fixes

Author: MatthewMiddlehurst

.codecov.yml

@@ -16,6 +16,10 @@ coverage:
 # post comment on PR
 comment: false
 
+# enable codecov to report to GitHub status checks
+github_checks:
+  annotations: false
+
 # ignore files
 ignore:
   - ".github/"

docs/conf.py

@@ -38,7 +38,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 project = "tsml"
-copyright = "2022 - 2023, The tsml developers (BSD-3 License)"
+copyright = "The tsml developers (BSD-3 License)"
 author = "Matthew Middlehurst"
 
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tsml"
-version = "0.0.1"
+version = "0.0.2"
 description = "A toolkit for time series machine learning algorithms."
 authors = [
     {name = "Matthew Middlehurst", email = "m.middlehurst@uea.ac.uk"},

tsml/datasets/_data_io.py

@@ -58,7 +58,12 @@ def load_from_ts_file(
     Examples
     --------
     >>> from tsml.datasets import load_from_ts_file
-    >>> X, y = load_from_ts_file("MinimalChinatown/MinimalChinatown_TRAIN.ts")
+    >>> path = (
+    ... "MinimalChinatown/MinimalChinatown_TRAIN.ts"
+    ...  if os.path.exists("MinimalChinatown/MinimalChinatown_TRAIN.ts") else
+    ... "tsml/datasets/MinimalChinatown/MinimalChinatown_TRAIN.ts"
+    ... )
+    >>> X, y = load_from_ts_file(path)
     """
     # Initialize flags and variables used when parsing the file
     timestamps = False
@@ -100,7 +105,8 @@ def load_from_ts_file(
                 tokens = line.split(" ")
                 if len(tokens) == 1:
                     raise IOError(
-                        "Invalid .ts file. @problemname tag requires str value (the problems name)."
+                        "Invalid .ts file. @problemname tag requires str value "
+                        "(the problems name)."
                     )
             elif line.startswith("@timestamps"):
                 tokens = line.split(" ")
@@ -154,14 +160,16 @@ def load_from_ts_file(
                 tokens = line.split(" ")
                 if len(tokens) != 2:
                     raise IOError(
-                        "Invalid .ts file. @dimension tag requires a int value (the number of dimensions for the problem)."
+                        "Invalid .ts file. @dimension tag requires a int value "
+                        "(the number of dimensions for the problem)."
                     )
 
                 try:
                     dimensions = int(tokens[1])
                 except ValueError:
                     raise IOError(
-                        "Invalid .ts file. @dimension tag requires a int value (the number of dimensions for the problem)."
+                        "Invalid .ts file. @dimension tag requires a int value "
+                        "(the number of dimensions for the problem)."
                     )
 
                 dimensions_tag = True
@@ -185,21 +193,24 @@ def load_from_ts_file(
                 tokens = line.split(" ")
                 if len(tokens) != 2:
                     raise IOError(
-                        "Invalid .ts file. @serieslength tag requires a int value (the number of dimensions for the problem)."
+                        "Invalid .ts file. @serieslength tag requires a int value "
+                        "(the number of dimensions for the problem)."
                     )
 
                 try:
                     serieslength = int(tokens[1])
                 except ValueError:
                     raise IOError(
-                        "Invalid .ts file. @serieslength tag requires a int value (the number of dimensions for the problem)."
+                        "Invalid .ts file. @serieslength tag requires a int value "
+                        "(the number of dimensions for the problem)."
                     )
 
                 serieslength_tag = True
             elif line.startswith("@targetlabel"):
                 if classlabels_tag:
                     raise IOError(
-                        "Invalid .ts file. @targetlabel tag cannot be used with @classlabel tag."
+                        "Invalid .ts file. @targetlabel tag cannot be used with "
+                        "@classlabel tag."
                     )
 
                 tokens = line.split(" ")
@@ -219,14 +230,16 @@ def load_from_ts_file(
 
                 if token_len > 2:
                     raise IOError(
-                        f"Invalid .ts file. @targetlabel tag should not be accompanied with info apart from True/False, but found {tokens}."
+                        f"Invalid .ts file. @targetlabel tag should not be accompanied "
+                        f"with info apart from True/False, but found {tokens}."
                     )
 
                 targetlabels_tag = True
             elif line.startswith("@classlabel"):
                 if targetlabels_tag:
                     raise IOError(
-                        "Invalid .ts file. @classlabel tag cannot be used with @targetlabel tag."
+                        "Invalid .ts file. @classlabel tag cannot be used with "
+                        "@targetlabel tag."
                     )
 
                 tokens = line.split(" ")
@@ -246,12 +259,14 @@ def load_from_ts_file(
 
                 if not classlabel and token_len > 2:
                     raise IOError(
-                        f"Invalid .ts file. @classlabel tag should not be accompanied with additional info when False, but found {tokens}."
+                        f"Invalid .ts file. @classlabel tag should not be accompanied "
+                        f"with additional info when False, but found {tokens}."
                     )
 
                 elif classlabel and token_len == 2:
                     raise IOError(
-                        "Invalid .ts file. @classlabel tag is true but no Class values are supplied."
+                        "Invalid .ts file. @classlabel tag is true but no Class values "
+                        "are supplied."
                     )
 
                 class_label_list = [token.strip() for token in tokens[2:]]
@@ -272,7 +287,8 @@ def load_from_ts_file(
 
             if len(lines) == data_start_line + 1:
                 raise IOError(
-                    "Invalid .ts file. A @data tag is present but no subsequent data is present."
+                    "Invalid .ts file. A @data tag is present but no subsequent data "
+                    "is present."
                 )
             else:
                 first_line = lines[data_start_line].split(":")
@@ -284,7 +300,8 @@ def load_from_ts_file(
                 has_labels = classlabel
             else:
                 raise IOError(
-                    "Unable to read .ts file. A @classlabel or @targetlabel tag is required."
+                    "Unable to read .ts file. A @classlabel or @targetlabel tag is "
+                    "required."
                 )
 
             # Equal length tag is required.
@@ -309,7 +326,8 @@ def load_from_ts_file(
                 not timestamps_tag or (timestamps_tag and not timestamps)
             ) and has_timestamps:
                 raise IOError(
-                    "Value mismatch in .ts file. @timestamps tag is missing or False but data has timestamps. Timestamps are currently not supported."
+                    "Value mismatch in .ts file. @timestamps tag is missing or False "
+                    "but data has timestamps. Timestamps are currently not supported."
                 )
             elif has_timestamps:
                 raise IOError(
@@ -322,17 +340,20 @@ def load_from_ts_file(
                 not univariate_tag or (univariate_tag and univariate)
             ) and data_dims > 1:
                 raise IOError(
-                    "Value mismatch in .ts file. @univariate tag is missing or True but data has more than one dimension."
+                    "Value mismatch in .ts file. @univariate tag is missing or True "
+                    "but data has more than one dimension."
                 )
 
             if dimensions_tag and dimensions != data_dims:
                 raise IOError(
-                    f"Value mismatch in .ts file. @dimensions tag value {dimensions} and read number of dimensions {data_dims} do not match."
+                    f"Value mismatch in .ts file. @dimensions tag value {dimensions} "
+                    f"and read number of dimensions {data_dims} do not match."
                 )
 
             if serieslength_tag and serieslength != data_length:
                 raise IOError(
-                    f"Value mismatch in .ts file. @serieslength tag value {serieslength} and read series length {data_length} do not match."
+                    f"Value mismatch in .ts file. @serieslength tag value "
+                    f"{serieslength} and read series length {data_length} do not match."
                 )
 
             if equallength:
@@ -390,7 +411,8 @@ def load_from_ts_file(
 
     if not data_started:
         raise IOError(
-            "Invalid .ts file. File contains metadata but no @data tag to signal the start of data."
+            "Invalid .ts file. File contains metadata but no @data tag to signal the "
+            "start of data."
         )
 
     if has_labels:

tsml/utils/numba_functions/general.py

@@ -60,7 +60,7 @@ def unique_count(X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
             else:
                 counts[uc] += 1
         return unique[: uc + 1], counts[: uc + 1]
-    return np.zeros(0, dtype=np.int32), np.zeros(0, dtype=np.int32)
+    return np.zeros(0), np.zeros(0, dtype=np.int32)
 
 
 @njit(fastmath=True, cache=True)

tsml/utils/testing.py

@@ -36,7 +36,7 @@ def generate_test_estimators() -> list[BaseEstimator]:
     """
     classes = all_estimators()
     estimators = []
-    for i, c in enumerate(classes):
+    for c in classes:
         m = getattr(c[1], "get_test_params", None)
         if callable(m):
             params = c[1].get_test_params()
@@ -81,10 +81,10 @@ def parametrize_with_checks(estimators: list[BaseEstimator]) -> Callable:
     Examples
     --------
     >>> from tsml.utils.testing import parametrize_with_checks
-    >>> from tsml.interval_based import DrCIFRegressor
+    >>> from tsml.interval_based import TSFRegressor
     >>> from tsml.sklearn import RotationForestClassifier
     >>> @parametrize_with_checks(
-    ...     [DrCIFRegressor(), RotationForestClassifier()]
+    ...     [TSFRegressor(), RotationForestClassifier()]
     ... )
     ... def test_sklearn_compatible_estimator(estimator, check):
     ...     check(estimator)