Data Science

Below is the list of broken links in the documention from a make linkcheck run, together with the file the link appears in and the error message.

If you want to work on this, please:

• do one Pull Request per link
• add a comment in this issue saying which link you want to tackle so that different people can work on this issue in parallel
• **mention this issue (#23631) in yo

The Mixed Time-Series chart type allows for configuring the title of the primary and the secondary y-axis.
However, while only the title of the primary axis is shown next to the axis, the title of the secondary one is placed at the upper end of the axis where it gets hidden by bar values and zoom controls.

How to reproduce the bug

1. Create a mixed time-series chart
2. Configure axi

Reporters do not apply sort_by_metric if metric is passed through tune.run.

I tried to pass metric argument to CLIReporter, however, I obtained this error:

raise ValueError(
                "You passed a `metric` or `mode` argument to `tune.run()`, but "
                "the reporter you are using was already instantiated with their "
                "own `metric` and `mode

Problem

See #3856 . Developer would like the ability to configure whether the developer menu or viewer menu is displayed while they are developing on cloud IDEs like Gitpod or Github Codespaces

Solution

Create a config option

showDeveloperMenu: true | false | auto

where

• true: always shows the developer menu locally and while deployed
• false: always sho

README.md contains an execution example of the program in mnist_examples, but mnist_examples does not exist. If the code exists in any branch or in the past, recover it.

I wanted to solve the broken link of lightning_lite.rst together with this problem, but the PR Lightning-AI/lightning#13331 of only the fix of the link has been issued.

cc @Borda @rohitgr7

Describe your context
Please provide us your environment, so we can easily reproduce the issue.

• replace the result of pip list | grep dash below

dash                      2.0.0
dash-bootstrap-components 1.0.0

• if frontend related, tell us your Browser, Version and OS

  • OS: [e.g. iOS] Windows
  • Browser [e.g. chrome, safari]: Chrome 96.0x, Edge 96.0x, Firefox

Bug summary

( This bug was originally issued in ipympl: matplotlib/ipympl#471 )

I want to use a selector matplotlib.widgets.RectangleSelector , it is incompatible with zoom and pan
So I try to call widgetlock to prevent user from using zoom when the selector is active, hoping that the zoom or pan tools will not be blocked.
This does not work, but crashes rather.

The warnings at

https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html

do not mention the issues with reloading modules with enums:

• Enum and Flag are compared by identity (is, even if == is used (similarly to None))
• reloading a module, or importing the same module by a different name, creates new enums (look the same, but are not the same)

• Base README.md
• Quizzes
• Introduction base README
  • Defining Data Science README
  • Defining Data Science assignment
  • Ethics README
  • Ethics assignment
  • Defining Data README
  • Defining Data assignment
  • Stats and Probability README
  • Stats and Probability assignment
• Working with Data base README
  • Rel

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Bug with GPU Model

Currently, while using pruning methods like TaylorFOWeight Pruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.