From 26f75d8bffa589180394401c90ba130a1a7e50fe Mon Sep 17 00:00:00 2001 From: Francesco Bruzzesi <42817048+FBruzzesi@users.noreply.github.com> Date: Wed, 8 Nov 2023 13:21:51 +0100 Subject: [PATCH 1/5] Docstrings standardization (#586) * docstring standardization * adding numpydoc dep * mkdocs * model selection fix, show_full_path: true --- doc/conf.py | 3 +- mkdocs.yaml | 129 +++ mkdocs/api/base.md | 31 + mkdocs/api/common.md | 28 + mkdocs/api/datasets.md | 41 + mkdocs/api/decomposition.md | 11 + mkdocs/api/dummy.md | 6 + mkdocs/api/linear-model.md | 46 ++ mkdocs/api/meta.md | 51 ++ mkdocs/api/metrics.md | 21 + mkdocs/api/mixture.md | 21 + mkdocs/api/model-selection.md | 16 + mkdocs/api/naive-bayes.md | 11 + mkdocs/api/neighbors.md | 6 + mkdocs/api/pandas-utils.md | 16 + mkdocs/api/pipeline.md | 18 + mkdocs/api/preprocessing.md | 66 ++ mkdocs/index.md | 3 + setup.py | 3 +- sklego/base.py | 78 ++ sklego/common.py | 329 ++++++-- sklego/datasets.py | 546 +++++++------ sklego/decomposition/pca_reconstruction.py | 98 ++- sklego/decomposition/umap_reconstruction.py | 94 ++- sklego/dummy.py | 70 +- sklego/linear_model.py | 847 +++++++++++++------- sklego/meta/_grouped_utils.py | 20 +- sklego/meta/confusion_balancer.py | 104 ++- sklego/meta/decay_estimator.py | 103 ++- sklego/meta/estimator_transformer.py | 73 +- sklego/meta/grouped_estimator.py | 1 + sklego/meta/grouped_predictor.py | 202 ++--- sklego/meta/grouped_transformer.py | 112 ++- sklego/meta/outlier_classifier.py | 84 +- sklego/meta/outlier_remover.py | 51 +- sklego/meta/regression_outlier_detector.py | 130 ++- sklego/meta/subjective_classifier.py | 151 ++-- sklego/meta/thresholder.py | 94 ++- sklego/meta/zero_inflated_regressor.py | 88 +- sklego/metrics.py | 180 +++-- sklego/mixture/__init__.py | 11 +- sklego/mixture/bayesian_gmm_classifier.py | 66 +- sklego/mixture/bayesian_gmm_detector.py | 97 ++- sklego/mixture/gmm_classifier.py | 67 +- sklego/mixture/gmm_outlier_detector.py | 92 ++- sklego/model_selection.py | 398 +++++---- sklego/naive_bayes.py | 146 +++- sklego/neighbors.py | 88 +- sklego/notinstalled.py | 26 +- sklego/pandas_utils.py | 250 +++--- sklego/pipeline.py | 530 ++++++------ sklego/preprocessing/__init__.py | 14 +- sklego/preprocessing/columncapper.py | 204 +++-- sklego/preprocessing/dictmapper.py | 61 +- sklego/preprocessing/identitytransformer.py | 57 +- sklego/preprocessing/intervalencoder.py | 137 +++- sklego/preprocessing/outlier_remover.py | 48 +- sklego/preprocessing/pandastransformers.py | 391 ++++++--- sklego/preprocessing/patsytransformer.py | 53 +- sklego/preprocessing/projections.py | 197 +++-- sklego/preprocessing/randomadder.py | 69 +- sklego/preprocessing/repeatingbasis.py | 211 +++-- sklego/testing.py | 2 +- 63 files changed, 4950 insertions(+), 2247 deletions(-) create mode 100644 mkdocs.yaml create mode 100644 mkdocs/api/base.md create mode 100644 mkdocs/api/common.md create mode 100644 mkdocs/api/datasets.md create mode 100644 mkdocs/api/decomposition.md create mode 100644 mkdocs/api/dummy.md create mode 100644 mkdocs/api/linear-model.md create mode 100644 mkdocs/api/meta.md create mode 100644 mkdocs/api/metrics.md create mode 100644 mkdocs/api/mixture.md create mode 100644 mkdocs/api/model-selection.md create mode 100644 mkdocs/api/naive-bayes.md create mode 100644 mkdocs/api/neighbors.md create mode 100644 mkdocs/api/pandas-utils.md create mode 100644 mkdocs/api/pipeline.md create mode 100644 mkdocs/api/preprocessing.md create mode 100644 mkdocs/index.md diff --git a/doc/conf.py b/doc/conf.py index e68216e10..eeef9d311 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -43,7 +43,8 @@ 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'nbsphinx', - 'recommonmark' + 'recommonmark', + 'numpydoc' ] # Add any paths that contain templates here, relative to this directory. diff --git a/mkdocs.yaml b/mkdocs.yaml new file mode 100644 index 000000000..dff49300b --- /dev/null +++ b/mkdocs.yaml @@ -0,0 +1,129 @@ +# Project information +site_name: scikit-lego + +# Repository information +repo_name: koaning/scikit-lego +repo_url: https://github.com/koaning/scikit-lego +edit_uri: edit/main/docs/ + +# Configuration +watch: + - sklego + +docs_dir: mkdocs + +use_directory_urls: true +theme: + name: material + logo: _static/logo.png + favicon: _static/logo.png + font: + text: Ubuntu + code: Ubuntu Mono + highlightjs: true + hljs_languages: + - bash + - python + - r + features: + - navigation.tabs + - navigation.tabs.sticky + - navigation.sections + - navigation.expand + - navigation.path + - navigation.indexes + - navigation.footer + - navigation.top + - navigation.tracking + + - content.action.edit + - content.action.view + - content.code.annotate + - content.code.copy + - content.tooltips + - content.tabs.link + + - search.suggest + - search.highlight + - search.share + + - toc.follow + +# Plugins +plugins: + - autorefs + - mkdocstrings: + handlers: + python: + options: + docstring_style: numpy + docstring_section_style: table # table, list or spacy + filters: + - "!^_" # Hide semi-private functions and methods + - "!^__" # Hide private functions and methods + - search: + separator: '[\s\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])' + +# Extensions +markdown_extensions: + - abbr + - admonition + - attr_list + - codehilite + - def_list + - footnotes + - md_in_html + - tables + - toc: + permalink: true + - pymdownx.arithmatex: + generic: true + - pymdownx.betterem: + smart_enable: all + - pymdownx.snippets: + check_paths: true + - pymdownx.caret + - pymdownx.details + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.keys + - pymdownx.magiclink: + repo_url_shorthand: true + user: squidfunk + repo: mkdocs-material + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences + - pymdownx.tabbed: + alternate_style: true + - pymdownx.tasklist: + custom_checkbox: true + - pymdownx.tilde + - pymdownx.snippets + +extra_javascript: + - javascripts/mathjax.js + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + +nav: + - Home: index.md + - API Reference: + - Base: api/base.md + - Common: api/common.md + - Datasets: api/datasets.md + - Decomposition: api/decomposition.md + - Dummy: api/dummy.md + - Linear Model: api/linear-model.md + - Meta: api/meta.md + - Metrics: api/metrics.md + - Mixture: api/mixture.md + - Model Selection: api/model-selection.md + - Naive Bayes: api/naive-bayes.md + - Neighbors: api/neighbors.md + - Pandas Utils: api/pandas-utils.md + - Pipeline: api/pipeline.md + - Preprocessing: api/preprocessing.md diff --git a/mkdocs/api/base.md b/mkdocs/api/base.md new file mode 100644 index 000000000..39231eee4 --- /dev/null +++ b/mkdocs/api/base.md @@ -0,0 +1,31 @@ +# Base + +::: sklego.base.ClustererMeta + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.base.Clusterer + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.base.OutlierModelMeta + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.base.OutlierModel + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.base.ProbabilisticClassifierMeta + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.base.ProbabilisticClassifier + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/common.md b/mkdocs/api/common.md new file mode 100644 index 000000000..30bda3abd --- /dev/null +++ b/mkdocs/api/common.md @@ -0,0 +1,28 @@ +# Common + +Module with common classes and functions used across the package. + +::: sklego.common.TrainOnlyTransformerMixin + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.common.as_list + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.common.flatten + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.common.expanding_list + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.common.sliding_window + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/datasets.md b/mkdocs/api/datasets.md new file mode 100644 index 000000000..560d4ddf2 --- /dev/null +++ b/mkdocs/api/datasets.md @@ -0,0 +1,41 @@ +# Datasets + +::: sklego.datasets.load_abalone + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.load_arrests + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.load_chicken + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.load_heroes + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.load_hearts + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.load_penguins + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.make_simpleseries + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.datasets.fetch_creditcard + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/decomposition.md b/mkdocs/api/decomposition.md new file mode 100644 index 000000000..6b27e090c --- /dev/null +++ b/mkdocs/api/decomposition.md @@ -0,0 +1,11 @@ +# Decomposition + +::: sklego.decomposition.pca_reconstruction.PCAOutlierDetection + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.decomposition.umap_reconstruction.UMAPOutlierDetection + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/dummy.md b/mkdocs/api/dummy.md new file mode 100644 index 000000000..63750bd83 --- /dev/null +++ b/mkdocs/api/dummy.md @@ -0,0 +1,6 @@ +# Dummy + +::: sklego.dummy.RandomRegressor + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/linear-model.md b/mkdocs/api/linear-model.md new file mode 100644 index 000000000..3185a18bf --- /dev/null +++ b/mkdocs/api/linear-model.md @@ -0,0 +1,46 @@ +# Linear Models + +::: sklego.linear_model.LowessRegression + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.ProbWeightRegression + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.DeadZoneRegressor + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.DemographicParityClassifier + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.EqualOpportunityClassifier + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.BaseScipyMinimizeRegressor + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.ImbalancedLinearRegression + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.QuantileRegression + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.linear_model.LADRegression + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/meta.md b/mkdocs/api/meta.md new file mode 100644 index 000000000..f076becd2 --- /dev/null +++ b/mkdocs/api/meta.md @@ -0,0 +1,51 @@ +# Meta Models + +::: sklego.meta.confusion_balancer.ConfusionBalancer + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.decay_estimator.DecayEstimator + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.estimator_transformer.EstimatorTransformer + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.grouped_predictor.GroupedPredictor + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.grouped_transformer.GroupedTransformer + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.outlier_classifier.OutlierClassifier + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.regression_outlier_detector.RegressionOutlierDetector + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.subjective_classifier.SubjectiveClassifier + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.thresholder.Thresholder + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.meta.zero_inflated_regressor.ZeroInflatedRegressor + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/metrics.md b/mkdocs/api/metrics.md new file mode 100644 index 000000000..f978e9022 --- /dev/null +++ b/mkdocs/api/metrics.md @@ -0,0 +1,21 @@ +# Metrics + +::: sklego.metrics.correlation_score + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.metrics.equal_opportunity_score + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.metrics.p_percent_score + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.metrics.subset_score + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/mixture.md b/mkdocs/api/mixture.md new file mode 100644 index 000000000..6d1abcd35 --- /dev/null +++ b/mkdocs/api/mixture.md @@ -0,0 +1,21 @@ +# Mixture Models + +:::sklego.mixture.bayesian_gmm_classifier.BayesianGMMClassifier + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.mixture.bayesian_gmm_detector.BayesianGMMOutlierDetector + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.mixture.gmm_classifier.GMMClassifier + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.mixture.gmm_outlier_detector.GMMOutlierDetector + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/model-selection.md b/mkdocs/api/model-selection.md new file mode 100644 index 000000000..2e6ee29c8 --- /dev/null +++ b/mkdocs/api/model-selection.md @@ -0,0 +1,16 @@ +# Model Selection + +:::sklego.model_selection.GroupTimeSeriesSplit + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.model_selection.KlusterFoldValidation + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.model_selection.TimeGapSplit + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/naive-bayes.md b/mkdocs/api/naive-bayes.md new file mode 100644 index 000000000..6faec56f4 --- /dev/null +++ b/mkdocs/api/naive-bayes.md @@ -0,0 +1,11 @@ +# Naive Bayes + +:::sklego.naive_bayes.GaussianMixtureNB + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.naive_bayes.BayesianGaussianMixtureNB + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/neighbors.md b/mkdocs/api/neighbors.md new file mode 100644 index 000000000..e8a00bb03 --- /dev/null +++ b/mkdocs/api/neighbors.md @@ -0,0 +1,6 @@ +# Neighbors + +:::sklego.neighbors.BayesianKernelDensityClassifier + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/pandas-utils.md b/mkdocs/api/pandas-utils.md new file mode 100644 index 000000000..f9148494c --- /dev/null +++ b/mkdocs/api/pandas-utils.md @@ -0,0 +1,16 @@ +# Pandas Utils + +::: sklego.pandas_utils.add_lags + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.pandas_utils.log_step + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.pandas_utils.log_step_extra + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/pipeline.md b/mkdocs/api/pipeline.md new file mode 100644 index 000000000..e99584e32 --- /dev/null +++ b/mkdocs/api/pipeline.md @@ -0,0 +1,18 @@ +# Pipeline + +Pipelines, variances to the sklearn.pipeline.Pipeline object. + +::: sklego.pipeline.DebugPipeline + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.pipeline.make_debug_pipeline + options: + show_root_full_path: true + show_root_heading: true + +::: sklego.pipeline.default_log_callback + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/api/preprocessing.md b/mkdocs/api/preprocessing.md new file mode 100644 index 000000000..5aadb5ca8 --- /dev/null +++ b/mkdocs/api/preprocessing.md @@ -0,0 +1,66 @@ +# Preprocessing + +:::sklego.preprocessing.columncapper.ColumnCapper + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.pandastransformers.ColumnDropper + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.pandastransformers.ColumnSelector + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.dictmapper.DictMapper + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.identitytransformer.IdentityTransformer + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.projections.InformationFilter + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.intervalencoder.IntervalEncoder + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.projections.OrthogonalTransformer + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.outlier_remover.OutlierRemover + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.pandastransformers.PandasTypeSelector + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.patsytransformer.PatsyTransformer + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.randomadder.RandomAdder + options: + show_root_full_path: true + show_root_heading: true + +:::sklego.preprocessing.repeatingbasis.RepeatingBasisFunction + options: + show_root_full_path: true + show_root_heading: true diff --git a/mkdocs/index.md b/mkdocs/index.md new file mode 100644 index 000000000..48772ee12 --- /dev/null +++ b/mkdocs/index.md @@ -0,0 +1,3 @@ +# scikit-lego + +Placeholder for the scikit-lego documentation. \ No newline at end of file diff --git a/setup.py b/setup.py index 348e4bb4f..f1c0c289f 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,8 @@ "sphinx==4.5.0", "sphinx_rtd_theme==1.0.0", "nbsphinx==0.8.8", - "recommonmark==0.7.1" + "recommonmark==0.7.1", + "numpydoc", ] test_packages = all_packages + [ # we need extras packages for their tests "flake8>=3.6.0", diff --git a/sklego/base.py b/sklego/base.py index ff4097b0e..13e5801b7 100644 --- a/sklego/base.py +++ b/sklego/base.py @@ -2,27 +2,105 @@ class ProbabilisticClassifierMeta(type): + """Metaclass for `ProbabilisticClassifier`. + + This metaclass is responsible for checking whether a class can be considered a `ProbabilisticClassifier`. + A class is considered a `ProbabilisticClassifier` if it has a "predict_proba" method. + """ + def __instancecheck__(self, other): + """Checks if the provided object is a `ProbabilisticClassifier`. + + Parameters + ---------- + self : ProbabilisticClassifierMeta + `ProbabilisticClassifierMeta` class. + other : object + The object to check for `ProbabilisticClassifier` compatibility. + + Returns + ------- + bool + True if the object is a `ProbabilisticClassifier` (has a "predict_proba" method ), False otherwise. + """ return hasattr(other, "predict_proba") class ProbabilisticClassifier(metaclass=ProbabilisticClassifierMeta): + """Base class for `ProbabilisticClassifier`. + + This base class defines the `ProbabilisticClassifier` interface, indicating that subclasses should have a + "predict_proba" method. + """ + pass class ClustererMeta(type): + """Metaclass for `Clusterer`. + + This metaclass is responsible for checking whether a class can be considered a `Clusterer`. + A class is considered a `Clusterer` if it has a "fit_predict" method. + """ + def __instancecheck__(self, other): + """Checks if the provided object is a `Clusterer`. + + Parameters + ---------- + self : ClustererMeta + `ClustererMeta` class. + other : object + The object to check for `Clusterer` compatibility. + + Returns + ------- + bool + True if the object is a `Clusterer` (has a "fit_predict" method ), False otherwise. + """ return hasattr(other, "fit_predict") class Clusterer(metaclass=ClustererMeta): + """Base class for `Clusterer`. + + This base class defines the `Clusterer` interface, indicating that subclasses should have a "fit_predict" method. + """ + pass class OutlierModelMeta(type): + """Metaclass for `OutlierModel`. + + This metaclass is responsible for checking whether a class can be considered an `OutlierModel`. + A class is considered an `OutlierModel` if it is an instance of the `sklearn.base.OutlierMixin` class. + """ + def __instancecheck__(self, other): + """ + Check if the provided object is an `OutlierModel`. + + Parameters + ---------- + self : OutlierModelMeta + The `OutlierModelMeta` class. + other : object + The object to check for `OutlierModel` compatibility. + + Returns + ------- + bool + True if the object is an `OutlierModel` (an instance of "OutlierMixin"), False otherwise. + """ return isinstance(other, OutlierMixin) class OutlierModel(metaclass=OutlierModelMeta): + """Base class for `OutlierModel`. + + This base class defines the `OutlierModel` interface, indicating that subclasses should be instances of the + "OutlierMixin" class. + """ + pass diff --git a/sklego/common.py b/sklego/common.py index 892b94b5c..2f109a5b1 100644 --- a/sklego/common.py +++ b/sklego/common.py @@ -9,34 +9,52 @@ class TrainOnlyTransformerMixin(TransformerMixin): - """ - Allows using a separate function for transforming train and test data - - Usage: - >>> from sklearn.base import BaseEstimator - >>> class TrainOnlyTransformer(TrainOnlyTransformerMixin, BaseEstimator): - ... def fit(self, X, y): - ... super().fit(X, y) - ... - ... def transform_train(self, X, y=None): - ... return X + np.random.normal(0, 1, size=X.shape) - ... - >>> X_train, X_test = np.random.randn(100, 4), np.random.randn(100, 4) - >>> y_train, y_test = np.random.randn(100), np.random.randn(100) - >>> - >>> trf = TrainOnlyTransformer() - >>> trf.fit(X_train, y_train) - >>> - >>> assert np.all(trf.transform(X_train) != X_train) - >>> assert np.all(trf.transform(X_test) == X_test) - - .. warning:: Transformers using this class as a mixin should at a minimum: - - - call `super().fit` in their fit method - - implement `transform_train()` - - They may also implement `transform_test()`. If it is not implemented, - `transform_test` will simply return the untransformed dataframe + """Mixin class for transformers that can handle training and test data differently. + + This mixin allows using a separate function for transforming training and test data. + + !!! warning + + Transformers using this class as a mixin should: + + - Call `super().fit` in their fit method. + - Implement `transform_train()` method. + + They may also implement `transform_test()` method, if not implemented, `transform_test()` will simply return + the untransformed data. + + Attributes + ---------- + X_hash_ : hash + The hash of the training data - used to determine whether to use `transform_train` or `transform_test`. + n_features_in_ : int + The number of features seen during `.fit()` in the training data. + dim_ : int + Deprecated, use `n_features_in_` instead. + + Examples + -------- + ```py + from sklearn.base import BaseEstimator + from sklego.common import TrainOnlyTransformerMixin + + class TrainOnlyTransformer(TrainOnlyTransformerMixin, BaseEstimator): + def fit(self, X, y): + super().fit(X, y) + + def transform_train(self, X, y=None): + '''Add random noise to the training data.''' + return X + np.random.normal(0, 1, size=X.shape) + + X_train, X_test = np.random.randn(100, 4), np.random.randn(100, 4) + y_train, y_test = np.random.randn(100), np.random.randn(100) + + trf = TrainOnlyTransformer() + trf.fit(X_train, y_train) + + assert np.all(trf.transform(X_train) != X_train) + assert np.all(trf.transform(X_test) == X_test) + ``` """ _HASHERS = { @@ -48,7 +66,20 @@ class TrainOnlyTransformerMixin(TransformerMixin): } def fit(self, X, y=None): - """Calculates the hash of X_train""" + """Fit the mixin by calculating the hash of `X` and stores it in `self.X_hash_`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) | None, default=None + The target values. + + Returns + ------- + self : TrainOnlyTransformerMixin + The fitted transformer. + """ if y is None: check_array(X, estimator=self) else: @@ -59,23 +90,61 @@ def fit(self, X, y=None): @staticmethod def _hash(X): - """Returns a hash of X based on the type of X. Hashers are defined in TrainOnlyMixin.HASHERS""" + """Calculate a hash of X based on its type. Hashers are defined in TrainOnlyMixin.HASHERS. + + Supported types are: + + - pd.DataFrame + - np.ndarray + - np.memmap + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to hash. + + Returns + ------- + hash + The calculated hash value. + + Raises + ------ + ValueError + If the type of `X` is not supported. + """ try: hasher = TrainOnlyTransformerMixin._HASHERS[type(X)] except KeyError: raise ValueError( f"Unknown datatype {type(X)}, " - f"TransformerSelector only supports {TrainOnlyTransformerMixin.HASHERS.keys()}" + f"`TrainOnlyTransformerMixin` only supports {TrainOnlyTransformerMixin.HASHERS.keys()}" ) else: return hasher(X) def transform(self, X, y=None): - """ - Dispatcher for transform method. - - It will dispatch to `self.transform_train` if X is the same as X passed to `fit`, otherwise, it will dispatch - to `self.trainsform_test` + """Dispatch to `transform_train()` or `transform_test()` based on the data passed. + + This method will check whether the hash of `X` matches the hash of the training data. If it does, it will + dispatch to `transform_train()`, otherwise it will dispatch to `transform_test()`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + y : array-like of shape (n_samples,) or None, default=None. + The target values. + + Returns + ------- + array-like of shape (n_samples, n_features) + The transformed data. + + Raises + ------ + ValueError + If the input dimension does not match the training dimension. """ check_is_fitted(self, ["X_hash_", "n_features_in_"]) check_array(X, estimator=self) @@ -91,11 +160,44 @@ def transform(self, X, y=None): return self.transform_test(X) def transform_train(self, X, y=None): + """Transform the training data. + + This method should be implemented in subclasses to specify how training data should be transformed. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) or None, default=None + The target values. + + Returns + ------- + array-like of shape (n_samples, n_features) + The transformed training data. + """ raise NotImplementedError( - "Subclasses of TrainOnlyMixin should implement `transform_train`" + "Subclasses of `TrainOnlyTransformerMixin` should implement `transform_train` method" ) def transform_test(self, X, y=None): + """Transform the test data. + + This method can be implemented in subclasses to specify how test data should be transformed. + If not implemented, it will return the untransformed data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The test data. + y : array-like of shape (n_samples,) or None, default=None + The target values. + + Returns + ------- + array-like of shape (n_samples, n_features) + The transformed test data or untransformed data if not implemented. + """ return X @property @@ -108,19 +210,32 @@ def dim_(self): def as_list(val): - """ - Helper function, always returns a list of the input value. + """Ensure the input value is converted into a list. + + This helper function takes an input value and ensures that it is always returned as a list. + + - If the input is a single value, it will be wrapped in a list. + - If the input is an iterable, it will be converted into a list. - :param val: the input value. - :returns: the input value as a list. + Parameters + ---------- + val : object + The input value that needs to be converted into a list. - :Example: + Returns + ------- + list + The input value as a list. - >>> as_list('test') - ['test'] + Examples + -------- + ```py + as_list("test") + # ['test'] - >>> as_list(['test1', 'test2']) - ['test1', 'test2'] + as_list(["test1", "test2"]) + # ['test1', 'test2'] + ``` """ treat_single_value = str @@ -134,14 +249,30 @@ def as_list(val): def flatten(nested_iterable): - """ - Helper function, returns an iterator of flattened values from an arbitrarily nested iterable - - >>> list(flatten([['test1', 'test2'], ['a', 'b', ['c', 'd']]])) - ['test1', 'test2', 'a', 'b', 'c', 'd'] - - >>> list(flatten(['test1', ['test2']])) - ['test1', 'test2'] + """Recursively flatten an arbitrarily nested iterable into an iterator of values. + + This helper function takes an arbitrarily nested iterable and returns an iterator of flattened values. + It recursively processes the input to extract individual elements and yield them in a flat structure. + + Parameters + ---------- + nested_iterable : Iterable + An arbitrarily nested iterable to be flattened. + + Yields + ------ + Generator + A generator of flattened values from the nested iterable. + + Examples + -------- + ```py + list(flatten([["test1", "test2"], ["a", "b", ["c", "d"]])) + # ['test1', 'test2', 'a', 'b', 'c', 'd'] + + list(flatten(["test1", ["test2"]]) + # ['test1', 'test2'] + ``` """ for el in nested_iterable: if isinstance(el, collections.abc.Iterable) and not isinstance( @@ -153,47 +284,67 @@ def flatten(nested_iterable): def expanding_list(list_to_extent, return_type=list): - """ - Make a expanding list of lists by making tuples of the first element, the first 2 elements etc. - - :param list_to_extent: - :param return_type: type of the elements of the list (tuple or list) - - :Example: - - >>> expanding_list('test') - [['test']] - - >>> expanding_list(['test1', 'test2', 'test3']) - [['test1'], ['test1', 'test2'], ['test1', 'test2', 'test3']] - - >>> expanding_list(['test1', 'test2', 'test3'], tuple) - [('test1',), ('test1', 'test2'), ('test1', 'test2', 'test3')] + """Create an expanding list of lists or tuples by making combinations of elements. + + This function takes an input list and creates an expanding list, where each element is a list or tuple containing a + subset of elements from the input list. The resulting list can be composed of lists or tuples, depending on the + specified `return_type`. + + Parameters + ---------- + list_to_extent : object + The input to be extended. + return_type : type, default=list + The type of elements in the resulting list (list or tuple). + + Returns + ------- + list + An expanding list of `list`s or `tuple`s containing combinations of elements from the input. + + Examples + -------- + ```py + expanding_list("test") + # [['test']] + + expanding_list(["test1", "test2", "test3"]) + # [['test1'], ['test1', 'test2'], ['test1', 'test2', 'test3']] + + expanding_list(["test1", "test2", "test3"], tuple) + # [('test1',), ('test1', 'test2'), ('test1', 'test2', 'test3')] + ``` """ listed = as_list(list_to_extent) - if len(listed) <= 1: - return [listed] - return [return_type(listed[: n + 1]) for n in range(len(listed))] def sliding_window(sequence, window_size, step_size): - """Returns sliding window generator object from a sequence - - :param sequence: e.g. a list - :type sequence: Iterable - :param window_size: the size of each window - :type window_size: int - :param step_size: the amount of steps to the next window - :type step_size: int - :return: a sliding window generator object - :rtype: Generator - - :Example: - - >>> generator = sliding_window([1,2,4,5], 2, 1) - >>> [i for i in generator] - [[1,2], [2,4], [4,5], [5]] + """Generate sliding windows over a sequence. + + This function generates sliding windows of a specified size over a given sequence, where each window is a list of + elements from the sequence. + + Parameters + ---------- + sequence : Iterable + The input sequence (e.g., a list). + window_size : int + The size of each sliding window. + step_size : int + The amount of steps to the next window. + + Returns + ------- + Generator + A generator object that yields sliding windows. + + Examples + -------- + ``` + list(sliding_window([1, 2, 4, 5], 2, 1)) + # [[1, 2], [2, 4], [4, 5], [5]] + ``` """ return ( sequence[pos : pos + window_size] for pos in range(0, len(sequence), step_size) diff --git a/sklego/datasets.py b/sklego/datasets.py index b8a76efaa..34bfff5f8 100644 --- a/sklego/datasets.py +++ b/sklego/datasets.py @@ -1,92 +1,95 @@ import os + import numpy as np import pandas as pd from pkg_resources import resource_filename - from sklearn.datasets import fetch_openml def load_penguins(return_X_y=False, as_frame=False): - """ - Loads the penguins dataset, which is a lovely alternative for the iris dataset. We've - added this dataset for educational use. + """Loads the penguins dataset, which is a lovely alternative for the iris dataset. + We've added this dataset for educational use. - Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, - Antarctica LTER, a member of the Long Term Ecological Research Network. The goal - of the dataset is to predict which species of penguin a penguin belongs to. + Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of + the Long Term Ecological Research Network. The goal of the dataset is to predict which species of penguin a penguin + belongs to. - This data originally appeared as a R package and R users can find this data - in the palmerpenguins package https://github.com/allisonhorst/palmerpenguins. + This data originally appeared as a R package and R users can find this data in the + [palmerpenguins package](https://github.com/allisonhorst/palmerpenguins). You can also go to the repository for some lovely images that explain the dataset. To cite this dataset in publications use: - Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism - and Environmental Variability within a Community of Antarctic - Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. - https://doi.org/10.1371/journal.pone.0090081 - - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) - - :Example: - >>> from sklego.datasets import load_penguins - >>> X, y = load_penguins(return_X_y=True) - >>> X.shape - (344, 6) - >>> y.shape - (344,) - >>> load_penguins(as_frame=True).columns - Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm', - 'flipper_length_mm', 'body_mass_g', 'sex'], - dtype='object') - - Additional data use information - ############################### + Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism + and Environmental Variability within a Community of Antarctic + Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. + https://doi.org/10.1371/journal.pone.0090081 + + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + ------- + ```py + from sklego.datasets import load_penguins + X, y = load_penguins(return_X_y=True) + + X.shape + # (344, 6) + y.shape + # (344,) + load_penguins(as_frame=True).columns + # Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm', + # 'flipper_length_mm', 'body_mass_g', 'sex'], + # dtype='object') + ``` + + Notes + ----- Anyone interested in publishing the data should contact - `Dr. Kristen Gorman `_ + [`Dr. Kristen Gorman`](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php) about analysis and working together on any final products. - From Gorman et al. (2014): + !!! quote "From Gorman et al. (2014)" - > “Data reported here are publicly available within the PAL-LTER data - > system (datasets 219, 220, and 221): - > ``_. - > Individuals interested in using these data are therefore expected to - > follow the US LTER Network’s Data Access Policy, Requirements and Use - > Agreement: ``_.” + Data reported here are publicly available within the + [PAL-LTER data system (datasets 219, 220, and 221)](http://oceaninformatics.ucsd.edu/datazoo/data/pallter/datasets). - **Please cite data using the following:** + Individuals interested in using these data are therefore expected to follow the [US LTER Network's Data Access + Policy, Requirements and Use Agreement](https://lternet.edu/data-access-policy/) + Please cite data using the following + ------------------------------------ **Adélie penguins:** - - Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size - measurements and isotopic signatures of foraging among adult male - and female Adélie penguins (*Pygoscelis adeliae*) nesting along the - Palmer Archipelago near Palmer Station, 2007-2009 ver 5. - Environmental Data Initiative. - ``_ + - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of + foraging among adult male and female Adélie penguins (*Pygoscelis adeliae*) nesting along the Palmer Archipelago + near Palmer Station, 2007-2009 ver 5. Environmental Data + Initiative](https://doi.org/10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f). + (Accessed 2020-06-08). **Gentoo penguins:** - - Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size - measurements and isotopic signatures of foraging among adult male - and female Gentoo penguin (*Pygoscelis papua*) nesting along the - Palmer Archipelago near Palmer Station, 2007-2009 ver 5. - Environmental Data Initiative. - ``_ + - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of + foraging among adult male and female Gentoo penguin (*Pygoscelis papua*) nesting along the Palmer Archipelago + near Palmer Station, 2007-2009 ver 5. Environmental Data + Initiative](https://doi.org/10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689). + (Accessed 2020-06-08). **Chinstrap penguins:** - - Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size - measurements and isotopic signatures of foraging among adult male - and female Chinstrap penguin (*Pygoscelis antarcticus*) nesting - along the Palmer Archipelago near Palmer Station, 2007-2009 ver 6. - Environmental Data Initiative. - ``_ + - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of + foraging among adult male and female Chinstrap penguin (*Pygoscelis antarcticus*) nesting along the Palmer + Archipelago near Palmer Station, 2007-2009 ver 6. + Environmental Data Initiative](https://doi.org/10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e). + (Accessed 2020-06-08). """ filepath = resource_filename("sklego", os.path.join("data", "penguins.zip")) @@ -112,33 +115,41 @@ def load_penguins(return_X_y=False, as_frame=False): def load_arrests(return_X_y=False, as_frame=False): - """ - Loads the arrests dataset which can serve as a benchmark for fairness. It is data on - the police treatment of individuals arrested in Toronto for simple possession of small - quantities of marijuana. The goal is to predict whether or not the arrestee was released - with a summons while maintaining a degree of fairness. - - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) - - :Example: - >>> from sklego.datasets import load_arrests - >>> X, y = load_arrests(return_X_y=True) - >>> X.shape - (5226, 7) - >>> y.shape - (5226,) - >>> load_arrests(as_frame=True).columns - Index(['released', 'colour', 'year', 'age', 'sex', 'employed', 'citizen', - 'checks'], - dtype='object') - - The dataset was copied from the carData R package and can originally be found in: + """Loads the arrests dataset which can serve as a benchmark for fairness. - - Personal communication from Michael Friendly, York University. + It is data on the police treatment of individuals arrested in Toronto for simple possession of small quantities of + marijuana. The goal is to predict whether or not the arrestee was released with a summons while maintaining a + degree of fairness. + + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + ------- + ```py + from sklego.datasets import load_arrests + X, y = load_arrests(return_X_y=True) + + X.shape + # (5226, 7) + y.shape + # (5226,) + + load_arrests(as_frame=True).columns + # Index(['released', 'colour', 'year', 'age', 'sex', 'employed', 'citizen', + # 'checks'], + # dtype='object') + ``` + + The dataset was copied from the carData R package + ([dataset documentation](http://vincentarelbundock.github.io/Rdatasets/doc/carData/Arrests.html)) + and can originally be found in: - The documentation page of the dataset from the package can be viewed here: - http://vincentarelbundock.github.io/Rdatasets/doc/carData/Arrests.html + - Personal communication from Michael Friendly, York University. """ filepath = resource_filename("sklego", os.path.join("data", "arrests.zip")) df = pd.read_csv(filepath) @@ -154,26 +165,33 @@ def load_arrests(return_X_y=False, as_frame=False): def load_chicken(return_X_y=False, as_frame=False): - """ - Loads the chicken dataset. The chicken data has 578 rows and 4 columns - from an experiment on the effect of diet on early growth of chicks. - The body weights of the chicks were measured at birth and every second - day thereafter until day 20. They were also measured on day 21. - There were four groups on chicks on different protein diets. - - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) - - :Example: - - >>> from sklego.datasets import load_chicken - >>> X, y = load_chicken(return_X_y=True) - >>> X.shape - (578, 3) - >>> y.shape - (578,) - >>> load_chicken(as_frame=True).columns - Index(['weight', 'time', 'chick', 'diet'], dtype='object') + """Loads the chicken dataset. + + The chicken data has 578 rows and 4 columns from an experiment on the effect of diet on early growth of chicks. + The body weights of the chicks were measured at birth and every second day thereafter until day 20. + They were also measured on day 21. There were four groups on chicks on different protein diets. + + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + ------- + ```py + from sklego.datasets import load_chicken + X, y = load_chicken(return_X_y=True) + + X.shape + # (578, 3) + y.shape + # (578,) + + load_chicken(as_frame=True).columns + # Index(['weight', 'time', 'chick', 'diet'], dtype='object') + ``` The datasets can be found in the following sources: @@ -194,27 +212,37 @@ def load_abalone(return_X_y=False, as_frame=False): """ Loads the abalone dataset where the goal is to predict the gender of the creature. - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + --------- + ```py + from sklego.datasets import load_abalone + X, y = load_abalone(return_X_y=True) - :Example: + X.shape + # (4177, 8) + y.shape + # (4177,) - >>> from sklego.datasets import load_abalone - >>> X, y = load_abalone(return_X_y=True) - >>> X.shape - (4177, 8) - >>> y.shape - (4177,) - >>> load_abalone(as_frame=True).columns - Index(['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', - 'viscera_weight', 'shell_weight', 'rings'], - dtype='object') + load_abalone(as_frame=True).columns + # Index(['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', + # 'viscera_weight', 'shell_weight', 'rings'], + # dtype='object') + ``` - The dataset was copied from Kaggle and can originally be found in: can be found in the following sources: + The dataset was copied from Kaggle and can originally be found in the following sources: - Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) - "The Population Biology of Abalone (_Haliotis_ species) in Tasmania." - Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288) + + "The Population Biology of Abalone (_Haliotis_ species) in Tasmania." + + Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288) """ filepath = resource_filename("sklego", os.path.join("data", "abalone.zip")) df = pd.read_csv(filepath) @@ -239,22 +267,32 @@ def load_abalone(return_X_y=False, as_frame=False): def load_heroes(return_X_y=False, as_frame=False): - """ - A dataset from a video game: "heroes of the storm". The goal of the dataset - is to predict the attack type. Note that the pandas dataset returns more information. + """A dataset from the video game [Heroes of the storm](https://heroesofthestorm.blizzard.com/en-us/). + + The goal of the dataset is to predict the attack type. Note that the pandas dataset returns more information. This is because we wanted to keep the X simple in the return_X_y case. - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) - - :Example: - >>> X, y = load_heroes(return_X_y=True) - >>> X.shape - (84, 2) - >>> y.shape - (84,) - >>> df = load_heroes(as_frame=True) - >>> df.columns - Index(['name', 'attack_type', 'role', 'health', 'attack', 'attack_spd'], dtype='object') + + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + -------- + ```py + from sklego.datasets import load_heroes + X, y = load_heroes(return_X_y=True) + + X.shape + # (84, 2) + y.shape + # (84,) + + load_heroes(as_frame=True).columns + # Index(['name', 'attack_type', 'role', 'health', 'attack', 'attack_spd'], dtype='object') + ``` """ filepath = resource_filename("sklego", os.path.join("data", "heroes.zip")) df = pd.read_csv(filepath) @@ -267,64 +305,8 @@ def load_heroes(return_X_y=False, as_frame=False): return {"data": X, "target": y} -def make_simpleseries( - n_samples=365 * 5, - trend=0.001, - season_trend=0.001, - noise=0.5, - as_frame=False, - seed=None, - stack_noise=False, - start_date=None, -): - """ - Generate a very simple timeseries dataset to play with. The generator - returns a daily time-series with a yearly seasonality, trend, and noise. - - :param n_samples: The number of days to simulate the timeseries for. - :param trend: The long term trend in the dataset. - :param season_trend: The long term trend in the seasonality. - :param noise: The noise that is applied to the dataset. - :param as_frame: Return a pandas dataframe instead of a numpy array. - :param seed: The seed value for the randomness. - :param stack_noise: Set the noise to be stacked by a cumulative sum. - :param start_date: Also add a start date (only works if `as_frame`=True). - :return: numpy array unless dataframe is specified - - :Example: - - >>> from sklego.datasets import make_simpleseries - >>> make_simpleseries(seed=42) - array([-0.34078806, -0.61828731, -0.18458236, ..., -0.27547402, - -0.38237413, 0.13489355]) - >>> make_simpleseries(as_frame=True, start_date="2018-01-01", seed=42).head(3) - yt date - 0 -0.340788 2018-01-01 - 1 -0.618287 2018-01-02 - 2 -0.184582 2018-01-03 - """ - if seed: - np.random.seed(seed) - time = np.arange(0, n_samples) - noise = np.random.normal(0, noise, n_samples) - if stack_noise: - noise = noise.cumsum() - r1, r2 = np.random.normal(0, 1, 2) - seasonality = r1 * np.sin(time / 365 * 2 * np.pi) + r2 * np.cos( - time / 365 * 4 * np.pi + 1 - ) - result = seasonality + season_trend * seasonality * time + trend * time + noise - if as_frame: - if start_date: - stamps = pd.date_range(start_date, periods=n_samples) - return pd.DataFrame({"yt": result, "date": stamps}) - return pd.DataFrame({"yt": result}) - return result - - def load_hearts(return_X_y=False, as_frame=False): - """ - Loads the Cleveland Heart Diseases dataset. + """Loads the Cleveland Heart Diseases dataset. The goal is to predict the presence of a heart disease (target values 1, 2, 3, and 4). The data originates from research to heart diseases by four institutions and originally contains 76 attributes. @@ -332,23 +314,32 @@ def load_hearts(return_X_y=False, as_frame=False): This implementation loads the Cleveland dataset of the research which is the only set used by ML researchers to this date. - :param return_X_y: If True, returns ``(data, target)`` instead of a dict object. - :param as_frame: give the pandas dataframe instead of X, y matrices (default=False) - - :Example: - >>> X, y = load_hearts(return_X_y=True) - >>> X.shape - (303, 13) - >>> y.shape - (303,) - >>> df = load_hearts(as_frame=True) - >>> df.columns - Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', - 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'], - dtype='object') - - The dataset can originally be found here: - https://archive.ics.uci.edu/ml/datasets/Heart+Disease + Parameters + ---------- + return_X_y : bool, default=False + If True, returns `(data, target)` instead of a dict object. + as_frame : bool, default=False + If True, returns `data` as a pandas DataFrame instead of X, y matrices. + + Examples + -------- + ```py + from sklego.datasets import load_hearts + X, y = load_hearts(return_X_y=True) + + X.shape + # (303, 13) + y.shape + # (303,) + + load_hearts(as_frame=True).columns + # Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', + # 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'], + # dtype='object') + ``` + + The dataset can originally be found in the + [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) The responsible institutions for the entire database are: @@ -357,7 +348,7 @@ def load_hearts(return_X_y=False, as_frame=False): 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. - The documentation of the dataset can be viewed here: + The documentation of the dataset can be viewed at: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names """ filepath = resource_filename("sklego", os.path.join("data", "hearts.zip")) @@ -387,55 +378,122 @@ def load_hearts(return_X_y=False, as_frame=False): return {"data": X, "target": y} -def fetch_creditcard(*, cache=True, data_home=None, as_frame=False, return_X_y=False): +def make_simpleseries( + n_samples=365 * 5, + trend=0.001, + season_trend=0.001, + noise=0.5, + as_frame=False, + seed=None, + stack_noise=False, + start_date=None, +): + """Generate a very simple timeseries dataset to play with. + + The generator returns a daily time-series with a yearly seasonality, trend, and noise. + + Parameters + ---------- + n_samples : int, default=365 * 5 + The number of days to simulate the timeseries for. + trend : float, default=0.001 + The long term trend in the dataset. + season_trend : float, default=0.001 + The long term trend in the seasonality. + noise : float, default=0.5 + The noise that is applied to the dataset. + as_frame : bool, default=False + Whether to return a pandas dataframe instead of a numpy array. + seed : int | None, default=None + The seed value for the randomness. + stack_noise : bool, default=False + Set the noise to be stacked by a cumulative sum. + start_date : str | None, default=None + Also add a start date (only works if `as_frame`=True). + + Returns + ------- + np.ndarray | pd.DataFrame + Timeseries dataset with specified characteristics. + + Examples + -------- + ```py + from sklego.datasets import make_simpleseries + + make_simpleseries(seed=42) + # array([-0.34078806, -0.61828731, -0.18458236, ..., -0.27547402, + # -0.38237413, 0.13489355]) + + make_simpleseries(as_frame=True, start_date="2018-01-01", seed=42).head(3) + ''' + yt date + 0 -0.340788 2018-01-01 + 1 -0.618287 2018-01-02 + 2 -0.184582 2018-01-03 + ''' + ``` """ - Load the creditcard dataset. Download it if necessary. + if seed: + np.random.seed(seed) + time = np.arange(0, n_samples) + noise = np.random.normal(0, noise, n_samples) + if stack_noise: + noise = noise.cumsum() + r1, r2 = np.random.normal(0, 1, 2) + seasonality = r1 * np.sin(time / 365 * 2 * np.pi) + r2 * np.cos(time / 365 * 4 * np.pi + 1) + result = seasonality + season_trend * seasonality * time + trend * time + noise + if as_frame: + if start_date: + stamps = pd.date_range(start_date, periods=n_samples) + return pd.DataFrame({"yt": result, "date": stamps}) + return pd.DataFrame({"yt": result}) + return result + - Note that internally this is using `fetch_openml` from scikit-learn, which is experimental. +def fetch_creditcard(*, cache=True, data_home=None, as_frame=False, return_X_y=False): + """Load the creditcard dataset. Download it if necessary. + + Note that internally this is using + [`fetch_openml`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html) from + scikit-learn, which is experimental. - ============== ============== - Samples total 284807 - Dimensionality 29 - Features real - Target int 0, 1 - ============== ============== + ============== ============== + Samples total 284807 + Dimensionality 29 + Features real + Target int 0, 1 + ============== ============== - The datasets contains transactions made by credit cards in September 2013 by european - cardholders. This dataset present transactions that occurred in two days, where we have - 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive - class (frauds) account for 0.172% of all transactions. + The datasets contains transactions made by credit cards in September 2013 by european cardholders. + This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. + The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. Please cite: + Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 - :param version: integer or 'active', default='active' - Version of the dataset. Can only be provided if also ``name`` is given. - If 'active' the oldest version that's still active is used. Since - there may be more than one active version of a dataset, and those - versions may fundamentally be different from one another, setting an - exact version is highly recommended. - :param cache: boolean, default=True + Parameters + ---------- + cache : bool, default=True Whether to cache downloaded datasets using joblib. - :param data_home: optional, default: None - Specify another download and cache folder for the datasets. By default - all scikit-learn data is stored in '~/scikit_learn_data' subfolders. - :param as_frame: boolean, default=False - If True, the data is a pandas DataFrame including columns with - appropriate dtypes (numeric, string or categorical). The target is - a pandas DataFrame or Series depending on the number of target_columns. - The Bunch will contain a ``frame`` attribute with the target and the - data. If ``return_X_y`` is True, then ``(data, target)`` will be pandas - DataFrames or Series as describe above. - :param return_X_y: : boolean, default=False. - If True, returns ``(data.data, data.target)`` instead of a Bunch - object. + data_home : str | None, default=None + Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in + '~/scikit_learn_data' subfolders. + as_frame : bool, default=False + If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or + categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. + The Bunch will contain a `frame` attribute with the target and the data. If `return_X_y` is True, then + `(data, target)` will be pandas DataFrames or Series as describe above. + return_X_y : bool, default=False. + If True, returns `(data.data, data.target)` instead of a Bunch object. Returns ------- - :return: - Dictionary-like object, with the following attributes. + Dictionary-like + With the following attributes: * data ndarray, shape (284807, 29) if ``as_frame`` is True, ``data`` is a pandas object. diff --git a/sklego/decomposition/pca_reconstruction.py b/sklego/decomposition/pca_reconstruction.py index efe0901ea..5a5957d62 100644 --- a/sklego/decomposition/pca_reconstruction.py +++ b/sklego/decomposition/pca_reconstruction.py @@ -1,12 +1,41 @@ import numpy as np -from sklearn.decomposition import PCA from sklearn.base import BaseEstimator, OutlierMixin -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.decomposition import PCA +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class PCAOutlierDetection(BaseEstimator, OutlierMixin): - """ - Does outlier detection based on the reconstruction error from PCA. + """`PCAOutlierDetection` is an outlier detector based on the reconstruction error from PCA. + + If the difference between original and reconstructed data is larger than the `threshold`, the point is + considered an outlier. + + Parameters + ---------- + n_components : int | None, default=None + Number of components of the PCA model. + threshold : float | None, default=None + The threshold used for the decision function. + variant : Literal["relative", "absolute"], default="relative" + The variant used for the difference calculation. "relative" means that the difference between original and + reconstructed data is divided by the sum of the original data. + whiten : bool, default=False + `whiten` parameter of PCA model. + svd_solver : Literal["auto", "full", "arpack", "randomized"], default="auto" + `svd_solver` parameter of PCA model. + tol : float, default=0.0 + `tol` parameter of PCA model. + iterated_power : int | Literal["auto"], default="auto" + `iterated_power` parameter of PCA model. + random_state : int | None, default=None + `random_state` parameter of PCA model. + + Attributes + ---------- + pca_ : PCA + The underlying PCA model. + offset_ : float + The offset used for the decision function. """ def __init__( @@ -30,12 +59,25 @@ def __init__( self.random_state = random_state def fit(self, X, y=None): - """ - Fit the model using X as training data. + """Fit the `PCAOutlierDetection` model using `X` as training data by fitting the underlying PCA model, and + checking the `threshold` value. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) or None, default=None + Ignored, present for compatibility. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: ignored but kept in for pipeline support - :return: Returns an instance of self. + Returns + ------- + self : PCAOutlierDetection + The fitted estimator. + + Raises + ------ + ValueError + If `threshold` is `None`. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) if not self.threshold: @@ -54,19 +96,23 @@ def fit(self, X, y=None): return self def transform(self, X): - """ - Uses the underlying PCA method to transform the data. - """ + """Transform the data using the underlying PCA method.""" X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["pca_", "offset_"]) return self.pca_.transform(X) def difference(self, X): - """ - Shows the calculated difference between original and reconstructed data. Row by row. + """Return the calculated difference between original and reconstructed data. Row by row. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the difference + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + Data to calculate the difference for. + + Returns + ------- + array-like of shape (n_samples,) + The calculated difference. """ check_is_fitted(self, ["pca_", "offset_"]) reduced = self.pca_.transform(X) @@ -76,17 +122,29 @@ def difference(self, X): return diff def decision_function(self, X): + """Calculate the decision function for the data as the difference between `threshold` and the `.difference(X)` + (which is the difference between original data and reconstructed data).""" return self.threshold - self.difference(X) def score_samples(self, X): + """Calculate the score for the samples""" return -self.difference(X) def predict(self, X): - """ - Predict if a point is an outlier. + """Predict if a point is an outlier using fitted estimator. + + If the difference between original and reconstructed data is larger than the `threshold`, the point is + considered an outlier. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the predicted data. 1 for inliers, -1 for outliers. + Returns + ------- + array-like of shape (n_samples,) + The predicted data. 1 for inliers, -1 for outliers. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["pca_", "offset_"]) diff --git a/sklego/decomposition/umap_reconstruction.py b/sklego/decomposition/umap_reconstruction.py index 32566bfd1..6c239ca60 100644 --- a/sklego/decomposition/umap_reconstruction.py +++ b/sklego/decomposition/umap_reconstruction.py @@ -7,12 +7,41 @@ import numpy as np from sklearn.base import BaseEstimator, OutlierMixin -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class UMAPOutlierDetection(BaseEstimator, OutlierMixin): - """ - Does outlier detection based on the reconstruction error from UMAP. + """`UMAPOutlierDetection` is an outlier detector based on the reconstruction error from UMAP. + + If the difference between original and reconstructed data is larger than the `threshold`, the point is + considered an outlier. + + Parameters + ---------- + n_components : int, default=2 + Number of components of the UMAP model. + threshold : float | None, default=None + The threshold used for the decision function. + variant : Literal["relative", "absolute"], default="relative" + The variant used for the difference calculation. "relative" means that the difference between original and + reconstructed data is divided by the sum of the original data. + n_neighbors : int, default=15 + `n_neighbors` parameter of UMAP model. + min_dist : float, default=0.1 + `min_dist` parameter of UMAP model. + metric : str, default="euclidean" + `metric` parameter of UMAP model + (see [UMAP documentation](https://umap-learn.readthedocs.io/en/latest/parameters.html#metric) for the full list + of available metrics and more information). + random_state : int | None, default=None + `random_state` parameter of UMAP model. + + Attributes + ---------- + umap_ : UMAP + The underlying UMAP model. + offset_ : float + The offset used for the decision function. """ def __init__( @@ -34,12 +63,26 @@ def __init__( self.random_state = random_state def fit(self, X, y=None): - """ - Fit the model using X as training data. + """Fit the `UMAPOutlierDetection` model using `X` as training data by fitting the underlying UMAP model, and + checking the `threshold` value. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) or None, default=None + Ignored, present for compatibility. + + Returns + ------- + self : UMAPOutlierDetection + The fitted estimator. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: ignored but kept in for pipeline support - :return: Returns an instance of self. + Raises + ------ + ValueError + - If `n_components` is less than 2. + - If `threshold` is `None`. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) if self.n_components < 2: @@ -59,19 +102,23 @@ def fit(self, X, y=None): return self def transform(self, X): - """ - Uses the underlying UMAP method to transform the data. - """ + """Transform the data using the underlying UMAP method.""" X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["umap_", "offset_"]) return self.umap_.transform(X) def difference(self, X): - """ - Shows the calculated difference between original and reconstructed data. Row by row. + """Return the calculated difference between original and reconstructed data. Row by row. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + Data to calculate the difference for. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the difference + Returns + ------- + array-like of shape (n_samples,) + The calculated difference. """ check_is_fitted(self, ["umap_", "offset_"]) reduced = self.umap_.transform(X) @@ -81,11 +128,20 @@ def difference(self, X): return diff def predict(self, X): - """ - Predict if a point is an outlier. + """Predict if a point is an outlier using fitted estimator. + + If the difference between original and reconstructed data is larger than the `threshold`, the point is + considered an outlier. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the predicted data. 1 for inliers, -1 for outliers. + Returns + ------- + array-like of shape (n_samples,) + The predicted data. 1 for inliers, -1 for outliers. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["umap_", "offset_"]) diff --git a/sklego/dummy.py b/sklego/dummy.py index 3cf7caeb4..58f401609 100644 --- a/sklego/dummy.py +++ b/sklego/dummy.py @@ -4,21 +4,56 @@ from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils import check_X_y from sklearn.utils.validation import ( - check_is_fitted, + FLOAT_DTYPES, check_array, + check_is_fitted, check_random_state, - FLOAT_DTYPES, ) class RandomRegressor(BaseEstimator, RegressorMixin): - """ - A RandomRegressor makes random predictions only based on the "y" - value that is seen. The goal is that such a regressor can be used - for benchmarking. It should be easily beatable. - - :param str strategy: how we want to select random values, can be "uniform" or "normal" - :param int seed: the seed value, default: 42 + """A `RandomRegressor` makes random predictions only based on the `y` value that is seen. + + The goal is that such a regressor can be used for benchmarking. It _should be_ easily beatable. + + Parameters + ---------- + strategy : Literal["uniform", "normal"], default="uniform" + How we want to select random values, either "uniform" or "normal" + random_state : int | None, default=None + The seed value used for the random number generator. + + Attributes + ---------- + min_ : float + The minimum value of `y` seen during `fit`. + max_ : float + The maximum value of `y` seen during `fit`. + mu_ : float + The mean value of `y` seen during `fit`. + sigma_ : float + The standard deviation of `y` seen during `fit`. + n_features_in_ : int + The number of features seen during `fit`. + dim_ : int + Deprecated, please use `n_features_in_` instead. + + Examples + -------- + ```py + from sklego.dummy import RandomRegressor + from sklearn.datasets import make_regression + + X, y = make_regression(n_samples=10, n_features=2, random_state=42) + + RandomRegressor(strategy="uniform", random_state=123).fit(X, y).predict(X).round(2) + # array([ 57.63, -66.05, -83.92, 13.88, 64.56, -24.77, 143.33, 54.12, + # -7.34, -34.11]) + + RandomRegressor(strategy="normal", random_state=123).fit(X, y).predict(X).round(2) + # array([-128.45, 78.05, 7.23, -170.15, -78.18, 142.9 , -261.39, + # -63.34, 104.68, -106.75]) + ``` """ _ALLOWED_STRATEGIES = ("uniform", "normal") @@ -50,11 +85,18 @@ def fit(self, X: np.array, y: np.array) -> "RandomRegressor": return self def predict(self, X): - """ - Predict new data by making random guesses. - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data + """Predict new data by generating random guesses following the given `strategy` based on the `y` statistics seen + during `fit`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. """ rs = check_random_state(self.random_state) check_is_fitted(self, ["n_features_in_", "min_", "max_", "mu_", "sigma_"]) diff --git a/sklego/linear_model.py b/sklego/linear_model.py index 505473265..0a4461a41 100644 --- a/sklego/linear_model.py +++ b/sklego/linear_model.py @@ -15,24 +15,40 @@ from scipy.special._ufuncs import expit from sklearn.base import BaseEstimator, RegressorMixin from sklearn.linear_model._base import LinearClassifierMixin -from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier +from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier from sklearn.preprocessing import LabelEncoder from sklearn.utils import check_X_y from sklearn.utils.validation import ( + FLOAT_DTYPES, _check_sample_weight, - check_is_fitted, check_array, - FLOAT_DTYPES, + check_is_fitted, column_or_1d, ) class LowessRegression(BaseEstimator, RegressorMixin): - """ - Does LowessRegression. Note that this *can* get expensive to predict. + """`LowessRegression` estimator: LOWESS (Locally Weighted Scatterplot Smoothing) is a type of + [local regression](https://en.wikipedia.org/wiki/Local_regression). + + !!! warning + This model *can* get expensive to predict. + In fact the prediction step needs to compute the distance between each sample to predict `x_i` with all the + training samples. - :param sigma: float, how wide we will smooth the data - :param span: float, what percentage of the data is to be used. Defaults to using all data. + Parameters + ---------- + sigma : float, default=1.0 + The bandwidth parameter that determines the width of the smoothing. + span : float | None, default=None + The fraction of data points to consider during smoothing. + + Attributes + ---------- + X_ : np.ndarray of shape (n_samples, n_features) + The training data. + y_ : np.ndarray of shape (n_samples,) + The target (training) values. """ def __init__(self, sigma=1, span=None): @@ -40,12 +56,26 @@ def __init__(self, sigma=1, span=None): self.span = span def fit(self, X, y): - """ - Fit the model using X, y as training data. + """Fit the estimator on training data `X` and `y` by storing them in `self.X_` and `self.y_`, and + validating the parameters. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : LowessRegression + The fitted estimator. + + Raises + ------ + ValueError + - If `span` is not between 0 and 1. + - If `sigma` is negative. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if self.span is not None: @@ -60,6 +90,13 @@ def fit(self, X, y): return self def _calc_wts(self, x_i): + """Calculate the weights for a single point `x_i` using the training data `self.X_` and the parameters + `self.sigma` and `self.span`. The weights are calculated as `np.exp(-(distances**2) / self.sigma)`, + where distances are the distances between `x_i` and all the training samples. + + If `self.span` is not None, then the weights are multiplied by + `(distances <= np.quantile(distances, q=self.span))`. + """ distances = np.linalg.norm(self.X_ - x_i, axis=1) weights = np.exp(-(distances**2) / self.sigma) if self.span: @@ -67,11 +104,21 @@ def _calc_wts(self, x_i): return weights def predict(self, X): - """ - Predict using the LowessRegression. + """Predict target values for `X` using fitted estimator. This process is expensive because it needs to compute + the distance between each sample `x_i` with all the training samples. + + Then it calculates the weights for **each sample** `x_i` as `np.exp(-(distances**2) / self.sigma)` and finally + it computes the weighted average of the `y` values weighted by these weights. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: Returns an array of predictions shape=(n_samples,) + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted values. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["X_", "y_"]) @@ -83,25 +130,59 @@ def predict(self, X): class ProbWeightRegression(BaseEstimator, RegressorMixin): - """ - This regressor assumes that all input signals in `X` need to be reweighted - with weights that sum up to one in order to predict `y`. This can be very useful - in combination with `sklego.meta.EstimatorTransformer` because it allows you - to construct an ensemble. + """`ProbWeightRegression` assumes that all input signals in `X` need to be reweighted with weights that sum up to + one in order to predict `y`. - :param non_negative: boolean, default=True, setting that forces all weights to be >= 0 + This can be very useful in combination with `sklego.meta.EstimatorTransformer` because it allows to construct + an ensemble. + + Parameters + ---------- + non_negative : bool, default=True + If True, forces all weights to be non-negative. + + Attributes + ---------- + n_features_in_ : int + The number of features seen during `fit`. + coef_ : np.ndarray, shape (n_columns,) + The learned coefficients after fitting the model. + coefs_ : np.ndarray, shape (n_columns,) + Deprecated, please use `coef_` instead. + + !!! info + + This model requires [`cvxpy`](https://www.cvxpy.org/) to be installed. If you don't have it installed, you can + install it with: + + ```bash + pip install cvxpy + # or pip install scikit-lego"[cvxpy]" + ``` """ def __init__(self, non_negative=True): self.non_negative = non_negative def fit(self, X, y): - """ - Fit the model using X, y as training data. + r"""Fit the estimator on training data `X` and `y` by solving the following convex optimization problem: + + $$\begin{array}{ll}{\operatorname{minimize}} & {\sum_{i=1}^{N}\left(\mathbf{x}_{i} + \boldsymbol{\beta}-y_{i}\right)^{2}} \\ + {\text { subject to }} & {\sum_{j=1}^{p} \beta_{j}=1} \\ + {(\text{If non_negative=True})} & {\beta_{j} \geq 0, \quad j=1, \ldots, p} \end{array}$$ - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : ProbWeightRegression + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) @@ -121,11 +202,17 @@ def fit(self, X, y): return self def predict(self, X): - """ - Predict using ProbWeightRegression. + """Predict target values for `X` using fitted estimator by multiplying `X` with the learned coefficients. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: Returns an array of predictions shape=(n_samples,) + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["coef_"]) @@ -141,6 +228,56 @@ def coefs_(self): class DeadZoneRegressor(BaseEstimator, RegressorMixin): + r"""The `DeadZoneRegressor` estimator implements a regression model that incorporates a _dead zone effect_ for + improving the robustness of regression predictions. + + The dead zone effect allows the model to reduce the impact of small errors in the training data on the regression + results, which can be particularly useful when dealing with noisy or unreliable data. + + The estimator minimizes the following loss function using gradient descent: + + $$\frac{1}{n} \sum_{i=1}^{n} \text{deadzone}\left(\left|X_i \cdot w - y_i\right|\right)$$ + + where: + + $$\text{deadzone}(e) = + \begin{cases} + e & \text{if } e > \text{threshold} \text{ and effect="linear"} \\ + e^2 & \text{if } e > \text{threshold} \text{ and effect="quadratic"} \\ + 0 & \text{otherwise} + \end{cases} + $$ + + Parameters + ---------- + threshold : float, default=0.3 + The threshold value for the dead zone effect. + relative : bool, default=False + If True, the threshold is relative to the target value. Namely the _dead zone effect_ is applied to the + relative error between the predicted and target values. + effect : Literal["linear", "quadratic", "constant"], default="linear" + The type of dead zone effect to apply. It can be one of the following: + + - "linear": the errors within the threshold have no impact (their contribution is effectively zero), and errors + outside the threshold are penalized linearly. + - "quadratic": the errors within the threshold have no impact (their contribution is effectively zero), and + errors outside the threshold are penalized quadratically (squared). + - "constant": the errors within the threshold have no impact, and errors outside the threshold are penalized with a constant value. + n_iter : int, default=2000 + The number of iterations to run the gradient descent algorithm. + stepsize : float, default=0.01 + The step size for the gradient descent algorithm. + check_grad : bool, default=False + If True, check the gradients numerically, _just to be safe_. + + Attributes + ---------- + coef_ : np.ndarray, shape (n_columns,) + The learned coefficients after fitting the model. + coefs_ : np.ndarray, shape (n_columns,) + Deprecated, please use `coef_` instead. + """ + _ALLOWED_EFFECTS = ("linear", "quadratic", "constant") def __init__( @@ -154,12 +291,24 @@ def __init__( self.effect = effect def fit(self, X, y): - """ - Fit the model using X, y as training data. + """Fit the estimator on training data `X` and `y` by optimizing the loss function using gradient descent. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Returns + ------- + self : DeadZoneRegressor + The fitted estimator. + + Raises + ------ + ValueError + If `effect` is not one of "linear", "quadratic" or "constant". """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if self.effect not in self._ALLOWED_EFFECTS: @@ -226,11 +375,17 @@ def training_loss_derivative(weights): return self def predict(self, X): - """ - Predict using DeadZoneRegressor. + """Predict target values for `X` using fitted estimator by multiplying `X` with the learned coefficients. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: Returns an array of predictions shape=(n_samples,) + Returns + ------- + array-like of shape (n_samples,) + The predicted data. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["coef_"]) @@ -255,6 +410,48 @@ def allowed_effects(self): class _FairClassifier(BaseEstimator, LinearClassifierMixin): + """Base class for fair classifiers that address sensitive attribute fairness. + + This base class provides a foundation for fair classifiers that aim to mitigate bias and discrimination by taking + into account sensitive attributes during the classification process. + + This estimator works by solving a constrained optimization problem that maximizes the log-likelihood of the + training data while satisfying fairness constraints. + + !!! warning + The classification problem should be a binary one. + + Parameters + ---------- + sensitive_cols : List[str] | List[int] | None, default=None + A list of column names or column indexes in the input data that represent sensitive attributes. + C : float, default=1.0 + Inverse of regularization strength; must be a positive float. Smaller values specify stronger regularization. + penalty : Literal["l1", "none"], default="l1" + The type of penalty to apply to the model. "l1" applies L1 regularization, while "none" disables regularization. + fit_intercept : bool, default=True + Whether or not to fit an intercept term. If True, an intercept term is added to the model. + max_iter : int, default=100 + Maximum number of iterations for the solver. + train_sensitive_cols : bool, default=False + Whether or not to include sensitive columns during training. If False, sensitive columns are removed from the + input data during training. + + Attributes + ---------- + sensitive_col_idx_ : array + Indices of columns in the input data that correspond to sensitive attributes. + classes_ : array-like of shape (n_classes,) + The classes seen at `fit`. + + Note + ---- + Subclasses of `_FairClassifier` should implement the `constraints` method to specify fairness constraints. + This class primarily handles the logistic regression optimization and preprocessing steps. + + `_FairClassifier` should not be used directly; it serves as a base class for fair classification models. + """ + def __init__( self, sensitive_cols=None, @@ -272,6 +469,27 @@ def __init__( self.C = C def fit(self, X, y): + """Fit the estimator on training data `X` and `y`. + + It handles preprocessing and optimization. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : _FairClassifier + The fitted estimator. + + Raises + ------ + ValueError + If `penalty` is not one of "l1" or "none". + """ if self.penalty not in ["l1", "none"]: raise ValueError( f"penalty should be either 'l1' or 'none', got {self.penalty}" @@ -305,10 +523,11 @@ def fit(self, X, y): def constraints(self, y_hat, y_true, sensitive, n_obs): raise NotImplementedError( - "subclasses of _FairClassifier should implement constraints" + "subclasses of `_FairClassifier` should implement constraints" ) def _solve(self, sensitive, X, y): + """Solve the optimization problem for the fair classifier.""" n_obs, n_features = X.shape theta = cp.Variable(n_features) y_hat = X @ theta @@ -340,6 +559,20 @@ def _solve(self, sensitive, X, y): self.intercept_ = np.array([0.0]) def predict_proba(self, X): + """Predict class probabilities for `X`. + + This method predicts class probabilities for input data `X`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, 2) + The predicted class probabilities for each data point. + """ decision = self.decision_function(X) decision_2d = np.c_[-decision, decision] return expit(decision_2d) @@ -357,41 +590,48 @@ def _add_intercept(self, X): class DemographicParityClassifier(BaseEstimator, LinearClassifierMixin): - r""" - A logistic regression classifier which can be constrained on demographic parity (p% score). + r"""`DemographicParityClassifier` is a logistic regression classifier which can be constrained on demographic + parity (p% score). - Minimizes the Log loss while constraining the correlation between the specified `sensitive_cols` and the + It minimizes the log loss while constraining the correlation between the specified `sensitive_cols` and the distance to the decision boundary of the classifier. - Only works for binary classification problems + !!! warning + This classifier only works for binary classification problems. - .. math:: - \begin{array}{cl}{\operatorname{minimize}} & -\sum_{i=1}^{N} \log p\left(y_{i} | \mathbf{x}_{i}, + $$\begin{array}{cl}{\operatorname{minimize}} & -\sum_{i=1}^{N} \log p\left(y_{i} | \mathbf{x}_{i}, \boldsymbol{\theta}\right) \\ - {\text { subject to }} & {\frac{1}{N} \sum_{i=1}^{N}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) d - \boldsymbol{\theta}\left(\mathbf{x}_{i}\right) \leq \mathbf{c}} \\ + {\text { subject to }} & {\frac{1}{N} \sum_{i=1}^{N}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) + d_\boldsymbol{\theta}\left(\mathbf{x}_{i}\right) \leq \mathbf{c}} \\ {} & {\frac{1}{N} \sum_{i=1}^{N}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) - d_{\boldsymbol{\theta}}\left(\mathbf{x}_{i}\right) \geq-\mathbf{c}}\end{array} - - Source: - - M. Zafar et al. (2017), Fairness Constraints: Mechanisms for Fair Classification - - :param covariance_threshold: - The maximum allowed covariance between the sensitive attributes and the distance to the - decision boundary. If set to None, no fairness constraint is enforced - :param sensitive_cols: - List of sensitive column names(when X is a dataframe) - or a list of column indices when X is a numpy array. - :param C: - Inverse of regularization strength; must be a positive float. - Like in support vector machines, smaller values specify stronger regularization. - :param penalty: Used to specify the norm used in the penalization. Expects 'none' or 'l1' - :param fit_intercept: Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. - :param max_iter: Maximum number of iterations taken for the solvers to converge. - :param train_sensitive_cols: Indicates whether the model should use the sensitive columns in the fit step. - :param multi_class: The method to use for multiclass predictions - :param n_jobs: The amount of parallel jobs that should be used to fit multiclass models + d_{\boldsymbol{\theta}}\left(\mathbf{x}_{i}\right) \geq-\mathbf{c}}\end{array}$$ + Parameters + ---------- + covariance_threshold : float | None + The maximum allowed covariance between the sensitive attributes and the distance to the decision boundary. + If set to None, no fairness constraint is enforced. + sensitive_cols : List[str] | List[int] | None, default=None + List of sensitive column names (if X is a dataframe) or a list of column indices (if X is a numpy array). + C : float, default=1.0 + Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values + specify stronger regularization. + penalty : Literal["l1", "none"], default="l1" + Used to specify the norm used in the penalization. + fit_intercept : bool, default=True + Whether or not a constant term (a.k.a. bias or intercept) should be added to the decision function. + max_iter : int, default=100 + Maximum number of iterations taken for the solvers to converge. + train_sensitive_cols : bool, default=False + Indicates whether the model should use the sensitive columns in the fit step. + multi_class : Literal["ovr", "ovo"], default="ovr" + The method to use for multiclass predictions. + n_jobs : int | None, default=1 + The amount of parallel jobs that should be used to fit the model. + + Source + ------ + M. Zafar et al. (2017), Fairness Constraints: Mechanisms for Fair Classification """ def __new__(cls, *args, multi_class="ovr", n_jobs=1, **kwargs): @@ -413,6 +653,15 @@ def __init__(self, *args, **kwargs): class _DemographicParityClassifier(_FairClassifier): + r"""Classifier for Demographic Parity fairness constraint. + + This classifier extends the `_FairClassifier` and adds a Demographic Parity fairness constraint. + Demographic Parity ensures that the probability of a positive outcome is the same for all groups defined by + sensitive attributes. + + The class implements Demographic Parity fairness constraint to the `_FairClassifier` optimization problem. + """ + def __init__( self, covariance_threshold, @@ -435,6 +684,7 @@ def __init__( self.covariance_threshold = covariance_threshold def constraints(self, y_hat, y_true, sensitive, n_obs): + """Implement the Demographic Parity fairness constraint.""" if self.covariance_threshold is not None: dec_boundary_cov = y_hat @ (sensitive - np.mean(sensitive, axis=0)) / n_obs return [cp.abs(dec_boundary_cov) <= self.covariance_threshold] @@ -443,42 +693,48 @@ def constraints(self, y_hat, y_true, sensitive, n_obs): class EqualOpportunityClassifier(BaseEstimator, LinearClassifierMixin): - r""" - A logistic regression classifier which can be constrained on equal opportunity score. + r"""`EqualOpportunityClassifier` is a logistic regression classifier which can be constrained on equal opportunity + score. - Minimizes the Log loss while constraining the correlation between the specified `sensitive_cols` and the + It minimizes the log loss while constraining the correlation between the specified `sensitive_cols` and the distance to the decision boundary of the classifier for those examples that have a y_true of 1. - Only works for binary classification problems + !!! warning + This classifier only works for binary classification problems. - .. math:: - \begin{array}{cl}{\operatorname{minimize}} & -\sum_{i=1}^{N} \log p\left(y_{i} | \mathbf{x}_{i}, + $$\begin{array}{cl}{\operatorname{minimize}} & -\sum_{i=1}^{N} \log p\left(y_{i} | \mathbf{x}_{i}, \boldsymbol{\theta}\right) \\ - {\text { subject to }} & {\frac{1}{POS} \sum_{i=1}^{POS}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) d - \boldsymbol{\theta}\left(\mathbf{x}_{i}\right) \leq \mathbf{c}} \\ + {\text { subject to }} & {\frac{1}{POS} \sum_{i=1}^{POS}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) + d_\boldsymbol{\theta}\left(\mathbf{x}_{i}\right) \leq \mathbf{c}} \\ {} & {\frac{1}{POS} \sum_{i=1}^{POS}\left(\mathbf{z}_{i}-\overline{\mathbf{z}}\right) - d_{\boldsymbol{\theta}}\left(\mathbf{x}_{i}\right) \geq-\mathbf{c}}\end{array} - - where POS is the subset of the population where y_true = 1 - - - :param covariance_threshold: - The maximum allowed covariance between the sensitive attributes and the distance to the - decision boundary. If set to None, no fairness constraint is enforced - :param positive_target: The name of the class which is associated with a positive outcome - :param sensitive_cols: - List of sensitive column names(when X is a dataframe) - or a list of column indices when X is a numpy array. - :param C: - Inverse of regularization strength; must be a positive float. - Like in support vector machines, smaller values specify stronger regularization. - :param penalty: Used to specify the norm used in the penalization. Expects 'none' or 'l1' - :param fit_intercept: Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. - :param max_iter: Maximum number of iterations taken for the solvers to converge. - :param train_sensitive_cols: Indicates whether the model should use the sensitive columns in the fit step. - :param multi_class: The method to use for multiclass predictions - :param n_jobs: The amount of parallel jobs that should be used to fit multiclass models + d_{\boldsymbol{\theta}}\left(\mathbf{x}_{i}\right) \geq-\mathbf{c}}\end{array}$$ + + where POS is the subset of the population where $\text{y_true} = 1$ + Parameters + ---------- + covariance_threshold : float | None + The maximum allowed covariance between the sensitive attributes and the distance to the decision boundary. + If set to None, no fairness constraint is enforced. + positive_target : int + The name of the class which is associated with a positive outcome + sensitive_cols : List[str] | List[int] | None, default=None + List of sensitive column names (if X is a dataframe) or a list of column indices (if X is a numpy array). + C : float, default=1.0 + Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values + specify stronger regularization. + penalty : Literal["l1", "none"], default="l1" + Used to specify the norm used in the penalization. + fit_intercept : bool, default=True + Whether or not a constant term (a.k.a. bias or intercept) should be added to the decision function. + max_iter : int, default=100 + Maximum number of iterations taken for the solvers to converge. + train_sensitive_cols : bool, default=False + Indicates whether the model should use the sensitive columns in the fit step. + multi_class : Literal["ovr", "ovo"], default="ovr" + The method to use for multiclass predictions. + n_jobs : int | None, default=1 + The amount of parallel jobs that should be used to fit the model. """ def __new__(cls, *args, multi_class="ovr", n_jobs=1, **kwargs): @@ -530,46 +786,45 @@ def constraints(self, y_hat, y_true, sensitive, n_obs): class BaseScipyMinimizeRegressor(BaseEstimator, RegressorMixin, ABC): - """ - Base class for regressors relying on scipy's minimize method. Derive a class from this one and give it the function to be minimized. + """Abstract base class for regressors relying on Scipy's + [minimize method](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) to minimize a + (custom) loss function. + + Derive a class from this one and give it the function to be minimized. The derived class should implement the + `_get_objective` method, which should return the loss function and its gradient. + + !!! info + This implementation uses + [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). Parameters ---------- alpha : float, default=0.0 - Constant that multiplies the penalty terms. Defaults to 1.0. - + Constant that multiplies the penalty terms. l1_ratio : float, default=0.0 - The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For - ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it - is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a - combination of L1 and L2. + The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`: + - `l1_ratio = 0` is equivalent to an L2 penalty. + - `l1_ratio = 1` is equivalent to an L1 penalty. + - `0 < l1_ratio < 1` is the combination of L1 and L2. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to False, no intercept will be used in calculations + Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - copy_X : bool, default=True - If True, X will be copied; else, it may be overwritten. - + If True, `X` will be copied; else, it may be overwritten. positive : bool, default=False When set to True, forces the coefficients to be positive. - - method : str, default="SLSQP" - Type of solver. Should be one of "SLSQP", "TNC", "L-BFGS-B". + method : Literal["SLSQP", "TNC", "L-BFGS-B"], default="SLSQP" + Type of solver to use for optimization. Attributes ---------- - coef_ : np.array of shape (n_features,) + coef_ : np.ndarray of shape (n_features,) Estimated coefficients of the model. - intercept_ : float - Independent term in the linear model. Set to 0.0 if fit_intercept = False. - - Notes - ----- - This implementation uses scipy.optimize.minimize, see - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html. + Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. + n_features_in_ : int + Number of features seen during `fit`. """ def __init__( @@ -595,28 +850,25 @@ def __init__( @abstractmethod def _get_objective(self, X, y, sample_weight): - """ - Produce the loss function to be minimized, and its gradient to speed up computations. + """Produce the loss function to be minimized, and its gradient to speed up computations. Parameters ---------- - X : np.array of shape (n_samples, n_features) + X : np.ndarray of shape (n_samples, n_features) The training data. - - y : np.array, 1-dimensional + y : np.ndarray of shape (n_samples,) The target values. - - sample_weight : Optional[np.array], default=None + sample_weight : np.ndarray of shape (n_samples,) | None, default=None Individual weights for each sample. Returns ------- - loss : Callable[[np.array], float] + loss : Callable[[np.ndarray], float] The loss function to be minimized. - - grad_loss : Callable[[np.array], np.array] + grad_loss : Callable[[np.ndarray], np.ndarray] The gradient of the loss function. Speeds up finding the minimum. """ + ... def _regularized_loss(self, params): return +self.alpha * self.l1_ratio * np.sum( @@ -630,23 +882,21 @@ def _regularized_grad_loss(self, params): ) def fit(self, X, y, sample_weight=None): - """ - Fit the model using the chosen algorithm. + """Fit the linear model on training data `X` and `y` by optimizing the loss function using gradient descent. Parameters ---------- - X : np.array of shape (n_samples, n_features) + X : array-like of shape (n_samples, n_features ) The training data. - - y : np.array, 1-dimensional + y : array-like of shape (n_samples,) The target values. - - sample_weight : Optional[np.array], default=None + sample_weight : array-like of shape (n_samples,) | None, default=None Individual weights for each sample. Returns ------- - Fitted regressor. + self : BaseScipyMinimizeRegressor + Fitted linear model. """ X_, grad_loss, loss = self._prepare_inputs(X, sample_weight, y) @@ -677,6 +927,11 @@ def fit(self, X, y, sample_weight=None): return self def _prepare_inputs(self, X, sample_weight, y): + """Prepare the inputs for the optimization problem. + + This method is called by `fit` to prepare the inputs for the optimization problem. It adds an intercept column + to `X` if `fit_intercept=True`, and returns the loss function and its gradient. + """ X, y = check_X_y(X, y, y_numeric=True) sample_weight = _check_sample_weight(sample_weight, X) self.n_features_in_ = X.shape[1] @@ -694,18 +949,17 @@ def _prepare_inputs(self, X, sample_weight, y): return X_, grad_loss, loss def predict(self, X): - """ - Predict using the linear model. + """Predict target values for `X` using fitted linear model by multiplying `X` with the learned coefficients. Parameters ---------- - X : np.array, shape (n_samples, n_features) - Samples to get predictions of. + X : array-like of shape (n_samples, n_features) + The data to predict. Returns ------- - y : np.array, shape (n_samples,) - The predicted values. + array-like of shape (n_samples,) + The predicted data. """ check_is_fitted(self) X = check_array(X) @@ -714,76 +968,79 @@ def predict(self, X): class ImbalancedLinearRegression(BaseScipyMinimizeRegressor): - r""" - Linear regression where overestimating is `overestimation_punishment_factor` times worse than underestimating. + r"""Linear regression where overestimating is `overestimation_punishment_factor` times worse than underestimating. - A value of `overestimation_punishment_factor=5` implies that overestimations by the model are penalized with a factor of 5 - while underestimations have a default factor of 1. The formula optimized for is + A value of `overestimation_punishment_factor=5` implies that overestimations by the model are penalized with a + factor of 5 while underestimations have a default factor of 1. The formula optimized for is - .. math:: - \frac{1}{2 N} \|s \circ (y - Xw) \|_2^2 + \alpha \cdot l_1 \cdot\|w\|_1 + \frac{\alpha}{2} \cdot (1-l_1)\cdot \|w\|_2^2 + $$\frac{1}{2 N} \|s \circ (y - Xw) \|_2^2 + \alpha \cdot l_1 \cdot\|w\|_1 + \frac{\alpha}{2} \cdot (1-l_1)\cdot + \|w\|_2^2$$ - where `circ` is component-wise multiplication and s is a vector with value overestimation_punishment_factor if y - Xw < 0, else 1. + where $\circ$ is component-wise multiplication and - ImbalancedLinearRegression fits a linear model to minimize the residual sum of squares between - the observed targets in the dataset, and the targets predicted by the linear approximation. + $$ s = \begin{cases} + \text{overestimation_punishment_factor} & \text{if } y - Xw < 0 \\ + 1 & \text{otherwise} + \end{cases} + $$ + + `ImbalancedLinearRegression` fits a linear model to minimize the residual sum of squares between the observed + targets in the dataset, and the targets predicted by the linear approximation. Compared to normal linear regression, this approach allows for a different treatment of over or under estimations. + !!! info + This implementation uses + [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). + Parameters ---------- alpha : float, default=0.0 - Constant that multiplies the penalty terms. Defaults to 1.0. - + Constant that multiplies the penalty terms. l1_ratio : float, default=0.0 - The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For - ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it - is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a - combination of L1 and L2. + The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`: + - `l1_ratio = 0` is equivalent to an L2 penalty. + - `l1_ratio = 1` is equivalent to an L1 penalty. + - `0 < l1_ratio < 1` is the combination of L1 and L2. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to False, no intercept will be used in calculations + Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - copy_X : bool, default=True - If True, X will be copied; else, it may be overwritten. - + If True, `X` will be copied; else, it may be overwritten. positive : bool, default=False When set to True, forces the coefficients to be positive. - - method : str, default="SLSQP" - Type of solver. Should be one of "SLSQP", "TNC", "L-BFGS-B". - - overestimation_punishment_factor : float, default=1 + method : Literal["SLSQP", "TNC", "L-BFGS-B"], default="SLSQP" + Type of solver to use for optimization. + overestimation_punishment_factor : float, default=1.0 Factor to punish overestimations more (if the value is larger than 1) or less (if the value is between 0 and 1). Attributes ---------- - coef_ : np.array of shape (n_features,) + coef_ : np.ndarray of shape (n_features,) Estimated coefficients of the model. - intercept_ : float - Independent term in the linear model. Set to 0.0 if fit_intercept = False. - - Notes - ----- - This implementation uses scipy.optimize.minimize, see - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html. + Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. + n_features_in_ : int + Number of features seen during `fit`. Examples -------- - >>> import numpy as np - >>> np.random.seed(0) - >>> X = np.random.randn(100, 4) - >>> y = X @ np.array([1, 2, 3, 4]) + 2*np.random.randn(100) - >>> over_bad = ImbalancedLinearRegression(overestimation_punishment_factor=50).fit(X, y) - >>> over_bad.coef_ - array([0.36267036, 1.39526844, 3.4247146 , 3.93679175]) - - >>> under_bad = ImbalancedLinearRegression(overestimation_punishment_factor=0.01).fit(X, y) - >>> under_bad.coef_ - array([0.73519586, 1.28698197, 2.61362614, 4.35989806]) - + ```py + import numpy as np + from sklego.linear_model import ImbalancedLinearRegression + + np.random.seed(0) + X = np.random.randn(100, 4) + y = X @ np.array([1, 2, 3, 4]) + 2*np.random.randn(100) + + over_bad = ImbalancedLinearRegression(overestimation_punishment_factor=50).fit(X, y) + over_bad.coef_ + # array([0.36267036, 1.39526844, 3.4247146 , 3.93679175]) + + under_bad = ImbalancedLinearRegression(overestimation_punishment_factor=0.01).fit(X, y) + under_bad.coef_ + # array([0.73519586, 1.28698197, 2.61362614, 4.35989806]) + ``` """ def __init__( @@ -822,71 +1079,83 @@ def grad_imbalanced_loss(params): class QuantileRegression(BaseScipyMinimizeRegressor): - """ - Compute Quantile Regression. This can be used for computing confidence intervals of linear regressions. + r"""Compute quantile regression. This can be used for computing confidence intervals of linear regressions. + `QuantileRegression` fits a linear model to minimize a weighted residual sum of absolute deviations between the observed targets in the dataset and the targets predicted by the linear approximation, i.e. - 1 / (2 * n_samples) * switch * ||y - Xw||_1 - + alpha * l1_ratio * ||w||_1 - + 0.5 * alpha * (1 - l1_ratio) * ||w||_2 ** 2 - where switch is a vector with value `quantile` if y - Xw < 0, else `1 - quantile`. The regressor defaults to - `LADRegression` for its default value of `quantile=0.5`. + + $$\frac{\text{switch} \cdot ||y - Xw||_1}{2 N} + \alpha \cdot l_1 \cdot ||w||_1 + + \frac{\alpha}{2} \cdot (1 - l_1) \cdot ||w||^2_2$$ + + where + + $$\text{switch} = \begin{cases} + \text{quantile} & \text{if } y - Xw < 0 \\ + 1-\text{quantile} & \text{otherwise} + \end{cases}$$ + + The regressor defaults to `LADRegression` for its default value of `quantile=0.5`. + Compared to linear regression, this approach is robust to outliers. + + !!! info + This implementation uses + [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). + Parameters ---------- alpha : float, default=0.0 Constant that multiplies the penalty terms. - l1_ratio : float, default=0.0 - The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For - ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it - is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a - combination of L1 and L2. + The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`: + - `l1_ratio = 0` is equivalent to an L2 penalty. + - `l1_ratio = 1` is equivalent to an L1 penalty. + - `0 < l1_ratio < 1` is the combination of L1 and L2. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to False, no intercept will be used in calculations + Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - copy_X : bool, default=True - If True, X will be copied; else, it may be overwritten. - + If True, `X` will be copied; else, it may be overwritten. positive : bool, default=False When set to True, forces the coefficients to be positive. + method : Literal["SLSQP", "TNC", "L-BFGS-B"], default="SLSQP" + Type of solver to use for optimization. + quantile : float, default=0.5 + The line output by the model will have a share of approximately `quantile` data points under it. It should + be a value between 0 and 1. - method : str, default="SLSQP" - Type of solver. Should be one of "SLSQP", "TNC", "L-BFGS-B". - - quantile : float, between 0 and 1, default=0.5 - The line output by the model will have a share of approximately `quantile` data points under it. - A value of `quantile=1` outputs a line that is above each data point, for example. `quantile=0.5` corresponds to LADRegression. + A value of `quantile=1` outputs a line that is above each data point, for example. + `quantile=0.5` corresponds to LADRegression. Attributes ---------- coef_ : np.ndarray of shape (n_features,) Estimated coefficients of the model. intercept_ : float - Independent term in the linear model. Set to 0.0 if fit_intercept = False. - Notes - ----- - This implementation uses scipy.optimize.minimize, see - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html. + Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. + n_features_in_ : int + Number of features seen during `fit`. + Examples -------- - >>> import numpy as np - >>> np.random.seed(0) - >>> X = np.random.randn(100, 4) - >>> y = X @ np.array([1, 2, 3, 4]) - >>> l = QuantileRegression().fit(X, y) - >>> l.coef_ - array([1., 2., 3., 4.]) - >>> import numpy as np - >>> np.random.seed(0) - >>> X = np.random.randn(100, 4) - >>> y = X @ np.array([-1, 2, -3, 4]) - >>> l = QuantileRegression(quantile=0.8).fit(X, y) - >>> l.coef_ - array([-1., 2., -3., 4.]) + ```py + import numpy as np + from sklego.linear_model import QuantileRegression + + np.random.seed(0) + X = np.random.randn(100, 4) + y = X @ np.array([1, 2, 3, 4]) + + model = QuantileRegression().fit(X, y) + model.coef_ + # array([1., 2., 3., 4.]) + + y = X @ np.array([-1, 2, -3, 4]) + model = QuantileRegression(quantile=0.8).fit(X, y) + model.coef_ + # array([-1., 2., -3., 4.]) + ``` """ def __init__( @@ -899,7 +1168,6 @@ def __init__( method="SLSQP", quantile=0.5, ): - """Initialize.""" super().__init__(alpha, l1_ratio, fit_intercept, copy_X, positive, method) self.quantile = quantile @@ -925,19 +1193,26 @@ def grad_quantile_loss(params): return quantile_loss, grad_quantile_loss def fit(self, X, y, sample_weight=None): - """ - Fit the model using the SLSQP algorithm. + """Fit the estimator on training data `X` and `y` by minimizing the quantile loss function. + Parameters ---------- - X : np.ndarray of shape (n_samples, n_features) + X : array-like of shape (n_samples, n_features) The training data. - y : np.ndarray, 1-dimensional + y : array-like of shape (n_samples,) The target values. - sample_weight : Optional[np.ndarray], default=None + sample_weight : array-like of shape (n_samples,) | None, default=None Individual weights for each sample. + Returns ------- - Fitted regressor. + self : QuantileRegression + The fitted estimator. + + Raises + ------ + ValueError + If `quantile` is not between 0 and 1. """ if 0 <= self.quantile <= 1: super().fit(X, y, sample_weight) @@ -948,75 +1223,74 @@ def fit(self, X, y, sample_weight=None): class LADRegression(QuantileRegression): - r""" - Least absolute deviation Regression. + r"""Least absolute deviation Regression. + + `LADRegression` fits a linear model to minimize the residual sum of absolute deviations between the observed targets + in the dataset, and the targets predicted by the linear approximation, i.e. - LADRegression fits a linear model to minimize the residual sum of absolute deviations between - the observed targets in the dataset, and the targets predicted by the linear approximation, i.e. + $$\frac{1}{N}\|y - Xw \|_1 + \alpha \cdot l_1 \cdot\|w\|_1 + \frac{\alpha}{2} \cdot (1-l_1)\cdot \|w\|^2_2$$ - .. math:: - \frac{1}{N}\|y - Xw \|_1 + \alpha \cdot l_1 \cdot\|w\|_1 + \frac{\alpha}{2} \cdot (1-l_1)\cdot \|w\|_2^2 + Compared to linear regression, this approach is robust to outliers. You can even optimize for the lowest MAPE + (Mean Average Percentage Error), by providing `sample_weight=np.abs(1/y_train)` when fitting the regressor. - Compared to linear regression, this approach is robust to outliers. You can even - optimize for the lowest MAPE (Mean Average Percentage Error), if you pass in np.abs(1/y_train) for the - sample_weight keyword when fitting the regressor. + !!! info + This implementation uses + [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). Parameters ---------- alpha : float, default=0.0 - Constant that multiplies the penalty terms. Defaults to 1.0. - + Constant that multiplies the penalty terms. l1_ratio : float, default=0.0 - The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For - ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it - is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a - combination of L1 and L2. + The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`: + - `l1_ratio = 0` is equivalent to an L2 penalty. + - `l1_ratio = 1` is equivalent to an L1 penalty. + - `0 < l1_ratio < 1` is the combination of L1 and L2. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to False, no intercept will be used in calculations + Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). - copy_X : bool, default=True - If True, X will be copied; else, it may be overwritten. - + If True, `X` will be copied; else, it may be overwritten. positive : bool, default=False When set to True, forces the coefficients to be positive. + method : Literal["SLSQP", "TNC", "L-BFGS-B"], default="SLSQP" + Type of solver to use for optimization. + quantile : float, default=0.5 + The line output by the model will have a share of approximately `quantile` data points under it. It should + be a value between 0 and 1. - method : str, default="SLSQP" - Type of solver. Should be one of "SLSQP", "TNC", "L-BFGS-B". + A value of `quantile=1` outputs a line that is above each data point, for example. + `quantile=0.5` corresponds to LADRegression. Attributes ---------- - coef_ : np.array of shape (n_features,) + coef_ : np.ndarray of shape (n_features,) Estimated coefficients of the model. - intercept_ : float - Independent term in the linear model. Set to 0.0 if fit_intercept = False. - - Notes - ----- - This implementation uses scipy.optimize.minimize, see - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html. + Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. + n_features_in_ : int + Number of features seen during `fit`. Examples -------- - >>> import numpy as np - >>> np.random.seed(0) - >>> X = np.random.randn(100, 4) - >>> y = X @ np.array([1, 2, 3, 4]) - >>> l = LADRegression().fit(X, y) - >>> l.coef_ - array([1., 2., 3., 4.]) - - >>> import numpy as np - >>> np.random.seed(0) - >>> X = np.random.randn(100, 4) - >>> y = X @ np.array([-1, 2, -3, 4]) - >>> l = LADRegression(positive=True).fit(X, y) - >>> l.coef_ - array([7.39575926e-18, 1.42423304e+00, 2.80467827e-17, 4.29789588e+00]) - + ```py + import numpy as np + from sklego.linear_model import LADRegression + + np.random.seed(0) + X = np.random.randn(100, 4) + y = X @ np.array([1, 2, 3, 4]) + + model = LADRegression().fit(X, y) + model.coef_ + # array([1., 2., 3., 4.]) + + y = X @ np.array([-1, 2, -3, 4]) + model = LADRegression(positive=True).fit(X, y) + model.coef_ + # array([7.39575926e-18, 1.42423304e+00, 2.80467827e-17, 4.29789588e+00]) + ``` """ def __init__( @@ -1028,7 +1302,6 @@ def __init__( positive=False, method="SLSQP", ): - """Initialize.""" super().__init__( alpha, l1_ratio, fit_intercept, copy_X, positive, method, quantile=0.5 ) diff --git a/sklego/meta/_grouped_utils.py b/sklego/meta/_grouped_utils.py index 990bc4c1a..f54f5dc9b 100644 --- a/sklego/meta/_grouped_utils.py +++ b/sklego/meta/_grouped_utils.py @@ -10,8 +10,7 @@ def constant_shrinkage(group_sizes: list, alpha: float) -> np.ndarray: - r""" - The augmented prediction for each level is the weighted average between its prediction and the augmented + r"""The augmented prediction for each level is the weighted average between its prediction and the augmented prediction for its parent. Let $\hat{y}_i$ be the prediction at level $i$, with $i=0$ being the root, than the augmented prediction @@ -19,10 +18,7 @@ def constant_shrinkage(group_sizes: list, alpha: float) -> np.ndarray: """ return np.array( [alpha ** (len(group_sizes) - 1)] - + [ - alpha ** (len(group_sizes) - 1 - i) * (1 - alpha) - for i in range(1, len(group_sizes) - 1) - ] + + [alpha ** (len(group_sizes) - 1 - i) * (1 - alpha) for i in range(1, len(group_sizes) - 1)] + [(1 - alpha)] ) @@ -35,9 +31,7 @@ def relative_shrinkage(group_sizes: list) -> np.ndarray: def min_n_obs_shrinkage(group_sizes: list, min_n_obs) -> np.ndarray: """Use only the smallest group with a certain amount of observations""" if min_n_obs > max(group_sizes): - raise ValueError( - f"There is no group with size greater than or equal to {min_n_obs}" - ) + raise ValueError(f"There is no group with size greater than or equal to {min_n_obs}") res = np.zeros(len(group_sizes)) res[np.argmin(np.array(group_sizes) >= min_n_obs) - 1] = 1 @@ -79,14 +73,10 @@ def _data_format_checks(X, name): def _shape_check(X, min_value_cols): if min_value_cols > 1: if X.ndim == 1 or X.shape[1] < 2: - raise ValueError( - f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required." - ) + raise ValueError(f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required.") else: if X.ndim == 2 and X.shape[1] < 1: - raise ValueError( - f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required." - ) + raise ValueError(f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required.") def _check_grouping_columns(X_group, **kwargs) -> pd.DataFrame: diff --git a/sklego/meta/confusion_balancer.py b/sklego/meta/confusion_balancer.py index b0f89d64c..b3f505745 100644 --- a/sklego/meta/confusion_balancer.py +++ b/sklego/meta/confusion_balancer.py @@ -1,35 +1,36 @@ -from sklearn.base import ( - BaseEstimator, - ClassifierMixin, - MetaEstimatorMixin, -) +from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin from sklearn.metrics import confusion_matrix from sklearn.utils.multiclass import unique_labels -from sklearn.utils.validation import ( - check_is_fitted, - check_X_y, - check_array, - FLOAT_DTYPES, -) +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, check_X_y from sklego.base import ProbabilisticClassifier class ConfusionBalancer(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): - """ - The ConfusionBalancer attempts to give it's child estimator a more balanced - output by learning from the confusion matrix during training. The idea is that - the confusion matrix calculates P(C_i | M_i) where C_i is the actual class and - M_i is the class that the underlying model gives. We use these probabilities to - attempt a more balanced prediction by averaging the correction from the confusion - matrix with the original probabilities. - - .. math:: - p(\text{class_j}) = \alpha p(\text{model}_j) + (1-\alpha) p(\text{class_j} | \text{model}_j) p(\text{model}_j) - - :param model: a scikit learn compatible classification model that has predict_proba - :param alpha: a hyperparameter between 0 and 1, determines how much to apply smoothing - :param cfm_smooth: a smoothing parameter for the confusion matrices to ensure zeros don't exist + r"""The `ConfusionBalancer` estimator attempts to give it's child estimator a more balanced output by learning from + the confusion matrix during training. + + The idea is that the confusion matrix calculates $P(C_i | M_i)$ where $C_i$ is the actual class and $M_i$ is the + class that the underlying model gives. We use these probabilities to attempt a more balanced prediction by averaging + the correction from the confusion matrix with the original probabilities. + + $$P(\text{class_j}) = \alpha P(\text{model}_j) + (1-\alpha) P(\text{class_j} | \text{model}_j) P(\text{model}_j)$$ + + Parameters + ---------- + estimator : scikit-learn compatible classifier + The estimator to be wrapped, it must implement a `predict_proba` method. + alpha : float, default=0.5 + Hyperparameter which determines how much smoothing to apply. Must be between 0 and 1. + cfm_smooth : float, default=0 + Smoothing parameter for the confusion matrices to ensure zeros don't exist. + + Attributes + ---------- + classes_ : array-like of shape (n_classes,) + The target class labels. + cfm_ : array-like of shape (n_classes, n_classes) + The confusion matrix used for the correction. """ def __init__(self, estimator, alpha: float = 0.5, cfm_smooth=0): @@ -38,13 +39,27 @@ def __init__(self, estimator, alpha: float = 0.5, cfm_smooth=0): self.cfm_smooth = cfm_smooth def fit(self, X, y): - """ - Fit the data. + """Fit the underlying estimator on the training data `X` and `y`, it calculates the confusion matrix, + normalizes it and stores it for later use. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :return: Returns an instance of self. + Returns + ------- + self : ConfusionBalancer + The fitted estimator. + + Raises + ------ + ValueError + If the underlying estimator does not have a `predict_proba` method. """ + X, y = check_X_y(X, y, estimator=self.estimator, dtype=FLOAT_DTYPES) if not isinstance(self.estimator, ProbabilisticClassifier): raise ValueError( @@ -57,22 +72,35 @@ def fit(self, X, y): return self def predict_proba(self, X): - """ - Predict new data, with probabilities + """Predict probabilities for new data `X` using the underlying estimator and then applying the confusion matrix + correction. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples, n_classes) the predicted data + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted values. """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) preds = self.estimator.predict_proba(X) return (1 - self.alpha) * preds + self.alpha * preds @ self.cfm_ def predict(self, X): - """ - Predict new data. + """Predict most likely class for new data `X` using the underlying estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data + Returns + ------- + array-like of shape (n_samples,) + The predicted values. """ check_is_fitted(self, ["cfm_", "classes_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) diff --git a/sklego/meta/decay_estimator.py b/sklego/meta/decay_estimator.py index 65f005b06..064ffd016 100644 --- a/sklego/meta/decay_estimator.py +++ b/sklego/meta/decay_estimator.py @@ -1,47 +1,62 @@ import numpy as np from sklearn import clone from sklearn.base import BaseEstimator -from sklearn.utils.validation import ( - check_is_fitted, - check_X_y, - FLOAT_DTYPES, -) +from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y class DecayEstimator(BaseEstimator): - """ - Morphs an estimator suchs that the training weights can be - adapted to ensure that points that are far away have less weight. - Note that it is up to the user to sort the dataset appropriately. - This meta estimator will only work for estimators that have a - "sample_weights" argument in their `.fit()` method. + """Morphs an estimator such that the training weights can be adapted to ensure that points that are far away have + less weight. + + This meta estimator will only work for estimators that allow a `sample_weights` argument in their `.fit()` method. + The meta estimator `.fit()` method computes the weights to pass to the estimator's `.fit()` method. - The `fit` method computes the weights to pass to the estimator. + !!! warning + It is up to the user to sort the dataset appropriately. - .. warning:: By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator. + !!! warning + By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator. To change such behaviour, set `check_input` to `True`. - Remark that if the check is skipped, then `y` should have a `shape` attrbute, which is + Remark that if the check is skipped, then `y` should have a `shape` attribute, which is used to extract the number of samples in training data, and compute the weights. - The DecayEstimator will use exponential decay to weight the parameters. - - w_{t-1} = decay * w_{t} + !!! info + The DecayEstimator will use exponential decay to weight the parameters. + + $$w_{t-1} = decay * w_{t}$$ + + Parameters + ---------- + model : scikit-learn compatible estimator + The estimator to be wrapped. + decay : float, default=0.999 + The decay factor to use. + decay_func : str, default="exponential" + The decay function to use. Currently only exponential decay is supported. + check_input : bool, default=False + Whether or not to check the input data. If False, the checks are delegated to the wrapped estimator. + + Attributes + ---------- + estimator_ : scikit-learn compatible estimator + The fitted estimator. + weights_ : array-like of shape (n_samples,) + The weights used to train the estimator. + classes_ : array-like of shape (n_classes,) + The classes labels. Only present if the wrapped estimator is a classifier. """ - def __init__( - self, model, decay: float = 0.999, decay_func="exponential", check_input=False - ): + def __init__(self, model, decay: float = 0.999, decay_func="exponential", check_input=False): self.model = model self.decay = decay self.decay_func = decay_func self.check_input = check_input def _is_classifier(self): - return any( - ["ClassifierMixin" in p.__name__ for p in type(self.model).__bases__] - ) + """Checks if the wrapped estimator is a classifier.""" + return any(["ClassifierMixin" in p.__name__ for p in type(self.model).__bases__]) @property def _estimator_type(self): @@ -49,18 +64,23 @@ def _estimator_type(self): return self.model._estimator_type def fit(self, X, y): - """ - Fit the data after adapting the same weight. - - :param X: array-like, shape=(n_samples, n_features,) training data. - :param y: array-like, shape=(n_samples,) target values. - :return: Returns an instance of self. + """Fit the underlying estimator on the training data `X` and `y` using the calculated sample weights. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self : DecayEstimator + The fitted estimator. """ if self.check_input: - X, y = check_X_y( - X, y, estimator=self, dtype=FLOAT_DTYPES, ensure_min_features=0 - ) + X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES, ensure_min_features=0) self.weights_ = np.cumprod(np.ones(y.shape[0]) * self.decay)[::-1] self.estimator_ = clone(self.model) @@ -68,19 +88,23 @@ def fit(self, X, y): self.estimator_.fit(X, y, sample_weight=self.weights_) except TypeError as e: if "sample_weight" in str(e): - raise TypeError( - f"Model {type(self.model).__name__}.fit() does not have 'sample_weight'" - ) + raise TypeError(f"Model {type(self.model).__name__}.fit() does not have 'sample_weight'") if self._is_classifier(): self.classes_ = self.estimator_.classes_ return self def predict(self, X): - """ - Predict new data. + """Predict target values for `X` using trained underlying estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_samples, n_features,) data to predict. - :return: array, shape=(n_samples,) the predicted data + Returns + ------- + array-like of shape (n_samples,) + The predicted values. """ if self._is_classifier(): check_is_fitted(self, ["classes_"]) @@ -88,4 +112,5 @@ def predict(self, X): return self.estimator_.predict(X) def score(self, X, y): + """Alias for `.score()` method of the underlying estimator.""" return self.estimator_.score(X, y) diff --git a/sklego/meta/estimator_transformer.py b/sklego/meta/estimator_transformer.py index 6bf01f997..911d56c81 100644 --- a/sklego/meta/estimator_transformer.py +++ b/sklego/meta/estimator_transformer.py @@ -1,32 +1,55 @@ from sklearn import clone -from sklearn.base import ( - BaseEstimator, - TransformerMixin, - MetaEstimatorMixin, -) -from sklearn.utils.validation import ( - check_is_fitted, - check_X_y, - FLOAT_DTYPES, -) +from sklearn.base import BaseEstimator, MetaEstimatorMixin, TransformerMixin +from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y + class EstimatorTransformer(TransformerMixin, MetaEstimatorMixin, BaseEstimator): - """ - Allows using an estimator such as a model as a transformer in an earlier step of a pipeline + """Allow using an estimator as a transformer in an earlier step of a pipeline. - :param estimator: An instance of the estimator that should be used for the transformation - :param predict_func: The function called on the estimator when transforming e.g. (`predict`, `predict_proba`) - :param check_input: Whether or not to check the input data. If False, the checks are delegated to the wrapped estimator. - """ + !!! warning + By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator. + To change such behaviour, set `check_input` to `True`. + + Parameters + ---------- + estimator : scikit-learn compatible estimator + The estimator to be applied to the data, used as transformer. + predict_func : str, default="predict" + The method called on the estimator when transforming e.g. (`"predict"`, `"predict_proba"`). + check_input : bool, default=False + Whether or not to check the input data. If False, the checks are delegated to the wrapped estimator. + + Attributes + ---------- + estimator_ : scikit-learn compatible estimator + The fitted underlying estimator. + multi_output_ : bool + Whether or not the estimator is multi output. + """ def __init__(self, estimator, predict_func="predict", check_input=False): self.estimator = estimator self.predict_func = predict_func self.check_input = check_input def fit(self, X, y, **kwargs): - """Fits the estimator""" + """Fit the underlying estimator on training data `X` and `y`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + **kwargs : dict + Additional keyword arguments passed to the `fit` method of the underlying estimator. + + Returns + ------- + self : EstimatorTransformer + The fitted transformer. + """ if self.check_input: X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES, multi_output=True) @@ -37,12 +60,20 @@ def fit(self, X, y, **kwargs): return self def transform(self, X): - """ - Applies the `predict_func` on the fitted estimator. + """Transform the data by applying the `predict_func` of the fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to be transformed. - Returns array of shape `(X.shape[0], )` if estimator is not multi output. - For multi output estimators an array of shape `(X.shape[0], y.shape[1])` is returned. + Returns + ------- + output : array-like of shape (n_samples,) | (n_samples, n_outputs) + The transformed data. Array will be of shape `(X.shape[0], )` if estimator is not multi output. + For multi output estimators an array of shape `(X.shape[0], y.shape[1])` is returned. """ + check_is_fitted(self, "estimator_") output = getattr(self.estimator_, self.predict_func)(X) return output if self.multi_output_ else output.reshape(-1, 1) diff --git a/sklego/meta/grouped_estimator.py b/sklego/meta/grouped_estimator.py index ff7542e0f..534058c06 100644 --- a/sklego/meta/grouped_estimator.py +++ b/sklego/meta/grouped_estimator.py @@ -1,4 +1,5 @@ from deprecated import deprecated + from .grouped_predictor import GroupedPredictor diff --git a/sklego/meta/grouped_predictor.py b/sklego/meta/grouped_predictor.py index 57348daf2..00ac7c838 100644 --- a/sklego/meta/grouped_predictor.py +++ b/sklego/meta/grouped_predictor.py @@ -3,40 +3,42 @@ from sklearn import clone from sklearn.base import BaseEstimator, is_classifier from sklearn.utils.metaestimators import available_if -from sklearn.utils.validation import ( - check_is_fitted, - check_array, -) +from sklearn.utils.validation import check_array, check_is_fitted from sklego.common import as_list, expanding_list -from ._grouped_utils import relative_shrinkage, constant_shrinkage, min_n_obs_shrinkage -from ._grouped_utils import _split_groups_and_values + +from ._grouped_utils import _split_groups_and_values, constant_shrinkage, min_n_obs_shrinkage, relative_shrinkage class GroupedPredictor(BaseEstimator): - """ - Construct an estimator per data group. Splits data by values of a - single column and fits one estimator per such column. - - :param estimator: the model/pipeline to be applied per group - :param groups: the column(s) of the matrix/dataframe to select as a grouping parameter set - :param shrinkage: How to perform shrinkage. - None: No shrinkage (default) - {"constant", "min_n_obs", "relative"} or a callable - * constant: shrunk prediction for a level is weighted average of its prediction and its - parents prediction - * min_n_obs: shrunk prediction is the prediction for the smallest group with at least - n observations in it - * relative: each group-level is weight according to its size - * function: a function that takes a list of group lengths and returns an array of the - same size with the weights for each group - :param use_global_model: With shrinkage: whether to have a model over the entire input as first group - Without shrinkage: whether or not to fall back to a general model in case the group - parameter is not found during `.predict()` - :param check_X: Whether to validate X to be non-empty 2D array of finite values and attempt to cast X to float. - If disabled, the model/pipeline is expected to handle e.g. missing, - non-numeric, or non-finite values. - :param **shrinkage_kwargs: keyword arguments to the shrinkage function + """Construct an estimator per data group. Splits data by values of a single column and fits one estimator per such + column. + + Parameters + ---------- + estimator : scikit-learn compatible estimator/pipeline + The estimator/pipeline to be applied per group. + groups : int | str | List[int] | List[str] + The column(s) of the array/dataframe to select as a grouping parameter set. + shrinkage : Literal["constant", "min_n_obs", "relative"] | Callable | None, default=None + How to perform shrinkage: + + - `None`: No shrinkage (default) + - `"constant"`: shrunk prediction for a level is weighted average of its prediction and its parents prediction + - `"min_n_obs"`: shrunk prediction is the prediction for the smallest group with at least n observations in it + - `"relative"`: each group-level is weight according to its size + - `Callable`: a function that takes a list of group lengths and returns an array of the same size with the + weights for each group + use_global_model : bool, default=True + + - With shrinkage: whether to have a model over the entire input as first group + - Without shrinkage: whether or not to fall back to a general model in case the group parameter is not found + during `.predict()` + check_X : bool, default=True + Whether to validate `X` to be non-empty 2D array of finite values and attempt to cast `X` to float. + If disabled, the model/pipeline is expected to handle e.g. missing, non-numeric, or non-finite values. + **shrinkage_kwargs : dict + Keyword arguments to the shrinkage function """ # Number of features in value df can be 0, e.g. for dummy models @@ -61,14 +63,8 @@ def __init__( self.check_X = check_X def __set_shrinkage_function(self): - if ( - self.shrinkage - and len(as_list(self.groups)) == 1 - and not self.use_global_model - ): - raise ValueError( - "Cannot do shrinkage with a single group if use_global_model is False" - ) + if self.shrinkage and len(as_list(self.groups)) == 1 and not self.use_global_model: + raise ValueError("Cannot do shrinkage with a single group if use_global_model is False") if isinstance(self.shrinkage, str): # Predefined shrinkage functions @@ -89,9 +85,7 @@ def __set_shrinkage_function(self): self.__check_shrinkage_func() self.shrinkage_function_ = self.shrinkage else: - raise ValueError( - "Invalid shrinkage specified. Should be either None (no shrinkage), str or callable." - ) + raise ValueError("Invalid shrinkage specified. Should be either None (no shrinkage), str or callable.") def __check_shrinkage_func(self): """Validate the shrinkage function if a function is specified""" @@ -100,18 +94,12 @@ def __check_shrinkage_func(self): try: result = self.shrinkage(group_lengths) except Exception as e: - raise ValueError( - f"Caught an exception while checking the shrinkage function: {str(e)}" - ) from e + raise ValueError(f"Caught an exception while checking the shrinkage function: {str(e)}") from e else: if not isinstance(result, np.ndarray): - raise ValueError( - f"shrinkage_function({group_lengths}) should return an np.ndarray" - ) + raise ValueError(f"shrinkage_function({group_lengths}) should return an np.ndarray") if result.shape != expected_shape: - raise ValueError( - f"shrinkage_function({group_lengths}).shape should be {expected_shape}" - ) + raise ValueError(f"shrinkage_function({group_lengths}).shape should be {expected_shape}") def __get_shrinkage_factor(self, X_group): """Get for all complete groups an array of shrinkages""" @@ -119,16 +107,11 @@ def __get_shrinkage_factor(self, X_group): counts = X_group.groupby(group_colnames).size() # Groups that are split on all - most_granular_groups = [ - grp for grp in self.groups_ if len(as_list(grp)) == len(group_colnames) - ] + most_granular_groups = [grp for grp in self.groups_ if len(as_list(grp)) == len(group_colnames)] # For each hierarchy level in each most granular group, get the number of observations hierarchical_counts = { - granular_group: [ - counts[tuple(subgroup)].sum() - for subgroup in expanding_list(granular_group, tuple) - ] + granular_group: [counts[tuple(subgroup)].sum() for subgroup in expanding_list(granular_group, tuple)] for granular_group in most_granular_groups } @@ -139,19 +122,19 @@ def __get_shrinkage_factor(self, X_group): } # Make sure that the factors sum to one - shrinkage_factors = { - group: value / value.sum() for group, value in shrinkage_factors.items() - } + shrinkage_factors = {group: value / value.sum() for group, value in shrinkage_factors.items()} return shrinkage_factors def __fit_single_group(self, group, X, y=None): + """Fit estimator to the given group.""" try: return clone(self.estimator).fit(X, y) except Exception as e: raise type(e)(f"Exception for group {group}: {e}") def __fit_grouped_estimator(self, X_group, X_value, y=None, columns=None): + """Fit an estimator to each group""" # Reset indices such that they are the same in X and y if not columns: columns = X_group.columns.tolist() @@ -174,8 +157,7 @@ def __fit_grouped_estimator(self, X_group, X_value, y=None, columns=None): } else: grouped_estimators = { - group: self.__fit_single_group(group, X_value[indices, :]) - for group, indices in group_indices.items() + group: self.__fit_single_group(group, X_value[indices, :]) for group, indices in group_indices.items() } return grouped_estimators @@ -185,11 +167,7 @@ def __fit_shrinkage_groups(self, X_group, X_value, y): for grouping_colnames in self.group_colnames_hierarchical_: # Fit a grouped estimator to each (sub)group hierarchically - estimators.update( - self.__fit_grouped_estimator( - X_group, X_value, y, columns=grouping_colnames - ) - ) + estimators.update(self.__fit_grouped_estimator(X_group, X_value, y, columns=grouping_colnames)) return estimators @@ -211,14 +189,25 @@ def __add_shrinkage_column(self, X_group): return X_group def fit(self, X, y=None): - """ - Fit the model using X, y as training data. Will also learn the groups that exist within the dataset. + """Fit one estimator for each group of training data `X` and `y`. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :return: Returns an instance of self. - """ + Will also learn the groups that exist within the dataset. + + If `use_global_model=True` a fallback estimator will be fitted on the entire dataset in case a group is not + found during `.predict()`. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Target values. + + Returns + ------- + self : GroupedPredictor + The fitted estimator. + """ X_group, X_value = _split_groups_and_values( X, self.groups, min_value_cols=0, check_X=self.check_X, **self._check_kwargs ) @@ -256,20 +245,14 @@ def __predict_shrinkage_groups(self, X_group, X_value, method="predict"): # DataFrame with predictions for each hierarchy level, per row. Missing groups errors are thrown here. hierarchical_predictions = pd.concat( [ - pd.Series( - self.__predict_groups( - X_group, X_value, level_columns, method=method - ) - ) + pd.Series(self.__predict_groups(X_group, X_value, level_columns, method=method)) for level_columns in self.group_colnames_hierarchical_ ], axis=1, ) # This is a Series with values the tuples of hierarchical grouping - prediction_groups = pd.Series( - [tuple(_) for _ in X_group.itertuples(index=False)] - ) + prediction_groups = pd.Series([tuple(_) for _ in X_group.itertuples(index=False)]) # This is a Series of arrays shrinkage_factors = prediction_groups.map(self.shrinkage_factors_) @@ -338,13 +321,20 @@ def __predict_groups( ) def predict(self, X): + """Predict target values on new data `X` by predicting on each group. If a group is not found during + `.predict()` and `use_global_model=True` the fallback estimator will be used. If `use_global_model=False` a + `ValueError` will be raised. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to predict. + + Returns + ------- + array-like of shape (n_samples,) + Predicted target values. """ - Predict on new data. - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data - """ - check_is_fitted(self, ["estimators_", "groups_", "fallback_"]) X_group, X_value = _split_groups_and_values( @@ -361,13 +351,21 @@ def predict(self, X): # This ensures that the meta-estimator only has the predict_proba method if the estimator has it @available_if(lambda self: hasattr(self.estimator, "predict_proba")) def predict_proba(self, X): - """ - Predict probabilities on new data. + """Predict probabilities on new data `X`. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples, n_classes) the predicted data - """ + !!! warning + Available only if the underlying estimator implements `.predict_proba()` method. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + Predicted probabilities per class. + """ check_is_fitted(self, ["estimators_", "groups_", "fallback_"]) X_group, X_value = _split_groups_and_values( @@ -386,13 +384,23 @@ def predict_proba(self, X): # This ensures that the meta-estimator only has the predict_proba method if the estimator has it @available_if(lambda self: hasattr(self.estimator, "decision_function")) def decision_function(self, X): + """Predict confidence scores for samples in `X`. + + !!! warning + Available only if the underlying estimator implements `.decision_function()` method. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to predict. + + Returns + ------- + array-like of shape (n_samples,) or (n_samples, n_classes) + Confidence scores per (n_samples, n_classes) combination. + In the binary case, confidence score for self.classes_[1] where > 0 means this class would be + predicted. """ - Evaluate the decision function for the samples in X. - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: the decision function of the sample for each class in the model. - """ - check_is_fitted(self, ["estimators_", "groups_", "fallback_"]) X_group, X_value = _split_groups_and_values( diff --git a/sklego/meta/grouped_transformer.py b/sklego/meta/grouped_transformer.py index 03dbccd09..92108be50 100644 --- a/sklego/meta/grouped_transformer.py +++ b/sklego/meta/grouped_transformer.py @@ -1,6 +1,5 @@ import numpy as np import pandas as pd - from sklearn.base import BaseEstimator, TransformerMixin, clone from sklearn.utils.validation import check_is_fitted @@ -8,15 +7,26 @@ class GroupedTransformer(BaseEstimator, TransformerMixin): - """ - Construct a transformer per data group. Splits data by groups from single or multiple columns - and transforms remaining columns using the transformers corresponding to the groups. - - :param transformer: the transformer to be applied per group - :param groups: the column(s) of the matrix/dataframe to select as a grouping parameter set. If None, - the transformer will be applied to the entire input without grouping - :param use_global_model: Whether or not to fall back to a general transformation in case a group - is not found during `.transform()` + """Construct a transformer per data group. Splits data by groups from single or multiple columns and transforms + remaining columns using the transformers corresponding to the groups. + + Parameters + ---------- + transformer : scikit-learn compatible transformer + The transformer to be applied per group. + groups : int | str | List[int] | List[str] | None + The column(s) of the array/dataframe to select as a grouping parameter set. If `None`, the transformer will be + applied to the entire input without grouping. + use_global_model : bool, default=True + Whether or not to fall back to a general transformation in case a group is not found during `.transform()`. + + Attributes + ---------- + transformers_ : scikit-learn compatible transformer | dict[..., scikit-learn compatible transformer] + The fitted transformers per group or a single fitted transformer if `groups` is `None`. + fallback_ : scikit-learn compatible transformer | None + The fitted transformer to fall back to in case a group is not found during `.transform()`. Only present if + `use_global_model` is `True`. """ _check_kwargs = {"accept_large_sparse": False} @@ -27,14 +37,28 @@ def __init__(self, transformer, groups, use_global_model=True): self.use_global_model = use_global_model def __fit_single_group(self, group, X, y=None): + """Fit transformer to the given group. + + Parameters + ---------- + group : tuple + The group to fit the transformer to. + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Target values. + + Returns + ------- + transformer : scikit-learn compatible transformer + The fitted transformer for the group. + """ try: return clone(self.transformer).fit(X, y) except Exception as e: raise type(e)(f"Exception for group {group}: {e}") - def __fit_grouped_transformer( - self, X_group: pd.DataFrame, X_value: np.array, y=None - ): + def __fit_grouped_transformer(self, X_group: pd.DataFrame, X_value: np.ndarray, y=None): """Fit a transformer to each group""" # Make the groups based on the groups dataframe, use the indices on the values array try: @@ -54,25 +78,36 @@ def __fit_grouped_transformer( } else: grouped_transformers = { - group: self.__fit_single_group(group, X_value[indices, :]) - for group, indices in group_indices.items() + group: self.__fit_single_group(group, X_value[indices, :]) for group, indices in group_indices.items() } return grouped_transformers def __check_transformer(self): + """Check if the supplied transformer has a `transform` method and raise a `ValueError` if not.""" if not hasattr(self.transformer, "transform"): - raise ValueError( - "The supplied transformer should have a 'transform' method" - ) + raise ValueError("The supplied transformer should have a 'transform' method") def fit(self, X, y=None): - """ - Fit the transformers to the groups in X + """Fit one transformer for each group of training data `X`. + + Will also learn the groups that exist within the dataset. - :param X: Array-like with at least two columns, of which at least one corresponds to groups defined in init, - and the remaining columns represent the values to transform. - :param y: (Optional) target variable + If `use_global_model=True` a fallback transformer will be fitted on the entire dataset in case a group is not + found during `.transform()`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. If `groups` is not `None`, X should have at least two columns, of which at least one + corresponds to groups defined in `groups`, and the remaining columns represent the values to transform. + y : array-like of shape (n_samples,), default=None + Target values. + + Returns + ------- + self : GroupedTransformer + The fitted transformer. """ self.__check_transformer() @@ -82,9 +117,7 @@ def fit(self, X, y=None): self.transformers_ = clone(self.transformer).fit(X, y) return self - X_group, X_value = _split_groups_and_values( - X, self.groups, **self._check_kwargs - ) + X_group, X_value = _split_groups_and_values(X, self.groups, **self._check_kwargs) self.transformers_ = self.__fit_grouped_transformer(X_group, X_value, y) if self.use_global_model: @@ -102,13 +135,11 @@ def __transform_single_group(self, group, X): if self.fallback_: group_transformer = self.fallback_ else: - raise ValueError( - f"Found new group {group} during transform with use_global_model = False" - ) + raise ValueError(f"Found new group {group} during transform with use_global_model = False") return pd.DataFrame(group_transformer.transform(X)).set_index(index) - def __transform_groups(self, X_group: pd.DataFrame, X_value: np.array): + def __transform_groups(self, X_group: pd.DataFrame, X_value: np.ndarray): """Transform all groups""" # Reset indices such that they are the same in X_group (reset in __check_grouping_columns), # this way we can track the order of the result @@ -130,18 +161,25 @@ def __transform_groups(self, X_group: pd.DataFrame, X_value: np.array): ) def transform(self, X): - """ - Fit the transformers to the groups in X - - :param X: Array-like with columns corresponding to the ones in .fit() + """Transform new data `X` by transforming on each group. If a group is not found during `.transform()` and + `use_global_model=True` the fallback transformer will be used. If `use_global_model=False` a `ValueError` will + be raised. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to transform. + + Returns + ------- + array-like of shape (n_samples, n_features) + Data transformed per group. """ check_is_fitted(self, ["fallback_", "transformers_"]) if self.groups is None: return self.transformers_.transform(X) - X_group, X_value = _split_groups_and_values( - X, self.groups, **self._check_kwargs - ) + X_group, X_value = _split_groups_and_values(X, self.groups, **self._check_kwargs) return self.__transform_groups(X_group, X_value) diff --git a/sklego/meta/outlier_classifier.py b/sklego/meta/outlier_classifier.py index 6a7a82745..789770aa3 100644 --- a/sklego/meta/outlier_classifier.py +++ b/sklego/meta/outlier_classifier.py @@ -1,38 +1,66 @@ import numpy as np from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.calibration import _SigmoidCalibration -from sklego.base import OutlierModel from sklearn.utils.validation import check_is_fitted, check_X_y +from sklego.base import OutlierModel + class OutlierClassifier(BaseEstimator, ClassifierMixin): - """ - Morphs an estimator that performs outlier detection into a classifier. - When an outlier is detected it will output 1 and 0 otherwise. - This way you can use familiar metrics again and this allows you - to consider outlier models as a fraud detector. + """Morphs an outlier detection model into a classifier. + + When an outlier is detected it will output 1 and 0 otherwise. This way you can use familiar metrics again and + this allows you to consider outlier models as a fraud detector. + + Parameters + ---------- + model : scikit-learn compatible outlier detection model + An outlier detection model that will be used for prediction. + + Attributes + ---------- + estimator_ : scikit-learn compatible outlier detection model + The fitted underlying outlier detection model. + classes_ : array-like of shape (2,) + Classes used for prediction (0 or 1) """ def __init__(self, model): self.model = model def _is_outlier_model(self): + """Check if the underlying model is an outlier detection model.""" return isinstance(self.model, OutlierModel) def fit(self, X, y=None): - """ - Fit the data after adapting the same weight. + """Fit the underlying estimator to the training data `X` and `y`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Target values. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :return: Returns an instance of self. + Returns + ------- + self : OutlierClassifier + The fitted estimator. + + Raises + ------ + ValueError + - If the underlying model is not an outlier detection model. + - If the underlying model does not have a `decision_function` method. """ X, y = check_X_y(X, y, estimator=self) if not self._is_outlier_model(): raise ValueError("Passed model does not detect outliers!") - if not hasattr(self.model, 'decision_function'): - raise ValueError(f'Passed model {self.model} does not have a `decision_function` ' - f'method. This is required for `predict_proba` estimation.') + if not hasattr(self.model, "decision_function"): + raise ValueError( + f"Passed model {self.model} does not have a `decision_function` " + f"method. This is required for `predict_proba` estimation." + ) self.estimator_ = self.model.fit(X, y) self.classes_ = np.array([0, 1]) @@ -43,11 +71,17 @@ def fit(self, X, y=None): return self def predict(self, X): - """ - Predict new data. + """Predict new data. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + np.ndarray of shape (n_samples,) + The predicted values. 0 for inliers, 1 for outliers. """ check_is_fitted(self, ["estimator_", "classes_"]) preds = self.estimator_.predict(X) @@ -56,11 +90,17 @@ def predict(self, X): return result def predict_proba(self, X): - """ - Predict probability estimates for new data. + """Predict probability estimates for new data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples,) input data. - :return: array, shape=(n_samples,) the predicted data + Returns + ------- + np.ndarray of shape (n_samples,) + The predicted probabilities. """ check_is_fitted(self, ["estimator_", "classes_"]) decision_function_scores = self.estimator_.decision_function(X) diff --git a/sklego/meta/outlier_remover.py b/sklego/meta/outlier_remover.py index 7fbc58848..011aac21d 100644 --- a/sklego/meta/outlier_remover.py +++ b/sklego/meta/outlier_remover.py @@ -1,25 +1,30 @@ +from deprecated import deprecated from sklearn import clone from sklearn.base import BaseEstimator -from sklearn.utils.validation import ( - check_is_fitted, - check_array, -) +from sklearn.utils.validation import check_array, check_is_fitted from sklego.common import TrainOnlyTransformerMixin -from deprecated import deprecated @deprecated( version="0.4.2", - reason="Please use `sklego.preprocessing.OutlierRemovers` instead. " + reason="Please use `sklego.preprocessing.OutlierRemover` instead. " "This object will be removed from the meta submodule in version 0.6.0.", ) class OutlierRemover(TrainOnlyTransformerMixin, BaseEstimator): - """ - Removes outliers (train-time only) using the supplied removal model. + """Removes outliers (train-time only) using the supplied removal model. + + Parameters + ---------- + outlier_detector : object + An outlier detector that implements `fit` and `predict` methods. + refit : bool, default=True + If True, fits the estimator during pipeline.fit(). If False, the estimator is not fitted during pipeline.fit(). - :param outlier_detector: must implement `fit` and `predict` methods - :param refit: If True, fits the estimator during pipeline.fit(). + Attributes + ---------- + estimator_ : object + The fitted outlier detector. """ def __init__(self, outlier_detector, refit=True): @@ -28,6 +33,20 @@ def __init__(self, outlier_detector, refit=True): self.estimator_ = None def fit(self, X, y=None): + """Fits the estimator, and the outlier detector if `refit` is True. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Target values. + + Returns + ------- + self : OutlierRemover + The fitted transformer. + """ self.estimator_ = clone(self.outlier_detector) if self.refit: super().fit(X, y) @@ -35,6 +54,18 @@ def fit(self, X, y=None): return self def transform_train(self, X): + """Removes outliers from `X` using the fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the outliers will be removed. + + Returns + ------- + np.ndarray of shape (n_not_outliers, n_features) + The data with the outliers removed, where `n_not_outliers = n_samples - n_outliers`. + """ check_is_fitted(self, "estimator_") predictions = self.estimator_.predict(X) check_array(predictions, estimator=self.outlier_detector, ensure_2d=False) diff --git a/sklego/meta/regression_outlier_detector.py b/sklego/meta/regression_outlier_detector.py index 9d71f42e9..1882f9e49 100644 --- a/sklego/meta/regression_outlier_detector.py +++ b/sklego/meta/regression_outlier_detector.py @@ -1,18 +1,40 @@ import numpy as np import pandas as pd from sklearn.base import BaseEstimator, OutlierMixin -from sklearn.utils.validation import ( - check_is_fitted, - check_array -) +from sklearn.utils.validation import check_array, check_is_fitted class RegressionOutlierDetector(BaseEstimator, OutlierMixin): + """Morphs a regression estimator into one that can detect outliers. We will try to predict `column` in X. + + Parameters + ---------- + model : scikit-learn compatible regression model + A regression model that will be used for prediction. + column : int + The index of the target column to predict in the input data. + lower : float, default=2.0 + Lower threshold for outlier detection. The method used for detection depends on the `method` parameter. + upper : float, default=2.0 + Upper threshold for outlier detection. The method used for detection depends on the `method` parameter. + method : Literal["sd", "relative", "absolute"], default="sd" + The method to use for outlier detection. + + - `"sd"` uses standard deviation + - `"relative"` uses relative difference + - `"absolute"` uses absolute difference + + Attributes + ---------- + estimator_ : scikit-learn compatible regression model + The fitted underlying regression model. + sd_ : float + The standard deviation of the differences between true and predicted values. + idx_ : int + The index of the target column in the input data. """ - Morphs a regression model into one that can detect outliers. We will try - to predict `column` in X. - """ - def __init__(self, model, column, lower=2, upper=2, method='sd'): + + def __init__(self, model, column, lower=2, upper=2, method="sd"): self.model = model self.column = column self.lower = lower @@ -20,11 +42,11 @@ def __init__(self, model, column, lower=2, upper=2, method='sd'): self.method = method def _is_regression_model(self): - return any( - ["RegressorMixin" in p.__name__ for p in type(self.model).__bases__] - ) + """Check if the underlying model is a regression model.""" + return any(["RegressorMixin" in p.__name__ for p in type(self.model).__bases__]) def _handle_thresholds(self, y_true, y_pred): + """Compute if a sample is an outlier based on the `method` parameter.""" difference = y_true - y_pred results = np.ones(difference.shape, dtype=int) allowed_methods = ["sd", "relative", "absolute"] @@ -34,8 +56,8 @@ def _handle_thresholds(self, y_true, y_pred): lower_limit_hit = -self.lower * self.sd_ > difference upper_limit_hit = self.upper * self.sd_ < difference if self.method == "relative": - lower_limit_hit = -self.lower > difference/y_true - upper_limit_hit = self.upper < difference/y_true + lower_limit_hit = -self.lower > difference / y_true + upper_limit_hit = self.upper < difference / y_true if self.method == "absolute": lower_limit_hit = -self.lower > difference upper_limit_hit = self.upper < difference @@ -44,6 +66,22 @@ def _handle_thresholds(self, y_true, y_pred): return results def to_x_y(self, X): + """Split `X` into two arrays `X_to_use` and `y`. + `y` is the column we want to predict (specified in the `column` parameter) and `X_to_use` is the rest of the + data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data to split. + + Returns + ------- + X_to_use : array-like of shape (n_samples, n_features-1) + Data to use for prediction. + y : array-like of shape (n_samples,) + The target column. + """ y = X[:, self.idx_] cols_to_use = [i for i in range(X.shape[1]) if i != self.column] X_to_use = X[:, cols_to_use] @@ -52,12 +90,27 @@ def to_x_y(self, X): return X_to_use, y def fit(self, X, y=None): - """ - Fit the data after adapting the same weight. + """Fit the underlying model on `X_to_use` and `y` where: - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :return: Returns an instance of self. + - `y` is the column we want to predict (`X[:, self.column]`) + - `X_to_use` is the rest of the data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : RegressionOutlierDetector + The fitted estimator. + + Raises + ------ + ValueError + If the `model` is not a regression estimator. """ self.idx_ = np.argmax([i == self.column for i in X.columns]) if isinstance(X, pd.DataFrame) else self.column X = check_array(X, estimator=self) @@ -69,20 +122,47 @@ def fit(self, X, y=None): return self def predict(self, X, y=None): - """ - Predict new data. + """Predict which samples of `X` are outliers using the underlying estimator and given `method`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data + Returns + ------- + np.ndarray of shape (n_samples,) + The predicted values. 1 for inliers, -1 for outliers. """ - check_is_fitted(self, ['estimator_', 'sd_', 'idx_']) + check_is_fitted(self, ["estimator_", "sd_", "idx_"]) X = check_array(X, estimator=self) X, y = self.to_x_y(X) preds = self.estimator_.predict(X) return self._handle_thresholds(y, preds) def score_samples(self, X, y=None): - check_is_fitted(self, ['estimator_', 'sd_', 'idx_']) + """Calculate the outlier scores for the given data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data for which outlier scores are calculated. + y : array-like of shape shape=(n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + np.ndarray of shape (n_samples,) + The outlier scores for the input data. + + Raises + ------ + ValueError + If `method` is not one of "sd", "relative", or "absolute". + """ + check_is_fitted(self, ["estimator_", "sd_", "idx_"]) X = check_array(X, estimator=self) X, y_true = self.to_x_y(X) y_pred = self.estimator_.predict(X) @@ -93,6 +173,6 @@ def score_samples(self, X, y=None): if self.method == "sd": return difference if self.method == "relative": - return difference/y_true + return difference / y_true if self.method == "absolute": return difference diff --git a/sklego/meta/subjective_classifier.py b/sklego/meta/subjective_classifier.py index 2c36fd6cb..ce5d64fee 100644 --- a/sklego/meta/subjective_classifier.py +++ b/sklego/meta/subjective_classifier.py @@ -1,42 +1,49 @@ import numpy as np -from sklearn.base import ( - BaseEstimator, - ClassifierMixin, - MetaEstimatorMixin, -) +from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin from sklearn.metrics import confusion_matrix from sklearn.preprocessing import normalize -from sklearn.utils.validation import ( - check_is_fitted, - check_X_y, - check_array, - FLOAT_DTYPES, -) +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, check_X_y class SubjectiveClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): - """ - Corrects predictions of the inner classifier by taking into account a (subjective) prior distribution of the + """Corrects predictions of the inner classifier by taking into account a (subjective) prior distribution of the classes. - This can be useful when there is a difference in class distribution between the training data set and - the real world. Using the confusion matrix of the inner classifier and the prior, the posterior probability for a - class, given the prediction of the inner classifier, can be computed. The background for this posterior estimation - is given `in this article _`. + This can be useful when there is a difference in class distribution between the training data set and the real + world. Using the confusion matrix of the inner classifier and the prior, the posterior probability for a class, + given the prediction of the inner classifier, can be computed. + + The background for this posterior estimation is given in + [this article](https://lucdemortier.github.io/articles/16/PerformanceMetrics). Based on the `evidence` attribute, this meta estimator's predictions are based on simple weighing of the inner estimator's `predict_proba()` results, the posterior probabilities based on the confusion matrix, or a combination of the two approaches. - :param estimator: An sklearn-compatible classifier estimator - :param prior: A dict of class->frequency representing the prior (a.k.a. subjective real-world) class - distribution. The class frequencies should sum to 1. - :param evidence: A string indicating which evidence should be used to correct the inner estimator's predictions. - Should be one of 'predict_proba', 'confusion_matrix', or 'both' (default). If `predict_proba`, the inner estimator's - `predict_proba()` results are multiplied by the prior distribution. In case of `confusion_matrix`, the inner - estimator's discrete predictions are converted to posterior probabilities using the prior and the inner estimator's - confusion matrix (obtained from the train data used in `fit()`). In case of `both` (default), the the inner - estimator's `predict_proba()` results are multiplied by the posterior probabilities. + Parameters + ---------- + estimator : scikit-learn compatible classifier + Classifier that will be wrapped with SubjectiveClassifier. It should implement `predict_proba` method. + prior : dict[int, float] + A dictionary mapping `class -> frequency` representing the prior (a.k.a. subjective real-world) class + distribution. The class frequencies should sum to 1. + evidence : Literal["predict_proba", "confusion_matrix", "both"], default="both" + A string indicating which evidence should be used to correct the inner estimator's predictions. + + - If `"both"` the the inner estimator's `predict_proba()` results are multiplied by the posterior probabilities. + - If `"predict_proba"`, the inner estimator's `predict_proba()` results are multiplied by the prior + distribution. + - If `"confusion_matrix"`, the inner estimator's discrete predictions are converted to posterior probabilities + using the prior and the inner estimator's confusion matrix (obtained from the train data used in `fit()`). + + Attributes + ---------- + estimator_ : scikit-learn compatible classifier + The fitted classifier. + classes_ : array-like, shape=(n_classes,) + The classes labels. + posterior_matrix_ : array-like, shape=(n_classes, n_classes) + The posterior probabilities for each class, given the prediction of the inner classifier. """ def __init__(self, estimator, prior, evidence="both"): @@ -50,8 +57,7 @@ def _likelihood(self, predicted_class, given_class, cfm): def _evidence(self, predicted_class, cfm): return sum( [ - self._likelihood(predicted_class, given_class, cfm) - * self.prior[self.classes_[given_class]] + self._likelihood(predicted_class, given_class, cfm) * self.prior[self.classes_[given_class]] for given_class in range(cfm.shape[0]) ] ) @@ -59,25 +65,34 @@ def _evidence(self, predicted_class, cfm): def _posterior(self, y, y_hat, cfm): y_hat_evidence = self._evidence(y_hat, cfm) return ( - ( - self._likelihood(y_hat, y, cfm) - * self.prior[self.classes_[y]] - / y_hat_evidence - ) + (self._likelihood(y_hat, y, cfm) * self.prior[self.classes_[y]] / y_hat_evidence) if y_hat_evidence > 0 else self.prior[y] # in case confusion matrix has all-zero column for y_hat ) def fit(self, X, y): - """ - Fits the inner estimator based on the data. - - Raises a `ValueError` if the `y` vector contains classes that are not specified in the prior, or if the prior is - not a valid probability distribution (i.e. does not sum to 1). - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :return: Returns an instance of self. + """Fit the inner classfier using `X` and `y` as training data by fitting the underlying estimator and computing + the posterior probabilities. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : SubjectiveClassifier + The fitted estimator. + + Raises + ------ + ValueError + - If `estimator` is not a classifier. + - If `y` contains classes that are not specified in the `prior` + - If `prior` is not a valid probability distribution (i.e. does not sum to 1). + - If `evidence` is not one of "predict_proba", "confusion_matrix", or "both". """ if not isinstance(self.estimator, ClassifierMixin): raise ValueError( @@ -85,15 +100,11 @@ def fit(self, X, y): ) if not np.isclose(sum(self.prior.values()), 1): - raise ValueError( - "Invalid prior: the prior probabilities of all classes should sum to 1" - ) + raise ValueError("Invalid prior: the prior probabilities of all classes should sum to 1") valid_evidence_types = ["predict_proba", "confusion_matrix", "both"] if self.evidence not in valid_evidence_types: - raise ValueError( - f"Invalid evidence: the provided evidence should be one of {valid_evidence_types}" - ) + raise ValueError(f"Invalid evidence: the provided evidence should be one of {valid_evidence_types}") X, y = check_X_y(X, y, estimator=self.estimator, dtype=FLOAT_DTYPES) if set(y) - set(self.prior.keys()): @@ -104,10 +115,7 @@ def fit(self, X, y): self.estimator.fit(X, y) cfm = confusion_matrix(y, self.estimator.predict(X)) self.posterior_matrix_ = np.array( - [ - [self._posterior(y, y_hat, cfm) for y_hat in range(cfm.shape[0])] - for y in range(cfm.shape[0]) - ] + [[self._posterior(y, y_hat, cfm) for y_hat in range(cfm.shape[0])] for y in range(cfm.shape[0])] ) return self @@ -118,17 +126,21 @@ def _weighted_proba(weights, y_hat_probas): @staticmethod def _to_discrete(y_hat_probas): y_hat_discrete = np.zeros(y_hat_probas.shape) - y_hat_discrete[ - np.arange(y_hat_probas.shape[0]), y_hat_probas.argmax(axis=1) - ] = 1 + y_hat_discrete[np.arange(y_hat_probas.shape[0]), y_hat_probas.argmax(axis=1)] = 1 return y_hat_discrete def predict_proba(self, X): - """ - Returns probability distribution of the class, based on the provided data. + """Predict probability distribution of the class, based on the provided data. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples, n_classes) the predicted data + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities. """ check_is_fitted(self, ["posterior_matrix_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) @@ -139,18 +151,20 @@ def predict_proba(self, X): return self._weighted_proba(prior_weights, y_hats) else: posterior_probas = self._to_discrete(y_hats) @ self.posterior_matrix_.T - return ( - self._weighted_proba(posterior_probas, y_hats) - if self.evidence == "both" - else posterior_probas - ) + return self._weighted_proba(posterior_probas, y_hats) if self.evidence == "both" else posterior_probas def predict(self, X): - """ - Returns predicted class, based on the provided data. + """Predict target values for `X` using fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples, n_classes) the predicted data + Returns + ------- + array-like of shape (n_samples, ) + The predicted class. """ check_is_fitted(self, ["posterior_matrix_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) @@ -158,4 +172,5 @@ def predict(self, X): @property def classes_(self): + """Alias for `.classes_` attribute of the underlying estimator.""" return self.estimator.classes_ diff --git a/sklego/meta/thresholder.py b/sklego/meta/thresholder.py index 4fedc23f1..d6884cfeb 100644 --- a/sklego/meta/thresholder.py +++ b/sklego/meta/thresholder.py @@ -1,27 +1,36 @@ import numpy as np from sklearn import clone -from sklearn.base import ( - BaseEstimator, - ClassifierMixin, -) -from sklearn.utils.validation import ( - check_is_fitted -) +from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.exceptions import NotFittedError +from sklearn.utils.validation import check_is_fitted from sklego.base import ProbabilisticClassifier class Thresholder(BaseEstimator, ClassifierMixin): - """ - Takes a two class estimator and moves the threshold. This way you might - design the algorithm to only accept a certain class if the probability - for it is larger than, say, 90% instead of 50%. - - :param model: the model to threshold - :param threshold: the actual threshold to use - :param refit: if True, we will always retrain the model even if it is already fitted. - If False we only refit if the original model isn't fitted. + """Takes a binary classifier and moves the threshold. This way you might design the algorithm to only accept a + certain class if the probability for it is larger than, say, 90% instead of 50%. + + !!! info + Please note that this only works for binary classification problems. + + Parameters + ---------- + model : scikit-learn compatible classifier + Classifier that will be wrapped with Thresholder. It should implement `predict_proba` method. + threshold : float + The threshold value to use. + refit : bool, default=False + + - If True, we will always retrain the model even if it is already fitted. + - If False we only refit if the original model isn't fitted. + + Attributes + ---------- + estimator_ : scikit-learn compatible classifier + The fitted classifier. + classes_ : array-like, shape=(2,) + The classes labels. """ def __init__(self, model, threshold: float, refit=False): @@ -30,7 +39,7 @@ def __init__(self, model, threshold: float, refit=False): self.refit = refit def _handle_refit(self, X, y, sample_weight=None): - """Only refit when we need to, unless refit=True is present.""" + """Only refit when we need to, unless `refit=True` is present.""" if self.refit: self.estimator_ = clone(self.model) self.estimator_.fit(X, y, sample_weight=sample_weight) @@ -41,41 +50,60 @@ def _handle_refit(self, X, y, sample_weight=None): self.estimator_.fit(X, y, sample_weight=sample_weight) def fit(self, X, y, sample_weight=None): - """ - Fit the data. + """Fit the underlying estimator using `X` and `y` as training data. If `refit=True` we will always retrain + (a copy of) the estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + sample_weight : array-like of shape (n_samples, ), default=None + Individual weights for each sample. + + Returns + ------- + self : Thresholder + The fitted estimator. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: array-like, shape=(n_samples,) training data. - :param sample_weight: array-like, shape=(n_samples) Individual weights for each sample. - :return: Returns an instance of self. + Raises + ------ + ValueError + - If `model` is not a classifier or it does not implement `predict_proba` method. + - If `model` does not have two classes. """ self.estimator_ = self.model if not isinstance(self.estimator_, ProbabilisticClassifier): - raise ValueError( - "The Thresholder meta model only works on classification models with .predict_proba." - ) + raise ValueError("The Thresholder meta model only works on classification models with .predict_proba.") self._handle_refit(X, y, sample_weight) self.classes_ = self.estimator_.classes_ if len(self.classes_) != 2: - raise ValueError( - "The Thresholder meta model only works on models with two classes." - ) + raise ValueError("The `Thresholder` meta model only works on models with two classes.") return self def predict(self, X): - """ - Predict new data. + """Predict target values for `X` using fitted estimator and the given `threshold`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples,) training data. - :return: array, shape=(n_samples,) the predicted data + Returns + ------- + array-like of shape (n_samples,) + The predicted values. """ check_is_fitted(self, ["classes_", "estimator_"]) predicate = self.estimator_.predict_proba(X)[:, 1] > self.threshold return np.where(predicate, self.classes_[1], self.classes_[0]) def predict_proba(self, X): + """Alias for `.predict_proba()` method of the underlying estimator.""" check_is_fitted(self, ["classes_", "estimator_"]) return self.estimator_.predict_proba(X) def score(self, X, y): + """Alias for `.score()` method of the underlying estimator.""" return self.estimator_.score(X, y) diff --git a/sklego/meta/zero_inflated_regressor.py b/sklego/meta/zero_inflated_regressor.py index fa1d9b887..b5140e59d 100644 --- a/sklego/meta/zero_inflated_regressor.py +++ b/sklego/meta/zero_inflated_regressor.py @@ -1,15 +1,14 @@ -from inspect import signature import logging +from inspect import signature import numpy as np -from sklearn.base import BaseEstimator, RegressorMixin, clone, is_regressor, is_classifier -from sklearn.utils.validation import check_is_fitted, check_X_y, check_array +from sklearn.base import BaseEstimator, RegressorMixin, clone, is_classifier, is_regressor from sklearn.exceptions import NotFittedError +from sklearn.utils.validation import check_array, check_is_fitted, check_X_y class ZeroInflatedRegressor(BaseEstimator, RegressorMixin): - """ - A meta regressor for zero-inflated datasets, i.e. the targets contain a lot of zeroes. + """A meta regressor for zero-inflated datasets, i.e. the targets contain a lot of zeroes. `ZeroInflatedRegressor` consists of a classifier and a regressor. @@ -24,54 +23,64 @@ class ZeroInflatedRegressor(BaseEstimator, RegressorMixin): Parameters ---------- - classifier : Any, scikit-learn classifier + classifier : scikit-learn compatible classifier A classifier that answers the question "Should the output be zero?". + regressor : scikit-learn compatible regressor + A regressor for predicting the target. Its prediction is only used if `classifier` says that the output is + non-zero. - regressor : Any, scikit-learn regressor - A regressor for predicting the target. Its prediction is only used if `classifier` says that the output is non-zero. + Attributes + ---------- + classifier_ : scikit-learn compatible classifier + The fitted classifier. + regressor_ : scikit-learn compatible regressor + The fitted regressor. Examples -------- - >>> import numpy as np - >>> from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor - >>> np.random.seed(0) - >>> X = np.random.randn(10000, 4) - >>> y = ((X[:, 0]>0) & (X[:, 1]>0)) * np.abs(X[:, 2] * X[:, 3]**2) - >>> z = ZeroInflatedRegressor( - ... classifier=ExtraTreesClassifier(random_state=0), - ... regressor=ExtraTreesRegressor(random_state=0) - ... ) - >>> z.fit(X, y) - ZeroInflatedRegressor(classifier=ExtraTreesClassifier(random_state=0), - regressor=ExtraTreesRegressor(random_state=0)) - >>> z.predict(X)[:5] - array([4.91483294, 0. , 0. , 0.04941909, 0. ]) + ```py + import numpy as np + from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor + from sklego.meta import ZeroInflatedRegressor + + np.random.seed(0) + X = np.random.randn(10000, 4) + y = ((X[:, 0]>0) & (X[:, 1]>0)) * np.abs(X[:, 2] * X[:, 3]**2) + model = ZeroInflatedRegressor( + classifier=ExtraTreesClassifier(random_state=0), + regressor=ExtraTreesRegressor(random_state=0) + ) + + model.fit(X, y) + # ZeroInflatedRegressor(classifier=ExtraTreesClassifier(random_state=0), + # regressor=ExtraTreesRegressor(random_state=0)) + + model.predict(X)[:5] + # array([4.91483294, 0. , 0. , 0.04941909, 0. ]) + ``` """ def __init__(self, classifier, regressor) -> None: - """Initialize.""" self.classifier = classifier self.regressor = regressor def fit(self, X, y, sample_weight=None): - """ - Fit the model. + """Fit the underlying classifier and regressor using `X` and `y` as training data. The regressor is only trained + on examples where the target is non-zero. Parameters ---------- - X : np.ndarray of shape (n_samples, n_features) + X : array-like of shape (n_samples, n_features ) The training data. - - y : np.ndarray, 1-dimensional + y : array-like of shape (n_samples,) The target values. - - sample_weight : Optional[np.array], default=None + sample_weight : array-like of shape (n_samples, ), default=None Individual weights for each sample. Returns ------- - ZeroInflatedRegressor - Fitted regressor. + self : ZeroInflatedRegressor + The fitted estimator. Raises ------ @@ -82,7 +91,8 @@ def fit(self, X, y, sample_weight=None): self._check_n_features(X, reset=True) if not is_classifier(self.classifier): raise ValueError( - f"`classifier` has to be a classifier. Received instance of {type(self.classifier)} instead.") + f"`classifier` has to be a classifier. Received instance of {type(self.classifier)} instead." + ) if not is_regressor(self.regressor): raise ValueError(f"`regressor` has to be a regressor. Received instance of {type(self.regressor)} instead.") @@ -111,7 +121,7 @@ def fit(self, X, y, sample_weight=None): self.regressor_.fit( X[non_zero_indices], y[non_zero_indices], - sample_weight=sample_weight[non_zero_indices] if sample_weight is not None else None + sample_weight=sample_weight[non_zero_indices] if sample_weight is not None else None, ) else: logging.warning("Regressor ignores sample_weight.") @@ -128,17 +138,17 @@ def fit(self, X, y, sample_weight=None): return self def predict(self, X): - """ - Get predictions. + """Predict target values for `X` using fitted estimator by first asking the classifier if the output should be + zero. If yes, output zero. Otherwise, ask the regressor for its prediction and output it. Parameters ---------- - X : np.ndarray, shape (n_samples, n_features) - Samples to get predictions of. + X : array-like of shape (n_samples, n_features) + The data to predict. Returns ------- - y : np.ndarray, shape (n_samples,) + array-like of shape (n_samples,) The predicted values. """ check_is_fitted(self) diff --git a/sklego/metrics.py b/sklego/metrics.py index bda5693c3..b583b72b5 100644 --- a/sklego/metrics.py +++ b/sklego/metrics.py @@ -1,24 +1,35 @@ -import numpy as np import warnings - from typing import Callable +import numpy as np + def correlation_score(column): - """ - The correlation score can score how well the estimator predictions correlate with a given column. - This is especially useful to use in situations where "fairness" is a theme. + """The correlation score can score how well the estimator predictions correlate with a given column. - `correlation_score` takes a column on which to calculate the correlation and returns a metric function + This is especially useful to use in situations where "fairness" is a theme. - Usage: - `correlation_score('gender')(clf, X, y)` + `correlation_score` takes a column on which to calculate the correlation and returns a metric function. + Parameters + ---------- + column : str | int + Name of the column (when X is a dataframe) or the index of the column (when X is a numpy array) to score + against. - :param column: Name of the column (when X is a dataframe) or the index of the column (when X is a numpy array). - :return: - A function which calculates the negative correlation between estimator.predict(X) and X[column] + Returns + ------- + Callable[..., float] + A function which calculates the _negative_ correlation between `estimator.predict(X)` and `X[column]` (in gridsearch, larger is better and we want to typically punish correlation). + + Examples + -------- + ```py + from sklego.metrics import correlation_score + ... + correlation_score('gender')(clf, X, y) + ``` """ def correlation_metric(estimator, X, y_true=None): @@ -30,34 +41,42 @@ def correlation_metric(estimator, X, y_true=None): def p_percent_score(sensitive_column, positive_target=1): - r""" - The p_percent score calculates the ratio between the probability of a positive outcome - given the sensitive attribute (column) being true and the same probability given the - sensitive attribute being false. + r"""The p_percent score calculates the ratio between the probability of a positive outcome given the sensitive + attribute (column) being true and the same probability given the sensitive attribute being false. - .. math:: - \min \left(\frac{P(\hat{y}=1 | z=1)}{P(\hat{y}=1 | z=0)}, \frac{P(\hat{y}=1 | z=0)}{P(\hat{y}=1 | z=1)}\right) + $$\min \left(\frac{P(\hat{y}=1 | z=1)}{P(\hat{y}=1 | z=0)}, \frac{P(\hat{y}=1 | z=0)}{P(\hat{y}=1 | z=1)}\right)$$ This is especially useful to use in situations where "fairness" is a theme. - Usage: - `p_percent_score('gender')(clf, X, y)` - - source: - - M. Zafar et al. (2017), Fairness Constraints: Mechanisms for Fair Classification - - :param sensitive_column: - Name of the column containing the binary sensitive attribute (when X is a dataframe) - or the index of the column (when X is a numpy array). - :param positive_target: The name of the class which is associated with a positive outcome - :return: a function (clf, X, y_true) -> float that calculates the p percent score for z = column + Parameters + ---------- + sensitive_column : str | int + Name of the column containing the binary sensitive attribute (when X is a dataframe) or the index of the column + (when X is a numpy array). + positive_target : int, default=1 + The name of the class which is associated with a positive outcome + + Returns + ------- + Callable[..., float] + A function which calculates the p percent score for z = column + + Examples + -------- + ```py + from sklego.metrics import p_percent_score + ... + p_percent_score('gender')(clf, X, y) + ``` + + Source + ------ + M. Zafar et al. (2017), Fairness Constraints: Mechanisms for Fair Classification """ def impl(estimator, X, y_true=None): """Remember: X is the thing going *in* to your pipeline.""" - sensitive_col = ( - X[:, sensitive_column] if isinstance(X, np.ndarray) else X[sensitive_column] - ) + sensitive_col = X[:, sensitive_column] if isinstance(X, np.ndarray) else X[sensitive_column] if not np.all((sensitive_col == 0) | (sensitive_col == 1)): raise ValueError( @@ -94,35 +113,44 @@ def impl(estimator, X, y_true=None): def equal_opportunity_score(sensitive_column, positive_target=1): - r""" - The equality opportunity score calculates the ratio between the probability of a **true positive** outcome - given the sensitive attribute (column) being true and the same probability given the - sensitive attribute being false. + r"""The equality opportunity score calculates the ratio between the probability of a **true positive** outcome + given the sensitive attribute (column) being true and the same probability given the sensitive attribute being + false. - .. math:: - \min \left(\frac{P(\hat{y}=1 | z=1, y=1)}{P(\hat{y}=1 | z=0, y=1)}, - \frac{P(\hat{y}=1 | z=0, y=1)}{P(\hat{y}=1 | z=1, y=1)}\right) + $$\min \left(\frac{P(\hat{y}=1 | z=1, y=1)}{P(\hat{y}=1 | z=0, y=1)}, + \frac{P(\hat{y}=1 | z=0, y=1)}{P(\hat{y}=1 | z=1, y=1)}\right)$$ This is especially useful to use in situations where "fairness" is a theme. - Usage: - `equal_opportunity_score('gender')(clf, X, y)` - - Source: - - M. Hardt, E. Price and N. Srebro (2016), Equality of Opportunity in Supervised Learning - - :param sensitive_column: - Name of the column containing the binary sensitive attribute (when X is a dataframe) - or the index of the column (when X is a numpy array). - :param positive_target: The name of the class which is associated with a positive outcome - :return: a function (clf, X, y_true) -> float that calculates the equal opportunity score for z = column + Parameters + ---------- + sensitive_column : str | int + Name of the column containing the binary sensitive attribute (when X is a dataframe) or the index of the column + (when X is a numpy array). + positive_target: int, default=1 + The name of the class which is associated with a positive outcome + + Returns + ------- + Callable[..., float] + A function which calculates the equal opportunity score for z = column + + Examples + -------- + ```py + from sklego.metrics import equal_opportunity_score + ... + equal_opportunity_score('gender')(clf, X, y) + ``` + + Source + ------ + M. Hardt, E. Price and N. Srebro (2016), Equality of Opportunity in Supervised Learning """ def impl(estimator, X, y_true): """Remember: X is the thing going *in* to your pipeline.""" - sensitive_col = ( - X[:, sensitive_column] if isinstance(X, np.ndarray) else X[sensitive_column] - ) + sensitive_col = X[:, sensitive_column] if isinstance(X, np.ndarray) else X[sensitive_column] if not np.all((sensitive_col == 0) | (sensitive_col == 1)): raise ValueError( @@ -159,23 +187,37 @@ def impl(estimator, X, y_true): def subset_score(subset_picker: Callable, score: Callable, **kwargs): - r""" - Returns a method that applies the passed score only to a specific subset. The subset picker - is a method that is passed the corresponding X and y_true and returns a one-dimensional - boolean vector where every element corresponds to a row in the data. Only the elements - with a True value are taken into account for the passed score, representing a filter. - - This allows users to have an easy approach to measuring metrics over different slices of - the population which can give insights into the model performance, either specifically for - fairness or in general. - - Usage: - `subset_score(lambda X, y_true: X['column'] == 'A', accuracy_score)(clf, X, y)` - - :param subset_picker: Method that returns a boolean mask that is used for slicing the samples - :param score: The score that needs to be applied to the subset - :param kwargs: Additional keyword arguments to pass to score - :return: a function that calculates the passed score for the subset + """Return a method that applies the passed score only to a specific subset. + + The subset picker is a method that is passed the corresponding `X` and `y_true` and returns a one-dimensional + boolean vector where every element corresponds to a row in the data. + + Only the elements with a True value are taken into account for the passed score, representing a filter. + + This allows users to have an easy approach to measuring metrics over different slices of the population which can + give insights into the model performance, either specifically for fairness or in general. + + Parameters + ---------- + subset_picker : Callable + Method that returns a boolean mask that is used for slicing the samples + score : Callable[..., T] + The score that needs to be applied to the subset + kwargs : dict + Additional keyword arguments to pass to score + + Returns + ------- + Callable[..., T] + A function which calculates the passed score for the subset + + Examples + -------- + ```py + from sklego.metrics import subset_score + ... + subset_score(lambda X, y_true: X['column'] == 'A', accuracy_score)(clf, X, y) + ``` """ def sliced_metric(estimator, X, y_true=None): diff --git a/sklego/mixture/__init__.py b/sklego/mixture/__init__.py index eedc23f97..a660526ec 100644 --- a/sklego/mixture/__init__.py +++ b/sklego/mixture/__init__.py @@ -1,11 +1,6 @@ -__all__ = [ - "GMMClassifier", - "BayesianGMMClassifier", - "GMMOutlierDetector", - "BayesianGMMOutlierDetector" -] +__all__ = ["GMMClassifier", "BayesianGMMClassifier", "GMMOutlierDetector", "BayesianGMMOutlierDetector"] -from .gmm_classifier import GMMClassifier from .bayesian_gmm_classifier import BayesianGMMClassifier -from .gmm_outlier_detector import GMMOutlierDetector from .bayesian_gmm_detector import BayesianGMMOutlierDetector +from .gmm_classifier import GMMClassifier +from .gmm_outlier_detector import GMMOutlierDetector diff --git a/sklego/mixture/bayesian_gmm_classifier.py b/sklego/mixture/bayesian_gmm_classifier.py index 4446e0429..d5fabd5a0 100644 --- a/sklego/mixture/bayesian_gmm_classifier.py +++ b/sklego/mixture/bayesian_gmm_classifier.py @@ -4,10 +4,26 @@ from sklearn.mixture import BayesianGaussianMixture from sklearn.utils import check_X_y from sklearn.utils.multiclass import unique_labels -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class BayesianGMMClassifier(BaseEstimator, ClassifierMixin): + """The `BayesianGMMClassifier` trains a Gaussian Mixture Model for each class in `y` on a dataset `X`. + Once a density is trained for each class we can evaluate the likelihood scores to see which class is more likely. + + !!! note + All the parameters are an exact copy of those of + [sklearn.mixture.BayesianGaussianMixture]( https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html). + + + Attributes + ---------- + gmms_ : dict[int, BayesianGaussianMixture] + A dictionary of Bayesian Gaussian Mixture Models, one for each class. + classes_ : np.ndarray of shape (n_classes,) + The classes seen during `fit`. + """ + def __init__( self, n_components=1, @@ -28,11 +44,6 @@ def __init__( verbose=0, verbose_interval=10, ): - """ - The BayesianGMMClassifier trains a Gaussian Mixture Model for each class in y on a dataset X. Once - a density is trained for each class we can evaluate the likelihood scores to see which class - is more likely. All parameters of the model are an exact copy of the parameters in scikit-learn. - """ self.n_components = n_components self.covariance_type = covariance_type self.tol = tol @@ -51,13 +62,20 @@ def __init__( self.verbose = verbose self.verbose_interval = verbose_interval - def fit(self, X: np.array, y: np.array) -> "BayesianGMMClassifier": - """ - Fit the model using X, y as training data. + def fit(self, X: np.ndarray, y: np.ndarray) -> "BayesianGMMClassifier": + """Fit the `BayesianGMMClassifier` model using `X`, `y` as training data. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : BayesianGMMClassifier + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if X.ndim == 1: @@ -90,11 +108,35 @@ def fit(self, X: np.array, y: np.array) -> "BayesianGMMClassifier": return self def predict(self, X): + """Predict labels for `X` using fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. + """ check_is_fitted(self, ["gmms_", "classes_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) return self.classes_[self.predict_proba(X).argmax(axis=1)] def predict_proba(self, X): + """Predict probabilities for `X` using fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities. + """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["gmms_", "classes_"]) res = np.zeros((X.shape[0], self.classes_.shape[0])) diff --git a/sklego/mixture/bayesian_gmm_detector.py b/sklego/mixture/bayesian_gmm_detector.py index 0aefcbc41..e4188845d 100644 --- a/sklego/mixture/bayesian_gmm_detector.py +++ b/sklego/mixture/bayesian_gmm_detector.py @@ -2,33 +2,42 @@ import numpy as np from scipy.optimize import minimize_scalar +from scipy.stats import gaussian_kde from sklearn.base import BaseEstimator, OutlierMixin from sklearn.mixture import BayesianGaussianMixture -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES - -from scipy.stats import gaussian_kde +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class BayesianGMMOutlierDetector(OutlierMixin, BaseEstimator): - """ - The GMMDetector trains a Bayesian Gaussian Mixture Model on a dataset X. Once - a density is trained we can evaluate the likelihood scores to see if - it is deemed likely. By giving a threshold this model might then label - outliers if their likelihood score is too low. - - :param threshold: the limit at which the model thinks an outlier appears, must be between (0, 1) - :param method: the method that the threshold will be applied to, possible values = [stddev, default=quantile] - - If you select method="quantile" then the threshold value represents the - quantile value to start calling something an outlier. - - If you select method="stddev" then the threshold value represents the - numbers of standard deviations before calling something an outlier. - - There are other settings too, these are best described in the BayesianGaussianMixture - documentation found here: - - https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html. + """The `BayesianGMMOutlierDetector` trains a Bayesian Gaussian Mixture model on a dataset `X`. Once a density is + trained we can evaluate the likelihood scores to see if it is deemed likely. + + By giving a threshold this model might then label outliers if their likelihood score is too low. + + Parameters + ---------- + threshold : float, default=0.99 + The limit at which the model thinks an outlier appears, must be between (0, 1). + method : Literal["quantile", "stddev"], default="quantile" + The method to use to apply the `threshold`. + + !!! info + If you select `method="quantile"` then the threshold value represents the quantile value to start calling + something an outlier. + + If you select `method="stddev"` then the threshold value represents the + numbers of standard deviations before calling something an outlier. + + !!! note + The other parameters are an exact copy of the parameters in + [sklearn.mixture.BayesianGaussianMixture]( https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html). + + Attributes + ---------- + gmm_ : BayesianGaussianMixture + The trained Bayesian Gaussian Mixture Model. + likelihood_threshold_ : float + The threshold value used to determine if something is an outlier. """ _ALLOWED_METHODS = ("quantile", "stddev") @@ -76,13 +85,27 @@ def __init__( self.verbose = verbose self.verbose_interval = verbose_interval - def fit(self, X: np.array, y=None) -> "BayesianGMMOutlierDetector": - """ - Fit the model using X, y as training data. - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: ignored but kept in for pipeline support - :return: Returns an instance of self. + def fit(self, X: np.ndarray, y=None) -> "BayesianGMMOutlierDetector": + """Fit the `BayesianGMMOutlierDetector` model using `X`, `y` as training data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + Ignored, present for compatibility. + + Returns + ------- + self : BayesianGMMOutlierDetector + The fitted estimator. + + Raises + ------ + ValueError + - If `method="quantile"` and `threshold` is not between (0, 1). + - If `method="stddev"` and `threshold` is negative. + - If `method` is not in `["quantile", "stddev"]`. """ # GMM sometimes throws an error if you don't do this @@ -143,6 +166,7 @@ def fit(self, X: np.array, y=None) -> "BayesianGMMOutlierDetector": return self def score_samples(self, X): + """Compute the log likelihood for each sample and return the negative value.""" X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["gmm_", "likelihood_threshold_"]) if len(X.shape) == 1: @@ -155,10 +179,17 @@ def decision_function(self, X): return self.score_samples(X) + self.likelihood_threshold_ def predict(self, X): - """ - Predict if a point is an outlier. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the predicted data. 1 for inliers, -1 for outliers. + """Predict if a point is an outlier or not using the fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. 1 for inliers, -1 for outliers. """ predictions = (self.decision_function(X) >= 0).astype(int) predictions[predictions == 1] = -1 diff --git a/sklego/mixture/gmm_classifier.py b/sklego/mixture/gmm_classifier.py index 34ee753c9..bc0f2cbbf 100644 --- a/sklego/mixture/gmm_classifier.py +++ b/sklego/mixture/gmm_classifier.py @@ -4,10 +4,27 @@ from sklearn.mixture import GaussianMixture from sklearn.utils import check_X_y from sklearn.utils.multiclass import unique_labels -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class GMMClassifier(BaseEstimator, ClassifierMixin): + """The `GMMClassifier` trains a Gaussian Mixture Model for each class in `y` on a dataset `X`. Once a density is + trained for each class we can evaluate the likelihood scores to see which class is more likely. + + All parameters of the model are an exact copy of the parameters in scikit-learn. + + !!! note + All the parameters are an exact copy of those of + [sklearn.mixture.GaussianMixture]( https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html). + + Attributes + ---------- + gmms_ : dict[int, GaussianMixture] + A dictionary of Gaussian Mixture Models, one for each class. + classes_ : np.ndarray of shape (n_classes,) + The classes seen during `fit`. + """ + def __init__( self, n_components=1, @@ -25,11 +42,6 @@ def __init__( verbose=0, verbose_interval=10, ): - """ - The GMMClassifier trains a Gaussian Mixture Model for each class in y on a dataset X. Once - a density is trained for each class we can evaluate the likelihood scores to see which class - is more likely. All parameters of the model are an exact copy of the parameters in scikit-learn. - """ self.n_components = n_components self.covariance_type = covariance_type self.tol = tol @@ -45,13 +57,20 @@ def __init__( self.verbose = verbose self.verbose_interval = verbose_interval - def fit(self, X: np.array, y: np.array) -> "GMMClassifier": - """ - Fit the model using X, y as training data. + def fit(self, X: np.ndarray, y: np.ndarray) -> "GMMClassifier": + """Fit the `GMMClassifier` model using `X`, `y` as training data. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : GMMClassifier + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if X.ndim == 1: @@ -81,11 +100,35 @@ def fit(self, X: np.array, y: np.array) -> "GMMClassifier": return self def predict(self, X): + """Predict labels for `X` using fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. + """ check_is_fitted(self, ["gmms_", "classes_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) return self.classes_[self.predict_proba(X).argmax(axis=1)] def predict_proba(self, X): + """Predict probabilities for `X` using fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities. + """ X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["gmms_", "classes_"]) res = np.zeros((X.shape[0], self.classes_.shape[0])) diff --git a/sklego/mixture/gmm_outlier_detector.py b/sklego/mixture/gmm_outlier_detector.py index 45375cadf..cead9d534 100644 --- a/sklego/mixture/gmm_outlier_detector.py +++ b/sklego/mixture/gmm_outlier_detector.py @@ -2,28 +2,42 @@ import numpy as np from scipy.optimize import minimize_scalar +from scipy.stats import gaussian_kde from sklearn.base import BaseEstimator, OutlierMixin from sklearn.mixture import GaussianMixture -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES - -from scipy.stats import gaussian_kde +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class GMMOutlierDetector(OutlierMixin, BaseEstimator): - """ - The GMMDetector trains a Gaussian Mixture Model on a dataset X. Once - a density is trained we can evaluate the likelihood scores to see if - it is deemed likely. By giving a threshold this model might then label - outliers if their likelihood score is too low. - - :param threshold: the limit at which the model thinks an outlier appears, must be between (0, 1) - :param method: the method that the threshold will be applied to, possible values = [stddev, default=quantile] - - If you select method="quantile" then the threshold value represents the - quantile value to start calling something an outlier. - - If you select method="stddev" then the threshold value represents the - numbers of standard deviations before calling something an outlier. + """The `GMMDetector` trains a Gaussian Mixture model on a dataset `X`. Once a density is trained we can evaluate the + likelihood scores to see if it is deemed likely. + + By giving a threshold this model might then label outliers if their likelihood score is too low. + + Parameters + ---------- + threshold : float, default=0.99 + The limit at which the model thinks an outlier appears, must be between (0, 1). + method : Literal["quantile", "stddev"], default="quantile" + The method to use to apply the `threshold`. + + !!! info + If you select `method="quantile"` then the threshold value represents the quantile value to start calling + something an outlier. + + If you select `method="stddev"` then the threshold value represents the + numbers of standard deviations before calling something an outlier. + + !!! note + The other parameters are an exact copy of the parameters in + [sklearn.mixture.GaussianMixture]( https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html). + + Attributes + ---------- + gmm_ : GaussianMixture + The trained Gaussian Mixture model. + likelihood_threshold_ : float + The threshold value used to determine if something is an outlier. """ _ALLOWED_METHODS = ("quantile", "stddev") @@ -65,15 +79,28 @@ def __init__( self.verbose = verbose self.verbose_interval = verbose_interval - def fit(self, X: np.array, y=None) -> "GMMOutlierDetector": - """ - Fit the model using X, y as training data. - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param y: ignored but kept in for pipeline support - :return: Returns an instance of self. + def fit(self, X: np.ndarray, y=None) -> "GMMOutlierDetector": + """Fit the `BayesianGMMOutlierDetector` model using `X`, `y` as training data. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + Ignored, present for compatibility. + + Returns + ------- + self : GMMOutlierDetector + The fitted estimator. + + Raises + ------ + ValueError + - If `method="quantile"` and `threshold` is not between (0, 1). + - If `method="stddev"` and `threshold` is negative. + - If `method` is not in `["quantile", "stddev"]`. """ - # GMM sometimes throws an error if you don't do this X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) if len(X.shape) == 1: @@ -129,6 +156,7 @@ def fit(self, X: np.array, y=None) -> "GMMOutlierDetector": return self def score_samples(self, X): + """Compute the log likelihood for each sample and return the negative value.""" X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) check_is_fitted(self, ["gmm_", "likelihood_threshold_"]) if len(X.shape) == 1: @@ -141,11 +169,17 @@ def decision_function(self, X): return self.score_samples(X) + self.likelihood_threshold_ def predict(self, X): - """ - Predict if a point is an outlier. + """Predict if a point is an outlier or not using the fitted model. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :return: array, shape=(n_samples,) the predicted data. 1 for inliers, -1 for outliers. + Returns + ------- + array-like of shape (n_samples,) + The predicted data. 1 for inliers, -1 for outliers. """ predictions = (self.decision_function(X) >= 0).astype(int) predictions[predictions == 1] = -1 diff --git a/sklego/model_selection.py b/sklego/model_selection.py index ead332712..c2228559d 100644 --- a/sklego/model_selection.py +++ b/sklego/model_selection.py @@ -14,46 +14,57 @@ class TimeGapSplit: - """ - Provides train/test indices to split time series data samples. - This cross-validation object is a variation of TimeSeriesSplit with the following - differences: + r"""Provides train/test indices to split time series data samples. + + This cross-validation object is a variation of TimeSeriesSplit with the following differences: - The splits are made based on datetime duration, instead of number of rows. - - The user specifies the validation durations and either training_duration or n_splits. - - The user can specify a 'gap' duration that is added after the training split and before the validation split. + - The user specifies the `valid_duration` and either `train_duration` or `n_splits`. + - The user can specify a `gap_duration` that is added after the training split and before the validation split. + + The 3 duration parameters can be used to really replicate how the model is going to be used in production in batch + learning. - The 3 duration parameters can be used to really replicate how the model is going to - be used in production in batch learning. + Each validation fold doesn't overlap. The entire `window` moves by 1 `valid_duration` until there is not enough + data. - Each validation fold doesn't overlap. The entire 'window' moves by 1 'valid_duration' - until there is not enough data. + If this would lead to more splits then specified with `n_splits`, the `window` moves by `valid_duration` times the + fraction of possible splits and requested splits: - If this would lead to more splits then specified with 'n_splits', the 'window' moves by - the 'validation_duration' times the fraction of possible splits and requested splits: + - `n_possible_splits = (total_length - train_duration-gap_duration) // valid_duration` + - `time_shift = valid_duration * n_possible_splits / n_slits` - - n_possible_splits = (total_length - train_duration-gap_duration) // valid_duration - - time_shift = valid_duration * n_possible_splits / n_slits so the CV spans the whole dataset. - If 'train_duration' is not passed but 'n_splits' is, the training duration is increased to: + If `train_duration` is not passed but `n_splits` is, the training duration is increased to: - train_duration = total_length - (gap_duration + valid_duration * n_splits) + `train_duration = total_length - (gap_duration + valid_duration * n_splits)` such that the shifting the entire window by one validation duration spans the whole training set. - :param pandas.Series date_serie: series with the date, that should have all the indices of X used in the split() method. - :param datetime.timedelta train_duration: historical training data. - :param datetime.timedelta valid_duration: retraining period. - :param datetime.timedelta gap_duration: forward looking window of the target. - The period of the forward looking window necessary to create your target variable. + Parameters + ---------- + date_serie : pd.Series + Series with the date, that should have all the indices of X used in the split() method. + valid_duration : datetime.timedelta + Retraining period. + train_duration : datetime.timedelta | None, default=None + Historical training data. + gap_duration : datetime.timedelta, default=timedelta(0) + Forward looking window of the target. The period of the forward looking window necessary to create your target + variable. + This period is dropped at the end of your training folds due to lack of recent data. + In production you would have not been able to create the target for that period, and you would have drop it from the training data. - :param int n_splits: number of splits. - :param string window: either 'rolling' or 'expanding'. - 'rolling' window has fixed size and is shifted entirely. - 'expanding' left side of window is fixed, right border increases each fold. + n_splits : int | None, default=None + Number of splits. + window : Literal["rolling", "expanding"], default="rolling" + Type of moving window to use. + + - `"rolling"` window has fixed size and is shifted entirely. + - `"expanding"` left side of window is fixed, right border increases each fold. """ def __init__( @@ -85,11 +96,14 @@ def __init__( self.window = window def _join_date_and_x(self, X): - """ - Make a DataFrame indexed by the pandas index (the same as date_series) with date column joined with that index - and with the 'numpy index' column (i.e. just a range) that is required for the output and the rest of sklearn. - - :param pandas.DataFrame X: Dataframe with the data to split + """Creates a DataFrame indexed by the pandas index (the same as `date_serie`) with date column joined with that + index and with the 'numpy index' column (i.e. just a range) that is required for the output and the rest of + sklearn. + + Parameters + ---------- + X : pd.DataFrame + Dataframe with the data to split """ X_index_df = pd.DataFrame(range(len(X)), columns=["np_index"], index=X.index) X_index_df = X_index_df.join(self.date_serie) @@ -97,12 +111,21 @@ def _join_date_and_x(self, X): return X_index_df def split(self, X, y=None, groups=None): - """ - Generate indices to split data into training and test set. - - :param pandas.DataFrame X: Dataframe with the data to split - :param y: Always ignored, exists for compatibility - :param groups: Always ignored, exists for compatibility + """Generate indices to split data into training and test set. + + Parameters + ---------- + X : pd.DataFrame + Dataframe with the data to split. + y : array-like | None, default=None + Ignored, present for compatibility. + groups : array-like | None, default=None + Ignored, present for compatibility. + + Yields + ------- + tuple[np.ndarray, np.ndarray] + Train and test indices of the same fold. """ X_index_df = self._join_date_and_x(X) @@ -185,18 +208,36 @@ def split(self, X, y=None, groups=None): ) def get_n_splits(self, X=None, y=None, groups=None): - """Gets the number of splits - - :return: amount of n_splits - :rtype: int + """Get the number of splits + + Parameters + ---------- + X : pd.DataFrame + Dataframe with the data to split. + y : array-like | None, default=None + Ignored, present for compatibility. + groups : array-like | None, default=None + Ignored, present for compatibility. + + Returns + ------- + int + Number of splits. """ return sum(1 for x in self.split(X, y, groups)) def summary(self, X): - """Describes all folds + """Describe all folds. - :param pandas.DataFrame X: Dataframe with the data to split - :returns: ``pd.DataFrame`` summary of all folds + Parameters + ---------- + X : pd.DataFrame + Dataframe with the data to split. + + Returns + ------- + pd.DataFrame + Summary of all folds. """ summary = [] X_index_df = self._join_date_and_x(X) @@ -230,12 +271,12 @@ def get_split_info(X, indices, j, part, summary): class KlusterFoldValidation: - """ - KlusterFold cross validator + """KlusterFold cross validator. Create folds based on provided cluster method - - Create folds based on provided cluster method - - :param cluster_method: Clustering method with fit_predict attribute + Parameters + ---------- + cluster_method : Clusterer + Clustering method to use for the fold validation. """ def __init__(self, cluster_method=None): @@ -248,12 +289,21 @@ def __init__(self, cluster_method=None): self.n_splits = None def split(self, X, y=None, groups=None): - """ - Generator to iterate over the indices - - :param X: Array to split on - :param y: Always ignored, exists for compatibility - :param groups: Always ignored, exists for compatibility + """Generate indices to split data into training and test set. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Array to split on. + y : array-like of shape (n_samples,) | None, default=None + Ignored, present for compatibility. + groups : array-like of shape (n_samples,) | None, default=None + Ignored, present for compatibility. + + Yields + ------- + tuple[np.ndarray, np.ndarray] + Train and test indices of the same fold. """ X = check_array(X) @@ -276,10 +326,19 @@ def split(self, X, y=None, groups=None): ) def _method_is_fitted(self, X): + """Check if the `cluster_method` is fitted + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Array to use if the method is fitted + + Returns + ------- + bool + True if fitted, else False """ - :param X: Array to use if the method is fitted - :return: True if fitted, else False - """ + try: self.cluster_method.predict(X[0:1, :]) return True @@ -288,22 +347,23 @@ def _method_is_fitted(self, X): class GroupTimeSeriesSplit(_BaseKFold): - """ - Sliding window time series split + """Sliding window time series split. - Create n_splits folds with an as equally possible size through a smart variant of a brute - force search. Groups parameter in .split() should be filled with the time groups (e.g. years) + Create `n_splits` folds with an as equally possible size through a smart variant of a brute force search. + Groups parameter in `.split()` method should be filled with the time groups (e.g. years) - If n_splits is 3 ("*" = train, "x" = test):: + If `n_splits` is 3 ("*" = train, "x" = test): - |-----------------------| - | * * * x x x - - - - - | - | - - - * * * x x x - - | - | - - - - - - * * * x x | - |-----------------------| + ```console + * * * x x x - - - - - + - - - * * * x x x - - + - - - - - - * * * x x + ``` - :param n_splits: the amount of train-test combinations. - :type n_splits: int + Parameters + ---------- + n_splits : int + Amount of (train, test) splits to generate. """ # table above inspired by sktime @@ -326,14 +386,14 @@ def __init__(self, n_splits): self.n_splits = n_splits def summary(self): - """ - Generates a pd.DataFrame which displays the groups splits - and extra statistics about it. - Can only be run after having applied the .split() method to - the GroupTimeSeriesSplit instance. + """Describe all folds in a pd.DataFrame which displays the groups splits and extra statistics about it. - :return: a pd.DataFrame with info about where the splits were made - :rtype: pd.DataFrame + Can only be run after having applied the `.split()` method to the `GroupTimeSeriesSplit` instance. + + Returns + ------- + pd.DataFrame + Summary of all folds. """ try: return ( @@ -352,24 +412,25 @@ def summary(self): ) except AttributeError: raise AttributeError( - ".summary() only works after having ran" " .split(X, y, groups)." + ".summary() only works after having ran .split(X, y, groups)." ) def split(self, X=None, y=None, groups=None): - """Returns the train-test splits of all the folds - - :param X: array-like of shape (n_samples, n_features) - Training data, where `n_samples` is the number of samples - and `n_features` is the number of features, defaults to None - :type X: np.array, optional - :param y: array-like of shape (n_samples,) - The target variable for supervised learning problems, defaults to None - :type y: np.array, optional - :param groups: Group labels for the samples used while splitting the dataset - into train/test set, defaults to None - :type groups: np.array - :return: the indices of the train and test splits - :rtype: List[np.array] + """Generate the train-test splits of all the folds + + Parameters + ---------- + X : array-like of shape (n_samples, n_features), default=None + Data to split. + y : array-like of shape (n_samples,), default=None + The target variable for supervised learning problems. + groups : array-like of shape (n_samples,), default=None + Group labels for the samples used while splitting the dataset into train/test set, + + Returns + ------- + List[np.ndarray] + List containing train-test split indices of each fold. """ if groups is None: raise ValueError("Groups cannot be None") @@ -387,24 +448,35 @@ def split(self, X=None, y=None, groups=None): def get_n_splits(self, X=None, y=None, groups=None): """Get the amount of splits - :param X: Always ignored, exists for compatibality, defaults to None - :type X: Object, optional - :param y: Always ignored, exists for compatibality, defaults to None - :type y: Object, optional - :param groups: Always ignored, exists for compatibality, defaults to None - :type groups: Object, optional - :return: amount of n_splits - :rtype: int + Parameters + ---------- + X : array-like of shape (n_samples, n_features), default=None + Ignored, present for compatibility. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + groups : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + int + Amount of splits. """ return self.n_splits def _check_for_long_estimated_runtime(self, groups): - """ - Checks for combinations of n_splits and unique groups and raises UserWarning - if runtime is expected to take over one minute - - :param groups: array of the groups - :type groups: np.array + """Check for combinations of n_splits and unique groups and raises `UserWarning` if runtime is expected to take + over one minute. + + Parameters + ---------- + groups : array-like of shape (n_samples,) + Groups to check for unique groups and n_splits. + + Raises + ------ + UserWarning + If runtime is expected to take over one minute. """ unique_groups = len(set(groups)) warning = ( @@ -431,16 +503,21 @@ def _check_for_long_estimated_runtime(self, groups): ) def _iter_test_indices(self, X=None, y=None, groups=None): - """ - Calculates the optimal division of groups into folds so that every fold is as - equally large as possible - - :param X: Always ignored, exists for compatibility. - :param y: Always ignored, exists for compatibility. - :param groups: array with groups - :type groups: np.array - - :yields: two numpy arrays -> train indices and test indices of the same fold + """Calculate the optimal division of groups into folds so that every fold is as equally large as possible. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features), default=None + Ignored, present for compatibility. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + groups : array-like of shape (n_samples,), default=None + Array with groups. + + Yields + ------ + tuple[np.ndarray, np.ndarray] + Train and test indices of the same fold. """ self._check_for_long_estimated_runtime(groups) ( @@ -453,17 +530,22 @@ def _iter_test_indices(self, X=None, y=None, groups=None): yield np.where(groups == i)[0], np.where(groups == i + 1)[0] def _calc_first_and_last_split_index(self, X=None, y=None, groups=None): - """ - Calculates an approximate first and last split point to reduce the amount - of options during a brute force search. - - :param X: Always ignored, exists for compatibility. - :param y: Always ignored, exists for compatibility. - :param groups: array with groups - :type groups: np.array - - :return: approximate index of first and approx. index of last split - :rtype: tuple + """Calculate an approximate first and last split point to reduce the amount of options during a brute force + search. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features), default=None + Ignored, present for compatibility. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + groups : array-like of shape (n_samples,), default=None + Array with groups. + + Returns + ------- + tuple[int, int] + Approximate first and last split indexes. """ # get the counts (=amount of rows) for each group @@ -508,12 +590,13 @@ def _calc_first_and_last_split_index(self, X=None, y=None, groups=None): return first_split_index, last_split_index def _get_split_indices(self): - """ - Calculates for each possible splits the total absolute different of the groups - to the ideal group size and saves the split with the least absolute difference. + """Calculate for each possible splits the total absolute different of the groups to the ideal group size and + saves the split with the least absolute difference. - :return: indices of the best split points - :rtype: tuple + Returns + ------- + int + Index of the best split point. """ # set the index range to search possible splits for index_range = range(self._first_split_index, self._last_split_index) @@ -535,8 +618,7 @@ def _get_split_indices(self): # create a new generator that starts from the beginning again splits_generator = combinations(index_range, self.n_splits) - # generate a list, with for every group the difference between them and the ideal group size - # e.g. + # generate a list, with for every group the difference between them and the ideal group size, e.g. # ideal_group_size = 100 # group_sizes = [10,20,270] # diff_from_ideal_list = [-90, -80, 170] @@ -565,8 +647,7 @@ def _get_split_indices(self): ) new_diff = sum([abs(diff) for diff in diff_from_ideal_list]) - # if with the new split the difference is less than the current most optimal, save the - # new split + # if with the new split the difference is less than the current most optimal, save the new split if new_diff < min_diff: min_diff = new_diff best_splits = new_splits @@ -574,20 +655,23 @@ def _get_split_indices(self): @staticmethod def _calc_new_diffs(values, diff_list, prev_splits, new_splits): - """Calculates the new group size differences compared to the optimal group size - - :param values: list of values - :type values: list - :param diff_list: list of values with for each index split its difference - from the optimal group size - :type diff_list: list - :param prev_splits: the indices of the previous splits, excluding index 0 and the last index - :type prev_splits: tuple - :param new_splits: the indices of the new splits, excluding index 0 and the last index - :type new_splits: tuple - - :return: updated diff_list - :rtype: list + """Calculate the new group size differences compared to the optimal group size. + + Parameters + ---------- + values : list + List of values. + diff_list : list + List of values with for each index split its difference from the optimal group size. + prev_splits : tuple + Indices of the previous splits, excluding index 0 and the last index. + new_splits : tuple + Indices of the new splits, excluding index 0 and the last index. + + Returns + ------- + list + List of values with for each index split its difference from the optimal group size. """ # calculate which indices have changed, e.g.: # new_index = (1,2,5) @@ -621,15 +705,19 @@ def _calc_new_diffs(values, diff_list, prev_splits, new_splits): return new_diff_list def _regroup(self, groups): - """ - Specifies in which group every observation belongs + """Specify in which group every observation belongs. - :param groups: original groups in array - :type: groups: np.array + Parameters + ---------- + groups : array-like of shape (n_samples,) + Array with original groups. - :return: indices for the train and test splits of each fold - :rtype: np.array + Returns + ------- + np.ndarray + Indices for the train and test splits of each fold """ + df = self._grouped_df.copy().reset_index() # set each unique group to the right group_id to group them into folds df.loc[: self._best_splits[0], "group"] = 0 diff --git a/sklego/naive_bayes.py b/sklego/naive_bayes.py index 4d311d7f8..800ea12f4 100644 --- a/sklego/naive_bayes.py +++ b/sklego/naive_bayes.py @@ -1,21 +1,32 @@ from warnings import warn import numpy as np - from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.mixture import GaussianMixture, BayesianGaussianMixture +from sklearn.mixture import BayesianGaussianMixture, GaussianMixture from sklearn.utils import check_X_y from sklearn.utils.multiclass import unique_labels -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class GaussianMixtureNB(BaseEstimator, ClassifierMixin): - """ - The GaussianMixtureNB trains a Naive Bayes Classifier that uses a mixture - of gaussians instead of merely training a single one. + """The `GaussianMixtureNB` estimator is a naive bayes classifier that uses a mixture of gaussians instead of + merely a single one. In particular it trains a `GaussianMixture` model for each class in the target and for each + feature in the data, on the subset of `X` where `y == class`. + + You can pass any keyword parameter that scikit-learn's + [GaussianMixture](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) + model uses and it will be passed along to each of the models. - You can pass any keyword parameter that scikit-learn's Gaussian Mixture - Model uses and it will be passed along. + Attributes + ---------- + gmms_ : dict[int, List[GaussianMixture]] + A dictionary of Gaussian Mixture Models, one for each class. + classes_ : np.ndarray of shape (n_classes,) + The classes seen during `fit`. + n_features_in_ : int + The number of features seen during `fit`. + num_fit_cols_ : int + Deprecated, please use `n_features_in_` instead. """ def __init__( @@ -46,13 +57,21 @@ def __init__( self.random_state = random_state self.warm_start = warm_start - def fit(self, X: np.array, y: np.array) -> "GaussianMixtureNB": - """ - Fit the model using X, y as training data. + def fit(self, X, y) -> "GaussianMixtureNB": + """Fit the `GaussianMixtureNB` estimator using `X` and `y` as training data by fitting a `GaussianMixture` model + for each class in the target and for each feature in the data, on the subset of `X` where `y == class`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Returns + ------- + self : GaussianMixtureNB + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if X.ndim == 1: @@ -83,11 +102,37 @@ def fit(self, X: np.array, y: np.array) -> "GaussianMixtureNB": return self def predict(self, X): + """Predict labels for `X` using fitted estimator and `predict_proba` method, by picking the class with the + highest probability. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. + """ check_is_fitted(self, ["gmms_", "classes_", "n_features_in_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) return self.classes_[self.predict_proba(X).argmax(axis=1)] - def predict_proba(self, X: np.array): + def predict_proba(self, X: np.ndarray): + """Predict probabilities for `X` using fitted estimator by summing the probabilities of each gaussian for each + class. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities. + """ check_is_fitted(self, ["gmms_", "classes_", "n_features_in_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) if self.n_features_in_ != X.shape[1]: @@ -118,12 +163,24 @@ def num_fit_cols_(self): class BayesianGaussianMixtureNB(BaseEstimator, ClassifierMixin): - """ - The BayesianGaussianMixtureNB trains a Naive Bayes Classifier that uses a bayesian - mixture of gaussians instead of merely training a single one. + """The `BayesianGaussianMixtureNB` estimator is a naive bayes classifier that uses a bayesian mixture of gaussians + instead of merely a single one. In particular it trains a `BayesianGaussianMixture` model for each class in the + target and for each feature in the data, on the subset of `X` where `y == class`. + + You can pass any keyword parameter that scikit-learn's + [`BayesianGaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html) + model uses and it will be passed along to each of the models. - You can pass any keyword parameter that scikit-learn's Bayesian Gaussian Mixture - Model uses and it will be passed along. + Attributes + ---------- + gmms_ : dict[int, List[BayesianGaussianMixture]] + A dictionary of Gaussian Mixture Models, one for each class. + classes_ : np.ndarray of shape (n_classes,) + The classes seen during `fit`. + n_features_in_ : int + The number of features seen during `fit`. + num_fit_cols_ : int + Deprecated, please use `n_features_in_` instead. """ def __init__( @@ -164,13 +221,22 @@ def __init__( self.verbose = verbose self.verbose_interval = verbose_interval - def fit(self, X: np.array, y: np.array) -> "BayesianGaussianMixtureNB": - """ - Fit the model using X, y as training data. + def fit(self, X, y) -> "BayesianGaussianMixtureNB": + """Fit the `BayesianGaussianMixtureNB` estimator using `X` and `y` as training data by fitting a + `BayesianGaussianMixture` model for each class in the target and for each feature in the data, on the subset of + `X` where `y == class`. - :param X: array-like, shape=(n_columns, n_samples, ) training data. - :param y: array-like, shape=(n_samples, ) training data. - :return: Returns an instance of self. + Parameters + ---------- + X : array-like of shape (n_samples, n_features ) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : BayesianGaussianMixtureNB + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) if X.ndim == 1: @@ -206,11 +272,37 @@ def fit(self, X: np.array, y: np.array) -> "BayesianGaussianMixtureNB": return self def predict(self, X): + """Predict labels for `X` using fitted estimator and `predict_proba` method, by picking the class with the + highest probability. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. + """ check_is_fitted(self, ["gmms_", "classes_", "n_features_in_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) return self.classes_[self.predict_proba(X).argmax(axis=1)] - def predict_proba(self, X: np.array): + def predict_proba(self, X: np.ndarray): + """Predict probabilities for `X` using fitted estimator by summing the probabilities of each gaussian for each + class. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities. + """ check_is_fitted(self, ["gmms_", "classes_", "n_features_in_"]) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) if self.n_features_in_ != X.shape[1]: diff --git a/sklego/neighbors.py b/sklego/neighbors.py index d1c4464c6..623610649 100644 --- a/sklego/neighbors.py +++ b/sklego/neighbors.py @@ -3,10 +3,27 @@ from sklearn.neighbors import KernelDensity from sklearn.utils import check_X_y from sklearn.utils.multiclass import unique_labels -from sklearn.utils.validation import check_is_fitted, check_array, FLOAT_DTYPES +from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted class BayesianKernelDensityClassifier(BaseEstimator, ClassifierMixin): + """The `BayesianKernelDensityClassifier` estimator trains using Kernel Density estimations to generate the joint + distribution. + + You can pass any keyword parameter that scikit-learn's + [KernelDensity](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html) + model uses and it will be passed along. + + Attributes + ---------- + classes_ : np.ndarray of shape (n_classes,) + The classes seen during `fit`. + models_ : dict[int, KernelDensity] + The models for each class seen during `fit`. + priors_logp_ : dict + The log priors for each class seen during `fit` (estimated as `np.log(len(x_subset) / len(X))`) + """ + def __init__( self, bandwidth=0.2, @@ -19,11 +36,6 @@ def __init__( leaf_size=40, metric_params=None, ): - """ - Bayesian Classifier that uses Kernel Density Estimations to generate the joint distribution - - All parameters of the model are an exact copy of the parameters in scikit-learn. - """ self.bandwidth = bandwidth self.kernel = kernel self.algorithm = algorithm @@ -35,12 +47,20 @@ def __init__( self.metric_params = metric_params def fit(self, X: np.ndarray, y: np.ndarray): - """ - Fit the model using X, y as training data. - - :param X: array-like, shape=(n_features, n_samples) - :param y: array-like, shape=(n_samples) - :return: Returns an instance of self + """Fit the `BayesianKernelDensityClassifier` estimator using `X` and `y` as training data by fitting a + `KernelDensity` model for each class on the subset of X where y == class. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The training data. + y : array-like of shape (n_samples,) + The target values. + + Returns + ------- + self : BayesianKernelDensityClassifier + The fitted estimator. """ X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) @@ -68,29 +88,26 @@ def fit(self, X: np.ndarray, y: np.ndarray): return self def predict_proba(self, X): - """ - Probability estimates. + """Predict probabilities for `X` using fitted estimator and the joint distribution. The returned estimates for all classes are in the same order found in the `.classes_` attribute. - :param X: array-like of shape (n_samples, n_features) - :return: array-like of shape (n_samples, n_classes) - Returns the probability of the sample for each class in the model, - where classes are ordered as they are in self.classes_. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples, n_classes) + The predicted probabilities for each class, ordered as in `self.classes_`. """ check_is_fitted(self) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) - log_prior = np.array( - [self.priors_logp_[target_label] for target_label in self.classes_] - ) + log_prior = np.array([self.priors_logp_[target_label] for target_label in self.classes_]) - log_likelihood = np.array( - [ - self.models_[target_label].score_samples(X) - for target_label in self.classes_ - ] - ).T + log_likelihood = np.array([self.models_[target_label].score_samples(X) for target_label in self.classes_]).T log_likelihood_and_prior = np.exp(log_likelihood + log_prior) evidence = log_likelihood_and_prior.sum(axis=1, keepdims=True) @@ -98,11 +115,18 @@ def predict_proba(self, X): return posterior def predict(self, X): - """ - Predict class labels for samples in X. - - :param X: array_like, shape (n_samples, n_features) - :return: array, shape (n_samples) + """Predict labels for `X` using fitted estimator and `predict_proba()` method, by taking the class with the + highest probability. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to predict. + + Returns + ------- + array-like of shape (n_samples,) + The predicted data. """ check_is_fitted(self) X = check_array(X, estimator=self, dtype=FLOAT_DTYPES) diff --git a/sklego/notinstalled.py b/sklego/notinstalled.py index 2b76c995a..a3cecab5c 100644 --- a/sklego/notinstalled.py +++ b/sklego/notinstalled.py @@ -7,18 +7,24 @@ class NotInstalledPackage: - """ - Class to gracefully catch ImportErrors for modules and packages that are not installed + """Class to gracefully catch `ImportError`s for modules and packages that are not installed. - :param package_name (str): Name of the package you want to load - :param version (str, Optional): Version of the package + Parameters + ---------- + package_name : str + Name of the package you want to load + version : str | None, default=None + Version of the package - Usage: - >>> try: - ... import thispackagedoesnotexist as package - >>> except ImportError: - ... from sklego.notinstalled import NotInstalledPackage - ... package = NotInstalledPackage("thispackagedoesnotexist") + Examples + -------- + ```py + try: + import thispackagedoesnotexist as package + except ImportError: + from sklego.notinstalled import NotInstalledPackage + package = NotInstalledPackage("thispackagedoesnotexist") + ``` """ def __init__(self, package_name: str, version: str = None): diff --git a/sklego/pandas_utils.py b/sklego/pandas_utils.py index 3a839d15c..a59abd733 100644 --- a/sklego/pandas_utils.py +++ b/sklego/pandas_utils.py @@ -10,10 +10,8 @@ def _get_shape_delta(old_shape, new_shape): - diffs = [ - ("+" if new > old else "") + str(new - old) - for new, old in zip(new_shape, old_shape) - ] + """Returns a string with the difference in shape between old and new.""" + diffs = [("+" if new > old else "") + str(new - old) for new, old in zip(new_shape, old_shape)] return f"delta=({', '.join(diffs)})" @@ -30,29 +28,45 @@ def log_step( display_args=True, log_error=True, ): - """ - Decorates a function that transforms a pandas dataframe to add automated logging statements - - :param func: callable, function to log, defaults to None - :param time_taken: bool, log the time it took to run a function, defaults to True - :param shape: bool, log the shape of the output result, defaults to True - :param shape_delta: bool, log the difference in shape of input and output, defaults to False - :param names: bool, log the names of the columns of the result, defaults to False - :param dtypes: bool, log the dtypes of the results, defaults to False - :param print_fn: callable, print function (e.g. print or logger.info), defaults to print - :param print_args: bool, whether or not to print the arguments given to the function. - :param log_error: bool, whether to add the Exception message to the log if the function fails, defaults to True. - :returns: the result of the function - - :Example: - >>> @log_step - ... def remove_outliers(df, min_obs=5): - ... pass - - >>> @log_step(print_fn=logging.info, shape_delta=True) - ... def remove_outliers(df, min_obs=5): - ... pass - + """Decorates a function that transforms a pandas dataframe to add automated logging statements. + + Parameters + ---------- + func : Callable | None, default=None + The function to decorate with logs. If None, returns a partial function with the given arguments. + time_taken : bool, default=True + Whether or not to log the time it took to run a function. + shape : bool, default=True + Whether or not to log the shape of the output result. + shape_delta : bool, default=False + Whether or not to log the difference in shape of input and output. + names : bool, default=False + Whether or not to log the names of the columns of the result. + dtypes : bool, default=False + Whether or not to log the dtypes of the result. + print_fn : Callable, default=print + Print function to use (e.g. `print` or `logger.info`) + display_args : bool, default=True + Whether or not to display the arguments given to the function. + log_error : bool, default=True + Whether or not to add the Exception message to the log if the function fails. + + Returns + ------- + Callable + The decorated function. + + Examples + -------- + ```py + @log_step + def remove_outliers(df, min_obs=5): + pass + + @log_step(print_fn=logging.info, shape_delta=True) + def remove_outliers(df, min_obs=5): + pass + ``` """ if func is None: @@ -98,9 +112,7 @@ def wrapper(*args, **kwargs): if display_args: func_args = inspect.signature(func).bind(*args, **kwargs).arguments - func_args_str = "".join( - ", {} = {!r}".format(*item) for item in list(func_args.items())[1:] - ) + func_args_str = "".join(", {} = {!r}".format(*item) for item in list(func_args.items())[1:]) print_fn( f"[{func.__name__}(df{func_args_str})] " + combined, ) @@ -117,20 +129,29 @@ def log_step_extra( print_fn=print, **log_func_kwargs, ): - """ - Decorates a function that transforms a pandas dataframe to add automated logging statements - - :param *log_functions: callable(s), functions that take the output of the decorated function and turn it into a log. - Note that the output of each log_function is casted to string using `str()` - :param print_fn: callable, print function (e.g. print or logger.info), defaults to print - :param **log_func_kwargs: keyword arguments to be passed to log_functions - :returns: the result of the function - - :Example: - >>> @log_step_extra(lambda d: d["some_column"].value_counts()) - ... def remove_outliers(df, min_obs=5): - ... pass - + """Decorates a function that transforms a pandas dataframe to add automated logging statements. + + Parameters + ---------- + *log_functions : List[Callable] + Functions that take the output of the decorated function and turn it into a log. + Note that the output of each log_function is casted to string using `str()`. + print_fn: Callable, default=print + Print function (e.g. `print` or `logger.info`). + **log_func_kwargs: dict + Keyword arguments to be passed to `log_functions` + + Returns: + Callable + The decorated function. + + Examples + -------- + ```py + @log_step_extra(lambda d: d["some_column"].value_counts()) + def remove_outliers(df, min_obs=5): + pass + ``` """ if not log_functions: raise ValueError("Supply at least one log_function for log_step_extra") @@ -141,14 +162,10 @@ def wrapper(*args, **kwargs): result = func(*args, **kwargs) func_args = inspect.signature(func).bind(*args, **kwargs).arguments - func_args_str = "".join( - ", {} = {!r}".format(*item) for item in list(func_args.items())[1:] - ) + func_args_str = "".join(", {} = {!r}".format(*item) for item in list(func_args.items())[1:]) try: - extra_logs = " ".join( - [str(log_f(result, **log_func_kwargs)) for log_f in log_functions] - ) + extra_logs = " ".join([str(log_f(result, **log_func_kwargs)) for log_f in log_functions]) except TypeError: raise ValueError( f"All log functions should be callable, got {[type(log_f) for log_f in log_functions]}" @@ -166,44 +183,65 @@ def wrapper(*args, **kwargs): def add_lags(X, cols, lags, drop_na=True): - """ - Appends lag column(s). - - :param X: array-like, shape=(n_columns, n_samples,) training data. - :param cols: column name(s) or index (indices). - :param lags: the amount of lag for each col. - :param drop_na: remove rows that contain NA values. - :returns: ``pd.DataFrame | np.ndarray`` with only the selected cols. - - :Example: - - >>> import pandas as pd - >>> df = pd.DataFrame([[1, 2, 3], - ... [4, 5, 6], - ... [7, 8, 9]], - ... columns=['a', 'b', 'c'], - ... index=[1, 2, 3]) + """Appends lag column(s). + + Parameters + ---------- + X : array-like + Data to be lagged. + cols : str | int | List[str] | List[int] + Column name(s) or index (indices). + lags : int | List[int] + The amount of lag for each col. + drop_na : bool, default=True + Whether or not to remove rows that contain NA values. + + Returns + ------- + pd.DataFrame | np.ndarray + With only the selected cols. + + Raises + ------ + ValueError + If the input is not a `pd.DataFrame` or `np.ndarray`. + + Examples + -------- + ```py + import pandas as pd + df = pd.DataFrame([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]], + columns=["a", "b", "c"], + index=[1, 2, 3] + ) - >>> add_lags(df, 'a', [1]) # doctest: +NORMALIZE_WHITESPACE - a b c a1 + add_lags(df, "a", [1]) # doctest: +NORMALIZE_WHITESPACE + ''' + a b c a1 1 1 2 3 4.0 2 4 5 6 7.0 + ''' - >>> add_lags(df, ['a', 'b'], 2) # doctest: +NORMALIZE_WHITESPACE - a b c a2 b2 + add_lags(df, ["a", "b"], 2) # doctest: +NORMALIZE_WHITESPACE + ''' + a b c a2 b2 1 1 2 3 7.0 8.0 + ''' - >>> import numpy as np - >>> X = np.array([[1, 2, 3], - ... [4, 5, 6], - ... [7, 8, 9]]) + import numpy as np + X = np.array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) - >>> add_lags(X, 0, [1]) - array([[1, 2, 3, 4], - [4, 5, 6, 7]]) + add_lags(X, 0, [1]) + # array([[1, 2, 3, 4], + # [4, 5, 6, 7]]) - >>> add_lags(X, 1, [-1, 1]) - array([[4, 5, 6, 2, 8]]) + add_lags(X, 1, [-1, 1]) + # array([[4, 5, 6, 2, 8]]) + ``` """ # A single lag will be put in a list @@ -232,13 +270,23 @@ def add_lags(X, cols, lags, drop_na=True): def _add_lagged_numpy_columns(X, cols, lags, drop_na): - """ - Append a lag columns. - - :param df: the input ``np.ndarray``. - :param cols: column index / indices. - :param drop_na: remove rows that contain NA values. - :returns: ``np.ndarray`` with the concatenated lagged cols. + """Append a lag columns. + + Parameters + ---------- + X : np.ndarray + Data to be lagged. + cols : int | List[int] + Column index / indices. + lags : int | List[int] + The amount of lag for each col. + drop_na : bool + Whether or not to remove rows that contain NA values. + + Returns + ------- + np.ndarray + Array with concatenated lagged cols. """ cols = as_list(cols) @@ -268,13 +316,23 @@ def _add_lagged_numpy_columns(X, cols, lags, drop_na): def _add_lagged_pandas_columns(df, cols, lags, drop_na): - """ - Append a lag columns. - - :param df: the input ``pd.DataFrame``. - :param cols: column name(s). - :param drop_na: remove rows that contain NA values. - :returns: ``pd.DataFrame`` with the concatenated lagged cols. + """Append a lag columns. + + Parameters + ---------- + df : pd.DataFrame + Data to be lagged. + cols : str | List[str] + Column name / names. + lags : int | List[int] + The amount of lag for each col. + drop_na : bool + Whether or not to remove rows that contain NA values. + + Returns + ------- + pd.DataFrame + Dataframe with concatenated lagged cols. """ cols = as_list(cols) @@ -284,9 +342,7 @@ def _add_lagged_pandas_columns(df, cols, lags, drop_na): if not all([col in df.columns.values for col in cols]): raise KeyError("The column does not exist") - combos = ( - df[col].shift(-lag).rename(col + str(lag)) for col in cols for lag in lags - ) + combos = (df[col].shift(-lag).rename(col + str(lag)) for col in cols for lag in lags) answer = pd.concat([df, *combos], axis=1) diff --git a/sklego/pipeline.py b/sklego/pipeline.py index fab53eeb6..6eddfe108 100644 --- a/sklego/pipeline.py +++ b/sklego/pipeline.py @@ -1,34 +1,37 @@ """ -Pipelines, variances to the :class:`sklearn.pipeline.Pipeline` object. +Pipelines, variances to the `sklearn.pipeline.Pipeline` object. """ import logging import time -from sklearn.pipeline import Pipeline +from sklearn.pipeline import Pipeline, _name_estimators from sklearn.utils.validation import check_memory -from sklearn.pipeline import _name_estimators def default_log_callback(output, execution_time, **kwargs): - """ - The default log callback which logs the step name, shape of the output and - the execution time of the step. + """The default log callback which logs the step name, shape of the output and the execution time of the step. - :param tuple output: - (:class:`numpy.ndarray` or :class:`pandas.DataFrame`, \ - :class:`estimator` or :class:`transformer`) + Parameters + ---------- + output : tuple[np.ndarray | pd.DataFrame, estimator | transformer] The output of the step and a step in the pipeline. - :param float execution_time: The execution time of the step. + execution_time : float + The execution time of the step. + + !!! info - .. note:: If you write your custom callback function the input is: - :param function func: The function to be wrapped. - :param tuple input_args: The input arguments. - :param dict input_kwargs: The input key-word arguments. - :param output: The output of the function. - :param execution_time float: The execution time. + + | Parameter | Type | Description | + | ---------------- | ---------------- | ------------------------------ | + | `func` | Callable[..., T] | The function to be wrapped | + | `input_args` | tuple | The input arguments | + | `input_kwargs` | dict | The input key-word arguments | + | `output` | T | The output of the function | + | `execution_time` | float | The execution time of the step | + """ logger = logging.getLogger(__name__) step_result, step = output @@ -36,16 +39,19 @@ def default_log_callback(output, execution_time, **kwargs): def _log_wrapper(log_callback=default_log_callback): - """ - Function wrapper to log information after the function is called, about the - function, input args, input kwargs, output and the execution time. - - :param function log_callback: optional. - The log callback which is called after `func` is called. Defaults to - :func:`default_log_callback`. Note, this function should expect the - same arguments as the default. - :returns: The function wrapped with a log callback. - :rtype: function + """Function wrapper to log information after the function is called, about the function, input args, input kwargs, + output and the execution time. + + Parameters + ---------- + log_callback : Callable, default=default_log_callback + The log callback which is called after `func` is called. Note, this function should expect the same arguments + as the default. + + Returns + ------- + Callable + The function wrapped with a log callback. """ def _(func): @@ -68,14 +74,17 @@ def _(*args, **kwargs): def _cache_with_function_log_statement(log_callback=default_log_callback): - """ - Wraps the `func` with :func:`_log_wrapper` before passing it to - :method:`_cache`. + """Wraps the `func` with `_log_wrapper` before passing it to `_cache`. - :param function log_callback: optional. - The log callback function. Defaults to :func:default_log_callback. + Parameters + ---------- + log_callback : Callable, default=default_log_callback + The log callback function. - :returns: The cache where its function is wrapped with a log statement. + Returns + ------- + Callable + The function wrapped with a log callback. """ def _(self, func=None, *args, **kwargs): @@ -87,183 +96,184 @@ def _(self, func=None, *args, **kwargs): class DebugPipeline(Pipeline): - ''' - A pipeline that has a log statement in between each step, useful for - debugging. - - :ivar log_callback: optional. - The callback function that logs information in between each - intermediate step. Defaults to None. If set to `'default'`, - :func:`default_log_callback` is used. - - See :func:`default_log_callback` for an example. - - See :class:`sklearn.pipeline.Pipeline` for all other variables. - - .. note:: - This implementation is a hack on the original sklearn Pipeline. It aims - to have the same behaviour as the original sklearn Pipeline, while - changing minimal amount of code. - - The log statement is added by overwriting the cache method of the - memory, such that the function called in the cache is wrapped with a - functions that calls the log callback function (`log_callback`). - - This hack breaks will break when: - - The SKlearn pipeline initialization function is changed. - - The memory is used differently in the fit. - - The :class:`joblib.memory.Memory` changes behaviour of the - :func:`cache` method.. - - The :class:`joblib.memory.Memory` starts using a :func:`_cache` - method. - - :Example: - - >>> # Set-up for example - >>> import logging - >>> import sys - >>> - >>> import numpy as np - >>> import pandas as pd - >>> - >>> from sklearn.base import BaseEstimator, TransformerMixin - >>> from sklego.pipeline import DebugPipeline - >>> - >>> logging.basicConfig( - ... format=('[%(funcName)s:%(lineno)d] - %(message)s'), - ... level=logging.INFO, - ... stream=sys.stdout, - ... ) - >>> - >>> # DebugPipeline set-up - >>> n_samples, n_features = 3, 5 - >>> X = np.zeros((n_samples, n_features)) - >>> y = np.arange(n_samples) - >>> - >>> class Adder(TransformerMixin, BaseEstimator): - ... def __init__(self, value): - ... self._value = value - ... - ... def fit(self, X, y=None): - ... return self - ... - ... def transform(self, X): - ... return X + self._value - ... - ... def __repr__(self): - ... return f'Adder(value={self._value})' - >>> - >>> steps = [ - ... ('add_1', Adder(value=1)), - ... ('add_10', Adder(value=10)), - ... ('add_100', Adder(value=100)), - ... ('add_1000', Adder(value=1000)), - ... ] - >>> - >>> # The DebugPipeline behaves the sames as the Sklearn pipeline. - >>> pipe = DebugPipeline(steps) - >>> - >>> _ = pipe.fit(X, y=y) - >>> print(pipe.transform(X)) - [[1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.]] - >>> - >>> # But it has the option to set a `log_callback`, that logs in between - >>> # each step. - >>> pipe = DebugPipeline(steps, log_callback='default') - >>> - >>> _ = pipe.fit(X, y=y) - [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s - >>> print(pipe.transform(X)) - [[1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.]] - >>> - >>> # It is possible to set the `log_callback` later then initialisation. - >>> pipe = DebugPipeline(steps) - >>> pipe.log_callback = 'default' - >>> - >>> _ = pipe.fit(X, y=y) - [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s - >>> print(pipe.transform(X)) - [[1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.]] - >>> - >>> # It is possible to define your own `log_callback` - >>> def log_callback(output, execution_time, **kwargs): - ... """My custom `log_callback` function - ... - ... Parameters - ... ---------- - ... output : tuple( - ... numpy.ndarray or pandas.DataFrame - ... :class:estimator or :class:transformer - ... ) - ... The output of the step and a step in the pipeline. - ... execution_time : float - ... The execution time of the step. ... - ... Note - ... ---------- - ... The **kwargs are for arguments that are not used in this callback. - ... """ - ... logger = logging.getLogger(__name__) - ... step_result, step = output - ... logger.info( - ... f'[{step}] shape={step_result.shape} ' - ... f'nbytes={step_result.nbytes} time={int(execution_time)}s') - >>> - >>> pipe.log_callback = log_callback - >>> - >>> _ = pipe.fit(X, y=y) - [log_callback:20] - [Adder(value=1)] shape=(3, 5) nbytes=120 time=0s - [log_callback:20] - [Adder(value=10)] shape=(3, 5) nbytes=120 time=0s - [log_callback:20] - [Adder(value=100)] shape=(3, 5) nbytes=120 time=0s - >>> print(pipe.transform(X)) - [[1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.]] - >>> - >>> # Remove the `log_callback` when you want to stop logging. - >>> pipe.log_callback = None - >>> - >>> _ = pipe.fit(X, y=y) - >>> print(pipe.transform(X)) - [[1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.] - [1111. 1111. 1111. 1111. 1111.]] - >>> - >>> # Logging also works with FeatureUnion - >>> from sklearn.pipeline import FeatureUnion - >>> pipe_w_default_log_callback = DebugPipeline(steps, log_callback='default') - >>> pipe_w_custom_log_callback = DebugPipeline(steps, log_callback=log_callback) - >>> - >>> pipe_union = DebugPipeline([ - ... ('feature_union', FeatureUnion([ - ... ('pipe_w_default_log_callback', pipe_w_default_log_callback), - ... ('pipe_w_custom_log_callback', pipe_w_custom_log_callback), - ... ])), - ... ('final_adder', Adder(10000)) - ... ], log_callback='default') - >>> - >>> _ = pipe_union.fit(X, y=y) # doctest:+ELLIPSIS - [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s - [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s - [log_callback:20] - [Adder(value=1)] shape=(3, 5) nbytes=120 time=0s - [log_callback:20] - [Adder(value=10)] shape=(3, 5) nbytes=120 time=0s - [log_callback:20] - [Adder(value=100)] shape=(3, 5) nbytes=120 time=0s - [default_log_callback:34] - [FeatureUnion(...)] shape=(3, 10) time=0s - >>> print(pipe_union.transform(X)) - [[11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.] - [11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.] - [11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.]] - ''' + """A pipeline that has a log statement in between each step, useful for debugging purposes. + + Parameters + ---------- + log_callback : Callable | None, default=None + The callback function that logs information in between each intermediate step. + If set to `"default"`, `default_log_callback` is used. + + Notes + ----- + See [`sklearn.pipeline.Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline) + for all other variables. + + !!! note + This implementation is a hack on the original sklearn Pipeline. It aims to have the same behaviour as the + original sklearn Pipeline, while changing minimal amount of code. + + The log statement is added by overwriting the cache method of the memory, such that the function called in the + cache is wrapped with a functions that calls the log callback function (`log_callback`). + + This hack will break when: + + - The sklearn pipeline initialization function is changed. + - The memory is used differently in the fit. + - The [`joblib.memory.Memory`](https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html) + changes behaviour of the `cache` method. + - The [`joblib.memory.Memory`](https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html) + starts using a `_cache` method. + + + Examples + -------- + ```py + # Set-up for example + import logging + import sys + + import numpy as np + import pandas as pd + + from sklearn.base import BaseEstimator, TransformerMixin + from sklego.pipeline import DebugPipeline + + logging.basicConfig( + format=("[%(funcName)s:%(lineno)d] - %(message)s"), + level=logging.INFO, + stream=sys.stdout, + ) + + # DebugPipeline set-up + n_samples, n_features = 3, 5 + X = np.zeros((n_samples, n_features)) + y = np.arange(n_samples) + + class Adder(TransformerMixin, BaseEstimator): + def __init__(self, value): + self._value = value + + def fit(self, X, y=None): + return self + + def transform(self, X): + return X + self._value + + def __repr__(self): + return f"Adder(value={self._value})" + + steps = [ + ("add_1", Adder(value=1)), + ("add_10", Adder(value=10)), + ("add_100", Adder(value=100)), + ("add_1000", Adder(value=1000)), + ] + + # The DebugPipeline behaves the sames as the Sklearn pipeline. + pipe = DebugPipeline(steps) + + _ = pipe.fit(X, y=y) + print(pipe.transform(X)) + # [[1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.]] + + # But it has the option to set a `log_callback`, that logs in between each step. + pipe = DebugPipeline(steps, log_callback="default") + + _ = pipe.fit(X, y=y) + # [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s + + print(pipe.transform(X)) + # [[1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.]] + + # It is possible to set the `log_callback` later then initialisation. + pipe = DebugPipeline(steps) + pipe.log_callback = "default" + + _ = pipe.fit(X, y=y) + # [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s + + print(pipe.transform(X)) + # [[1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.]] + + # It is possible to define your own `log_callback` function. + def log_callback(output, execution_time, **kwargs): + '''My custom `log_callback` function + + Parameters + output : tuple[np.ndarray | pd.DataFrame, estimator | transformer] + The output of the step and a step in the pipeline. + execution_time : float + The execution time of the step. + + Note + The **kwargs are for arguments that are not used in this callback. + ''' + logger = logging.getLogger(__name__) + step_result, step = output + logger.info( + f"[{step}] shape={step_result.shape} " + f"nbytes={step_result.nbytes} time={int(execution_time)}s") + + pipe.log_callback = log_callback + + _ = pipe.fit(X, y=y) + # [log_callback:20] - [Adder(value=1)] shape=(3, 5) nbytes=120 time=0s + # [log_callback:20] - [Adder(value=10)] shape=(3, 5) nbytes=120 time=0s + # [log_callback:20] - [Adder(value=100)] shape=(3, 5) nbytes=120 time=0s + + print(pipe.transform(X)) + # [[1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.]] + + # Remove the `log_callback` when you want to stop logging. + pipe.log_callback = None + + _ = pipe.fit(X, y=y) + print(pipe.transform(X)) + # [[1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.] + # [1111. 1111. 1111. 1111. 1111.]] + + # Logging also works with FeatureUnion + from sklearn.pipeline import FeatureUnion + pipe_w_default_log_callback = DebugPipeline(steps, log_callback="default") + pipe_w_custom_log_callback = DebugPipeline(steps, log_callback=log_callback) + + pipe_union = DebugPipeline([ + ("feature_union", FeatureUnion([ + ("pipe_w_default_log_callback", pipe_w_default_log_callback), + ("pipe_w_custom_log_callback", pipe_w_custom_log_callback), + ])), + ("final_adder", Adder(10000)) + ], log_callback="default") + + _ = pipe_union.fit(X, y=y) # doctest:+ELLIPSIS + # [default_log_callback:34] - [Adder(value=1)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=10)] shape=(3, 5) time=0s + # [default_log_callback:34] - [Adder(value=100)] shape=(3, 5) time=0s + # [log_callback:20] - [Adder(value=1)] shape=(3, 5) nbytes=120 time=0s + # [log_callback:20] - [Adder(value=10)] shape=(3, 5) nbytes=120 time=0s + # [log_callback:20] - [Adder(value=100)] shape=(3, 5) nbytes=120 time=0s + # [default_log_callback:34] - [FeatureUnion(...)] shape=(3, 10) time=0s + + print(pipe_union.transform(X)) + # [[11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.] + # [11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.] + # [11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111. 11111.]] + ``` + """ def __init__(self, steps, memory=None, verbose=False, *, log_callback=None): self.log_callback = log_callback @@ -284,9 +294,9 @@ def memory(self): # output when the function is called if not hasattr(self._memory, "_cache"): self._memory._cache = self._memory.cache - self._memory.cache = _cache_with_function_log_statement( - self._log_callback - ).__get__(self._memory, self._memory.__class__) + self._memory.cache = _cache_with_function_log_statement(self._log_callback).__get__( + self._memory, self._memory.__class__ + ) return self._memory @memory.setter @@ -305,51 +315,65 @@ def log_callback(self, func): def make_debug_pipeline(*steps, **kwargs): - """Construct a DebugPipeline from the given estimators. - This is a shorthand for the DebugPipeline constructor; it does not require, and - does not permit, naming the estimators. Instead, their names will be set - to the lowercase of their types automatically. + """Construct a `DebugPipeline` from the given estimators. + + This is a shorthand for the `DebugPipeline` constructor; it does not require, and does not permit, naming the + estimators. Instead, their names will be set to the lowercase of their types automatically. + Parameters ---------- - *steps : list of estimators. - memory : None, str or object with the joblib.Memory interface, optional - Used to cache the fitted transformers of the pipeline. By default, - no caching is performed. If a string is given, it is the path to - the caching directory. Enabling caching triggers a clone of - the transformers before fitting. Therefore, the transformer - instance given to the pipeline cannot be inspected - directly. Use the attribute ``named_steps`` or ``steps`` to - inspect estimators within the pipeline. Caching the - transformers is advantageous when fitting is time consuming. - verbose : boolean, default=False - If True, the time elapsed while fitting each step will be printed as it - is completed. - log_callback: string, default=None. - The callback function that logs information in between each - intermediate step. Defaults to None. If set to `'default'`, - :func:`default_log_callback` is used. - - See :func:`default_log_callback` for an example. + *steps : list + List of estimators to be included in the pipeline. + **kwargs : dict + Additional keyword arguments passed to the `DebugPipeline` constructor. + Possible arguments are `memory`, `verbose` and `log_callback`: + + - `memory` : str | object with the joblib.Memory interface, default=None + + Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a + transformer. By default, no caching is performed. If a string is given, it is the path to the caching + directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer + instance given to the pipeline cannot be inspected directly. Use the attribute `named_steps` or `steps` to + inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time + consuming. + + - `verbose` : bool, default=False + + If True, the time elapsed while fitting each step will be printed as it is completed. + + - `log_callback` : str | Callable | None, default=None. + + The callback function that logs information in between each intermediate step. If set to `"default"`, + `default_log_callback` is used. - See Also - -------- - sklego.pipeline.DebugPipeline : Class for creating a pipeline of - transforms with a final estimator. - Examples - -------- - >>> from sklearn.naive_bayes import GaussianNB - >>> from sklearn.preprocessing import StandardScaler - >>> make_debug_pipeline(StandardScaler(), GaussianNB(priors=None)) - DebugPipeline(steps=[('standardscaler', StandardScaler()), - ('gaussiannb', GaussianNB())]) Returns ------- - p : DebugPipeline + DebugPipeline + Instance with given steps, `memory`, `verbose` and `log_callback`. + + Examples + -------- + ```py + from sklearn.naive_bayes import GaussianNB + from sklearn.preprocessing import StandardScaler + + make_debug_pipeline(StandardScaler(), GaussianNB(priors=None)) + # DebugPipeline(steps=[("standardscaler", StandardScaler()), + # ("gaussiannb", GaussianNB())]) + ``` + + See Also + -------- + sklego.pipeline.DebugPipeline : Class for creating a pipeline of transforms with a final estimator. """ - memory = kwargs.pop('memory', None) - verbose = kwargs.pop('verbose', False) - log_callback = kwargs.pop('log_callback', None) + memory = kwargs.pop("memory", None) + verbose = kwargs.pop("verbose", False) + log_callback = kwargs.pop("log_callback", None) if kwargs: - raise TypeError('Unknown keyword arguments: "{}"' - .format(list(kwargs.keys())[0])) - return DebugPipeline(_name_estimators(steps), memory=memory, verbose=verbose, log_callback=log_callback) + raise TypeError('Unknown keyword arguments: "{}"'.format(list(kwargs.keys())[0])) + return DebugPipeline( + _name_estimators(steps), + memory=memory, + verbose=verbose, + log_callback=log_callback, + ) diff --git a/sklego/preprocessing/__init__.py b/sklego/preprocessing/__init__.py index 532195e5a..af6676c5d 100644 --- a/sklego/preprocessing/__init__.py +++ b/sklego/preprocessing/__init__.py @@ -15,14 +15,14 @@ "FormulaicTransformer", ] +from .columncapper import ColumnCapper +from .dictmapper import DictMapper +from .identitytransformer import IdentityTransformer from .intervalencoder import IntervalEncoder -from .randomadder import RandomAdder +from .formulaictransformer import FormulaicTransformer +from .outlier_remover import OutlierRemover +from .pandastransformers import ColumnDropper, ColumnSelector, PandasTypeSelector from .patsytransformer import PatsyTransformer -from .pandastransformers import ColumnSelector, PandasTypeSelector, ColumnDropper from .projections import InformationFilter, OrthogonalTransformer +from .randomadder import RandomAdder from .repeatingbasis import RepeatingBasisFunction -from .columncapper import ColumnCapper -from .identitytransformer import IdentityTransformer -from .outlier_remover import OutlierRemover -from .dictmapper import DictMapper -from .formulaictransformer import FormulaicTransformer diff --git a/sklego/preprocessing/columncapper.py b/sklego/preprocessing/columncapper.py index b578d4701..933fcc846 100644 --- a/sklego/preprocessing/columncapper.py +++ b/sklego/preprocessing/columncapper.py @@ -7,69 +7,86 @@ class ColumnCapper(TransformerMixin, BaseEstimator): - """ - Caps the values of columns according to the given quantile thresholds. - - :type quantile_range: tuple or list, optional, default=(5.0, 95.0) - :param quantile_range: The quantile ranges to perform the capping. Their valus must - be in the interval [0; 100]. - - :type interpolation: str, optional, default='linear' - :param interpolation: The interpolation method to compute the quantiles when the - desired quantile lies between two data points `i` and `j`. The Available values - are: - - * ``'linear'``: `i + (j - i) * fraction`, where `fraction` is the fractional part of\ - the index surrounded by `i` and `j`. - * ``'lower'``: `i`. - * ``'higher'``: `j`. - * ``'nearest'``: `i` or `j` whichever is nearest. - * ``'midpoint'``: (`i` + `j`) / 2. - - :type discard_infs: bool, optional, default=False - :param discard_infs: Whether to discard ``-np.inf`` and ``np.inf`` values or not. If - ``False``, such values will be capped. If ``True``, they will be replaced by - ``np.nan``. - - .. note:: - Setting ``discard_infs=True`` is important if the `inf` values are results - of divisions by 0, which are interpreted by ``pandas`` as ``-np.inf`` or - ``np.inf`` depending on the signal of the numerator. - - :type copy: bool, optional, default=True - :param copy: If False, try to avoid a copy and do inplace capping instead. This is not - guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse - CSR matrix, a copy may still be returned. - - :raises: - ``TypeError``, ``ValueError`` - - :Example: - - >>> import pandas as pd - >>> import numpy as np - >>> from sklego.preprocessing import ColumnCapper - >>> df = pd.DataFrame({'a':[2, 4.5, 7, 9], 'b':[11, 12, np.inf, 14]}) - >>> df + """The `ColumnCapper` transformer caps the values of columns according to the given quantile thresholds. + + The capping is performed independently for each column of the input data. The quantile thresholds are computed + during the fitting phase. The capping is performed during the transformation phase. + + Parameters + ---------- + quantile_range : Tuple[float, float] | List[float], default=(5.0, 95.0) + The quantile ranges to perform the capping. Their values must be in the interval [0; 100]. + interpolation : Literal["linear", "lower", "higher", "midpoint", "nearest"], default="linear" + The interpolation method to compute the quantiles when the desired quantile lies between two data points `i` + and `j`. This value is passed to + [`numpy.nanquantile`](https://numpy.org/doc/stable/reference/generated/numpy.nanquantile.html) function. + + The Available values are: + + - `"linear"`: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` + and `j`. + * `"lower"`: `i`. + * `"higher"`: `j`. + * `"nearest"`: `i` or `j` whichever is nearest. + * `"midpoint"`: (`i` + `j`) / 2. + discard_infs : bool, default=False + Whether to discard `-np.inf` and `np.inf` values or not. If False, such values will be capped. If True, + they will be replaced by `np.nan`. + + !!! info + Setting `discard_infs=True` is important if the `inf` values are results of divisions by 0, which are + interpreted by `pandas` as `-np.inf` or `np.inf` depending on the sign of the numerator. + copy : bool, default=True + If False, try to avoid a copy and do inplace capping instead. This is not guaranteed to always work inplace; + e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. + + Attributes + ---------- + quantiles_ : np.ndarray of shape (2, n_features) + The computed quantiles for each column of the input data. The first row contains the lower quantile, the second + row contains the upper quantile. + n_features_in_ : int + Number of features seen during `fit`. + n_columns_ : int + Deprecated, please use `n_features_in_` instead. + + Examples + -------- + ```py + import pandas as pd + import numpy as np + from sklego.preprocessing import ColumnCapper + + df = pd.DataFrame({'a':[2, 4.5, 7, 9], 'b':[11, 12, np.inf, 14]}) + df + ''' a b 0 2.0 11.0 1 4.5 12.0 2 7.0 inf 3 9.0 14.0 - >>> capper = ColumnCapper() - >>> capper.fit_transform(df) + ''' + + capper = ColumnCapper() + capper.fit_transform(df) + ''' array([[ 2.375, 11.1 ], [ 4.5 , 12. ], [ 7. , 13.8 ], [ 8.7 , 13.8 ]]) - >>> capper = ColumnCapper(discard_infs=True) # Discarding infs - >>> df[['a', 'b']] = capper.fit_transform(df) - >>> df + ''' + + capper = ColumnCapper(discard_infs=True) # Discarding infs + df[['a', 'b']] = capper.fit_transform(df) + df + ''' a b 0 2.375 11.1 1 4.500 12.0 2 7.000 NaN 3 8.700 13.8 + ''' + ``` """ def __init__( @@ -88,19 +105,24 @@ def __init__( self.copy = copy def fit(self, X, y=None): - """ - Computes the quantiles for each column of ``X``. - - :type X: pandas.DataFrame or numpy.ndarray - :param X: The column(s) from which the capping limit(s) will be computed. - - :param y: Ignored. - - :rtype: sklego.preprocessing.ColumnCapper - :returns: The fitted object. - - :raises: - ``ValueError`` if ``X`` contains non-numeric columns + """Fit the `ColumnCapper` transformer by computing quantiles for each column of `X`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to compute the quantiles for capping. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : ColumnCapper + The fitted transformer. + + Raises + ------ + ValueError + If `X` contains non-numeric columns. """ X = check_array( X, copy=True, force_all_finite=False, dtype=FLOAT_DTYPES, estimator=self @@ -131,18 +153,22 @@ def fit(self, X, y=None): return self def transform(self, X): - """ - Performs the capping on the column(s) of ``X``. - - :type X: pandas.DataFrame or numpy.ndarray - :param X: The column(s) for which the capping limit(s) will be applied. - - :rtype: numpy.ndarray - :returns: ``X`` values with capped limits. - - :raises: - ``ValueError`` if the number of columns from ``X`` differs from the - number of columns when fitting + """Performs the capping on the column(s) of `X` according to the quantile thresholds computed during fitting. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the capping limit(s) will be applied. + + Returns + ------- + X : np.ndarray of shape (n_samples, n_features) + `X` values with capped limits. + + Raises + ------ + ValueError + If the number of columns from `X` differs from the number of columns when fitting. """ check_is_fitted(self, "quantiles_") X = check_array( @@ -169,8 +195,21 @@ def transform(self, X): @staticmethod def _check_quantile_range(quantile_range): - """ - Checks for the validity of quantile_range. + """Checks for the validity of quantile_range. + + Parameters + ---------- + quantile_range : Tuple[float, float] | List[float] + The quantile ranges to perform the capping. Their values must be in the interval [0; 100]. + + Raises + ------ + TypeError + If `quantile_range` is not a tuple or a list. + ValueError + - If `quantile_range` does not contain exactly 2 elements. + - If `quantile_range` contains values outside of [0; 100]. + - If `quantile_range` contains values in the wrong order. """ if not isinstance(quantile_range, tuple) and not isinstance( quantile_range, list @@ -194,8 +233,17 @@ def _check_quantile_range(quantile_range): @staticmethod def _check_interpolation(interpolation): - """ - Checks for the validity of interpolation + """Checks for the validity of interpolation. + + Parameters + ---------- + interpolation : Literal["linear", "lower", "higher", "midpoint", "nearest"] + Interpolation method to compute the quantiles + + Raises + ------ + ValueError + If `interpolation` is not one of the allowed values. """ allowed_interpolations = ("linear", "lower", "higher", "midpoint", "nearest") if interpolation not in allowed_interpolations: diff --git a/sklego/preprocessing/dictmapper.py b/sklego/preprocessing/dictmapper.py index 62d8e0a56..14a23c805 100644 --- a/sklego/preprocessing/dictmapper.py +++ b/sklego/preprocessing/dictmapper.py @@ -7,12 +7,22 @@ class DictMapper(TransformerMixin, BaseEstimator): - """ - Map the values of values of columns according to the input dictionary, - fall back to the default if the key is not present in the dictionary. + """The `DictMapper` transformer maps the values of columns according to the input `mapper` dictionary, fall back to + the `default` value if the key is not present in the dictionary. + + Parameters + ---------- + mapper : dict[..., int] + The dictionary containing the mapping of the values. + default : int + The value to fall back to if the value is not in the mapper. - :param mapper: The dictionary containing the mapping of the values - :param default: The value to fall back to if the value is not in the mapper + Attributes + ---------- + n_features_in_ : int + Number of features seen during `fit`. + dim_ : int + Deprecated, please use `n_features_in_` instead. """ def __init__(self, mapper, default): @@ -20,16 +30,19 @@ def __init__(self, mapper, default): self.default = default def fit(self, X, y=None): - """ - Checks the input dataframe and records the shape of it + """Checks the input data and records the number of features. - :type X: pandas.DataFrame or numpy.ndarray - :param X: The column(s) from which the mapping will be applied + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to fit. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. - :param y: Ignored. - - :rtype: sklego.preprocessing.DictMapper - :returns: The fitted object. + Returns + ------- + self : DictMapper + The fitted transformer. """ X = check_array( X, @@ -43,18 +56,22 @@ def fit(self, X, y=None): return self def transform(self, X): - """ - Performs the mapping on the column(s) of ``X``. + """Performs the mapping on the column(s) of `X`. - :type X: pandas.DataFrame or numpy.ndarray - :param X: The column(s) for which the mapping will be applied. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the mapping will be applied. - :rtype: numpy.ndarray - :returns: ``X`` values with the mapping applied + Returns + ------- + np.ndarray of shape (n_samples, n_features) + The data with the mapping applied. - :raises: - ``ValueError`` if the number of columns from ``X`` differs from the - number of columns when fitting + Raises + ------ + ValueError + If the number of columns from `X` differs from the number of columns when fitting. """ check_is_fitted(self, ["n_features_in_"]) X = check_array( diff --git a/sklego/preprocessing/identitytransformer.py b/sklego/preprocessing/identitytransformer.py index 6b360287a..63e9ee88a 100644 --- a/sklego/preprocessing/identitytransformer.py +++ b/sklego/preprocessing/identitytransformer.py @@ -1,35 +1,72 @@ from warnings import warn from sklearn.base import BaseEstimator, TransformerMixin - from sklearn.utils import check_array from sklearn.utils.validation import check_is_fitted class IdentityTransformer(BaseEstimator, TransformerMixin): - """ - The identity transformer returns what it is fed. Does not apply anything useful. + """The `IdentityTransformer` returns what it is fed. Does not apply any transformation. + The reason for having it is because you can build more expressive pipelines. - :type check_X: bool, optional, default=False - :param check_X: Whether to validate X to be non-empty 2D array of finite values - and attempt to cast X to float. - If disabled, the model/pipeline is expected to handle e.g. missing, - non-numeric, or non-finite values. + Parameters + ---------- + check_X : bool, default=False + Whether to validate `X` to be non-empty 2D array of finite values and attempt to cast `X` to float. + If disabled, the model/pipeline is expected to handle e.g. missing, non-numeric, or non-finite values. + + Attributes + ---------- + n_samples_ : int + The number of samples seen during `fit`. + n_features_in_ : int + The number of features seen during `fit`. + shape_ : tuple[int, int] + Deprecated, please use `n_samples_` and `n_features_in_` instead. """ def __init__(self, check_X: bool = False): self.check_X = check_X def fit(self, X, y=None): - """'Fits' the estimator.""" + """Check the input data if `check_X` is enabled and and records its shape. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to fit. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : IdentityTransformer + The fitted transformer. + """ if self.check_X: X = check_array(X, copy=True, estimator=self) self.n_samples_, self.n_features_in_ = X.shape return self def transform(self, X): - """'Applies' the estimator.""" + """Performs identity "transformation" on `X` - which is no transformation at all. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Input data. + + Returns + ------- + array-like of shape (n_samples, n_features) + Unchanged input data. + + Raises + ------ + ValueError + If the number of columns from `X` differs from the number of columns when fitting. + """ if self.check_X: X = check_array(X, copy=True, estimator=self) check_is_fitted(self, "n_features_in_") diff --git a/sklego/preprocessing/intervalencoder.py b/sklego/preprocessing/intervalencoder.py index 01c1c3ada..428be75a5 100644 --- a/sklego/preprocessing/intervalencoder.py +++ b/sklego/preprocessing/intervalencoder.py @@ -14,14 +14,23 @@ def _mk_monotonic_average(xs, ys, intervals, method="increasing", **kwargs): - """ - Creates smoothed averages of `ys` at the intervals given by `intervals`. - :param xs: all the datapoints of a feature (represents the x-axis) - :param ys: all the datapoints what we'd like to predict (represents the y-axis) - :param intervals: the intervals at which we'd like to get a good average value - :param method: the method that is used for smoothing, can be either `increasing` or `decreasing`. - :return: - An array as long as `intervals` that represents the average `y`-values at those intervals, + """Creates smoothed averages of `ys` at the intervals given by `intervals`. + + Parameters + ---------- + xs : array-like of shape (n_samples,) + All the datapoints of a feature (represents the x-axis). + ys : array-like of shape (n_samples,) + All the datapoints what we'd like to predict (represents the y-axis). + intervals : array-like of shape (n_intervals,) + The intervals at which we'd like to get a good average value. + method : Literal["increasing", "decreasing"]}, default="increasing" + The method that is used for smoothing, can be either `"increasing"` or `"decreasing"`. + + Returns + ------- + np.ndarray of shape (n_intervals,) + An array of the same shape of `intervals` that represents the average `y`-values at those intervals, keeping the constraint in mind. """ x_internal = np.array([xs >= i for i in intervals]).T.astype(float) @@ -41,17 +50,26 @@ def _mk_monotonic_average(xs, ys, intervals, method="increasing", **kwargs): def _mk_average(xs, ys, intervals, method="average", span=1, **kwargs): - """ - Creates smoothed averages of `ys` at the intervals given by `intervals`. - :param xs: all the datapoints of a feature (represents the x-axis) - :param ys: all the datapoints what we'd like to predict (represents the y-axis) - :param intervals: the intervals at which we'd like to get a good average value - :param method: the method that is used for smoothing, can be either `average` or `normal`. - :param span: if the method is `average` then this is the span around the interval - that is used to determine the average `y`-value, if the method is `normal` the span - becomes the value of sigma that is used for weighted averaging - :return: - An array as long as `intervals` that represents the average `y`-values at those intervals. + """Creates smoothed averages of `ys` at the intervals given by `intervals`. + + Parameters + ---------- + xs : array-like of shape (n_samples,) + All the datapoints of a feature (represents the x-axis). + ys : array-like of shape (n_samples,) + All the datapoints what we'd like to predict (represents the y-axis). + intervals : array-like of shape (n_intervals,) + The intervals at which we'd like to get a good average value + method : Literal["average", "normal"], default="average" + The method that is used for smoothing, can be either `"average"` or `"normal"`. + span : float, default=1.0 + If the method is `"average"` then this is the span around the interval that is used to determine the average + `y`-value, if the method is `"normal"` the span becomes the value of sigma that is used for weighted averaging. + + Returns + ------- + np.ndarray of shape (n_intervals,) + An array of the same shape of `intervals` that represents the average `y`-values at those intervals. """ results = np.zeros(intervals.shape) for idx, interval in enumerate(intervals): @@ -70,20 +88,33 @@ def _mk_average(xs, ys, intervals, method="average", span=1, **kwargs): class IntervalEncoder(TransformerMixin, BaseEstimator): - """ - The interval encoder bends features in `X` with regards to`y`. - We take each column in X separately and smooth it towards `y` using - the strategy that is defined in `method`. - Note that this allows us to make certain features strictly monotonic - in your machine learning model if you follow this with an appropriate - model. - :param n_chunks: the number of cuts that makes the interval - :param method: the interpolation method used, must be in - ["average", "normal", "increasing", "decreasing"], default: "normal" - :param span: a hyperparameter for the interpolation method, if the - method is `normal` it resembles the width of the radial basis - function used to weigh the points. It is ignored if if the method is - "increasing" or "decreasing". + """The `IntervalEncoder` transformer bends features in `X` with regards to`y`. + + We take each column in `X` separately and smooth it towards `y` using the strategy that is defined in `method`. + + Note that this allows us to make certain features strictly monotonic in your machine learning model if you follow + this with an appropriate model. + + Parameters + ---------- + n_chunks : int, default=10 + The number of cuts that makes the interval. + span : float, default=1.0 + A hyperparameter for the interpolation method, if the method is `"normal"` it resembles the width of the radial + basis function used to weigh the points. It is ignored if the method is `"increasing"` or `"decreasing"`. + method : Literal["average", "normal", "increasing", "decreasing"], default="normal" + The interpolation method used, can be either `"average"`, `"normal"`, `"increasing"` or `"decreasing"`. + + Attributes + ---------- + quantiles_ : np.ndarray of shape (n_features, n_chunks) + The quantiles that are used to cut the interval. + heights_ : np.ndarray of shape (n_features, n_chunks) + The heights of the quantiles that are used to cut the interval. + n_features_in_ : int + Number of features seen during `fit`. + num_cols_ : int + Deprecated, please use `n_features_in_` instead. """ _ALLOWED_METHODS = ("average", "normal", "increasing", "decreasing") @@ -94,7 +125,27 @@ def __init__(self, n_chunks=10, span=1, method="normal"): self.n_chunks = n_chunks def fit(self, X, y): - """Fits the estimator""" + """Fit the `IntervalEncoder` transformer by computing interpolation quantiles for each column of `X`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self : IntervalEncoder + The fitted transformer. + + Raises + ------ + ValueError + - If `method` is not one of `"average"`, `"normal"`, `"increasing"` or `"decreasing"`. + - If `n_chunks` is not a positive integer. + - If `span` is not between 0 and 1. + """ if self.method not in self._ALLOWED_METHODS: raise ValueError( @@ -139,8 +190,22 @@ def fit(self, X, y): return self def transform(self, X): - """ - Transform each column such that it is bends smoothly towards y. + """Performs smoothing on the column(s) of `X` according to the quantile values computed during fitting. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the smoothing will be applied. + + Returns + ------- + X : np.ndarray of shape (n_samples, n_features) + `X` values with smoothed values. + + Raises + ------ + ValueError + If the number of columns from `X` differs from the number of columns when fitting. """ check_is_fitted(self, ["quantiles_", "heights_", "n_features_in_"]) X = check_array(X, estimator=self) diff --git a/sklego/preprocessing/outlier_remover.py b/sklego/preprocessing/outlier_remover.py index 8aba3b4a7..d9167c923 100644 --- a/sklego/preprocessing/outlier_remover.py +++ b/sklego/preprocessing/outlier_remover.py @@ -1,20 +1,26 @@ from sklearn import clone from sklearn.base import BaseEstimator -from sklearn.utils.validation import ( - check_is_fitted, - check_array, -) +from sklearn.utils.validation import check_array, check_is_fitted from sklego.common import TrainOnlyTransformerMixin class OutlierRemover(TrainOnlyTransformerMixin, BaseEstimator): - """ - Removes outliers (train-time only) using the supplied removal model. + """The `OutlierRemover` transformer removes outliers (train-time only) using the supplied removal model. The + removal model should implement `.fit()` and `.predict()` methods. - :param outlier_detector: must implement `fit` and `predict` methods - :param refit: If True, fits the estimator during pipeline.fit(). + Parameters + ---------- + outlier_detector : object + An outlier detector that implements `.fit()` and `.predict()` methods. + refit : bool, default=True + If True, fits the estimator during `pipeline.fit()`. If False, the estimator is not fitted during + `pipeline.fit()`. + Attributes + ---------- + estimator_ : object + The fitted outlier detector. """ def __init__(self, outlier_detector, refit=True): @@ -23,6 +29,20 @@ def __init__(self, outlier_detector, refit=True): self.estimator_ = None def fit(self, X, y=None): + """Fit the estimator on training data `X` and `y` by fitting the underlying outlier detector if `refit` is True. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,), default=None + Target values. + + Returns + ------- + self : OutlierRemover + The fitted transformer. + """ self.estimator_ = clone(self.outlier_detector) if self.refit: super().fit(X, y) @@ -30,6 +50,18 @@ def fit(self, X, y=None): return self def transform_train(self, X): + """Removes outliers from `X` using the fitted estimator. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the outliers will be removed. + + Returns + ------- + np.ndarray of shape (n_not_outliers, n_features) + The data with the outliers removed, where `n_not_outliers = n_samples - n_outliers`. + """ check_is_fitted(self, "estimator_") predictions = self.estimator_.predict(X) check_array(predictions, estimator=self.outlier_detector, ensure_2d=False) diff --git a/sklego/preprocessing/pandastransformers.py b/sklego/preprocessing/pandastransformers.py index 0438de7f8..3b51c85b2 100644 --- a/sklego/preprocessing/pandastransformers.py +++ b/sklego/preprocessing/pandastransformers.py @@ -6,68 +6,104 @@ class ColumnDropper(BaseEstimator, TransformerMixin): - """ - Allows dropping specific columns from a pandas DataFrame by name. Can be useful in a sklearn Pipeline. - - :param columns: column name ``str`` or list of column names to be selected - - .. note:: - Raises a ``TypeError`` if input provided is not a DataFrame - - Raises a ``ValueError`` if columns provided are not in the input DataFrame - - :Example: - - >>> # Selecting a single column from a pandas DataFrame - >>> import pandas as pd - >>> df = pd.DataFrame({ - ... 'name': ['Swen', 'Victor', 'Alex'], - ... 'length': [1.82, 1.85, 1.80], - ... 'shoesize': [42, 44, 45] - ... }) - >>> ColumnDropper(['name']).fit_transform(df) + """The `ColumnDropper` transformer allows dropping specific columns from a pandas DataFrame by name. + Can be useful in a sklearn Pipeline. + + Parameters + ---------- + columns : str | list[str] + Column name(s) to be selected. + + Attributes + ---------- + feature_names_ : list[str] + The names of the features to keep during transform. + + Examples + -------- + ```py + # Selecting a single column from a pandas DataFrame + import pandas as pd + from sklego.preprocessing import ColumnDropper + + df = pd.DataFrame({ + "name": ["Swen", "Victor", "Alex"], + "length": [1.82, 1.85, 1.80], + "shoesize": [42, 44, 45] + }) + ColumnDropper(["name"]).fit_transform(df) + ''' length shoesize 0 1.82 42 1 1.85 44 2 1.80 45 + ''' - >>> # Selecting multiple columns from a pandas DataFrame - >>> ColumnDropper(['length', 'shoesize']).fit_transform(df) + # Selecting multiple columns from a pandas DataFrame + ColumnDropper(["length", "shoesize"]).fit_transform(df) + ''' name 0 Swen 1 Victor 2 Alex - - - >>> # Selecting non-existent columns returns in a KeyError - >>> ColumnDropper(['weight']).fit_transform(df) - Traceback (most recent call last): - ... - KeyError: "['weight'] column(s) not in DataFrame" - - >>> # How to use the ColumnSelector in a sklearn Pipeline - >>> from sklearn.pipeline import Pipeline - >>> from sklearn.preprocessing import StandardScaler - >>> pipe = Pipeline([ - ... ('select', ColumnDropper(['name', 'shoesize'])), - ... ('scale', StandardScaler()), - ... ]) - >>> pipe.fit_transform(df) - array([[-0.16222142], - [ 1.29777137], - [-1.13554995]]) + ''' + + # Selecting non-existent columns returns in a KeyError + ColumnDropper(["weight"]).fit_transform(df) + # Traceback (most recent call last): + # ... + # KeyError: "['weight'] column(s) not in DataFrame" + + # How to use the ColumnSelector in a sklearn Pipeline + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + pipe = Pipeline([ + ("select", ColumnDropper(["name", "shoesize"])), + ("scale", StandardScaler()), + ]) + pipe.fit_transform(df) + # array([[-0.16222142], + # [ 1.29777137], + # [-1.13554995]]) + ``` + + !!! warning + + - Raises a `TypeError` if input provided is not a DataFrame. + - Raises a `ValueError` if columns provided are not in the input DataFrame. """ def __init__(self, columns: list): self.columns = columns def fit(self, X, y=None): - """ - Checks 1) if input is a DataFrame, and 2) if column names are in this DataFrame - - :param X: ``pd.DataFrame`` on which we apply the column selection - :param y: ``pd.Series`` labels for X. unused for column selection - :returns: ``ColumnSelector`` object. + """Fit the transformer by storing the column names to keep during `.transform()` step. + + Checks: + + 1. If input is a `pd.DataFrame` object + 2. If column names are in such DataFrame + + Parameters + ---------- + X : pd.DataFrame + The data on which we apply the column selection. + y : pd.Series, default=None + Ignored, present for compatibility. + + Returns + ------- + self : ColumnDropper + The fitted transformer. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. + KeyError + If one or more of the columns provided doesn't exist in the input DataFrame. + ValueError + If dropping the specified columns would result in an empty output DataFrame. """ self.columns_ = as_list(self.columns) self._check_X_for_type(X) @@ -77,10 +113,22 @@ def fit(self, X, y=None): return self def transform(self, X): - """Returns a pandas DataFrame with only the specified columns - - :param X: ``pd.DataFrame`` on which we apply the column selection - :returns: ``pd.DataFrame`` with only the selected columns + """Returns a pandas DataFrame with only the specified columns. + + Parameters + ---------- + X : pd.DataFrame + The data on which we apply the column selection. + + Returns + ------- + pd.DataFrame + The data with the specified columns dropped. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. """ check_is_fitted(self, ["feature_names_"]) self._check_X_for_type(X) @@ -89,14 +137,13 @@ def transform(self, X): return X def get_feature_names(self): + """Alias for `.feature_names_` attribute""" return self.feature_names_ def _check_column_length(self): """Check if all columns are dropped""" if len(self.feature_names_) == 0: - raise ValueError( - f"Dropping {self.columns_} would result in an empty output DataFrame" - ) + raise ValueError(f"Dropping {self.columns_} would result in an empty output DataFrame") def _check_column_names(self, X): """Check if one or more of the columns provided doesn't exist in the input DataFrame""" @@ -112,11 +159,30 @@ def _check_X_for_type(X): class PandasTypeSelector(BaseEstimator, TransformerMixin): - """ - Select columns in a pandas dataframe based on their dtype - - :param include: types to be included in the dataframe - :param exclude: types to be excluded in the dataframe + """The `PandasTypeSelector` transformer allows to select columns in a pandas DataFrame based on their type. + Can be useful in a sklearn Pipeline. + + It uses + [pandas.DataFrame.select_dtypes](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.select_dtypes.html) + method. + + Parameters + ---------- + include : scalar or list-like + Column type(s) to be selected + exclude : scalar or list-like + Column type(s) to be excluded from selection + + Attributes + ---------- + feature_names_ : list[str] + The names of the features to keep during transform. + X_dtypes_ : pd.Series + The dtypes of the columns in the input DataFrame. + + !!! warning + + Raises a `TypeError` if input provided is not a DataFrame. """ def __init__(self, include=None, exclude=None): @@ -124,16 +190,30 @@ def __init__(self, include=None, exclude=None): self.exclude = exclude def fit(self, X, y=None): - """ - Saves the column names for check during transform - :param X: pandas dataframe to select dtypes out of - :param y: not used in this class + """Fit the transformer by saving the column names to keep during transform. + + Parameters + ---------- + X : pd.DataFrame + The data on which we apply the column selection. + y : pd.Series, default=None + Ignored, present for compatibility. + + Returns + ------- + self : PandasTypeSelector + The fitted transformer. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. + ValueError + If provided type(s) results in empty dataframe. """ self._check_X_for_type(X) self.X_dtypes_ = X.dtypes - self.feature_names_ = list( - X.select_dtypes(include=self.include, exclude=self.exclude).columns - ) + self.feature_names_ = list(X.select_dtypes(include=self.include, exclude=self.exclude).columns) if len(self.feature_names_) == 0: raise ValueError("Provided type(s) results in empty dataframe") @@ -141,12 +221,28 @@ def fit(self, X, y=None): return self def get_feature_names(self, *args, **kwargs): + """Alias for `.feature_names_` attribute""" return self.feature_names_ def transform(self, X): - """ - Transforms pandas dataframe by (de)selecting columns based on their dtype - :param X: pandas dataframe to select dtypes for + """Returns a pandas DataFrame with columns (de)selected based on their dtype. + + Parameters + ---------- + X : pd.DataFrame + The data to select dtype for. + + Returns + ------- + pd.DataFrame + The data with the specified columns selected. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. + ValueError + If column dtypes were not equal during fit and transform. """ check_is_fitted(self, ["X_dtypes_", "feature_names_"]) @@ -174,55 +270,72 @@ def _check_X_for_type(X): class ColumnSelector(BaseEstimator, TransformerMixin): - """ - Allows selecting specific columns from a pandas DataFrame by name. Can be useful in a sklearn Pipeline. - - :param columns: column name ``str`` or list of column names to be selected - - .. note:: - Raises a ``TypeError`` if input provided is not a DataFrame - - Raises a ``ValueError`` if columns provided are not in the input DataFrame - - :Example: - - >>> # Selecting a single column from a pandas DataFrame - >>> import pandas as pd - >>> df = pd.DataFrame({ - ... 'name': ['Swen', 'Victor', 'Alex'], - ... 'length': [1.82, 1.85, 1.80], - ... 'shoesize': [42, 44, 45] - ... }) - >>> ColumnSelector(['length']).fit_transform(df) - length + """The `ColumnSelector` transformer allows selecting specific columns from a pandas DataFrame by name. + Can be useful in a sklearn Pipeline. + + Parameters + ---------- + columns : str | list[str] + Column name(s) to be selected. + + Attributes + ---------- + columns_ : list[str] + The names of the features to keep during transform. + + Examples + -------- + ```py + # Selecting a single column from a pandas DataFrame + import pandas as pd + from sklego.preprocessing import ColumnSelector + + df = pd.DataFrame({ + "name": ["Swen", "Victor", "Alex"], + "length": [1.82, 1.85, 1.80], + "shoesize": [42, 44, 45] + }) + ColumnSelector(["length"]).fit_transform(df) + ''' + length 0 1.82 1 1.85 2 1.80 + ''' - >>> # Selecting multiple columns from a pandas DataFrame - >>> ColumnSelector(['length', 'shoesize']).fit_transform(df) + # Selecting multiple columns from a pandas DataFrame + ColumnSelector(["length", "shoesize"]).fit_transform(df) + ''' length shoesize 0 1.82 42 1 1.85 44 2 1.80 45 - - >>> # Selecting non-existent columns returns in a KeyError - >>> ColumnSelector(['weight']).fit_transform(df) - Traceback (most recent call last): - ... - KeyError: "['weight'] column(s) not in DataFrame" - - >>> # How to use the ColumnSelector in a sklearn Pipeline - >>> from sklearn.pipeline import Pipeline - >>> from sklearn.preprocessing import StandardScaler - >>> pipe = Pipeline([ - ... ('select', ColumnSelector(['length'])), - ... ('scale', StandardScaler()), - ... ]) - >>> pipe.fit_transform(df) - array([[-0.16222142], - [ 1.29777137], - [-1.13554995]]) + ''' + + # Selecting non-existent columns returns in a KeyError + ColumnSelector(["weight"]).fit_transform(df) + # Traceback (most recent call last): + # ... + # KeyError: "['weight'] column(s) not in DataFrame" + + # How to use the ColumnSelector in a sklearn Pipeline + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import StandardScaler + pipe = Pipeline([ + ("select", ColumnSelector(["length"])), + ("scale", StandardScaler()), + ]) + pipe.fit_transform(df) + # array([[-0.16222142], + # [ 1.29777137], + # [-1.13554995]]) + ``` + + !!! warning + + Raises a `TypeError` if input provided is not a DataFrame. + + Raises a `ValueError` if columns provided are not in the input DataFrame. """ def __init__(self, columns: list): @@ -230,12 +343,33 @@ def __init__(self, columns: list): self.columns = columns def fit(self, X, y=None): - """ - Checks 1) if input is a DataFrame, and 2) if column names are in this DataFrame - - :param X: ``pd.DataFrame`` on which we apply the column selection - :param y: ``pd.Series`` labels for X. unused for column selection - :returns: ``ColumnSelector`` object. + """Fit the transformer by storing the column names to keep during transform. + + Checks: + + 1. If input is a `pd.DataFrame` object + 2. If column names are in such DataFrame + + Parameters + ---------- + X : pd.DataFrame + The data on which we apply the column selection. + y : pd.Series, default=None + Ignored, present for compatibility. + + Returns + ------- + self : ColumnSelector + The fitted transformer. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. + KeyError + If one or more of the columns provided doesn't exist in the input DataFrame. + ValueError + If dropping the specified columns would result in an empty output DataFrame. """ self.columns_ = as_list(self.columns) self._check_X_for_type(X) @@ -244,10 +378,22 @@ def fit(self, X, y=None): return self def transform(self, X): - """Returns a pandas DataFrame with only the specified columns - - :param X: ``pd.DataFrame`` on which we apply the column selection - :returns: ``pd.DataFrame`` with only the selected columns + """Returns a pandas DataFrame with only the specified columns. + + Parameters + ---------- + X : pd.DataFrame + The data on which we apply the column selection. + + Returns + ------- + pd.DataFrame + The data with the specified columns dropped. + + Raises + ------ + TypeError + If `X` is not a `pd.DataFrame` object. """ self._check_X_for_type(X) if self.columns: @@ -255,14 +401,13 @@ def transform(self, X): return X def get_feature_names(self): + """Alias for `.columns_` attribute""" return self.columns_ def _check_column_length(self): """Check if no column is selected""" if len(self.columns_) == 0: - raise ValueError( - "Expected columns to be at least of length 1, found length of 0 instead" - ) + raise ValueError("Expected columns to be at least of length 1, found length of 0 instead") def _check_column_names(self, X): """Check if one or more of the columns provided doesn't exist in the input DataFrame""" diff --git a/sklego/preprocessing/patsytransformer.py b/sklego/preprocessing/patsytransformer.py index d964737c6..6ab9161a4 100644 --- a/sklego/preprocessing/patsytransformer.py +++ b/sklego/preprocessing/patsytransformer.py @@ -17,13 +17,22 @@ "This object will be removed from the preprocessing submodule in version 0.8.0.", ) class PatsyTransformer(TransformerMixin, BaseEstimator): - """ - The patsy transformer offers a method to select the right columns - from a dataframe as well as a DSL for transformations. It is inspired - from R formulas. - This is can be useful as a first step in the pipeline. - :param formula: a patsy-compatible formula - :return_type: Either "matrix" or "dataframe", passed on to patsy + """The `PatsyTransformer` offers a method to select the right columns from a dataframe as well as a DSL for + transformations. + + It is inspired from R formulas. This is can be useful as a first step in the pipeline. + + Parameters + ---------- + formula : str + A patsy-compatible formula. + return_type : Literal["matrix", "dataframe"], default="matrix" + Either "matrix" or "dataframe", passed on to patsy. + + Attributes + ---------- + design_info_ : [patsy.DesignInfo](https://patsy.readthedocs.io/en/latest/API-reference.html#patsy.DesignInfo) + A DesignInfo object holds metadata about a design matrix. """ def __init__(self, formula, return_type="matrix"): @@ -31,7 +40,20 @@ def __init__(self, formula, return_type="matrix"): self.return_type = return_type def fit(self, X, y=None): - """Fits the estimator""" + """Fits the transformer on input data `X` by constructing a design matrix given the `formula`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to fit. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : PatsyTransformer + The fitted transformer. + """ X_ = patsy.dmatrix( self.formula, X, NA_action="raise", return_type=self.return_type ) @@ -48,12 +70,19 @@ def fit(self, X, y=None): return self def transform(self, X): - """ - Applies the formula to the matrix/dataframe X. + """Transform `X` by applying the fitted formula. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. Returns - - A patsy.DesignMatrix, if return_type="matrix" (the default) - - A pandas.DataFrame, if return_type="dataframe" + ------- + patsy.DesignMatrix | pd.DataFrame + + - DesignMatrix if return_type="matrix" (the default) + - pd.DataFrame if return_type="dataframe" """ check_is_fitted(self, "design_info_") try: diff --git a/sklego/preprocessing/projections.py b/sklego/preprocessing/projections.py index 8e5e3c181..1d2e7d9a1 100644 --- a/sklego/preprocessing/projections.py +++ b/sklego/preprocessing/projections.py @@ -8,19 +8,63 @@ class OrthogonalTransformer(BaseEstimator, TransformerMixin): - """ - Transform the columns of a dataframe or numpy array to a column orthogonal or orthonormal matrix. - Q, R such that X = Q*R, with Q orthogonal, from which follows Q = X*inv(R) - :param normalize: whether orthogonal matrix should be orthonormal as well + r"""The `OrthogonalTransformer` transforms the columns of a dataframe or numpy array to orthogonal (or + orthonormal if `normalize=True`) matrix. + + It learns matrices $Q, R$ such that $X = Q \cdot R$, with $Q$ orthogonal, from which follows $Q = X \cdot R^{-1}$ + + Parameters + ---------- + normalize : bool, default=False + Whether or not orthogonal matrix should be orthonormal as well. + + Attributes + ---------- + inv_R_ : array-like of shape (n_features, n_features) + The inverse of R of the QR decomposition of `X`. + normalization_vector_ : array-like of shape (n_features,) + The normalization terms to make the orthogonal matrix orthonormal. + + Examples + -------- + ``` + from sklearn.datasets import make_regression + from sklego.preprocessing import OrthogonalTransformer + + # Generate a synthetic dataset + X, y = make_regression(n_samples=100, n_features=3, noise=0.1, random_state=42) + + # Instantiate the transformer + transformer = OrthogonalTransformer(normalize=True) + + # Fit the pipeline with the training data + transformer.fit(X) + + # Transform the data using the fitted transformer + X_transformed = transformer.transform(X) + ``` """ def __init__(self, normalize=False): self.normalize = normalize def fit(self, X, y=None): - """ - Store the inverse of R of the QR decomposition of X, which can be used to calculate the orthogonal projection - of X. If normalization is required, also stores a vector with normalization terms + """Fit the transformer to the input data by calculating the inverse of R of the QR decomposition of `X`. + This can be used to calculate the orthogonal projection of `X`. + + If normalization is required, also stores a vector with normalization terms. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to fit. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : OrthogonalTransformer + The fitted transformer. """ X = check_array(X, estimator=self) @@ -39,7 +83,18 @@ def fit(self, X, y=None): return self def transform(self, X): - """Transforms X using the fitted inverse of R. Normalizes the result if required""" + """Transforms `X` using the fitted inverse of R. Normalizes the result if required. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + + Returns + ------- + array-like of shape (n_samples, n_features) + The transformed data. + """ if self.normalize: check_is_fitted(self, ["inv_R_", "normalization_vector_"]) else: @@ -59,28 +114,47 @@ def vector_projection(vec, unto): class InformationFilter(BaseEstimator, TransformerMixin): - """ - The `InformationFilter` uses a variant of the gram smidt process - to filter information out of the dataset. This can be useful if you - want to filter information out of a dataset because of fairness. - To explain how it works: given a training matrix :math:`X` that contains - columns :math:`x_1, ..., x_k`. If we assume columns :math:`x_1` and :math:`x_2` - to be the sensitive columns then the information-filter will - remove information by applying these transformations; - .. math:: - \\begin{split} - v_1 & = x_1 \\\\ - v_2 & = x_2 - \\frac{x_2 v_1}{v_1 v_1}\\\\ - v_3 & = x_3 - \\frac{x_k v_1}{v_1 v_1} - \\frac{x_2 v_2}{v_2 v_2}\\\\ - ... \\\\ - v_k & = x_k - \\frac{x_k v_1}{v_1 v_1} - \\frac{x_2 v_2}{v_2 v_2} - \\end{split} - Concatenating our vectors (but removing the sensitive ones) gives us - a new training matrix :math:`X_{fair} = [v_3, ..., v_k]`. - :param columns: the columns to filter out this can be a sequence of either int - (in the case of numpy) or string (in the case of pandas). - :param alpha: parameter to control how much to filter, for alpha=1 we filter out - all information while for alpha=0 we don't apply any. + r"""The `InformationFilter` transformer uses a variant of the + [Gram-Schmidt process](https://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process) to filter information out of the + dataset. + + This can be useful if you want to filter information out of a dataset because of fairness. + + To explain how it works: given a training matrix $X$ that contains columns $x_1, ..., x_k$. + If we assume columns $x_1$ and $x_2$ to be the _sensitive_ columns then the information-filter will remove + information by applying these transformations: + + $$\begin{split} + v_1 & = x_1 \\ + v_2 & = x_2 - \frac{x_2 v_1}{v_1 v_1} \\ + v_3 & = x_3 - \frac{x_k v_1}{v_1 v_1} - \frac{x_2 v_2}{v_2 v_2} \\ + & ... \\ + v_k & = x_k - \frac{x_k v_1}{v_1 v_1} - \frac{x_2 v_2}{v_2 v_2} + \end{split}$$ + + Concatenating our vectors (but removing the sensitive ones) gives us a new training matrix + + $$X_{fair} = [v_3, ..., v_k]$$ + + Parameters + ---------- + columns : int | str | Sequence[int] | Sequence[str] + The columns to filter out. This can be a sequence of either int (in the case of numpy) or string + (in the case of pandas). + alpha : float, default=1.0 + Parameter to control how much to filter: + + - `alpha=1` we filter out all information. + - `alpha=0` we don't apply any filtering. + + Should be between 0 and 1. + + Attributes + ---------- + projection_ : array-like of shape (n_features, n_features) + The projection matrix that can be used to filter information out of a dataset. + col_ids_ : List[int] of length `len(columns)` + The list of column ids of the sensitive columns. """ def __init__(self, columns, alpha=1): @@ -88,31 +162,28 @@ def __init__(self, columns, alpha=1): self.alpha = alpha def _check_coltype(self, X): + """Check if the `columns` type(s) are compatible with `X` type.""" for col in as_list(self.columns): if isinstance(col, str): if isinstance(X, np.ndarray): - raise ValueError( - f"column {col} is a string but datatype receive is numpy." - ) + raise ValueError(f"column {col} is a string but datatype receive is numpy.") if isinstance(X, pd.DataFrame): if col not in X.columns: raise ValueError(f"column {col} is not in {X.columns}") if isinstance(col, int): if col not in range(np.atleast_2d(np.array(X)).shape[1]): - raise ValueError( - f"column {col} is out of bounds for input shape {X.shape}" - ) + raise ValueError(f"column {col} is out of bounds for input shape {X.shape}") def _col_idx(self, X, name): + """Get the column index of a column name.""" if isinstance(name, str): if isinstance(X, np.ndarray): - raise ValueError( - "You cannot have a column of type string on a numpy input matrix." - ) + raise ValueError("You cannot have a column of type string on a numpy input matrix.") return {name: i for i, name in enumerate(X.columns)}[name] return name def _make_v_vectors(self, X, col_ids): + """Make the v vectors that we will use to filter out information.""" vs = np.zeros((X.shape[0], len(col_ids))) for i, c in enumerate(col_ids): vs[:, i] = X[:, col_ids[i]] @@ -121,12 +192,28 @@ def _make_v_vectors(self, X, col_ids): return vs def fit(self, X, y=None): - """Learn the projection required to make the dataset orthogonal to sensitive columns.""" + """Fit the transformer by learning the projection required to make the dataset orthogonal to sensitive + columns. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to fit. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : InformationFilter + The fitted transformer. + + Raises + ------ + ValueError + If `columns` type(s) are incompatible with input data `X` type. + """ self._check_coltype(X) - self.col_ids_ = [ - v if isinstance(v, int) else self._col_idx(X, v) - for v in as_list(self.columns) - ] + self.col_ids_ = [v if isinstance(v, int) else self._col_idx(X, v) for v in as_list(self.columns)] X = check_array(X, estimator=self) X_fair = X.copy() v_vectors = self._make_v_vectors(X, self.col_ids_) @@ -140,7 +227,23 @@ def fit(self, X, y=None): return self def transform(self, X): - """Transforms X by applying the information filter.""" + """Transforms `X` by applying the information filter. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + + Returns + ------- + array-like of shape (n_samples, n_features) + The transformed data. + + Raises + ------ + ValueError + If `columns` type(s) are incompatible with input data `X` type. + """ check_is_fitted(self, ["projection_", "col_ids_"]) self._check_coltype(X) X = check_array(X, estimator=self) @@ -148,6 +251,4 @@ def transform(self, X): X_fair = X @ self.projection_ X_removed = np.delete(X_fair, self.col_ids_, axis=1) X_orig = np.delete(X, self.col_ids_, axis=1) - return self.alpha * np.atleast_2d(X_removed) + (1 - self.alpha) * np.atleast_2d( - X_orig - ) + return self.alpha * np.atleast_2d(X_removed) + (1 - self.alpha) * np.atleast_2d(X_orig) diff --git a/sklego/preprocessing/randomadder.py b/sklego/preprocessing/randomadder.py index 472c2b4f2..8d7435b36 100644 --- a/sklego/preprocessing/randomadder.py +++ b/sklego/preprocessing/randomadder.py @@ -2,17 +2,72 @@ from sklearn.base import BaseEstimator from sklearn.utils import check_array, check_X_y -from sklearn.utils.validation import FLOAT_DTYPES, check_random_state, check_is_fitted +from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_random_state from sklego.common import TrainOnlyTransformerMixin class RandomAdder(TrainOnlyTransformerMixin, BaseEstimator): + """The `RandomAdder` transformer adds random noise to the input data. + + This class is designed to be used during the training phase and not for transforming test data. + Noise added is sampled from a normal distribution with mean 0 and standard deviation `noise`. + + Parameters + ---------- + noise : float, default=1.0 + The standard deviation of the normal distribution from which the noise is sampled. + random_state : int | None + The seed used by the random number generator. + + Attributes + ---------- + n_features_in_ : int + Number of features seen during `fit`. + dim_ : int + Deprecated, please use `n_features_in_` instead. + + Examples + -------- + ```py + from sklearn.pipeline import Pipeline + from sklearn.linear_model import LinearRegression + from sklego.preprocessing import RandomAdder + + # Create a pipeline with the RandomAdder and a LinearRegression model + pipeline = Pipeline([ + ('random_adder', RandomAdder(noise=0.5, random_state=42)), + ('linear_regression', LinearRegression()) + ]) + + # Fit the pipeline with training data + pipeline.fit(X_train, y_train) + + # Use the fitted pipeline to make predictions + y_pred = pipeline.predict(X_test) + ``` + """ + def __init__(self, noise=1, random_state=None): self.noise = noise self.random_state = random_state def fit(self, X, y): + """Fit the transformer on training data `X` and `y` by checking the input data and record the number of + input features. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self : RandomAdder + The fitted transformer. + """ super().fit(X, y) X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES) self.n_features_in_ = X.shape[1] @@ -20,6 +75,18 @@ def fit(self, X, y): return self def transform_train(self, X): + r"""Transform training data by adding random noise sampled from $N(0, \text{noise})$. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data for which the noise will be added. + + Returns + ------- + np.ndarray of shape (n_samples, n_features) + The data with the noise added. + """ rs = check_random_state(self.random_state) check_is_fitted(self, ["n_features_in_"]) diff --git a/sklego/preprocessing/repeatingbasis.py b/sklego/preprocessing/repeatingbasis.py index 8950fd22f..5f83be109 100644 --- a/sklego/preprocessing/repeatingbasis.py +++ b/sklego/preprocessing/repeatingbasis.py @@ -6,42 +6,44 @@ class RepeatingBasisFunction(TransformerMixin, BaseEstimator): - """ - This is a transformer for features with some form of circularity. - E.g. for days of the week you might face the problem that, conceptually, day 7 is as - close to day 6 as it is to day 1. While numerically their distance is different. - This transformer remedies that problem. - The transformer selects a column and transforms it with a given number of repeating - (radial) basis functions. Radial basis functions are bell-curve shaped functions - which take the original data as input. The basis functions are equally spaced over - the input range. The key feature of repeating basis functions is that they are - continuous when moving from the max to the min of the input range. As a result these - repeating basis functions can capture how close each datapoint is to the center of - each repeating basis function, even when the input data has a circular nature. - - :type column: int or list, default=0 - :param column: Indexes the data on its second axis. Integers are interpreted as - positional columns, while strings can reference DataFrame columns by name. - - :type remainder: {'drop', 'passthrough'}, default="drop" - :param remainder: By default, only the specified column is transformed, and the - non-specified columns are dropped. (default of ``'drop'``). By specifying - ``remainder='passthrough'``, all remaining columns will be automatically passed - through. This subset of columns is concatenated with the output of the transformer. - - :type n_periods: int, default=12 - :param n_periods: number of basis functions to create, i.e., the number of columns that - will exit the transformer. - - :type input_range: tuple or None, default=None - :param input_range: the values at which the data repeats itself. For example, for days of - the week this is (1,7). If input_range=None it is inferred from the training data. - - :type width: float, default=1. - :param width: determines the width of the radial basis functions. + """The `RepeatingBasisFunction` transformer is designed to be used when the input data has a circular nature. + + For example, for days of the week you might face the problem that, conceptually, day 7 is as close to day 6 as it is + to day 1. While numerically their distance is different. + + This transformer remedies that problem. The transformer selects a column and transforms it with a given number of + repeating (radial) basis functions. + + Radial basis functions are bell-curve shaped functions which take the original data as input. The basis functions + are equally spaced over the input range. The key feature of repeating basis functions is that they are continuous + when moving from the max to the min of the input range. As a result these repeating basis functions can capture how + close each datapoint is to the center of each repeating basis function, even when the input data has a circular + nature. + + Parameters + ---------- + column : int | str, default=0 + Index or column name of the data to transform. Integers are interpreted as positional columns, while + strings can reference DataFrame columns by name. + remainder : Literal["drop", "passthrough"], default="drop" + By default, only the specified column is transformed, and the non-specified columns are dropped. + By specifying `remainder="passthrough"`, all remaining columns will be automatically passed through. + This subset of columns is concatenated with the output of the transformer. + n_periods : int, default=12 + Number of basis functions to create, i.e., the number of columns that will exit the transformer. + input_range : Tuple[float, float] | List[float] | None, default=None + The values at which the data repeats itself. For example, for days of the week this is (1,7). + If `input_range=None` it is inferred from the training data. + width : float, default=1.0. + Determines the width of the radial basis functions. + + Attributes + ---------- + pipeline_ : ColumnTransformer + Fitted `ColumnTransformer` object used to transform data with repeating basis functions. """ - def __init__(self, column=0, remainder="drop", n_periods=12, input_range=None, width=1.): + def __init__(self, column=0, remainder="drop", n_periods=12, input_range=None, width=1.0): self.column = column self.remainder = remainder self.n_periods = n_periods @@ -49,12 +51,29 @@ def __init__(self, column=0, remainder="drop", n_periods=12, input_range=None, w self.width = width def fit(self, X, y=None): + """Fit `RepeatingBasisFunction` transformer on input data `X`. + It uses `sklearn.compose.ColumnTransformer`. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data used to compute the quantiles for capping. + y : array-like of shape (n_samples,), default=None + Ignored, present for compatibility. + + Returns + ------- + self : RepeatingBasisFunction + The fitted transformer. + """ self.pipeline_ = ColumnTransformer( [ ( "repeatingbasis", _RepeatingBasisFunction( - n_periods=self.n_periods, input_range=self.input_range, width=self.width + n_periods=self.n_periods, + input_range=self.input_range, + width=self.width, ), [self.column], ) @@ -67,17 +86,67 @@ def fit(self, X, y=None): return self def transform(self, X): + """Transform input data `X` with fitted `RepeatingBasisFunction` transformer. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + + Returns + ------- + X_transformed : array-like of shape (n_samples, n_periods) + Transformed data. + """ check_is_fitted(self, ["pipeline_"]) return self.pipeline_.transform(X) class _RepeatingBasisFunction(TransformerMixin, BaseEstimator): - def __init__(self, n_periods: int = 12, input_range=None, width: float = 1.): + """Transformer for generating repeating basis functions. + + This transformer generates a set of repeating basis functions for a given input data. Each basis function is + defined by its center, and the width of the functions is adjusted based on the number of periods. It is + particularly useful in applications where periodic patterns need to be captured. + + Parameters + ---------- + n_periods : int, default=12 + The number of repeating periods or basis functions to generate. + input_range : Tuple[float, float] | List[float] | None, default=None + The values at which the data repeats itself. For example, for days of the week this is (1,7). + If `input_range=None` it is inferred from the training data. + width : float, default=1.0 + The width of the basis functions. This parameter controls how narrow or wide the basis functions are. + + Attributes + ---------- + bases_ : numpy.ndarray of shape (n_periods,) + The centers of the repeating basis functions. + width_ : float + The adjusted width of the basis functions based on the number of periods and the provided width. + """ + + def __init__(self, n_periods: int = 12, input_range=None, width: float = 1.0): self.n_periods = n_periods self.input_range = input_range self.width = width def fit(self, X, y=None): + """Fit the transformer to the input data and compute the basis functions. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input data used to compute the basis functions. + y : array-like of shape (n_samples), default=None + Ignored, present for compatibility. + + Returns + ------- + self : _RepeatingBasisFunction + The fitted transformer. + """ X = check_array(X, estimator=self) # find min and max for standardization if not given explicitly @@ -93,6 +162,23 @@ def fit(self, X, y=None): return self def transform(self, X): + """Transform the input data into features based on the repeating basis functions. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input data to be transformed using the basis functions. + + Returns + ------- + array-like of shape (n_samples, n_periods) + The transformed data with features generated from the basis functions. + + Raises + ------ + ValueError + If X has more than one column, as this transformer only accepts one feature as input. + """ X = check_array(X, estimator=self, ensure_2d=True) check_is_fitted(self, ["bases_", "width_"]) # This transformer only accepts one feature as input @@ -108,24 +194,59 @@ def transform(self, X): return self._rbf(base_distances) def _array_base_distance(self, arr: np.ndarray, base: float) -> np.ndarray: - """Calculates the distances between all array values and the base, - where 0 and 1 are assumed to be at the same position""" + """Calculate the distances between all array values and the base, where 0 and 1 are assumed to be at the same + positions + + Parameters + ---------- + arr : np.ndarray, shape (n_samples,) + The input array for which distances to the base are calculated. + base : float + The base value to which distances are calculated. + + Returns + ------- + np.ndarray, shape (n_samples,) + An array of distances between the values in `arr` and the `base`, with consideration of 0 and 1 as + equivalent positions. + """ abs_diff_0 = np.abs(arr - base) abs_diff_1 = 1 - abs_diff_0 - concat = np.concatenate( - (abs_diff_0.reshape(-1, 1), abs_diff_1.reshape(-1, 1)), axis=1 - ) + concat = np.concatenate((abs_diff_0.reshape(-1, 1), abs_diff_1.reshape(-1, 1)), axis=1) final = concat.min(axis=1) return final def _array_bases_distances(self, array, bases): - """Calculates the distances between all combinations of array and bases values""" + """Calculate distances between all combinations of array and bases values. + + Parameters + ---------- + array : np.ndarray, shape (n_samples,) + The input array for which distances to the bases are calculated. + bases : np.ndarray, shape (n_bases,) + The bases values to which distances are calculated. + + Returns + ------- + np.ndarray, shape (n_samples, n_bases) + An array of distances between the elements of 'array' and the specified 'bases'. + """ array = array.reshape(-1, 1) bases = bases.reshape(1, -1) - return np.apply_along_axis( - lambda b: self._array_base_distance(array, base=b), axis=0, arr=bases - ) + return np.apply_along_axis(lambda b: self._array_base_distance(array, base=b), axis=0, arr=bases) def _rbf(self, arr): + """Apply the Radial Basis Function (RBF) to the input array. + + Parameters + ---------- + arr : np.ndarray + The input array to which the RBF is applied. + + Returns + ------- + np.ndarray + An array with the RBF applied to the input array. + """ return np.exp(-((arr / self.width_) ** 2)) diff --git a/sklego/testing.py b/sklego/testing.py index 0f0dd0911..7f1663081 100644 --- a/sklego/testing.py +++ b/sklego/testing.py @@ -1,8 +1,8 @@ import itertools as it from copy import copy -from sklearn.utils._testing import ignore_warnings from sklearn.datasets import make_classification, make_regression +from sklearn.utils._testing import ignore_warnings @ignore_warnings(category=(DeprecationWarning, FutureWarning)) From 4ae226e15846b23862d88230c1adbd1a6ce3a145 Mon Sep 17 00:00:00 2001 From: Marco Edward Gorelli Date: Wed, 15 Nov 2023 10:17:58 +0000 Subject: [PATCH 2/5] fix HASHERS -> _HASHERS typo (#598) --- sklego/common.py | 4 ++-- tests/test_common/test_transformerselectormixin.py | 9 +++++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/sklego/common.py b/sklego/common.py index 2f109a5b1..a99a51146 100644 --- a/sklego/common.py +++ b/sklego/common.py @@ -90,7 +90,7 @@ def fit(self, X, y=None): @staticmethod def _hash(X): - """Calculate a hash of X based on its type. Hashers are defined in TrainOnlyMixin.HASHERS. + """Calculate a hash of X based on its type. Hashers are defined in TrainOnlyMixin._HASHERS. Supported types are: @@ -118,7 +118,7 @@ def _hash(X): except KeyError: raise ValueError( f"Unknown datatype {type(X)}, " - f"`TrainOnlyTransformerMixin` only supports {TrainOnlyTransformerMixin.HASHERS.keys()}" + f"`TrainOnlyTransformerMixin` only supports: {set(TrainOnlyTransformerMixin._HASHERS.keys())}" ) else: return hasher(X) diff --git a/tests/test_common/test_transformerselectormixin.py b/tests/test_common/test_transformerselectormixin.py index ea9855968..e25da4c8b 100644 --- a/tests/test_common/test_transformerselectormixin.py +++ b/tests/test_common/test_transformerselectormixin.py @@ -2,6 +2,7 @@ import numpy as np import pandas as pd +import pytest from sklearn.base import BaseEstimator from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline @@ -39,6 +40,14 @@ def test_hash_pandas(): assert len(hashes) == len(set(hashes)) +def test_hash_invalid(): + """Tests whether the hash function raises on non-supported class""" + with pytest.raises( + ValueError, + match="Unknown datatype , `TrainOnlyTransformerMixin` only " + "supports", + ): + TrainOnlyTransformerMixin._hash({'a': [1, 1, 2], 'b': [4, 5, 6]}) def test_bare_trainonlytransformer(random_xy_dataset_regr): """Tests whether the trainonlytransformer will only transform train when used directly""" From 2831264685fc06898fcccc03077eeba5c4990ad5 Mon Sep 17 00:00:00 2001 From: Francesco Bruzzesi <42817048+FBruzzesi@users.noreply.github.com> Date: Wed, 29 Nov 2023 17:23:39 +0100 Subject: [PATCH 3/5] Docs via mkdocs (#589) * mkdocs site * merge with main and update installation * feedback adjustment * rstudio link fix * fix Docs can build step * ignore validation --------- Co-authored-by: vincent d warmerdam --- .github/workflows/dependencies.yml | 8 +- .gitignore | 1 - Makefile | 5 +- doc/Makefile | 19 - doc/_static/css/custom.css | 16 - doc/_static/decay1.png | Bin 24133 -> 0 bytes doc/_static/decay2.png | Bin 35712 -> 0 bytes doc/_static/grouped-chick-1.png | 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docs/user-guide/linear-models.md create mode 100644 docs/user-guide/meta-models.md create mode 100644 docs/user-guide/mixture-methods.md create mode 100644 docs/user-guide/naive-bayes.md create mode 100644 docs/user-guide/outliers.md create mode 100644 docs/user-guide/pandas-pipelines.md create mode 100644 docs/user-guide/preprocessing.md delete mode 100644 mkdocs/index.md diff --git a/.github/workflows/dependencies.yml b/.github/workflows/dependencies.yml index c73485a21..ac5de6d9b 100644 --- a/.github/workflows/dependencies.yml +++ b/.github/workflows/dependencies.yml @@ -28,23 +28,23 @@ jobs: - name: Run Checks run: | python tests/scripts/check_pip.py missing cvxpy - python tests/scripts/check_pip.py contains scikit-learn + python tests/scripts/check_pip.py installed scikit-learn python tests/scripts/import_all.py - name: Install cvxpy run: | python -m pip install -e ".[cvxpy]" - name: Run Checks run: | - python tests/scripts/check_pip.py contains cvxpy scikit-learn + python tests/scripts/check_pip.py installed cvxpy scikit-learn python tests/scripts/import_all.py - name: Install All run: | python -m pip install -e ".[all]" - name: Run Checks run: | - python tests/scripts/check_pip.py contains cvxpy scikit-learn + python tests/scripts/check_pip.py installed cvxpy formulaic patsy scikit-learn umap-learn - name: Docs can Build run: | sudo apt-get update && sudo apt-get install pandoc python -m pip install -e ".[docs]" - sphinx-build doc docs + mkdocs build diff --git a/.gitignore b/.gitignore index 1bcb3d7b5..8da68c282 100644 --- a/.gitignore +++ b/.gitignore @@ -117,7 +117,6 @@ dmypy.json # editor .vscode .idea -docs/ .DS_Store # Local Netlify folder diff --git a/Makefile b/Makefile index 7c7d4b16b..38fa056d7 100644 --- a/Makefile +++ b/Makefile @@ -24,8 +24,8 @@ precommit: pre-commit run docs: - rm -rf doc/.ipynb_checkpoints - sphinx-build -a -E doc docs + pip install -e ".[docs]" + mkdocs serve docs-deploy: docs netlify deploy --dir=docs --prod @@ -34,7 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