[ENH] Normalise/TimeSeriesScaler reconciliation (#2289)

* normalise reconciliation

* split classes

* refactor TimeSeriesScalar

* notebook

* Standardizer - Centerer

* Standardizer - Centerer

* corrections

* corrections

Author: TonyBagnall

aeon/classification/compose/tests/test_pipeline.py

@@ -20,9 +20,9 @@
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
+    Normalizer,
     Padder,
     Tabularizer,
-    TimeSeriesScaler,
 )
 from aeon.transformations.collection.feature_based import SevenNumberSummary
 
@@ -110,7 +110,7 @@ def test_unequal_tag_inference():
 
     t1 = SevenNumberSummary()
     t2 = Padder()
-    t3 = TimeSeriesScaler()
+    t3 = Normalizer()
     t4 = AutocorrelationFunctionTransformer(n_lags=5)
     t5 = StandardScaler()
     t6 = Tabularizer()
@@ -181,7 +181,7 @@ def test_missing_tag_inference():
     t1 = t1.set_tags(
         **{"capability:missing_values": True, "removes_missing_values": True}
     )
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = StandardScaler()
     t4 = Tabularizer()
 
@@ -230,7 +230,7 @@ def test_multivariate_tag_inference():
     X, y = make_example_3d_numpy(n_cases=10, n_channels=2, n_timepoints=12)
 
     t1 = SevenNumberSummary()
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = HOG1DTransformer()
     t4 = StandardScaler()
 

aeon/classification/dictionary_based/_redcomets.py

@@ -17,7 +17,7 @@
 from sklearn.utils import check_random_state
 
 from aeon.classification.base import BaseClassifier
-from aeon.transformations.collection import TimeSeriesScaler
+from aeon.transformations.collection import Normalizer
 from aeon.transformations.collection.dictionary_based import SAX, SFA
 from aeon.utils.validation._dependencies import _check_soft_dependencies
 
@@ -201,7 +201,7 @@ def _build_univariate_ensemble(self, X, y):
 
         from imblearn.over_sampling import SMOTE, RandomOverSampler
 
-        X = TimeSeriesScaler().fit_transform(X).squeeze()
+        X = Normalizer().fit_transform(X).squeeze()
 
         if self.variant in [1, 2, 3]:
             perc_length = self.perc_length / self._n_channels
@@ -411,7 +411,7 @@ def _predict_proba_unvivariate(self, X) -> np.ndarray:
             2D np.ndarray of shape (n_cases, n_classes_)
             Predicted probabilities using the ordering in ``classes_``.
         """
-        X = TimeSeriesScaler().fit_transform(X).squeeze()
+        X = Normalizer().fit_transform(X).squeeze()
 
         pred_mat = np.zeros((X.shape[0], self.n_classes_))
 
@@ -455,7 +455,7 @@ def _predict_proba_dimension_ensemble(self, X) -> np.ndarray:
             2D np.ndarray of shape (n_cases, n_classes_)
             Predicted probabilities using the ordering in ``classes_``.
         """
-        X = TimeSeriesScaler().fit_transform(X)
+        X = Normalizer().fit_transform(X)
 
         ensemble_pred_mats = None
 

aeon/clustering/compose/tests/test_pipeline.py

@@ -18,9 +18,9 @@
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
+    Normalizer,
     Padder,
     Tabularizer,
-    TimeSeriesScaler,
 )
 from aeon.transformations.collection.feature_based import SevenNumberSummary
 
@@ -109,7 +109,7 @@ def test_unequal_tag_inference():
 
     t1 = SevenNumberSummary()
     t2 = Padder()
-    t3 = TimeSeriesScaler()
+    t3 = Normalizer()
     t4 = AutocorrelationFunctionTransformer(n_lags=5)
     t5 = StandardScaler()
     t6 = Tabularizer()
@@ -179,7 +179,7 @@ def test_missing_tag_inference():
 
     t1 = MockCollectionTransformer()
     t1.set_tags(**{"capability:missing_values": True, "removes_missing_values": True})
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = StandardScaler()
     t4 = Tabularizer()
 
@@ -229,7 +229,7 @@ def test_multivariate_tag_inference():
     X, y = make_example_3d_numpy(n_cases=10, n_channels=2, n_timepoints=12)
 
     t1 = SevenNumberSummary()
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = HOG1DTransformer()
     t4 = StandardScaler()
 

aeon/regression/compose/tests/test_pipeline.py

@@ -20,9 +20,9 @@
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,
+    Normalizer,
     Padder,
     Tabularizer,
-    TimeSeriesScaler,
 )
 from aeon.transformations.collection.feature_based import SevenNumberSummary
 
@@ -110,7 +110,7 @@ def test_unequal_tag_inference():
 
     t1 = SevenNumberSummary()
     t2 = Padder()
-    t3 = TimeSeriesScaler()
+    t3 = Normalizer()
     t4 = AutocorrelationFunctionTransformer(n_lags=5)
     t5 = StandardScaler()
     t6 = Tabularizer()
@@ -179,7 +179,7 @@ def test_missing_tag_inference():
 
     t1 = MockCollectionTransformer()
     t1.set_tags(**{"capability:missing_values": True, "removes_missing_values": True})
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = StandardScaler()
     t4 = Tabularizer()
 
@@ -230,7 +230,7 @@ def test_multivariate_tag_inference():
     )
 
     t1 = SevenNumberSummary()
-    t2 = TimeSeriesScaler()
+    t2 = Normalizer()
     t3 = HOG1DTransformer()
     t4 = StandardScaler()
 

aeon/testing/estimator_checking/tests/test_check_estimator.py

@@ -15,7 +15,7 @@
 )
 from aeon.testing.mock_estimators._mock_anomaly_detectors import MockAnomalyDetector
 from aeon.testing.utils.deep_equals import deep_equals
-from aeon.transformations.collection import TimeSeriesScaler
+from aeon.transformations.collection import Normalizer
 
 test_classes = [
     MockClassifier,
@@ -24,7 +24,7 @@
     MockSegmenter,
     MockAnomalyDetector,
     # MockMultivariateSeriesTransformer,
-    TimeSeriesScaler,
+    Normalizer,
     MockClassifierParams,
 ]
 test_classes = {c.__name__: c for c in test_classes}

aeon/transformations/collection/__init__.py

@@ -6,20 +6,21 @@
     # transformers
     "AutocorrelationFunctionTransformer",
     "ARCoefficientTransformer",
+    "Centerer",
     "DownsampleTransformer",
     "ElbowClassSum",
     "ElbowClassPairwise",
     "DWTTransformer",
     "HOG1DTransformer",
     "MatrixProfile",
+    "MinMaxScaler",
+    "Normalizer",
     "Padder",
     "PeriodogramTransformer",
-    "Tabularizer",
     "Resizer",
     "SlopeTransformer",
-    "TimeSeriesScaler",
     "Truncator",
-    "Normalise",
+    "Tabularizer",
 ]
 
 from aeon.transformations.collection._acf import AutocorrelationFunctionTransformer
@@ -28,12 +29,11 @@
 from aeon.transformations.collection._dwt import DWTTransformer
 from aeon.transformations.collection._hog1d import HOG1DTransformer
 from aeon.transformations.collection._matrix_profile import MatrixProfile
-from aeon.transformations.collection._normalise import Normalise
 from aeon.transformations.collection._pad import Padder
 from aeon.transformations.collection._periodogram import PeriodogramTransformer
 from aeon.transformations.collection._reduce import Tabularizer
+from aeon.transformations.collection._rescale import Centerer, MinMaxScaler, Normalizer
 from aeon.transformations.collection._resize import Resizer
-from aeon.transformations.collection._scaler import TimeSeriesScaler
 from aeon.transformations.collection._slope import SlopeTransformer
 from aeon.transformations.collection._truncate import Truncator
 from aeon.transformations.collection.base import BaseCollectionTransformer