data_analysis | Fork of the python libs in the data_hacking repo

You and your collaborators have different cwd sets. It looks like your collaborators have reset the cwd, such as add this in the settings.json file:

The immediate problem here is that in foo || bar; baz, baz happens no matter what foo's exit status was. In that respect, a semicolon, as a command separator, is handled in just the same way as a newline. That can be fixed with explicit grouping, as in, foo || { bar; baz; } -- or even more explicitly, if foo; then bar; baz; fi

The relevant code would be better written as:

This error is most likely happening due to your Docker image being based on the SDK harness image (apache/beam_python3.8_sdk). SDK harness images are used in portable pipelines; When a portable runner needs to execute stages of a pipeline that must be executed in their original language, it starts a container with the SDK harness and delegates execution of that stage of the pipeline to the SDK harness. Therefore, when the SDK harness boots up it is expecting to have various configuration details provided by the runner that started it, one of which is the ID. When you start this container directly, those configuration details are not provided and it crashes.

For context into your specific use-case, let me first diagram out the different processes involved in running a portable pipeline.

Since you:

• Only want '.docx' (i.e. as determined by suffix)
• Do not want: '.pdf', 'SS.docx', or 'ss.docx' (i.e. fies with these endings)

This could be done more simply as follows.

Code--Option 1 using str endswith

Removing the display: flex property from this class will fix it.

I don't see an alternative to dropping RandomizedSearchCV. Internally RandomSearchCV calls sample_without_replacement to sample from your feature space. When your feature space is larger than C's long size, scikit-learn's sample_without_replacement simply breaks down.

Luckily, random search kind of sucks anyway. Check out optuna as an alternative. It is way smarter about where in your feature space to spend time evaluating (paying more attention to high-performing areas), and does not require you to limit your feature space precision beforehand (that is, you can omit the step size). More generally, check out the field of AutoML.

If you insist on random search however, you'll have to find another implementation. Actually, optuna also supports a random sampler.

using ax.patches you can achieve it.

This will do:

As per Hive-6384 Jira, Starting from Hive-1.2 you can use Timestamp,date types in parquet tables.

Workarounds for Hive < 1.2 version:

1. Using String type:

Try the following (documentation is inside the code):