fix code formatting

settylab · Dec 14, 2022 · 450bbc5 · 450bbc5
1 parent f40933c
commit 450bbc5
Showing 1 changed file with 8 additions and 7 deletions.
diff --git a/mellon/model.py b/mellon/model.py
@@ -39,6 +39,7 @@
 
 DEFAULT_COV_FUNC = Matern52
 
+
 class BaseEstimator:
     R"""
     Base class for the mellon estimators.
@@ -117,7 +118,7 @@ def _set_x(self, x):
     def _compute_landmarks(self):
         x = self.x
         n_landmarks = self.n_landmarks
-        logger.info(f'Computing {n_landmarks:,} landmarks with k-means clustering.')
+        logger.info(f"Computing {n_landmarks:,} landmarks with k-means clustering.")
         landmarks = compute_landmarks(x, n_landmarks=n_landmarks)
         return landmarks
 
@@ -136,7 +137,7 @@ def _compute_cov_func(self):
         cov_func_curry = self.cov_func_curry
         ls = self.ls
         cov_func = compute_cov_func(cov_func_curry, ls)
-        logger.info('Using covariance function %s.', str(cov_func))
+        logger.info("Using covariance function %s.", str(cov_func))
         return cov_func
 
     def _compute_L(self):
@@ -150,7 +151,7 @@ def _compute_L(self):
         if isinstance(rank, float):
             logger.info(
                 f'Computing rank reduction using "{method}" method '
-                f'retaining > {rank:.2%} of variance.'
+                f"retaining > {rank:.2%} of variance."
             )
         else:
             logger.info(
@@ -166,7 +167,7 @@ def _compute_L(self):
                 "indicates underrepresentation by landmarks. Consider "
                 "increasing n_landmarks!"
             )
-        logger.info(f'Using rank {new_rank:,} covariance representation.')
+        logger.info(f"Using rank {new_rank:,} covariance representation.")
         return L
 
     def _run_inference(self):
@@ -175,7 +176,7 @@ def _run_inference(self):
         n_iter = self.n_iter
         init_learn_rate = self.init_learn_rate
         optimizer = self.optimizer
-        logger.info('Running inference using %s.', optimizer)
+        logger.info("Running inference using %s.", optimizer)
         if optimizer == "adam":
             results = minimize_adam(
                 function,
@@ -549,7 +550,7 @@ def _compute_loss_func(self):
     def _set_log_density_x(self):
         pre_transformation = self.pre_transformation
         transform = self.transform
-        logger.info('Decoding latent density representation.')
+        logger.info("Decoding latent density representation.")
         log_density_x = compute_log_density_x(pre_transformation, transform)
         self.log_density_x = log_density_x
 
@@ -560,7 +561,7 @@ def _set_log_density_func(self):
         mu = self.mu
         cov_func = self.cov_func
         jitter = self.jitter
-        logger.info('Computing predictive function.')
+        logger.info("Computing predictive function.")
         log_density_func = compute_conditional_mean(
             x,
             landmarks,