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[MRG] Added the "error-strict" option to OneHotEncoder and start deprecating unknown values in range #7327

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[MRG] Added the "error-strict" option to OneHotEncoder and start deprecating unknown values in range #7327

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d73d1e7
Refactored OneHotEncoder to work with strings
vighneshbirodkar Mar 30, 2016
d67989f
ported functions to fixes.py
vighneshbirodkar May 2, 2016
f7731de
unique arrays are now sorted
vighneshbirodkar May 2, 2016
92e6f71
revert selection logic
vighneshbirodkar May 2, 2016
2aa5f12
Added copy argument
vighneshbirodkar May 2, 2016
b7e218d
Inbetween adding the seen option
vighneshbirodkar Aug 31, 2016
f7e4f12
remove seen argument and support range case with FutureWarning
vighneshbirodkar Sep 1, 2016
7f5a86a
Made label_encoders_ private
vighneshbirodkar Sep 2, 2016
542da50
Added new attributes and tests for OHE
vighneshbirodkar Sep 2, 2016
232b3fc
Fixed doctests
vighneshbirodkar Sep 2, 2016
52c4813
Fixed rst doc tests
vighneshbirodkar Sep 2, 2016
7a4cf51
Replaced type in array with ellipsis
vighneshbirodkar Sep 2, 2016
48a68e7
flake fixes
vighneshbirodkar Sep 2, 2016
7e3c48e
Add NORMALIZE_WHITESPACE for python3 tests
vighneshbirodkar Sep 2, 2016
633631c
normalize whitespace for rst docs
vighneshbirodkar Sep 2, 2016
b9fad64
normalizing whitespace again
vighneshbirodkar Sep 2, 2016
faebd86
docstring changes and minor optimizations
vighneshbirodkar Sep 6, 2016
f9d6f9a
Made tests pass by creating arrays with object dtype
vighneshbirodkar Dec 28, 2016
75a4526
Assign both values and n_values to self._values and remove redundant …
vighneshbirodkar Dec 28, 2016
97a2112
removed extra spaces for flake8 compat
vighneshbirodkar Dec 28, 2016
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43 changes: 27 additions & 16 deletions doc/modules/preprocessing.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -397,31 +397,42 @@ only one active.
Continuing the example above::

>>> enc = preprocessing.OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) # doctest: +ELLIPSIS
OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
handle_unknown='error', n_values='auto', sparse=True)
>>> enc.transform([[0, 1, 3]]).toarray()
array([[ 1., 0., 0., 1., 0., 0., 0., 0., 1.]])
>>> enc.fit([['female', 'from US', 'uses Chrome'],
... ['male', 'from Asia', 'uses Firefox']]) \
... # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
OneHotEncoder(categorical_features='all', copy=True,
dtype=<... 'numpy.float64'>, handle_unknown='error', n_values=None,
sparse=True, values='auto')
>>> enc.transform([['female', 'from Asia', 'uses Firefox']]).toarray()
array([[ 1., 0., 1., 0., 0., 1.]])

By default, how many values each feature can take is inferred automatically from the dataset.
It is possible to specify this explicitly using the parameter ``n_values``.
It is possible to specify this explicitly using the parameter ``values``.
There are two genders, three possible continents and four web browsers in our
dataset.
Then we fit the estimator, and transform a data point.
In the result, the first two numbers encode the gender, the next set of three
numbers the continent and the last four the web browser.
In the result, the first two values are genders, the next set of three
values are the continents and the last values are web browsers.

Note that, if there is a possibilty that the training data might have missing categorical
features, one has to explicitly set ``n_values``. For example,

>>> enc = preprocessing.OneHotEncoder(n_values=[2, 3, 4])
>>> # Note that there are missing categorical values for the 2nd and 3rd
>>> # features
>>> enc.fit([[1, 2, 3], [0, 2, 0]]) # doctest: +ELLIPSIS
OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
handle_unknown='error', n_values=[2, 3, 4], sparse=True)
>>> enc.transform([[1, 0, 0]]).toarray()
array([[ 0., 1., 1., 0., 0., 1., 0., 0., 0.]])
>>> browsers = ['uses Internet Explorer', 'uses Chrome' , 'uses Safari', 'uses Firefox']
>>> genders = ['male', 'female']
>>> locations = ['from Europe', 'from Asia', 'from US']
>>> enc = preprocessing.OneHotEncoder(values=[genders, locations, browsers])
>>> # Note that for there are missing categorical values for the 2nd and 3rd
>>> # feature
>>> enc.fit([['female', 'from US', 'uses Chrome'],
... ['male', 'from Asia', 'uses Internet Explorer']]) \
... # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
OneHotEncoder(categorical_features='all', copy=True,
dtype=<... 'numpy.float64'>, handle_unknown='error', n_values=None,
sparse=True,
values=[...])

>>> enc.transform([['male', 'from Europe', 'uses Safari']]).toarray()
array([[ 0., 1., 0., 1., 0., 0., 0., 0., 1.]])

See :ref:`dict_feature_extraction` for categorical features that are represented
as a dict, not as integers.
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