Data Science

PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.

Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR should mention which estimator it's dealing with and the description of the PR should begin with towards #.

Steps

• The estimator must define a cl

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

Description

The upscaling_speed and idle_timeout_minutes properties are useful and it already in RayCluster. However, it not ex

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

Proposed refactor

The current import time for the pytorch_lightning package on my machine is several seconds. There are some opportunities to improve this.

Motivation

High import times have an impact on the development and debugging speed.

Benchmark

I benchmarked the import time in two environments:

1. Fresh environment with pytorch lightning installed, no extras.

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

Problem

do_3d_projection() reorders vertices of various 3D collection artists in z-depth. Once the drawing is done, it would be useful to restore back the original order, so that e.g. interactive tools (mplcursors) reporting the index of a picked collection member get the original index as set by the user (anntzer/mplcursors#49). This should be reasonably cheap t

https://ipython.readthedocs.io/en/stable/api/generated/IPython.lib.demo.html

The example of demo.py uses print statements instead of print function, which does not work in python 3.x

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

• 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

Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct

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.