DISCO.

doc/streams.rst

@@ -105,7 +105,7 @@ The cyclic repetition of class distributions is a completely different property
 
 .. image:: _static/recurring.png
 
-Non-recurring gradual drift
+Non-recurring drift
 ---------------------------
 
 The default mode of consecutive concept occurences is a non-recurring drift, where in each concept drift we are generating a completely new, previously unseen  class distribution.
@@ -121,9 +121,20 @@ The default mode of consecutive concept occurences is a non-recurring drift, whe
 Class imbalance
 ===============
 
+Another area of data stream properties, different from the concept drift phenomenon, is the a priori probability of problem classes. By default, a balanced stream is generated, i.e. one in which patterns of all classes are present in a similar number.
+
+.. code-block:: python
+
+  StreamGenerator()
+
+.. image:: _static/stationary.png
+
+
 Stationary imbalanced stream
 ----------------------------
 
+The basic type of problem in which we are dealing with disturbed class distribution is a *dataset imbalanced stationary*, where the classes maintain a predetermined proportion in each chunk of data stream. To acquire this type of a stream, one should pass the ``list`` to the ``weights`` parameter of the generator (i) consisting of as many elements as the classes in the problem and (ii) adding to one.
+
 
 .. code-block:: python
 
@@ -134,9 +145,29 @@ Stationary imbalanced stream
 Dynamically imbalanced stream
 -----------------------------
 
+A less common type of *imbalanced data*, impossible to obtain in static datasets, is *data imbalanced dynamically*. In this case, the class distribution is not constant throughout the course of a stream, but changes over time, similar to changing the concept presence in gradual streams. To get this type of data stream, we pass a ``tuple`` of three numeric values to the ``weights`` parameter of the generator:
+
+- the number of cycles of distribution changes,
+- ``concept_sigmoid_spacing`` parameter, deciding about the dynamics of changes on the same principle as in gradual and incremental drifts,
+- range within which oscillation is to take place.
+
 .. code-block:: python
 
   StreamGenerator(weights=(2, 5, 0.9))
 
 
 .. image:: _static/dynamic-imbalanced.png
+
+Mixing drift properties
+=======================
+
+Of course, when generating data streams, we don't have to limit ourselves to just one modification of their properties. We can easily prepare a stream with many drifts, any dynamics of changes, a selected type of drift and a diverse, dynamic imbalanced ratio. The last example in this chapter of User Guide is such proposition, namely, DISCO (Dynamically Imbalanced Stream with Concept Oscillation).
+
+.. code-block:: python
+
+  StreamGenerator(
+      weights=(2, 5, 0.9), n_drifts=3, concept_sigmoid_spacing=5,
+      recurring=True, incremental=True
+  )
+
+.. image:: _static/disco.png

examples/plot_stream_plotting.py

@@ -134,3 +134,21 @@ def plot_stream(stream, filename="foo", title=""):
 plot_stream(
     stream, "dynamic-imbalanced", "Data stream with dynamically imbalanced drift"
 )
+
+##############################################################################
+# DISCO
+#
+# inne
+
+stream = sl.streams.StreamGenerator(
+    **concept_kwargs,
+    weights=(2, 5, 0.9),
+    n_drifts=3,
+    concept_sigmoid_spacing=5,
+    recurring=True,
+    incremental=True
+)
+
+plot_stream(
+    stream, "disco", "Dynamically Imbalanced Stream with Concept Oscillation (DISCO)"
+)