release 1.1.1

release 1.1.1

.gitignore

@@ -37,7 +37,7 @@ MANIFEST
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+#  before PyInstaller builds the exe, so as to inject date/other infos into it
 *.manifest
 *.spec
 

CHANGELOG.md

@@ -1,3 +1,18 @@
+# Changelog
+
+
+## [1.1.1](https://github.com/eonu/sequentia/releases/tag/v1.1.1)
+
+#### Major changes
+
+- Remove `scikit-learn` validation constraints from `IndependentFunctionTransformer`. ([#237](https://github.com/eonu/sequentia/pull/237))
+
+#### Minor changes
+
+- Change default `mean_filter`/`median_filter` width to 5. ([#238](https://github.com/eonu/sequentia/pull/238))
+- Update repository documentation. ([#239](https://github.com/eonu/sequentia/pull/239)) 
+
+
 ## [1.1.0](https://github.com/eonu/sequentia/releases/tag/v1.1.0)
 
 #### Major changes

CODE_OF_CONDUCT.md

@@ -30,7 +30,7 @@ characteristics above, including participants with disabilities.
 
 ### Reporting Issues
 
-If you experience or witness unacceptable behavior—or have any other concerns—please report it by contacting us via [ed@eonu.net](mailto:ed@eonu.net). All reports will be handled with discretion. In your report please include:
+If you experience or witness unacceptable behavior—or have any other concerns—please report it by contacting Edwin Onuonga via [ed@eonu.net](mailto:ed@eonu.net). All reports will be handled with discretion. In your report please include:
 
 - Your contact information.
 - Names (real, nicknames, or pseudonyms) of any individuals involved. If there are additional witnesses, please

CONTRIBUTING.md

@@ -36,12 +36,11 @@ If you intend to help contribute to Sequentia, you will need some additional dep
 You can specify the `dev` extra when installing Sequentia to do this.
 
 ```console
-pip install --pre sequentia[dev]
+# from inside the sequentia repository folder
+pip install -e .[dev]
 ```
 
-If installing a Sequentia from a local directory, you can use `pip install -e .` from within that directory, or `pip install -e .[xxx]` to install with extras.
-
-Note that on some shells you may have to use quote marks, e.g. `pip install -e ".[xxx]"`.
+> **Note**: On some shells you may have to escape, e.g. `pip install -e ".[dev]"`
 
 ## License
 

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019-2023 Edwin Onuonga <ed@eonu.net>
+Copyright (c) 2019-2023 Edwin Onuonga (eonu) <ed@eonu.net>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

lib/sequentia/__init__.py

@@ -5,7 +5,7 @@
 from .utils import *
 
 __name__ = "sequentia"
-__version__ = "1.1.0"
+__version__ = "1.1.1"
 __author__ = "Edwin Onuonga"
 __email__ = "ed@eonu.net"
 __copyright__ = f"2019-2023, {__author__}"

lib/sequentia/preprocessing/transforms.py

@@ -97,14 +97,6 @@ class IndependentFunctionTransformer(Transform):
         Xt = transform.transform(data.X, data.lengths)
     """
 
-    _parameter_constraints: dict = {
-        "func": [callable, None],
-        "inverse_func": [callable, None],
-        "validate": ["boolean"],
-        "check_inverse": ["boolean"],
-        "kw_args": [dict, None],
-        "inv_kw_args": [dict, None],
-    }
 
     def __init__(
         self,
@@ -146,19 +138,9 @@ def __init__(
         self.kw_args: Optional[Dict[str, Any]] = kw_args
         self.inv_kw_args: Optional[Dict[str, Any]] = inv_kw_args
 
-    def _check_input(self, X, lengths, *, reset):
+    def _check_input(self, X, lengths):
         data = _BaseSequenceValidator(X=X, lengths=lengths)
-        X, lengths = data.X, data.lengths
-        if self.validate:
-            X = self._validate_data(X, reset=reset)
-            return X, lengths
-        elif reset:
-            # Set feature_names_in_ and n_features_in_ even if validate=False
-            # We run this only when reset==True to store the attributes but not
-            # validate them, because validate=False
-            self._check_n_features(X, reset=reset)
-            self._check_feature_names(X, reset=reset)
-        return X, lengths
+        return data.X, data.lengths
 
     def _check_inverse_transform(self, X, lengths):
         """Check that func and inverse_func are the inverse."""
@@ -200,8 +182,7 @@ def fit(
 
         :return: The fitted transformer.
         """
-        self._validate_params()
-        X, lengths = self._check_input(X, lengths, reset=True)
+        X, lengths = self._check_input(X, lengths)
         if self.check_inverse and not (self.func is None or self.inverse_func is None):
             self._check_inverse_transform(X, lengths)
         return self
@@ -227,7 +208,7 @@ def transform(
 
         :return: The transformed array.
         """
-        X, lengths = self._check_input(X, lengths, reset=False)
+        X, lengths = self._check_input(X, lengths)
         return self._transform(X, lengths, func=self.func, kw_args=self.kw_args)
 
     def inverse_transform(
@@ -251,8 +232,7 @@ def inverse_transform(
 
         :return: The inverse transformed array.
         """
-        data = _BaseSequenceValidator(X=X, lengths=lengths)
-        X, lengths = data.X, data.lengths
+        X, lengths = self._check_input(X, lengths)
         if self.validate:
             X = check_array(X, accept_sparse=False)
         return self._transform(X, lengths, func=self.inverse_func, kw_args=self.inv_kw_args)
@@ -272,7 +252,7 @@ def _more_tags(self):
         return {"no_validation": not self.validate, "stateless": True}
 
 
-def mean_filter(x: Array, k: PositiveInt = 3) -> Array:
+def mean_filter(x: Array, k: PositiveInt = 5) -> Array:
     """Applies a mean filter of size ``k`` independently to each feature of the sequence,
     retaining the original input shape by using appropriate padding.
 
@@ -300,10 +280,10 @@ def mean_filter(x: Array, k: PositiveInt = 3) -> Array:
 
         # Apply the mean filter to the first sequence
         x, _ = data[0]
-        xt = mean_filter(x, k=5)
+        xt = mean_filter(x, k=7)
 
         # Create an independent mean filter transform
-        transform = IndependentFunctionTransformer(mean_filter, kw_args={"k": 5})
+        transform = IndependentFunctionTransformer(mean_filter, kw_args={"k": 7})
 
         # Apply the transform to all sequences
         Xt = transform.transform(data.X, data.lengths)
@@ -312,7 +292,7 @@ def mean_filter(x: Array, k: PositiveInt = 3) -> Array:
     return convolve(data.X, np.ones((k, 1)) / k, mode="same")
 
 
-def median_filter(x: Array, k: PositiveInt = 3) -> Array:
+def median_filter(x: Array, k: PositiveInt = 5) -> Array:
     """Applies a median filter of size ``k`` independently to each feature of the sequence,
     retaining the original input shape by using appropriate padding.
 
@@ -338,10 +318,10 @@ def median_filter(x: Array, k: PositiveInt = 3) -> Array:
 
         # Apply the median filter to the first sequence
         x, _ = data[0]
-        xt = median_filter(x, k=5)
+        xt = median_filter(x, k=7)
 
         # Create an independent median filter transform
-        transform = IndependentFunctionTransformer(median_filter, kw_args={"k": 5})
+        transform = IndependentFunctionTransformer(median_filter, kw_args={"k": 7})
 
         # Apply the transform to all sequences
         Xt = transform.transform(data.X, data.lengths)