fix combiner n_jobs

scorecardpipeline/feature_selection.py

@@ -275,13 +275,13 @@ def _IV(x, y, regularization=1.0, n_jobs=None):
 
 class InformationValueSelector(SelectorMixin):
 
-    def __init__(self, threshold=0.02, target="target", regularization=1.0, methods=None, n_jobs=None, **kwargs):
+    def __init__(self, threshold=0.02, target="target", regularization=1.0, methods=None, n_jobs=None, combiner=None, **kwargs):
         super().__init__()
         self.dropped = None
         self.select_columns = None
         self.scores_ = None
         self.n_features_in_ = None
-        self.combiner = None
+        self.combiner = combiner
         self.threshold = threshold
         self.target = target
         self.regularization = regularization
@@ -298,7 +298,9 @@ def fit(self, x: pd.DataFrame, y=None):
 
         self.n_features_in_ = x.shape[1]
 
-        if self.methods:
+        if self.combiner:
+            xt = self.combiner.transform(x)
+        elif self.methods:
             temp = x.copy()
             temp[self.target] = y
             self.combiner = Combiner(target=self.target, method=self.methods, n_jobs=self.n_jobs, **self.kwargs)
@@ -350,7 +352,7 @@ class LiftSelector(SelectorMixin):
     :param select_columns : array-like
     :param dropped : DataFrame
     """
-    def __init__(self, target="target", threshold=3.0, n_jobs=None, methods=None, **kwargs):
+    def __init__(self, target="target", threshold=3.0, n_jobs=None, methods=None, combiner=None, **kwargs):
         """
         :param target: target
         :param threshold: float or str (default=3.0). Feature which has a lift score greater than `threshold` will be kept.
@@ -362,6 +364,7 @@ def __init__(self, target="target", threshold=3.0, n_jobs=None, methods=None, **
         self.n_jobs = n_jobs
         self.target = target
         self.methods = methods
+        self.combiner = combiner
         self.kwargs = kwargs
 
     def fit(self, x: pd.DataFrame, y=None, **fit_params):
@@ -372,8 +375,10 @@ def fit(self, x: pd.DataFrame, y=None, **fit_params):
             x = x.drop(columns=self.target)
 
         self.n_features_in_ = x.shape[1]
-
-        if self.methods:
+
+        if self.combiner:
+            xt = self.combiner.transform(x)
+        elif self.methods:
             temp = x.copy()
             temp[self.target] = y
             self.combiner = Combiner(target=self.target, method=self.methods, n_jobs=self.n_jobs, **self.kwargs)

scorecardpipeline/processing.py

@@ -502,7 +502,7 @@ def fit(self, x: pd.DataFrame, y=None):
 
         if self.method in ["cart", "mdlp", "uniform"]:
             feature_optbinning_bins = partial(self.optbinning_bins, data=x, target=self.target, min_n_bins=self.min_n_bins, max_n_bins=self.max_n_bins, max_n_prebins=self.max_n_prebins, min_prebin_size=self.min_prebin_size, min_bin_size=self.min_bin_size, max_bin_size=self.max_bin_size, gamma=self.gamma, monotonic_trend=self.monotonic_trend, **self.kwargs)
-            if self.n_jobs > 1:
+            if self.n_jobs is not None:
                 rules = Parallel(n_jobs=self.n_jobs)(delayed(feature_optbinning_bins)(feature) for feature in x.columns.drop(self.target))
                 [self.combiner.update(r) for r in rules]
                 # with ProcessPoolExecutor(max_workers=self.n_jobs) as executor: