Use immutable default arguments throughtout repo

bryandeng · bryandeng · commit 956f88a2fc60 · 2015-03-18T21:35:17.000+01:00
Replace mutable default arguments in examples

Replace mutable default arguments in sklearn/manifold/t_sne.py

Replace mutable default arguments in sklearn/metrics/pairwise.py

Replace mutable default arguments in sklearn/feature_selection/rfe.py
diff --git a/examples/applications/plot_species_distribution_modeling.py b/examples/applications/plot_species_distribution_modeling.py
@@ -87,8 +87,8 @@ def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
     return bunch
 
 
-def plot_species_distribution(species=["bradypus_variegatus_0",
-                                       "microryzomys_minutus_0"]):
+def plot_species_distribution(species=("bradypus_variegatus_0",
+                                       "microryzomys_minutus_0")):
     """
     Plot the species distribution.
     """
diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
@@ -102,7 +102,7 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
            Mach. Learn., 46(1-3), 389--422, 2002.
     """
     def __init__(self, estimator, n_features_to_select=None, step=1,
-                 estimator_params={}, verbose=0):
+                 estimator_params=None, verbose=0):
         self.estimator = estimator
         self.n_features_to_select = n_features_to_select
         self.step = step
@@ -316,7 +316,7 @@ class RFECV(RFE, MetaEstimatorMixin):
            Mach. Learn., 46(1-3), 389--422, 2002.
     """
     def __init__(self, estimator, step=1, cv=None, scoring=None,
-                 estimator_params={}, verbose=0):
+                 estimator_params=None, verbose=0):
         self.estimator = estimator
         self.step = step
         self.cv = cv
diff --git a/sklearn/manifold/t_sne.py b/sklearn/manifold/t_sne.py
@@ -114,7 +114,7 @@ def _kl_divergence(params, P, alpha, n_samples, n_components):
 def _gradient_descent(objective, p0, it, n_iter, n_iter_without_progress=30,
                       momentum=0.5, learning_rate=1000.0, min_gain=0.01,
                       min_grad_norm=1e-7, min_error_diff=1e-7, verbose=0,
-                      args=[]):
+                      args=None):
     """Batch gradient descent with momentum and individual gains.
 
     Parameters
@@ -173,6 +173,9 @@ def _gradient_descent(objective, p0, it, n_iter, n_iter_without_progress=30,
     i : int
         Last iteration.
     """
+    if args is None:
+        args = []
+
     p = p0.copy().ravel()
     update = np.zeros_like(p)
     gains = np.ones_like(p)
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -346,7 +346,7 @@ def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",
 
 
 def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
-                              batch_size=500, metric_kwargs={}):
+                              batch_size=500, metric_kwargs=None):
     """Compute minimum distances between one point and a set of points.
 
     This function computes for each row in X, the index of the row of Y which
@@ -419,6 +419,9 @@ def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
     sklearn.metrics.pairwise_distances
     sklearn.metrics.pairwise_distances_argmin_min
     """
+    if metric_kwargs is None:
+        metric_kwargs = {}
+
     return pairwise_distances_argmin_min(X, Y, axis, metric, batch_size,
                                          metric_kwargs)[0]
 
