Set default n_jobs=None

README.md

@@ -118,8 +118,8 @@ A decision rule is a logical expression of the form "IF conditions THEN response
 
 2) In [1], rules are extracted from an ensemble of trees; a weighted combination of these rules is then built by solving a L1-regularized optimization problem over the weights as described in [5].
 
-In this package, we use the second approach. Rules are extracted from tree ensemble, which allow us to take advantage of existing fast algorithms (such as bagged decision trees, or gradient boosting) to produce such tree ensemble. Too similar or duplicated rules are then removed, based on a similarity threshold of their supports..
-The main goal of this package is to provide rules verifying precision and recall conditions. It still implement a score (`decision_function`) method, but which does not solve the L1-regularized optimization problem as in [1]. Instead, weights are simply proportional to the OOB associated precision of the rule.
+In this package, we use the second approach. Rules are extracted from tree ensemble, which allow us to take advantage of existing fast algorithms (such as bagged decision trees, or gradient boosting) to produce such tree ensemble. Too similar or duplicated rules are then removed, based on a similarity threshold of their supports.
+The main goal of this package is to provide rules verifying precision and recall conditions. It still implements a score (`decision_function`) method, but which does not solve the L1-regularized optimization problem as in [1]. Instead, weights are simply proportional to the OOB associated precision of the rule.
 
 This package also offers convenient methods to compute predictions with the k most precise rules (cf score_top_rules() and predict_top_rules() functions).
 

doc/_build/html/_sources/index.txt

@@ -25,7 +25,7 @@ estimator with unit tests, along with examples and benchmarks.
        auto_examples/index
        ...
 
-See the `README <https://github.com/skope-rules/skope-rules/blob/master/README.md>`_
+See the `README <https://github.com/scikit-learn-contrib/skope-rules/blob/master/README.md>`_
 for more information.
 
 

skrules/skope_rules.py

@@ -95,7 +95,7 @@ class SkopeRules(BaseEstimator):
               `ceil(min_samples_split * n_samples)` are the minimum
               number of samples for each split.
 
-    n_jobs : integer, optional (default=1)
+    n_jobs : integer, optional (default=None)
         The number of jobs to run in parallel for both `fit` and `predict`.
         If -1, then the number of jobs is set to the number of cores.
 
@@ -150,7 +150,7 @@ def __init__(self,
                  max_depth_duplication=None,
                  max_features=1.,
                  min_samples_split=2,
-                 n_jobs=1,
+                 n_jobs=None,
                  random_state=None,
                  verbose=0):
         self.precision_min = precision_min