From 368ee03fbc5f31e6a0e964dae4565e80380eba83 Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Tue, 28 Feb 2023 10:25:46 +0100
Subject: [PATCH] some minor improvements

---
 quapy/__init__.py               | 12 ++++++------
 quapy/classification/methods.py |  3 ++-
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/quapy/__init__.py b/quapy/__init__.py
index 47a7388..72218e5 100644
--- a/quapy/__init__.py
+++ b/quapy/__init__.py
@@ -1,6 +1,7 @@
+"""QuaPy module for quantification"""
+from quapy.data import datasets
 from . import error
 from . import data
-from quapy.data import datasets
 from . import functional
 # from . import method
 from . import evaluation
@@ -25,7 +26,8 @@ environ = {
 
 def _get_njobs(n_jobs):
     """
-    If `n_jobs` is None, then it returns `environ['N_JOBS']`; if otherwise, returns `n_jobs`.
+    If `n_jobs` is None, then it returns `environ['N_JOBS']`;
+    if otherwise, returns `n_jobs`.
 
     :param n_jobs: the number of `n_jobs` or None if not specified
     :return: int
@@ -35,7 +37,8 @@ def _get_njobs(n_jobs):
 
 def _get_sample_size(sample_size):
     """
-    If `sample_size` is None, then it returns `environ['SAMPLE_SIZE']`; if otherwise, returns `sample_size`.
+    If `sample_size` is None, then it returns `environ['SAMPLE_SIZE']`;
+    if otherwise, returns `sample_size`.
     If none of these are set, then a ValueError exception is raised.
 
     :param sample_size: integer or None
@@ -45,6 +48,3 @@ def _get_sample_size(sample_size):
     if sample_size is None:
         raise ValueError('neither sample_size nor qp.environ["SAMPLE_SIZE"] have been specified')
     return sample_size
-
-
-
diff --git a/quapy/classification/methods.py b/quapy/classification/methods.py
index 01f4654..e26e990 100644
--- a/quapy/classification/methods.py
+++ b/quapy/classification/methods.py
@@ -21,10 +21,11 @@ class LowRankLogisticRegression(BaseEstimator):
         self.n_components = n_components
         self.learner = LogisticRegression(**kwargs)
 
-    def get_params(self):
+    def get_params(self, deep=True):
         """
         Get hyper-parameters for this estimator.
 
+        :param deep: compatibility with sklearn
         :return: a dictionary with parameter names mapped to their values
         """
         params = {'n_components': self.n_components}