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

In many other great docs sites, like https://www.tensorflow.org/api_docs, there's a button at the end of the page to collect simple feedback.

This will help us more accurately improve our docs

Use cas

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

Documentation Link

https://matplotlib.org/devdocs/users/prev_whats_new/whats_new_3.4.0.html?highlight=bracket#angles-on-bracket-arrow-styles

Problem

I can't tell from this figure what Angle = 60 is relative to, since the top angle is >60 and the lower angle is <60 in the first example, and '0 degrees means perpendicular to the line' is buried in the api docs (I missed what that mean

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)

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

• 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

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

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.