Iguanas

Added:
- Added `Bounds` class to `metrics` module.
- Added examples in docstrings of all classes.
- Added `warnings` modules for custom warnings.
- Added `infer_dtypes` parameter to rule generator classes. If set to `True`, column datatypes are inferred from the data (same as previous versions); if set to `False`, the datatypes from the dataset are used.

Changed:
- Allow empty `Rules` class to be created.

Improvements:
- Added the following operators to those supported by rule converters: `greater_field`, `greater_or_equal_field`, `less_field`, `less_or_equal_field`.
- Parallelised `DirectSearchOptimiser` class.
- Added try/except to `ParallelPipeline` to allow it to run even when a step fails to generate a rule set.
- Added verbosity > 1 to `BayesSearchCV`.

Improvements
* Added verbosity to `LinearPipeline` and `ParallelPipeline` classes.
* Added check for duplicate columns in `X` or `X_rules` for relevant classes - an exception is now thrown if duplicate columns are found.
* Updated rule optimiser classes to include non-optimisable rules in the final `rule_strings` attribute (this ensures that these rules remain in the set when a rule optimiser is used in a pipeline)

Changes
* In the rule optimiser, rules which use exclusively all null features are allocated to the zero variance group, rather than the non-optimisable group.
* Updated the `create_x0`, `create_bounds` and `create_initial_simplexes` methods in the `DirectSearchOptimiser` class so that `np.nan` values are converted to 0.
* Added pre- and post-optimisation methods to `_base_pipeline`.
* Updated documentation and notebooks to reflect changes.

Changed
* Updated URLs in `LICENSE` to use https.

Fixes
* Updated `_calc_tps_fps_tns_fns_numpy` to use `.to_numpy()` when converting Pandas objects to numpy - fixes error seen when Pandas objects of `Int` dtype are used.

Fixes:
* Fixed error thrown when converting rules with exponentials (e.g. `1e-05`) present in a condition from string to dictionary format.

Added:
* Added `ParallelPipeline` class to pipeline module & unit tests.
* Added `rules` parameter to filter modules, allowing a given rule set to be filtered.
* Added `rules` parameter to `RBSOptimiser`, allowing a given rule set to be filtered to the rules remaining after optimisation.
* Added `rules` attribute to rule generators (which is a `Rules` class containing the generated rules).
* Added example notebook for `ParallelPipeline` class.
* Added advanced example notebook for `BayesSearchCV` class.
* Added `__repr__` to `AgglomerativeClusteringReducer` class.

Changed:
* The `get_params` method in the `LinearPipeline` and `ParallelPipeline` classes now returns a dictionary of parameters and their values for each pipeline step.
* Removed `num_cores` parameter from `RBSPipeline` and `RBSOptimiser` classes (as it was ignored anyway).
* Updated `LinearPipeline` and `BayesSearchCV` example notebook with diagrams.
* Relaxed versions of packages required in setup.py.

Improvements:
* Classes in later stages of a `LinearPipeline` can now use the initial datasets if required (e.g. if a rule optimiser is placed after a rule generator, the rule optimiser can be configured to use the initial feature set to optimise the generated rules). See the `use_init_data` param in `LinearPipeline` class.
* `ClassAccessor` classes can now be used in parameters that are mutable iterables - this allows attributes from classes earlier in a pipeline to be passed to parameters of classes later in the pipeline that are mutable iterables (e.g. lists).
* Users can set whether they want to use the `sample_weight` when calculating the `metric` of the validation fold in the `BayesSearchCV` class (prior to this, the `sample_weight` was always used if provided). See the `sample_weight_in_val` parameter in the `BayesSearchCV` class.
* `Rules` classes can now be combined using `+` and `sum()`.

Fixes:
* Updated rule generators so rule names remain constant if the `fit` method is applied more than once after class instantiation.
* `int` values are now properly parsed when converting from `rule_strings` to `rule_dicts` in the `Rules` class.
* Added a check for the parameter name when updating a class parameter in a `LinearPipeline` or `ParallelPipeline` - if the parameter does not exist, an error is thrown (prior to this, no error was thrown).
* Updated the `RBSPipeline` class to return the index of `X_rules` in the prediction.

Added:
* Code of conduct
* Issue templates
* Contributing guidelines
* rule_names check in rule generator/optimiser unit tests
* Added Github workflow for CI/CD for Ubuntu and Mac OS

Changed:
* Reduced rule generator naming methods to one method in _BaseGenerator
* Rule generators calculate rule_lambdas at the end
* Removed TypeVars from iguanas.utils.typing, replaced with strings. Added unit tests.

Improvements:
* Reduced runtime of RBSPipeline
* Updated wording of introduction in documentation
* Updated test_bayes_search_cv to cover all methods

Fixes:
* Bug in AgglomerativeClusteringReducer which duplicated column names in cols_to_keep when cols_to_drop was empty
* Bug in BayesSearchCV which omitted all but one param from best_params when multiple params were including in search_space for a given pipeline step