Fix: dependency on PyYaml version 6.0

Removed PyYAML~=6.0 from the dependencies

Code Style and Object Types Checks

• Added flake8 for code style checks
• Added mypy for object types checks
• Improved code style
• Added contribution guide

Support for widget visualization in Colab, Pylab, Kaggle, Deepnote

1. Supported dashboard visualization in Google Colab
2. Supported dashboard visualization in python Pylab
3. Added a parameter mode for dashboard.show(), which can take the following options:

• auto - the default option. Ideally, you will not need to specify the value for mode and use the default. But, if it does not work (in case we failed to determine the environment automatically), consider setting the correct value explicitly.
• nbextention - to show the UI using nbextension. Use this option to display dashboards in jupyter notebooks (should work automatically).
• inline - to insert the UI directly into the cell. Use this option for Google Colab, Kaggle Kernels and Deepnote. For Google Colab this should work automatically, for Kaggle Kernels and Deepnote option should be specified explicitly.

Support for widget visualization in Colab and Pylab

• Supported dashboard visualization in Google Colab
• Supported dashboard visualization in python Pylab
• Added a parameter to switch on pylab visualization model: dashboard.show(mode='pylab')

Data Drift Monitoring with Grafana

• Added a way to calculate metrics with moving window
• Added metrics logging to Prometheus
• Added an example of Data Drift Monitoring with Prometheus and Grafana
• Added a config for Data drift dashboard at Grafana

Fix: Regression Performance Dashboard

Fix for #61

UI and architecture update

• Added the source code for the UI (now it can be built from the source)
• Created utils.py with helper functions
• Added config for Pylint
• Added some unit tests

Dataset Drift and Customised Bins

Added the following options to configure data drift report:

• 'drift_conf_level' confidence level for the individual features (default value = 0.95)
• 'drift_features_share' - share of the drifted features to detect dataset drift (default value = 0.5)
• 'xbins' - the custom bins to plot in the datadrift table
• 'nbinsx' - the custom number of bins to plot in the datadrift table

If share of the features drifted at the 'drift_conf_level' confidence level is higher than the 'drift_features_share' threshold, than Dataset drift will be detected. Otherwise Dataset drift will not be detected.

Telemetry for CLI

• When you use Evidently in the command-line interface, we collect basic telemetry. It includes data on the environment (e.g. Python version) and usage (type of report or profile generated).
• Our telemetry is intentionally limited in scope. We DO NOT collect any sensitive information and never see the data, its structure, or column names.
• You can read more about what we collect here.
• You can opt-out from telemetry collection by setting the environment variable EVIDENTLY_DISABLE_TELEMETRY=1

Proportion Difference Test

• Added proportion difference test for binary categorical target/prediction drift
• Added proportion difference test for data drift (categorical features)