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[ENH] Replace SFA with SFAFast in REDCOMETS #2418

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[ENH] Replace SFA with SFAFast in REDCOMETS #2418

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29 changes: 14 additions & 15 deletions aeon/classification/dictionary_based/_redcomets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@

from aeon.classification.base import BaseClassifier
from aeon.transformations.collection import Normalizer
from aeon.transformations.collection.dictionary_based import SAX, SFA
from aeon.transformations.collection.dictionary_based import SAX, SFAFast
from aeon.utils.validation._dependencies import _check_soft_dependencies


Expand Down Expand Up @@ -62,7 +62,7 @@ class REDCOMETS(BaseClassifier):

See Also
--------
SAX, SFA
SAX, SFA, SFAFast

Notes
-----
Expand Down Expand Up @@ -182,9 +182,9 @@ def _build_univariate_ensemble(self, X, y):
Returns
-------
sfa_transforms :
List of ``SFA()`` instances with random word length and alpabet size
List of ``SFAFast()`` instances with random word length and alpabet size
sfa_clfs :
List of ``(RandomForestClassifier(), weight)`` tuples fitted on `SFA`
List of ``(RandomForestClassifier(), weight)`` tuples fitted on `SFAFast`
transformed training data
sax_transforms :
List of ``SAX()`` instances with random word length and alpabet size
Expand Down Expand Up @@ -241,7 +241,7 @@ def _build_univariate_ensemble(self, X, y):
cv = np.min([5, len(y_smote) // len(list(set(y_smote)))])

sfa_transforms = [
SFA(
SFAFast(
word_length=w,
alphabet_size=a,
window_size=X_smote.shape[1],
Expand All @@ -254,8 +254,9 @@ def _build_univariate_ensemble(self, X, y):

sfa_clfs = []
for sfa in sfa_transforms:
sfa_dics = sfa.fit_transform(X_smote, y_smote)
X_sfa = np.array([sfa.word_list(list(d.keys())[0]) for d in sfa_dics[0]])
sfa.fit(X_smote, y_smote)
sfa_dics = sfa.transform_words(X_smote)
X_sfa = sfa_dics[:, 0, :]

rf = RandomForestClassifier(
n_estimators=self.n_trees,
Expand Down Expand Up @@ -318,11 +319,11 @@ def _build_dimension_ensemble(self, X, y):
Returns
-------
sfa_transforms : list
List of lists of ``SFA()`` instances with random word length and alpabet
List of lists of ``SFAFast()`` instances with random word length and alpabet
size
sfa_clfs : list
List of lists of ``(RandomForestClassifier(), weight)`` tuples fitted on
`SFA` transformed training data
`SFAFast` transformed training data
sax_transforms : list
List of lists of ``SAX()`` instances with random word length and alpabet
size
Expand Down Expand Up @@ -416,8 +417,8 @@ def _predict_proba_unvivariate(self, X) -> np.ndarray:
pred_mat = np.zeros((X.shape[0], self.n_classes_))

for sfa, (rf, weight) in zip(self.sfa_transforms, self.sfa_clfs):
sfa_dics = sfa.transform(X)
X_sfa = np.array([sfa.word_list(list(d.keys())[0]) for d in sfa_dics[0]])
sfa_dics = sfa.transform_words(X)
X_sfa = sfa_dics[:, 0, :]

rf_pred_mat = rf.predict_proba(X_sfa)

Expand Down Expand Up @@ -471,10 +472,8 @@ def _predict_proba_dimension_ensemble(self, X) -> np.ndarray:
if self.variant in [6, 7, 8, 9]:
dimension_pred_mats = None
for sfa, (rf, _) in zip(sfa_transforms, sfa_clfs):
sfa_dics = sfa.transform(X_d)
X_sfa = np.array(
[sfa.word_list(list(d.keys())[0]) for d in sfa_dics[0]]
)
sfa_dics = sfa.transform_words(X_d)
X_sfa = sfa_dics[:, 0, :]

rf_pred_mat = rf.predict_proba(X_sfa)

Expand Down