Time series classifiers refactor/Shape_DTW

sktime/classification/distance_based/_shape_dtw.py

@@ -6,26 +6,22 @@
 
 import numpy as np
 import pandas as pd
-from sktime.utils.validation.panel import check_X, check_X_y
-from sktime.datatypes._panel._convert import from_nested_to_2d_array
 
 # Tuning
-from sklearn.model_selection import GridSearchCV
-from sklearn.model_selection import KFold
+from sklearn.model_selection import GridSearchCV, KFold
 
-# Transforms
-from sktime.transformations.panel.segment import SlidingWindowSegmenter
+# Classifiers
+from sktime.classification.base import BaseClassifier
+from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
+from sktime.datatypes._panel._convert import from_nested_to_2d_array
 from sktime.transformations.panel.dictionary_based._paa import PAA
 from sktime.transformations.panel.dwt import DWTTransformer
-from sktime.transformations.panel.slope import SlopeTransformer
-from sktime.transformations.panel.summarize._extract import (
-    DerivativeSlopeTransformer,
-)
 from sktime.transformations.panel.hog1d import HOG1DTransformer
 
-# Classifiers
-from sktime.classification.base import BaseClassifier
-from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
+# Transforms
+from sktime.transformations.panel.segment import SlidingWindowSegmenter
+from sktime.transformations.panel.slope import SlopeTransformer
+from sktime.transformations.panel.summarize._extract import DerivativeSlopeTransformer
 
 __author__ = ["Vincent Nicholson"]
 
@@ -114,15 +110,6 @@ class ShapeDTW(BaseClassifier):
 
     """
 
-    # Capability tags
-    capabilities = {
-        "multivariate": False,
-        "unequal_length": False,
-        "missing_values": False,
-        "train_estimate": False,
-        "contractable": False,
-    }
-
     def __init__(
         self,
         n_neighbours=1,
@@ -138,7 +125,7 @@ def __init__(
         self.metric_params = metric_params
         super(ShapeDTW, self).__init__()
 
-    def fit(self, X, y):
+    def _fit(self, X, y):
         """Train the classifier.
 
         Parameters
@@ -159,8 +146,6 @@ def fit(self, X, y):
                 + "' instead."
             )
 
-        X, y = check_X_y(X, y, enforce_univariate=False)
-
         if self.metric_params is None:
             self.metric_params = {}
 
@@ -261,7 +246,7 @@ def _preprocess(self, X):
 
         return X
 
-    def predict_proba(self, X):
+    def _predict_proba(self, X):
         """Perform predictions on the testing data X.
 
         This function returns the probabilities for each class.
@@ -275,15 +260,13 @@ def predict_proba(self, X):
         output : numpy array of shape =
                 [n_instances, num_classes] of probabilities
         """
-        X = check_X(X, enforce_univariate=False)
-
         # Transform the test data in the same way as the training data.
         X = self._preprocess(X)
 
         # Classify the test data
         return self.knn.predict_proba(X)
 
-    def predict(self, X):
+    def _predict(self, X):
         """Find predictions for all cases in X.
 
         Parameters
@@ -294,8 +277,6 @@ def predict(self, X):
         -------
         output : numpy array of shape = [n_instances]
         """
-        X = check_X(X, enforce_univariate=False)
-
         # Transform the test data in the same way as the training data.
         X = self._preprocess(X)