Evidently

**Colour Scheme**
Support for custom colours in the Dashboards:
- primary_color
- secondary_color
- current_data_color
- reference_data_color
- color_sequence
- fill_color
- zero_line_color
- non_visible_color
- underestimation_color
- overestimation_color
- majority_color

**Statistical Test:**
Support for user implemented statistical tests
Support for custom statistical tests in Dashboards and Profiles
Available tests:
- 'ks'
- 'z'
- 'chisquare'
- 'jensenshannon'
- 'kl_div'
- 'psi'
- 'wasserstein'
more info: [docs](https://github.com/evidentlyai/evidently/blob/main/docs/book/customization/options-for-statistical-tests.md)

**Fixes:**
193

Custom Text Comments in Dashboards
- Added type type="text” for BaseWidgetInfo (for text widgets implementation)
- Markdown syntax is supported

see the example: https://github.com/evidentlyai/evidently/blob/main/examples/how_to_questions/text_widget_usage_iris.ipynb

- Data Quality Dashboard: add dataset overview widget
- Data Quality Dashboard: add correlations widget
- Speeded uploading via preview plots optimisation
- Paging in Data Quality feature table widget

- DataQualityTab() is now available for Dashboards! The Tab helps to observe data columns, explore their properties and compare datasets.
- DataQualityProfileSection() is available for Profiles as well.
- ColumnMapping update: added task parameter to specify the type of machine learning problem. This parameter is used by DataQualityAnalyzer and some data quality widgets to calculate metrics and visuals with the respect to the task.

- Added monitors for NumTargetDrift, CatTargetDrift, ClassificationPerformance, ProbClassificationPerformance
- Fixed RegressionPerformance monitor
- Supported strings as a categorical features in DataDrift and CatTargetDrift dashboards
- Supported boolean features in DataDrift dashboard

Analyzers Refactoring: analyzer result became a structured object instead of a dictionary for all Analyzers

The following Quality Metrics Options are added:
- conf_interval_n_sigmas (the width of confidence intervals ): int = DEFAULT_CONF_INTERVAL_SIZE
- classification_treshold (the threshold for true labels): float = DEFAULT_CLASSIFICATION_TRESHOLD
- cut_quantile (cut the data by right, left and two-sided quantiles): Union[None, Tuple[str, float], Dict[str, Tuple[str, float]]] = None