labeling | d3 plugin for automatic labeling | Data Labeling library

My Excel workbook features a table containing approximately 50K record. I am currently using a formula to count distinct values based on two criteria, namely: ID & Region

Doing this via a formula makes my workbook incredibly slow. I was therefore wondering if you had any idea how I could convert it into an efficient VBA loop instead.

customeradd.dart error image I tried to create |CustomerAddingpage layout that contains user inputs usingmixinsinFlutter`.

I made a globalkey to validate the form.

and I had 4 Textfield methods as widgets represents user inputs, but when I try to call mixins my validator it gives red sign saying;

The argument type Widget Function() can't be assigned to the parameter type String?

"validation_mixin.dart" page below

Let's say we have a example dataframe like below,

i need some help with labeling data inside dataframe, based on dynamic conditions. I have a dataframe

It is longitudinal data; ID wise values are repeating 4 times in every tick of 20 steps. Then this experiments repeats. For the datafarme below I want bins based for every tick time steps for the categories of land based on the values of X. Bins can be 3 for every time interval for land type (Small, medium and large) each. I want to see timeline of bins of X based on categories of Land. Any help will be appreciated. I have added possibly a picture of how data may look like for ggplot and plot as bins or dots may look like as in picture.

What is the best practice to label a part of a form?

Creating a

I have a large form with different parts everything is on one site (no wizzard or something). I guess users with a screen reader just use TAB to navigate between the different inputs and once they reach a different area of the form I would like to explain what it is about. So kinda like labeling a div and once they reach an input in that div -> read something.

Just a simplified example:

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'm using Plotly to produce a similar graph from https://plotly.com/python/horizontal-bar-charts/, section "Color Palette for Bar Chart".

Here's the code, based on the reference link, but with few modifications (values in x_data and showlegend = True) to show my problem:

I have a problem labelling my matplot x-axis row. So I have 1388 instances, but I want my X-axis to a custom labeling in the form of a sequence in dates. My R code looks like this: