Merge pull request #30 from ntucllab/interface-documentation

Update interfaces.py documentation

libact/base/interfaces.py

@@ -12,7 +12,6 @@ class QueryStrategy(metaclass=ABCMeta):
     A QueryStrategy advices on which unlabeled data to be queried next given 
     a pool of labeled and unlabeled data.
     """
-
     def __init__(self, dataset, **kwargs):
         self._dataset = dataset
         dataset.on_update(self.update)
@@ -22,53 +21,136 @@ def dataset(self):
         return self._dataset
 
     def update(self, entry_id, label):
+        """Update the internal states of the QueryStrategy after each queried 
+        sample being labeled.
+
+        Parameters
+        ----------
+        entry_id : int
+            The index of the newly labeled sample.
+            
+        label : float
+            The label of the queried sample.
+        """
         pass
 
     @abstractmethod
     def make_query(self):
+        """Return the index of the sample to be queried and labeled. Read-only.
+        
+        No modification to the internal states.
+        
+        Returns
+        -------
+        ask_id : int
+            The index of the next unlabeled sample to be queried and labeled.
+        """
         pass
 
 
 class Labeler(metaclass=ABCMeta):
     """Label the queries made by QueryStrategies
     
-    A Labeler assigns labels to the features queried by QueryStrategies.
+    Assign labels to the samples queried by QueryStrategies.
     """
-
     @abstractmethod
     def label(self, feature):
+        """Return the class labels for the input feature array. 
+    
+        Parameters
+        ----------
+        feature : array-like, shape (n_features,)
+            The feature vector whose label is to queried.
+            
+        Returns
+        -------
+        label : int
+            The class label of the queried feature.
+        """
         pass
 
 
 class Model(metaclass=ABCMeta):
     """Classification Model
 
-    A Model is trained on a training dataset and produces a class-predicting
-    function for future features.
+    A Model returns a class-predicting function for future samples after 
+    trained on a training dataset.
     """
-
     @abstractmethod
     def train(self, dataset, *args, **kwargs):
+        """Train a model according to the given training dataset. 
+        
+        Parameters
+        ----------
+        dataset : Dataset object
+             The training dataset the model is to be trained on.
+
+        Returns
+        -------
+        self : object
+            Returns self.
+        """        
         pass
 
     @abstractmethod
     def predict(self, feature, *args, **kwargs):
+        """Predict the class labels for the input samples
+        
+        Parameters
+        ----------
+        feature : array-like, shape (n_samples, n_features)
+            The unlabeled samples whose labels are to be predicted.
+            
+        Returns
+        -------
+        y_pred : array-like, shape (n_samples,)
+            The class labels for samples in the feature array.
+        """
         pass
 
     @abstractmethod
     def score(self, testing_dataset, *args, **kwargs):
+        """Return the mean accuracy on the test dataset
+        
+        Parameters
+        ----------
+        testing_dataset : Dataset object
+            The testing dataset used to measure the perforance of the trained model.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) wrt. y.
+        """
         pass
 
 
 class ContinuousModel(Model):
     """Classification Model with intermediate continuous output
     
     A continuous classification model is able to output a real-valued vector
-    for each features provided. The output vector is of shape (n_samples, n_classs)
-    for an input feature matrix X of shape (n_samples, n_features). The larger the 
-    kth-column value is, the more likely a feature x belongs the class k.
+    for each features provided.
     """
-
     @abstractmethod
     def predict_real(self, feature, *args, **kwargs):
+        """Predict confidence scores for samples. 
+        
+        Returns the confidence score for each (sample, class) combination.
+        
+        The larger the value for entry (sample=x, class=k) is, the more confident 
+        the model is about the sample x belonging to the class k.
+        
+        Take Logistic Regression as example, the return value is the signed dis-
+        tance of that sample to the hyperplane.
+        
+        Parameters
+        ----------
+        feature : array-like, shape (n_samples, n_features)
+            The samples whose confidence scores are to be predicted.
+
+        Returns
+        -------
+        X : array-like, shape (n_samples, n_classes)
+            Each entry is the confidence scores per (sample, class) combination.        
+        """
         pass

libact/labelers/ideal_labeler.py

@@ -2,20 +2,19 @@
 Ideal/Noiseless labeler that returns true label
 
 """
-from libact.base.interfaces import Labeler
 import numpy as np
 
+from libact.base.interfaces import Labeler
+
 class IdealLabeler(Labeler):
     """    
     Provide the errorless/noiseless label to any feature vectors being queried.
 
-    Attributes
+    Parameters
     ----------
-    features: numpy array
-        an array of features used as the search keys for labels
-    
-    label: numpy array
-        an array of noiesless labels corresponding to the features
+    dataset: Dataset object
+        Dataset object with the ground-truth label for each sample.
+        
     """ 
 
     def __init__(self, dataset, **kwargs):