atypical | Find the junk data hidden amongst the good data

Say I have a dataframe like below.

I am partitioning by the "ID" and ordering by the "VALUE" desc.

If for an ID there is a tie, take the greater "disc" value.

If the greatest "disc" value is the same then I want to assign "True" to the row where description is "general".

Original df

Found a nasty bug in HTMLAgilityPack whereby some attribute values are NOT returned fully - they are truncated. Specifically, when attempting to get the href value out of an anchor tag, only the root domain is returned, anything following (the query string) is completely ignored. Anyone know a good workaround?

Example:

Just a heads up, I'm working with a very odd data frame, and I'm struggling to adjust it into a usable format.

Basically, I have a grouping variable Game, an individual-level variable Player, and Player_Grade, which takes on an atypical format.

Here is an example:

I am making the convergence graph of the chains generated using the traceplot function. However, see what unusual lines are appearing on the chart. How would you go about removing them?

data: https://drive.google.com/file/d/1iOuGbjNI_caLWBIz4s7hZX5GlfhLrwr9/view?usp=sharing

Below are the codes.

I have used the skin cancer classification competition data in Kaggle. There are 4 labels and the entire data is imbalanced. I ran the resnet 18 model on a 10 fold cross validation split to train the data and each fold was given around 2 epochs. The code has been attached below. Basically the model gave 98.2% accuracy with 0.07 loss value in the train data and 98.1% accuracy and 0.06 loss value in the validation data. So this seemed pretty good. However the problem is...prediction.py(code attached below). When I tried to predict, the model keeps giving the result as [0]. Even if it's a train image data.

Is there something wrong with my code?

Expected result: if the image is the input, the output should be either 0,1,2 or 3

model.py(where the training happens)

I am currently working on a computer vision project. I keep getting a runtime error that says "CUDA out of memory". I have tried all possible ways like reducing batch size and image resolution, clearing the cache, deleting variables after training starts, reducing image data and so on... Unfortunately, this error doesn't stop. I have a Nvidia Geforce 940MX graphics card on my HP Pavilion laptop. I have installed cuda 10.2 and cudNN from the pytorch installation page. My aim was to create a flask website out of this model but I am stuck with this issue. Any suggestions to this problem will be helpful.

This is my code

I am trying to two data frames (df_a and df_b) in R (essentially I want to repopulate df_a with the updated data contained within df_b). The columns in df_b are all present in df_a. Within df_b there is (important) redundancy in ref_transcript_name, ref_transcript_id, and ref_gene_name, but all values of qry_transcript_id are unique and have a one-to-one relationship with df_a. My assumption here is that a left_join() would do the trick. I've tried:

1. df_c <- left_join(df_a, df_b) - here df_c is identical to df_b
2. df_c <- left_join(df_a, df_b, by = "qry_transcript_id") - here df_c contains the three non-guide columns of df_b as new columns of df_c.

I'm clearly missing something fundamental about the join functions here, but essentially I want to populate (most of) the missing values in df_a with the values from df_b.

Here are my data:

I'm currently using VS code to write a PowerShell script. As part of this script REGEX is used to replace/remove an atypical character that ends up in the data fairly often and causes trouble down the line. The character is (U+2019) and when the script is opened in code it is replaced permanently with (U+FFFD)

thus the line: $user.Name = $user.Name -Replace "'|\’|\(|\)|\s+",""

Permanently becomes: $user.Name = $user.Name -Replace "'|\�|\(|\)|\s+",""

until it is manually changed. Seeing as I can paste the U+2019 character in once the file is open and then run the code, I assume that VS code can interpret it okay and the problem is with loading the file in. Is there some option that I can set to stop this being replaced when I open the file?