Create classes random_variable_uncertainty.py and variable_uncertaint…

…y.py.

base.py

@@ -11,8 +11,4 @@
 
 class ActiveLearningBase():
 
-    """ Base classe for Active Learning Library """
-
-    @abc.abstractmethod()
-    def describe(self, describe):
-        ...
+    """ Base classe for Active Learning Library """

fixed_uncertainty.py

@@ -25,17 +25,12 @@ class FixedUncertainty(ActiveLearningBase):
 
 """
 
-    def __init__(self, theta: float = 0.95, seed=None):
+    def __init__(self, theta: float = 0.95):
         super().__init__()
         self.theta = theta
 
 
-    def describe(self):
-        print('Strategy of Active Learning to select instances more significative based on uncertainty.'+
-              'The fixed uncertainty sampler selects samples for labeling based on the uncertainty of the prediction.'+
-              'The higher the uncertainty, the more likely the sample will be selected for labeling. The uncertainty'+
-              'measure is compared with a fixed uncertainty limit.'+
-              'References [^1]: I. Zliobaite, A. Bifet, B.Pfahringer, G. Holmes. “Active Learning with Drifting Streaming Data”, IEEE Transactions on Neural Netowrks and Learning Systems, Vol.25 (1), pp.27-39, 2014.')
+
 
     def isSignificative(self, x, y_pred) -> bool:
         """Ask for the label of a current instance.

random_variable_uncertainty.py

@@ -0,0 +1,73 @@
+""" 
+Active Learning 18
+
+@author: Albert França Josuá Costa
+@email: [email protected]
+"""
+
+import numpy as np
+
+from .base import ActiveLearningBase
+
+
+
+class RandomVariableUncertainty (ActiveLearningBase):
+
+    r"""Strategy of Active Learning to select instances more significative based on uncertainty.
+
+   The random variable uncertainty sampler selects samples for labeling based on the uncertainty of the prediction.
+   The higher the uncertainty, the more likely the sample will be selected for labeling. The uncertainty
+   measure is compared with a random variable uncertainty limit.
+
+
+    References
+    ----------
+    [^1]: I. Zliobaite, A. Bifet, B.Pfahringer, G. Holmes. “Active Learning with Drifting Streaming Data”, IEEE Transactions on Neural Netowrks and Learning Systems, Vol.25 (1), pp.27-39, 2014.
+
+"""
+
+    def __init__(self, theta: float = 0.95, s=0.5, delta=1.0):
+        super().__init__()
+
+        self.theta = theta
+        self.s = s
+        self.delta = delta
+
+
+    def isSignificative(self, x, y_pred) -> bool:
+        """Ask for the label of a current instance.
+
+        Based on the uncertainty of the base classifier, it checks whether the current instance should be labeled.
+
+        Parameters
+        ----------
+        x
+            Instance
+
+        y_pred
+
+           Arrays of predicted labels
+
+
+        Returns
+        -------
+        selected
+            A boolean indicating whether a label is needed.
+            True for selected instance.
+            False for not selecte instance.
+
+
+        """
+        maximum_posteriori = max(y_pred.values())
+        selected = False
+
+        thetaRand = self.theta * np.random.normal(1,self.delta)
+
+        if maximum_posteriori < thetaRand:
+            self.theta = self.theta*(1-self.s)
+            selected = True
+        else:
+            self.theta = self.theta*(1+self.s)
+            selected = False
+
+        return selected

variable_uncertainty.py

@@ -0,0 +1,70 @@
+""" 
+Active Learning 18
+
+@author: Albert França Josuá Costa
+@email: [email protected]
+"""
+
+
+from .base import ActiveLearningBase
+
+
+
+class VariableUncertainty  (ActiveLearningBase):
+
+    r"""Strategy of Active Learning to select instances more significative based on uncertainty.
+
+    The variable uncertainty sampler selects samples for labeling based on the uncertainty of the prediction.
+    The higher the uncertainty, the more likely the sample will be selected for labeling. The uncertainty
+    measure is compared with a random variable uncertainty limit.
+
+
+    References
+    ----------
+    [^1]: I. Zliobaite, A. Bifet, B.Pfahringer, G. Holmes. “Active Learning with Drifting Streaming Data”, IEEE Transactions on Neural Netowrks and Learning Systems, Vol.25 (1), pp.27-39, 2014.
+
+"""
+
+    def __init__(self, theta: float = 0.95, s=0.5):
+        super().__init__()
+
+        self.theta = theta
+        self.s = s
+
+
+    def isSignificative(self, x, y_pred) -> bool:
+        """Ask for the label of a current instance.
+
+        Based on the uncertainty of the base classifier, it checks whether the current instance should be labeled.
+
+        Parameters
+        ----------
+        x
+            Instance
+
+        y_pred
+
+           Arrays of predicted labels
+
+
+        Returns
+        -------
+        selected
+            A boolean indicating whether a label is needed.
+            True for selected instance.
+            False for not selecte instance.
+
+
+        """
+        maximum_posteriori = max(y_pred.values())
+        selected = False
+
+
+        if maximum_posteriori < self.theta:
+            self.theta = self.theta*(1-self.s)
+            selected = True
+        else:
+            self.theta = self.theta*(1+self.s)
+            selected = False
+
+        return selected