[M] paper JOSS

predihood/classes/Classifier.py

@@ -2,7 +2,7 @@ class Classifier:
     # def __init__(self):
 
     def fit(self, X, y):
-        return None
+        raise Exception("You must implement your own fit function.")
 
     def predict(self, df):
-        return ["default_value"]
+        raise Exception("You must implement your own predict function.")

predihood/classes/MethodPrediction.py

@@ -32,6 +32,8 @@ class MethodPrediction(Method):
         """
         Compute performance metrics, i.e. accuracy.
         """
+        print(self.dataset.X)
+        print(self.dataset.Y)
         scores = cross_val_score(self.classifier, self.dataset.X, self.dataset.Y, cv=5, scoring="accuracy")
         self.accuracy = scores.mean() * 100
         print(self.accuracy)

predihood/classes/NewAlgorithm.py

@@ -2,14 +2,24 @@ from predihood.classes.Classifier import Classifier
 
 
 class NewAlgorithm(Classifier):
+    """
+    A new algorithm.
+    Parameters
+    ----------
+    alpha : int, default=1
+        The first parameter.
+    beta : str, default="2"
+        The second parameter.
+    """
     def __init__(self, alpha=1, beta="2"):
+
         Classifier.__init__(self)
         self.alpha = alpha
         self.beta = beta
 
     def fit(self, X, y):
         print("here")
-        return None
+        return self
 
     def predict(self, df):
         print("there")

predihood/classifiers_list.py

@@ -7,12 +7,14 @@ from sklearn.svm import SVC
 from sklearn.tree import DecisionTreeClassifier
 
 
+# to add a new classifier, add as key the name of your classifier and as value the name of the class that implement your classifier.
+# do not forget to import your classifier
 AVAILABLE_CLASSIFIERS = {
-    "RandomForestClassifier": RandomForestClassifier,
-    "KNeighborsClassifier": KNeighborsClassifier,
-    "DecisionTreeClassifier": DecisionTreeClassifier,
+    "Random Forest Classifier": RandomForestClassifier,
+    "KNeighbors Classifier": KNeighborsClassifier,
+    "Decision Tree Classifier": DecisionTreeClassifier,
     "SVC": SVC,
-    "AdaBoostClassifier": AdaBoostClassifier,
-    "MLPClassifier": MLPClassifier,
-    "NewAlgorithm": NewAlgorithm
+    "AdaBoost Classifier": AdaBoostClassifier,
+    "MLP Classifier": MLPClassifier,
+    "New Algorithm": NewAlgorithm
 }

predihood/utility_functions.py

@@ -221,13 +221,16 @@ def signature(chosen_algorithm):
     try:
         # model = eval(_chosen_algorithm) # never use eval on untrusted strings
         model = get_classifier(chosen_algorithm)
+        print(model)
         doc = model.__doc__  # TODO: specify case when there is no doc (user-implemented algorithm)
+        print(doc)
         param_section = "Parameters"
         dashes = "-" * len(param_section)  # -------
         number_spaces = doc.find(dashes) - (doc.find(param_section) + len(param_section))
         attribute_section = "Attributes\n"
         # sub_doc is the param section of the docs (i.e. without attributes and some text)
         sub_doc = doc[doc.find(param_section) + len(param_section) + number_spaces + len(dashes) + len("\n"):doc.find(attribute_section)]
+        print(sub_doc)
     except:
         raise Exception("This algorithm does not exist for the moment...")
     params = inspect.getfullargspec(model.__init__).args[1:]  # get parameter' names -- [1:] to remove self parameter