Skip to content

[MRG] Remove random_seed in fit and use the one in init #224

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 13 commits into from
Jul 3, 2019
Merged

[MRG] Remove random_seed in fit and use the one in init #224

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
13 commits
Select commit Hold shift + click to select a range
710ff12
Remove random_seed in fit and use the one in init
Jun 25, 2019
3a27064
update tests with the new API
Jun 25, 2019
2d0337d
Update test_RCA in sklearn_compat
Jun 25, 2019
d907d30
Update test_SDML in sklearn_compat
Jun 25, 2019
c055e94
Remove testing of pca_comps since it's deprecated
Jun 25, 2019
4eba167
Fix sklearn test
Jun 25, 2019
4c791b4
Fix random_seed for test_iris in TestRCA
Jun 25, 2019
38dc337
Relaunch CI
Jun 25, 2019
f2baeef
Augment tolerance rather than fix random_seed
Jun 25, 2019
0be553a
Add ChangedBehaviorWarning if the random_state is left default
Jun 25, 2019
8f9067e
Merge branch 'master' into maint/remove_random_seed_fit
Jul 3, 2019
1c05dbf
Update the merge
Jul 3, 2019
7ada336
Address https://github.com/metric-learn/metric-learn/pull/224#pullreq…
Jul 3, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 8 additions & 4 deletions metric_learn/constraints.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import warnings
from six.moves import xrange
from scipy.sparse import coo_matrix
from sklearn.utils import check_random_state

__all__ = ['Constraints']

Expand All @@ -23,7 +24,8 @@ def __init__(self, partial_labels):
self.known_label_idx, = np.where(partial_labels >= 0)
self.known_labels = partial_labels[self.known_label_idx]

def adjacency_matrix(self, num_constraints, random_state=np.random):
def adjacency_matrix(self, num_constraints, random_state=None):
random_state = check_random_state(random_state)
a, b, c, d = self.positive_negative_pairs(num_constraints,
random_state=random_state)
row = np.concatenate((a, c))
Expand All @@ -35,7 +37,8 @@ def adjacency_matrix(self, num_constraints, random_state=np.random):
return adj + adj.T

def positive_negative_pairs(self, num_constraints, same_length=False,
random_state=np.random):
random_state=None):
random_state = check_random_state(random_state)
a, b = self._pairs(num_constraints, same_label=True,
random_state=random_state)
c, d = self._pairs(num_constraints, same_label=False,
Expand Down Expand Up @@ -68,13 +71,14 @@ def _pairs(self, num_constraints, same_label=True, max_iter=10,
ab = np.array(list(ab)[:num_constraints], dtype=int)
return self.known_label_idx[ab.T]

def chunks(self, num_chunks=100, chunk_size=2, random_state=np.random):
def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
"""
the random state object to be passed must be a numpy random seed
"""
random_state = check_random_state(random_state)
chunks = -np.ones_like(self.known_label_idx, dtype=int)
uniq, lookup = np.unique(self.known_labels, return_inverse=True)
all_inds = [set(np.where(lookup==c)[0]) for c in xrange(len(uniq))]
all_inds = [set(np.where(lookup == c)[0]) for c in xrange(len(uniq))]
idx = 0
while idx < num_chunks and all_inds:
if len(all_inds) == 1:
Expand Down
29 changes: 23 additions & 6 deletions metric_learn/itml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import warnings
import numpy as np
from six.moves import xrange
from sklearn.exceptions import ChangedBehaviorWarning
from sklearn.metrics import pairwise_distances
from sklearn.utils.validation import check_array
from sklearn.base import TransformerMixin
Expand Down Expand Up @@ -298,7 +299,6 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
A positive definite (PD) matrix of shape
(n_features, n_features), that will be used as such to set the
prior.

A0 : Not used
.. deprecated:: 0.5.0
`A0` was deprecated in version 0.5.0 and will
Expand All @@ -310,7 +310,9 @@ class ITML_Supervised(_BaseITML, TransformerMixin):
tuples will be formed like this: X[indices].
random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int. If
``prior='random'``, ``random_state`` is used to set the prior.
``prior='random'``, ``random_state`` is used to set the prior. In any
case, `random_state` is also used to randomly sample constraints from
labels.


Attributes
Expand Down Expand Up @@ -350,7 +352,7 @@ def __init__(self, gamma=1., max_iter=1000, convergence_threshold=1e-3,
self.num_constraints = num_constraints
self.bounds = bounds

def fit(self, X, y, random_state=np.random, bounds=None):
def fit(self, X, y, random_state='deprecated', bounds=None):
"""Create constraints from labels and learn the ITML model.


Expand All @@ -362,8 +364,11 @@ def fit(self, X, y, random_state=np.random, bounds=None):
y : (n) array-like
Data labels.

random_state : numpy.random.RandomState, optional
If provided, controls random number generation.
random_state : Not used
.. deprecated:: 0.5.0
`random_state` in the `fit` function was deprecated in version 0.5.0
and will be removed in 0.6.0. Set `random_state` at initialization
instead (when instantiating a new `ITML_Supervised` object).

bounds : array-like of two numbers
Bounds on similarity, aside slack variables, s.t.
Expand All @@ -384,6 +389,18 @@ def fit(self, X, y, random_state=np.random, bounds=None):
' It has been deprecated in version 0.5.0 and will be'
' removed in 0.6.0. Use the "bounds" parameter of this '
'fit method instead.', DeprecationWarning)
if random_state != 'deprecated':
warnings.warn('"random_state" parameter in the `fit` function is '
'deprecated. Set `random_state` at initialization '
'instead (when instantiating a new `ITML_Supervised` '
'object).', DeprecationWarning)
else:
warnings.warn('As of v0.5.0, `ITML_Supervised` now uses the '
'`random_state` given at initialization to sample '
'constraints, not the default `np.random` from the `fit` '
'method, since this argument is now deprecated. '
'This warning will disappear in v0.6.0.',
ChangedBehaviorWarning)
X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
num_constraints = self.num_constraints
if num_constraints is None:
Expand All @@ -392,6 +409,6 @@ def fit(self, X, y, random_state=np.random, bounds=None):

c = Constraints(y)
pos_neg = c.positive_negative_pairs(num_constraints,
random_state=random_state)
random_state=self.random_state)
pairs, y = wrap_pairs(X, pos_neg)
return _BaseITML._fit(self, pairs, y, bounds=bounds)
26 changes: 21 additions & 5 deletions metric_learn/lsml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -286,7 +286,8 @@ class LSML_Supervised(_BaseLSML, TransformerMixin):
random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int. If
``init='random'``, ``random_state`` is used to set the random
prior.
prior. In any case, `random_state` is also used to randomly sample
constraints from labels.

Attributes
----------
Expand All @@ -308,7 +309,7 @@ def __init__(self, tol=1e-3, max_iter=1000, prior=None,
self.num_constraints = num_constraints
self.weights = weights

def fit(self, X, y, random_state=np.random):
def fit(self, X, y, random_state='deprecated'):
"""Create constraints from labels and learn the LSML model.

Parameters
Expand All @@ -319,13 +320,28 @@ def fit(self, X, y, random_state=np.random):
y : (n) array-like
Data labels.

random_state : numpy.random.RandomState, optional
If provided, controls random number generation.
random_state : Not used
.. deprecated:: 0.5.0
`random_state` in the `fit` function was deprecated in version 0.5.0
and will be removed in 0.6.0. Set `random_state` at initialization
instead (when instantiating a new `LSML_Supervised` object).
"""
if self.num_labeled != 'deprecated':
warnings.warn('"num_labeled" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
' removed in 0.6.0', DeprecationWarning)
if random_state != 'deprecated':
warnings.warn('"random_state" parameter in the `fit` function is '
'deprecated. Set `random_state` at initialization '
'instead (when instantiating a new `LSML_Supervised` '
'object).', DeprecationWarning)
else:
warnings.warn('As of v0.5.0, `LSML_Supervised` now uses the '
'`random_state` given at initialization to sample '
'constraints, not the default `np.random` from the `fit` '
'method, since this argument is now deprecated. '
'This warning will disappear in v0.6.0.',
ChangedBehaviorWarning)
X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
num_constraints = self.num_constraints
if num_constraints is None:
Expand All @@ -334,6 +350,6 @@ def fit(self, X, y, random_state=np.random):

c = Constraints(y)
pos_neg = c.positive_negative_pairs(num_constraints, same_length=True,
random_state=random_state)
random_state=self.random_state)
return _BaseLSML._fit(self, X[np.column_stack(pos_neg)],
weights=self.weights)
26 changes: 21 additions & 5 deletions metric_learn/mmc.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -538,7 +538,8 @@ class MMC_Supervised(_BaseMMC, TransformerMixin):
random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int. If
``init='random'``, ``random_state`` is used to initialize the random
Mahalanobis matrix.
Mahalanobis matrix. In any case, `random_state` is also used to
randomly sample constraints from labels.

`MMC_Supervised` creates pairs of similar sample by taking same class
samples, and pairs of dissimilar samples by taking different class
Expand Down Expand Up @@ -566,7 +567,7 @@ def __init__(self, max_iter=100, max_proj=10000, convergence_threshold=1e-6,
self.num_labeled = num_labeled
self.num_constraints = num_constraints

def fit(self, X, y, random_state=np.random):
def fit(self, X, y, random_state='deprecated'):
"""Create constraints from labels and learn the MMC model.

Parameters
Expand All @@ -575,13 +576,28 @@ def fit(self, X, y, random_state=np.random):
Input data, where each row corresponds to a single instance.
y : (n) array-like
Data labels.
random_state : numpy.random.RandomState, optional
If provided, controls random number generation.
random_state : Not used
.. deprecated:: 0.5.0
`random_state` in the `fit` function was deprecated in version 0.5.0
and will be removed in 0.6.0. Set `random_state` at initialization
instead (when instantiating a new `MMC_Supervised` object).
"""
if self.num_labeled != 'deprecated':
warnings.warn('"num_labeled" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
' removed in 0.6.0', DeprecationWarning)
if random_state != 'deprecated':
warnings.warn('"random_state" parameter in the `fit` function is '
'deprecated. Set `random_state` at initialization '
'instead (when instantiating a new `MMC_Supervised` '
'object).', DeprecationWarning)
else:
warnings.warn('As of v0.5.0, `MMC_Supervised` now uses the '
'`random_state` given at initialization to sample '
'constraints, not the default `np.random` from the `fit` '
'method, since this argument is now deprecated. '
'This warning will disappear in v0.6.0.',
ChangedBehaviorWarning)
X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
num_constraints = self.num_constraints
if num_constraints is None:
Expand All @@ -590,6 +606,6 @@ def fit(self, X, y, random_state=np.random):

c = Constraints(y)
pos_neg = c.positive_negative_pairs(num_constraints,
random_state=random_state)
random_state=self.random_state)
pairs, y = wrap_pairs(X, pos_neg)
return _BaseMMC._fit(self, pairs, y)
32 changes: 28 additions & 4 deletions metric_learn/rca.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -184,11 +184,17 @@ class RCA_Supervised(RCA):
be removed in 0.6.0. Use `n_components` instead.

num_chunks: int, optional

chunk_size: int, optional

preprocessor : array-like, shape=(n_samples, n_features) or callable
The preprocessor to call to get tuples from indices. If array-like,
tuples will be formed like this: X[indices].

random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int.
It is used to randomly sample constraints from labels.

Attributes
----------
transformer_ : `numpy.ndarray`, shape=(n_components, n_features)
Expand All @@ -197,13 +203,15 @@ class RCA_Supervised(RCA):

def __init__(self, num_dims='deprecated', n_components=None,
pca_comps='deprecated', num_chunks=100, chunk_size=2,
preprocessor=None):
preprocessor=None, random_state=None):
"""Initialize the supervised version of `RCA`."""
RCA.__init__(self, num_dims=num_dims, n_components=n_components,
pca_comps=pca_comps, preprocessor=preprocessor)
self.num_chunks = num_chunks
self.chunk_size = chunk_size
self.random_state = random_state

def fit(self, X, y, random_state=np.random):
def fit(self, X, y, random_state='deprecated'):
"""Create constraints from labels and learn the RCA model.
Needs num_constraints specified in constructor.

Expand All @@ -212,10 +220,26 @@ def fit(self, X, y, random_state=np.random):
X : (n x d) data matrix
each row corresponds to a single instance
y : (n) data labels
random_state : a random.seed object to fix the random_state if needed.
random_state : Not used
.. deprecated:: 0.5.0
`random_state` in the `fit` function was deprecated in version 0.5.0
and will be removed in 0.6.0. Set `random_state` at initialization
instead (when instantiating a new `RCA_Supervised` object).
"""
if random_state != 'deprecated':
warnings.warn('"random_state" parameter in the `fit` function is '
'deprecated. Set `random_state` at initialization '
'instead (when instantiating a new `RCA_Supervised` '
'object).', DeprecationWarning)
else:
warnings.warn('As of v0.5.0, `RCA_Supervised` now uses the '
'`random_state` given at initialization to sample '
'constraints, not the default `np.random` from the `fit` '
'method, since this argument is now deprecated. '
'This warning will disappear in v0.6.0.',
ChangedBehaviorWarning)
X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
chunks = Constraints(y).chunks(num_chunks=self.num_chunks,
chunk_size=self.chunk_size,
random_state=random_state)
random_state=self.random_state)
return RCA.fit(self, X, chunks)
27 changes: 21 additions & 6 deletions metric_learn/sdml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -310,7 +310,8 @@ class SDML_Supervised(_BaseSDML, TransformerMixin):
random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int. If
``init='random'``, ``random_state`` is used to set the random
prior.
prior. In any case, `random_state` is also used to randomly sample
constraints from labels.

Attributes
----------
Expand All @@ -336,7 +337,7 @@ def __init__(self, balance_param=0.5, sparsity_param=0.01, prior=None,
self.num_labeled = num_labeled
self.num_constraints = num_constraints

def fit(self, X, y, random_state=np.random):
def fit(self, X, y, random_state='deprecated'):
"""Create constraints from labels and learn the SDML model.

Parameters
Expand All @@ -345,9 +346,11 @@ def fit(self, X, y, random_state=np.random):
data matrix, where each row corresponds to a single instance
y : array-like, shape (n,)
data labels, one for each instance
random_state : {numpy.random.RandomState, int}, optional
Random number generator or random seed. If not given, the singleton
numpy.random will be used.
random_state : Not used
.. deprecated:: 0.5.0
`random_state` in the `fit` function was deprecated in version 0.5.0
and will be removed in 0.6.0. Set `random_state` at initialization
instead (when instantiating a new `SDML_Supervised` object).

Returns
-------
Expand All @@ -358,6 +361,18 @@ def fit(self, X, y, random_state=np.random):
warnings.warn('"num_labeled" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
' removed in 0.6.0', DeprecationWarning)
if random_state != 'deprecated':
warnings.warn('"random_state" parameter in the `fit` function is '
'deprecated. Set `random_state` at initialization '
'instead (when instantiating a new `SDML_Supervised` '
'object).', DeprecationWarning)
else:
warnings.warn('As of v0.5.0, `SDML_Supervised` now uses the '
'`random_state` given at initialization to sample '
'constraints, not the default `np.random` from the `fit` '
'method, since this argument is now deprecated. '
'This warning will disappear in v0.6.0.',
ChangedBehaviorWarning)
X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
num_constraints = self.num_constraints
if num_constraints is None:
Expand All @@ -366,6 +381,6 @@ def fit(self, X, y, random_state=np.random):

c = Constraints(y)
pos_neg = c.positive_negative_pairs(num_constraints,
random_state=random_state)
random_state=self.random_state)
pairs, y = wrap_pairs(X, pos_neg)
return _BaseSDML._fit(self, pairs, y)
Loading