I used Artificial Neural Network . So I need to seed value but I do not what value will I set ?
You can use whatever number you want as seed. It is used for the initilization and shouldn't make a big difference. Setting a seed is useful if you want to have reproducable code/workflow and is mostly used for testing purposes.
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Is there a reason why we need to do BOTH local SA and global SA when we want to determine the most influential parameters?
I am planning to do sensitivity analysis using normalised forward sensitivity index (local SA) and the LHS/PRCC approach (global SA). I am wondering, is one better than the other? Or just performing either one of it will suffice?
It depends on your study purpose. The local SA is mainly used to conduct parameter prioritization (which parameters are important to model output), but the global SA is used for parameter fixing (which parameters are "not" important to model output).
In a nutshell, if you have a metamodel with dozens of parameters that need to be calibrated with the advanced statistical approach (e.g., Bayesian), you might need to consider using global SA to find the non-influential parameters and fix them to improve the computational efficiency. Otherwise, you can just use local SA to investigate which parameters that dominate the model output.
I have a object with many fields. Each field has different range of values. I want to use hypothesis to generate different instances of this object.
Is there a limit to the number of combination of field values Hypothesis can handle? Or what does the search tree hypothesis creates look like? I don't need all the combinations but I want to make sure that I get a fair number of combinations where I test many different values for each field. I want to make sure Hypothesis is not doing a DFS until it hits the max number of examples to generate
TLDR: don't worry, this is a common use-case and even a naive strategy works very well.
The actual search process used by Hypothesis is complicated (as in, "lead author's PhD topic"), but it's definitely not a depth-first search! Briefly, it's a uniform distribution layered on a psudeo-random number generator, with a coverage-guided fuzzer biasing that towards less-explored code paths, with strategy-specific heuristics on top of that.
In general, I trust this process to pick good examples far more than I trust my own judgement, or that of anyone without years of experience in QA or testing research!
I am implementing a Go playing program roughly according to the architecture of earlier versions of AlphaGo(AlphaGo Fan or AlphaGo Lee), e.g. using policy network, value network, and Monte Carlo tree search(MCTS). Currently I have trained a decent policy network and an insensitive value network, and I don't have a fast roll-out policy. By "insensitive" I mean, the value network is not able to judge complicated situations, only outputing a win rate around 50% unless the situation is concise. The value network can judge concise board(no big fight going on) correctly.
Using this policy network and value network, I also implemented MCTS algorithm(The evaluation of a tree node is done only by value network). Since the value network is not accurate, I am afraid MCTS is prone to be trapped in bad moves before the time of MCTS is up. In order to better fine-tune the hyper parameters of MCTS to remedy the bad influence brought by inaccurate value network, I have two questions to ask:
Node selection is done by arg max (p_value + lambda * p_policy/visit_cnt). Does fine-tune the parameter lambda help?
Intuitively I want MCTS to explore as further as possible. In node expansion stage, does setting the expansion condition as expand a leaf once it is visited a very small number of times, like 3 help? What expansion method should I use?
EDIT: The second question is about the 'expand' stage of typical 'selection, expand, evaluation, backup' MCTS algorithm. I reckon by expand as quickly as possible, the MCTS can explore deeper, and give more accurate value approximations. I set a parameter n as how many times a leaf node is visited before it is expanded. I want to know intuitively, what a large n and a small n would influence the performance of MCTS.
Node selection is done by arg max (p_value + lambda * p_policy/visit_cnt). Does fine-tune the parameter lambda help?
Let's first try to develop a good understanding of what all the terms in that formula do:
p_value: average of all the evaluations at the end of iterations that went through this node. This is our current estimate of how good this node is according to the value network's evaluations at the end of rollouts.
p_policy/visit_cnt: p_policy will be high for nodes that seem good according to your policy network, and low for nodes that seem bad according to your policy network. visit_cnt will be high for nodes which we've already visited often, and low for nodes we have not yet visited often. This complete term makes us initially ''lean towards'' the policy network, but move away from the policy network as time goes on (because the nodes that are good according to policy network will have high visit counts).
lambda: A parameter that determines a balance between the first and the second term of the two points above. If lambda is high, we'll rely more on the information that the policy network gives us, and less on the information that the value network gives us. If lambda is low, we'll more quickly start relying on the information provided to us by earlier MCTS iterations + value network evaluations, and rely less on the policy network.
In the text of your question, you have stated that you believe the policy network is decent, and the value network isn't really informative. So, if that's the case, I'd try using a high value for lambda: if you believe the policy network to be more informative than the value network, you'll want to rely more on the policy network than the value network, so you'll want a high lambda.
Intuitively I want MCTS to explore as further as possible. In node expansion stage, does setting the expansion condition as expand a leaf once it is visited a very small number of times, like 3 help? What expansion method should I use?
The only reason why the expansion phase is often rather limited in classical MCTS implementations (e.g. often only expands a single node per iteration) is memory concerns; if you expand too often, your tree grows too quickly, and you run out of memory.
In these AlphaGo-style setups (mixed Deep Learning + MCTS), you often use a lot more computation time in these networks, and therefore get much fewer MCTS iterations than a raw, pure MCTS algorithm without any Deep Learning (but often higher quality / more informative iterations, which makes up for the lower iteration count). This lower iteration count significantly reduces the risk of running out of memory due to over-enthusiastic expansion, so I suspect you can afford to expand more aggressively. The only possible negative effect of expanding too much will be that you run out of RAM, and you'll easily notice when that happens because your program will crash.
Is there any way to test multiple algorithms rather than doing it once for each and every algorithm; then checking the result? There are a lot of times where I don’t really know which one to use, so I would like to test multiple and get the result (error rate) fairly quick in Azure Machine Learning Studio.
You could connect the scores of multiple algorithms with an 'Evaluate Model' button to evaluate algorithms against each other.
Hope this helps.
The module you are looking for, is the one called “Cross-Validate Model”. It basically splits whatever comes in from the input-port (dataset) into 10 pieces, then reserves the last piece as the “answer”; and trains the nine other subset models and returns a set of accuracy statistics measured towards the last subset. What you would look at is the column called “Mean absolute error” which is the average error for the trained models. You can connect whatever algorithm you want to one of the ports, and subsequently you will receive the result for that algorithm in particular after you “right-click” the port which gives the score.
After that you can assess which algorithm did the best. And as a pro-tip; you could use the Filter-based-feature selection to actually see which column had a significant impact on the result.
You can check section 6.2.4 of hands-on-lab at GitHub https://github.com/Azure-Readiness/hol-azure-machine-learning/blob/master/006-lab-model-evaluation.md which focuses on the evaluation of multiple algorithms etc.
I have a set of examples, which are each annotated with feature data. The examples and features describe the settings of an experiment in an arbitrary domain (e.g. number-of-switches, number-of-days-performed, number-of-participants, etc.). Certain features are fixed (i.e. static), while others I can manually set (i.e. variable) in future experiments. Each example also has a "reward" feature, which is a continuous number bounded between 0 and 1, indicating the success of the experiment as determined by an expert.
Based on this example set, and given a set of static features for a future experiment, how would I determine the optimal value to use for a specific variable so as to maximise the reward?
Also, does this process have a formal name? I've done some research, and this sounds similar to regression analysis, but I'm still not sure if it's the same thing.
The process is called "design of experiments." There are various techniques that can be used depending on the number of parameters, and whether you are able to do computations between trials or if you have to pick all your treatments in advance.
full factorial - try each combination, the brute force method
fractional factorial - eliminate some of the combinations in a pattern and use regression to fill in the missing data
Plackett-Burman, response surface - more sophisticated methods, trading off statistical effort for experimental effort
...and many others. This is an active area of statistical research.
Once you've built a regression model from the data in your experiments, you can find an optimum by applying the usual numerical optimization techniques.