Remove deprecated code (#626)

* remove deprecated but patsy * patsy removal and formulaic docs * docs * import fix
koaning · Mar 15, 2024 · 47c29a5 · 47c29a5
1 parent b7e9f77
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diff --git a/.github/workflows/dependencies.yml b/.github/workflows/dependencies.yml
@@ -39,7 +39,7 @@ jobs:
         python -m pip install -e ".[all]"
     - name: Run Checks
       run: |
-        python tests/scripts/check_pip.py installed cvxpy formulaic patsy scikit-learn umap-learn
+        python tests/scripts/check_pip.py installed cvxpy formulaic scikit-learn umap-learn
     - name: Docs can Build
       run: |
         sudo apt-get update && sudo apt-get install pandoc

diff --git a/docs/_scripts/preprocessing.py b/docs/_scripts/preprocessing.py
@@ -118,12 +118,12 @@
 # --8<-- [end:column-capper-inf]
 
 
-######################################## Patsy ###########################################
+######################################## Formulaic #######################################
 ##########################################################################################
 
-# --8<-- [start:patsy-1]
+# --8<-- [start:formulaic-1]
 import pandas as pd
-from sklego.preprocessing import PatsyTransformer
+from sklego.preprocessing import FormulaicTransformer
 
 df = pd.DataFrame({
     "a": [1, 2, 3, 4, 5],
@@ -132,15 +132,26 @@
 })
 X, y = df[["a", "b"]], df[["y"]].to_numpy()
 
-pt = PatsyTransformer("a + np.log(a) + b")
-pt.fit(X, y).transform(X)
-# --8<-- [end:patsy-1]
+formulaic_transformer = FormulaicTransformer(
+    formula="a + np.log(a) + b",
+    return_type="pandas"
+)
+formulaic_transformer.fit(X, y).transform(X)
+# --8<-- [end:formulaic-1]
+
+with open(_static_path / "formulaic-1.md", "w") as f:
+    f.write(formulaic_transformer.fit(X, y).transform(X).head().to_markdown(index=False))
 
-# --8<-- [start:patsy-2]
-pt = PatsyTransformer("a + np.log(a) + b - 1")
-pt.fit(X, y).transform(X)
-# --8<-- [end:patsy-2]
+# --8<-- [start:formulaic-2]
+formulaic_transformer = FormulaicTransformer(
+    formula="a + np.log(a) + b - 1",
+    return_type="pandas"
+)
+formulaic_transformer.fit(X, y).transform(X)
+# --8<-- [end:formulaic-2]
 
+with open(_static_path / "formulaic-2.md", "w") as f:
+    f.write(formulaic_transformer.fit(X, y).transform(X).head().to_markdown(index=False))
 
 ######################################## RBF ###########################################
 ##########################################################################################

diff --git a/docs/_static/preprocessing/formulaic-1.md b/docs/_static/preprocessing/formulaic-1.md
@@ -0,0 +1,7 @@
+|   Intercept |   a |   np.log(a) |   b[T.no] |   b[T.yes] |
+|------------:|----:|------------:|----------:|-----------:|
+|           1 |   1 |    0        |         0 |          1 |
+|           1 |   2 |    0.693147 |         0 |          1 |
+|           1 |   3 |    1.09861  |         1 |          0 |
+|           1 |   4 |    1.38629  |         0 |          0 |
+|           1 |   5 |    1.60944  |         0 |          1 |
diff --git a/docs/_static/preprocessing/formulaic-2.md b/docs/_static/preprocessing/formulaic-2.md
@@ -0,0 +1,7 @@
+|   a |   np.log(a) |   b[T.maybe] |   b[T.no] |   b[T.yes] |
+|----:|------------:|-------------:|----------:|-----------:|
+|   1 |    0        |            0 |         0 |          1 |
+|   2 |    0.693147 |            0 |         0 |          1 |
+|   3 |    1.09861  |            0 |         1 |          0 |
+|   4 |    1.38629  |            1 |         0 |          0 |
+|   5 |    1.60944  |            0 |         0 |          1 |
diff --git a/docs/_static/preprocessing/interval-encoder-1.png b/docs/_static/preprocessing/interval-encoder-1.png
diff --git a/docs/_static/preprocessing/interval-encoder-2.png b/docs/_static/preprocessing/interval-encoder-2.png
diff --git a/docs/_static/preprocessing/interval-encoder-3.png b/docs/_static/preprocessing/interval-encoder-3.png
diff --git a/docs/_static/preprocessing/monotonic-2.png b/docs/_static/preprocessing/monotonic-2.png
diff --git a/docs/_static/preprocessing/monotonic-3.png b/docs/_static/preprocessing/monotonic-3.png
diff --git a/docs/_static/preprocessing/rbf-data.png b/docs/_static/preprocessing/rbf-data.png
diff --git a/docs/_static/preprocessing/rbf-plot.png b/docs/_static/preprocessing/rbf-plot.png
diff --git a/docs/_static/preprocessing/rbf-regr.png b/docs/_static/preprocessing/rbf-regr.png
diff --git a/docs/api/features-selection.md b/docs/api/features-selection.md
diff --git a/docs/api/preprocessing.md b/docs/api/preprocessing.md
@@ -35,22 +35,22 @@
         show_root_full_path: true
         show_root_heading: true
 
-:::sklego.preprocessing.projections.OrthogonalTransformer
+:::sklego.preprocessing.formulaictransformer.FormulaicTransformer
     options:
         show_root_full_path: true
         show_root_heading: true
 
-:::sklego.preprocessing.outlier_remover.OutlierRemover
+:::sklego.preprocessing.projections.OrthogonalTransformer
     options:
         show_root_full_path: true
         show_root_heading: true
 
-:::sklego.preprocessing.pandastransformers.PandasTypeSelector
+:::sklego.preprocessing.outlier_remover.OutlierRemover
     options:
         show_root_full_path: true
         show_root_heading: true
 
-:::sklego.preprocessing.patsytransformer.PatsyTransformer
+:::sklego.preprocessing.pandastransformers.PandasTypeSelector
     options:
         show_root_full_path: true
         show_root_heading: true

diff --git a/docs/installation.md b/docs/installation.md
@@ -37,14 +37,13 @@ Install **scikit-lego**:
 
 Some functionality can only be used if certain dependencies are installed. This can be done by specifying the extra dependencies in square brackets after the package name.
 
-Currently supported extras are [**cvxpy**][cvxpy], [**formulaic**][formulaic], [**patsy**][patsy] and [**umap**][umap]. You can specify these as follows:
+Currently supported extras are [**cvxpy**][cvxpy], [**formulaic**][formulaic] and [**umap**][umap]. You can specify these as follows:
 
 === "pip"
 
     ```bash
     python -m pip install scikit-lego"[cvxpy]"
     python -m pip install scikit-lego"[formulaic]"
-    python -m pip install scikit-lego"[patsy]"
     python -m pip install scikit-lego"[umap]"
     python -m pip install scikit-lego"[all]"
     ```
@@ -57,12 +56,10 @@ Currently supported extras are [**cvxpy**][cvxpy], [**formulaic**][formulaic], [
 
     python -m pip install ".[cvxpy]"
     python -m pip install ."[formulaic]"
-    python -m pip install ."[patsy]"
     python -m pip install ."[umap]"
     python -m pip install ".[all]"
     ```
 
 [cvxpy]: https://www.cvxpy.org/
 [formulaic]: https://matthewwardrop.github.io/formulaic/
-[patsy]: https://patsy.readthedocs.io/en/latest/
 [umap]: https://umap-learn.readthedocs.io/en/latest/index.html
diff --git a/docs/user-guide/preprocessing.md b/docs/user-guide/preprocessing.md
@@ -114,60 +114,35 @@ Let's demonstrate how [`ColumnCapper`][column-capper-api] works in a few example
         [0.10029693, 0.89859006]])
     ```
 
-## Patsy Formulas
+## Formulaic (Wilkinson formulas)
 
 If you're used to the statistical programming language R you might have seen a formula object before. This is an object that represents a shorthand way to design variables used in a statistical model.
 
-The [patsy][patsy-docs] python project took this idea and made it available for python. From sklego we've made a wrapper, called [`PatsyTransformer`][patsy-api], such that you can also use these in your pipelines.
+The [formulaic][formulaic-docs] python project took this idea and made it available for python. From sklego we've made a wrapper, called [`FormulaicTransformer`][formulaic-api], such that you can also use these in your pipelines.
 
 ```py
---8<-- "docs/_scripts/preprocessing.py:patsy-1"
+--8<-- "docs/_scripts/preprocessing.py:formulaic-1"
 ```
 
-```console
-DesignMatrix with shape (5, 5)
-  Intercept  b[T.no]  b[T.yes]  a  np.log(a)
-          1        0         1  1    0.00000
-          1        0         1  2    0.69315
-          1        1         0  3    1.09861
-          1        0         0  4    1.38629
-          1        0         1  5    1.60944
-  Terms:
-    'Intercept' (column 0)
-    'b' (columns 1:3)
-    'a' (column 3)
-    'np.log(a)' (column 4)
-```
+--8<-- "docs/_static/preprocessing/formulaic-1.md"
 
 You might notice that the first column contains the constant array equal to one. You might also expect 3 dummy variable columns instead of 2.
 
-This is because the design matrix from patsy attempts to keep the columns in the matrix linearly independent of each other.
+This is because the design matrix from formulaic attempts to keep the columns in the matrix linearly independent of each other.
 
 If this is not something you'd want to create you can choose to omit
 it by indicating "-1" in the formula.
 
 ```py
---8<-- "docs/_scripts/preprocessing.py:patsy-2"
+--8<-- "docs/_scripts/preprocessing.py:formulaic-2"
 ```
 
-```console
-DesignMatrix with shape (5, 5)
-  b[maybe]  b[no]  b[yes]  a  np.log(a)
-         0      0       1  1    0.00000
-         0      0       1  2    0.69315
-         0      1       0  3    1.09861
-         1      0       0  4    1.38629
-         0      0       1  5    1.60944
-  Terms:
-    'b' (columns 0:3)
-    'a' (column 3)
-    'np.log(a)' (column 4)
-```
+--8<-- "docs/_static/preprocessing/formulaic-2.md"
 
-You'll notice that now the constant array is gone and it is replaced with a dummy array. Again this is now possible because patsy wants to guarantee that each column in this matrix is linearly independent of each other.
+You'll notice that now the constant array is gone and it is replaced with a dummy array. Again this is now possible because formulaic wants to guarantee that each column in this matrix is linearly independent of each other.
 
 The formula syntax is pretty powerful, if you'd like to learn we refer you
-to [formulas][patsy-formulas] documentation.
+to [formulas][formulaic-formulas] documentation.
 
 ## Repeating Basis Function Transformer
 
@@ -282,10 +257,10 @@ If these features are now passed to a model that supports monotonicity constrain
 [meta-module]: ../../api/meta
 [id-transformer-api]: ../../api/preprocessing#sklego.preprocessing.identitytransformer.IdentityTransformer
 [column-capper-api]: ../../api/preprocessing#sklego.preprocessing.columncapper.ColumnCapper
-[patsy-api]: ../../api/preprocessing#sklego.preprocessing.patsytransformer.PatsyTransformer
+[formulaic-api]: ../../api/preprocessing#sklego.preprocessing.formulaictransformer.FormulaicTransformer
 [rbf-api]: ../../api/preprocessing#sklego.preprocessing.repeatingbasis.RepeatingBasisFunction
 [interval-encoder-api]: ../../api/preprocessing#sklego.preprocessing.intervalencoder.IntervalEncoder
 [decay-section]: ../../user-guide/meta#decayed-estimation
 
-[patsy-docs]: https://patsy.readthedocs.io/en/latest/
-[patsy-formulas]: https://patsy.readthedocs.io/en/latest/formulas.html
+[formulaic-docs]: https://matthewwardrop.github.io/formulaic/
+[formulaic-formulas]: https://matthewwardrop.github.io/formulaic/formulas/
diff --git a/pyproject.toml b/pyproject.toml
@@ -20,7 +20,6 @@ maintainers = [
 ]
 
 dependencies = [
-    "Deprecated>=1.2.6",
     "pandas>=1.1.5",
     "scikit-learn>=1.0",
     "importlib-metadata >= 1.0; python_version < '3.8'",
@@ -46,10 +45,9 @@ documentation = "https://koaning.github.io/scikit-lego/"
 [project.optional-dependencies]
 cvxpy = ["cmake", "osqp", "cvxpy>=1.1.8"]
 formulaic = ["formulaic>=0.6.0"]
-patsy = ["patsy>=0.5.1"]
 umap = ["umap-learn>=0.4.6"]
 
-all = ["scikit-lego[cvxpy,formulaic,patsy,umap]"]
+all = ["scikit-lego[cvxpy,formulaic,umap]"]
 
 docs = [
     "mkdocs>=1.5.3",

diff --git a/readme.md b/readme.md
@@ -121,7 +121,6 @@ Here's a list of features that this library currently offers:
 - `sklego.preprocessing.IdentityTransformer` returns the same data, allows for concatenating pipelines
 - `sklego.preprocessing.OrthogonalTransformer` makes all features linearly independent
 - `sklego.preprocessing.PandasTypeSelector` selects columns based on pandas type
-- `sklego.preprocessing.PatsyTransformer` applies a [patsy](https://patsy.readthedocs.io/en/latest/formulas.html) formula
 - `sklego.preprocessing.RandomAdder` adds randomness in training
 - `sklego.preprocessing.RepeatingBasisFunction` repeating feature engineering, useful for timeseries
 - `sklego.preprocessing.DictMapper` assign numeric values on categorical columns

diff --git a/sklego/linear_model.py b/sklego/linear_model.py
@@ -10,7 +10,6 @@
 
 import numpy as np
 import pandas as pd
-from deprecated.sphinx import deprecated
 from scipy.optimize import minimize
 from scipy.special._ufuncs import expit
 from sklearn.base import BaseEstimator, RegressorMixin
@@ -629,15 +628,6 @@ def __new__(cls, *args, multi_class="ovr", n_jobs=1, **kwargs):
         return multiclass_meta(_DemographicParityClassifier(*args, **kwargs), n_jobs=n_jobs)
 
 
-@deprecated(
-    version="0.4.0",
-    reason="Please use `sklego.linear_model.DemographicParityClassifier instead`",
-)
-class FairClassifier(DemographicParityClassifier):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-
 class _DemographicParityClassifier(_FairClassifier):
     """Classifier for Demographic Parity fairness constraint.
 

diff --git a/sklego/meta/__init__.py b/sklego/meta/__init__.py
@@ -10,7 +10,6 @@
     "HierarchicalPredictor",
     "HierarchicalRegressor",
     "OrdinalClassifier",
-    "OutlierRemover",
     "SubjectiveClassifier",
     "Thresholder",
     "RegressionOutlierDetector",
@@ -25,7 +24,6 @@
 from sklego.meta.hierarchical_predictor import HierarchicalClassifier, HierarchicalPredictor, HierarchicalRegressor
 from sklego.meta.ordinal_classification import OrdinalClassifier
 from sklego.meta.outlier_classifier import OutlierClassifier
-from sklego.meta.outlier_remover import OutlierRemover
 from sklego.meta.regression_outlier_detector import RegressionOutlierDetector
 from sklego.meta.subjective_classifier import SubjectiveClassifier
 from sklego.meta.thresholder import Thresholder

diff --git a/sklego/meta/outlier_remover.py b/sklego/meta/outlier_remover.py
diff --git a/sklego/notinstalled.py b/sklego/notinstalled.py
@@ -2,7 +2,6 @@
     "cvxpy": {"version": ">=1.0.24", "extra_name": "cvxpy"},
     "umap-learn": {"version": ">=0.4.6", "extra_name": "umap"},
     "formulaic": {"version": ">=0.6.0", "extra_name": "formulaic"},
-    "patsy": {"version": ">=0.5.1", "extra_name": "patsy"},
 }