Add docstring todos

divik/_feature_selection.py

@@ -103,6 +103,7 @@ class GMMSelector(BaseEstimator, SelectorMixin):
 
 
     """
+    # TODO: Improve docstring so it will work with doctest
     def __init__(self, stat: str, use_log: bool = False,
                  n_candidates: int = None, min_features: int = 1,
                  min_features_rate: float = .0, preserve_high: bool = True,
@@ -256,6 +257,7 @@ class HighAbundanceAndVarianceSelector(BaseEstimator, SelectorMixin):
     array([[1 1 1 1 1 ...2 2 2]])
 
     """
+    # TODO: Improve docstring so it will work with doctest
     def __init__(self, use_log: bool = False, min_features: int = 1,
                  min_features_rate: float = 0., max_components: int = 10):
         self.use_log = use_log

divik/_kmeans/_auto.py

@@ -118,6 +118,7 @@ class AutoKMeans(BaseEstimator, ClusterMixin, TransformerMixin):
         The optimal estimator.
 
     """
+    # TODO: Add example of usage.
     def __init__(self, max_clusters: int, min_clusters: int = 1,
                  n_jobs: int = 1, method: str = 'dunn',
                  distance: str = 'euclidean', init: str = 'percentile',

divik/_kmeans/_core.py

@@ -158,6 +158,7 @@ class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
         Labels of each point
 
     """
+    # TODO: Add example of usage.
     def __init__(self, n_clusters: int, distance: str = 'euclidean',
                  init: str = 'percentile', percentile: float = 95.,
                  max_iter: int = 100, normalize_rows: bool = False):

divik/_sklearn.py

@@ -151,6 +151,7 @@ class DiviK(BaseEstimator, ClusterMixin, TransformerMixin):
            [ 1, ...,  2.]])
 
     """
+    # TODO: Improve docstring so it will work with doctest
 
     def __init__(self,
                  gap_trials: int = 10,