From f01b0322817fb8b3e0979bb84569a3c55a0f6ccd Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 17:08:59 +0100
Subject: [PATCH 01/28] base

---
 .gitignore                    | 1 +
 sktime/classification/base.py | 8 ++++++++
 2 files changed, 9 insertions(+)

diff --git a/.gitignore b/.gitignore
index 569c3277c5f..73d7ddd9b33 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,4 @@ sktime/contrib/nothing_to_see_here.py
 sktime/distances/elastic_cython.html
 *.html
 sktime/contrib/distance_based/old_time_series_neighbors.py
+sktime/contrib/debug.py
diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 0d48d814d7b..7a2a3afa48f 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -75,6 +75,11 @@ class BaseClassifier(BaseEstimator):
     _tags = {
         "coerce-X-to-numpy": True,
         "coerce-X-to-pandas": False,
+        "capability:multivariate": False,
+        "capability:unequal_length": False,
+        "capability:missing_values": False,
+        "capability:train_estimate": False,
+        "capability:contractable": False,
     }
 
     def __init__(self):
@@ -105,6 +110,9 @@ def fit(self, X, y):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy", False)
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas", False)
+
+
+        enforce_univariate = self.get_tag("coerce-X-to-pandas", False)
         X, y = check_X_y(
             X, y, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas
         )

From 3c14b91d0ccef1f6579578551300d48d1dbbce3d Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 17:59:06 +0100
Subject: [PATCH 02/28] enforce univariate in base class

---
 sktime/classification/base.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 7a2a3afa48f..328ac4857de 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -108,13 +108,13 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", False)
-
-
-        enforce_univariate = self.get_tag("coerce-X-to-pandas", False)
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
+        enforce_univariate = self.get_tag("capability:multivariate",
+                                          tag_value_default=False)
         X, y = check_X_y(
-            X, y, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas
+            X, y, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            enforce_univariate=enforce_univariate
         )
 
         self._fit(X, y)

From 498b8ddf89104a612bf2d10eed0f62138b6f6560 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 18:03:42 +0100
Subject: [PATCH 03/28] remove unnecessary classifier tags

---
 sktime/registry/_tags.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sktime/registry/_tags.py b/sktime/registry/_tags.py
index 608a7b8182e..fa7fb61d57b 100644
--- a/sktime/registry/_tags.py
+++ b/sktime/registry/_tags.py
@@ -89,13 +89,13 @@
     ),
     (
         "X-y-must-have-same-index",
-        ["forecaster", "classifier", "regressor"],
+        ["forecaster", "regressor"],
         "bool",
         "do X/y in fit/update and X/fh in predict have to be same indices?",
     ),
     (
         "enforce_index_type",
-        ["forecaster", "classifier", "regressor"],
+        ["forecaster", "regressor"],
         "type",
         "passed to input checks, input conversion index type to enforce",
     ),

From 8b72f4daab0b8711c8c82079c8d73e5e35e7834d Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 18:08:27 +0100
Subject: [PATCH 04/28] predict and predict_proba

---
 sktime/classification/base.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 328ac4857de..8a51874b1b6 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -138,10 +138,13 @@ def predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", False)
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
+        enforce_univariate = self.get_tag("capability:multivariate",
+                                          tag_value_default=False)
         X = check_X(
-            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas
+            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            enforce_univariate=enforce_univariate
         )
         self.check_is_fitted()
 
@@ -163,10 +166,13 @@ def predict_proba(self, X):
         -------
         y : array-like, shape =  [n_instances, n_classes] - predictive pmf
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", False)
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
+        enforce_univariate = self.get_tag("capability:multivariate",
+                                          tag_value_default=False)
         X = check_X(
-            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas
+            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            enforce_univariate=enforce_univariate
         )
         self.check_is_fitted()
         return self._predict_proba(X)

From b79f65bfbaa4c6c64dc8431ccfe9343a6abf45d0 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:01:22 +0100
Subject: [PATCH 05/28] tweaks to classifier base class

---
 sktime/base/_base.py          |  8 +++--
 sktime/classification/base.py | 57 +++++++++--------------------------
 2 files changed, 19 insertions(+), 46 deletions(-)

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index b1b75665d0d..7cc2eb47b7d 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -142,17 +142,19 @@ def get_tag(self, tag_name, tag_value_default=None, raise_error=True):
         tag_value_default : any type, optional; default=None
             Default/fallback value if tag is not found
         raise_error : bool
-            whether a ValueError is raised when the tag is not found
+            whether a ValueError is raised when the tag is not found and
+            tag_value_default is None
 
         Returns
         -------
         tag_value :
             Value of the `tag_name` tag in self. If not found, returns
-            `tag_value_default`.
+            `tag_value_default`, or raises a ValueError if `tag_value_default` is None.
 
         Raises
         ------
-        ValueError if raise_error is True and tag_name does not exist
+        ValueError if raise_error is True, tag_name does not exist and
+        `tag_value_default` is None.
             i.e., if tag_name is not in self.get_tags().keys()
         """
         collected_tags = self.get_tags()
diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 8a51874b1b6..f1234a4e96e 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -8,6 +8,7 @@ class name: BaseClassifier
 Scitype defining methods:
     fitting         - fit(self, X, y)
     predicting      - predict(self, X)
+                    - predict_proba(self, X)
 
 State:
     fitted model/strategy   - by convention, any attributes ending in "_"
@@ -26,50 +27,20 @@ class name: BaseClassifier
 
 __all__ = [
     "BaseClassifier",
-    "classifier_list",
 ]
-__author__ = ["mloning", "fkiraly"]
+__author__ = ["mloning", "fkiraly","TonyBagnall"]
 
 import numpy as np
 
 from sktime.base import BaseEstimator
 from sktime.utils.validation.panel import check_X, check_X_y
 
-"""
-Main list of classifiers extending this class. For clarity, some utility classifiers,
-such as Proximity Stump, are not listed.
-"""
-classifier_list = [
-    # in classification/distance_based
-    "ProximityForest",
-    # "KNeighborsTimeSeriesClassifier",
-    # "ElasticEnsemble",
-    # "ShapeDTW",
-    # in classification/dictionary_based
-    "BOSS",
-    "ContractableBOSS",
-    "TemporalDictionaryEnsemble",
-    "WEASEL",
-    "MUSE",
-    # in classification/interval_based
-    "RandomIntervalSpectralForest",
-    "TimeSeriesForest",
-    "CanonicalIntervalForest",
-    # in classification/shapelet_based
-    "ShapeletTransformClassifier",
-    "ROCKET",
-    "MrSEQLClassifier",
-]
-
 
 class BaseClassifier(BaseEstimator):
     """Base time series classifier template class.
 
-    The base classifier specifies the methods and method
-    signatures that all forecasters have to implement.
-
-    Specific implementations of these methods is deferred to concrete
-    forecasters.
+    The base classifier specifies the methods and method signatures that all
+    classifiers have to implement.
     """
 
     _tags = {
@@ -94,8 +65,7 @@ def fit(self, X, y):
         ----------
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
-                or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
         y : array-like, shape =  [n_instances] - the class labels.
 
         Returns
@@ -132,7 +102,7 @@ def predict(self, X):
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
 
         Returns
         -------
@@ -160,11 +130,12 @@ def predict_proba(self, X):
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
 
         Returns
         -------
-        y : array-like, shape =  [n_instances, n_classes] - predictive pmf
+        y : array-like, shape =  [n_instances, n_classes] - estimated class
+        probabilities
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
@@ -185,7 +156,7 @@ def score(self, X, y):
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
         y : array-like, shape =  [n_instances] - predicted class labels
 
         Returns
@@ -199,14 +170,14 @@ def score(self, X, y):
     def _fit(self, X, y):
         """Fit time series classifier to training data.
 
-        core logic
+       Abstract method
 
         Parameters
         ----------
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
         y : array-like, shape = [n_instances] - the class labels
 
         Returns
@@ -224,14 +195,14 @@ def _fit(self, X, y):
     def _predict(self, X):
         """Predicts labels for sequences in X.
 
-        core logic
+        Default behaviour: make predictions from predict_proba
 
         Parameters
         ----------
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
 
         Returns
         -------

From 9d36d91394e6b8480306258ae7ff34f7e9cdf7ae Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:10:56 +0100
Subject: [PATCH 06/28] formatting 1

---
 sktime/classification/base.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index f1234a4e96e..c66ab6eba1f 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -28,7 +28,7 @@ class name: BaseClassifier
 __all__ = [
     "BaseClassifier",
 ]
-__author__ = ["mloning", "fkiraly","TonyBagnall"]
+__author__ = ["mloning", "fkiraly", "TonyBagnall"]
 
 import numpy as np
 
@@ -80,8 +80,9 @@ def fit(self, X, y):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag("capability:multivariate",
-                                          tag_value_default=False)
+        enforce_univariate = self.get_tag(
+            "capability:multivariate", tag_value_default=False
+        )
         X, y = check_X_y(
             X, y, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=enforce_univariate
@@ -110,10 +111,13 @@ def predict(self, X):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag("capability:multivariate",
-                                          tag_value_default=False)
+        enforce_univariate = self.get_tag(
+            "capability:multivariate", tag_value_default=False
+        )
         X = check_X(
-            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            X,
+            coerce_to_numpy=coerce_to_numpy,
+            coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=enforce_univariate
         )
         self.check_is_fitted()
@@ -139,10 +143,13 @@ def predict_proba(self, X):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag("capability:multivariate",
-                                          tag_value_default=False)
+        enforce_univariate = self.get_tag(
+            "capability:multivariate", tag_value_default=False
+        )
         X = check_X(
-            X, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            X,
+            coerce_to_numpy=coerce_to_numpy,
+            coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=enforce_univariate
         )
         self.check_is_fitted()
@@ -170,7 +177,7 @@ def score(self, X, y):
     def _fit(self, X, y):
         """Fit time series classifier to training data.
 
-       Abstract method
+        Abstract method
 
         Parameters
         ----------

From 4ccd2dfecd2e4a1a8aba44d6a4ad51c60153bdff Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:13:49 +0100
Subject: [PATCH 07/28] formatting 3

---
 sktime/classification/base.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index c66ab6eba1f..0aecd48d875 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -84,7 +84,10 @@ def fit(self, X, y):
             "capability:multivariate", tag_value_default=False
         )
         X, y = check_X_y(
-            X, y, coerce_to_numpy=coerce_to_numpy, coerce_to_pandas=coerce_to_pandas,
+            X,
+            y,
+            coerce_to_numpy=coerce_to_numpy,
+            coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=enforce_univariate
         )
 
@@ -118,7 +121,7 @@ def predict(self, X):
             X,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate
+            enforce_univariate=enforce_univariate,
         )
         self.check_is_fitted()
 
@@ -150,7 +153,7 @@ def predict_proba(self, X):
             X,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate
+            enforce_univariate=enforce_univariate,
         )
         self.check_is_fitted()
         return self._predict_proba(X)

From 5503c54debe4b80f338356497d3ac80015f2adbd Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:16:03 +0100
Subject: [PATCH 08/28] formatting 4

---
 sktime/classification/base.py | 2 +-
 sktime/registry/_tags.py      | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 0aecd48d875..f28b42ca7c3 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -88,7 +88,7 @@ def fit(self, X, y):
             y,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate
+            enforce_univariate=enforce_univariate,
         )
 
         self._fit(X, y)
diff --git a/sktime/registry/_tags.py b/sktime/registry/_tags.py
index fa7fb61d57b..b1f7b6842f5 100644
--- a/sktime/registry/_tags.py
+++ b/sktime/registry/_tags.py
@@ -43,7 +43,6 @@
 
 import pandas as pd
 
-
 ESTIMATOR_TAG_REGISTER = [
     (
         "ignores-exogeneous-X",

From 50ca231ef97dec47c539d69561054fb11019cf33 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:19:35 +0100
Subject: [PATCH 09/28] formatting 6?

---
 sktime/base/_base.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index 7cc2eb47b7d..cf442e57596 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -49,13 +49,10 @@ class name: BaseEstimator
 __all__ = ["BaseEstimator", "BaseObject"]
 
 import inspect
-
 from copy import deepcopy
-
 from sklearn import clone
 from sklearn.base import BaseEstimator as _BaseEstimator
 from sklearn.ensemble._base import _set_random_states
-
 from sktime.exceptions import NotFittedError
 
 

From f7dc72901a9d652bacf46f4eb09477443a86bcee Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Wed, 13 Oct 2021 20:22:23 +0100
Subject: [PATCH 10/28] blank lines or no blank lines?

---
 sktime/base/_base.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index cf442e57596..c7645cac09d 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -50,9 +50,11 @@ class name: BaseEstimator
 
 import inspect
 from copy import deepcopy
+
 from sklearn import clone
 from sklearn.base import BaseEstimator as _BaseEstimator
 from sklearn.ensemble._base import _set_random_states
+
 from sktime.exceptions import NotFittedError
 
 

From 5919fbb8c55f30d707866be31c4bee9e8277f3e6 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 14 Oct 2021 05:00:47 +0100
Subject: [PATCH 11/28] remove unnecessary argument to get_tag

---
 sktime/classification/base.py | 24 +++++++++---------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index f28b42ca7c3..21e14d7ee29 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -78,11 +78,9 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        enforce_univariate = self.get_tag("capability:multivariate")
         X, y = check_X_y(
             X,
             y,
@@ -112,11 +110,9 @@ def predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        enforce_univariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,
@@ -144,11 +140,9 @@ def predict_proba(self, X):
         y : array-like, shape =  [n_instances, n_classes] - estimated class
         probabilities
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        enforce_univariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,

From 4bdb4574053ae56b4cb3a87842602cfecaafba9c Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 14 Oct 2021 10:17:52 +0100
Subject: [PATCH 12/28] negate tag correctly, remove unnecessary get_tag
 argument

---
 sktime/classification/base.py | 30 ++++++++++++------------------
 1 file changed, 12 insertions(+), 18 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index f28b42ca7c3..1b3934a656b 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -78,17 +78,15 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X, y = check_X_y(
             X,
             y,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate,
+            enforce_univariate=not allow_multivariate,
         )
 
         self._fit(X, y)
@@ -112,16 +110,14 @@ def predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate,
+            enforce_univariate=not allow_multivariate,
         )
         self.check_is_fitted()
 
@@ -144,16 +140,14 @@ def predict_proba(self, X):
         y : array-like, shape =  [n_instances, n_classes] - estimated class
         probabilities
         """
-        coerce_to_numpy = self.get_tag("coerce-X-to-numpy", tag_value_default=False)
-        coerce_to_pandas = self.get_tag("coerce-X-to-pandas", tag_value_default=False)
-        enforce_univariate = self.get_tag(
-            "capability:multivariate", tag_value_default=False
-        )
+        coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
+        coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,
             coerce_to_pandas=coerce_to_pandas,
-            enforce_univariate=enforce_univariate,
+            enforce_univariate=not allow_multivariate,
         )
         self.check_is_fitted()
         return self._predict_proba(X)

From 6accbc6dee25a8752af0a92b79973710bc4bc8a2 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 14 Oct 2021 10:21:13 +0100
Subject: [PATCH 13/28] correct tag negation

---
 sktime/classification/base.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 59d623e8859..1b3934a656b 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -80,7 +80,7 @@ def fit(self, X, y):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
-        enforce_univariate = self.get_tag("capability:multivariate")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X, y = check_X_y(
             X,
             y,
@@ -112,7 +112,7 @@ def predict(self, X):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
-        enforce_univariate = self.get_tag("capability:multivariate")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,
@@ -142,7 +142,7 @@ def predict_proba(self, X):
         """
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
-        enforce_univariate = self.get_tag("capability:multivariate")
+        allow_multivariate = self.get_tag("capability:multivariate")
         X = check_X(
             X,
             coerce_to_numpy=coerce_to_numpy,

From d40840885b6a8925737656062677d7fc203809bc Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Sat, 16 Oct 2021 13:20:49 +0100
Subject: [PATCH 14/28] _predict _predict_proba

_predict made abstract, _
predict_proba given a default implmentation
n_classes_ added to base class
---
 sktime/classification/base.py | 34 ++++++++++++++++++++--------------
 1 file changed, 20 insertions(+), 14 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 1b3934a656b..607f0bb25a6 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -55,6 +55,7 @@ class BaseClassifier(BaseEstimator):
 
     def __init__(self):
         self._is_fitted = False
+        self.n_classes_ = 0
 
         super(BaseClassifier, self).__init__()
 
@@ -88,7 +89,7 @@ def fit(self, X, y):
             coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=not allow_multivariate,
         )
-
+        self.n_classes_ = np.unique(y).shape[0]
         self._fit(X, y)
 
         # this should happen last
@@ -194,12 +195,13 @@ def _fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-        raise NotImplementedError("abstract method")
+        raise NotImplementedError("_fit is a protected abstract method, it must be "
+                                  "implemented.")
 
     def _predict(self, X):
         """Predicts labels for sequences in X.
 
-        Default behaviour: make predictions from predict_proba
+        Abstract class, must be implemented.
 
         Parameters
         ----------
@@ -212,28 +214,32 @@ def _predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
-        distributions = self.predict_proba(X)
-        predictions = []
-        for instance_index in range(0, X.shape[0]):
-            distribution = distributions[instance_index]
-            prediction = np.argmax(distribution)
-            predictions.append(prediction)
-        y = self.label_encoder.inverse_transform(predictions)
 
-        return y
+        raise NotImplementedError("_predict is a protected abstract method, it must be "
+                                  "implemented.")
 
     def _predict_proba(self, X):
         """Predicts labels probabilities for sequences in X.
 
+        Default behaviour is to call _predict and set the predicted class probability
+        to 1, other class probabilities to 0. Override if better estimates are
+        obtainable.
+
         Parameters
         ----------
         X : 3D np.array, array-like or sparse matrix
                 of shape = [n_instances,n_dimensions,series_length]
                 or shape = [n_instances,series_length]
-            or single-column pd.DataFrame with pd.Series entries
+            or pd.DataFrame with each column a dimension, each cell a pd.Series
 
         Returns
         -------
-        y : array-like, shape =  [n_instances, n_classes] - predictive pmf
+        y : array-like, shape =  [n_instances, n_classes] - estimated probabilities
+        of class membership.
         """
-        raise NotImplementedError("abstract method")
+        dists = np.zeros((X.shape[0], self.n_classes_))
+        preds = self._predict(X)
+        for i in range(0,len(preds)):
+            dists[preds[i]] = 1
+
+        return dists

From b17f9a7da271055f096e690aaa35e17758b1a082 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Sat, 16 Oct 2021 13:26:33 +0100
Subject: [PATCH 15/28] formatting 1

---
 sktime/classification/base.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 607f0bb25a6..917812ecc36 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -195,8 +195,9 @@ def _fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-        raise NotImplementedError("_fit is a protected abstract method, it must be "
-                                  "implemented.")
+        raise NotImplementedError(
+            "_fit is a protected abstract method, it must be implemented."
+        )
 
     def _predict(self, X):
         """Predicts labels for sequences in X.
@@ -215,8 +216,9 @@ def _predict(self, X):
         y : array-like, shape =  [n_instances] - predicted class labels
         """
 
-        raise NotImplementedError("_predict is a protected abstract method, it must be "
-                                  "implemented.")
+        raise NotImplementedError(
+            "_predict is a protected abstract method, it must be implemented."
+        )
 
     def _predict_proba(self, X):
         """Predicts labels probabilities for sequences in X.
@@ -239,7 +241,7 @@ def _predict_proba(self, X):
         """
         dists = np.zeros((X.shape[0], self.n_classes_))
         preds = self._predict(X)
-        for i in range(0,len(preds)):
+        for i in range(0, len(preds)):
             dists[preds[i]] = 1
 
         return dists

From 57347f783e2a8354fd62088dcfbe4240e1fc2d81 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Sat, 16 Oct 2021 13:29:00 +0100
Subject: [PATCH 16/28] formatting 2

---
 sktime/classification/base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 917812ecc36..b1aaac23138 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -215,7 +215,6 @@ def _predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
-
         raise NotImplementedError(
             "_predict is a protected abstract method, it must be implemented."
         )

From 8b29a99e983d2ff86e9d4a8aac913e4efa11ea74 Mon Sep 17 00:00:00 2001
From: Matthew Middlehurst <pfm15hbu@uea.ac.uk>
Date: Sun, 17 Oct 2021 21:12:44 +0100
Subject: [PATCH 17/28] HC comments an experiments fixes

---
 sktime/classification/base.py | 38 ++++++++++++++++++++++++++---------
 sktime/registry/_tags.py      | 10 +++++++--
 2 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index b1aaac23138..eb83de5beab 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -33,6 +33,7 @@ class name: BaseClassifier
 import numpy as np
 
 from sktime.base import BaseEstimator
+from sktime.utils.validation import check_n_jobs
 from sktime.utils.validation.panel import check_X, check_X_y
 
 
@@ -51,12 +52,14 @@ class BaseClassifier(BaseEstimator):
         "capability:missing_values": False,
         "capability:train_estimate": False,
         "capability:contractable": False,
+        "capability:multithreading": False,
     }
 
     def __init__(self):
-        self._is_fitted = False
+        self.classes_ = []
         self.n_classes_ = 0
-
+        self._class_dictionary = {}
+        self._threads_to_use = 1
         super(BaseClassifier, self).__init__()
 
     def fit(self, X, y):
@@ -89,7 +92,21 @@ def fit(self, X, y):
             coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=not allow_multivariate,
         )
-        self.n_classes_ = np.unique(y).shape[0]
+
+        multithread = self.get_tag("capability:multithreading")
+        if multithread:
+            try:
+                self._threads_to_use = check_n_jobs(self.n_jobs)
+            except NameError:
+                raise AttributeError(
+                    "self.n_jobs must be set if capability:multithreading is True"
+                )
+
+        self.classes_ = np.unique(y)
+        self.n_classes_ = self.classes_.shape[0]
+        for index, classVal in enumerate(self.classes_):
+            self._class_dictionary[classVal] = index
+
         self._fit(X, y)
 
         # this should happen last
@@ -111,6 +128,8 @@ def predict(self, X):
         -------
         y : array-like, shape =  [n_instances] - predicted class labels
         """
+        self.check_is_fitted()
+
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
         allow_multivariate = self.get_tag("capability:multivariate")
@@ -120,11 +139,8 @@ def predict(self, X):
             coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=not allow_multivariate,
         )
-        self.check_is_fitted()
-
-        y = self._predict(X)
 
-        return y
+        return self._predict(X)
 
     def predict_proba(self, X):
         """Predicts labels probabilities for sequences in X.
@@ -141,6 +157,8 @@ def predict_proba(self, X):
         y : array-like, shape =  [n_instances, n_classes] - estimated class
         probabilities
         """
+        self.check_is_fitted()
+
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
         allow_multivariate = self.get_tag("capability:multivariate")
@@ -150,7 +168,7 @@ def predict_proba(self, X):
             coerce_to_pandas=coerce_to_pandas,
             enforce_univariate=not allow_multivariate,
         )
-        self.check_is_fitted()
+
         return self._predict_proba(X)
 
     def score(self, X, y):
@@ -240,7 +258,7 @@ def _predict_proba(self, X):
         """
         dists = np.zeros((X.shape[0], self.n_classes_))
         preds = self._predict(X)
-        for i in range(0, len(preds)):
-            dists[preds[i]] = 1
+        for i in range(0, X.shape[0]):
+            dists[i, np.where(self.classes_ == preds[i])] = 1
 
         return dists
diff --git a/sktime/registry/_tags.py b/sktime/registry/_tags.py
index b1f7b6842f5..4867ec0932c 100644
--- a/sktime/registry/_tags.py
+++ b/sktime/registry/_tags.py
@@ -138,13 +138,13 @@
         "capability:multivariate",
         "classifier",
         "bool",
-        "can classifier classify time series with 2 or more variables?",
+        "can the classifier classify time series with 2 or more variables?",
     ),
     (
         "capability:unequal_length",
         "classifier",
         "bool",
-        "can classifier handle unequal length time series?",
+        "can the classifier handle unequal length time series?",
     ),
     # "capability:missing_values" is same as "handles-missing-data" tag.
     # They are kept distinct intentionally for easier TSC refactoring.
@@ -167,6 +167,12 @@
         "bool",
         "contract time setting, i.e. does the estimator support limiting max fit time?",
     ),
+    (
+        "capability:multithreading",
+        "classifier",
+        "bool",
+        "can the classifier set n_jobs to use multiple threads?",
+    ),
     (
         "coerce-X-to-pandas",
         ["classifier", "transformer"],

From b31afcda35f8349a83c052986180208a013cced7 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:16:08 +0100
Subject: [PATCH 18/28] Update base.py

---
 sktime/classification/base.py | 51 +++++++++++++++++++++--------------
 1 file changed, 31 insertions(+), 20 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index eb83de5beab..ea066a0f0da 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -67,10 +67,13 @@ def fit(self, X, y):
 
         Parameters
         ----------
-        X : 3D np.array, array-like or sparse matrix
-                of shape = [n_instances,n_dimensions,series_length]
-            or pd.DataFrame with each column a dimension, each cell a pd.Series
-        y : array-like, shape =  [n_instances] - the class labels.
+        X : 2D np.array (univariate, equal length series) of shape = [n_instances,
+        series_length]
+            or 3D np.array (any number of dimensions, equal length series) of shape =
+            [n_instances,n_dimensions,series_length]
+            or pd.DataFrame with each column a dimension, each cell a pd.Series (any
+            number of dimensions, equal or unequal length series)
+        y : 1D np.array of shape =  [n_instances] - the class labels.
 
         Returns
         -------
@@ -119,14 +122,16 @@ def predict(self, X):
 
         Parameters
         ----------
-        X : 3D np.array, array-like or sparse matrix
-                of shape = [n_instances,n_dimensions,series_length]
-                or shape = [n_instances,series_length]
-            or pd.DataFrame with each column a dimension, each cell a pd.Series
+        X : 2D np.array (univariate, equal length series) of shape = [n_instances,
+        series_length]
+            or 3D np.array (any number of dimensions, equal length series) of shape =
+            [n_instances,n_dimensions,series_length]
+            or pd.DataFrame with each column a dimension, each cell a pd.Series (any
+            number of dimensions, equal or unequal length series)
 
         Returns
         -------
-        y : array-like, shape =  [n_instances] - predicted class labels
+        y : 1D np.array of shape =  [n_instances] - predicted class labels
         """
         self.check_is_fitted()
 
@@ -147,14 +152,18 @@ def predict_proba(self, X):
 
         Parameters
         ----------
-        X : 3D np.array, array-like or sparse matrix
-                of shape = [n_instances,n_dimensions,series_length]
-                or shape = [n_instances,series_length]
-            or pd.DataFrame with each column a dimension, each cell a pd.Series
+        Parameters
+        ----------
+        X : 2D np.array (univariate, equal length series) of shape = [n_instances,
+        series_length]
+            or 3D np.array (any number of dimensions, equal length series) of shape =
+            [n_instances,n_dimensions,series_length]
+            or pd.DataFrame with each column a dimension, each cell a pd.Series (any
+            number of dimensions, equal or unequal length series)
 
         Returns
         -------
-        y : array-like, shape =  [n_instances, n_classes] - estimated class
+        y : 2D array of shape =  [n_instances, n_classes] - estimated class
         probabilities
         """
         self.check_is_fitted()
@@ -171,16 +180,18 @@ def predict_proba(self, X):
 
         return self._predict_proba(X)
 
-    def score(self, X, y):
+    def score(self, X, y) -> float:
         """Scores predicted labels against ground truth labels on X.
 
         Parameters
         ----------
-        X : 3D np.array, array-like or sparse matrix
-                of shape = [n_instances,n_dimensions,series_length]
-                or shape = [n_instances,series_length]
-            or pd.DataFrame with each column a dimension, each cell a pd.Series
-        y : array-like, shape =  [n_instances] - predicted class labels
+        X : 2D np.array (univariate, equal length series) of shape = [n_instances,
+        series_length]
+            or 3D np.array (any number of dimensions, equal length series) of shape =
+            [n_instances,n_dimensions,series_length]
+            or pd.DataFrame with each column a dimension, each cell a pd.Series (any
+            number of dimensions, equal or unequal length series)
+        y : array-like, shape =  [n_instances] - actual class labels
 
         Returns
         -------

From 26069f775d59257ea1da63758628020a9110b19b Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:19:41 +0100
Subject: [PATCH 19/28] Update base.py

---
 sktime/classification/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index ea066a0f0da..fa497e3c7ad 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -231,7 +231,7 @@ def _fit(self, X, y):
     def _predict(self, X):
         """Predicts labels for sequences in X.
 
-        Abstract class, must be implemented.
+        Abstract method, must be implemented.
 
         Parameters
         ----------

From 27949f6900b1e97c063ca02097403cc5194df017 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:30:29 +0100
Subject: [PATCH 20/28] fix comments in get_tags

---
 sktime/base/_base.py          | 9 ++++-----
 sktime/classification/base.py | 5 +++++
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index c7645cac09d..9c19b55e8d8 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -147,14 +147,13 @@ def get_tag(self, tag_name, tag_value_default=None, raise_error=True):
         Returns
         -------
         tag_value :
-            Value of the `tag_name` tag in self. If not found, returns
-            `tag_value_default`, or raises a ValueError if `tag_value_default` is None.
+            Value of the `tag_name` tag in self. If not found, returns an error if
+            raise_error is True, otherwise it returns `tag_value_default`.
 
         Raises
         ------
-        ValueError if raise_error is True, tag_name does not exist and
-        `tag_value_default` is None.
-            i.e., if tag_name is not in self.get_tags().keys()
+        ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(
+        ).keys()
         """
         collected_tags = self.get_tags()
 
diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index fa497e3c7ad..7f70c8d462f 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -85,6 +85,11 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
+#        blah = self.get_tag("not-here")
+#        print(" blah = ", blah)
+        blah = self.get_tag("not-here", raise_error=False)
+        print(" blah = ", blah)
+
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
         allow_multivariate = self.get_tag("capability:multivariate")

From 031a18b921c5dbd78b7b862f7294234451c4869f Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:34:13 +0100
Subject: [PATCH 21/28] remove debug

---
 sktime/classification/base.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 7f70c8d462f..fa497e3c7ad 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -85,11 +85,6 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-#        blah = self.get_tag("not-here")
-#        print(" blah = ", blah)
-        blah = self.get_tag("not-here", raise_error=False)
-        print(" blah = ", blah)
-
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
         allow_multivariate = self.get_tag("capability:multivariate")

From dee524b4323b11e6da4c31e37052f8222031756e Mon Sep 17 00:00:00 2001
From: Matthew Middlehurst <pfm15hbu@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:36:19 +0100
Subject: [PATCH 22/28] get_tag comment revert

---
 sktime/base/_base.py          | 3 +--
 sktime/classification/base.py | 7 -------
 2 files changed, 1 insertion(+), 9 deletions(-)

diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index 9c19b55e8d8..ea32fc4351d 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -141,8 +141,7 @@ def get_tag(self, tag_name, tag_value_default=None, raise_error=True):
         tag_value_default : any type, optional; default=None
             Default/fallback value if tag is not found
         raise_error : bool
-            whether a ValueError is raised when the tag is not found and
-            tag_value_default is None
+            whether a ValueError is raised when the tag is not found
 
         Returns
         -------
diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 7f70c8d462f..ffc52daf406 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -85,11 +85,6 @@ def fit(self, X, y):
         Changes state by creating a fitted model that updates attributes
         ending in "_" and sets is_fitted flag to True.
         """
-#        blah = self.get_tag("not-here")
-#        print(" blah = ", blah)
-        blah = self.get_tag("not-here", raise_error=False)
-        print(" blah = ", blah)
-
         coerce_to_numpy = self.get_tag("coerce-X-to-numpy")
         coerce_to_pandas = self.get_tag("coerce-X-to-pandas")
         allow_multivariate = self.get_tag("capability:multivariate")
@@ -155,8 +150,6 @@ def predict(self, X):
     def predict_proba(self, X):
         """Predicts labels probabilities for sequences in X.
 
-        Parameters
-        ----------
         Parameters
         ----------
         X : 2D np.array (univariate, equal length series) of shape = [n_instances,

From feade0ebc14157bce957a6fd976a342ad1f6dcde Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:36:22 +0100
Subject: [PATCH 23/28] format 1

---
 sktime/classification/base.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index fa497e3c7ad..ffc52daf406 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -150,8 +150,6 @@ def predict(self, X):
     def predict_proba(self, X):
         """Predicts labels probabilities for sequences in X.
 
-        Parameters
-        ----------
         Parameters
         ----------
         X : 2D np.array (univariate, equal length series) of shape = [n_instances,

From 87dfa72dc161f25ac1cc949965f3fdd898743282 Mon Sep 17 00:00:00 2001
From: Matthew Middlehurst <pfm15hbu@uea.ac.uk>
Date: Tue, 19 Oct 2021 10:40:38 +0100
Subject: [PATCH 24/28] doc consistency

---
 sktime/classification/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index ffc52daf406..3dcdef22f8f 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -202,7 +202,7 @@ def score(self, X, y) -> float:
     def _fit(self, X, y):
         """Fit time series classifier to training data.
 
-        Abstract method
+        Abstract method, must be implemented.
 
         Parameters
         ----------

From d85c4c6e53b5c1dd5de074fe3d668530bab395a6 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 13:38:07 +0100
Subject: [PATCH 25/28] change contributors on both base

Use markus's github instead of full name, added matthew.
---
 sktime/classification/base.py    | 11 +++--------
 sktime/forecasting/base/_base.py |  2 +-
 2 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 3dcdef22f8f..1bf399f2b12 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -1,20 +1,15 @@
 # -*- coding: utf-8 -*-
 # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
 """
-Base class template for time series classifier scitype.
+Base class template for time series classifier.
 
     class name: BaseClassifier
 
-Scitype defining methods:
+Defining methods:
     fitting         - fit(self, X, y)
     predicting      - predict(self, X)
                     - predict_proba(self, X)
 
-State:
-    fitted model/strategy   - by convention, any attributes ending in "_"
-    fitted state flag       - is_fitted (property)
-    fitted state inspection - check_is_fitted()
-
 Inspection methods:
     hyper-parameter inspection  - get_params()
     fitted parameter inspection - get_fitted_params()
@@ -28,7 +23,7 @@ class name: BaseClassifier
 __all__ = [
     "BaseClassifier",
 ]
-__author__ = ["mloning", "fkiraly", "TonyBagnall"]
+__author__ = ["mloning", "fkiraly", "TonyBagnall", "MatthewMiddlehurst"]
 
 import numpy as np
 
diff --git a/sktime/forecasting/base/_base.py b/sktime/forecasting/base/_base.py
index f2479325a5f..c47bb2f3426 100644
--- a/sktime/forecasting/base/_base.py
+++ b/sktime/forecasting/base/_base.py
@@ -26,7 +26,7 @@ class name: BaseForecaster
 """
 
 
-__author__ = ["Markus Löning", "@big-o", "fkiraly"]
+__author__ = ["mloning", "@big-o", "fkiraly"]
 __all__ = ["BaseForecaster"]
 
 from sktime.base import BaseEstimator

From f0b64bc97783d448c3d2d51b91209fef6e5ee85a Mon Sep 17 00:00:00 2001
From: Matthew Middlehurst <pfm15hbu@uea.ac.uk>
Date: Tue, 19 Oct 2021 14:00:31 +0100
Subject: [PATCH 26/28] class dictionary useage in predict_proba default

---
 sktime/classification/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 3dcdef22f8f..093da115e22 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -268,6 +268,6 @@ def _predict_proba(self, X):
         dists = np.zeros((X.shape[0], self.n_classes_))
         preds = self._predict(X)
         for i in range(0, X.shape[0]):
-            dists[i, np.where(self.classes_ == preds[i])] = 1
+            dists[i, self._class_dictionary[preds[i]]] = 1
 
         return dists

From aa7f500379077bb7f8cb7dd1be7c52af4c805269 Mon Sep 17 00:00:00 2001
From: Matthew Middlehurst <pfm15hbu@uea.ac.uk>
Date: Tue, 19 Oct 2021 14:08:05 +0100
Subject: [PATCH 27/28] code quality

---
 sktime/forecasting/base/_base.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/sktime/forecasting/base/_base.py b/sktime/forecasting/base/_base.py
index c47bb2f3426..be356537254 100644
--- a/sktime/forecasting/base/_base.py
+++ b/sktime/forecasting/base/_base.py
@@ -29,24 +29,23 @@ class name: BaseForecaster
 __author__ = ["mloning", "@big-o", "fkiraly"]
 __all__ = ["BaseForecaster"]
 
-from sktime.base import BaseEstimator
-
 from contextlib import contextmanager
 from warnings import warn
 
 import numpy as np
 import pandas as pd
 
-from sktime.utils.datetime import _shift
-from sktime.utils.validation.forecasting import check_X
-from sktime.utils.validation.forecasting import check_alpha
-from sktime.utils.validation.forecasting import check_cv
-from sktime.utils.validation.forecasting import check_fh
-from sktime.utils.validation.forecasting import check_y_X
-from sktime.utils.validation.series import check_series, check_equal_time_index
-
+from sktime.base import BaseEstimator
 from sktime.datatypes import convert_to, mtype
-
+from sktime.utils.datetime import _shift
+from sktime.utils.validation.forecasting import (
+    check_alpha,
+    check_cv,
+    check_fh,
+    check_X,
+    check_y_X,
+)
+from sktime.utils.validation.series import check_equal_time_index, check_series
 
 DEFAULT_ALPHA = 0.05
 

From a4b5b1a4250d033cb8ca863002e200d65df22475 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Tue, 19 Oct 2021 14:44:40 +0100
Subject: [PATCH 28/28] Update base.py

---
 sktime/classification/base.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sktime/classification/base.py b/sktime/classification/base.py
index 48d4163bb32..8767fdea9ba 100644
--- a/sktime/classification/base.py
+++ b/sktime/classification/base.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
 """
-Base class template for time series classifier.
+Abstract base class for time series classifiers.
 
     class name: BaseClassifier
 
@@ -33,7 +33,7 @@ class name: BaseClassifier
 
 
 class BaseClassifier(BaseEstimator):
-    """Base time series classifier template class.
+    """Abstract base class for time series classifiers.
 
     The base classifier specifies the methods and method signatures that all
     classifiers have to implement.