add example with basic clf pipeline. minor refactoring

aimclub · Sep 18, 2023 · 8370a28 · 8370a28
1 parent e1d5909
commit 8370a28
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Showing 5 changed files with 44 additions and 88 deletions.
diff --git a/examples/time_series_classification/ts_classification_basic.py b/examples/time_series_classification/ts_classification_basic.py
@@ -0,0 +1,41 @@
+import numpy as np
+from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+from examples.fedot.fedot_ex import init_input_data
+from fedot_ind.core.architecture.preprocessing.DatasetLoader import DataLoader
+from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
+from sklearn.metrics import f1_score, roc_auc_score
+
+model_dict = {'basic_quantile': PipelineBuilder().add_node('quantile_extractor',
+                                                           params={'window_size': 10,
+                                                                   'window_mode': True}).add_node('rf'),
+              'basic_topological': PipelineBuilder().add_node('topological_extractor',
+                                                              params={'window_size': 10}).add_node('rf'),
+              'basic_recurrence': PipelineBuilder ().add_node('recurrence_extractor').add_node('rf'),
+              'advanced_quantile': PipelineBuilder().add_node('fourier_basis').add_node('quantile_extractor',
+                                                                                        params={'window_size': 10,
+                                                                                                'window_mode': True}).add_node(
+                  'rf'),
+              'advanced_topological': PipelineBuilder().add_node('eigen_basis').add_node('topological_extractor',
+                                                                                         params={
+                                                                                             'window_size': 10}).add_node(
+                  'rf'),
+              'advanced_reccurence': PipelineBuilder().add_node('wavelet_basis').add_node(
+                  'recurrence_extractor').add_node(
+                  'rf')
+              }
+metric_dict = {}
+train_data, test_data = DataLoader(dataset_name='Ham').load_data()
+
+with IndustrialModels():
+    for model in model_dict.keys():
+        pipeline = model_dict[model].build()
+        input_data = init_input_data(train_data[0], train_data[1])
+        val_data = init_input_data(test_data[0], test_data[1])
+        pipeline.fit(input_data)
+        features = pipeline.predict(val_data).predict
+        if len(np.unique(test_data[1])) > 2:
+            metric = f1_score(test_data[1], features, average='weighted')
+        else:
+            metric = roc_auc_score(test_data[1], features, average='weighted')
+        metric_dict.update({model: metric})
+    print(metric_dict)
diff --git a/examples/time_series_regression/ts_regression_example.py b/examples/time_series_regression/ts_regression_example.py
@@ -2,7 +2,7 @@
 from fedot_ind.core.architecture.preprocessing.DatasetLoader import DataLoader
 
 if __name__ == "__main__":
-    dataset_name = 'LiveFuelMoistureContent'
+    dataset_name = 'ApplianceEnergy'
     industrial = FedotIndustrial(task='ts_regression',
                                  dataset=dataset_name,
                                  strategy='quantile',

diff --git a/fedot_ind/core/architecture/preprocessing/InputData.py b/fedot_ind/core/architecture/preprocessing/InputData.py
diff --git a/fedot_ind/core/architecture/settings/task_factory.py b/fedot_ind/core/architecture/settings/task_factory.py
@@ -19,7 +19,7 @@ class TaskEnum(Enum):
     ts_classification = dict(fedot_preset=TimeSeriesClassifierPreset,
                              nn=TimeSeriesClassifierNN,
                              default=TimeSeriesClassifier)
-
+    ts_forecasting = (TimeSeriesForecasingWithDecomposition,)
     ts_regression = (TimeSeriesRegression,)
     anomaly_detection = (TimeSeriesAnomalyDetectionPreset,)
     image_classification = (CVExperimenter,)

diff --git a/fedot_ind/core/models/quantile/quantile_extractor.py b/fedot_ind/core/models/quantile/quantile_extractor.py
@@ -99,6 +99,7 @@ def generate_features_from_ts(self,
                                   window_length: int = None) -> InputData:
 
         ts = np.nan_to_num(ts)
+        ts = ts.flatten()
 
         if len(ts.shape) == 1:
             aggregation_df = self.extract_stats_features(ts)