628 bugfix (#1145)

* Fix path to data

* Fix tests

cases/multi_target_levels_forecasting.py

@@ -7,6 +7,7 @@
 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.utils import fedot_project_root
 
 warnings.filterwarnings('ignore')
 
@@ -99,5 +100,5 @@ def run_multi_output_case(path, vis=False):
 
 
 if __name__ == '__main__':
-    path_file = './data/lena_levels/multi_sample.csv'
+    path_file = fedot_project_root() / 'cases/data/lena_levels/multi_sample.csv'
     run_multi_output_case(path_file, vis=True)

cases/multi_ts_level_forecasting.py

@@ -1,7 +1,6 @@
 import os
 
 import numpy as np
-from matplotlib import pyplot as plt
 from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
 
 from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
@@ -52,7 +51,6 @@ def run_multi_ts_forecast(forecast_length, is_multi_ts):
                   pop_size=15,
                   max_arity=4,
                   cv_folds=None,
-                  validation_blocks=None,
                   initial_assumption=init_pipeline
                   )
     # fit model

test/integration/composer/test_history.py

@@ -3,6 +3,8 @@
 
 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
@@ -118,8 +120,11 @@ def test_collect_intermediate_metric(pipeline: Pipeline, input_data: InputData,
     graph_gen_params = get_pipeline_generation_params()
     metrics = [metric]
 
-    data_source = DataSourceSplitter(validation_blocks=1).build(input_data)
-    objective_eval = PipelineObjectiveEvaluate(MetricsObjective(metrics), data_source)
+    validation_blocks = 1 if input_data.task.task_type is TaskTypesEnum.ts_forecasting else None
+    data_source = DataSourceSplitter(validation_blocks=validation_blocks).build(input_data)
+    objective_eval = PipelineObjectiveEvaluate(MetricsObjective(metrics),
+                                               data_source,
+                                               validation_blocks=validation_blocks)
     dispatcher = MultiprocessingDispatcher(graph_gen_params.adapter)
     dispatcher.set_graph_evaluation_callback(objective_eval.evaluate_intermediate_metrics)
     evaluate = dispatcher.dispatch(objective_eval)