From 4ef1c8e5596472ceeed9bf2544faedba98ca7f10 Mon Sep 17 00:00:00 2001
From: Peter Shevcnenko <57573631+MorrisNein@users.noreply.github.com>
Date: Wed, 6 Dec 2023 14:55:55 +0300
Subject: [PATCH] Metrics fixes, tests, refactoring, typing improvement (#1214)

* fix f1 & precision evaluation

* rem flattening from QualityMetric._simple_prediction

* fix metrics hierarchy

* improve typing of QualityMetricCallable

* add tests for all supported types of metrics

* return **kwargs to complexity metrics

* use MetricCallable where it is appropriate

* tests: simplify regression pipeline, type hints & additional assertion

* remove __self__ from MetricCallable definition;
remove default value for metric_by_id

* rename & redo repository methods

* rename MetricType -> MetricIDType

* reorder module

* rename metrics repo module

* rename metrics test module

* update test data pipeline pickle

* correct typos

* adapt documentation

* fix inheritance

* add kwargs and metric() to complexity metrics

* better types for metrics_repository.py

* combine all metrics to a single test; add default values check

* unwrap pipeline predictions for time series

* fix confusing naming

* add fixture for expected values for faster tests

* remove test for binary classification

* add a less strict test for binary classification

* fix random seed for binary classification

* delete simple test for binary classification

* delete binary test from `test_metrics`

* fix structural_analysis_example.py

* make `test_metrics` fall if `update_expected_values=True`

* fix unused imports

* add `str` to `MetricIDType`

---------

Co-authored-by: kasyanovse <kus7466@gmail.com>
---
 .../credit_scoring_problem_multiobj.py        |   2 +-
 .../evo_operators_comparison.py               |   2 +-
 .../river_level_case_composer.py              |   2 +-
 .../river_level_case_manual.py                |   2 +-
 .../advanced/hyperparameters_tuning.rst       |  12 +-
 docs/source/api/repository.rst                |   2 +-
 .../classification_refinement_example.py      |   2 +-
 .../regression_refinement_example.py          |   2 +-
 .../multitask_classification_regression.py    |   2 +-
 examples/advanced/pipeline_sensitivity.py     |   8 +-
 .../structural_analysis_example.py            |  25 +-
 .../composing_pipelines.py                    |   2 +-
 .../custom_model_tuning.py                    |   2 +-
 .../multi_ts_arctic_forecasting.py            |   2 +-
 .../sparse_lagged_tuning.py                   |   2 +-
 .../classification_with_tuning.py             |   2 +-
 .../classification/multiclass_prediction.py   |   2 +-
 .../simple/classification/resample_example.py |   2 +-
 examples/simple/pipeline_tune.py              |   2 +-
 examples/simple/pipeline_tuning_with_iopt.py  |   2 +-
 .../regression/regression_with_tuning.py      |   2 +-
 .../tuning_pipelines.py                       |   2 +-
 fedot/api/api_utils/api_composer.py           |   4 +-
 fedot/api/builder.py                          |  16 +-
 fedot/api/main.py                             |   5 +-
 fedot/core/composer/composer_builder.py       |  12 +-
 fedot/core/composer/metrics.py                | 163 ++++++-------
 fedot/core/operations/atomized_model.py       |  20 +-
 .../optimisers/objective/metrics_objective.py |   6 +-
 fedot/core/pipelines/pipeline.py              |   3 +
 fedot/core/pipelines/tuning/tuner_builder.py  |   6 +-
 ...cs_repository.py => metrics_repository.py} |  54 +++--
 fedot/utilities/define_metric_by_task.py      |   4 +-
 test/data/expected_metric_values.json         |  65 +++++
 test/integration/composer/test_composer.py    |   2 +-
 test/integration/composer/test_history.py     |   9 +-
 test/integration/optimizer/test_evaluation.py |   2 +-
 .../pipelines/tuning/test_pipeline_tuning.py  |   2 +-
 .../pipelines/tuning/test_tuner_builder.py    |   4 +-
 test/integration/validation/test_table_cv.py  |   2 +-
 test/unit/api/test_api_params.py              |   2 +-
 test/unit/composer/test_metrics.py            | 227 ++++++++++++++++++
 test/unit/composer/test_quality_metrics.py    | 119 ---------
 .../optimizer/test_pipeline_objective_eval.py |   4 +-
 .../data/pred_ints_model_test.pickle          | Bin 171132 -> 173414 bytes
 .../test_solver_mutations.py                  |   8 +-
 test/unit/tasks/test_regression.py            |  10 +-
 test/unit/validation/test_table_cv.py         |   2 +-
 test/unit/validation/test_time_series_cv.py   |   4 +-
 49 files changed, 510 insertions(+), 328 deletions(-)
 rename fedot/core/repository/{quality_metrics_repository.py => metrics_repository.py} (60%)
 create mode 100644 test/data/expected_metric_values.json
 create mode 100644 test/unit/composer/test_metrics.py
 delete mode 100644 test/unit/composer/test_quality_metrics.py

diff --git a/cases/credit_scoring/credit_scoring_problem_multiobj.py b/cases/credit_scoring/credit_scoring_problem_multiobj.py
index 01fccbb658..4a104531cb 100644
--- a/cases/credit_scoring/credit_scoring_problem_multiobj.py
+++ b/cases/credit_scoring/credit_scoring_problem_multiobj.py
@@ -15,7 +15,7 @@
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.operation_types_repository import get_operations_for_task
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 
diff --git a/cases/evo_operators_comparison/evo_operators_comparison.py b/cases/evo_operators_comparison/evo_operators_comparison.py
index c9554b7cea..1a89fe81be 100644
--- a/cases/evo_operators_comparison/evo_operators_comparison.py
+++ b/cases/evo_operators_comparison/evo_operators_comparison.py
@@ -16,7 +16,7 @@
 
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.repository.operation_types_repository import get_operations_for_task
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 
diff --git a/cases/river_levels_prediction/river_level_case_composer.py b/cases/river_levels_prediction/river_level_case_composer.py
index 138dd1efc9..b60f51e3cc 100644
--- a/cases/river_levels_prediction/river_level_case_composer.py
+++ b/cases/river_levels_prediction/river_level_case_composer.py
@@ -16,7 +16,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import \
+from fedot.core.repository.metrics_repository import \
     RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 
diff --git a/cases/river_levels_prediction/river_level_case_manual.py b/cases/river_levels_prediction/river_level_case_manual.py
index d63431e0ac..a85308ce73 100644
--- a/cases/river_levels_prediction/river_level_case_manual.py
+++ b/cases/river_levels_prediction/river_level_case_manual.py
@@ -11,7 +11,7 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 
 warnings.filterwarnings('ignore')
diff --git a/docs/source/advanced/hyperparameters_tuning.rst b/docs/source/advanced/hyperparameters_tuning.rst
index 65484a2a50..0a6c377130 100644
--- a/docs/source/advanced/hyperparameters_tuning.rst
+++ b/docs/source/advanced/hyperparameters_tuning.rst
@@ -378,7 +378,7 @@ Example for ``SimultaneousTuner``:
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+    from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task
 
     task = Task(TaskTypesEnum.classification)
@@ -466,7 +466,7 @@ Example for ``IOptTuner``:
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+    from fedot.core.repository.metrics_repository import RegressionMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task
 
     task = Task(TaskTypesEnum.regression)
@@ -517,7 +517,7 @@ Example for ``OptunaTuner``:
     from fedot.core.data.data import InputData
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+    from fedot.core.repository.metrics_repository import RegressionMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task
 
     task = Task(TaskTypesEnum.regression)
@@ -568,7 +568,7 @@ and obtain a list of tuned pipelines representing a pareto front after tuning.
     from fedot.core.pipelines.pipeline import Pipeline
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+    from fedot.core.repository.metrics_repository import RegressionMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task
 
     task = Task(TaskTypesEnum.regression)
@@ -603,7 +603,7 @@ Sequential tuning
     from fedot.core.data.data import InputData
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+    from fedot.core.repository.metrics_repository import RegressionMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
 
     task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=10))
@@ -663,7 +663,7 @@ Tuning of a node
     from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
     from fedot.core.pipelines.pipeline_builder import PipelineBuilder
     from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-    from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+    from fedot.core.repository.metrics_repository import RegressionMetricsEnum
     from fedot.core.repository.tasks import TaskTypesEnum, Task
     from test.integration.quality.test_synthetic_tasks import get_regression_data
 
diff --git a/docs/source/api/repository.rst b/docs/source/api/repository.rst
index 962d106259..1f2d3b58b4 100644
--- a/docs/source/api/repository.rst
+++ b/docs/source/api/repository.rst
@@ -16,7 +16,7 @@ Operation Types
 Quality metrics
 ---------------
 
-.. automodule:: fedot.core.repository.quality_metrics_repository
+.. automodule:: fedot.core.repository.metrics_repository
    :members:
    :no-undoc-members:
 
diff --git a/examples/advanced/decompose/classification_refinement_example.py b/examples/advanced/decompose/classification_refinement_example.py
index 5dc05ec479..1806571eb3 100644
--- a/examples/advanced/decompose/classification_refinement_example.py
+++ b/examples/advanced/decompose/classification_refinement_example.py
@@ -5,7 +5,7 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 
diff --git a/examples/advanced/decompose/regression_refinement_example.py b/examples/advanced/decompose/regression_refinement_example.py
index c7a3c93d81..e371375937 100644
--- a/examples/advanced/decompose/regression_refinement_example.py
+++ b/examples/advanced/decompose/regression_refinement_example.py
@@ -11,7 +11,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 
diff --git a/examples/advanced/multitask_classification_regression.py b/examples/advanced/multitask_classification_regression.py
index 9d3dda5719..e4f31ee7c5 100644
--- a/examples/advanced/multitask_classification_regression.py
+++ b/examples/advanced/multitask_classification_regression.py
@@ -12,7 +12,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import TaskTypesEnum, Task
 from fedot.core.utils import fedot_project_root
 
diff --git a/examples/advanced/pipeline_sensitivity.py b/examples/advanced/pipeline_sensitivity.py
index fe6ada2a7c..4ea82fb952 100644
--- a/examples/advanced/pipeline_sensitivity.py
+++ b/examples/advanced/pipeline_sensitivity.py
@@ -11,7 +11,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.repository.operation_types_repository import get_operations_for_task
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, MetricsRepository, \
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, MetricsRepository, \
     RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import default_fedot_data_dir, fedot_project_root
@@ -123,7 +123,7 @@ def run_class_scoring_case(is_composed: bool, path_to_save=None):
     train_data, test_data = get_scoring_data()
     task = Task(TaskTypesEnum.classification)
     # the choice of the metric for the pipeline quality assessment during composition
-    metric_function = MetricsRepository().metric_by_id(ClassificationMetricsEnum.ROCAUC_penalty)
+    metric_function = MetricsRepository.get_metric(ClassificationMetricsEnum.ROCAUC_penalty)
 
     if is_composed:
         case = 'scoring_composed'
@@ -141,7 +141,7 @@ def run_class_kc2_case(is_composed: bool = False, path_to_save=None):
     train_data, test_data = get_kc2_data()
     task = Task(TaskTypesEnum.classification)
     # the choice of the metric for the pipeline quality assessment during composition
-    metric_function = MetricsRepository().metric_by_id(ClassificationMetricsEnum.ROCAUC_penalty)
+    metric_function = MetricsRepository.get_metric(ClassificationMetricsEnum.ROCAUC_penalty)
 
     if is_composed:
         case = 'kc2_composed'
@@ -159,7 +159,7 @@ def run_regr_case(is_composed: bool = False, path_to_save=None):
     train_data, test_data = get_cholesterol_data()
     task = Task(TaskTypesEnum.regression)
     # the choice of the metric for the pipeline quality assessment during composition
-    metric_function = MetricsRepository().metric_by_id(RegressionMetricsEnum.RMSE)
+    metric_function = MetricsRepository.get_metric(RegressionMetricsEnum.RMSE)
 
     if is_composed:
         case = 'cholesterol_composed'
diff --git a/examples/advanced/structural_analysis/structural_analysis_example.py b/examples/advanced/structural_analysis/structural_analysis_example.py
index 1a58e5c0f7..482f92b538 100644
--- a/examples/advanced/structural_analysis/structural_analysis_example.py
+++ b/examples/advanced/structural_analysis/structural_analysis_example.py
@@ -1,15 +1,15 @@
 import os
 from copy import deepcopy
 from functools import partial
+from typing import Callable, Dict, List, Optional, Tuple
 
 from golem.core.dag.graph_verifier import GraphVerifier
-from golem.core.dag.verification_rules import has_one_root, has_no_cycle, has_no_isolated_components, \
-    has_no_isolated_nodes, has_no_self_cycled_nodes
+from golem.core.dag.verification_rules import (has_no_cycle, has_no_isolated_components, has_no_isolated_nodes,
+                                               has_no_self_cycled_nodes, has_one_root)
 from golem.core.optimisers.graph import OptGraph
 from golem.core.optimisers.objective import Objective
 from golem.structural_analysis.graph_sa.graph_structural_analysis import GraphStructuralAnalysis
 from golem.structural_analysis.graph_sa.sa_requirements import StructuralAnalysisRequirements
-from typing import Callable, Dict, Any, Optional, Tuple, List
 
 from examples.advanced.structural_analysis.dataset_access import get_scoring_data
 from examples.advanced.structural_analysis.pipelines_access import get_three_depth_manual_class_pipeline
@@ -20,8 +20,9 @@
 from fedot.core.pipelines.pipeline_advisor import PipelineChangeAdvisor
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.pipeline_node_factory import PipelineOptNodeFactory
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, QualityMetricsEnum, \
-    MetricsRepository
+from fedot.core.repository.metrics_repository import (ClassificationMetricsEnum, ComplexityMetricCallable,
+                                                      ComplexityMetricsEnum, MetricsRepository,
+                                                      QualityMetricCallable, QualityMetricsEnum)
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.utilities.project_import_export import DEFAULT_PATH
 
@@ -31,19 +32,20 @@ class SAObjective(Objective):
     This objective has to evaluate pipeline in __call__ method and have 'metrics' field to identify
     which metrics are optimized.
     """
+
     def __init__(self,
                  objective: Callable,
-                 quality_metrics: Dict[Any, Callable],
-                 complexity_metrics: Optional[Dict[Any, Callable]] = None,
+                 quality_metrics: Dict[QualityMetricsEnum, QualityMetricCallable],
+                 complexity_metrics: Optional[Dict[ComplexityMetricsEnum, ComplexityMetricCallable]] = None,
                  is_multi_objective: bool = False,
                  ):
         self.objective = objective
         super().__init__(quality_metrics=quality_metrics, complexity_metrics=complexity_metrics,
                          is_multi_objective=is_multi_objective)
 
-    def __call__(self, graph: OptGraph) -> float:
+    def __call__(self, graph: OptGraph, **kwargs) -> float:
         pip = PipelineAdapter().restore(graph)
-        return self.objective(pip)
+        return self.objective(pip, **kwargs)
 
 
 def structural_analysis_set_up(train_data: InputData, test_data: InputData,
@@ -53,11 +55,12 @@ def structural_analysis_set_up(train_data: InputData, test_data: InputData,
         -> Tuple[PipelineOptNodeFactory, SAObjective, SAObjective]:
     """ Build initial infrastructure for performing SA: node factory, objectives.
     Can be reused for other SA applications, appropriate parameters must be specified then. """
+
     def _construct_objective(data: InputData, metric: QualityMetricsEnum) -> SAObjective:
         """ Build objective function with fit and predict functions inside. """
-        metric_func = MetricsRepository.metric_by_id(metric)
+        metric_func = MetricsRepository.get_metric(metric)
         get_value = partial(metric_func, reference_data=data)
-        metrics_ = {metric: data}
+        metrics_ = {metric: metric_func}
 
         data_producer = DataSourceSplitter().build(data=data)
         objective_function = PipelineObjectiveEvaluate(objective=Objective(quality_metrics=get_value),
diff --git a/examples/advanced/time_series_forecasting/composing_pipelines.py b/examples/advanced/time_series_forecasting/composing_pipelines.py
index f5d25cda85..a2cacd3e20 100644
--- a/examples/advanced/time_series_forecasting/composing_pipelines.py
+++ b/examples/advanced/time_series_forecasting/composing_pipelines.py
@@ -16,7 +16,7 @@
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import \
+from fedot.core.repository.metrics_repository import \
     RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
 from fedot.core.utils import fedot_project_root
diff --git a/examples/advanced/time_series_forecasting/custom_model_tuning.py b/examples/advanced/time_series_forecasting/custom_model_tuning.py
index e8b97f19ad..32f0c5d078 100644
--- a/examples/advanced/time_series_forecasting/custom_model_tuning.py
+++ b/examples/advanced/time_series_forecasting/custom_model_tuning.py
@@ -12,7 +12,7 @@
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
 
 
diff --git a/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py b/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
index 36aa166bec..9c4f309cab 100644
--- a/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
+++ b/examples/advanced/time_series_forecasting/multi_ts_arctic_forecasting.py
@@ -15,7 +15,7 @@
 from fedot.core.composer.composer_builder import ComposerBuilder
 from fedot.core.composer.gp_composer.specific_operators import parameter_change_mutation
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import \
+from fedot.core.repository.metrics_repository import \
     RegressionMetricsEnum
 
 
diff --git a/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py b/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
index d3d0d77690..ddbf36e46e 100644
--- a/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
+++ b/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
@@ -12,7 +12,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
 from fedot.core.utils import fedot_project_root
 
diff --git a/examples/simple/classification/classification_with_tuning.py b/examples/simple/classification/classification_with_tuning.py
index 1eef76d04c..c1b024c91f 100644
--- a/examples/simple/classification/classification_with_tuning.py
+++ b/examples/simple/classification/classification_with_tuning.py
@@ -7,7 +7,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 from fedot.utilities.synth_dataset_generator import classification_dataset
diff --git a/examples/simple/classification/multiclass_prediction.py b/examples/simple/classification/multiclass_prediction.py
index cb0a69457c..eca3ca3897 100644
--- a/examples/simple/classification/multiclass_prediction.py
+++ b/examples/simple/classification/multiclass_prediction.py
@@ -16,7 +16,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.repository.operation_types_repository import OperationTypesRepository
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import (
     ensure_directory_exists,
diff --git a/examples/simple/classification/resample_example.py b/examples/simple/classification/resample_example.py
index 27259974c3..fdf505526b 100644
--- a/examples/simple/classification/resample_example.py
+++ b/examples/simple/classification/resample_example.py
@@ -12,7 +12,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import TaskTypesEnum, Task
 from fedot.core.utils import fedot_project_root
 
diff --git a/examples/simple/pipeline_tune.py b/examples/simple/pipeline_tune.py
index f6902ee9e8..66ce2608d8 100644
--- a/examples/simple/pipeline_tune.py
+++ b/examples/simple/pipeline_tune.py
@@ -9,7 +9,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 
 
 def get_case_train_test_data():
diff --git a/examples/simple/pipeline_tuning_with_iopt.py b/examples/simple/pipeline_tuning_with_iopt.py
index 1665514c81..dee9153183 100644
--- a/examples/simple/pipeline_tuning_with_iopt.py
+++ b/examples/simple/pipeline_tuning_with_iopt.py
@@ -7,7 +7,7 @@
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import TaskTypesEnum, Task
 from fedot.core.utils import fedot_project_root
 
diff --git a/examples/simple/regression/regression_with_tuning.py b/examples/simple/regression/regression_with_tuning.py
index 665a6725bd..88acfbb903 100644
--- a/examples/simple/regression/regression_with_tuning.py
+++ b/examples/simple/regression/regression_with_tuning.py
@@ -9,7 +9,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 from fedot.utilities.synth_dataset_generator import regression_dataset
diff --git a/examples/simple/time_series_forecasting/tuning_pipelines.py b/examples/simple/time_series_forecasting/tuning_pipelines.py
index 18d12ec7a9..4127dc6b4f 100644
--- a/examples/simple/time_series_forecasting/tuning_pipelines.py
+++ b/examples/simple/time_series_forecasting/tuning_pipelines.py
@@ -10,7 +10,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
 from fedot.core.utils import fedot_project_root
 
diff --git a/fedot/api/api_utils/api_composer.py b/fedot/api/api_utils/api_composer.py
index 7bb1f2dabf..5a0545a85b 100644
--- a/fedot/api/api_utils/api_composer.py
+++ b/fedot/api/api_utils/api_composer.py
@@ -17,12 +17,12 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import MetricType
+from fedot.core.repository.metrics_repository import MetricIDType
 
 
 class ApiComposer:
 
-    def __init__(self, api_params: ApiParams, metrics: Union[str, MetricType, Sequence]):
+    def __init__(self, api_params: ApiParams, metrics: Union[MetricIDType, Sequence[MetricIDType]]):
         self.log = default_log(self)
         self.params = api_params
         self.metrics = metrics
diff --git a/fedot/api/builder.py b/fedot/api/builder.py
index 449b076833..52b8f85ca8 100644
--- a/fedot/api/builder.py
+++ b/fedot/api/builder.py
@@ -1,12 +1,12 @@
 from __future__ import annotations
 
-from typing import Any, Callable, Dict, List, Optional, Type, Union
+from typing import Any, Dict, List, Optional, Sequence, Type, Union
 
 from golem.core.optimisers.optimizer import GraphOptimizer
 
 from fedot.api.main import Fedot
 from fedot.core.pipelines.pipeline import Pipeline
-from fedot.core.repository.quality_metrics_repository import MetricsEnum
+from fedot.core.repository.metrics_repository import MetricIDType
 from fedot.core.repository.tasks import TaskParams
 
 
@@ -274,7 +274,7 @@ def setup_pipeline_structure(
 
     def setup_pipeline_evaluation(
             self,
-            metric: Union[str, Callable, MetricsEnum, List[Union[str, Callable, MetricsEnum]]] = DEFAULT_VALUE,
+            metric: Union[MetricIDType, Sequence[MetricIDType]] = DEFAULT_VALUE,
             cv_folds: int = DEFAULT_VALUE,
             max_pipeline_fit_time: Optional[int] = DEFAULT_VALUE,
             collect_intermediate_metric: bool = DEFAULT_VALUE,
@@ -287,19 +287,19 @@ def setup_pipeline_evaluation(
 
                 .. details:: Default value depends on a given task:
 
-                    - ``roc_auc`` -> for classification
+                    - ``roc_auc_pen`` -> for classification
                     - ``rmse`` -> for regression & time series forecasting
 
                 .. details:: Available metrics are listed in the following enumerations:
 
                     - classification -> \
-                        :class:`~fedot.core.repository.quality_metrics_repository.ClassificationMetricsEnum`
+                        :class:`~fedot.core.repository.metrics_repository.ClassificationMetricsEnum`
                     - regression -> \
-                        :class:`~fedot.core.repository.quality_metrics_repository.RegressionMetricsEnum`
+                        :class:`~fedot.core.repository.metrics_repository.RegressionMetricsEnum`
                     - time series forcasting -> \
-                        :class:`~fedot.core.repository.quality_metrics_repository.TimeSeriesForecastingMetricsEnum`
+                        :class:`~fedot.core.repository.metrics_repository.TimeSeriesForecastingMetricsEnum`
                     - pipeline complexity (task-independent) -> \
-                        :class:`~fedot.core.repository.quality_metrics_repository.ComplexityMetricsEnum`
+                        :class:`~fedot.core.repository.metrics_repository.ComplexityMetricsEnum`
 
             cv_folds: number of folds for cross-validation.
 
diff --git a/fedot/api/main.py b/fedot/api/main.py
index f1e6718adc..2c19b86f5f 100644
--- a/fedot/api/main.py
+++ b/fedot/api/main.py
@@ -1,6 +1,6 @@
 import logging
 from copy import deepcopy
-from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -26,6 +26,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.ts_wrappers import convert_forecast_to_output, out_of_sample_ts_forecast
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
+from fedot.core.repository.metrics_repository import MetricCallable
 from fedot.core.repository.tasks import TaskParams, TaskTypesEnum
 from fedot.core.utils import set_random_seed
 from fedot.explainability.explainer_template import Explainer
@@ -187,7 +188,7 @@ def fit(self,
 
     def tune(self,
              input_data: Optional[InputData] = None,
-             metric_name: Optional[Union[str, Callable]] = None,
+             metric_name: Optional[Union[str, MetricCallable]] = None,
              iterations: int = DEFAULT_TUNING_ITERATIONS_NUMBER,
              timeout: Optional[float] = None,
              cv_folds: Optional[int] = None,
diff --git a/fedot/core/composer/composer_builder.py b/fedot/core/composer/composer_builder.py
index a3eae7e1cf..73217e2575 100644
--- a/fedot/core/composer/composer_builder.py
+++ b/fedot/core/composer/composer_builder.py
@@ -6,8 +6,8 @@
 from golem.core.log import LoggerAdapter, default_log
 from golem.core.optimisers.genetic.gp_optimizer import EvoGraphOptimizer
 from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
-from golem.core.optimisers.initial_graphs_generator import InitialPopulationGenerator, GenerationFunction
-from golem.core.optimisers.optimizer import GraphOptimizer, AlgorithmParameters, GraphGenerationParams
+from golem.core.optimisers.initial_graphs_generator import GenerationFunction, InitialPopulationGenerator
+from golem.core.optimisers.optimizer import AlgorithmParameters, GraphGenerationParams, GraphOptimizer
 from golem.utilities.data_structures import ensure_wrapped_in_sequence
 
 from fedot.core.caching.pipelines_cache import OperationsCache
@@ -20,11 +20,7 @@
 from fedot.core.pipelines.pipeline_graph_generation_params import get_pipeline_generation_params
 from fedot.core.pipelines.verification import rules_by_task
 from fedot.core.repository.operation_types_repository import get_operations_for_task
-from fedot.core.repository.quality_metrics_repository import (
-    ComplexityMetricsEnum,
-    MetricsEnum,
-    MetricType
-)
+from fedot.core.repository.metrics_repository import ComplexityMetricsEnum, MetricIDType, MetricsEnum
 from fedot.core.repository.tasks import Task
 from fedot.remote.remote_evaluator import RemoteEvaluator
 from fedot.utilities.define_metric_by_task import MetricByTask
@@ -88,7 +84,7 @@ def with_graph_generation_param(self, graph_generation_params: GraphGenerationPa
         self.graph_generation_params = graph_generation_params
         return self
 
-    def with_metrics(self, metrics: Union[MetricType, List[MetricType]]):
+    def with_metrics(self, metrics: Union[MetricIDType, Sequence[MetricIDType]]):
         self.metrics = ensure_wrapped_in_sequence(metrics)
         return self
 
diff --git a/fedot/core/composer/metrics.py b/fedot/core/composer/metrics.py
index f0eb97f886..20c6fdd395 100644
--- a/fedot/core/composer/metrics.py
+++ b/fedot/core/composer/metrics.py
@@ -1,7 +1,9 @@
 import os.path
 import sys
 from abc import abstractmethod
+from functools import wraps
 from pathlib import Path
+from typing import Optional, Tuple
 from uuid import uuid4
 
 import numpy as np
@@ -14,13 +16,13 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.tasks import TaskTypesEnum
 from fedot.core.utils import default_fedot_data_dir
 from fedot.utilities.custom_errors import AbstractMethodNotImplementError
 from fedot.utilities.debug import is_analytic_mode
 
 
 def from_maximised_metric(metric_func):
+    @wraps(metric_func)
     def wrapper(*args, **kwargs):
         return -metric_func(*args, **kwargs)
 
@@ -38,23 +40,14 @@ def smape(y_true: np.ndarray, y_pred: np.ndarray) -> float:
 
 
 class Metric:
-    output_mode = 'default'
-    default_value = 0
-
     @classmethod
     @abstractmethod
-    def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
-                  validation_blocks: int) -> float:
-        """ Get metrics values based on pipeline and InputData for validation """
+    def get_value(cls, **kwargs) -> float:
+        """ Get metrics value based on pipeline and other optional arguments. """
         raise AbstractMethodNotImplementError
 
-    @staticmethod
-    @abstractmethod
-    def metric(reference: InputData, predicted: OutputData) -> float:
-        raise AbstractMethodNotImplementError
 
-
-class QualityMetric:
+class QualityMetric(Metric):
     max_penalty_part = 0.01
     output_mode = 'default'
     default_value = 0
@@ -65,13 +58,20 @@ def metric(reference: InputData, predicted: OutputData) -> float:
         raise AbstractMethodNotImplementError
 
     @classmethod
-    def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
-                  validation_blocks: int = None) -> float:
+    def get_value(cls, pipeline: Pipeline, reference_data: InputData,
+                  validation_blocks: Optional[int] = None) -> float:
+        """ Get metric value based on pipeline, reference data, and number of validation blocks.
+        Args:
+            pipeline: a :class:`Pipeline` instance for evaluation.
+            reference_data: :class:`InputData` for evaluation.
+            validation_blocks: number of validation blocks. Used only for time series forecasting.
+                If ``None``, data separation is not performed.
+        """
         metric = cls.default_value
         try:
             if validation_blocks is None:
                 # Time series or regression classical hold-out validation
-                results, reference_data = cls._simple_prediction(pipeline, reference_data)
+                reference_data, results = cls._simple_prediction(pipeline, reference_data)
             else:
                 # Perform time series in-sample validation
                 reference_data, results = cls._in_sample_prediction(pipeline, reference_data, validation_blocks)
@@ -95,43 +95,14 @@ def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
         return metric
 
     @classmethod
-    def _simple_prediction(cls, pipeline: 'Pipeline', reference_data: InputData):
-        """ Method prepares data for metric evaluation and perform simple validation """
-        results = pipeline.predict(reference_data, output_mode=cls.output_mode)
-
-        # Define conditions for target and predictions transforming
-        is_regression = reference_data.task.task_type == TaskTypesEnum.regression
-        is_multi_target = len(np.array(results.predict).shape) > 1
-        is_multi_target_regression = is_regression and is_multi_target
-
-        # Time series forecasting
-        is_ts_forecasting = reference_data.task.task_type == TaskTypesEnum.ts_forecasting
-        if is_ts_forecasting or is_multi_target_regression:
-            results, reference_data = cls.flatten_convert(results, reference_data)
-
-        return results, reference_data
-
-    @staticmethod
-    def flatten_convert(results, reference_data):
-        """ Transform target and predictions by converting them into
-        one-dimensional array
-
-        :param results: output from pipeline
-        :param reference_data: actual data for validation
-        """
-        # Predictions convert into uni-variate array
-        forecast_values = np.ravel(np.array(results.predict))
-        results.predict = forecast_values
-        # Target convert into uni-variate array
-        target_values = np.ravel(np.array(reference_data.target))
-        reference_data.target = target_values
-
-        return results, reference_data
+    def _simple_prediction(cls, pipeline: Pipeline, reference_data: InputData) -> Tuple[InputData, OutputData]:
+        """ Method calls pipeline.predict() and returns the result. """
+        return reference_data, pipeline.predict(reference_data, output_mode=cls.output_mode)
 
     @classmethod
-    def get_value_with_penalty(cls, pipeline: 'Pipeline', reference_data: InputData,
-                               validation_blocks: int = None) -> float:
-        quality_metric = cls.get_value(pipeline, reference_data)
+    def get_value_with_penalty(cls, pipeline: Pipeline, reference_data: InputData,
+                               validation_blocks: Optional[int] = None) -> float:
+        quality_metric = cls.get_value(pipeline, reference_data, validation_blocks)
         structural_metric = StructuralComplexity.get_value(pipeline)
 
         penalty = abs(structural_metric * quality_metric * cls.max_penalty_part)
@@ -140,7 +111,8 @@ def get_value_with_penalty(cls, pipeline: 'Pipeline', reference_data: InputData,
         return metric_with_penalty
 
     @staticmethod
-    def _in_sample_prediction(pipeline: 'Pipeline', data: InputData, validation_blocks: int):
+    def _in_sample_prediction(pipeline: Pipeline, data: InputData, validation_blocks: int
+                              ) -> Tuple[InputData, OutputData]:
         """ Performs in-sample pipeline validation for time series prediction """
 
         horizon = int(validation_blocks * data.task.task_params.forecast_length)
@@ -160,6 +132,14 @@ def _in_sample_prediction(pipeline: 'Pipeline', data: InputData, validation_bloc
 
         return reference_data, results
 
+    @staticmethod
+    def _get_least_frequent_val(array: np.ndarray):
+        """ Returns the least frequent value in a flattened numpy array. """
+        unique_vals, count = np.unique(np.ravel(array), return_counts=True)
+        least_frequent_idx = np.argmin(count)
+        least_frequent_val = unique_vals[least_frequent_idx]
+        return least_frequent_val
+
 
 class RMSE(QualityMetric):
     default_value = sys.maxsize
@@ -214,16 +194,12 @@ class F1(QualityMetric):
     @staticmethod
     @from_maximised_metric
     def metric(reference: InputData, predicted: OutputData) -> float:
-        n_classes = reference.num_classes
-        if n_classes > 2:
-            additional_params = {'average': F1.multiclass_averaging_mode}
+        if reference.num_classes == 2:
+            pos_label = QualityMetric._get_least_frequent_val(reference.target)
+            additional_params = dict(average=F1.binary_averaging_mode, pos_label=pos_label)
         else:
-            u, count = np.unique(np.ravel(reference.target), return_counts=True)
-            count_sort_ind = np.argsort(count)
-            pos_label = u[count_sort_ind[0]].item()
-            additional_params = {'average': F1.binary_averaging_mode, 'pos_label': pos_label}
-        return f1_score(y_true=reference.target, y_pred=predicted.predict,
-                        **additional_params)
+            additional_params = dict(average=F1.multiclass_averaging_mode)
+        return f1_score(y_true=reference.target, y_pred=predicted.predict, **additional_params)
 
 
 class MAE(QualityMetric):
@@ -259,15 +235,13 @@ class ROCAUC(QualityMetric):
     def metric(reference: InputData, predicted: OutputData) -> float:
         n_classes = reference.num_classes
         if n_classes > 2:
-            additional_params = {'multi_class': 'ovr', 'average': 'macro'}
+            additional_params = dict(multi_class='ovr', average='macro')
         else:
-            additional_params = {}
+            additional_params = dict()
 
-        score = round(roc_auc_score(y_score=predicted.predict,
-                                    y_true=reference.target,
-                                    **additional_params), 3)
-
-        return score
+        return roc_auc_score(y_score=predicted.predict,
+                             y_true=reference.target,
+                             **additional_params)
 
     @staticmethod
     def roc_curve(target: np.ndarray, predict: np.ndarray, pos_label=None):
@@ -281,20 +255,19 @@ def auc(cls, fpr, tpr):
 
 class Precision(QualityMetric):
     output_mode = 'labels'
+    binary_averaging_mode = 'binary'
+    multiclass_averaging_mode = 'macro'
 
     @staticmethod
     @from_maximised_metric
     def metric(reference: InputData, predicted: OutputData) -> float:
         n_classes = reference.num_classes
         if n_classes > 2:
-            return precision_score(y_true=reference.target, y_pred=predicted.predict)
+            additional_params = dict(average=Precision.multiclass_averaging_mode)
         else:
-            u, count = np.unique(np.ravel(reference.target), return_counts=True)
-            count_sort_ind = np.argsort(count)
-            pos_label = u[count_sort_ind[0]].item()
-            additional_params = {'pos_label': pos_label}
-            return precision_score(y_true=reference.target, y_pred=predicted.predict,
-                                   **additional_params)
+            pos_label = QualityMetric._get_least_frequent_val(reference.target)
+            additional_params = dict(pos_label=pos_label, average=Precision.binary_averaging_mode)
+        return precision_score(y_true=reference.target, y_pred=predicted.predict, **additional_params)
 
 
 class Logloss(QualityMetric):
@@ -324,21 +297,41 @@ def metric(reference: InputData, predicted: OutputData) -> float:
         return silhouette_score(reference.features, labels=predicted.predict)
 
 
-class StructuralComplexity(Metric):
+class ComplexityMetric(Metric):
+    default_value = 0
+    norm_constant = 1
+
+    @classmethod
+    def get_value(cls, pipeline: Pipeline, **kwargs) -> float:
+        """ Get metric value and apply norm_constant to it. """
+        return cls.metric(pipeline, **kwargs) / cls.norm_constant
+
+    @classmethod
+    @abstractmethod
+    def metric(cls, pipeline: Pipeline, **kwargs) -> float:
+        """ Get metrics value based on pipeline. """
+        raise AbstractMethodNotImplementError
+
+
+class StructuralComplexity(ComplexityMetric):
+    norm_constant = 30
+
     @classmethod
-    def get_value(cls, pipeline: 'Pipeline', **args) -> float:
-        norm_constant = 30
-        return (pipeline.depth ** 2 + pipeline.length) / norm_constant
+    def metric(cls, pipeline: Pipeline, **kwargs) -> float:
+        return pipeline.depth ** 2 + pipeline.length
+
 
+class NodeNum(ComplexityMetric):
+    norm_constant = 10
 
-class NodeNum(Metric):
     @classmethod
-    def get_value(cls, pipeline: 'Pipeline', **args) -> float:
-        norm_constant = 10
-        return pipeline.length / norm_constant
+    def metric(cls, pipeline: Pipeline, **kwargs) -> float:
+        return pipeline.length
 
 
-class ComputationTime(Metric):
+class ComputationTime(ComplexityMetric):
+    default_value = sys.maxsize
+
     @classmethod
-    def get_value(cls, pipeline: 'Pipeline', **args) -> float:
+    def metric(cls, pipeline: Pipeline, **kwargs) -> float:
         return pipeline.computation_time
diff --git a/fedot/core/operations/atomized_model.py b/fedot/core/operations/atomized_model.py
index 5fba8f6d44..3fce78bc22 100644
--- a/fedot/core/operations/atomized_model.py
+++ b/fedot/core/operations/atomized_model.py
@@ -2,18 +2,18 @@
 from datetime import timedelta
 from functools import reduce
 from operator import and_, or_
-from typing import Callable, Union, Optional, Set, List, Any, Dict
+from typing import Any, Callable, Dict, List, Optional, Set, Union
 
-from fedot.core.pipelines.node import PipelineNode
 from golem.core.tuning.simultaneous import SimultaneousTuner
 
 from fedot.core.data.data import InputData, OutputData
 from fedot.core.operations.operation import Operation
 from fedot.core.operations.operation_parameters import OperationParameters
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.operation_types_repository import OperationMetaInfo, \
-    atomized_model_type
+from fedot.core.repository.metrics_repository import MetricCallable
+from fedot.core.repository.operation_types_repository import OperationMetaInfo, atomized_model_type
 
 
 class AtomizedModel(Operation):
@@ -50,16 +50,16 @@ def predict_for_fit(self,
         return self.predict(fitted_operation, data, params, output_mode)
 
     def fine_tune(self,
-                  metric_function: Callable,
+                  metric_function: MetricCallable,
                   input_data: Optional[InputData] = None,
                   iterations: int = 50,
                   timeout: int = 5) -> 'AtomizedModel':
         """ Method for tuning hyperparameters """
-        tuner = TunerBuilder(input_data.task)\
-            .with_tuner(SimultaneousTuner)\
-            .with_metric(metric_function)\
-            .with_iterations(iterations)\
-            .with_timeout(timedelta(minutes=timeout))\
+        tuner = TunerBuilder(input_data.task) \
+            .with_tuner(SimultaneousTuner) \
+            .with_metric(metric_function) \
+            .with_iterations(iterations) \
+            .with_timeout(timedelta(minutes=timeout)) \
             .build(input_data)
         tuned_pipeline = tuner.tune(self.pipeline)
         tuned_atomized_model = AtomizedModel(tuned_pipeline)
diff --git a/fedot/core/optimisers/objective/metrics_objective.py b/fedot/core/optimisers/objective/metrics_objective.py
index c6b54448b0..d7ae719192 100644
--- a/fedot/core/optimisers/objective/metrics_objective.py
+++ b/fedot/core/optimisers/objective/metrics_objective.py
@@ -3,12 +3,12 @@
 from golem.core.optimisers.objective import Objective
 from golem.utilities.data_structures import ensure_wrapped_in_sequence
 
-from fedot.core.repository.quality_metrics_repository import MetricType, MetricsRepository, ComplexityMetricsEnum
+from fedot.core.repository.metrics_repository import MetricIDType, MetricsRepository, ComplexityMetricsEnum
 
 
 class MetricsObjective(Objective):
     def __init__(self,
-                 metrics: Union[MetricType, Iterable[MetricType]],
+                 metrics: Union[MetricIDType, Iterable[MetricIDType]],
                  is_multi_objective: bool = False):
         quality_metrics = {}
         complexity_metrics = {}
@@ -18,7 +18,7 @@ def __init__(self,
                 metric_id = str(metric)
                 quality_metrics[metric_id] = metric
             else:
-                metric_func = MetricsRepository.metric_by_id(metric)
+                metric_func = MetricsRepository.get_metric(metric)
                 if metric_func:
                     if ComplexityMetricsEnum.has_value(metric):
                         complexity_metrics[metric] = metric_func
diff --git a/fedot/core/pipelines/pipeline.py b/fedot/core/pipelines/pipeline.py
index c5a727108c..526fb263ba 100644
--- a/fedot/core/pipelines/pipeline.py
+++ b/fedot/core/pipelines/pipeline.py
@@ -274,6 +274,9 @@ def predict(self, input_data: Union[InputData, MultiModalData], output_mode: str
         copied_input_data = self._assign_data_to_nodes(copied_input_data)
         result = self.root_node.predict(input_data=copied_input_data, output_mode=output_mode)
 
+        if input_data.task.task_type == TaskTypesEnum.ts_forecasting:
+            result.predict = result.predict.ravel()
+
         result = self._postprocess(copied_input_data, result, output_mode)
         return result
 
diff --git a/fedot/core/pipelines/tuning/tuner_builder.py b/fedot/core/pipelines/tuning/tuner_builder.py
index c061768436..379028a959 100644
--- a/fedot/core/pipelines/tuning/tuner_builder.py
+++ b/fedot/core/pipelines/tuning/tuner_builder.py
@@ -1,5 +1,5 @@
 from datetime import timedelta
-from typing import Type, Union, Iterable, Sequence
+from typing import Iterable, Sequence, Type, Union
 
 from golem.core.tuning.optuna_tuner import OptunaTuner
 from golem.core.tuning.simultaneous import SimultaneousTuner
@@ -14,7 +14,7 @@
 from fedot.core.pipelines.adapters import PipelineAdapter
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
-from fedot.core.repository.quality_metrics_repository import MetricType, MetricsEnum
+from fedot.core.repository.metrics_repository import MetricIDType, MetricsEnum
 from fedot.core.repository.tasks import Task
 from fedot.utilities.define_metric_by_task import MetricByTask
 
@@ -55,7 +55,7 @@ def with_n_jobs(self, n_jobs: int):
         self.n_jobs = n_jobs
         return self
 
-    def with_metric(self, metrics: Union[MetricType, Iterable[MetricType]]):
+    def with_metric(self, metrics: Union[MetricIDType, Iterable[MetricIDType]]):
         self.metric = ensure_wrapped_in_sequence(metrics)
         return self
 
diff --git a/fedot/core/repository/quality_metrics_repository.py b/fedot/core/repository/metrics_repository.py
similarity index 60%
rename from fedot/core/repository/quality_metrics_repository.py
rename to fedot/core/repository/metrics_repository.py
index 537d87bd20..7c02d4b934 100644
--- a/fedot/core/repository/quality_metrics_repository.py
+++ b/fedot/core/repository/metrics_repository.py
@@ -1,15 +1,15 @@
-from numbers import Real
-from typing import Callable, Union, TypeVar
+from typing import Dict, Optional, Protocol, TypeVar, Union
 
-from golem.core.dag.graph import Graph
-from golem.utilities.data_structures import ComparableEnum as Enum
+from golem.utilities.data_structures import ComparableEnum
 
-from fedot.core.composer.metrics import (ComputationTime, Accuracy, F1, Logloss, MAE,
-                                         MAPE, SMAPE, MSE, MSLE, Metric, NodeNum, Precision, R2,
-                                         RMSE, ROCAUC, Silhouette, StructuralComplexity, MASE)
+from fedot.core.composer.metrics import (Accuracy, ComplexityMetric, ComputationTime, F1, Logloss, MAE, MAPE, MASE, MSE,
+                                         MSLE, NodeNum, Precision, QualityMetric, R2, RMSE, ROCAUC, SMAPE, Silhouette,
+                                         StructuralComplexity)
+from fedot.core.data.data import InputData
+from fedot.core.pipelines.pipeline import Pipeline
 
 
-class MetricsEnum(Enum):
+class MetricsEnum(ComparableEnum):
     def __str__(self):
         return self.value
 
@@ -18,13 +18,7 @@ def has_value(cls, value):
         return value in cls._value2member_map_
 
 
-G = TypeVar('G', bound=Graph, covariant=True)
-MetricCallable = Callable[[G], Real]
-MetricType = Union[MetricCallable, MetricsEnum]
-
-
-class QualityMetricsEnum(MetricsEnum):
-    pass
+class QualityMetricsEnum(MetricsEnum): pass
 
 
 class ComplexityMetricsEnum(MetricsEnum):
@@ -69,8 +63,25 @@ class TimeSeriesForecastingMetricsEnum(QualityMetricsEnum):
     RMSE_penalty = 'rmse_pen'
 
 
+NumberType = Union[int, float, complex]
+PipelineType = TypeVar('PipelineType', bound=Pipeline, covariant=True)
+
+
+class QualityMetricCallable(Protocol):
+    def __call__(self, pipeline: PipelineType, reference_data: InputData,
+                 validation_blocks: Optional[int] = None) -> NumberType: pass
+
+
+class ComplexityMetricCallable(Protocol):
+    def __call__(self, pipeline: PipelineType, **kwargs) -> NumberType: pass
+
+
+MetricCallable = Union[QualityMetricCallable, ComplexityMetricCallable]
+MetricIDType = Union[str, MetricsEnum, MetricCallable]
+
+
 class MetricsRepository:
-    _metrics_implementations = {
+    _metrics_implementations: Dict[MetricsEnum, MetricCallable] = {
         # classification
         ClassificationMetricsEnum.ROCAUC: ROCAUC.get_value,
         ClassificationMetricsEnum.ROCAUC_penalty: ROCAUC.get_value_with_penalty,
@@ -101,10 +112,13 @@ class MetricsRepository:
         ComplexityMetricsEnum.computation_time: ComputationTime.get_value
     }
 
+    _metrics_classes = {metric_id: getattr(metric_func, '__self__')
+                        for metric_id, metric_func in _metrics_implementations.items()}
+
     @staticmethod
-    def metric_by_id(metric_id: MetricsEnum, default_callable: MetricCallable = None) -> MetricCallable:
-        return MetricsRepository._metrics_implementations.get(metric_id, default_callable)
+    def get_metric(metric_name: MetricsEnum) -> MetricCallable:
+        return MetricsRepository._metrics_implementations[metric_name]
 
     @staticmethod
-    def metric_class_by_id(metric_id: MetricsEnum) -> Metric:
-        return MetricsRepository._metrics_implementations[metric_id].__self__()
+    def get_metric_class(metric_name: MetricsEnum) -> Union[QualityMetric, ComplexityMetric]:
+        return MetricsRepository._metrics_classes[metric_name]
diff --git a/fedot/utilities/define_metric_by_task.py b/fedot/utilities/define_metric_by_task.py
index c168ad1605..b11ca03612 100644
--- a/fedot/utilities/define_metric_by_task.py
+++ b/fedot/utilities/define_metric_by_task.py
@@ -1,7 +1,7 @@
 from typing import List
 
 from fedot.core.data.data import InputData, OutputData
-from fedot.core.repository.quality_metrics_repository import (
+from fedot.core.repository.metrics_repository import (
     MetricsEnum,
     RegressionMetricsEnum,
     ClassificationMetricsEnum,
@@ -27,7 +27,7 @@ def compute_default_metric(task_type: TaskTypesEnum, true: InputData, predicted:
                                round_up_to: int = 6) -> float:
         """Returns the value of metric defined by task"""
         metric_id = MetricByTask.get_default_quality_metrics(task_type)[0]
-        metric = MetricsRepository.metric_class_by_id(metric_id)
+        metric = MetricsRepository.get_metric_class(metric_id)
         try:
             return round(metric.metric(reference=true, predicted=predicted), round_up_to)
         except ValueError:
diff --git a/test/data/expected_metric_values.json b/test/data/expected_metric_values.json
new file mode 100644
index 0000000000..8a293325c6
--- /dev/null
+++ b/test/data/expected_metric_values.json
@@ -0,0 +1,65 @@
+{
+  "complexity": {
+    "node_number": 0.4,
+    "structural": 0.43333333333333335,
+    "computation_time_in_seconds": 0.0
+  },
+  "binary": {
+    "roc_auc": -0.9799498746867168,
+    "precision": -0.9473684210526315,
+    "f1": -0.9473684210526315,
+    "neg_log_loss": 0.19864695688257933,
+    "roc_auc_pen": -0.9757034252297411,
+    "accuracy": -0.95
+  },
+  "multiclass": {
+    "roc_auc": -0.9832500832500832,
+    "precision": -0.9777777777777779,
+    "f1": -0.9719701552732407,
+    "neg_log_loss": 0.17094588819131074,
+    "roc_auc_pen": -0.9789893328893329,
+    "accuracy": -0.9722222222222222
+  },
+  "regression": {
+    "rmse": 52.5400204534369,
+    "mse": 2760.4537492475683,
+    "neg_mean_squared_log_error": 0.11968905129112402,
+    "mape": 0.32791432529967834,
+    "smape": 49.48675123994897,
+    "mae": 41.55089094096007,
+    "r2": 0.389822739375963,
+    "rmse_pen": 52.64510049434378
+  },
+  "multitarget": {
+    "rmse": 15.753366859480218,
+    "mse": 377.5025166058113,
+    "neg_mean_squared_log_error": 0.030627538521796293,
+    "mape": 0.15337090733886807,
+    "smape": 14.144394353302935,
+    "mae": 13.50645038033778,
+    "r2": -2.9713973901034954,
+    "rmse_pen": 15.784873593199178
+  },
+  "ts": {
+    "mase": 0.6080909603204148,
+    "rmse": 6.977694399080989,
+    "mse": 48.6882191269662,
+    "neg_mean_squared_log_error": 0.005433112046490622,
+    "mape": 0.05897445640267307,
+    "smape": 5.602921894471363,
+    "mae": 4.960742044719172,
+    "r2": 0.8502177471021775,
+    "rmse_pen": 6.991649787879151
+  },
+  "multits": {
+    "mase": 0.6080543658437002,
+    "rmse": 6.977578875807477,
+    "mse": 48.68660696811473,
+    "neg_mean_squared_log_error": 0.005434854312738277,
+    "mape": 0.05898285612710732,
+    "smape": 5.603672314933528,
+    "mae": 4.960443510830185,
+    "r2": 0.8502227066753377,
+    "rmse_pen": 6.991534033559091
+  }
+}
\ No newline at end of file
diff --git a/test/integration/composer/test_composer.py b/test/integration/composer/test_composer.py
index a30d2a933b..2cb174c4bf 100644
--- a/test/integration/composer/test_composer.py
+++ b/test/integration/composer/test_composer.py
@@ -24,7 +24,7 @@
 from fedot.core.pipelines.pipeline_graph_generation_params import get_pipeline_generation_params
 from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.operation_types_repository import OperationTypesRepository, get_operations_for_task
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root, set_random_seed
 from test.unit.pipelines.test_pipeline_comparison import pipeline_first, pipeline_second
diff --git a/test/integration/composer/test_history.py b/test/integration/composer/test_history.py
index 70807c14ad..c266ba7539 100644
--- a/test/integration/composer/test_history.py
+++ b/test/integration/composer/test_history.py
@@ -3,8 +3,6 @@
 
 import numpy as np
 import pytest
-
-from fedot.core.repository.tasks import TaskTypesEnum
 from golem.core.dag.graph import Graph
 from golem.core.optimisers.fitness import SingleObjFitness
 from golem.core.optimisers.genetic.evaluation import MultiprocessingDispatcher
@@ -20,8 +18,9 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_graph_generation_params import get_pipeline_generation_params
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, \
-    RegressionMetricsEnum, MetricType
+from fedot.core.repository.metrics_repository import (ClassificationMetricsEnum, MetricIDType,
+                                                      RegressionMetricsEnum)
+from fedot.core.repository.tasks import TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 from test.unit.tasks.test_forecasting import get_ts_data
 from test.unit.validation.test_table_cv import get_classification_data
@@ -116,7 +115,7 @@ def assert_intermediate_metrics(pipeline: Graph):
                            get_ts_data()[0],
                            RegressionMetricsEnum.RMSE),
                           ])
-def test_collect_intermediate_metric(pipeline: Pipeline, input_data: InputData, metric: MetricType):
+def test_collect_intermediate_metric(pipeline: Pipeline, input_data: InputData, metric: MetricIDType):
     graph_gen_params = get_pipeline_generation_params()
     metrics = [metric]
 
diff --git a/test/integration/optimizer/test_evaluation.py b/test/integration/optimizer/test_evaluation.py
index 52a5a616c8..1629736e9d 100644
--- a/test/integration/optimizer/test_evaluation.py
+++ b/test/integration/optimizer/test_evaluation.py
@@ -10,7 +10,7 @@
 from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
 from fedot.core.pipelines.adapters import PipelineAdapter
 from fedot.core.pipelines.pipeline import Pipeline
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from test.unit.pipelines.test_node_cache import pipeline_first, pipeline_second, pipeline_third, pipeline_fourth
 from test.unit.validation.test_table_cv import get_classification_data
 
diff --git a/test/integration/pipelines/tuning/test_pipeline_tuning.py b/test/integration/pipelines/tuning/test_pipeline_tuning.py
index be281a92c0..48510f0339 100644
--- a/test/integration/pipelines/tuning/test_pipeline_tuning.py
+++ b/test/integration/pipelines/tuning/test_pipeline_tuning.py
@@ -23,7 +23,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum, ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum, ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root, NESTED_PARAMS_LABEL
 from test.unit.multimodal.data_generators import get_single_task_multimodal_tabular_data, get_multimodal_pipeline
diff --git a/test/integration/pipelines/tuning/test_tuner_builder.py b/test/integration/pipelines/tuning/test_tuner_builder.py
index 18cb50a3dd..c2bb4ebc7c 100644
--- a/test/integration/pipelines/tuning/test_tuner_builder.py
+++ b/test/integration/pipelines/tuning/test_tuner_builder.py
@@ -16,13 +16,13 @@
 from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, MetricType
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, MetricIDType
 from test.integration.pipelines.tuning.test_pipeline_tuning import get_not_default_search_space
 from test.unit.optimizer.test_pipeline_objective_eval import pipeline_first_test
 from test.unit.validation.test_table_cv import get_classification_data
 
 
-def get_objective_evaluate(metric: MetricType, data: InputData,
+def get_objective_evaluate(metric: MetricIDType, data: InputData,
                            cv_folds: Optional[int] = None) \
         -> PipelineObjectiveEvaluate:
     objective = MetricsObjective(metric)
diff --git a/test/integration/validation/test_table_cv.py b/test/integration/validation/test_table_cv.py
index b0a0cd09bb..a7ae120ebb 100644
--- a/test/integration/validation/test_table_cv.py
+++ b/test/integration/validation/test_table_cv.py
@@ -7,7 +7,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.repository.operation_types_repository import OperationTypesRepository
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from test.unit.validation.test_table_cv import get_classification_data
 
diff --git a/test/unit/api/test_api_params.py b/test/unit/api/test_api_params.py
index 7295ababa9..43479aeb9d 100644
--- a/test/unit/api/test_api_params.py
+++ b/test/unit/api/test_api_params.py
@@ -11,7 +11,7 @@
 from fedot.core.constants import AUTO_PRESET_NAME
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
-from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum
+from fedot.core.repository.metrics_repository import RegressionMetricsEnum
 from fedot.core.repository.tasks import TaskTypesEnum
 
 fedot_params_full = dict(parallelization_mode='populational',
diff --git a/test/unit/composer/test_metrics.py b/test/unit/composer/test_metrics.py
new file mode 100644
index 0000000000..b8b868a9e7
--- /dev/null
+++ b/test/unit/composer/test_metrics.py
@@ -0,0 +1,227 @@
+import json
+import sys
+from itertools import product
+from typing import Callable, Dict, Tuple, Union
+
+import numpy as np
+import pandas as pd
+import pytest
+from sklearn.datasets import load_breast_cancer, load_diabetes, load_linnerud, load_wine
+
+from fedot.core.composer.metrics import QualityMetric, ROCAUC
+from fedot.core.data.data import InputData, OutputData
+from fedot.core.data.data_split import train_test_data_setup
+from fedot.core.pipelines.node import PipelineNode
+from fedot.core.pipelines.pipeline import Pipeline
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.metrics_repository import (ClassificationMetricsEnum, ComplexityMetricsEnum,
+                                                      MetricsRepository, RegressionMetricsEnum,
+                                                      TimeSeriesForecastingMetricsEnum)
+from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
+from fedot.core.utils import fedot_project_root, set_random_seed
+
+
+@pytest.fixture(scope='session')
+def data_setup(request):
+    task_type = request.param
+    validation_blocks = None
+    if task_type in ('binary', 'complexity'):
+        x, y = load_breast_cancer(return_X_y=True)
+        task = Task(TaskTypesEnum.classification)
+        data_type = DataTypesEnum.table
+    elif task_type == 'multiclass':
+        x, y = load_wine(return_X_y=True)
+        task = Task(TaskTypesEnum.classification)
+        data_type = DataTypesEnum.table
+    elif task_type == 'regression':
+        x, y = load_diabetes(return_X_y=True)
+        task = Task(TaskTypesEnum.regression)
+        data_type = DataTypesEnum.table
+    elif task_type == 'multitarget':
+        x, y = load_linnerud(return_X_y=True)
+        task = Task(TaskTypesEnum.regression)
+        data_type = DataTypesEnum.table
+    elif task_type == 'ts':
+        file_path = fedot_project_root() / 'test/data/short_time_series.csv'
+        df = pd.read_csv(file_path)
+        x = y = df['sea_height'].to_numpy()
+        task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=10))
+        data_type = DataTypesEnum.ts
+        validation_blocks = 2
+    elif task_type == 'multits':
+        file_path = fedot_project_root() / 'test/data/short_time_series.csv'
+        df = pd.read_csv(file_path)
+        x = df[['sea_height', 'sea_height']].to_numpy()
+        y = df['sea_height'].to_numpy()
+        task = Task(TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=10))
+        data_type = DataTypesEnum.multi_ts
+        validation_blocks = 2
+    else:
+        raise ValueError(f'Unsupported task type: {task_type}')
+
+    x, y = x[:200], y[:200]
+
+    # Wrap data into InputData
+    input_data = InputData(features=x,
+                           target=y,
+                           idx=np.arange(len(x)),
+                           task=task,
+                           data_type=data_type)
+    # Train test split
+    train_data, test_data = train_test_data_setup(input_data, validation_blocks=validation_blocks)
+    return train_data, test_data, task_type, validation_blocks
+
+
+def get_classification_pipeline():
+    first = PipelineNode(operation_type='logit')
+    second = PipelineNode(operation_type='logit', nodes_from=[first])
+    third = PipelineNode(operation_type='logit', nodes_from=[first])
+    final = PipelineNode(operation_type='logit', nodes_from=[second, third])
+
+    pipeline = Pipeline(final)
+
+    return pipeline
+
+
+def get_regression_pipeline():
+    first = PipelineNode(operation_type='scaling')
+    final = PipelineNode(operation_type='linear', nodes_from=[first])
+
+    pipeline = Pipeline(final)
+
+    return pipeline
+
+
+def get_ts_pipeline(window_size=30):
+    """ Function return pipeline with lagged transformation in it """
+    node_lagged = PipelineNode('lagged')
+    node_lagged.parameters = {'window_size': window_size}
+
+    node_final = PipelineNode('ridge', nodes_from=[node_lagged])
+    pipeline = Pipeline(node_final)
+    return pipeline
+
+
+@pytest.fixture(scope='session')
+def expected_values() -> Dict[str, Dict[str, float]]:
+    with open(fedot_project_root() / 'test/data/expected_metric_values.json', 'r') as f:
+        return json.load(f)
+
+
+@pytest.mark.parametrize(
+    'metric, pipeline_func, data_setup',
+    [  # TODO: Add binary classification to the test after completion of https://github.com/aimclub/FEDOT/issues/1221.
+        *product(ComplexityMetricsEnum, [get_classification_pipeline], ['complexity']),
+        *product(ClassificationMetricsEnum, [get_classification_pipeline], ['multiclass']),
+        *product(RegressionMetricsEnum, [get_regression_pipeline], ['regression', 'multitarget']),
+        *product(TimeSeriesForecastingMetricsEnum, [get_ts_pipeline], ['ts', 'multits'])
+    ],
+    indirect=['data_setup']
+)
+def test_metrics(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline],
+                 data_setup: Tuple[InputData, InputData, str, Union[int, None]],
+                 expected_values: Dict[str, Dict[str, float]]):
+    set_random_seed(0)
+    update_expected_values: bool = False
+
+    train, test, task_type, validation_blocks = data_setup
+
+    pipeline = pipeline_func()
+    pipeline.fit(input_data=train)
+    metric_function = MetricsRepository.get_metric(metric)
+    metric_class = MetricsRepository.get_metric_class(metric)
+    metric_value = metric_function(pipeline=pipeline, reference_data=test, validation_blocks=validation_blocks)
+
+    if not update_expected_values:
+        expected_value = expected_values[task_type][str(metric)]
+        assert np.isclose(metric_value, expected_value, rtol=0.001, atol=0.001)
+        assert not np.isclose(metric_value, metric_class.default_value, rtol=0.01, atol=0.01)
+    else:
+        with open(fedot_project_root() / 'test/data/expected_metric_values.json', 'w') as f:
+            expected_values[task_type] = expected_values.get(task_type) or {}
+            expected_values[task_type][str(metric)] = metric_value
+            json.dump(expected_values, f, indent=2)
+        raise ValueError('The value of `update_expected_values` should equal to `False` '
+                         'in order for this test to pass.')
+
+
+@pytest.mark.parametrize(
+    'metric, pipeline_func, data_setup',
+    [
+        *product(ClassificationMetricsEnum, [get_classification_pipeline], ['binary']),
+    ],
+    indirect=['data_setup']
+)
+def test_binary_classification(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline],
+                               data_setup: Tuple[InputData, InputData, str, Union[int, None]],
+                               expected_values: Dict[str, Dict[str, float]]):
+    train, test, task_type, validation_blocks = data_setup
+
+    pipeline = pipeline_func()
+    pipeline.fit(input_data=train)
+    metric_function = MetricsRepository.get_metric(metric)
+    metric_class = MetricsRepository.get_metric_class(metric)
+    metric_value = metric_function(pipeline=pipeline, reference_data=test, validation_blocks=validation_blocks)
+
+    assert not np.isclose(metric_value, metric_class.default_value, rtol=0.01, atol=0.01)
+    assert 0 < abs(metric_value) < sys.maxsize
+
+
+@pytest.mark.parametrize(
+    'metric, pipeline_func, data_setup, validation_blocks',
+    [
+        *product(ClassificationMetricsEnum, [get_classification_pipeline], ['binary', 'multiclass'], [None]),
+        *product(RegressionMetricsEnum, [get_regression_pipeline], ['regression', 'multitarget'], [None]),
+        *product(TimeSeriesForecastingMetricsEnum, [get_ts_pipeline], ['ts', 'multits'], [2]),
+    ],
+    indirect=['data_setup']
+)
+def test_ideal_case_metrics(metric: ClassificationMetricsEnum, pipeline_func: Callable[[], Pipeline],
+                            validation_blocks: Union[int, None], data_setup: Tuple[InputData, InputData, str],
+                            expected_values):
+    reference, _, task_type, _ = data_setup
+    metric_class = MetricsRepository.get_metric_class(metric)
+    predicted = OutputData(idx=reference.idx, task=reference.task, data_type=reference.data_type)
+    if task_type == 'multiclass' and metric_class.output_mode != 'labels':
+        label_vals = np.unique(reference.target)
+        predicted.predict = np.identity(len(label_vals))[reference.target]
+    else:
+        predicted.predict = reference.target
+    if task_type == 'multits':
+        reference.features = reference.features[:, 0]
+
+    ideal_value = metric_class.metric(reference, predicted)
+
+    assert ideal_value != metric_class.default_value
+
+
+@pytest.mark.parametrize('data_setup', ['multitarget'], indirect=True)
+def test_predict_shape_multi_target(data_setup: Tuple[InputData, InputData, str]):
+    train, test, _, _ = data_setup
+    simple_pipeline = Pipeline(PipelineNode('linear'))
+    simple_pipeline.fit(input_data=train)
+
+    target_shape = test.target.shape
+    # Get converted data
+    _, results = QualityMetric()._simple_prediction(simple_pipeline, test)
+    predict_shape = results.predict.shape
+    assert target_shape == predict_shape
+
+
+def test_roc_auc_multiclass_correct():
+    data = InputData(features=np.array([[1, 2], [2, 3], [3, 4], [4, 1]]),
+                     target=np.array([['x'], ['y'], ['z'], ['x']]),
+                     idx=np.arange(4),
+                     task=Task(TaskTypesEnum.classification),
+                     data_type=DataTypesEnum.table)
+    prediction = OutputData(features=np.array([[1, 2], [2, 3], [3, 4], [4, 1]]),
+                            predict=np.array([[0.4, 0.3, 0.3],
+                                              [0.2, 0.5, 0.3],
+                                              [0.1, 0.2, 0.7],
+                                              [0.8, 0.1, 0.1]]),
+                            idx=np.arange(4), task=Task(TaskTypesEnum.classification), data_type=DataTypesEnum.table)
+    for i in range(data.num_classes):
+        fpr, tpr, threshold = ROCAUC.roc_curve(data.target, prediction.predict[:, i],
+                                               pos_label=data.class_labels[i])
+        roc_auc = ROCAUC.auc(fpr, tpr)
+        assert roc_auc
diff --git a/test/unit/composer/test_quality_metrics.py b/test/unit/composer/test_quality_metrics.py
deleted file mode 100644
index 426faef66d..0000000000
--- a/test/unit/composer/test_quality_metrics.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import os
-import sys
-
-import numpy as np
-import pytest
-from sklearn.datasets import load_breast_cancer
-
-from fedot.core.composer.metrics import QualityMetric, ROCAUC
-from fedot.core.data.data import InputData, OutputData
-from fedot.core.data.data_split import train_test_data_setup
-from fedot.core.pipelines.node import PipelineNode
-from fedot.core.pipelines.pipeline import Pipeline
-from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import \
-    (ClassificationMetricsEnum,
-     ComplexityMetricsEnum,
-     MetricsRepository,
-     RegressionMetricsEnum)
-from fedot.core.repository.tasks import Task, TaskTypesEnum
-
-
-@pytest.fixture()
-def data_setup():
-    predictors, response = load_breast_cancer(return_X_y=True)
-    response = response[:100]
-    predictors = predictors[:100]
-
-    # Wrap data into InputData
-    input_data = InputData(features=predictors,
-                           target=response,
-                           idx=np.arange(0, len(predictors)),
-                           task=Task(TaskTypesEnum.classification),
-                           data_type=DataTypesEnum.table)
-    # Train test split
-    train_data, test_data = train_test_data_setup(input_data)
-    return train_data, test_data
-
-
-@pytest.fixture()
-def multi_target_data_setup():
-    test_file_path = str(os.path.dirname(__file__))
-    file = '../../data/multi_target_sample.csv'
-    path = os.path.join(test_file_path, file)
-
-    target_columns = ['1_day', '2_day', '3_day', '4_day', '5_day', '6_day', '7_day']
-    task = Task(TaskTypesEnum.regression)
-    data = InputData.from_csv(path, target_columns=target_columns,
-                              index_col=None, columns_to_drop=['date'], task=task)
-    train, test = train_test_data_setup(data)
-    return train, test
-
-
-def default_valid_pipeline():
-    first = PipelineNode(operation_type='logit')
-    second = PipelineNode(operation_type='logit', nodes_from=[first])
-    third = PipelineNode(operation_type='logit', nodes_from=[first])
-    final = PipelineNode(operation_type='logit', nodes_from=[second, third])
-
-    pipeline = Pipeline(final)
-
-    return pipeline
-
-
-def test_structural_quality_correct():
-    pipeline = default_valid_pipeline()
-    metric_function = MetricsRepository().metric_by_id(ComplexityMetricsEnum.structural)
-    expected_metric_value = 13
-    actual_metric_value = metric_function(pipeline)
-    assert actual_metric_value <= expected_metric_value
-
-
-def test_classification_quality_metric(data_setup):
-    train, _ = data_setup
-    pipeline = default_valid_pipeline()
-    pipeline.fit(input_data=train)
-
-    for metric in ClassificationMetricsEnum:
-        metric_function = MetricsRepository().metric_by_id(metric)
-        metric_value = metric_function(pipeline=pipeline, reference_data=train)
-        assert 0 < abs(metric_value) < sys.maxsize
-
-
-def test_regression_quality_metric(data_setup):
-    train, _ = data_setup
-    pipeline = default_valid_pipeline()
-    pipeline.fit(input_data=train)
-
-    for metric in RegressionMetricsEnum:
-        metric_function = MetricsRepository().metric_by_id(metric)
-        metric_value = metric_function(pipeline=pipeline, reference_data=train)
-        assert metric_value > 0
-
-
-def test_data_preparation_for_multi_target_correct(multi_target_data_setup):
-    train, test = multi_target_data_setup
-    simple_pipeline = Pipeline(PipelineNode('linear'))
-    simple_pipeline.fit(input_data=train)
-
-    source_shape = test.target.shape
-    # Get converted data
-    results, new_test = QualityMetric()._simple_prediction(simple_pipeline, test)
-    number_elements = len(new_test.target)
-    assert source_shape[0] * source_shape[1] == number_elements
-
-
-def test_roc_auc_multiclass_correct():
-    data = InputData(features=[[1, 2], [2, 3], [3, 4], [4, 1]], target=np.array([['x'], ['y'], ['z'], ['x']]),
-                     idx=np.arange(4),
-                     task=Task(TaskTypesEnum.classification), data_type=DataTypesEnum.table)
-    prediction = OutputData(features=[[1, 2], [2, 3], [3, 4], [4, 1]], predict=np.array([[0.4, 0.3, 0.3],
-                                                                                         [0.2, 0.5, 0.3],
-                                                                                         [0.1, 0.2, 0.7],
-                                                                                         [0.8, 0.1, 0.1]]),
-                            idx=np.arange(4), task=Task(TaskTypesEnum.classification), data_type=DataTypesEnum.table)
-    for i in range(data.num_classes):
-        fpr, tpr, threshold = ROCAUC.roc_curve(data.target, prediction.predict[:, i],
-                                               pos_label=data.class_labels[i])
-        roc_auc = ROCAUC.auc(fpr, tpr)
-        assert roc_auc
diff --git a/test/unit/optimizer/test_pipeline_objective_eval.py b/test/unit/optimizer/test_pipeline_objective_eval.py
index 1c1ce688e7..145a28d3db 100644
--- a/test/unit/optimizer/test_pipeline_objective_eval.py
+++ b/test/unit/optimizer/test_pipeline_objective_eval.py
@@ -13,7 +13,7 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum, MetricsRepository, \
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, MetricsRepository, \
     RegressionMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from test.integration.models.test_model import classification_dataset, classification_dataset_with_str_labels
@@ -51,7 +51,7 @@ def actual_fitness(data_split, pipeline, metric):
     metric_values = []
     for (train_data, test_data) in data_split():
         pipeline.fit(train_data)
-        metric_function = MetricsRepository().metric_by_id(metric, default_callable=metric)
+        metric_function = MetricsRepository.get_metric(metric)
         metric_values.append(metric_function(pipeline=pipeline, reference_data=test_data))
     mean_metric = np.mean(metric_values, axis=0)
     return SingleObjFitness(mean_metric)
diff --git a/test/unit/pipelines/prediction_intervals/data/pred_ints_model_test.pickle b/test/unit/pipelines/prediction_intervals/data/pred_ints_model_test.pickle
index 88fe04d97a98914f1643a5387910376d0fc55780..4c89b978be62a63f87f0e8a9afcc07e1d7edcef5 100644
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diff --git a/test/unit/pipelines/prediction_intervals/test_solver_mutations.py b/test/unit/pipelines/prediction_intervals/test_solver_mutations.py
index e73e1473dc..61d15dee4f 100644
--- a/test/unit/pipelines/prediction_intervals/test_solver_mutations.py
+++ b/test/unit/pipelines/prediction_intervals/test_solver_mutations.py
@@ -2,14 +2,14 @@
 
 import numpy as np
 import pytest
-from golem.core.log import default_log, Log
+from golem.core.log import Log, default_log
 
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.prediction_intervals.params import PredictionIntervalsParams
 from fedot.core.pipelines.prediction_intervals.solvers.mutation_of_best_pipeline import solver_mutation_of_best_pipeline
 from fedot.core.pipelines.prediction_intervals.utils import get_last_generations
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum
+from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
 from fedot.core.utils import fedot_project_root
 
 
@@ -42,9 +42,9 @@ def test_solver_mutation_of_best_pipeline(params):
     params_default = {'choice': 'different', 'discard': True, 'percentage': 0.66, 'number_mutations': 10,
                       'message': 'default solver_mutation_of_best_pipeline failed.'}
     params_with_replacement = {'choice': 'with_replacement', 'discard': True, 'percentage': 0.5, 'number_mutations': 30,
-                               'message': 'solver_mutation_of_best_pipeline with inapropriate pipelines failed.'}
+                               'message': 'solver_mutation_of_best_pipeline with inappropriate pipelines failed.'}
     params_different = {'choice': 'different', 'discard': False, 'percentage': 0.8, 'number_mutations': 10,
-                        'mesage': 'solver_mutation_of_best_pipeline failed.'}
+                        'message': 'solver_mutation_of_best_pipeline failed.'}
 
     for x in [params_default, params_with_replacement, params_different]:
         res = solver_mutation_of_best_pipeline(train_input=params['train_input'],
diff --git a/test/unit/tasks/test_regression.py b/test/unit/tasks/test_regression.py
index e63ba6473c..7489d1c5b6 100644
--- a/test/unit/tasks/test_regression.py
+++ b/test/unit/tasks/test_regression.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 from sklearn.datasets import make_regression
 from sklearn.metrics import mean_squared_error as mse
 
@@ -10,9 +11,7 @@
 from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from test.unit.common_tests import is_predict_ignores_target
-from test.unit.composer.test_quality_metrics import multi_target_data_setup
-
-_ = multi_target_data_setup
+from test.unit.composer.test_metrics import data_setup  # noqa
 
 
 def check_predict_correct(pipeline, input_data):
@@ -101,9 +100,10 @@ def test_regression_pipeline_with_data_operation_fit_predict_correct():
     assert check_predict_correct(pipeline, train_data)
 
 
-def test_multi_target_regression_composing_correct(multi_target_data_setup):
+@pytest.mark.parametrize('data_setup', ['multitarget'], indirect=True)
+def test_multi_target_regression_composing_correct(data_setup):
     # Load simple dataset for multi-target
-    train, test = multi_target_data_setup
+    train, test, _, _ = data_setup
 
     problem = 'regression'
     timeout = 0.1
diff --git a/test/unit/validation/test_table_cv.py b/test/unit/validation/test_table_cv.py
index 61d8f5bfef..8ec6ce1a16 100644
--- a/test/unit/validation/test_table_cv.py
+++ b/test/unit/validation/test_table_cv.py
@@ -15,7 +15,7 @@
 from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.operation_types_repository import OperationTypesRepository
-from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
+from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
 from test.integration.models.test_model import classification_dataset
diff --git a/test/unit/validation/test_time_series_cv.py b/test/unit/validation/test_time_series_cv.py
index 9f2e99db8f..3acfcc64ce 100644
--- a/test/unit/validation/test_time_series_cv.py
+++ b/test/unit/validation/test_time_series_cv.py
@@ -14,7 +14,7 @@
 from fedot.core.composer.composer_builder import ComposerBuilder
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.quality_metrics_repository import \
+from fedot.core.repository.metrics_repository import \
     MetricsRepository, RegressionMetricsEnum
 from fedot.core.repository.tasks import TsForecastingParams
 from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
@@ -112,7 +112,7 @@ def test_composer_cv_correct():
     )
 
     init_pipeline = get_simple_ts_pipeline()
-    metric_function = MetricsRepository().metric_by_id(RegressionMetricsEnum.RMSE)
+    metric_function = MetricsRepository.get_metric(RegressionMetricsEnum.RMSE)
     builder = ComposerBuilder(task=time_series.task). \
         with_optimizer_params(parameters). \
         with_requirements(composer_requirements). \