The canonical use case for scikit-optimize is an optimization objective given a fixed set of hyperparameters, where skopt is given full control to explore the space. However, one may wish to simultaneously expose a variable to skopt and fix it to a certain value on a subsequent iteration because it is outside of one's control. Is this possible using the current API?
Hypothetical use case:
We wish to maximize bike sale profit. Price is a free parameter to be optimized. The rain forecast is outside of our control, but we wish to control for it in skopt.
Related
I want to change the height of all walls but the length of walls only in a particular axis, for instance, along the x-axis.
Consecutively, could you also tell how I could alter the similar dimensions for a house? Where there are connected walls?
I see nothing in this code that means it does not work.
However, it seems to me that it does not make much sense.
One would seldom constrain all wall heights to be user defined to a certain value; instead, in most Revit models, walls are constrained to reach from a bottom level to a top level. Then, if the height of all walls needs to be modified, you would modify the elevation of the top level only.
The logic of the code guarantees that the wall location line will only be modified if the newWallLine equals XYZ.BasisX. This may never be the case, since the line is a Line object and the vector an XYZ.
I would recommend researching exactly what you wish you achieve and how to do so manually in the end user interface before addressing the task programmatically.
In general, if a feature is not available in the Revit product manually through the user interface, then the Revit API will not provide it either.
You should therefore research the optimal workflow and best practices to address your task at hand manually through the user interface first.
To do so, please discuss and analyse it with an experienced application engineer, product usage expert, or product support.
Once you have got that part sorted out, it is time to step up into the programming environment.
I hope this clarifies.
I'm a mechanical engineer, and I have developed a pretty cool spreadsheet that I use to size some steel members for lifting beams. The set back is that I need to do some trial and error in the selection of the member until I get one that gets as close to the allowable limits as possible.
What I'm hoping to improve on is to develop a function that based upon a length and weight variable that I enter, the program runs a loop and automatically selects the best member size(s) based upon a list of the members and their physical properties. Is this possible?
Yeah, depending on the complexity, either a simple search through parameters (less than, more than etc) might bring you the answer. You can do it quite easily via Pandas library. Just load up the excel as pandas DataFrame (pandas.read_excel()), which then will allow you to perform the searches on that DataFrame object.
If you want to run some optimization algo, you should look into SciPy's optimize to get what you're looking for based on the input data (it handles unconstrained and constrained functions).
Of course, the question you've stated is quite general, so I only pointed the direction. More info would be better.
I am trying to build a system that on providing an image of a car can assess the damage percentage of it and also find out which parts are damaged in the car.
Is there any possible way to do this using Python and open-cv or tensorflow ?
The GitHub repositories I found that were relevant to my work are these
https://github.com/VakhoQ/damage-car-detector/tree/master/DamageCarDetector
https://github.com/neokt/car-damage-detective
But what they provide is a qualitative output( like they say the car damage is high or low), I wanted to print out a quantitative output( percentage of damage ) along with the individual part names which are damaged
Is this possible ?
If so please help me out.
Thank you.
To extend the good answers given by #yves-daoust: It is not a trivial task and you should not try to do it at once with one single approach.
You should question yourself how a human with a comparable task, i.e. say an expert who reviews these cars after a leasing contract, proceeds with this. Then you have to formulate requirements and also restrictions for your system.
For instance, an expert first checks for any visual occurences and rates these, then they may check technical issues which may well be hidden from optical sensors (i.e. if the car is drivable, driving a round and estimate if the engine is running smoothly, the steering geometry is aligned (i.e. if the car manages to stay in line), if there are any minor vibrations which should not be there and so on) and they may also apply force (trying to manually shake the wheels to check if the bearings are ok).
If you define your measurement system as restricted to just a normal camera sensor, you are somewhat limited within to what extend your system is able to deliver.
If you just want to spot cosmetic damages, i.e. classification of scratches in paint and rims, I'd say a state of the art machine vision application should be able to help you to some extent:
First you'd need to detect the scratches. Bear in mind that visibility of scratches, especially in the field with changing conditions (sunlight) may be a very hard to impossible task for a cheap sensor. I.e. to cope with reflections a system might need to make use of polarizing filters, special effect paints may interfere with your optical system in a way you are not able to spot anything.
Secondly, after you detect the position and dimension of these scratches in the camera coordinates, you need to transform them into real world coordinates for getting to know the real dimensions of these scratches. It would also be of great use to know the exact location of the scratch on the car (which would require a digital twin of the car - which is not to be trivially done anymore).
After determining the extent of the scratch and its position on the car, you need to apply a cost model. Because some car parts are easily fixable, say a scratch in the bumper, just respray the bumper, but scratch in the C-Pillar easily is a repaint for the whole back quarter if it should not be noticeable anymore.
Same goes with bigger scratches / cracks: The optical detection model needs to be able to distinguish between scratches and cracks (which is very hard to do, just by looking at it) and then the cost model can infer the cost i.e. if a bumper needs just respray or needs complete replacement (because it is cracked and not just scratched). This cost model may seem to be easy but bear in mind this needs to be adopted to every car you "scan". Because one cheap damage for the one car body might be a very hard to fix damage for a different car body. I'd say this might even be harder than to spot the inital scratches because you'd need to obtain the construction plans/repair part lists (the repair handbooks / repair part lists are mostly accessible if you are a registered mechanic but they might cost licensing fees) of any vehicle you want to quote.
You see, this is a very complex problem which is composed of multiple hard sub-problems. The easiest or probably the best way to do this would be to do a bottom up approach, i.e. starting with a simple "scratch detector" which just spots scratches in paint. Then go from there and you easily see what is possible and what is not
I hope it belongs here.
Can anyone please tell me is there any method to compare different search applications working in the same domain with the same dataset?
The problem is they are quite different - one is a web application which looks up the database where items are grouped in categories, and another one is a rich client which makes search by keywords.
Is there any standard test giudes for that purpose?
There are testing methods. You may use e.g. Precision/Recall or the F beta method to estimate a rate which computes the "efficiency". However you need to make a reference set by yourself. That means you will somehow measure not the efficiency in the domain, more likely the efficiency compared to your own reasoning.
The more you need to make sure that your reference set is representative for the data you have.
In most cases common reasoning will give you also the result.
If you want to measure the performance in matters of speed you need to formulate a set of assumed queries against the search and query your search engine with these at a given rate. Thats doable with every common loadtesting tool.
For an ecommerce website how do you measure if a change to your site actually improved usability? What kind of measurements should you gather and how would you set up a framework for making this testing part of development?
Multivariate testing and reporting is a great way to actually measure these kind of things.
It allows you to test what combination of page elements has the greatest conversion rate, providing continual improvement on your site design and usability.
Google Web Optimiser has support for this.
Similar methods that you used to identify the usability problems to begin with-- usability testing. Typically you identify your use-cases and then have a lab study evaluating how users go about accomplishing certain goals. Lab testing is typically good with 8-10 people.
The more information methodology we have adopted to understand our users is to have anonymous data collection (you may need user permission, make your privacy policys clear, etc.) This is simply evaluating what buttons/navigation menus users click on, how users delete something (i.e. changing quantity - are more users entering 0 and updating quantity or hitting X)? This is a bit more complex to setup; you have to develop an infrastructure to hold this data (which is actually just counters, i.e. "Times clicked x: 138838383, Times entered 0: 390393") and allow data points to be created as needed to plug into the design.
To push the measurement of an improvement of a UI change up the stream from end-user (where the data gathering could take a while) to design or implementation, some simple heuristics can be used:
Is the number of actions it takes to perform a scenario less? (If yes, then it has improved). Measurement: # of steps reduced / added.
Does the change reduce the number of kinds of input devices to use (even if # of steps is the same)? By this, I mean if you take something that relied on both the mouse and keyboard and changed it to rely only on the mouse or only on the keyboard, then you have improved useability. Measurement: Change in # of devices used.
Does the change make different parts of the website consistent? E.g. If one part of the e-Commerce site loses changes made while you are not logged on and another part does not, this is inconsistent. Changing it so that they have the same behavior improves usability (preferably to the more fault tolerant please!). Measurement: Make a graph (flow chart really) mapping the ways a particular action could be done. Improvement is a reduction in the # of edges on the graph.
And so on... find some general UI tips, figure out some metrics like the above, and you can approximate usability improvement.
Once you have these design approximations of user improvement, and then gather longer term data, you can see if there is any predictive ability for the design-level usability improvements to the end-user reaction (like: Over the last 10 projects, we've seen an average of 1% quicker scenarios for each action removed, with a range of 0.25% and standard dev of 0.32%).
The first way can be fully subjective or partly quantified: user complaints and positive feedbacks. The problem with this is that you may have some strong biases when it comes to filter those feedbacks, so you better make as quantitative as possible. Having some ticketing system to file every report from the users and gathering statistics about each version of the interface might be useful. Just get your statistics right.
The second way is to measure the difference in a questionnaire taken about the interface by end-users. Answers to each question should be a set of discrete values and then again you can gather statistics for each version of the interface.
The latter way may be much harder to setup (designing a questionnaire and possibly the controlled environment for it as well as the guidelines to interpret the results is a craft by itself) but the former makes it unpleasantly easy to mess up with the measurements. For example, you have to consider the fact that the number of tickets you get for each version is dependent on the time it is used, and that all time ranges are not equal (e.g. a whole class of critical issues may never be discovered before the third or fourth week of usage, or users might tend not to file tickets the first days of use, even if they find issues, etc.).
Torial stole my answer. Although if there is a measure of how long it takes to do a certain task. If the time is reduced and the task is still completed, then that's a good thing.
Also, if there is a way to record the number of cancels, then that would work too.