Vectorize metric computation

[Mr-Geekman]

🚀 Feature Request

Currently our metrics are calculated in per-segment fashion. We could fix it to optimize metric computation speed.

Proposal

Add new parameter to Metric: metric_fn_signature. It could have two possible values:

• array_to_float: default, it expects two vectors and returns float,
• matrix_to_array: it expects two matrices and returns vector.

Rework functional metrics to work with new matrix mode by checking the input shape:

• mape
• smape
• sign
• max_deviation
• wape

Rework all current metrics (except for interval metrics) to work in matrix mode. For metrics based on sklearn we should use multioutput parameter.

To discuss:

• Could we add more metrics? For example, we could follow this logic for Sign, Width.
• Setting multioutput parameter manually could interfere with self.kwargs. What are we going to do about it?

Test cases

• Make sure current tests pass
• Fix test_metrics_values
• Rework tests for functional metrics

Additional context

Related: #1338.