Saving Output of Confusionmatrix as a .Csv Table

Saving output of confusionMatrix as a .csv table

Ok, so if you inspect the output of confusionMatrix(xtab, prevalence = 0.25) , it's a list:

cm <- confusionMatrix(pred, truth)
str(cm)

    List of 5
 $ positive: chr "abnormal"
 $ table   : 'table' int [1:2, 1:2] 231 27 32 54
  ..- attr(*, "dimnames")=List of 2
  .. ..$ Prediction: chr [1:2] "abnormal" "normal"
  .. ..$ Reference : chr [1:2] "abnormal" "normal"
 $ overall : Named num [1:7] 0.828 0.534 0.784 0.867 0.75 ...
  ..- attr(*, "names")= chr [1:7] "Accuracy" "Kappa" "AccuracyLower" "AccuracyUpper" ...
 $ byClass : Named num [1:8] 0.895 0.628 0.878 0.667 0.75 ...
  ..- attr(*, "names")= chr [1:8] "Sensitivity" "Specificity" "Pos Pred Value" "Neg Pred Value" ...
 $ dots    : list()
 - attr(*, "class")= chr "confusionMatrix"

From here on you select the appropriate objects that you want to create a csv from and make a data.frame that will have a column for each variable. In your case, this will be:

tocsv <- data.frame(cbind(t(cm$overall),t(cm$byClass)))

# You can then use
write.csv(tocsv,file="file.csv")

How to save the output of multiple confusionMatrix to single csv file in r?

The problem with using the as.matrix() function in this example is you're creating a list of lists. Instead of:

as.matrix(result, what = "classes")
as.matrix(result, what = "overall")

try creating data frames to house your results which you can populate by iterating through your original result list. The code below should do the trick.

## iterate through all six parts of the confusionMatrix: "positive", "table", "overall", "byClass", "mode", "dots" 
for(i in 1:length(names(result[[1]]))){
  ##create a data frame to house the data for export
  data <- data.frame()
  ## iterate through all results; in the example we have Aizawl" and "Serchhip"
  for(j in 1:length(names(result))){
    ## load the data into a data frame
    df <- data.frame(result[[j]][i])
    ## if data is empty no need to alter or append to data frame so skip to next
    if(nrow(df)==0){next}
    ## add a name column for identifying between result sets; in the example we have Aizawl" and "Serchhip"
    df$name <- names(result)[j]
    ## append the loaded data to the data frame for export
    data <- rbind(data, df)
  }
  ## if data is empty no need to export, therefore skip to next
  if(nrow(data)==0){next}
  ## write the data to a csv with the name of the part of the condusionMatrix it contains
  ## row.names changed to TRUE based on OP's comments
  write.csv(data, file = paste0(names(result[[1]])[i],".csv"), row.names = TRUE, na = "")
}

Unless, you'd like you can transform the data frames before using the write.csv() function. In which case you can use

for(i in 1:length(names(result[[1]]))){
  data <- data.frame()
  for(j in 1:length(names(result))){
    df <- data.frame(result[[j]][i])
    if(nrow(df)==0){next}
    df$name <- names(result)[j]
    data <- rbind(data, df)
  }
  if(nrow(data)==0){next}
  assign(names(result[[1]])[i], data)
}

Python: how to save a confusion matrix

df_confusion.to_csv('your_output_file_name.csv')
df_confusion.to_html('your_output_file_name.html')

check the documentation for full details and parameters:

http://pandas.pydata.org/pandas-docs/version/0.20.3/generated/pandas.DataFrame.to_csv.html

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_html.html

also, a search would have answered it very fast, please do that in future.

How to read a csv file and plot confusion matrix in python

pd.read_csv is a function. You call a function in Python by using parenthesis.

You should use pd.read_csv(CSVFILE) instead of pd.read_csv[CSVFILE].

Exporting table as a csv file while preserving correct column numbers

Try using the quote=TRUE option. Excel should be able to distinguish between , used to delimit columns vs. part of the data.

Here is the command with the one additional option.

write.csv(
    summary(univariateTable(gender ~ Q(value) + genotype, data=df)),
    file="file.csv", quote = TRUE)

Apply confusionMatrix using pipe operator in R

If you do it on the whole data.frame, you get a array of tables separated by stations, hence confusionMatrix() complains:

xtabs( ~ Observed + Forecasted + Station, data =df)
, , Station = Aizawl

        Forecasted
Observed  1  3  4  5  6
       1 56  2 13 13  0
       3 12  0  4  8  0
       4  4  0  9 11  0
       5  3  0  7 23  0
       6  0  0  0  0  0

, , Station = Serchhip

        Forecasted
Observed  1  3  4  5  6
       1 76  3 18 18  0
       3  4  0  2  5  0
       4  2  0  4 12  0
       5  1  0  9 10  2
       6  0  0  0  2  0

So you can try using array_tree() with margin = 3 to convert it into a list, with which you can use map() to apply the confusion matrix:

df %>% 
xtabs( ~ Observed + Forecasted + Station, data =.) %>% 
array_tree(.,margin=3) %>% 
map(~caret::confusionMatrix(as.table(.x)))

How to log Keras loss output to a file

There is a simple solution to your problem. Every time any of the fit methods are used - as a result the special callback called History Callback is returned. It has a field history which is a dictionary of all metrics registered after every epoch. So to get list of loss function values after every epoch you can easly do:

history_callback = model.fit(params...)
loss_history = history_callback.history["loss"]

It's easy to save such list to a file (e.g. by converting it to numpy array and using savetxt method).

UPDATE:

Try:

import numpy
numpy_loss_history = numpy.array(loss_history)
numpy.savetxt("loss_history.txt", numpy_loss_history, delimiter=",")

UPDATE 2:

The solution to the problem of recording a loss after every batch is written in Keras Callbacks Documentation in a Create a Callback paragraph.

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