booster | fastest way to get a full-fledged REST service

There's no need for you to put the hexcolor value in quotes

You can have a look at VanillaJS DatePicker. It has all your required options and is completely written in JavaScript with no external dependencies. In the below code, you can see a minimal example of conditions that you stated in your question.

daysOfWeekDisabled - 0 and 6 disables Sunday and Saturday

datesDisabled - Dates to disable including next Monday if it is Friday today

minDate - Minimum Date that can be picked is +2days

maxDate - Maximum Date that can be picked is +60days

Please check where 249-235=14 features are in test data.
Or fit on same data
best_xgb.fit(X[test_data.columns], y)

I am not an expert with scikit-learn but it seems that one of the requirements of various objects used by this framework is that they can be cloned by calling the sklearn.base.clone method. This appears to be something that the existing XGBRegressor class does, so is something your subclass of XGBRegressor must also do.

What may help is to pass any other unexpected keyword arguments as a **kwargs parameter. In your constructor, kwargs will contain a dict of all of the other keyword parameters that weren't assigned to other constructor parameters. You can pass this dict of parameters on to the call to the superclass constructor by referring to them as **kwargs again: this will cause Python to expand them out:

Your selection is just too general, cause it is selecting all

Select the headlines mor specific:

If I try with single-quoted I get

Although I am not 100% sure, I think I know what happened.

This issue happens because some parameters are not suitable for certain booster type and the trial will return nan as result and be stuck at the step - calculating the MSE score.

To solve the problem, you just need to delete the "booster": "dart".

In other words, using "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear"]), rather than "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]), can solve the problem.

I got the idea when I tuned my LightGBMRegressor Model. I found many trials fail because these trials returned nan and they all used the same "boosting_type"="rf". So I deleted the rf and all 100 trials were completed without any error. Then I looked for the XGBRegressor issue which I posted above. I found all the trials which were stuck had the same "booster":"dart" either. So I deleted the dart, and the XGBRegressor run normally.

As stated in the XGBoost Docs

Parameter tuning is a dark art in machine learning, the optimal parameters of a model can depend on many scenarios.

You asked for suggestions for your specific scenario, so here are some of mine.

1. Drop the dimensions booster from your hyperparameter search space. You probably want to go with the default booster 'gbtree'. If you are interested in the performance of a linear model you could just try linear or ridge regression, but don't bother with it during your XGBoost parameter tuning.
2. Drop the dimension base_score from your hyperparameter search space. This should not have much of an effect with sufficiently many boosting iterations (see XGB parameter docs).
3. Currently you have 3200 hyperparameter combinations in your grid. Expecting to find a good one by looking at 50 random ones might be a bit too optimistic. After dropping the booster and base_score dimensions you would be down to

That’s not what’s happening. Unix systems, at least, will happily let you open a file multiple times.

However, Python’s IO is buffered by default. You need to flush the data you’ve written out to the file before you can read the data from it. See https://docs.python.org/3/library/io.html#io.IOBase.flush for more information about this. (Summary: put wf.flush() after the wf.write(…) call and before attempting to read from it.