Evidently

Fix for https://github.com/evidentlyai/evidently/issues/61

- 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

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.

* 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](https://docs.evidentlyai.com/support/telemetry).
* You can opt-out from telemetry collection by setting the environment variable EVIDENTLY_DISABLE_TELEMETRY=1

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

- Fixed Regression Performance Analyzer (underperformance segments table)
- Fixed Prob Classification Performance Analyzer (precision-recall table)
- Fixed Classification Performance Analyzer (Classification Confusion Based Feature Distribution Table)
- Updated CatTargetDriftTab to use analyzers
- Updated NumTargetDriftTab to use analyzers
- Updated RegressionRerformanceTab to use analyzers
- Updated ClassificationPerformanceTab to use analyzers
- Updated ProbClassificationPerformanceTab to use Analyzers