8000 [WIP] gamma=auto in SVC #8361 by neokt · Pull Request #8535 · scikit-learn/scikit-learn · GitHub
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[WIP] gamma=auto in SVC #8361 #8535

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6 changes: 3 additions & 3 deletions sklearn/ensemble/tests/test_bagging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ def test_classification():
Perceptron(),
DecisionTreeClassifier(),
KNeighborsClassifier(),
SVC()]:
SVC(gamma="scale")]:
for params in grid:
BaggingClassifier(base_estimator=base_estimator,
random_state=rng,
Expand Down Expand Up @@ -309,7 +309,7 @@ def test_oob_score_classification():
iris.target,
random_state=rng)

for base_estimator in [DecisionTreeClassifier(), SVC()]:
for base_estimator in [DecisionTreeClassifier(), SVC(gamma="scale")]:
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I think we can use this PR to also set a random_state, to reduce diff of #8563.

It will anyway have a merge conflict for all these lines in that PR...

@jnothman Ok with you?

clf = BaggingClassifier(base_estimator=base_estimator,
n_estimators=100,
bootstrap=True,
Expand Down Expand Up @@ -493,7 +493,7 @@ def test_gridsearch():
parameters = {'n_estimators': (1, 2),
'base_estimator__C': (1, 2)}

GridSearchCV(BaggingClassifier(SVC()),
GridSearchCV(BaggingClassifier(SVC(gamma="scale")),
parameters,
scoring="roc_auc").fit(X, y)

Expand Down
4 changes: 2 additions & 2 deletions sklearn/ensemble/tests/test_weight_boosting.py
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Expand Up @@ -287,7 +287,7 @@ def test_base_estimator():
clf = AdaBoostClassifier(RandomForestClassifier())
clf.fit(X, y_regr)

clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
clf = AdaBoostClassifier(SVC(gamma="scale"), algorithm="SAMME")
clf.fit(X, y_class)

from sklearn.ensemble import RandomForestRegressor
Expand All @@ -302,7 +302,7 @@ def test_base_estimator():
# Check that an empty discrete ensemble fails in fit, not predict.
X_fail = [[1, 1], [1, 1], [1, 1], [1, 1]]
y_fail = ["foo", "bar", 1, 2]
clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
clf = AdaBoostClassifier(SVC(gamma="scale"), algorithm="SAMME")
assert_raises_regexp(ValueError, "worse than random",
clf.fit, X_fail, y_fail)

Expand Down
2 changes: 1 addition & 1 deletion sklearn/grid_search.py
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Expand Up @@ -743,7 +743,7 @@ class GridSearchCV(BaseSearchCV):
>>> from sklearn import svm, grid_search, datasets
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> svr = svm.SVC(gamma="scale")
>>> clf = grid_search.GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Expand Down
4 changes: 2 additions & 2 deletions sklearn/metrics/tests/test_score_objects.py
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Expand Up @@ -180,11 +180,11 @@ def test_check_scoring_gridsearchcv():
# test that check_scoring works on GridSearchCV and pipeline.
# slightly redundant non-regression test.

grid = GridSearchCV(LinearSVC(), param_grid={'C': [.1, 1]})
grid = GridSearchCV(LinearSVC(gamma="scale"), param_grid={'C': [.1, 1]})
scorer = check_scoring(grid, "f1")
assert_true(isinstance(scorer, _PredictScorer))

pipe = make_pipeline(LinearSVC())
pipe = make_pipeline(LinearSVC(gamma="scale"))
scorer = check_scoring(pipe, "f1")
assert_true(isinstance(scorer, _PredictScorer))

Expand Down
2 changes: 1 addition & 1 deletion sklearn/model_selection/_search.py
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Expand Up @@ -814,7 +814,7 @@ class GridSearchCV(BaseSearchCV):
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> svr = svm.SVC(gamma="scale")
>>> clf = GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Expand Down
30 changes: 15 additions & 15 deletions sklearn/model_selection/tests/test_search.py
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Expand Up @@ -399,7 +399,7 @@ def test_grid_search_one_grid_point():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
param_dict = {"C": [1.0], "kernel": ["rbf"], "gamma": [0.1]}

clf = SVC()
clf = SVC(gamma="scale")
cv = GridSearchCV(clf, param_dict)
cv.fit(X_, y_)

Expand All @@ -423,30 +423,30 @@ def test_grid_search_when_param_grid_includes_range():

def test_grid_search_bad_param_grid():
param_dict = {"C": 1.0}
clf = SVC()
clf = SVC(gamma="scale")
assert_raise_message(
ValueError,
"Parameter values for parameter (C) need to be a sequence"
"(but not a string) or np.ndarray.",
GridSearchCV, clf, param_dict)

param_dict = {"C": []}
clf = SVC()
clf = SVC(gamma="scale")
assert_raise_message(
ValueError,
"Parameter values for parameter (C) need to be a non-empty sequence.",
GridSearchCV, clf, param_dict)

param_dict = {"C": "1,2,3"}
clf = SVC()
clf = SVC(gamma="scale")
assert_raise_message(
ValueError,
"Parameter values for parameter (C) need to be a sequence"
"(but not a string) or np.ndarray.",
GridSearchCV, clf, param_dict)

param_dict = {"C": np.ones(6).reshape(3, 2)}
clf = SVC()
clf = SVC(gamma="scale")
assert_raises(ValueError, GridSearchCV, clf, param_dict)


Expand Down Expand Up @@ -742,10 +742,10 @@ def test_grid_search_cv_results():
n_grid_points = 6
params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),
dict(kernel=['poly', ], degree=[1, 2])]
grid_search = GridSearchCV(SVC(), cv=n_splits, iid=False,
grid_search = GridSearchCV(SVC(gamma="scale"), cv=n_splits, iid=False,
param_grid=params)
grid_search.fit(X, y)
grid_search_iid = GridSearchCV(SVC(), cv=n_splits, iid=True,
grid_search_iid = GridSearchCV(SVC(gamma="scale"), cv=n_splits, iid=True,
param_grid=params)
grid_search_iid.fit(X, y)

Expand Down Expand Up @@ -802,11 +802,11 @@ def test_random_search_cv_results():
n_splits = 3
n_search_iter = 30
params = dict(C=expon(scale=10), gamma=expon(scale=0.1))
random_search = RandomizedSearchCV(SVC(), n_iter=n_search_iter,
random_search = RandomizedSearchCV(SVC(gamma="scale"), n_iter=n_search_iter,
cv=n_splits, iid=False,
param_distributions=params)
random_search.fit(X, y)
random_search_iid = RandomizedSearchCV(SVC(), n_iter=n_search_iter,
random_search_iid = RandomizedSearchCV(SVC(gamma="scale"), n_iter=n_search_iter,
cv=n_splits, iid=True,
param_distributions=params)
random_search_iid.fit(X, y)
Expand Down Expand Up @@ -850,8 +850,8 @@ def test_search_iid_param():
# create "cv" for splits
cv = [[mask, ~mask], [~mask, mask]]
# once with iid=True (default)
grid_search = GridSearchCV(SVC(), param_grid={'C': [1, 10]}, cv=cv)
random_search = RandomizedSearchCV(SVC(), n_iter=2,
grid_search = GridSearchCV(SVC(gamma="scale"), param_grid={'C': [1, 10]}, cv=cv)
random_search = RandomizedSearchCV(SVC(gamma="scale"), n_iter=2,
param_distributions={'C': [1, 10]},
cv=cv)
for search in (grid_search, random_search):
Expand Down Expand Up @@ -893,10 +893,10 @@ def test_search_iid_param():
assert_almost_equal(train_std, 0)

# once with iid=False
grid_search = GridSearchCV(SVC(),
grid_search = GridSearchCV(SVC(gamma="scale"),
param_grid={'C': [1, 10]},
cv=cv, iid=False)
random_search = RandomizedSearchCV(SVC(), n_iter=2,
random_search = RandomizedSearchCV(SVC(gamma="scale"), n_iter=2,
param_distributions={'C': [1, 10]},
cv=cv, iid=False)

Expand Down Expand Up @@ -936,8 +936,8 @@ def test_search_cv_results_rank_tie_breaking():
# which would result in a tie of their mean cv-scores
param_grid = {'C': [1, 1.001, 0.001]}

grid_search = GridSearchCV(SVC(), param_grid=param_grid)
random_search = RandomizedSearchCV(SVC(), n_iter=3,
grid_search = GridSearchCV(SVC(gamma="scale"), param_grid=param_grid)
random_search = RandomizedSearchCV(SVC(gamma="scale"), n_iter=3,
param_distributions=param_grid)

for search in (grid_search, random_search):
Expand Down
11 changes: 8 additions & 3 deletions sklearn/svm/base.py
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Expand Up @@ -168,9 +168,14 @@ def fit(self, X, y, sample_weight=None):
"%r vs %r\n"
"Note: Sparse matrices cannot be indexed w/"
"boolean masks (use `indices=True` in CV)."
% (sample_weight.shape, X.shape))

if self.gamma == 'auto':
% (sample_weight.shape, X.shape))

if self.gamma == 'scale':
self._gamma = 1.0 / (X.shape[1] * X.std())
elif self.gamma == 'auto':
warnings.warn("The default gamma parameter value 'auto', calculated as 1 / n_features,"
" is depreciated in version 0.19 and will be replaced by 'scale',"
" calculated as 1 / (n_features * X.std()) in version 0.21.", DeprecationWarning)
self._gamma = 1.0 / X.shape[1]
else:
self._gamma = self.gamma
Expand Down
45 changes: 40 additions & 5 deletions sklearn/svm/classes.py
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Expand Up @@ -418,7 +418,14 @@ class SVC(BaseSVC):

gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
If gamma is 'auto' then 1/n_features will be used.
If gamma is 'scale' then 1/(n_features * X.std()) will be used.
The current default 'auto' is deprecated in version 0.19 and will
be replaced by 'scale' in version 0.21.

.. versionchanged:: 0.19
Default parameter value 'auto' is deprecated and will be replaced by
'scale' in version 0.21

coef0 : float, optional (default=0.0)
Independent term in kernel function.
Expand Down Expand Up @@ -572,7 +579,14 @@ class NuSVC(BaseSVC):

gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
If gamma is 'auto' then 1/n_features will be used.
If gamma is 'scale' then 1/(n_features * X.std()) will be used.
The current default 'auto' is deprecated in version 0.19 and will
be replaced by 'scale' in version 0.21.

.. versionchanged:: 0.19
Default parameter value 'auto' is deprecated and will be replaced by
'scale' in version 0.21

coef0 : float, optional (default=0.0)
Independent term in kernel function.
Expand Down Expand Up @@ -725,7 +739,14 @@ class SVR(BaseLibSVM, RegressorMixin):

gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
If gamma is 'auto' then 1/n_features will be used.
If gamma is 'scale' then 1/(n_features * X.std()) will be used.
The current default 'auto' is deprecated in version 0.19 and will
be replaced by 'scale' in version 0.21.

.. versionchanged:: 0.19
Default parameter value 'auto' is deprecated and will be replaced by
'scale' in version 0.21

coef0 : float, optional (default=0.0)
Independent term in kernel function.
Expand Down Expand Up @@ -840,7 +861,14 @@ class NuSVR(BaseLibSVM, RegressorMixin):

gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
If gamma is 'auto' then 1/n_features will be used.
If gamma is 'scale' then 1/(n_features * X.std()) will be used.
The current default 'auto' is deprecated in version 0.19 and will
be replaced by 'scale' in version 0.21.

.. versionchanged:: 0.19
Default parameter value 'auto' is deprecated and will be replaced by
'scale' in version 0.21

coef0 : float, optional (default=0.0)
Independent term in kernel function.
Expand Down Expand Up @@ -949,7 +977,14 @@ class OneClassSVM(BaseLibSVM):

gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
If gamma is 'auto' then 1/n_features will be used.
If gamma is 'scale' then 1/(n_features * X.std()) will be used.
The current default 'auto' is deprecated in version 0.19 and will
be replaced by 'scale' in version 0.21.

.. versionchanged:: 0.19
Default parameter value 'auto' is deprecated and will be replaced by
'scale' in version 0.21

coef0 : float, optional (default=0.0)
Independent term in kernel function.
Expand Down
6 changes: 3 additions & 3 deletions sklearn/svm/tests/test_sparse.py
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Expand Up @@ -181,7 +181,7 @@ def test_error():
Y2 = Y[:-1] # wrong dimensions for labels
assert_raises(ValueError, clf.fit, X_sp, Y2)

clf = svm.SVC()
clf = svm.SVC(gamma="scale")
clf.fit(X_sp, Y)
assert_array_equal(clf.predict(T), true_result)

Expand Down Expand Up @@ -238,7 +238,7 @@ def test_weight():
X_ = sparse.csr_matrix(X_)
for clf in (linear_model.LogisticRegression(),
svm.LinearSVC(random_state=0),
svm.SVC()):
svm.SVC(gamma="scale")):
clf.set_params(class_weight={0: 5})
clf.fit(X_[:180], y_[:180])
y_pred = clf.predict(X_[180:])
Expand All @@ -247,7 +247,7 @@ def test_weight():

def test_sample_weights():
# Test weights on individual samples
clf = svm.SVC()
clf = svm.SVC(gamma="scale")
clf.fit(X_sp, Y)
assert_array_equal(clf.predict([X[2]]), [1.])

Expand Down
34 changes: 26 additions & 8 deletions sklearn/svm/tests/test_svm.py
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E F438 xpand Up @@ -405,7 +405,7 @@ def test_weight():
weights=[0.833, 0.167], random_state=2)

for clf in (linear_model.LogisticRegression(),
svm.LinearSVC(random_state=0), svm.SVC()):
svm.LinearSVC(random_state=0), svm.SVC(gamma="scale")):
clf.set_params(class_weight={0: .1, 1: 10})
clf.fit(X_[:100], y_[:100])
y_pred = clf.predict(X_[100:])
Expand All @@ -415,7 +415,7 @@ def test_weight():
def test_sample_weights():
# Test weights on individual samples
# TODO: check on NuSVR, OneClass, etc.
clf = svm.SVC()
clf = svm.SVC(gamma="scale")
clf.fit(X, Y)
assert_array_equal(clf.predict([X[2]]), [1.])

Expand All @@ -424,7 +424,7 @@ def test_sample_weights():
assert_array_equal(clf.predict([X[2]]), [2.])

# test that rescaling all samples is the same as changing C
clf = svm.SVC()
clf = svm.SVC(gamma="scale")
clf.fit(X, Y)
dual_coef_no_weight = clf.dual_coef_
clf.set_params(C=100)
Expand Down Expand Up @@ -472,7 +472,7 @@ def test_bad_input():
assert_raises(ValueError, clf.fit, X, Y2)

# Test with arrays that are non-contiguous.
for clf in (svm.SVC(), svm.LinearSVC(random_state=0)):
for clf in (svm.SVC(gamma="scale"), svm.LinearSVC(random_state=0)):
Xf = np.asfortranarray(X)
assert_false(Xf.flags['C_CONTIGUOUS'])
yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T)
Expand All @@ -487,18 +487,18 @@ def test_bad_input():
assert_raises(ValueError, clf.fit, X, Y)

# sample_weight bad dimensions
clf = svm.SVC()
clf = svm.SVC(gamma="scale")
assert_raises(ValueError, clf.fit, X, Y, sample_weight=range(len(X) - 1))

# predict with sparse input when trained with dense
clf = svm.SVC().fit(X, Y)
clf = svm.SVC(gamma="scale").fit(X, Y)
assert_raises(ValueError, clf.predict, sparse.lil_matrix(X))

Xt = np.array(X).T
clf.fit(np.dot(X, Xt), Y)
assert_raises(ValueError, clf.predict, X)

clf = svm.SVC()
clf = svm.SVC(gamma="scale")
clf.fit(X, Y)
assert_raises(ValueError, clf.predict, Xt)

Expand Down Expand Up @@ -844,7 +844,7 @@ def test_timeout():
def test_unfitted():
X = "foo!" # input validation not required when SVM not fitted

clf = svm.SVC()
clf = svm.SVC(gamma="scale")
assert_raises_regexp(Exception, r".*\bSVC\b.*\bnot\b.*\bfitted\b",
clf.predict, X)

Expand Down Expand Up @@ -974,3 +974,21 @@ def test_ovr_decision_function():
# Test if the first point has lower decision value on every quadrant
# compared to the second point
assert_true(np.all(pred_class_deci_val[:, 0] < pred_class_deci_val[:, 1]))

def test_gamma_auto():
X, y = [[0.0], [1.0]], [0, 1]

msg = ("The default gamma parameter value 'auto', calculated as 1 / n_features,"
" is depreciated in version 0.19 and will be replaced by 'scale',"
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we use "deprecated" not "depreciated"

" calculated as 1 / (n_features * X.std()) in version 0.21.")

assert_warns_message(DeprecationWarning,
msg,
svm.SVC(gamma='auto').fit, X, y)
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But this means that a user can't intentionally pass 'auto' without receiving a warning, which isn't great. We could solve this by making the default actually 'auto_deprecated' which behaves like 'auto' with a warning. Using 'auto' explicitly would be same without warning.


def test_gamma_scale():
X, y = [[0.0], [1.0]], [0, 1]

clf = svm.SVC(gamma='scale').fit(X, y)
assert_equal(clf._gamma, 2.0)
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please check for more than one X


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there should be a newline at the end fo the file.

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