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')