Evidently

- 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

Added backward compatibility for imports:
- Widgets and Tabs can be imported from evidently directly, but this is deprecated behavior and cause warning
- Sections can be imported from evidently directly, but this is deprecated behavior and cause warning

- Library source code is moved to the src/evidently folder
- Docs, Tests, and Examples are moved to the top level of the repo
- Widgets and Tabs are moved inside of the src/evidently/dashboard folder, as those are parts of the Dashboard
- Sections are moved inside of the src/evidently/model_profile folder, as those are parts of the Model_profiles
- Docs are stored in the repo: docs/book folder
- DataDriftAnalyzer refactoring: analyzer results became a structured object instead of a dictionary

* fixed: input DataFrames cannot be changed during any calculations (fixed by making shallow copies)
* fixed: chi-square statistical test uses normalized frequencies (with respect to the latest scipy version)
* current dataset is optional for Performace Tabs and Sections calculation (None value can be passed)
* improved readme

**Data Drift Options**:
* Created confidence: Union[float, Dict[str, float]] - option can take a float or a dict as an argument. If float has passed, then this confidence level will be used for all features. If dict has passed, then specified features will have a custom confidence levels (all the rest will have default confidence level = 0.95)
* Updated nbinsx: Union[int, Dict[str, int]] - option can take an int or a dict as an argument. If int has passed, then this number of bins will be used for all features. If dict has passed, then specified features will have a custom number of bins (all the rest will have default number of bins = 10)
* Updated feature_stattest_func: Union[None, Callable, Dict[str, Callable]] - option can take a function or a dict as an argument. If a function has passed, then this function will be used to measure drift for all features. If dict has passed, then for specified features custom functions will be used (all the rest features will be processed by an internal algorithm of drift measurement)

**Package building**:
* Fixed dependencies