From 7bb8ba0cb0ebeca0652ad2e71cdf83a5f6edae63 Mon Sep 17 00:00:00 2001
From: valer1435 <valp998@gmail.com>
Date: Tue, 8 Aug 2023 09:55:23 +0300
Subject: [PATCH] made working example

---
 examples/anomaly_detection/detection_classification.py     | 6 +++---
 .../architecture/experiment/TimeSeriesClassifierPreset.py  | 7 +++----
 fedot_ind/core/models/statistical/StatsExtractor.py        | 2 +-
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/examples/anomaly_detection/detection_classification.py b/examples/anomaly_detection/detection_classification.py
index b158ec511..f23bdfebf 100644
--- a/examples/anomaly_detection/detection_classification.py
+++ b/examples/anomaly_detection/detection_classification.py
@@ -52,8 +52,8 @@ def generate_time_series(to_plot: bool = True,
                                          label=cls) for cls in anomaly_intervals.keys()]
 
         for anomaly_class, intervals in anomaly_intervals.items():
-            for interval in intervals.split(', '):
-                start_idx, end_idx = map(int, interval.split(':'))
+            for interval in intervals:
+                start_idx, end_idx = map(int, interval)
                 ax.axvspan(start_idx, end_idx, alpha=0.3, color=color_dict[anomaly_class])
 
         plt.legend(handles=set(legend_patches))
@@ -69,7 +69,7 @@ def generate_colors(num_colors):
 
 
 if __name__ == "__main__":
-    time_series, anomaly_intervals = generate_time_series(to_plot=False,
+    time_series, anomaly_intervals = generate_time_series(to_plot=True,
                                                           ts_length=1000,
                                                           num_anomaly_classes=4,
                                                           num_of_anomalies=50)
diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py
index 75f085ea2..ed18df338 100644
--- a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py
+++ b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py
@@ -15,7 +15,6 @@
 from fedot.core.repository.quality_metrics_repository import ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from golem.core.tuning.simultaneous import SimultaneousTuner
-from golem.core.tuning.sequential import SequentialTuner
 
 from fedot_ind.api.utils.saver_collections import ResultSaver
 from fedot_ind.core.architecture.postprocessing.Analyzer import PerformanceAnalyzer
@@ -210,8 +209,8 @@ def fit(self, features,
                                                      'mlp',
                                                      'knn',
                                                      'lgbm',
-                                                     'pca']
-                               , **self.model_params)
+                                                     'pca'],
+                               **self.model_params)
 
         self.predictor.fit(train_data_preprocessed)
 
@@ -247,8 +246,8 @@ def predict(self, features: pd.DataFrame, target: np.array) -> dict:
                                                 data_type=test_data_preprocessed.data_type,
                                                 task=test_data_preprocessed.task)
 
-        self.prediction_label_baseline = self.baseline_model.predict(self.test_data_preprocessed).predict
         self.prediction_label = self.predictor.predict(self.test_data_preprocessed)
+        self.prediction_label_baseline = self.baseline_model.predict(self.test_data_preprocessed).predict
 
         return self.prediction_label
 
diff --git a/fedot_ind/core/models/statistical/StatsExtractor.py b/fedot_ind/core/models/statistical/StatsExtractor.py
index bc6db9683..1e06e7ebb 100644
--- a/fedot_ind/core/models/statistical/StatsExtractor.py
+++ b/fedot_ind/core/models/statistical/StatsExtractor.py
@@ -50,7 +50,7 @@ def _transform(self, input_data: InputData) -> np.array:
         # predict = self.drop_features(predict=predict,
         #                              columns=stat_features,
         #                              n_components=n_components)
-        if predict.shape[1] == 29:
+        if predict.shape[1] == 36:
             print('a')
         return predict