Boosting | Decision stumps as the weak classifiers

This is related to a change in scipy 1.8.0, One should use -np.squeeze(res.fun) instead of -res.fun[0]

https://github.com/fmfn/BayesianOptimization/issues/300

The comments in the bug report indicate reverting to scipy 1.7.0 fixes this,

It seems the fix is been proposed in the BayesianOptimization package: https://github.com/fmfn/BayesianOptimization/pull/303

But this has not been merged and released yet, so you could either:

• fall back to scipy 1.7.0
• use the forked github version of BayesianOptimization with the patch (https://github.com/samFarrellDay/BayesianOptimization)
• apply the patch in issue 303 manually on your system

Are you looking for something like the following?

You can read the documentation of boolean query here, there is huge difference in the should and boost.

Should and must both contributes to the _score of the document, and as mentioned in the above documentation, follows the

The bool query takes a more-matches-is-better approach, so the score from each matching must or should clause will be added together to provide the final _score for each document.

While boost is a parameter, using which you can increase the weight according to your value, let me explain that using an example.

Index sample docs

SQL, as a declarative language, is about the "What", not the "How".
You declare what you want to get as a result and it is up to the database to decide how to do it depends on your code as well as the specific SQL engine, the system configuration, the database configuration, the data demographics, the collected statistics and more.
In some SQL engines you might hint the database as to your preferred execution plan, however, this hint might cause an execution error or be ignored. I have even seen scenarios where the execution plan did not reflect some optimizations done by the database.

In other words -
Your assumption as to how a database execute your 2 queries might be completely wrong.
There is no reason to assume that the 2nd query will store temp results or even that any of the queries will use sorting at all.

And last -
What you are trying to achieve can actually be done without any sorting with complexity of o(n).

Thank you for asking this question; we definitely are not supporting this use case (passing non-default arguments to the recipe or model) very well right now. We've opened an issue here where you can track our work on this.

In the meantime, you could try a bit of a hacky workaround by manually using update_recipe() on the workflow you are interested in:

Elasticsearch "replaces" every instance of a synonym all other synonyms, and does so on both indexing and searching (unless you provide a separate search_analyzer) so you're losing the exact token. To keep this information, use a subfield with standard analyzer and then use multi_match query to match either synonyms or exact value + boost the exact field.

You can use different environments for the comparison of Numpy with and without MKL. In each environment you can install the needed packages(numpy with MKL or without) using package installer. Then on that environments you can run your program to compare the performance of Numpy with and without MKL.

NumPy doesn’t depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically Intel MKL or OpenBLAS.

• The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS.

• In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy.

• When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library.But it can also be MKL (from the defaults channel), or even BLIS or reference BLAS.

Please refer this link to know about installing Numpy in detail.

You can create two different environments to compare the NumPy performance with MKL and without it. In the first environment install the stand-alone NumPy (that is, the NumPy without MKL) and in the second environment install the one with MKL.

To create environment using NumPy without MKL.

After fitting your model, you need to actually predict the predictions, with clf.predict, lets say that we want to save y_pred_train & y_pred_test:

In a more complex project, unit tests will help you test every functionality of your code without having to manually check if it is still working.

So unit tests will save time you would be spending on manually running the tests in postman.