From 3dffa08e4349166148cd7da4f07c006c642f86d2 Mon Sep 17 00:00:00 2001
From: Yichuan Liu <yichuanliu2004@gmail.com>
Date: Mon, 12 Sep 2016 20:35:32 +0200
Subject: [PATCH] FIX 7155: GridSearchCV predict_proba delegation to
SDGClassifier
---
sklearn/grid_search.py | 12 ++--
sklearn/model_selection/_search.py | 12 ++--
sklearn/model_selection/tests/test_search.py | 39 +++++++++++-
sklearn/tests/test_metaestimators.py | 3 +-
sklearn/utils/metaestimators.py | 63 +++++++++++---------
sklearn/utils/tests/test_metaestimators.py | 56 ++++++++++++++++-
6 files changed, 140 insertions(+), 45 deletions(-)
diff --git a/sklearn/grid_search.py b/sklearn/grid_search.py
index 202327574e..0de08ee9e8 100644
--- a/sklearn/grid_search.py
+++ b/sklearn/grid_search.py
@@ -426,7 +426,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
ChangedBehaviorWarning)
return self.scorer_(self.best_estimator_, X, y)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict(self, X):
"""Call predict on the estimator with the best found parameters.
@@ -442,7 +442,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
"""
return self.best_estimator_.predict(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_proba(self, X):
"""Call predict_proba on the estimator with the best found parameters.
@@ -458,7 +458,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
"""
return self.best_estimator_.predict_proba(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_log_proba(self, X):
"""Call predict_log_proba on the estimator with the best found parameters.
@@ -474,7 +474,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
"""
return self.best_estimator_.predict_log_proba(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def decision_function(self, X):
"""Call decision_function on the estimator with the best found parameters.
@@ -490,7 +490,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
"""
return self.best_estimator_.decision_function(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def transform(self, X):
"""Call transform on the estimator with the best found parameters.
@@ -506,7 +506,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
"""
return self.best_estimator_.transform(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found parameters.
diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index f1880555df..7c6344c02c 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -426,7 +426,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
else:
check_is_fitted(self, 'best_estimator_')
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict(self, X):
"""Call predict on the estimator with the best found parameters.
@@ -443,7 +443,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
self._check_is_fitted('predict')
return self.best_estimator_.predict(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_proba(self, X):
"""Call predict_proba on the estimator with the best found parameters.
@@ -460,7 +460,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
self._check_is_fitted('predict_proba')
return self.best_estimator_.predict_proba(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_log_proba(self, X):
"""Call predict_log_proba on the estimator with the best found parameters.
@@ -477,7 +477,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
self._check_is_fitted('predict_log_proba')
return self.best_estimator_.predict_log_proba(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def decision_function(self, X):
"""Call decision_function on the estimator with the best found parameters.
@@ -494,7 +494,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
self._check_is_fitted('decision_function')
return self.best_estimator_.decision_function(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def transform(self, X):
"""Call transform on the estimator with the best found parameters.
@@ -511,7 +511,7 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
self._check_is_fitted('transform')
return self.best_estimator_.transform(X)
- @if_delegate_has_method(delegate='estimator')
+ @if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found params.
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 141c1a21b4..bb21a386d3 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -58,6 +58,7 @@ from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import Imputer
from sklearn.pipeline import Pipeline
+from sklearn.linear_model import SGDClassifier
# Neither of the following two estimators inherit from BaseEstimator,
@@ -967,11 +968,13 @@ def test_grid_search_failing_classifier():
refit=False, error_score=0.0)
assert_warns(FitFailedWarning, gs.fit, X, y)
n_candidates = len(gs.cv_results_['params'])
+
# Ensure that grid scores were set to zero as required for those fits
# that are expected to fail.
- get_cand_scores = lambda i: np.array(list(
- gs.cv_results_['split%d_test_score' % s][i]
- for s in range(gs.n_splits_)))
+ def get_cand_scores(i):
+ return np.array(list(gs.cv_results_['split%d_test_score' % s][i]
+ for s in range(gs.n_splits_)))
+
assert all((np.all(get_cand_scores(cand_i) == 0.0)
for cand_i in range(n_candidates)
if gs.cv_results_['param_parameter'][cand_i] ==
@@ -1028,3 +1031,33 @@ def test_parameters_sampler_replacement():
sampler = ParameterSampler(params_distribution, n_iter=7)
samples = list(sampler)
assert_equal(len(samples), 7)
+
+
+def test_stochastic_gradient_loss_param():
+ # Make sure the predict_proba works when loss is specified
+ # as one of the parameters in the param_grid.
+ param_grid = {
+ 'loss': ['log'],
+ }
+ X = np.arange(20).reshape(5, -1)
+ y = [0, 0, 1, 1, 1]
+ clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
+ param_grid=param_grid)
+
+ # When the estimator is not fitted, `predict_proba` is not available as the
+ # loss is 'hinge'.
+ assert_false(hasattr(clf, "predict_proba"))
+ clf.fit(X, y)
+ clf.predict_proba(X)
+ clf.predict_log_proba(X)
+
+ # Make sure `predict_proba` is not available when setting loss=['hinge']
+ # in param_grid
+ param_grid = {
+ 'loss': ['hinge'],
+ }
+ clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
+ param_grid=param_grid)
+ assert_false(hasattr(clf, "predict_proba"))
+ clf.fit(X, y)
+ assert_false(hasattr(clf, "predict_proba"))
diff --git a/sklearn/tests/test_metaestimators.py b/sklearn/tests/test_metaestimators.py
index 4c6ace3d3a..f0f30cb91a 100644
--- a/sklearn/tests/test_metaestimators.py
+++ b/sklearn/tests/test_metaestimators.py
@@ -39,7 +39,8 @@ DELEGATING_METAESTIMATORS = [
skip_methods=['transform', 'inverse_transform', 'score']),
DelegatorData('BaggingClassifier', BaggingClassifier,
skip_methods=['transform', 'inverse_transform', 'score',
- 'predict_proba', 'predict_log_proba', 'predict'])
+ 'predict_proba', 'predict_log_proba',
+ 'predict'])
]
diff --git a/sklearn/utils/metaestimators.py b/sklearn/utils/metaestimators.py
index 9850ea50ea..346064448b 100644
--- a/sklearn/utils/metaestimators.py
+++ b/sklearn/utils/metaestimators.py
@@ -14,16 +14,22 @@ class _IffHasAttrDescriptor(object):
"""Implements a conditional property using the descriptor protocol.
Using this class to create a decorator will raise an ``AttributeError``
- if the ``attribute_name`` is not present on the base object.
+ if none of the delegates (specified in ``delegate_names``) is an attribute
+ of the base object or the first found delegate does not have an attribute
+ ``attribute_name``.
- This allows ducktyping of the decorated method based on ``attribute_name``.
+ This allows ducktyping of the decorated method based on
+ ``delegate.attribute_name``. Here ``delegate`` is the first item in
+ ``delegate_names`` for which ``hasattr(object, delegate) is True``.
See https://docs.python.org/3/howto/descriptor.html for an explanation of
descriptors.
"""
- def __init__(self, fn, attribute_name):
+ def __init__(self, fn, delegate_names, attribute_name):
self.fn = fn
- self.get_attribute = attrgetter(attribute_name)
+ self.delegate_names = delegate_names
+ self.attribute_name = attribute_name
+
# update the docstring of the descriptor
update_wrapper(self, fn)
@@ -32,7 +38,17 @@ class _IffHasAttrDescriptor(object):
if obj is not None:
# delegate only on instances, not the classes.
# this is to allow access to the docstrings.
- self.get_attribute(obj)
+ for delegate_name in self.delegate_names:
+ try:
+ delegate = attrgetter(delegate_name)(obj)
+ except AttributeError:
+ continue
+ else:
+ getattr(delegate, self.attribute_name)
+ break
+ else:
+ attrgetter(self.delegate_names[-1])(obj)
+
# lambda, but not partial, allows help() to work with update_wrapper
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
# update the docstring of the returned function
@@ -46,27 +62,18 @@ def if_delegate_has_method(delegate):
This enables ducktyping by hasattr returning True according to the
sub-estimator.
- >>> from sklearn.utils.metaestimators import if_delegate_has_method
- >>>
- >>>
- >>> class MetaEst(object):
- ... def __init__(self, sub_est):
- ... self.sub_est = sub_est
- ...
- ... @if_delegate_has_method(delegate='sub_est')
- ... def predict(self, X):
- ... return self.sub_est.predict(X)
- ...
- >>> class HasPredict(object):
- ... def predict(self, X):
- ... return X.sum(axis=1)
- ...
- >>> class HasNoPredict(object):
- ... pass
- ...
- >>> hasattr(MetaEst(HasPredict()), 'predict')
- True
- >>> hasattr(MetaEst(HasNoPredict()), 'predict')
- False
+ Parameters
+ ----------
+ delegate : string, list of strings or tuple of strings
+ Name of the sub-estimator that can be accessed as an attribute of the
+ base object. If a list or a tuple of names are provided, the first
+ sub-estimator that is an attribute of the base object will be used.
+
"""
- return lambda fn: _IffHasAttrDescriptor(fn, '%s.%s' % (delegate, fn.__name__))
+ if isinstance(delegate, list):
+ delegate = tuple(delegate)
+ if not isinstance(delegate, tuple):
+ delegate = (delegate,)
+
+ return lambda fn: _IffHasAttrDescriptor(fn, delegate,
+ attribute_name=fn.__name__)
diff --git a/sklearn/utils/tests/test_metaestimators.py b/sklearn/utils/tests/test_metaestimators.py
index cb1f46ef80..d73c67d0d1 100644
--- a/sklearn/utils/tests/test_metaestimators.py
+++ b/sklearn/utils/tests/test_metaestimators.py
@@ -1,5 +1,5 @@
+from nose.tools import assert_true, assert_false
from sklearn.utils.metaestimators import if_delegate_has_method
-from nose.tools import assert_true
class Prefix(object):
@@ -24,3 +24,57 @@ def test_delegated_docstring():
in str(MockMetaEstimator.func.__doc__))
assert_true("This is a mock delegated function"
in str(MockMetaEstimator().func.__doc__))
+
+
+class MetaEst(object):
+ """A mock meta estimator"""
+ def __init__(self, sub_est, better_sub_est=None):
+ self.sub_est = sub_est
+ self.better_sub_est = better_sub_est
+
+ @if_delegate_has_method(delegate='sub_est')
+ def predict(self):
+ pass
+
+
+class MetaEstTestTuple(MetaEst):
+ """A mock meta estimator to test passing a tuple of delegates"""
+
+ @if_delegate_has_method(delegate=('sub_est', 'better_sub_est'))
+ def predict(self):
+ pass
+
+
+class MetaEstTestList(MetaEst):
+ """A mock meta estimator to test passing a list of delegates"""
+
+ @if_delegate_has_method(delegate=['sub_est', 'better_sub_est'])
+ def predict(self):
+ pass
+
+
+class HasPredict(object):
+ """A mock sub-estimator with predict method"""
+
+ def predict(self):
+ pass
+
+
+class HasNoPredict(object):
+ """A mock sub-estimator with no predict method"""
+ pass
+
+
+def test_if_delegate_has_method():
+ assert_true(hasattr(MetaEst(HasPredict()), 'predict'))
+ assert_false(hasattr(MetaEst(HasNoPredict()), 'predict'))
+ assert_false(
+ hasattr(MetaEstTestTuple(HasNoPredict(), HasNoPredict()), 'predict'))
+ assert_true(
+ hasattr(MetaEstTestTuple(HasPredict(), HasNoPredict()), 'predict'))
+ assert_false(
+ hasattr(MetaEstTestTuple(HasNoPredict(), HasPredict()), 'predict'))
+ assert_false(
+ hasattr(MetaEstTestList(HasNoPredict(), HasPredict()), 'predict'))
+ assert_true(
+ hasattr(MetaEstTestList(HasPredict(), HasPredict()), 'predict'))
--
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