DOC/MAINT final touches to FunctionTransformer

larsmans · larsmans · commit b09afb08be74 · 2015-08-03T22:12:19.000+02:00
* copyedit doc
* give the module an underscore (we should do this to all private modules...)
* use public import path in test
* change all logs to log1p to prevent warning in test and because it's
  genuinely useful for frequency data
* what's new
diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst
@@ -509,22 +509,22 @@ Note that polynomial features are used implicitily in `kernel methods <http://en
 
 See :ref:`example_linear_model_plot_polynomial_interpolation.py` for Ridge regression using created polynomial features.
 
-Custom Transformers
+Custom transformers
 ===================
 
-Often, you will want to convert an existing python function into a transformer
-to assist in data cleaning or processing. Users may implement a transformer from
-an arbitrary function with :class:`FunctionTransformer`. For example, one could
-apply a log transformation in a pipeline like::
+Often, you will want to convert an existing Python function into a transformer
+to assist in data cleaning or processing. You can implement a transformer from
+an arbitrary function with :class:`FunctionTransformer`. For example, to build
+a transformer that applies a log transformation in a pipeline, do::
 
     >>> import numpy as np
     >>> from sklearn.preprocessing import FunctionTransformer
-    >>> transformer = FunctionTransformer(np.log)
-    >>> X = np.array([[1, 2], [3, 4]])
+    >>> transformer = FunctionTransformer(np.log1p)
+    >>> X = np.array([[0, 1], [2, 3]])
     >>> transformer.transform(X)
     array([[ 0.        ,  0.69314718],
            [ 1.09861229,  1.38629436]])
 
 For a full code example that demonstrates using a :class:`FunctionTransformer`
-to do column selection,
+to do custom feature selection,
 see :ref:`example_preprocessing_plot_function_transformer.py`
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -29,6 +29,10 @@ New features
      range normalization when the data is already centered or sparse.
      By `Thomas Unterthiner`_.
 
+   - The new class :class:`preprocessing.FunctionTransformer` turns a Python
+     function into a ``Pipeline``-compatible transformer object.
+     By Joe Jevnik.
+
 Enhancements
 ............
 
diff --git a/sklearn/preprocessing/__init__.py b/sklearn/preprocessing/__init__.py
@@ -3,7 +3,7 @@
 normalization, binarization and imputation methods.
 """
 
-from .function_transformer import FunctionTransformer
+from ._function_transformer import FunctionTransformer
 
 from .data import Binarizer
 from .data import KernelCenterer
diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py
diff --git a/sklearn/preprocessing/tests/test_function_transformer.py b/sklearn/preprocessing/tests/test_function_transformer.py
@@ -1,7 +1,7 @@
 from nose.tools import assert_equal
 import numpy as np
 
-from ..function_transformer import FunctionTransformer
+from sklearn.preprocessing import FunctionTransformer
 
 
 def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
@@ -78,6 +78,6 @@ def test_np_log():
 
     # Test that the numpy.log example still works.
     np.testing.assert_array_equal(
-        FunctionTransformer(np.log).transform(X),
-        np.log(X),
+        FunctionTransformer(np.log1p).transform(X),
+        np.log1p(X),
     )
