Create class fixed_uncertainty.py.

__init__.py

@@ -5,4 +5,9 @@
 @email: [email protected]
 """
 
-from .import base
+from .import base
+from .fixed_uncertainty import FixedUncertainty
+from .random_variable_uncertainty import RandomVariableUncertainty
+from .variable_uncertainty import VariableUncertainty
+
+__all__ = ["base", "FixedUncertainty", "RandomVariableUncertainty", "VariableUncertainty"]

base.py

@@ -7,6 +7,8 @@
 
 import abc
 
+__all__ = ["ActiveLearningBase"]
+
 class ActiveLearningBase():
 
     """ Base classe for Active Learning Library """

fixed_uncertainty.py

@@ -0,0 +1,67 @@
+""" 
+Active Learning 18
+
+@author: Albert França Josuá Costa
+@email: [email protected]
+"""
+
+
+from .base import ActiveLearningBase
+
+
+
+class FixedUncertainty(ActiveLearningBase):
+
+    """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 __init__(self, theta: float = 0.95, seed=None):
+        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.
+
+        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:
+            selected = True
+        return selected