def test_max_binary_matrix():
print "\n-- 'max_binary_matrix' --"
X = np.array([[1, 0, 0], [10, 8, 5], [1. / 3, 1. / 3, 1. / 3], [0, 0, 1],
[0, 0.9, 1], [0.5, 0, 0.5]])
print "X original:\n", X
Xb = max_binary_matrix(X)
print "X with winning classes (no tolerance):\n", Xb
Xb = max_binary_matrix(X, 0.2)
print "X with winning classes (with 0.2 tolerance):\n", Xb
X = np.array([[10, 9, 0]])
print "\nX original:\n", X
Xb = max_binary_matrix(X, 2)
print "X with winning classes (with 2 tolerance):\n", Xb
def test_max_binary_matrix():
print "\n-- 'max_binary_matrix' --"
X = np.array( [[1,0,0],
[10,8,5],
[1./3,1./3,1./3],
[0,0,1],
[0,0.9,1],
[0.5,0,0.5]])
print "X original:\n", X
Xb = max_binary_matrix(X)
print "X with winning classes (no tolerance):\n", Xb
Xb = max_binary_matrix(X, 0.2)
print "X with winning classes (with 0.2 tolerance):\n", Xb
X = np.array( [[10,9,0]])
print "\nX original:\n", X
Xb = max_binary_matrix(X,2)
print "X with winning classes (with 2 tolerance):\n", Xb
def test_matrix_difference_with_accuracy_etc():
print "\n-- 'matrix_difference' (precision/recall/accuracy/cosine), 'max_binary_matrix' --"
X_true = np.array([[2, 0, 0],
[2, 0, 2],
[0, 1, 0],
[0, 0, 3],
[0, 0, 3],
[1, 0, 2],
[0, 3, 3]])
X_pred = np.array([[1, 1, 2],
[2, 1, 2],
[3, 4, 0],
[1, 1, 2],
[2, 1, 1],
[1, 2, 2],
[1, 2.99, 3]])
X_true_b = max_binary_matrix(X_true)
X_pred_b = max_binary_matrix(X_pred)
X_pred_b1 = max_binary_matrix(X_pred, threshold=0.1)
print "X_true:\n", X_true
print "X_pred:\n", X_pred
print "X_true binary:\n", X_true_b
print "X_pred binary:\n", X_pred_b
print "X_pred binary with threshold 0.1:\n", X_pred_b1
ind = list([])
# ind = list([0, 1])
# ind = list([1, 2, 3, 4, 5])
# ind = list([0, 2, 3, 4, 5, 6])
print "\nPrecision:\n", matrix_difference(X_true, X_pred, ind, vector=True, similarity='precision')
print "*** type:", type (matrix_difference(X_true, X_pred, ind, vector=True, similarity='precision'))
print "Recall:\n", matrix_difference(X_true, X_pred, ind, vector=True, similarity='recall')
print "Accuracy:\n", matrix_difference(X_true, X_pred, ind, vector=True, similarity='accuracy')
cosine_list = matrix_difference(X_true, X_pred, ind, vector=True, similarity='cosine')
print "Cosine:\n", cosine_list
print "Cosine sorted:\n", sorted(cosine_list, reverse=True)
print "\nPrecision:\n", matrix_difference(X_true, X_pred, ind, similarity='precision')
print "Recall:\n", matrix_difference(X_true, X_pred, ind, similarity='recall')
print "Accuracy:\n", matrix_difference(X_true, X_pred, ind)
print "Cosine:\n", matrix_difference(X_true, X_pred, ind, similarity='cosine')
def test_matrix_difference_with_accuracy_etc():
print "\n-- 'matrix_difference' (precision/recall/accuracy/cosine), 'max_binary_matrix' --"
X_true = np.array([[2, 0, 0], [2, 0, 2], [0, 1, 0], [0, 0, 3], [0, 0, 3],
[1, 0, 2], [0, 3, 3]])
X_pred = np.array([[1, 1, 2], [2, 1, 2], [3, 4, 0], [1, 1, 2], [2, 1, 1],
[1, 2, 2], [1, 2.99, 3]])
X_true_b = max_binary_matrix(X_true)
X_pred_b = max_binary_matrix(X_pred)
X_pred_b1 = max_binary_matrix(X_pred, threshold=0.1)
print "X_true:\n", X_true
print "X_pred:\n", X_pred
print "X_true binary:\n", X_true_b
print "X_pred binary:\n", X_pred_b
print "X_pred binary with threshold 0.1:\n", X_pred_b1
ind = list([])
# ind = list([0, 1])
# ind = list([1, 2, 3, 4, 5])
# ind = list([0, 2, 3, 4, 5, 6])
print "\nPrecision:\n", matrix_difference(X_true,
X_pred,
ind,
vector=True,
similarity='precision')
print "*** type:", type(
matrix_difference(X_true,
X_pred,
ind,
vector=True,
similarity='precision'))
print "Recall:\n", matrix_difference(X_true,
X_pred,
ind,
vector=True,
similarity='recall')
print "Accuracy:\n", matrix_difference(X_true,
X_pred,
ind,
vector=True,
similarity='accuracy')
cosine_list = matrix_difference(X_true,
X_pred,
ind,
vector=True,
similarity='cosine')
print "Cosine:\n", cosine_list
print "Cosine sorted:\n", sorted(cosine_list, reverse=True)
print "\nPrecision:\n", matrix_difference(X_true,
X_pred,
ind,
similarity='precision')
print "Recall:\n", matrix_difference(X_true,
X_pred,
ind,
similarity='recall')
print "Accuracy:\n", matrix_difference(X_true, X_pred, ind)
print "Cosine:\n", matrix_difference(X_true,
X_pred,
ind,
similarity='cosine')
