have 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 affordable variety of observations to work with by way of runtime (and the aim of this doc is to indicate syntax, not construct significantly predictive fashions).

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

  [1] 2840   42

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

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

    [1] 0.7051952

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

Getting probably the most out of formulation

Let’s suppose we’re focused on placing tweets into classes based mostly on reputation however we’re undecided how finely-grained we need to make distinctions. A few of the information, like rstats$mentions_screen_name is available in a listing of various lengths, so let’s write a helper perform to depend non-NA entries.

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

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

reputation$confusion

reputation$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 information accurately and that predictive accuracy doesn’t enhance after the primary ten epochs. The confusion matrix means that mannequin does greatest with tweets which are retweeted a handful of instances however overpredicts the 1-10 stage. The historical past plot additionally means that out-of-sample accuracy will not be 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)
reputation <- kms(lower(retweet_count + favorite_count, breaks) ~  
                  n(hashtags) + n(mentions_screen_name) + n(urls_url) +
                  nchar(textual content) +
                  screen_name + supply +
                  grepl('photograph', media_type) +
                  weekdays(created_at) + 
                  format(created_at, '%H'), rstats, Nepochs = 10)

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

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

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

Right here we use paste0 so as to add to the formulation by looping over person 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)")

reputation <- 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 information, maybe add particular person phrases from the textual content or another abstract stat (imply(textual content %in% LETTERS) to see if all caps explains reputation). However let’s alter the neural internet.

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

    [1] 1277

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

reputation <- kms(pop_input, rstats,
                  layers = record(
                    models = 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 (reputation$P) however after that customers are free to find out the variety of layers and so forth. The kms argument layers expects a listing, the primary entry of which is a vector models with which to name keras::layer_dense(). The primary ingredient the variety of models within the first layer, the second ingredient for the second layer, and so forth (NA as the ultimate ingredient connotes to auto-detect the ultimate variety of models based mostly on the noticed variety of outcomes). activation can be handed to layer_dense() and will take values reminiscent of softmax, relu, elu, and linear. (kms additionally has a separate parameter to regulate the optimizer; by default kms(... optimizer="rms_prop").) The dropout that follows every dense layer charge 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 have been proud of our mannequin however didn’t have any specific instinct about what the dimensions must 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 quick however, from these outcomes, 64 is the perfect batch measurement.

Making predictions for brand spanking new information

So far, we now have been utilizing the default settings for kms which first splits information 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 needed to make predictions on a brand new information set…

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

    [1] 0.579

As a result of the formulation creates a dummy variable for every display title and point out, any given set of tweets is all however assured to have totally different columns. predict.kms_fit is an S3 technique that takes the brand new information 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 can even use keras::predict_classes on object$mannequin.

Utilizing a compiled Keras mannequin

This part reveals how one can enter a mannequin compiled within the trend 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.

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

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

popularity_lstm <- kms(pop_input, rstats, ok)

Drop me a line by way of the undertaking’s Github repo. Particular because of @dfalbel and @jjallaire for useful recommendations!!