Changelog#

+
+

0.4.1, 3 October 2023#

+
    +

  • This is a minor release intended to preview new pandas-related features for version 0.5.0.

    • +

  • Added another pandas Series accessor, is_imbalanced().

    • +

  • Added two pandas DataFrame accessors, feature_importances() and correlation_detector(). These are experimental features.

    • +
+

0.4.0, 28 September 2023#

    @@ -385,6 +393,7 @@

    0.1.0, 30 January 2022
    • Changelog
      • consecutive() (in module redflag.utils) +
        • +
      • correlation_detector() (redflag.pandas.DataFrameAccessor method)
      • CorrelationDetector (class in redflag.sklearn)
        • @@ -272,6 +274,8 @@

        C

        D

          +
        • docstring_from() (in module redflag.utils) +
        • dummy_classification_scores() (in module redflag.target)
        • dummy_regression_scores() (in module redflag.target) @@ -323,7 +329,11 @@

          F

          • feature_importances() (in module redflag.importance) + +
          • find_large_peaks() (in module redflag.distributions)
          • fit() (redflag.sklearn.BaseRedflagDetector method) @@ -453,7 +463,11 @@

            I

          • is_correlated() (in module redflag.independence)
          • is_imbalanced() (in module redflag.imbalance) + +
          • is_multiclass() (in module redflag.target)
          • is_multimodal() (in module redflag.distributions) diff --git a/objects.inv b/objects.inv index 5e7071c..a6a30b3 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/redflag.html b/redflag.html index 9aa9543..d7eee27 100644 --- a/redflag.html +++ b/redflag.html @@ -1462,6 +1462,23 @@

            Submodules

            redflag.pandas module#

            Pandas accessors.

            +
            +
            +class redflag.pandas.DataFrameAccessor(pandas_obj)#
            +

            Bases: object

            +
            +
            +correlation_detector(features=None, target=None, n=20, s=20, threshold=0.1)#
            +

            This is an experimental feature.

            +
            + +
            +
            +feature_importances(features=None, target=None, n: int = 3, task: Optional[str] = None, random_state: Optional[int] = None, standardize: bool = True)#
            +
            + +
            +
            class redflag.pandas.SeriesAccessor(pandas_obj)#
            @@ -1476,6 +1493,11 @@

            Submodulesimbalance_degree()#

            +
            +
            +is_imbalanced(threshold=0.4, method='tv', classes=None)#
            +
            +
            is_ordered(q=0.95)#
            @@ -2609,17 +2631,23 @@

            Submodulesredflag.utils.deprecated(instructions)#

            Flags a method as deprecated. This decorator can be used to mark functions as deprecated. It will result in a warning being emitted when the function -is used. -:param instructions: A human-friendly string of instructions, such -:type instructions: str -:param as: ‘Please migrate to add_proxy() ASAP.’

            +is used.

            -
            Returns:
            -

            The decorated function.

            +
            Parameters:
            +

            instructions (str) – A human-friendly string of instructions.

            +
            +
            Returns:
            +

            The decorated function.

            +
            +
            +redflag.utils.docstring_from(source_func)#
            +

            Decorator copying the docstring one function to another.

            +
            +
            redflag.utils.ecdf(arr: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], start: str = '1/N', downsample: Optional[int] = None) → tuple[numpy.ndarray, numpy.ndarray]#
            @@ -3291,9 +3319,15 @@

            Submodulesredflag.pandas module
              +
            • DataFrameAccessor +
            • SeriesAccessor
              • SeriesAccessor.dummy_scores()
              • SeriesAccessor.imbalance_degree()
                • +
              • SeriesAccessor.is_imbalanced()
              • SeriesAccessor.is_ordered()
              • SeriesAccessor.minority_classes()
              • SeriesAccessor.report()
                • @@ -3390,6 +3424,7 @@

                Submodulesconsecutive()
              • cv()
              • deprecated()
                • +
              • docstring_from()
              • ecdf()
              • flatten()
              • generate_data()
                • diff --git a/searchindex.js b/searchindex.js index a5e618c..4a9ef88 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["_notebooks/Basic_usage", "_notebooks/Tutorial", "_notebooks/Using_redflag_with_Pandas", "_notebooks/Using_redflag_with_sklearn", "authors", "changelog", "contributing", "development", "index", "installation", "license", "redflag"], "filenames": ["_notebooks/Basic_usage.ipynb", "_notebooks/Tutorial.ipynb", "_notebooks/Using_redflag_with_Pandas.ipynb", "_notebooks/Using_redflag_with_sklearn.ipynb", "authors.md", "changelog.md", "contributing.md", "development.md", "index.rst", "installation.md", "license.md", "redflag.rst"], "titles": ["\ud83d\udea9 Basic usage", "\ud83d\udea9 Tutorial", "\ud83d\udea9 Using redflag with Pandas", "\ud83d\udea9 Using redflag with sklearn", "Authors", "Changelog", "Contributing", "Development", "Redflag: safer ML by design", "\ud83d\udea9 Installation", "License", "redflag 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"series-accessor"]], "DataFrame accessor": [[2, "dataframe-accessor"]], "\ud83d\udea9 Using redflag with sklearn": [[3, "using-redflag-with-sklearn"]], "The redflag detector classes": [[3, "the-redflag-detector-classes"]], "Using the pre-built redflag pipeline": [[3, "using-the-pre-built-redflag-pipeline"]], "Using the \u2018detector\u2019 transformers": [[3, "using-the-detector-transformers"]], "The imbalance comparator": [[3, "the-imbalance-comparator"]], "Making your own smoke detector": [[3, "making-your-own-smoke-detector"]], "What to do about the warnings": [[3, "what-to-do-about-the-warnings"]], "ImbalanceDetector and ImbalanceComparator": [[3, "imbalancedetector-and-imbalancecomparator"]], "ClipDetector": [[3, "clipdetector"]], "CorrelationDetector": [[3, "correlationdetector"]], "OutlierDetector": [[3, "outlierdetector"]], "DistributionComparator": [[3, "distributioncomparator"]], "ImportanceDetector": [[3, "importancedetector"]], "Authors": [[4, "authors"]], "Changelog": [[5, 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"redflag.sklearn.ImportanceDetector.transform"]], "transform() (redflag.sklearn.insufficientdatadetector method)": [[11, "redflag.sklearn.InsufficientDataDetector.transform"]], "transform() (redflag.sklearn.multimodalitydetector method)": [[11, "redflag.sklearn.MultimodalityDetector.transform"]], "transform() (redflag.sklearn.multivariateoutlierdetector method)": [[11, "redflag.sklearn.MultivariateOutlierDetector.transform"]], "transform() (redflag.sklearn.outlierdetector method)": [[11, "redflag.sklearn.OutlierDetector.transform"]], "transform() (redflag.sklearn.rfpipeline method)": [[11, "redflag.sklearn.RfPipeline.transform"]], "update_p() (in module redflag.utils)": [[11, "redflag.utils.update_p"]], "wasserstein() (in module redflag.distributions)": [[11, "redflag.distributions.wasserstein"]], "wasserstein_multi() (in module redflag.distributions)": [[11, "redflag.distributions.wasserstein_multi"]], "wasserstein_ovo() (in module redflag.distributions)": [[11, "redflag.distributions.wasserstein_ovo"]], "wasserstein_ovr() (in module redflag.distributions)": [[11, "redflag.distributions.wasserstein_ovr"]], "zscore() (in module redflag.utils)": [[11, "redflag.utils.zscore"]]}}) \ No newline at end of file