Shapash

Fix maximum version for category_encoders waiting for change to adapt new version 2.6.x:
418 maximum version for category_encoders

**This patch release fixes several bugs on webapp:**
403 Webapp : when zooming, labels keeps the short format with "..."
405 Minor bug on Webapp, When filter and zoom on contribution_plot for global population
406 Webapp: A small bug with groups of variables and selecting a point
415 Webapp: bug when click on a single sample, it removes the sub-selection of the feature importance

**And fix maximum version for sklearn waiting for change to adapt new version 1.2.x:**
414 maximum version for sklearn

These 2 new features are designed to **select samples in the Webapp**
- With a new tab "Dataset Filter" to filter more easily with the characteristics of the features
- With a graph that represents the "True values vs Predicted values"

✨ Features
389 Webapp: Improve the top menu for class selection
388 Create to tab which contains prediction picking graph and connexion with other graph
https://github.com/MAIF/shapash/issues/387 add responsive titles, subtitles, axis titles and axis labels to each graph
https://github.com/MAIF/shapash/issues/386 Add explanation button and popup
https://github.com/MAIF/shapash/issues/385 Adapt the labels of graphs according to their size
https://github.com/MAIF/shapash/issues/384 Add tab that contains dataset filters
https://github.com/MAIF/shapash/issues/378 Adding a plot to the webapp and interactivity with other plots
https://github.com/MAIF/shapash/issues/377 Add of a prediction error distribution graph

✨ **Features**
New feature 376
Clustering of the correlation matrix in order to visualize correlations between variables easily.

**New support in Python version**
:arrow_up: Support to Python 3.10 297
:arrow_down: Stop to support Python 3.6 297

**Upgrade dependencies**
:arrow_up: scikit-learn>=0.24.0 297
:arrow_up: acv-exp>=1.2.0 297
:arrow_up: category_encoders>=2.2.2 372

✨ **Features**
New feature 364
Pairwise comparison of Consistency : How are differences in contributions distributed across features ?