Now tuner uses metrics that not compatible with composer metrics. Also it has a monolith structure.

What should be done:

• Union metrics for composer and tuner
• Make tuner more flexible using decomposition
• Implement self._objective as separate module
• Refactor view of model parameters. Now they become too complicated to store them into dict (see Redesign representation of tunable model parameters FEDOT#862)

UPD (from @gkirgizov) Additional subtasks:

• HyperOpt.get_metric_value seems to duplicate logic of PipelineObjectiveEvaluate that's concerned with how to validate the model -- so it's better to use it instead.
• Abstract Tuner to arbitrary Graphs.
  The only place where the specifics of Pipeline are required -- is for evaluation of objective. But, I think, currently this can be encapsualted with Objective/ObjectiveEvaluate classes.

Regarding 1st and 3rd points by Valera, possibly Objective class is useful here.