Analyzing rtweet Knowledge with kerasformula

Overview

The kerasformula bundle affords a high-level interface for the R interface to Keras. It’s foremost interface is the kms perform, a regression-style interface to keras_model_sequential that makes use of formulation and sparse matrices.

The kerasformula bundle is on the market on CRAN, and might be put in with:

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

library(kerasformula)

# set up the core keras library (if you have not already finished 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 in a comparatively homogenous type (e.g., pixels for digit recognition or phrase counts or ranks) which might make coding considerably cumbersome when information is contained in a heterogenous information body. kms() takes benefit of the flexibleness 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 numerous 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 exhibits how kms can assist 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 present us a pleasant cheap variety of observations to work with when it comes to runtime (and the aim of this doc is to indicate syntax, not construct notably predictive fashions).

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

  [1] 2840   42

Suppose our aim is to foretell how common tweets can be primarily based 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 direction of zero.

Getting essentially the most out of formulation

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

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

breaks <- c(-1, 0, 1, 10, 100, 1000, 10000)
recognition <- kms(cut(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$historical past) 
  + ggtitle(paste("#rstat recognition:", 
            paste0(round(100*recognition$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 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 might be retweeted a handful of instances however overpredicts the 1-10 stage. The historical past plot additionally means that out-of-sample accuracy shouldn’t be very secure. 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(cut(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$historical past) 
  + ggtitle(paste("#rstat recognition (new breakpoints):",
            paste0(round(100*recognition$evaluations$acc, 1), "%"),
            "out-of-sample accuracy")) 
  + theme_minimal()

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

pop_input <- "minimize(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 formulation by looping over consumer IDs including one thing like:

grepl("12233344455556", mentions_user_id)

mentions <- unlist(rstats$mentions_user_id)
mentions <- unique(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 may 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 recognition). However let’s alter the neural web.

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

    [1] 1277

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

recognition <- kms(pop_input, rstats,
                  layers = list(
                    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 (recognition$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 primarily based on the noticed variety of outcomes). activation can be handed to layer_dense() and will take values comparable 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 price 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 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 = length(Nbatch))
colnames(accuracy) <- paste0("Nbatch_", Nbatch)

est <- list()
for(i in 1:Nruns){
  for(j in 1:length(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 new information

So far, now we 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 barely used on the finish to evaluate predictive accuracy.
However suppose you needed to make predictions on a brand new information set…

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

    [1] 0.579

As a result of the formulation creates a dummy variable for every display screen identify 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 too can use keras::predict_classes on object$mannequin.

Utilizing a compiled Keras mannequin

This part exhibits the right way to 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 example.

ok <- keras_model_sequential()
ok %>%
  layer_embedding(input_dim = recognition$P, output_dim = recognition$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 venture’s Github repo. Particular due to @dfalbel and @jjallaire for useful strategies!!