Merge pull request #208 from dpr1005/deepsource-transform-49e39c06

Format code with yapf, black, autopep8 and isort

Author: dpuenteramirez

semisupervised/DensityPeaks.py

@@ -16,6 +16,7 @@
 from sklearn.svm import SVC
 
 from instance_selection import ENN
+
 from .utils import split
 
 
@@ -31,19 +32,19 @@ class STDPNF:
     """
 
     def __init__(
-            self,
-            dc=None,
-            distance_metric="euclidean",
-            k=3,
-            gauss_cutoff=True,
-            percent=2.0,
-            density_threshold=None,
-            distance_threshold=None,
-            anormal=True,
-            filtering=False,
-            classifier=None,
-            classifier_params=None,
-            filter_method=None,
+        self,
+        dc=None,
+        distance_metric="euclidean",
+        k=3,
+        gauss_cutoff=True,
+        percent=2.0,
+        density_threshold=None,
+        distance_threshold=None,
+        anormal=True,
+        filtering=False,
+        classifier=None,
+        classifier_params=None,
+        filter_method=None,
     ):
         """Semi Supervised Algorithm based on Density Peaks."""
         self.dc = dc
@@ -121,8 +122,7 @@ def __auto_select_dc(self):
 
         while True:
             nneighs = (
-                    sum([1 for v in self.distances.values() if
-                         v < dc]) / self.n_id ** 2
+                sum([1 for v in self.distances.values() if v < dc]) / self.n_id**2
             )
             if 0.01 <= nneighs <= 0.02:
                 break
@@ -476,7 +476,7 @@ def _fit_stdpnf(self):
         while count <= max(self.order.values()):
             unlabeled_rows = self.structure_stdnpf.loc[
                 self.structure_stdnpf["label"] == -1
-                ].index.to_list()
+            ].index.to_list()
             unlabeled_indexes = []
             for row in unlabeled_rows:
                 if self.order[row] == count:
@@ -492,7 +492,7 @@ def _fit_stdpnf(self):
             else:
                 labeled_data = self.structure_stdnpf.loc[
                     self.structure_stdnpf["label"] != -1
-                    ]
+                ]
                 complete = labeled_data["sample"]
                 complete_y = labeled_data["label"]
 
@@ -502,15 +502,14 @@ def _fit_stdpnf(self):
 
             labeled_data = self.structure_stdnpf.loc[
                 self.structure_stdnpf["label"] != -1
-                ]
+            ]
             self.classifier_stdpnf.fit(
                 labeled_data["sample"].tolist(), labeled_data["label"].tolist()
             )
 
             count += 1
 
-        labeled_data = self.structure_stdnpf.loc[
-            self.structure_stdnpf["label"] != -1]
+        labeled_data = self.structure_stdnpf.loc[self.structure_stdnpf["label"] != -1]
         self.classifier_stdpnf.fit(
             labeled_data["sample"].tolist(), labeled_data["label"].tolist()
         )
@@ -533,8 +532,7 @@ def _results_to_structure(self, complete, result):
             if not is_in:
                 results_to_unlabeled.append(r)
         for r in results_to_unlabeled:
-            self.structure_stdnpf.at[
-                np.array(self.structure_stdnpf["sample"], r)][
+            self.structure_stdnpf.at[np.array(self.structure_stdnpf["sample"], r)][
                 "label"
             ] = -1