Feature/local outlier factor

docs/modules/datasets.rst

@@ -17,6 +17,7 @@ The following functions are used to retrieve specific functional datasets:
    skfda.datasets.fetch_medflies
    skfda.datasets.fetch_weather
    skfda.datasets.fetch_aemet
+   skfda.datasets.fetch_octane
 
 Those functions return a dictionary with at least a "data" field containing the
 instance data, and a "target" field containing the class labels or regression values,

docs/modules/exploratory/outliers.rst

@@ -4,12 +4,15 @@ Outlier detection
 Functional outlier detection is the identification of functions that do not seem to behave like the others in the
 dataset. There are several ways in which a function may be different from the others. For example, a function may
 have a different shape than the others, or its values could be more extreme. Thus, outlyingness is difficult to
-categorize exactly as each outlier detection method looks at different features of the functions in order to 
+categorize exactly as each outlier detection method looks at different features of the functions in order to
 identify the outliers.
 
 Each of the outlier detection methods in scikit-fda has the same API as the outlier detection methods of
 `scikit-learn <https://scikit-learn.org/stable/modules/outlier_detection.html>`_.
 
+Interquartilic Range Outlier Detector
+------------------------------------
+
 One of the most common ways of outlier detection is given by the functional data boxplot. An observation is marked
 as an outlier if it has points :math:`1.5 \cdot IQR` times outside the region containing the deepest 50% of the curves
 (the central region), where :math:`IQR` is the interquartilic range.
@@ -18,19 +21,23 @@ as an outlier if it has points :math:`1.5 \cdot IQR` times outside the region co
    :toctree: autosummary
 
    skfda.exploratory.outliers.IQROutlierDetector
-
+
+
+DirectionalOutlierDetector
+--------------------------
+
 Other more novel way of outlier detection takes into account the magnitude and shape of the curves. Curves which have
 a very different shape or magnitude are considered outliers.
 
 .. autosummary::
    :toctree: autosummary
 
    skfda.exploratory.outliers.DirectionalOutlierDetector
-   
+
 For this method, it is necessary to compute the mean and variation of the directional outlyingness, which can be done
 with the following function.
 
 .. autosummary::
    :toctree: autosummary
 
-   skfda.exploratory.outliers.directional_outlyingness_stats
+   skfda.exploratory.outliers.directional_outlyingness_stats

skfda/_neighbors/base.py

@@ -97,11 +97,11 @@ def multivariate_metric(x, y, _check=False, **kwargs):
 class NeighborsBase(ABC, BaseEstimator):
     """Base class for nearest neighbors estimators."""
 
-    @abstractmethod
     def __init__(self, n_neighbors=None, radius=None,
                  weights='uniform', algorithm='auto',
                  leaf_size=30, metric='l2', metric_params=None,
                  n_jobs=None, multivariate_metric=False):
+        """Initializes the nearest neighbors estimator"""
 
         self.n_neighbors = n_neighbors
         self.radius = radius
@@ -166,6 +166,7 @@ def fit(self, X, y=None):
                     metric = lp_distance
                 else:
                     metric = self.metric
+
                 sklearn_metric = _to_multivariate_metric(metric,
                                                          self._sample_points)
             else:
@@ -203,7 +204,7 @@ def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
                 Indices of the nearest points in the population matrix.
 
         Examples:
-            Firstly, we will create a toy dataset with 2 classes
+            Firstly, we will create a toy dataset.
 
             >>> from skfda.datasets import make_sinusoidal_process
             >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0)
@@ -260,7 +261,7 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'):
             A[i, j] is assigned the weight of edge that connects i to j.
 
         Examples:
-            Firstly, we will create a toy dataset with 2 classes.
+            Firstly, we will create a toy dataset.
 
             >>> from skfda.datasets import make_sinusoidal_process
             >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0)
@@ -329,7 +330,7 @@ def radius_neighbors(self, X=None, radius=None, return_distance=True):
                 within a ball of size ``radius`` around the query points.
 
         Examples:
-            Firstly, we will create a toy dataset with 2 classes.
+            Firstly, we will create a toy dataset.
 
             >>> from skfda.datasets import make_sinusoidal_process
             >>> fd1 = make_sinusoidal_process(phase_std=.25, random_state=0)

skfda/_neighbors/classification.py

@@ -59,8 +59,9 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin,
         Doesn't affect :meth:`fit` method.
     multivariate_metric : boolean, optional (default = False)
         Indicates if the metric used is a sklearn distance between vectors (see
-        :class:`sklearn.neighbors.DistanceMetric`) or a functional metric of
-        the module :mod:`skfda.misc.metrics`.
+        :class:`~sklearn.neighbors.DistanceMetric`) or a functional metric of
+        the module `skfda.misc.metrics` if ``False``.
+
     Examples
     --------
     Firstly, we will create a toy dataset with 2 classes
@@ -96,6 +97,7 @@ class KNeighborsClassifier(NeighborsBase, NeighborsMixin, KNeighborsMixin,
     :class:`~skfda.ml.regression.KNeighborsRegressor`
     :class:`~skfda.ml.regression.RadiusNeighborsRegressor`
     :class:`~skfda.ml.clustering.NearestNeighbors`
+
 
     Notes
     -----
@@ -254,6 +256,7 @@ class RadiusNeighborsClassifier(NeighborsBase, NeighborsMixin,
     :class:`~skfda.ml.regression.RadiusNeighborsRegressor`
     :class:`~skfda.ml.clustering.NearestNeighbors`
 
+
     Notes
     -----
     See Nearest Neighbors in the sklearn online documentation for a discussion
@@ -358,6 +361,7 @@ class and return a :class:`FData` object with only one sample
     :class:`~skfda.ml.regression.RadiusNeighborsRegressor`
     :class:`~skfda.ml.clustering.NearestNeighbors`
 
+
     """
 
     def __init__(self, metric='l2', mean='mean'):