From daeaefacb89dba23412ae6738e212ef3e6048016 Mon Sep 17 00:00:00 2001
From: ASPP Student <student@localhost>
Date: Tue, 3 Sep 2019 11:57:34 +0200
Subject: [PATCH 1/2] test for voting.py Fixes: #2

---
 .../pyanno_voting/pyanno/tests/test_voting.py | 19 +++++++++++++++++++
 hands_on/pyanno_voting/pyanno/voting.py       | 17 +++++++++++++++++
 2 files changed, 36 insertions(+)

diff --git a/hands_on/pyanno_voting/pyanno/tests/test_voting.py b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
index f21a10c..a9fde4d 100644
--- a/hands_on/pyanno_voting/pyanno/tests/test_voting.py
+++ b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
@@ -3,6 +3,8 @@
 from pyanno import voting
 from pyanno.voting import MISSING_VALUE as MV
 
+from math import isclose
+
 
 def test_labels_count():
     annotations = [
@@ -41,3 +43,20 @@ def test_majority_vote_empty_item():
     expected = [1, MV, 2]
     result = voting.majority_vote(annotations)
     assert result == expected
+
+def test_labels_frequency():
+    matrix = [
+        [1, 2, 2, -1],
+        [2, 2, 2, 2],
+        [1, 1, 3, 3],
+        [1, 3, 3, 2],
+        [-1, 2, 3, 1],
+        [-1, -1, -1, 3],
+    ]
+    result = voting.labels_frequency(matrix, 4)
+
+    assert np.all([res != None for res in result])
+    assert len(result) == 4
+    assert np.all(voting.labels_frequency([[-1, -1, -1, -1],[-1, -1, -1, -1]], 4) == np.zeros(4))
+    assert np.all([i >= 0 and i <= 1 for i in result])
+    assert isclose(np.sum(result),1) or isclose(np.sum(result), 0,abs_tol=1e-12)
diff --git a/hands_on/pyanno_voting/pyanno/voting.py b/hands_on/pyanno_voting/pyanno/voting.py
index d5b5747..3694cc7 100644
--- a/hands_on/pyanno_voting/pyanno/voting.py
+++ b/hands_on/pyanno_voting/pyanno/voting.py
@@ -100,3 +100,20 @@ def labels_frequency(annotations, nclasses):
         freq[k] is the frequency of elements of class k in `annotations`, i.e.
         their count over the number of total of observed (non-missing) elements
     """
+    annotations_array = np.ravel(annotations)
+    result = np.zeros(nclasses)
+
+    dim = 0
+    for number in annotations_array:
+        if number != -1:
+            dim = dim + 1
+    if dim !=0:
+        for cl in np.arange(nclasses):
+            aux = 0
+            for anot in annotations_array:
+                if cl == anot:
+                    aux = aux + 1
+            result[cl] = aux / dim
+        return(result)
+    else:
+        return(0)

From 30522c5b9f6b5b491cebf40016d93ace744da519 Mon Sep 17 00:00:00 2001
From: ASPP Student <student@localhost>
Date: Tue, 3 Sep 2019 12:29:08 +0200
Subject: [PATCH 2/2] refactored test_labels_frequency and test_labels_count

---
 .../pyanno_voting/pyanno/tests/test_voting.py | 57 +++++++++++++------
 1 file changed, 40 insertions(+), 17 deletions(-)

diff --git a/hands_on/pyanno_voting/pyanno/tests/test_voting.py b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
index a9fde4d..7e89da1 100644
--- a/hands_on/pyanno_voting/pyanno/tests/test_voting.py
+++ b/hands_on/pyanno_voting/pyanno/tests/test_voting.py
@@ -7,6 +7,7 @@
 
 
 def test_labels_count():
+    #given
     annotations = [
         [1,  2, MV, MV],
         [MV, MV,  3,  3],
@@ -15,10 +16,49 @@ def test_labels_count():
     ]
     nclasses = 5
     expected = [0, 3, 1, 3, 0]
+
+    #when
     result = voting.labels_count(annotations, nclasses)
+
+    #then
     assert result == expected
 
 
+def test_labels_frequency():
+    #given
+    matrix = [
+        [1, 2, 2, -1],
+        [2, 2, 2, 2],
+        [1, 1, 3, 3],
+        [1, 3, 3, 2],
+        [-1, 2, 3, 1],
+        [-1, -1, -1, 3],
+    ]
+
+    matrix2 = [
+        [-1, -1, -1, -1],
+        [-1, -1, -1, -1]
+    ]
+
+    lowerlimit = 0
+    upperlimit = 1
+    nclasses = 4
+    
+    expected2 = np.zeros(nclasses)
+
+
+    #when
+    result = voting.labels_frequency(matrix, nclasses)
+    result2 = voting.labels_frequency(matrix2, nclasses)
+
+    #then
+    assert np.all([res != None for res in result])
+    assert len(result) == nclasses
+    assert np.all(result2 == expected2)
+    assert np.all([i >= lowerlimit and i <= upperlimit for i in result])
+    assert isclose(np.sum(result),upperlimit) or isclose(np.sum(result), lowerlimit,abs_tol=1e-12)
+
+
 def test_majority_vote():
     annotations = [
         [1, 2, 2, MV],
@@ -43,20 +83,3 @@ def test_majority_vote_empty_item():
     expected = [1, MV, 2]
     result = voting.majority_vote(annotations)
     assert result == expected
-
-def test_labels_frequency():
-    matrix = [
-        [1, 2, 2, -1],
-        [2, 2, 2, 2],
-        [1, 1, 3, 3],
-        [1, 3, 3, 2],
-        [-1, 2, 3, 1],
-        [-1, -1, -1, 3],
-    ]
-    result = voting.labels_frequency(matrix, 4)
-
-    assert np.all([res != None for res in result])
-    assert len(result) == 4
-    assert np.all(voting.labels_frequency([[-1, -1, -1, -1],[-1, -1, -1, -1]], 4) == np.zeros(4))
-    assert np.all([i >= 0 and i <= 1 for i in result])
-    assert isclose(np.sum(result),1) or isclose(np.sum(result), 0,abs_tol=1e-12)