Interval classifiers (#5)

* actually change python version

* dummy classifiers and sklearn lower bound change

* test fix

* test fix

* dev

* early sklearn version fixes

* all interval classifiers

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tsml"
-version = "0.0.4"
+version = "0.0.5"
 description = "A toolkit for time series machine learning algorithms."
 authors = [
     {name = "Matthew Middlehurst", email = "[email protected]"},
@@ -42,8 +42,9 @@ dependencies = [
 
 [project.optional-dependencies]
 extras = [
-    "pycatch22",
-    "pyfftw"
+    "pycatch22>=0.4.2",
+    "pyfftw>=0.12.0",
+    "statsmodels>=0.12.1",
 ]
 dev = [
     "pre-commit",

tsml/__init__.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """tsml."""
 
-__version__ = "0.0.4"
+__version__ = "0.0.5"

tsml/dummy/_dummy.py

@@ -104,7 +104,7 @@ def fit(self, X, y):
         for index, classVal in enumerate(self.classes_):
             self.class_dictionary_[classVal] = index
 
-        if len(self.classes_) == 1:
+        if self.n_classes_ == 1:
             return self
 
         self._clf = SklearnDummyClassifier(
@@ -120,12 +120,12 @@ def predict(self, X) -> np.ndarray:
         """"""
         check_is_fitted(self)
 
+        X = self._validate_data(X=X, reset=False, ensure_min_series_length=1)
+
         # treat case of single class seen in fit
         if self.n_classes_ == 1:
             return np.repeat(list(self.class_dictionary_.keys()), X.shape[0], axis=0)
 
-        X = self._validate_data(X=X, reset=False, ensure_min_series_length=1)
-
         return self._clf.predict(np.zeros(X.shape))
 
     def predict_proba(self, X) -> np.ndarray:

tsml/feature_based/__init__.py

@@ -6,4 +6,4 @@
     "Catch22Regressor",
 ]
 
-from tsml.feature_based._catch22_classifier import Catch22Classifier, Catch22Regressor
+from tsml.feature_based._catch22 import Catch22Classifier, Catch22Regressor

tsml/feature_based/_catch22_classifier.py → tsml/feature_based/_catch22.py

@@ -123,6 +123,9 @@ def fit(self, X, y):
         for index, classVal in enumerate(self.classes_):
             self.class_dictionary_[classVal] = index
 
+        if self.n_classes_ == 1:
+            return self
+
         self._n_jobs = check_n_jobs(self.n_jobs)
 
         self._transformer = Catch22Transformer(
@@ -164,6 +167,10 @@ def predict(self, X) -> np.ndarray:
         """
         check_is_fitted(self)
 
+        # treat case of single class seen in fit
+        if self.n_classes_ == 1:
+            return np.repeat(list(self.class_dictionary_.keys()), X.shape[0], axis=0)
+
         X = self._validate_data(X=X, reset=False)
 
         return self._estimator.predict(self._transformer.transform(X))
@@ -183,6 +190,10 @@ def predict_proba(self, X) -> np.ndarray:
         """
         check_is_fitted(self)
 
+        # treat case of single class seen in fit
+        if self.n_classes_ == 1:
+            return np.repeat([[1]], X.shape[0], axis=0)
+
         X = self._validate_data(X=X, reset=False)
 
         m = getattr(self._estimator, "predict_proba", None)

tsml/interval_based/__init__.py

@@ -5,23 +5,28 @@
     "BaseIntervalForest",
     "CIFClassifier",
     "CIFRegressor",
-    # "DrCIFClassifier",
-    # "DrCIFRegressor",
+    "DrCIFClassifier",
+    "DrCIFRegressor",
     "IntervalForestClassifier",
     "IntervalForestRegressor",
     "RandomIntervalClassifier",
     "RandomIntervalRegressor",
     "SupervisedIntervalClassifier",
-    # "RISEClassifier",
-    # "RISERegressor",
-    # "STSFClassifier",
-    # "RSTSFClassifier",
+    "RISEClassifier",
+    "RISERegressor",
+    "STSFClassifier",
+    "RSTSFClassifier",
     "TSFClassifier",
     "TSFRegressor",
 ]
 
 from tsml.interval_based._base import BaseIntervalForest
-from tsml.interval_based._cif import CIFClassifier, CIFRegressor
+from tsml.interval_based._cif import (
+    CIFClassifier,
+    CIFRegressor,
+    DrCIFClassifier,
+    DrCIFRegressor,
+)
 from tsml.interval_based._interval_forest import (
     IntervalForestClassifier,
     IntervalForestRegressor,
@@ -31,4 +36,6 @@
     RandomIntervalRegressor,
     SupervisedIntervalClassifier,
 )
+from tsml.interval_based._rise import RISEClassifier, RISERegressor
+from tsml.interval_based._stsf import RSTSFClassifier, STSFClassifier
 from tsml.interval_based._tsf import TSFClassifier, TSFRegressor