feat: add arctic sea (environment monitoring ts reg) new api example

examples/real_world_examples/industrial_examples/enviroment_monitoring/ts_forecasting/arctic_sea.py

@@ -1,70 +1,72 @@
 from pathlib import Path
 
-import matplotlib
+from matplotlib import use
+import matplotlib.pyplot as plt
 import pandas as pd
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum
-from matplotlib import pyplot as plt
 from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
 
 from fedot_ind.core.architecture.settings.computational import backend_methods as np
-from fedot_ind.tools.serialisation.path_lib import PROJECT_PATH
+from fedot_ind.tools.serialisation.path_lib import EXAMPLES_DATA_PATH
 
-matplotlib.use('TKagg')
 
-horizon = 365
-PATH = Path(PROJECT_PATH, 'examples', 'data', 'ices_areas_ts.csv')
 
-time_series_df = pd.read_csv(PATH).iloc[:, 1:]
-target_series = time_series_df['Карское'].values
+if __name__ == '__main__':
+    use('TKagg')
+    horizon = 365
 
-input_data = InputData.from_numpy_time_series(
-    target_series,
-    task=Task(
-        TaskTypesEnum.ts_forecasting,
-        task_params=TsForecastingParams(
-            forecast_length=horizon)))
-train_data, test_data = train_test_data_setup(input_data)
+    time_series_df = pd.read_csv(Path(EXAMPLES_DATA_PATH,
+                                      'real_world/ice_forecasting/ices_areas_ts.csv'))
+    time_series_df = time_series_df.iloc[:, 1:]
+    target_series = time_series_df['Карское'].values
 
-pipeline_based = PipelineBuilder().add_node('lagged').add_node('rfr').build()
-pipeline_based.fit(train_data)
+    input_data = InputData.from_numpy_time_series(
+        target_series,
+        task=Task(TaskTypesEnum.ts_forecasting,
+                  task_params=TsForecastingParams(forecast_length=horizon)))
+    train_data, test_data = train_test_data_setup(input_data)
 
-topological_pipeline = PipelineBuilder().add_node('lagged').add_node(
-    'topological_features') .add_node('lagged', branch_idx=2).join_branches('rfr').build()
-topological_pipeline.fit(train_data)
+    pipeline_based = (
+        PipelineBuilder()
+        .add_node('lagged')
+        .add_node('rfr')
+        .build()
+    )
+    pipeline_based.fit(train_data)
 
-forecast_base = np.ravel(pipeline_based.predict(test_data).predict)
-forecast_topo = np.ravel(topological_pipeline.predict(test_data).predict)
+    topological_pipeline = (
+        PipelineBuilder()
+        .add_node('lagged')
+        .add_node('topological_features')
+        .add_node('lagged', branch_idx=2)
+        .join_branches('rfr')
+        .build()
+    )
+    topological_pipeline.fit(train_data)
 
-forecast_base[forecast_base < 0] = 0
-forecast_topo[forecast_topo < 0] = 0
+    forecast_base = np.ravel(pipeline_based.predict(test_data).predict)
+    forecast_topo = np.ravel(topological_pipeline.predict(test_data).predict)
 
-plt.plot(input_data.features, label='real data')
-plt.plot(np.arange(len(target_series) - horizon, len(target_series)),
-         forecast_base, label='forecast base')
-plt.plot(np.arange(len(target_series) - horizon, len(target_series)),
-         forecast_topo, label='forecast topo')
+    forecast_base[forecast_base < 0] = 0
+    forecast_topo[forecast_topo < 0] = 0
 
-plt.grid()
-plt.legend()
-plt.show()
+    plt.plot(input_data.features, label='real data')
+    plt.plot(np.arange(len(target_series) - horizon, len(target_series)),
+             forecast_base, label='forecast base')
+    plt.plot(np.arange(len(target_series) - horizon, len(target_series)),
+             forecast_topo, label='forecast topo')
 
-print('base')
-print(mean_squared_error(test_data.target, forecast_base, squared=False))
-print(
-    mean_absolute_percentage_error(
-        test_data.target +
-        1000,
-        forecast_base +
-        1000))
+    plt.grid()
+    plt.legend()
+    plt.show()
 
-print('topo')
-print(mean_squared_error(test_data.target, forecast_topo, squared=False))
-print(
-    mean_absolute_percentage_error(
-        test_data.target +
-        1000,
-        forecast_topo +
-        1000))
+    print('base')
+    print(mean_squared_error(test_data.target, forecast_base, squared=False))
+    print(mean_absolute_percentage_error(test_data.target + 1000, forecast_base + 1000))
+
+    print('topo')
+    print(mean_squared_error(test_data.target, forecast_topo, squared=False))
+    print(mean_absolute_percentage_error(test_data.target + 1000, forecast_topo + 1000))