API: SVMs: eps -> tol

Change the eps argument to 'tol' and expose it in base classes.

The reasonning behind calling this argument tol rather than eps is that
it is a bound on the tolerance of the optimization, and it does not
relate to the machine precision (eps).

doc/modules/svm.rst

@@ -72,7 +72,7 @@ training samples::
     >>> Y = [0, 1]
     >>> clf = svm.SVC()
     >>> clf.fit(X, Y)
-    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, eps=0.001,
+    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, tol=0.001,
       cache_size=100.0, shrinking=True, gamma=0.5)
 
 After being fitted, the model can then be used to predict new values::
@@ -110,7 +110,7 @@ classifiers are constructed and each one trains data from two classes.
     >>> Y = [0, 1, 2, 3]
     >>> clf = svm.SVC()
     >>> clf.fit(X, Y)
-    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, eps=0.001,
+    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, tol=0.001,
       cache_size=100.0, shrinking=True, gamma=0.25)
     >>> dec = clf.decision_function([[1]])
     >>> dec.shape[1] # 4 classes: 4*3/2 = 6
@@ -124,8 +124,8 @@ two classes, only one model is trained.
 
     >>> lin_clf = svm.LinearSVC()
     >>> lin_clf.fit(X, Y)
-    LinearSVC(loss='l2', C=1.0, intercept_scaling=1, fit_intercept=True,
-         eps=0.0001, penalty='l2', multi_class=False, dual=True)
+    LinearSVC(loss='l2', C=1.0, dual=True, fit_intercept=True, penalty='l2',
+         multi_class=False, tol=0.0001, intercept_scaling=1)
     >>> dec = lin_clf.decision_function([[1]])
     >>> dec.shape[1]
     4
@@ -169,8 +169,8 @@ floating point values instead of integer values.
     >>> y = [0.5, 2.5]
     >>> clf = svm.SVR()
     >>> clf.fit(X, y)
-    SVR(kernel='rbf', C=1.0, probability=False, degree=3, shrinking=True,
-      eps=0.001, p=0.1, cache_size=100.0, coef0=0.0, nu=0.5, gamma=0.5)
+    SVR(kernel='rbf', C=1.0, probability=False, degree=3, shrinking=True, p=0.1,
+      tol=0.001, cache_size=100.0, coef0=0.0, nu=0.5, gamma=0.5)
     >>> clf.predict([[1, 1]])
     array([ 1.5])
 
@@ -270,7 +270,7 @@ Tips on Practical Use
   * The underlying :class:`LinearSVC` implementation uses a random
     number generator to select features when fitting the model. It is
     thus not uncommon, to have slightly different results for the same
-    input data. If that happens, try with a smaller eps parameter.
+    input data. If that happens, try with a smaller tol parameter.
 
 
 .. _svm_kernels:

scikits/learn/linear_model/logistic.py

@@ -41,6 +41,9 @@ class LogisticRegression(BaseLibLinear, ClassifierMixin,
         To lessen the effect of regularization on synthetic feature weight
         (and therefore on the intercept) intercept_scaling has to be increased
 
+    tol: float, optional
+         tolerance for stopping criteria
+
     Attributes
     ----------
 
@@ -60,19 +63,19 @@ class LogisticRegression(BaseLibLinear, ClassifierMixin,
     The underlying C implementation uses a random number generator to
     select features when fitting the model. It is thus not uncommon,
     to have slightly different results for the same input data. If
-    that happens, try with a smaller eps parameter.
+    that happens, try with a smaller tol parameter.
 
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification
     http://www.csie.ntu.edu.tw/~cjlin/liblinear/
     """
 
-    def __init__(self, penalty='l2', dual=False, eps=1e-4, C=1.0,
+    def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
                  fit_intercept=True, intercept_scaling=1):
 
         super(LogisticRegression, self).__init__ (penalty=penalty,
-            dual=dual, loss='lr', eps=eps, C=C,
+            dual=dual, loss='lr', tol=tol, C=C,
             fit_intercept=fit_intercept, intercept_scaling=intercept_scaling)
 
     def predict_proba(self, X):
@@ -96,7 +99,7 @@ def predict_proba(self, X):
         X = np.asanyarray(X, dtype=np.float64, order='C')
         probas = _liblinear.predict_prob_wrap(X, self.raw_coef_,
                                       self._get_solver_type(),
-                                      self.eps, self.C,
+                                      self.tol, self.C,
                                       self.class_weight_label,
                                       self.class_weight, self.label_,
                                       self._get_bias())

scikits/learn/linear_model/sparse/logistic.py

@@ -48,6 +48,9 @@ class LogisticRegression(SparseBaseLibLinear, ClassifierMixin,
         To lessen the effect of regularization on synthetic feature weight
         (and therefore on the intercept) intercept_scaling has to be increased
 
+    tol: float, optional
+         tolerance for stopping criteria
+
     Attributes
     ----------
 
@@ -67,19 +70,19 @@ class LogisticRegression(SparseBaseLibLinear, ClassifierMixin,
     The underlying C implementation uses a random number generator to
     select features when fitting the model. It is thus not uncommon,
     to have slightly different results for the same input data. If
-    that happens, try with a smaller eps parameter.
+    that happens, try with a smaller tol parameter.
 
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification
     http://www.csie.ntu.edu.tw/~cjlin/liblinear/
     """
 
-    def __init__(self, penalty='l2', dual=False, eps=1e-4, C=1.0,
+    def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
                  fit_intercept=True, intercept_scaling=1):
 
         super(LogisticRegression, self).__init__ (penalty=penalty,
-            dual=dual, loss='lr', eps=eps, C=C,
+            dual=dual, loss='lr', tol=tol, C=C,
             fit_intercept=fit_intercept, intercept_scaling=intercept_scaling)
 
     def predict_proba(self, X):
@@ -95,7 +98,7 @@ def predict_proba(self, X):
         probas = csr_predict_prob(X.shape[1], X.data, X.indices,
                                   X.indptr, self.raw_coef_,
                                   self._get_solver_type(),
-                                  self.eps, self.C,
+                                  self.tol, self.C,
                                   self.class_weight_label,
                                   self.class_weight, self.label_,
                                   self._get_bias())

scikits/learn/pipeline.py

@@ -73,7 +73,7 @@ class Pipeline(BaseEstimator):
         >>> # For instance, fit using a k of 10 in the SelectKBest
         >>> # and a parameter 'C' of the svn
         >>> anova_svm.fit(X, y, anova__k=10, svc__C=.1) #doctest: +ELLIPSIS
-        Pipeline(steps=[('anova', SelectKBest(k=10, score_func=<function f_regression at ...>)), ('svc', SVC(kernel='linear', C=0.1, probability=False, degree=3, coef0=0.0, eps=0.001,
+        Pipeline(steps=[('anova', SelectKBest(k=10, score_func=<function f_regression at ...>)), ('svc', SVC(kernel='linear', C=0.1, probability=False, degree=3, coef0=0.0, tol=0.001,
           cache_size=100.0, shrinking=True, gamma=0.0))])
 
         >>> prediction = anova_svm.predict(X)

scikits/learn/svm/base.py

@@ -37,7 +37,7 @@ class BaseLibSVM(BaseEstimator):
     _svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
 
     def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
-                 eps, C, nu, p, shrinking, probability):
+                 tol, C, nu, p, shrinking, probability):
 
         if not impl in self._svm_types:
             raise ValueError("impl should be one of %s, %s was given" % (
@@ -53,7 +53,7 @@ def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
         self.gamma = gamma
         self.coef0 = coef0
         self.cache_size = cache_size
-        self.eps = eps
+        self.tol = tol
         self.C = C
         self.nu = nu
         self.p = p
@@ -143,7 +143,7 @@ def fit(self, X, y, class_weight={}, sample_weight=[], **params):
         self.dual_coef_, self.intercept_, self.label_, self.probA_, \
         self.probB_ = \
         libsvm_train(_X, y, solver_type, kernel_type, self.degree,
-                      self.gamma, self.coef0, self.eps, self.C,
+                      self.gamma, self.coef0, self.tol, self.C,
                       self.nu, self.cache_size, self.p,
                       self.class_weight_label, self.class_weight,
                       sample_weight, int(self.shrinking),
@@ -185,7 +185,7 @@ def predict(self, X):
         return libsvm_predict(X, self.support_vectors_,
                       self.dual_coef_, self.intercept_,
                       self._svm_types.index(self.impl), kernel_type,
-                      self.degree, self.gamma, self.coef0, self.eps,
+                      self.degree, self.gamma, self.coef0, self.tol,
                       self.C, self.class_weight_label,
                       self.class_weight, self.nu, self.cache_size,
                       self.p, int(self.shrinking),
@@ -225,7 +225,7 @@ def predict_proba(self, T):
         pprob = libsvm_predict_proba(T, self.support_vectors_,
                       self.dual_coef_, self.intercept_,
                       self._svm_types.index(self.impl), kernel_type,
-                      self.degree, self.gamma, self.coef0, self.eps,
+                      self.degree, self.gamma, self.coef0, self.tol,
                       self.C, self.class_weight_label,
                       self.class_weight, self.nu, self.cache_size,
                       self.p, int(self.shrinking),
@@ -280,7 +280,7 @@ def decision_function(self, T):
         dec_func = libsvm_decision_function(T, self.support_vectors_,
                       self.dual_coef_, self.intercept_,
                       self._svm_types.index(self.impl), kernel_type,
-                      self.degree, self.gamma, self.coef0, self.eps,
+                      self.degree, self.gamma, self.coef0, self.tol,
                       self.C, self.class_weight_label,
                       self.class_weight, self.nu, self.cache_size,
                       self.p, int(self.shrinking),
@@ -319,12 +319,12 @@ class BaseLibLinear(BaseEstimator):
         'PL2_LLR_D1' : 7,  # L2 penalty, logistic regression, dual form
         }
 
-    def __init__(self, penalty='l2', loss='l2', dual=True, eps=1e-4, C=1.0,
+    def __init__(self, penalty='l2', loss='l2', dual=True, tol=1e-4, C=1.0,
                  multi_class=False, fit_intercept=True, intercept_scaling=1):
         self.penalty = penalty
         self.loss = loss
         self.dual = dual
-        self.eps = eps
+        self.tol = tol
         self.C = C
         self.fit_intercept = fit_intercept
         self.intercept_scaling = intercept_scaling
@@ -377,7 +377,7 @@ def fit(self, X, y, class_weight={}, **params):
         y = np.asanyarray(y, dtype=np.int32, order='C')
 
         self.raw_coef_, self.label_ = _liblinear.train_wrap(X, y,
-                       self._get_solver_type(), self.eps,
+                       self._get_solver_type(), self.tol,
                        self._get_bias(), self.C,
                        self.class_weight_label, self.class_weight)
 
@@ -402,7 +402,7 @@ def predict(self, X):
 
         return _liblinear.predict_wrap(X, coef,
                                       self._get_solver_type(),
-                                      self.eps, self.C,
+                                      self.tol, self.C,
                                       self.class_weight_label,
                                       self.class_weight, self.label_,
                                       self._get_bias())
@@ -426,7 +426,7 @@ def decision_function(self, X):
         self._check_n_features(X)
 
         dec_func = _liblinear.decision_function_wrap(
-            X, self.raw_coef_, self._get_solver_type(), self.eps,
+            X, self.raw_coef_, self._get_solver_type(), self.tol,
             self.C, self.class_weight_label, self.class_weight,
             self.label_, self._get_bias())
 

scikits/learn/svm/liblinear.py

@@ -28,8 +28,8 @@ class LinearSVC(BaseLibLinear, ClassifierMixin, CoefSelectTransformerMixin):
         Select the algorithm to either solve the dual or primal
         optimization problem.
 
-    eps: float, optional
-         precision for stopping criteria
+    tol: float, optional
+         tolerance for stopping criteria
 
     multi_class: boolean, optional
          perform multi-class SVM by Cramer and Singer. If active,
@@ -60,7 +60,7 @@ class LinearSVC(BaseLibLinear, ClassifierMixin, CoefSelectTransformerMixin):
     The underlying C implementation uses a random number generator to
     select features when fitting the model. It is thus not uncommon,
     to have slightly different results for the same input data. If
-    that happens, try with a smaller eps parameter.
+    that happens, try with a smaller tol parameter.
 
     See also
     --------

scikits/learn/svm/libsvm.py

@@ -35,7 +35,7 @@ class SVC(BaseLibSVM, ClassifierMixin):
     shrinking: boolean, optional
          wether to use the shrinking heuristic.
 
-    eps: float, optional
+    tol: float, optional
          precision for stopping criteria
 
     cache_size: float, optional
@@ -72,7 +72,7 @@ class SVC(BaseLibSVM, ClassifierMixin):
     >>> from scikits.learn.svm import SVC
     >>> clf = SVC()
     >>> clf.fit(X, y)
-    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, eps=0.001,
+    SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, tol=0.001,
       cache_size=100.0, shrinking=True, gamma=0.25)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
@@ -84,10 +84,10 @@ class SVC(BaseLibSVM, ClassifierMixin):
 
     def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=0.0,
                  coef0=0.0, shrinking=True, probability=False,
-                 eps=1e-3, cache_size=100.0):
+                 tol=1e-3, cache_size=100.0):
 
         BaseLibSVM.__init__(self, 'c_svc', kernel, degree, gamma, coef0,
-                         cache_size, eps, C, 0., 0.,
+                         cache_size, tol, C, 0., 0.,
                          shrinking, probability)
 
 
@@ -125,7 +125,7 @@ class NuSVC(BaseLibSVM, ClassifierMixin):
     shrinking: boolean, optional
          wether to use the shrinking heuristic.
 
-    eps: float, optional
+    tol: float, optional
          precision for stopping criteria
 
     cache_size: float, optional
@@ -179,7 +179,7 @@ class NuSVC(BaseLibSVM, ClassifierMixin):
     >>> from scikits.learn.svm import NuSVC
     >>> clf = NuSVC()
     >>> clf.fit(X, y)
-    NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, eps=0.001,
+    NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, tol=0.001,
        cache_size=100.0, shrinking=True, nu=0.5, gamma=0.25)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
@@ -191,10 +191,10 @@ class NuSVC(BaseLibSVM, ClassifierMixin):
 
     def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma=0.0,
                  coef0=0.0, shrinking=True, probability=False,
-                 eps=1e-3, cache_size=100.0):
+                 tol=1e-3, cache_size=100.0):
 
         BaseLibSVM.__init__(self, 'nu_svc', kernel, degree, gamma, coef0,
-                         cache_size, eps, 0., nu, 0.,
+                         cache_size, tol, 0., nu, 0.,
                          shrinking, probability)
 
 
@@ -231,7 +231,7 @@ class SVR(BaseLibSVM, RegressorMixin):
         enable probability estimates. This must be enabled prior
         to calling prob_predict.
 
-    eps: float, optional
+    tol: float, optional
          precision for stopping criteria
 
     coef0 : float, optional
@@ -267,11 +267,11 @@ class SVR(BaseLibSVM, RegressorMixin):
     NuSVR
     """
     def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
-                 cache_size=100.0, eps=1e-3, C=1.0, nu=0.5, p=0.1,
+                 cache_size=100.0, tol=1e-3, C=1.0, nu=0.5, p=0.1,
                  shrinking=True, probability=False):
 
         BaseLibSVM.__init__(self, 'epsilon_svr', kernel, degree, gamma, coef0,
-                         cache_size, eps, C, nu, p,
+                         cache_size, tol, C, nu, p,
                          shrinking, probability)
 
     def fit(self, X, y, sample_weight=[]):
@@ -325,7 +325,7 @@ class NuSVR(BaseLibSVM, RegressorMixin):
         kernel coefficient for rbf and poly, by default 1/n_features
         will be taken.
 
-    eps: float, optional
+    tol: float, optional
          precision for stopping criteria
 
     probability: boolean, optional (False by default)
@@ -367,10 +367,10 @@ class NuSVR(BaseLibSVM, RegressorMixin):
 
     def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3,
                  gamma=0.0, coef0=0.0, shrinking=True,
-                 probability=False, cache_size=100.0, eps=1e-3):
+                 probability=False, cache_size=100.0, tol=1e-3):
 
         BaseLibSVM.__init__(self, 'epsilon_svr', kernel, degree, gamma, coef0,
-                         cache_size, eps, C, nu, 0.,
+                         cache_size, tol, C, nu, 0.,
                          shrinking, probability)
 
     def fit(self, X, y):
@@ -423,7 +423,7 @@ class OneClassSVM(BaseLibSVM):
         Independent term in kernel function. It is only significant in
         poly/sigmoid.
 
-    eps: float, optional
+    tol: float, optional
          precision for stopping criteria
 
     shrinking: boolean, optional
@@ -452,9 +452,9 @@ class OneClassSVM(BaseLibSVM):
 
     """
     def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
-                 cache_size=100.0, eps=1e-3, nu=0.5, shrinking=True):
+                 cache_size=100.0, tol=1e-3, nu=0.5, shrinking=True):
         BaseLibSVM.__init__(self, 'one_class', kernel, degree, gamma, coef0,
-                             cache_size, eps, 0.0, nu, 0.0, shrinking, False)
+                             cache_size, tol, 0.0, nu, 0.0, shrinking, False)
 
     def fit(self, X, class_weight={}, sample_weight=[], **params):
         """