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Free TON Solidity. How to get hash from data encoded into cell? tvm.hash() analogue?

I am dealing with a classification problem with 3 classes [0,1,2], and imbalanced class distribution as shown below.

I want to apply XGBClassifier (in Python) to this classification problem, but the model does not respond to class_weight adjustments and skews towards the majority class 0, and ignores the minority classes 1,2. Which hyperparameters other than class_weight can help me?

I tried 1) computing class weights using sklearn compute_class_weight; 2) setting weights according to the relative frequency of the classes; 3) and also manually adjusting classes with extreme values to see if any change happens at all, such as {0:0.5,1:100,2:200}. But in any case, it does not help the classifier to take the minority classes into account.

Observations:

• I can handle the problem in the binary case: If I make the problem a binary classification by identifying classes [1,2], then I can get the classifier work properly by adjusting scale_pos_weight (even in this case class_weight alone does not help). But scale_pos_weight, as far as I know, works for binary classification. Is there an analogue of this parameter for the multi-classification problems?

• Using RandomForestClassifier instead of XGBClassifier, I can handle the problem by setting class_weight='balanced_subsample' and tunning max_leaf_nodes. But, for some reason, this approach does not work for XGBClassifier.

Remark: I know about balancing techniques, such as over/undersampling, or SMOTE. But I want to avoid them as much as possible, and prefer a solutions using hyperparameter tunning of the model if possible. My observation above shows that this can work for the binary case.

Coming from an R background, and just started learning Julia, I wonder how's documentation in Julia, and if there are analogues to R's help pages and vignettes.

Furthermore, in R, one can document functions using roxygen comment blocks, is there also something similar in Julia?

I am creating an app which is requires to shows analogue clock at the top left.

here is my code

I'm trying to apply a function f(x::Array{Float64, 1}) -> Array{Float64, 2} to each row in an m x n array in Julia (I'm using v1.1, if it's relevant).

Now, I was hoping that I could simply apply mapslices as follows:

I am getting Mono from response, User has a list of subscription, I have to check(through another api) if at least one of them matches the condition and then based on that I have to send further event.

I have produced this plot, which basically depicts the predicted 1- and 3-yrs risk of progression obtained from a Cox regression and for a certain tumor as function of the cumulative dosage applied.

Problem: I have used ggarrange() to merge the two plots, but unfortunately the upper plot is squeezed more together due to applying theme(legend.position = "bottom") for this plot, but show.legend = F for the lower. Consequently, the y-axes have different heights.

Question: Is there a way to manually adjust height for the arranged plots to obtain identical heights for the y-axes while keeping the legend for the upper plot? I am willing to use other plot-merging approaches if that solves it.

My script:

I have been experimenting with Splunk, trying to emulate some basic functionality from the OSISoft PI Time Series database.

I have two data points that I wish to display trends for over time in order to compare fluctuations between them, specifically power network MW analogue tags.

In PI this is very easy to do, however I am having difficulty figuring out how to do it in Splunk.

How do I achieve this given the field values "SubstationA_T1_MW", & "SubstationA_T2_MW" in the field Tag?

The fields involved are TimeStamp, Tag, Value, and Status

Edit:

Sample Input and Output listed below:

I need to insert into the database an object of the type Entity