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StephanAkkerman / crypto-forecasting-benchmark

This repository contains the codebase used in the research conducted for the paper titled "Benchmarking Cryptocurrency Forecasting Models in the Context of Data Properties and Market Factors." The study involved a rigorous assessment of thirteen different time series forecasting models over twenty-one cryptocurrencies and four distinct time frames.
MIT License
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Bump seaborn from 0.12.2 to 0.13.0 #42

Closed dependabot[bot] closed 11 months ago

dependabot[bot] commented 1 year ago

Bumps seaborn from 0.12.2 to 0.13.0.

Release notes

Sourced from seaborn's releases.

v0.13.0 (September 2023)

See the online docs for an annotated version of these notes with working links.

This is a major release with a number of important new features and changes. The highlight is a major overhaul to seaborn's categorical plotting functions, providing them with many new capabilities and better aligning their API with the rest of the library. There is also provisional support for alternate dataframe libraries like polars, a new theme and display configuration system for objects.Plot, and many smaller bugfixes and enhancements.

Updating is recommended, but users are encouraged to carefully check the outputs of existing code that uses the categorical functions, and they should be aware of some deprecations and intentional changes to the default appearance of the resulting plots (see notes below with and tags).

Major enhancements to categorical plots

Seaborn's categorical functions <categorical_api> have been completely rewritten for this release. This provided the opportunity to address some longstanding quirks as well as to add a number of smaller but much-desired features and enhancements.

Support for numeric and datetime data

The categorical functions have historically treated all data as categorical, even when it has a numeric or datetime type. This can now be controlled with the new native_scale parameter. The default remains False to preserve existing behavior. But with native_scale=True, values will be treated as they would by other seaborn or matplotlib functions. Element widths will be derived from the minimum distance between two unique values on the categorical axis.

Additionally, while seaborn previously determined the mapping from categorical values to ordinal positions internally, this is now delegated to matplotlib. The change should mostly be transparent to the user, but categorical plots (even with native_scale=False) will better align with artists added by other seaborn or matplotlib functions in most cases, and matplotlib's interactive machinery will work better.

Changes to color defaults and specification

The categorical functions now act more like the rest of seaborn in that they will produce a plot with a single main color unless the hue variable is assigned. Previously, there would be an implicit redundant color mapping (e.g., each box in a boxplot would get a separate color from the default palette). To retain the previous behavior, explicitly assign a redundant hue variable (e.g., boxplot(data, x="x", y="y", hue="x")).

Two related idiosyncratic color specifications are deprecated, but they will continue to work (with a warning) for one release cycle:

  • Passing a palette without explicitly assigning hue is no longer supported (add an explicitly redundant hue assignment instead).
  • Passing a color while assigning hue to produce a gradient is no longer supported (use palette="dark:{color}" or palette="light:{color}" instead).

Finally, like other seaborn functions, the default palette now depends on the variable type, and a sequential palette will be used with numeric data. To retain the previous behavior, pass the name of a qualitative palette (e.g., palette="deep" for seaborn's default). Accordingly, the functions have gained a parameter to control numeric color mappings (hue_norm).

Other features, enhancements, and changes

The following updates apply to multiple categorical functions.

  • All functions now accept a legend parameter, which can be a boolean (to suppress the legend) or one of {"auto", "brief", "full"} to control the amount of information shown in the legend for a numerical color mapping.
  • All functions now accept a callable formatter parameter to control the string representation of the data.
  • All functions that draw a solid patch now accept a boolean fill parameter, which when set to False will draw line-art elements.
  • All functions that support dodging now have an additional gap parameter that can be set to a non-zero value to leave space between dodged elements.
  • The boxplot, boxenplot, and violinplot functions now support a single linecolor parameter.
  • The default value for dodge has changed from True to "auto". With "auto", elements will dodge only when at least one set of elements would otherwise overlap.
  • When the value axis of the plot has a non-linear scale, the statistical operations (e.g. an aggregation in pointplot or the kernel density fit in violinplot) are now applied in that scale space.
  • All functions now accept a log_scale parameter. With a single argument, this will set the scale on the "value" axis (opposite the categorical axis). A tuple will set each axis directly (although setting a log scale categorical axis also requires native_scale=True).
  • The orient parameter now accepts "x"/"y" to specify the categorical axis, matching the objects interface.
  • The categorical functions are generally more deferential to the user's additional matplotlib keyword arguments.
  • Using "gray" to select an automatic gray value that complements the main palette is now deprecated in favor of "auto".

The following updates are function-specific.

  • In pointplot, a single matplotlib.lines.Line2D artist is now used rather than adding separate matplotlib.collections.PathCollection artist for the points. As a result, it is now possible to pass additional keyword arguments for complete customization the appearance of both the lines and markers; additionally, the legend representation is improved. Accordingly, parameters that previously allowed only partial customization (scale, join, and errwidth) are now deprecated. The old parameters will now trigger detailed warning messages with instructions for adapting existing code.
  • The bandwidth specification in violinplot better aligns with kdeplot, as the bw parameter is now deprecated in favor of bw_method and bw_adjust.
  • In boxenplot, the boxen are now drawn with separate patch artists in each tail. This may have consequences for code that works with the underlying artists, but it produces a better result for low-alpha / unfilled plots and enables proper area/density scaling.
  • In barplot, the errcolor and errwidth parameters are now deprecated in favor of a more general err_kws` dictionary. The existing parameters will continue to work for two releases.

... (truncated)

Commits
  • f2abeb0 Update version metadata to 0.13.0
  • d657c54 Merge branch 'master' into v0.13
  • a8b6cac Assorted small doc updates (#3502)
  • 4888797 Avoid a matplotlib warning for strip/swarmplot with unfilled marker (#3501)
  • 782b71e Address a couple of warnings that turned up only in the docs (#3500)
  • 6b9e2fb Fix catplot with kind='point' to avoid color warning (#3499)
  • 43d762e Set version string for v0.13.0rc0
  • 0d46709 Postpone some removals of deprecated features (#3497)
  • b2f0de0 Update release notes
  • 92c22bc Restore implicit hue for wide categorical data (#3496)
  • Additional commits viewable in compare view


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dependabot[bot] commented 11 months ago

Superseded by #48.