Merge branch 'master' of github.com:scikit-multiflow/scikit-multiflow

src/skmultiflow/bayes/naive_bayes.py

@@ -7,8 +7,8 @@ class NaiveBayes(StreamModel):
     
     A mask for scikit-learn's Naive Bayes classifier.
     
-    Because scikit-multiflow's framework require a few interfaces, not present 
-    int scikit-learn, this mask allows the first to use classifiers native to 
+    Because scikit-multiflow's framework requires a few interfaces, not present
+    in scikit-learn, this mask allows the first to use classifiers native to
     the latter.
     
     """

src/skmultiflow/core/base.py

@@ -78,7 +78,7 @@ def predict(self, X):
         Parameters
         ----------
         X : Numpy.ndarray of shape (n_samples, n_features)
-            The matrix of samples one wants to predict.
+            The matrix of samples one wants to predict the labels for.
 
         Returns
         -------
@@ -94,7 +94,7 @@ def predict_proba(self, X):
         Parameters
         ----------
         X : Numpy.ndarray of shape (n_samples, n_features)
-            The matrix of samples one wants to predict.
+            The matrix of samples one wants to predict the class probabilities for.
 
         Returns
         -------

src/skmultiflow/core/base_object.py

@@ -5,8 +5,8 @@ class BaseObject(metaclass=ABCMeta):
     """ BaseObject
     
     The most basic object, from which classes in scikit-multiflow
-    derive from. It guarantees that all classes have at least the
-    two basic functions described in this base class.
+    derive. It guarantees that all classes have at least the two
+    basic functions described in this base class.
     
     """
 
@@ -28,7 +28,7 @@ def get_class_type(self):
     def get_info(self):
         """ get_info
         
-        A sum up of all important characteristics of a class. 
+        A sum-up of all important characteristics of a class.
         
         The default format of the return string is as follows: 
         ClassName: attribute_one: value_one - attribute_two: value_two \ 

src/skmultiflow/core/pipeline.py

@@ -17,7 +17,7 @@ class Pipeline(BaseObject):
     The last step should be an estimator (learner), so it should implement
     partial_fit, and predict at least.
 
-    Since it has an estimator as the last step, the Pipeline will act like as
+    Since it has an estimator as the last step, the Pipeline will act like
     an estimator itself, in a way that it can be directly passed to evaluation
     objects, as if it was a learner.
 
@@ -250,7 +250,6 @@ def _validate_steps(self):
         """
 
         names, estimators = zip(*self.steps)
-        transforms = classifier = None
         classifier = estimators[-1]
         transforms = estimators[:-1]
 

src/skmultiflow/lazy/sam_knn.py

@@ -376,7 +376,7 @@ def predict(self, X):
         for i in range(r):
             distancesSTM = SAMKNN.get_distances(X[i], self._STMSamples)
             predictedLabel.append(self.predictFct(X[i], None, distancesSTM))
-        return predictedLabel
+        return np.asarray(predictedLabel)
 
     def predict_proba(self, X):
         raise NotImplementedError

src/skmultiflow/meta/adaptive_random_forests.py

@@ -1,4 +1,5 @@
 from copy import deepcopy
+import numpy as np
 from sklearn.preprocessing import normalize
 
 from skmultiflow.core.base_object import BaseObject
@@ -230,7 +231,7 @@ def predict(self, X):
             Samples for which we want to predict the labels.
         Returns
         -------
-        list
+        numpy.ndarray
             Predicted labels for all instances in X.
         """
         r, _ = get_dimensions(X)
@@ -242,7 +243,7 @@ def predict(self, X):
                 predictions.append(0)
             else:
                 predictions.append(max(votes, key=votes.get))
-        return predictions
+        return np.asarray(predictions)
 
     def predict_proba(self, X):
         """ Predicts the class probabilities for the X instance(s).

src/skmultiflow/meta/leverage_bagging.py

@@ -316,8 +316,8 @@ def predict(self, X):
         
         Returns
         -------
-        list
-            A list with the label prediction for all the samples in X.
+        numpy.ndarray
+            A numpy.ndarray with the label prediction for all the samples in X.
         
         """
         r, c = get_dimensions(X)
@@ -328,7 +328,7 @@ def predict(self, X):
             proba = np.zeros((r, 1))
         for i in range(r):
             predictions.append(np.argmax(proba[i]))
-        return predictions
+        return np.asarray(predictions)
 
     def predict_proba(self, X):
         """ predict_proba
@@ -392,7 +392,7 @@ def predict_proba(self, X):
                 aux.append([x / sum_proba[i] for x in proba[i]])
             else:
                 aux.append(proba[i])
-        return aux
+        return np.asarray(aux)
 
     def predict_binary_proba(self, X):
         """ predict_binary_proba

src/skmultiflow/meta/oza_bagging.py

@@ -196,8 +196,8 @@ def predict(self, X):
         
         Returns
         -------
-        list
-            A list with the label prediction for all the samples in X.
+        numpy.ndarray
+            A numpy.ndarray with the label prediction for all the samples in X.
         
         """
         r, c = get_dimensions(X)
@@ -207,7 +207,7 @@ def predict(self, X):
             return None
         for i in range(r):
             predictions.append(np.argmax(proba[i]))
-        return predictions
+        return np.asarray(predictions)
 
     def predict_proba(self, X):
         """ predict_proba
@@ -267,7 +267,7 @@ def predict_proba(self, X):
                 aux.append([x / sum_proba[i] for x in proba[i]])
             else:
                 aux.append(proba[i])
-        return aux
+        return np.asarray(aux)
 
     def score(self, X, y):
         raise NotImplementedError

src/skmultiflow/neural_networks/perceptron.py

@@ -1,3 +1,4 @@
+import numpy as np
 from skmultiflow.core.base import StreamModel
 from sklearn.linear_model.perceptron import Perceptron
 
@@ -107,11 +108,11 @@ def predict(self, X):
 
         Returns
         -------
-        list
-            A list containing the predicted labels for all instances in X.
+        numpy.ndarray
+            A numpy.ndarray containing the predicted labels for all instances in X.
 
         """
-        return self.classifier.predict(X)
+        return np.asarray(self.classifier.predict(X))
 
     def predict_proba(self, X):
         """ predict_proba