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I want to forecast upcoming total users on a daily basis within Python using a machine learning algorithm. Check the pattern below:
Looking at this graph, I was wondering if someone knows which forecasting method in Python I should use to predict?
Thanks!
If you have no additional data expect the user data over time which you have shown, the only thing you can do is try to find a function dependent on time which gives you a good approximation for that plot (ordinary curve fitting). I suppose that's not what you want.
To do a predection (which can be done not only by a machine learning approach), you need other data which is somehow correlated to the data you want to predict.
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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I'm trying to get some approximation ratios for the Maximum Independent Set Problem and so I need some exact solutions !
I've found libraries written in C++ (i.e https://github.com/iPapatsoris/Maximum-Independent-Set)
but wondered if there were any directly in Python. I know of the `networkx' maximal indepedent set function but these are only approximations.
I realise it's far from the most efficient language to use but I'm only solving small Erdős–Rényi graphs (N<20).
In addition to this, are there any libraries that solve this for the weighted problem, where some nodes matter more than others?
This is the only python library I could find:
https://github.com/pchervi/Graph-Coloring/blob/master/Coloring_MWIS_heuristics.py
I haven't checked that it works correctly however.
I've been using KaMIS instead, which is a C++ implementation.
https://github.com/KarlsruheMIS/KaMIS
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Why it is so important to do pre-processing and what are the simple steps doing it? Can anyone help. I am working on python.
I have a dataframe consisting of null values. The data consist of outliers, moreover it is not distributed uniformly.
My question is what protocol I should follow inorder to fill null values, should I remove outliers because this might lead to loss of information and what are the steps to make data distributed uniformly?
Firstly it really doesnot matter which language you are working on. Both python and R are popular in Data Science.
Secondly, you cannot insert raw data to any machine learning models. Before you need to clean it. Here are some simple steps:
1. Remove missing values: Many a times there are missing values present in the data. So you have to fill those data. Question arises how? There are planty of methods which you can google out.
2. Remove skewness and outliers: Generally data contains values that are not within the range of other data. So you have to bring those data with that range.
3. One-hot-encoding: Categorical values needs to be transformed to encoding format.
Still there are more steps but you to google it out there are tons of blogs you can go through.
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I have a system which is configurable. It has 3 parameters
Parameter1 - can vary between [0, 2^30]
Parameter2 - can vary between [0, 2^30]
Parameter3 - can vary between [0, 2^12]
I have a python code which when given a set of valid numbers corresponding to (parameter1, parameter2, parameter3) can configure the system and after few mins return a number say a score for the configuration.
Suppose the aim to maximize the score. Is there a python library to generate the parameters with constraints intelligently.
Thanks in advance
This is a bounded optimization problem. Depending on how the "system" is structured different optimization strategies will behave differently well.
My suggestions is to either look at scipy.optimize.minimize or a genetic algorithm. I know there are multiple GA implementations in Python, but I haven't tried them.
Since a single evaluation takes on the order of minutes, you are looking at long optimization times though.
If you can compute the gradient to your score function, and if it is well-behaved (i.e. convex) then the scipy.optimize.minimize is probably your best bet. Otherwise you might have better luck with a GA.
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I am looking for an implementation of Continuous Wavelet Transform for Python that includes Haar Wavelet.
I would like to reproduce the experiment given by MathWorks for Matlab, at this link.
I tried with Pyscellania but I obtain completely different coefficients.
Is there a Python implementation of the CWT out there that includes the Haar Wavalet apart from Pyscellania?
Your request is clear.
Have you tried Pyscellania's normalised or standard Haar Wavelet?
Maybe you are just using the wrong one.