COSMIT pep8

Author: amueller

sklearn/hmm.py

@@ -952,12 +952,13 @@ class GMMHMM(_BaseHMM):
     >>> GMMHMM(n_components=2, n_mix=10, covariance_type='diag')
     ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
     GMMHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01,
-        gmms=[GMM(covariance_type=None, init_params='wmc', min_covar=0.001, 
-        n_components=10, n_init=1, n_iter=100, params='wmc', random_state=None, 
-        thresh=0.01), GMM(covariance_type=None, init_params='wmc', min_covar=0.001, 
-        n_components=10, n_init=1, n_iter=100, params='wmc', random_state=None, 
-        thresh=0.01)], n_components=2, n_mix=10, random_state=None, startprob=None,
-        startprob_prior=1.0, transmat=None, transmat_prior=1.0)
+        gmms=[GMM(covariance_type=None, init_params='wmc', min_covar=0.001,
+        n_components=10, n_init=1, n_iter=100, params='wmc', random_state=None,
+        thresh=0.01), GMM(covariance_type=None, init_params='wmc',
+        min_covar=0.001, n_components=10, n_init=1, n_iter=100, params='wmc',
+        random_state=None, thresh=0.01)], n_components=2, n_mix=10,
+        random_state=None, startprob=None, startprob_prior=1.0, transmat=None,
+        transmat_prior=1.0)
 
     See Also
     --------

sklearn/mixture/dpgmm.py

@@ -17,7 +17,6 @@
 
 from ..utils import check_random_state
 from ..utils.extmath import norm
-from ..utils import deprecated
 from .. import cluster
 from .gmm import GMM
 
@@ -203,7 +202,7 @@ def __init__(self, n_components=1, covariance_type='diag', alpha=1.0,
         super(DPGMM, self).__init__(n_components, covariance_type,
                                     random_state=random_state,
                                     thresh=thresh, min_covar=min_covar,
-                                    n_iter=n_iter, params=params, 
+                                    n_iter=n_iter, params=params,
                                     init_params=init_params)
 
     def _get_precisions(self):
@@ -480,7 +479,7 @@ def fit(self, X, **kwargs):
         A initialization step is performed before entering the em
         algorithm. If you want to avoid this step, set the keyword
         argument init_params to the empty string '' when when creating
-        the object. Likewise, if you would like just to do an 
+        the object. Likewise, if you would like just to do an
         initialization, set n_iter=0.
 
         Parameters
@@ -491,15 +490,15 @@ def fit(self, X, **kwargs):
         """
         self.random_state = check_random_state(self.random_state)
         if kwargs:
-            warnings.warn("Setting parameters in the 'fit' method is deprecated"
-                     "Set it on initialization instead.",
+            warnings.warn("Setting parameters in the 'fit' method is"
+                    "deprecated. Set it on initialization instead.",
                      DeprecationWarning)
             # initialisations for in case the user still adds parameters to fit
             # so things don't break
             if 'n_iter' in kwargs:
-                self.n_iter =  kwargs['n_iter']
+                self.n_iter = kwargs['n_iter']
             if 'params' in kwargs:
-                self.params = kwargs['params']            
+                self.params = kwargs['params']
             if 'init_params' in kwargs:
                 self.init_params = kwargs['init_params']
 

sklearn/mixture/gmm.py

@@ -196,8 +196,9 @@ class GMM(BaseEstimator):
     >>> obs = np.concatenate((np.random.randn(100, 1),
     ...                       10 + np.random.randn(300, 1)))
     >>> g.fit(obs) # doctest: +NORMALIZE_WHITESPACE
-    GMM(covariance_type=None, init_params='wmc', min_covar=0.001, n_components=2,
-      n_init=1, n_iter=100, params='wmc', random_state=None, thresh=0.01)
+    GMM(covariance_type=None, init_params='wmc', min_covar=0.001,
+            n_components=2, n_init=1, n_iter=100, params='wmc',
+            random_state=None, thresh=0.01)
     >>> np.round(g.weights_, 2)
     array([ 0.75,  0.25])
     >>> np.round(g.means_, 2)
@@ -213,8 +214,9 @@ class GMM(BaseEstimator):
     >>> # Refit the model on new data (initial parameters remain the
     >>> # same), this time with an even split between the two modes.
     >>> g.fit(20 * [[0]] +  20 * [[10]]) # doctest: +NORMALIZE_WHITESPACE
-    GMM(covariance_type=None, init_params='wmc', min_covar=0.001, n_components=2,
-      n_init=1, n_iter=100, params='wmc', random_state=None, thresh=0.01)
+    GMM(covariance_type=None, init_params='wmc', min_covar=0.001,
+            n_components=2, n_init=1, n_iter=100, params='wmc',
+            random_state=None, thresh=0.01)
     >>> np.round(g.weights_, 2)
     array([ 0.5,  0.5])
 
@@ -435,15 +437,15 @@ def fit(self, X, **kwargs):
 
         A initialization step is performed before entering the em
         algorithm. If you want to avoid this step, set the keyword
-        argument init_params to the empty string '' when creating the 
-        GMM object. Likewise, if you would like just to do an 
+        argument init_params to the empty string '' when creating the
+        GMM object. Likewise, if you would like just to do an
         initialization, set n_iter=0.
 
         Parameters
         ----------
         X : array_like, shape (n, n_features)
             List of n_features-dimensional data points.  Each row
-            corresponds to a single data point.       
+            corresponds to a single data point.
         """
         ## initialization step
         X = np.asarray(X)
@@ -454,13 +456,13 @@ def fit(self, X, **kwargs):
                 'GMM estimation with %s components, but got only %s samples' %
                 (self.n_components, X.shape[0]))
         if kwargs:
-            warnings.warn("Setting parameters in the 'fit' method is deprecated"
-                    "Set it on initialization instead.",
+            warnings.warn("Setting parameters in the 'fit' method is"
+                    "deprecated. Set it on initialization instead.",
                     DeprecationWarning)
             # initialisations for in case the user still adds parameters to fit
             # so things don't break
             if 'n_iter' in kwargs:
-                self.n_iter =  kwargs['n_iter']
+                self.n_iter = kwargs['n_iter']
             if 'n_init' in kwargs:
                 if kwargs['n_init'] < 1:
                     raise ValueError('GMM estimation requires at least one run')
@@ -470,7 +472,6 @@ def fit(self, X, **kwargs):
                 self.params = kwargs['params']
             if 'init_params' in kwargs:
                 self.init_params = kwargs['init_params']
-            
 
         max_log_prob = - np.infty
 

sklearn/mixture/tests/test_gmm.py

@@ -228,7 +228,7 @@ def test_train_degenerate(self, params='wmc'):
         X = rng.randn(100, self.n_features)
         X.T[1:] = 0
         g = self.model(n_components=2, covariance_type=self.covariance_type,
-                       random_state=rng, min_covar=1e-3, n_iter=5, 
+                       random_state=rng, min_covar=1e-3, n_iter=5,
                        init_params=params)
         g.fit(X)
         trainll = g.score(X)
@@ -241,7 +241,7 @@ def test_train_1d(self, params='wmc'):
         X = rng.randn(100, 1)
         #X.T[1:] = 0
         g = self.model(n_components=2, covariance_type=self.covariance_type,
-                       random_state=rng, min_covar=1e-7, n_iter=5, 
+                       random_state=rng, min_covar=1e-7, n_iter=5,
                        init_params=params)
         g.fit(X)
         trainll = g.score(X)

sklearn/neighbors/base.py

@@ -16,12 +16,14 @@
 from ..metrics import pairwise_distances
 from ..utils import safe_asarray, atleast2d_or_csr
 
+
 class NeighborsWarning(UserWarning):
     pass
 
 # Make sure that NeighborsWarning are displayed more than once
 warnings.simplefilter("always", NeighborsWarning)
 
+
 def warn_equidistant():
     msg = ("kneighbors: neighbor k+1 and neighbor k have the same "
            "distance: results will be dependent on data order.")

sklearn/utils/__init__.py

@@ -11,6 +11,7 @@
 # Make sure that DeprecationWarning get printed
 warnings.simplefilter("always", DeprecationWarning)
 
+
 class deprecated(object):
     """Decorator to mark a function or class as deprecated.