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Neural Network Architecture for Graph Inputs [closed]

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I have an undirected graph with edges of equal distance with 7 features per node. I want to train a Neural Network with this graph as an input and output a scalar. What network architecture do I need for my network to analyse the graph locally (for example, a node and it's neighbours) and to generalise, much like Convolutional Neural Networks operate on grid data. I have heard of Graph Neural Networks however I don't know if this is what i'm looking for. Will it be able to analyse my graph much like a CNN does with an image, sharing the generalisation benefits that convolution kernels bring?
I want to implement the solution in TensorFlow, ideally with Keras.
Thank you

The performance will most likely depend on the exact output that you're hoping to get. From your description a 2D-CNN should be good enough and easier to implement with Keras than a GNN.
However, there are some advantages to retaining the graph structure from your data. I think this is too much to present here, but you can find a proper explanation on "Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis" by Wang et al.
This paper also has the benefit of describing data processing to input into the network.
If you don't want to use basic Keras models to assemble your own GNN you may also want to take a look at Spektral, which is a python library for graph deep learning.
Without any other constraints I would firstly use a CNN, because it will be faster to implement with almost ready to use models from Keras.

Is Tensorflow worth using for simple optimization problems? [closed]

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I have started learning Tensorflow recently and I am wondering if it is worth using in simple optimization problems (least squares, maximum likelihood estimation, ...) instead of more traditional libraries (scikit-learn, statsmodel)?
I have implemented a basic AR model estimator using Tensorflow with MLE and the AdamOptimizer and the results are not convincing either performance or computation speed wise.
What do you think?

This is somewhat opinion based, but Tensorflow and similar frameworks such as PyTorch are useful when you want to optimize an arbitrary, parameter-rich non-linear function (e.g., a deep neural network). For a 'standard' statistical model, I would use code that was already tailored to it instead of reinventing the wheel. This is true especially when there are closed-form solutions (as in linear least squares) - why go into to the murky water of local optimization when you don't have to? Another advantage of using existing statistical libraries is that they usually provide you with measures of uncertainty about your point estimates.
I see one potential case in which you might want to use Tensorflow for a simple linear model: when the number of variables is so big the model can't be estimated using closed-form approaches. Then gradient descent based optimization makes sense, and tensorflow is a viable tool for that.

How to train using neural network in python? [closed]

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I am in my final year Project. In my project,I will collect data from a specific road. I have choosen 5 points in that road.From each point i will collect data from GPS about which day of the week,time of the day and time Taken to reach from previous point to that point.
I want to train neural network using this data.
So,the input is which day of the week,time,source and destination & output will be the time needed to reach the destination point from the source point.
What will be easiest to complete this job in python? which library should i choose?

I don't actually know about the conditions of you final year's project, but just a few sidenotes:
Using 4 inputs to your perceptron layer (weekday, hourofday, source, destination) to predict one final neuron (timedelta), you will most likely not need the non-linear powers of a neural network.
If you collect data on your own, you will most likely have too few observations to actually train a neural network. And with too few observations, it will probably overfit to your data.
You are very likely perfectly fine with a linear regression.
If you want to try using a neural network whatsoever, take a look at h2o - it offers a broad variety of machine learning / AI functionality to train models and make predictions.
However, to me it seems that you may require additional reading on this topic. You must understand how to interpret results (if any) and you should know about the pros and cons of each method - this includes knowing about data types and values being appropriate or not for certain models to be applied.

Simple SVM algorithm to predict an integer [closed]

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In my project, we are supposed to use an SVM based algorithm. So to get a basic idea about implementation of SVM, we are trying to implement an algorithm, which when fed with an array of 1000 integers where first 95 integers are of values ranging from 0-5, then the next 5 around 10,000 and then again 95 integers of values ranging from 0-5 and next 5 around 10,000 and so on, will be able to predict the next 100 integers (1001st - 1100th) with first 95 integers around 0-5 and the last 5 around 10,000 ...
How to implement it? Preferred programming language is python. So are there any svm modules like libsvm which will facilitate this?
I know this might be a stupid question, but any help would be appreciated a lot !!
Please reply

Here are some resources on AI (SVM specifically) from the Python wiki:
Milk - Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.
LibSVM - LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. A Python interface is available by by default.
Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS, Liblinear, LibSVM, SVMLight, SVMLin and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels. SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source Software

I'd go with SVM from SciKit. Other options include svmlight and LaSVM.

Using python generators in scikit-learn [closed]

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I was wondering whether and how it is possible to use a python generator as data input to scikit-learn classifier's .fit() functions? Due to huge amounts of data, this seems to make sense to me.
In particular I am about to implement a random forest approach.
Regards
K

The answer is "no". To do out of core learning with random forests, you should
Split your data into reasonably-sized batches (restricted by the amount of RAM you have; bigger is better);
train separate random forests;
append all the underlying trees together in the estimators_ member of one of the trees (untested):
for i in xrange(1, len(forests)):
forests[0].estimators_.extend(forests[i].estimators_)`
(Yes, this is hacky, but no solution to this problem has been found yet. Note that with very large datasets, it might pay to just sample a number training examples that fits in the RAM of a big machine instead of training on all of it. Another option is to switch to linear models with SGD, those implement a partial_fit method, but obviously they're limited in the kind of functions they can learn.)

The short answer is "No, you can't". Classical Random Forest classifier is not an incremental or online classifier, so you can't discard training data while learning, and have to provide all the dataset at once.
Due to popularity of RF in machine learning (not least because of the good prediction results for some interesting cases), there are some attempts to implement online variation of Random Forest, but to my knowledge those are not yet implemented in any python ML package.
See Amir Saffari's page for such an approach (not Python).