added shrinkage to riemann extractor

fedot_ind/core/models/manifold/riemann_embeding.py

@@ -1,5 +1,6 @@
 import numpy as np
-from pyriemann.estimation import XdawnCovariances, Covariances
+import pandas as pd
+from pyriemann.estimation import XdawnCovariances, Covariances, Shrinkage
 from pyriemann.tangentspace import TangentSpace
 from typing import Optional
 from sklearn.utils.extmath import softmax
@@ -66,13 +67,16 @@ def __init__(self, params: Optional[OperationParameters] = None):
     def _init_spaces(self):
         self.covarince_transformer = Covariances(estimator='scm')
         self.tangent_projector = TangentSpace(metric=self.distance_metric)
+        self.shinkage = Shrinkage()
 
     def extract_riemann_features(self, input_data: InputData) -> InputData:
         if not self.fit_stage:
             SPD = self.covarince_transformer.transform(input_data.features)
+            SPD = self.shinkage.transform(SPD)
             ref_point = self.tangent_projector.transform(SPD)
         else:
             SPD = self.covarince_transformer.fit_transform(input_data.features, input_data.target)
+            SPD = self.shinkage.fit_transform(SPD)
             ref_point = self.tangent_projector.fit_transform(SPD)
             self.fit_stage = False
         return ref_point
@@ -81,9 +85,11 @@ def extract_centroid_distance(self, input_data: InputData):
         self.classes_ = np.unique(input_data.target)
         if not self.fit_stage:
             SPD = self.covarince_transformer.transform(input_data.features)
+            SPD = self.shinkage.transform(SPD)
         else:
             SPD = self.covarince_transformer.fit_transform(input_data.features, input_data.target)
-        self.covmeans_ = [mean_covariance(SPD[input_data.target == ll], metric=self.covariance_metric)
+            SPD = self.shinkage.fit_transform(SPD)
+        self.covmeans_ = [mean_covariance(SPD[input_data.target.flatten() == ll], metric=self.covariance_metric)
                           for ll in self.classes_]
 
         n_centroids = len(self.covmeans_)
@@ -106,3 +112,36 @@ def _transform(self, input_data: InputData) -> np.array:
         feature_matrix = self.extraction_func(input_data)
         self.predict = self._clean_predict(feature_matrix)
         return self.predict
+
+
+if __name__ == "__main__":
+    from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+    from sklearn.metrics import accuracy_score
+    from fedot_ind.api.utils.data import init_input_data
+    from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
+    from sklearn.model_selection import train_test_split
+
+    path_x = '/Users/technocreep/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/0_lavence/valence_data/pt1/X_eeg_4_24_old_resave.npy'
+    path_y = '/Users/technocreep/Desktop/Working-Folder/fedot-industrial/Fedot.Industrial/0_lavence/valence_data/pt1/y_old.npy'
+    X = np.load(path_x)
+    y = np.load(path_y)
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
+    x_train = pd.DataFrame([[pd.Series(i) for i in k] for k in X_train])
+    x_test = pd.DataFrame([[pd.Series(i) for i in k] for k in X_test])
+
+    init_train = init_input_data(x_train, y_train)
+    init_test = init_input_data(x_test, y_test)
+
+    with IndustrialModels():
+        # riemann_extractor
+        # pipeline = PipelineBuilder().add_node('eigen_basis').add_node('quantile_extractor').add_node('rf').build()
+        pipeline = PipelineBuilder().add_node('riemann_extractor', params={'n_filter': 3})\
+                                    .add_node('quantile_extractor')\
+                                    .add_node('mlp')\
+                                    .build()
+        pipeline.fit(init_train)
+        pred = pipeline.predict(init_test)
+        acc = accuracy_score(y_test, np.round(pred.predict))
+        print(acc)
+

fedot_ind/core/repository/model_repository.py

@@ -15,8 +15,9 @@
     *
 
     # FastTopologicalFeaturesImplementation
-from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.topological_extractor import \
-    TopologicalFeaturesImplementation
+# from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.topological_extractor import \
+#     TopologicalFeaturesImplementation
+from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.fast_topological_extractor import TopologicalFeaturesImplementation
 from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import \
     ExogDataTransformationImplementation, GaussianFilterImplementation, LaggedTransformationImplementation, \
     SparseLaggedTransformationImplementation, TsSmoothingImplementation