Fix linting issues with Codacy.

nicodv · Apr 5, 2019 · 033d087 · 033d087
1 parent 63e351b
commit 033d087
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Showing 2 changed files with 28 additions and 24 deletions.
diff --git a/kmodes/kmodes.py b/kmodes/kmodes.py
@@ -390,16 +390,18 @@ def fit(self, X, y=None, **kwargs):
         """
 
         random_state = check_random_state(self.random_state)
-        self._enc_cluster_centroids, self._enc_map, self.labels_,\
-        self.cost_, self.n_iter_, self.epoch_costs_ = k_modes(X,
-                                                          self.n_clusters,
-                                                          self.max_iter,
-                                                          self.cat_dissim,
-                                                          self.init,
-                                                          self.n_init,
-                                                          self.verbose,
-                                                          random_state,
-                                                          self.n_jobs)
+        self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_, \
+        self.n_iter_, self.epoch_costs_ = k_modes(
+                X,
+                self.n_clusters,
+                self.max_iter,
+                self.cat_dissim,
+                self.init,
+                self.n_init,
+                self.verbose,
+                random_state,
+                self.n_jobs,
+        )
         return self
 
     def fit_predict(self, X, y=None, **kwargs):

diff --git a/kmodes/kprototypes.py b/kmodes/kprototypes.py
@@ -451,20 +451,22 @@ def fit(self, X, y=None, categorical=None):
         random_state = check_random_state(self.random_state)
         # If self.gamma is None, gamma will be automatically determined from
         # the data. The function below returns its value.
-        self._enc_cluster_centroids, self._enc_map, \
-        self.labels_, self.cost_, self.n_iter_, \
-        self.epoch_costs_, self.gamma = k_prototypes(X,
-                                            categorical,
-                                            self.n_clusters,
-                                            self.max_iter,
-                                            self.num_dissim,
-                                            self.cat_dissim,
-                                            self.gamma,
-                                            self.init,
-                                            self.n_init,
-                                            self.verbose,
-                                            random_state,
-                                            self.n_jobs)
+        self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_, \
+        self.n_iter_, self.epoch_costs_, self.gamma = k_prototypes(
+                X,
+                categorical,
+                self.n_clusters,
+                self.max_iter,
+                self.num_dissim,
+                self.cat_dissim,
+                self.gamma,
+                self.init,
+                self.n_init,
+                self.verbose,
+                random_state,
+                self.n_jobs
+        )
+
         return self
 
     def predict(self, X, categorical=None):