refit | The automatic type-safe REST library for .NET Core, Xamarin and .NET. Heavily inspired by Square's R | Form library

 by   reactiveui C# Version: 7.0.0-beta.1 License: MIT

kandi X-RAY | refit Summary

kandi X-RAY | refit Summary

refit is a C# library typically used in User Interface, Form, Xamarin applications. refit has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Refit is a library heavily inspired by Square's Retrofit library, and it turns your REST API into a live interface:.

            kandi-support Support

              refit has a medium active ecosystem.
              It has 7251 star(s) with 697 fork(s). There are 172 watchers for this library.
              There were 3 major release(s) in the last 12 months.
              There are 154 open issues and 687 have been closed. On average issues are closed in 127 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of refit is 7.0.0-beta.1

            kandi-Quality Quality

              refit has 0 bugs and 0 code smells.

            kandi-Security Security

              refit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              refit code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              refit is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              refit releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.
              It has 387 lines of code, 0 functions and 86 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            refit Key Features

            No Key Features are available at this moment for refit.

            refit Examples and Code Snippets

            No Code Snippets are available at this moment for refit.

            Community Discussions


            How to choose LinearSVC instead of SVC if kernel=linear in param_grid?
            Asked 2022-Apr-17 at 18:30

            I have the following way to create the grid_cv_object. Where hyperpam_grid={"C":c, "kernel":kernel, "gamma":gamma, "degree":degree}.



            Answered 2022-Apr-17 at 18:30

            Try making parameter grids in the following form



            How can update trained IsolationForest model with new datasets/datafarmes in python?
            Asked 2022-Mar-02 at 20:42

            Let's say I fit IsolationForest() algorithm from scikit-learn on time-series based Dataset1 or dataframe1 df1 and save the model using the methods mentioned here & here. Now I want to update my model for new dataset2 or df2.

            My findings:

            ...learn incrementally from a mini-batch of instances (sometimes called “online learning”) is key to out-of-core learning as it guarantees that at any given time, there will be only a small amount of instances in the main memory. Choosing a good size for the mini-batch that balances relevancy and memory footprint could involve tuning.

            but Sadly IF algorithm doesn't support estimator.partial_fit(newdf)

            • auto-sklearn offers refit() is also not suitable for my case based on this post.

            How I can update the trained on Dataset1 and saved IF model with a new Dataset2?



            Answered 2022-Mar-02 at 17:41

            You can simply reuse the .fit() call available to the estimator on the new data.

            This would be preferred, especially in a time series, as the signal changes and you do not want older, non-representative data to be understood as potentially normal (or anomalous).

            If old data is important, you can simply join the older training data and newer input signal data together, and then call .fit() again.

            Also sidenote, according to sklearn documentation, it is better to use joblib than pickle

            An MRE with resources below:



            Unpickle instance from Jupyter Notebook in Flask App
            Asked 2022-Feb-28 at 18:03

            I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

            AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

            I am creating the model on Google Colab.

            Code in Jupyter Notebook:



            Answered 2022-Feb-24 at 11:48

            Import GensimWord2VecVectorizer in your Flask Web app python file.



            Modify surface code to solve for 4 dimensions instead of 3 [edited]
            Asked 2022-Feb-22 at 00:46

            I found this great question with some concise code that, with a couple of tweaks, fits a 3D polynomial surface onto a set of points of in space.

            Python 3D polynomial surface fit, order dependent

            My version is below.

            Ultimately, I've realized that I need to fit a surface over time, i.e. I need to solve for a 4 dimensional surface, and I've struggled with it.

            I came up with a very hacky and computationally intensive work-around. I create a surface for each time interval. Then I create a grid of points and find the Z value for each point on each surface. So now I have a bunch of x,y points and each one has a list of z values that need to flow smoothly from one interval to the next. So we do a regression on the z values. Now that the z-flow is smooth, I refit a surface for each time interval based on the x,y points and whatever their smoothed Z value is for the relevant time interval.

            Its what it sounds like. Clunky and suboptimal. The resulting surfaces flow more smoothly and still perform okay but there's gotta be a way to cut out the middle man and solve for that 4th dimension directly in the fitSurface function.



            Answered 2022-Feb-22 at 00:46

            Alright so I think I got this dialed in. I wont go over the how, other than to say that once you study the code enough the black magic doesn't go away but patterns do emerge. I just extended those patterns and it looks like it works.

            End result

            Admittedly this is so low res that it look like its not changing from C=1 to C=2 but it is. Load it up and you'll see. The gif should show the flow more cleary now.

            First the methodology behind the proof. I found a funky surface equation and added a third input variable C (in-effect creating a 4D surface), then studied the surface shape with fixed C values using the original 3D fitter/renderer as a point of trusted reference.

            When C is 1, you get a half pipe from hell. A slightly lopsided downsloping halfpipe.

            Whence C is 2, you get much the same but the lopsidedness is even more exaggerated.

            When C is 3, you get a very different shape. Like the exaggerated half pipe from above was cut in half, reversed, and glued back together.

            When you run the below code, you get a 3D render with a slider that allows you to flow through the C values from 1 to 3. The values at 1, 2, and 3 look like solid matches to the references. I also added a randomizer to the data to see how it would perform at approximating a surface from imperfect noisy data and I like what I see there too.

            Props to the below questions for their code and ideas.

            Python 3D polynomial surface fit, order dependent

            python visualize 4d data with surface plot and slider for 4th variable



            Code undoes itself before returning value
            Asked 2022-Feb-13 at 00:03
            from time import sleep
            def refit(i, n, c=[]):
             if i[:n] != '': refit(i[n:],n,c+[i[:n]])
             return c


            Answered 2022-Feb-13 at 00:03

            Within a given call to refit, you're not changing c, so c has the same value in the second print() that it does in the first print(). All the recursive calls happen in between those two print calls.

            On the first call, c is []:



            Why doesn't GridSearchCV have best_estimator_ even after fitting?
            Asked 2022-Feb-12 at 22:05

            I am learning about multiclass classification using scikit learn. My goal is to develop a code which tries to include all the possible metrics needed to evaluate the classification. This is my code:



            Answered 2022-Feb-12 at 22:05

            The point of refit is that the model will be refitted using the best parameter set found before and the entire dataset. To find the best parameters, cross-validation is used which means that the dataset is always split into a training and a validation set, i.e. not the entire dataset is used for training here.

            When you define multiple metrics, you have to tell scikit-learn how it should determine what best means for you. For convenience, you can just specify any of your scorers to be used as the decider so to say. In that case, the parameter set that maximizes this metric will be used for refitting.

            If you want something more sophisticated, like taking the parameter set that returned the highest mean of all scorers, you have to pass a function to refit that given all the created metrics returns the index of the corresponding best parameter set. This parameter set will then be used to refit the model.

            Those metrics will be passed as a dictionary of strings as keys and NumPy arrays as values. Those NumPy arrays have as many entries as parameter sets that have been evaluated. You find a lot of things in there. What is probably the most relevant is mean_test_*scorer-name*. Those arrays contain for each tested parameter set the mean scorer-name-scorer computed across the cv splits.

            In code, to get the index of the parameter set, that returns the highest mean across all scorers, you can do the following



            All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough
            Asked 2022-Jan-21 at 17:06

            Can I ask, when I run this code, it produces an output without error:



            Answered 2022-Jan-21 at 17:06

            The order of the steps in a Pipeline matters, and only the last step can be a non-transformer like your svc.



            NullReferenceException when testing API implemented by Refit
            Asked 2021-Nov-22 at 15:07

            I'm trying to test some error responses (BadRequest, Unauthorized, ...) with Refit and so I implemented a TestHandler that returns any desired response. The response works fine with an "OK" (HTTP status code 200) response:



            Answered 2021-Nov-22 at 15:07

            Found it, with help from a colleague! Turns out the HttpResponseMessage needs some/any RequestMessage.




            GridSearchCV VS CV of the model
            Asked 2021-Nov-19 at 07:37

            What is the difference between using RidgeClassifierCV and tuning the model after training it



            Answered 2021-Nov-19 at 07:37

            RidgeClassifierCV allows you to perform cross validation and find the best alpha with respect to your dataset.

            GridSearchCV allows you not only to finetune an estimator but the preprocessing steps of a Pipeline as well.

            From the documentation, The advantage of an EstimatorCV such as RidgeClassifierCV is that they can take advantage of warm-starting by reusing precomputed results in the previous steps of the cross-validation process. This generally leads to speed improvements.

            As a conclusion if you are only trying to finetune a ridge classifier, RidgeClassifierCV should be the best choice as it might be faster. However if you are having extra preprocessing steps, it should be better to use GridSearchCV.



            How to find coefficients for LinearRegression problem with Pipeline and GridSearchCV
            Asked 2021-Oct-22 at 10:55

            I'm performing a LinearRegression model with a pipeline and GridSearchCV, I can not manage to make it to the coefficients that are calculated for each feature of X_train.



            Answered 2021-Oct-22 at 10:55

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


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            Install refit

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