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6_Text_als_Daten.R
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library(caret)
library(dplyr)
library(doc2vec)
library(quanteda)
library(quanteda.textmodels)
library(quanteda.classifiers)
library(udpipe)
library(glmnet)
library(plotly)
library(ROCR)
movie <- readr::read_csv('C:\\Users\\Drecker\\Documents\\Lehre\\Daten\\movie_review.csv')
movie <- movie %>% select( text,tag)
movie_cor <- quanteda::corpus(movie) %>%
corpus_trim(
what = "documents",
min_ntoken = 3
)
lemma_en <- udpipe_download_model(language = "english")
lemma_en <- udpipe_load_model(file = lemma_en$file_model)
lemma_en <- udpipe(movie_cor, lemma_en, parallel.cores = 8)
lemma_en <- lemma_en %>% filter(upos != 'PUNCT' & is.na(lemma) == F)
movie_cor <- quanteda::corpus(movie) %>%
tokens(remove_punct = TRUE, remove_numbers = TRUE, remove_symbols = TRUE) %>%
tokens_remove( c(stopwords("english"))) %>%
tokens_ngrams( n = 1)
movie_cor_le <- tokens_replace(tokens(movie_cor), pattern = lemma_en$token, replacement = lemma_en$lemma)
movie_cor[1]
movie_cor_le[1]
training_id <- sample(1:nrow(movie), 0.66 * nrow(movie),replace = FALSE)
docvars(movie_cor_le, "id_numeric") <- 1:ndoc(movie_cor)
movie_cor_training <-movie_cor_le %>%
dfm() %>%
dfm_subset(id_numeric %in% training_id)
movie_cor_test <- movie_cor_le %>%
tokens() %>%
dfm() %>%
dfm_subset(!id_numeric %in% training_id)
print(prop.table(table(docvars(
movie_cor_training, "tag"
))) * 100)
print(prop.table(table(docvars(
movie_cor_test, "tag"
))) * 100)
lasso <- cv.glmnet(x = movie_cor_training,
y = as.integer(movie_cor_training$tag == "pos"),
alpha = 1,
nfold = 5,
family = "binomial", type.measure = 'class')
plot(lasso)
lasso$cvm[lasso$lambda==lasso$lambda.min]
true_class <-as.integer(movie_cor_test$tag == 'pos')
predicted_class <- predict(lasso, movie_cor_test, type = "class", s = lasso$lambda.min)
tab_class <- table(true_class, predicted_class)
tab_class
confusionMatrix(tab_class, mode = "everything")
predicted_class <- predict(lasso, movie_cor_test,type="response")
pred_movie <- prediction(as.numeric(predicted_class), movie_cor_test$tag)
perf_movie <- performance(pred_movie,"tpr","fpr")
data_roc <- data.frame(x = [email protected], y = [email protected])
names(data_roc) <- c('x', 'y')
plot_ly(data = data_roc ,x = ~x, y = ~y, type = 'scatter', mode = 'lines', fill = 'tozeroy') %>%
layout(title = 'ROC_Curve',xaxis = list(title = "False Positive Rate"), yaxis = list(title = "True Positive Rate")) %>%
add_segments(x = 0, xend = 1, y = 0, yend = 1, line = list(dash = "dash", color = 'black'),inherit = FALSE, showlegend = FALSE)
#######N Bayes
model_NB <-textmodel_nb(movie_cor_training, movie_cor_training$tag, prior = "docfreq")
crossval(model_NB, k = 10, by_class = F)
true_class <-movie_cor_test$tag
predicted_class <- predict(model_NB, newdata = movie_cor_test)
tab_class <- table(true_class, predicted_class)
tab_class
confusionMatrix(tab_class, mode = "everything")
library(plotly)
library(ROCR)
predicted_class <- predict(model_NB, newdata = movie_cor_test,type="probability")
pred_movie <- prediction(as.numeric(predicted_class[,-1]), movie_cor_test$tag)
perf_movie <- performance(pred_movie,"tpr","fpr")
data_roc <- data.frame(x = [email protected], y = [email protected])
names(data_roc) <- c('x', 'y')
plot_ly(data = data_roc ,x = ~x, y = ~y, type = 'scatter', mode = 'lines', fill = 'tozeroy') %>%
layout(title = 'ROC_Curve',xaxis = list(title = "False Positive Rate"), yaxis = list(title = "True Positive Rate")) %>%
add_segments(x = 0, xend = 1, y = 0, yend = 1, line = list(dash = "dash", color = 'black'),inherit = FALSE, showlegend = FALSE)
# Neuronales Netz
# Benötigt Anaconda
install.packages("tensorflow")
library(tensorflow)
library(dplyr)
library(ggplot2)
library(purrr)
library(keras)
friends_data <- friends::friends %>%
filter(speaker == "Monica Geller" | speaker == "Joey Tribbiani" | speaker == "Chandler Bing" | speaker == "Phoebe Buffay"
| speaker =="Ross Geller")
class_df <- data.frame(speaker = c("Monica Geller", "Joey Tribbiani", "Ross Geller","Chandler Bing", "Phoebe Buffay", "Rachel Green"),
Class = c(1, 0, 2,0,0,0),
stringsAsFactors = FALSE)
friends_data <- merge(friends_data, class_df, by = "speaker", all.x = TRUE)
training_id <- sample.int(nrow(friends_data), size = nrow(friends_data)*0.8)
training <- friends_data[training_id,]
testing <- friends_data[-training_id,]
friends_data$text %>%
strsplit(" ") %>%
sapply(length) %>%
summary()
num_words <- 10000
max_length <- 30
text_vectorization <- layer_text_vectorization(
max_tokens = num_words,
output_sequence_length = max_length,
)
text_vectorization %>%
adapt(friends_data$text)
text_vectorization(matrix(friends_data$text[1], ncol = 1))
input <- layer_input(shape = c(1), dtype = "string")
output <- input %>%
text_vectorization() %>%
layer_embedding(input_dim = num_words + 1, output_dim = 16) %>%
layer_global_average_pooling_1d() %>%
layer_dense(units = 16, activation = "relu") %>%
layer_dropout(0.5) %>%
layer_dense(units = 3, activation = "sigmoid")
model <- keras_model(input, output)
model %>% compile(
optimizer = 'adam',
loss = 'sparse_categorical_crossentropy',
metrics = list('accuracy')
)
history <- model %>% fit(
training$text,
training$Class,
epochs = 10,
batch_size = 512,
validation_split = 0.2,
verbose=2
)
results <- model %>% evaluate(testing$text, testing$Class, verbose = 0)
results
# Beispiel IMDB Dataset
df <- readr::read_csv('C:\\Users\\Drecker\\Documents\\Lehre\\Daten\\movie_review.csv')
head(df)
training_id <- sample.int(nrow(df), size = nrow(df)*0.8)
training <- df[training_id,]
testing <- df[-training_id,]
num_words <- 10000
max_length <- 50
text_vectorization <- layer_text_vectorization(
max_tokens = num_words,
output_sequence_length = max_length,
)
text_vectorization %>%
adapt(df$text)
text_vectorization(matrix(df$text[1], ncol = 1))
input <- layer_input(shape = c(1), dtype = "string")
output <- input %>%
text_vectorization() %>%
layer_embedding(input_dim = num_words + 1, output_dim = 16) %>%
layer_global_average_pooling_1d() %>%
layer_dense(units = 16, activation = "relu") %>%
layer_dropout(0.5) %>%
layer_dense(units = 1, activation = "sigmoid")
model <- keras_model(input, output)
model %>% compile(
optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = list('accuracy')
)
history <- model %>% fit(
training$text,
as.numeric(training$tag == "pos"),
epochs = 10,
batch_size = 512,
validation_split = 0.2,
verbose=2
)
results <- model %>% evaluate(testing$text, as.numeric(testing$tag == "pos"), verbose = 0)
results