def run(clf):
acc_records, y_records = [], []
bar = ProgressBar(max_value=10, name="Main")
for _ in range(10):
if clf == "Naive Bayes":
_clf = SKMultinomialNB(alpha=0.1)
elif clf == "Non-linear SVM":
_clf = SKSVM()
else:
_clf = SKLinearSVM()
rs = main(_clf)
acc_records.append(rs[0])
y_records += rs[1]
bar.update()
acc_records = np.array(acc_records) * 100
plt.figure()
plt.boxplot(acc_records, vert=False, showmeans=True)
plt.show()
from Util.DataToolkit import DataToolkit
idx = np.argmax(acc_records) # type: int
print(
metrics.classification_report(y_records[idx][0],
y_records[idx][1],
target_names=np.load(
os.path.join("_Data",
"LABEL_DIC.npy"))))
toolkit = DataToolkit(acc_records[np.argmax(np.average(acc_records,
axis=1))])
print("Acc Mean : {:8.6}".format(toolkit.mean))
print("Acc Variance : {:8.6}".format(toolkit.variance))
print("Done")
def main():
# x, y = DataUtil.gen_xor(100, one_hot=False)
x, y = DataUtil.gen_spiral(20, 4, 2, 2, one_hot=False)
# x, y = DataUtil.gen_two_clusters(n_dim=2, one_hot=False)
y[y == 0] = -1
animation_params = {
"show": False,
"mp4": False,
"period": 50,
"dense": 400,
"draw_background": True
}
svm = SVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=600)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
svm = GDSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=10000)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
if TorchSVM is not None:
svm = TorchSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12)
svm.evaluate(x, y)
svm.visualize2d(x,
y,
padding=0.1,
dense=400,
emphasize=svm["alpha"] > 0)
svm = TFSVM()
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400)
svm = SKSVM()
# svm = SKSVM(kernel="poly", degree=12)
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm.support_)
(x_train,
y_train), (x_test,
y_test), *_ = DataUtil.get_dataset("mushroom",
"../_Data/mushroom.txt",
n_train=100,
quantize=True,
tar_idx=0)
y_train[y_train == 0] = -1
y_test[y_test == 0] = -1
svm = SKSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TFSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
if TorchSVM is not None:
svm = TorchSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TorchSVM()
logs = [
log[0] for log in svm.fit(x_train,
y_train,
metrics=["acc"],
x_test=x_test,
y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = SVM()
logs = [
log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = GDSVM()
logs = [
log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm.show_timing_log()
def main():
# x, y = DataUtil.gen_xor(100, one_hot=False)
x, y = DataUtil.gen_spiral(20, 4, 2, 2, one_hot=False)
# x, y = DataUtil.gen_two_clusters(n_dim=2, one_hot=False)
y[y == 0] = -1
#
# svm = SKSVM()
# # svm = SKSVM(kernel="poly", degree=12)
# svm.fit(x, y)
# svm.evaluate(x, y)
# svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm.support_)
#
# svm = TFSVM()
# svm.fit(x, y, lr=0.0001)
# svm.evaluate(x, y)
# svm.visualize2d(x, y, padding=0.1, dense=400)
#
svm = SVM()
svm.fit(x, y, kernel="poly", p=12)
# _logs = [_log[0] for _log in svm.fit(x, y, metrics=["acc"])]
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
(x_train,
y_train), (x_test,
y_test), *_ = DataUtil.get_dataset("mushroom",
"../_Data/mushroom.txt",
train_num=100,
quantize=True,
tar_idx=0)
y_train[y_train == 0] = -1
y_test[y_test == 0] = -1
svm = SKSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TFSVM()
_logs = [
_log[0] for _log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(_logs)), _logs)
plt.show()
svm = SVM()
_logs = [
_log[0] for _log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(_logs)), _logs)
plt.show()
svm.show_timing_log()