sizhky

########################################
'''Simple Bar Chart'''

x_axis=['a','b','c']
y_axis=[10,12,11]

fig, ax = plt.subplots(figsize=(20, 8))
ax.set_xticks(range(len(x_axis)))
ax.bar(range(len(x_axis)), y_axis, align='center')
ax.set_xticklabels(x_axis, rotation='vertical')

sizhky

import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = 5, 10
%matplotlib inline

sizhky

## arrow up
"\e[A":history-search-backward
## arrow down
"\e[B":history-search-forward

sizhky

test_train_split <- function(data, train_frac = 0.66){
  library(caret)
  inTrain <- createDataPartition(1:nrow(data), p = train_frac, list = FALSE)
  training <- data[inTrain,]
  testing <- data[-inTrain,]
  return(list(train = training, test = testing))
}

sizhky

get_metrics <- function(model, test_data, truth, table = FALSE){
  preds <- predict(model, test_data);
  table_ <- table(truth, preds);
  acc_ <- sum(diag(table_))/sum(table_);
  rec_ <- table_[2,2]/sum(table_[,2]);
  prec_ <- table_[2,2]/sum(table_[2,]);
  f_ <- 2*rec_*prec_/(rec_+prec_)
  if (table == FALSE){
    cat('Accuracy:', acc_, '\nRecall:', rec_, '\nPrecision:', prec_, '\nF-Score:', f_);
  } else {

sizhky

datatypes <- function(dataframe){
  types <- split(names(dataframe),sapply(dataframe, function(x) paste(class(x), collapse=" ")))
  return(types)
}
                                         
factor_cols <- c()
raw_data[,factor_cols] <- lapply(raw_data[,factor_cols], 
                                  function(x) factor(x))