def circle_results(file):
t = PrettyTable(['Description', 'Total', 'True Pos.', 'False Pos.', 'True Neg.', 'False Neg.', 'Precision', 'Sensitivity',
'Dice Coeff.', 'Jaccard Ind.', 'Jaccard Dist.'])
f = open(file, 'r')
for line in f:
totals = line.split()
description = totals[0]
tp = totals[1]
fp = totals[2]
tn = totals[3]
fn = totals[4]
total = int(tp) + int(fp) + int(tn) + int(fn)
precision = evaluation.precision(tp, fp)
sensitivity = evaluation.sensitivity(tp, fn)
# fmeasure = evaluation.fmeasure(tp, fp, fn)
dicecoeff = evaluation.dicecoeff(tp, fp, fn)
jaccardindex = evaluation.jaccardindex(tp, fp, fn)
jaccarddifference = 1 - jaccardindex
t.add_row([description, str(total), str(tp), str(fp), str(tn), str(fn), str(precision), str(sensitivity), str(dicecoeff),
str(jaccardindex), str(jaccarddifference)])
print "Circle Detection\n"
print t
now = "\n" + str(datetime.now()) + "\n"
data = t.get_string()
r = open('circle_test_results.txt', 'ab')
r.write(now)
r.write(data)
r.write("\n")
r.close()
def test_octavo(
num_points,
classes,
xbound,
ybound,
zbound,
max_depth,
min_node_size,
min_loss,
expected,
):
xy_parent = data_for_tests.make_octavo(
num_points, classes, xbound, ybound, zbound
).values
X = xy_parent[:, :-1]
y = xy_parent[:, -1]
forest = random_forest.grow_random_forest(
X, y, num_trees=20, max_features=2, min_node_size=1
)
predictions = random_forest.forest_predict(forest, X)
targets = y
tfpns = evaluation.tfpn(predictions, targets)
cm = evaluation.make_confusion_matrix(*tfpns, percentage=True)
result = np.array(
[evaluation.precision(cm), evaluation.sensitivity(cm), evaluation.fpr(cm)]
)
expected = np.array(expected)
assert np.any(np.abs(expected - result) < 0.01)
def test_diagonal_ndim(num_points, dim, max_features, expected, precision_bound):
xy_parent = data_for_tests.make_diagonal_ndim(num_points, dim).values
X = xy_parent[:, :-1]
y = xy_parent[:, -1]
forest = random_forest.grow_random_forest(
X, y, num_trees=30, max_depth=20, max_features=max_features, min_node_size=1
)
predictions = random_forest.forest_predict(forest, X)
targets = y
tfpns = evaluation.tfpn(predictions, targets)
cm = evaluation.make_confusion_matrix(*tfpns, percentage=True)
result = np.array(
[evaluation.precision(cm), evaluation.sensitivity(cm), evaluation.fpr(cm)]
)
expected = np.array(expected)
print(precision_bound)
assert np.any(np.abs(expected - result) < precision_bound)
tn = 0
fn = 0
f = open(file, 'r')
for line in f:
lines += 1
totals = line.split()
# totals[0] is image name
tp += int(totals[1])
fp += int(totals[2])
tn += int(totals[3])
fn += int(totals[4])
f.close()
precision = evaluation.precision(tp, fp)
sensitivity = evaluation.sensitivity(tp, fn)
fmeasure = evaluation.fmeasure(tp, fp, fn)
dicecoeff = evaluation.dicecoeff(tp, fp, fn)
jaccardindex = evaluation.jaccardindex(tp, fp, fn)
r = open('circle_found_results.txt', 'a')
r.write('File name: ' + file + '\n')
r.write('Lines: ' + str(lines) + '\n')
r.write('True Positives: ' + str(tp) + '\n')
r.write('False Positives: ' + str(fp) + '\n')
r.write('True Negatives: ' + str(tn) + '\n')
r.write('False Negatives: ' + str(fn) + '\n')
r.write('Precision: ' + str(precision) + '\n')
r.write('Sensitivity: ' + str(sensitivity) + '\n')
r.write('F-Measure: ' + str(fmeasure) + '\n')
r.write('Dice Coefficent: ' + str(dicecoeff) + '\n')
fp = 0
tn = 0
fn = 0
f = open(file, 'r')
for line in f:
lines += 1
totals = line.split()
tp += int(totals[0])
fp += int(totals[1])
tn += int(totals[2])
fn += int(totals[3])
f.close()
precision = evaluation.precision(tp, fp)
sensitivity = evaluation.sensitivity(tp, fn)
fmeasure = evaluation.fmeasure(tp, fp, fn)
dicecoeff = evaluation.dicecoeff(tp, fp, fn)
jaccardindex = evaluation.jaccardindex(tp, fp, fn)
r = open('circle_found_results.txt', 'a')
r.write('File name: ' + file + '\n')
r.write('Lines: ' + str(lines) + '\n')
r.write('True Positives: ' + str(tp) + '\n')
r.write('False Positives: ' + str(fp) + '\n')
r.write('True Negatives: ' + str(tn) + '\n')
r.write('False Negatives: ' + str(fn) + '\n')
r.write('Precision: ' + str(precision) + '\n')
r.write('Sensitivity: ' + str(sensitivity) + '\n')
r.write('F-Measure: ' + str(fmeasure) + '\n')
r.write('Dice Coefficent: ' + str(dicecoeff) + '\n')
forest = random_forest.grow_random_forest(
X, y, num_trees=30, max_depth=20, max_features=max_features, min_node_size=1
)
# make predictions
predictions = random_forest.forest_predict(forest, X)
# calculate the numbers of true positives, false positives, true negatives, false negatives
tfpns = evaluation.tfpn(predictions, y)
# calculate the confusion matrix
cm = evaluation.make_confusion_matrix(*tfpns, percentage=True)
# calculate metrics: precision, sensitivity, false-positive-rate
metrics = np.array(
[evaluation.precision(cm), evaluation.sensitivity(cm), evaluation.fpr(cm)]
)
print(
f"{num_points} points are randomly generated in the unit cube in {dim}-dimensions.\n \
Those with the sum of coordinates >= {dim}/2 are labeled 1, \n those below are \
labeled 0."
)
print("The model achieves the following in sample metrics:")
print("precision:", metrics[0])
print("sensitivity:", metrics[1])
print("false-positive-rate:", metrics[2])
print('If the metrics are not 1,1,0, then there is a problem.')
# if (metrics[0] == 1) & (metrics[1] == 1) & (metrics[2] == 0):
# print(0)
# else:
