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[DOC] solves several small raised issues #266

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Merged
merged 5 commits into from
Nov 21, 2019
Merged

[DOC] solves several small raised issues #266

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438d15b
solves #258
RobinVogel Nov 20, 2019
77753cf
solves #261
RobinVogel Nov 20, 2019
4270107
Solves issue #256
RobinVogel Nov 20, 2019
b22af58
maj
RobinVogel Nov 20, 2019
8cae29a
typo
RobinVogel Nov 21, 2019
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19 changes: 11 additions & 8 deletions doc/getting_started.rst
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Expand Up @@ -29,11 +29,14 @@ Quick start
This example loads the iris dataset, and evaluates a k-nearest neighbors
algorithm on an embedding space learned with `NCA`.

>>> from metric_learn import NCA
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.pipeline import make_pipeline
>>>
>>> X, y = load_iris(return_X_y=True)
>>> clf = make_pipeline(NCA(), KNeighborsClassifier())
>>> cross_val_score(clf, X, y)
::

from metric_learn import NCA
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.neighbors import KNeighborsClassifier

X, y = load_iris(return_X_y=True)
clf = make_pipeline(NCA(), KNeighborsClassifier())
cross_val_score(clf, X, y)
7 changes: 6 additions & 1 deletion doc/supervised.rst
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Expand Up @@ -374,7 +374,12 @@ Supervised versions of weakly-supervised algorithms
Each :ref:`weakly-supervised algorithm <weakly_supervised_section>`
has a supervised version of the form `*_Supervised` where similarity tuples are
randomly generated from the labels information and passed to the underlying
algorithm.
algorithm.

.. warning::
Supervised versions of weakly-supervised algorithms interpret label -1
(or any negative label) as a point with unknown label.
Those points are discarded in the learning process.

For pairs learners (see :ref:`learning_on_pairs`), pairs (tuple of two points
from the dataset), and pair labels (`int` indicating whether the two points
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1 change: 1 addition & 0 deletions metric_learn/itml.py
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Expand Up @@ -273,6 +273,7 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
be removed in 0.6.0.
num_constraints: int, optional
number of constraints to generate
(`20 * num_classes**2` constraints by default)
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We may need to use double-backticks here to prevent the docs from interpreting this as a link target. Can you build the HTML docs with these changes to verify?

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I tried it locally and I see it works as expected, also there is a itml_supervised.fit(X, y, bounds=...) that does not uses double backticks in the code a few lines below.

Should I make a pull request for the documentation that I generated as well ?
The commits that update the documentation look a bit heavy for minor modifications and include some system-specific results.

bounds : Not used
.. deprecated:: 0.5.0
`bounds` was deprecated in version 0.5.0 and will
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1 change: 1 addition & 0 deletions metric_learn/lsml.py
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Expand Up @@ -276,6 +276,7 @@ class LSML_Supervised(_BaseLSML, TransformerMixin):
be removed in 0.6.0.
num_constraints: int, optional
number of constraints to generate
(`20 * num_classes**2` constraints by default)
weights : (m,) array of floats, optional
scale factor for each constraint
verbose : bool, optional
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1 change: 1 addition & 0 deletions metric_learn/mmc.py
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Expand Up @@ -498,6 +498,7 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
be removed in 0.6.0.
num_constraints: int, optional
number of constraints to generate
(`20 * num_classes**2` constraints by default)
init : None, string or numpy array, optional (default=None)
Initialization of the Mahalanobis matrix. Possible options are
'identity', 'covariance', 'random', and a numpy array of
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1 change: 1 addition & 0 deletions metric_learn/sdml.py
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Expand Up @@ -302,6 +302,7 @@ class SDML_Supervised(_BaseSDML, TransformerMixin):
be removed in 0.6.0.
num_constraints : int, optional (default=None)
number of constraints to generate
(`20 * num_classes**2` constraints by default)
verbose : bool, optional (default=False)
if True, prints information while learning
preprocessor : array-like, shape=(n_samples, n_features) or callable
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