Decimal places control implemented (#1161)

* Decimal places control implemented

examples/simple/classification/api_classification.py

@@ -1,6 +1,5 @@
 from fedot.api.main import Fedot
-from fedot.core.utils import fedot_project_root
-from fedot.core.utils import set_random_seed
+from fedot.core.utils import fedot_project_root, set_random_seed
 
 
 def run_classification_example(timeout: float = None, visualization=False, with_tuning=True):
@@ -19,7 +18,7 @@ def run_classification_example(timeout: float = None, visualization=False, with_
     auto_model.fit(features=train_data_path, target='target')
     prediction = auto_model.predict_proba(features=test_data_path)
 
-    print(auto_model.get_metrics())
+    print(auto_model.get_metrics(rounding_order=4))  # we can control the rounding of metrics
     if visualization:
         auto_model.plot_prediction()
     return prediction

fedot/api/main.py

@@ -1,11 +1,11 @@
 import logging
 from copy import deepcopy
-from typing import Any, List, Optional, Sequence, Tuple, Union, Callable
+from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import pandas as pd
 from golem.core.dag.graph_utils import graph_structure
-from golem.core.log import default_log, Log
+from golem.core.log import Log, default_log
 from golem.core.optimisers.opt_history_objects.opt_history import OptHistory
 from golem.core.tuning.simultaneous import SimultaneousTuner
 from golem.visualisation.opt_viz_extra import visualise_pareto
@@ -475,7 +475,8 @@ def get_metrics(self,
                     target: Union[np.ndarray, pd.Series] = None,
                     metric_names: Union[str, List[str]] = None,
                     in_sample: Optional[bool] = None,
-                    validation_blocks: Optional[int] = None) -> dict:
+                    validation_blocks: Optional[int] = None,
+                    rounding_order: int = 3) -> dict:
         """Gets quality metrics for the fitted graph
 
         Args:
@@ -484,6 +485,7 @@ def get_metrics(self,
             in_sample: used for time series forecasting.
                 If True prediction will be obtained as ``.predict(..., in_sample=True)``.
             validation_blocks: number of validation blocks for time series in-sample forecast.
+            rounding_order: number of decimal places for metrics
 
         Returns:
             The values of quality metrics.
@@ -517,7 +519,8 @@ def get_metrics(self,
                                              do_unfit=False)
 
         metrics = obj_eval.evaluate(self.current_pipeline).values
-        metrics = {metric_name: round(abs(metric), 3) for (metric_name, metric) in zip(metric_names, metrics)}
+        metrics = {metric_name: round(abs(metric), rounding_order) for (metric_name, metric) in
+                   zip(metric_names, metrics)}
 
         return metrics
 

test/integration/api/test_main_api.py

@@ -26,7 +26,7 @@
 from fedot.core.utils import fedot_project_root
 from test.integration.models.test_split_train_test import get_synthetic_input_data
 from test.unit.common_tests import is_predict_ignores_target
-from test.unit.tasks.test_classification import get_synthetic_classification_data, get_iris_data
+from test.unit.tasks.test_classification import get_iris_data, get_synthetic_classification_data
 from test.unit.tasks.test_forecasting import get_ts_data
 from test.unit.tasks.test_multi_ts_forecast import get_multi_ts_data
 from test.unit.tasks.test_regression import get_synthetic_regression_data
@@ -157,7 +157,7 @@ def test_api_predict_correct(task_type, predefined_model, metric_name):
     model = Fedot(problem=task_type, **TESTS_MAIN_API_DEFAULT_PARAMS)
     fedot_model = model.fit(features=train_data, predefined_model=predefined_model)
     prediction = model.predict(features=test_data)
-    metric = model.get_metrics(metric_names=metric_name)
+    metric = model.get_metrics(metric_names=metric_name, rounding_order=5)
     assert isinstance(fedot_model, Pipeline)
     assert len(prediction) == len(test_data.target)
     assert all(value > 0 for value in metric.values())

test/unit/validation/test_table_cv.py

@@ -2,13 +2,13 @@
 from datetime import timedelta
 
 import pytest
-
 from golem.core.tuning.simultaneous import SimultaneousTuner
 
 from fedot.api.main import Fedot
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.optimisers.objective import PipelineObjectiveEvaluate
+from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
 from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
@@ -18,7 +18,6 @@
 from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root
-from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
 from test.integration.models.test_model import classification_dataset
 from test.unit.tasks.test_classification import get_iris_data, pipeline_simple
 
@@ -91,7 +90,7 @@ def test_cv_api_correct():
     model = Fedot(problem='classification', logging_level=logging.DEBUG, **composer_params)
     fedot_model = model.fit(features=dataset_to_compose)
     prediction = model.predict(features=dataset_to_validate)
-    metric = model.get_metrics(metric_names='f1')
+    metric = model.get_metrics(metric_names='f1', rounding_order=1)
 
     assert isinstance(fedot_model, Pipeline)
     assert len(prediction) == len(dataset_to_validate.target)