indicative | simple yet powerful data validator for Node.js and browsers | Validation library

I have trained a doc2vec (PV-DM) model in gensim on documents which fall into a few classes. I am working in a non-linguistic setting where both the number of documents and the number of unique words are small (~100 documents, ~100 words) for practical reasons. Each document has perhaps 10k tokens. My goal is to show that the doc2vec embeddings are more predictive of document class than simpler statistics and to explain which words (or perhaps word sequences, etc.) in each document are indicative of class.

I have good performance of a (cross-validated) classifier trained on the embeddings compared to one compared on the other statistic, but I am still unsure of how to connect the results of the classifier to any features of a given document. Is there a standard way to do this? My first inclination was to simply pass the co-learned word embeddings through the document classifier in order to see which words inhabited which classifier-partitioned regions of the embedding space. The document classes output on word embeddings are very consistent across cross validation splits, which is encouraging, although I don't know how to turn these effective labels into a statement to the effect of "Document X got label Y because of such and such properties of words A, B and C in the document".

Another idea is to look at similarities between word vectors and document vectors. The ordering of similar word vectors is pretty stable across random seeds and hyperparameters, but the output of this sort of labeling does not correspond at all to the output from the previous method.

Thanks for help in advance.

Edit: Here are some clarifying points. The tokens in the "documents" are ordered, and they are measured from a discrete-valued process whose states, I suspect, get their "meaning" from context in the sequence, much like words. There are only a handful of classes, usually between 3 and 5. The documents are given unique tags and the classes are not used for learning the embedding. The embeddings have rather dimension, always < 100, which are learned over many epochs, since I am only worried about overfitting when the classifier is learned, not the embeddings. For now, I'm using a multinomial logistic regressor for classification, but I'm not married to it. On that note, I've also tried using the normalized regressor coefficients as vector in the embedding space to which I can compare words, documents, etc.

I have a database which records user interactions. There is an expectation of 2 per hour (minutes 00-59 inc) per user and any above that should be labelled as extra. The following table is indicative of the result I am trying to achieve with an update to the 'extra' column. Any idea how I would go about this?

I've been doing a lot of searching, and everything I can find regarding this error are deployment issues, which isn't my issue in this case. About 20 times a day, I will get a notification (I use Logentries) of a crash following the pattern of:

Premise: I have a calendar-like system that allows the creation/deletion of 'events' at a scheduled time in the future. The end goal is to perform an action (send message/reminder) prior to & at the start of the event. I've done a bit of searching & have narrowed down to what seems to be my two most viable choices

• Unix Cron Jobs
• Bree

I'm not quite sure which will best suit my end goal though, and additionally, it feels like there must be some additional established ways to do things like this that I just don't have proper knowledge of, or that I'm entirely skipping over.

My questions:

• If, theoretically, the system were to be handling an arbitrarily large amount of 'events', all for arbitrary times in the future, which of these options is more practical system-resource-wise? Is my concern in this regard even valid?

• Is there any foreseeable problem with filling up a crontab with a large volume of jobs - or, in bree's case, scheduling a large amount of jobs?

• Is there a better idea I've just completely missed so far?

This mainly stems from bree's use of node 'worker threads'. I'm very unfamiliar with this concept and concerned that since a 'worker thread' is spawned per every job, I could very quickly tie up all of my available threads and grind... something, to a halt. This, however, sounds somewhat silly & possibly wrong(possibly indicative of my complete lack of knowledge here), & thus, my question.

Thanks, Stark.

Main question: Is there a way to populate a df with the data frame name other than just typing it in and pasting it manually?

I have 20 csv files in a folder that have a grid of data that looks somewhat like this. File 1:

File 2:

Note: X1,2,3 and Y1,2,3 are co-ordinates, while the populated values are just example values and are not indicative of any pattern

Each file has a unique ID e.g., US_plot_1.csv, US_plot_2.csv, UK_plot_1.csv, US_plot_2.csv,

I want to populate a df that sorts these files into columns that R can analyse, grouped by filename i.e.,

I understand for the populating the data I can loop it.

I'm new to using jsoup and I am struggling to retrieve the tables with class name: verbtense with the headers: Present and Past, under the div named Indicative from the from this site: https://www.verbix.com/webverbix/Swedish/misslyckas

I have started off trying to do the following, but there are no results from the get go:

I've been working at a rcpsp problem using docplex in python. I consider 10 tasks with indicative costs and a worker that has to complete these tasks in 10 timeframes (can be weeks, days etc.).

One of my constraints is that the worker can perform a specific set of tasks per timeframe (worker_availability list). If I consider the example on the link below, one can restrict a worker's availability to not exceed a specific point i.e. mdl.sum(resources) <= capacity, where capacity is a fixed number.

I want to use a dynamic constraint that obeys worker_availability, i.e. at point 0 my worker can process 2 tasks, at 1 0 tasks etc.

Does anyone know how to that with docplex in python?

link: http://ibmdecisionoptimization.github.io/docplex-doc/cp/visu.rcpsp.py.html?highlight=rcpsp

When running valgrind memcheck, it keeps dying and killing the process with output like the following:

I am attempting to strip commas from columns that I will later convert to numeric and was hoping I could get some advice regarding this error.

I have defined my columns that I want to conduct the str.replace operation on. I can remove whitespace using the same approach with no issues, but when I run the below code I get the following error: