Improved documentation #194

Author: dpuenteramirez

semisupervised/CoTraining.py

@@ -54,27 +54,7 @@ class CoTraining:
     def __init__(self, p=1, n=3, k=30, u=75, random_state=None,
                  c1=None, c1_params=None,
                  c2=None, c2_params=None,):
-        """
-        The function takes in the parameters for the two classifiers, and if the
-        classifier is not None, it will use the parameters to create the
-        classifier. If the classifier is None, it will use the default
-        classifier, which is GaussianNB
-
-        :param p: The number of positive samples, defaults to 1
-        (optional)
-        :param n: The number of negative samples, defaults to 3
-        (optional)
-        :param k: The number of iterations to train the classifiers, defaults
-        to 30 (optional)
-        :param u: The number of unlabeled samples to use in the training set,
-        defaults to 75 (optional)
-        :param random_state: The random seed used to generate the initial
-        population
-        :param c1: The first classifier to be used
-        :param c1_params: parameters for the first classifier
-        :param c2: The second classifier to be used
-        :param c2_params: The parameters for the second classifier
-        """
+        """Co-Training."""
         self.p = p
         self.n = n
         self.k = k

semisupervised/DemocraticCoLearning.py

@@ -68,19 +68,7 @@ def __init__(self, random_state=None,
                  c1=None, c1_params=None,
                  c2=None, c2_params=None,
                  c3=None, c3_params=None):
-        """
-        The function takes in three classifiers and their parameters, and if
-        they are not provided, it uses the default classifiers and their
-        parameters.
-
-        :param random_state: The random seed used to initialize the classifiers
-        :param c1: The first classifier
-        :param c1_params: parameters for the first classifier
-        :param c2: The classifier to use for the second classifier
-        :param c2_params: The parameters for the second classifier
-        :param c3: The third classifier
-        :param c3_params: The parameters for the third classifier
-        """
+        """Democratic Co-Learning."""
         self.const = 1.96  # 95%
         self.random_state = random_state if random_state is not None else \
             np.random.randint(low=0, high=10e5, size=1)[0]

semisupervised/TriTraining.py

@@ -60,6 +60,7 @@ def __init__(self, random_state=None,
                  c1=None, c1_params=None,
                  c2=None, c2_params=None,
                  c3=None, c3_params=None):
+        """Tri-Training."""
 
         classifiers = [c1, c2, c3]
         classifiers_params = [c1_params, c2_params, c3_params]

semisupervised/utils/_split.py

@@ -18,7 +18,6 @@ def split(samples, y):
     :param y: the labels of the data
     :return: L, U, y
     """
-
     if isinstance(y, pd.DataFrame):
         y = y.to_numpy()