def cardio_re():
epss = [3, 4, 5]
for e in epss:
mat = loadmat('../data/cardio.mat')
X_car = mat['X']
y = pd.DataFrame(mat['y'])
y = y[0].to_numpy()
X_car = pd.DataFrame(X_car)
print(X_car.head(12))
print(y)
# clusterlmat = kmeansm(X_car, 2, 176, 10, 0.05, 21)
clusterlmat = dbscan.dbscanp(X_car, 21, eps=e, minpts=10, factor=1)
# print(clusterlmat[0][13])
y_t = clusterlmat[0][21].to_numpy()
print(y_t)
index = 0
for i in y_t.copy():
if i == -1:
y_t[index] = 1
else:
y_t[index] = 0
index += 1
print(y_t)
print(f1_score(y, y_t, average='weighted'))
print(assess.falsealarmrate(y, [0], y_t, 1))
print(adjusted_rand_score(y, y_t))
print(jaccard_score(y, y_t))
print(e)
def pima_re():
epss = [(45, 0.2, 0.9), (35, 0.35, 2), (25, 0.2, 0.0005)]
for t in epss:
data = pd.read_csv('../data/pimaindiansdiabetes.csv', header=None)
print(data.head(12))
y = data[8].to_numpy()
print(y)
# clusterlmat = kmeansm(X_car, 2, 176, 10, 0.05, 21)
clusterlmat = dbscan.dbscanp(
data,
8,
eps=t[2],
minpts=t[0],
factor=t[1],
initialization=dbscan.Initialization.UNIFORM,
plot=True)
# print(clusterlmat[0][13])
y_t = clusterlmat[0][9].to_numpy()
# print(y_t)
index = 0
for i in y_t.copy():
if i == -1:
y_t[index] = 1
else:
y_t[index] = 0
index += 1
print(y_t)
print(f1_score(y, y_t, average='weighted'))
print(assess.falsealarmrate(y, [0], y_t, 1))
print(adjusted_rand_score(y, y_t))
print(jaccard_score(y, y_t))
print(t[2])
def glass_re():
epss = [0.4, 0.9, 1, 1.2, 2]
for e in epss:
mat = loadmat('../data/glass.mat')
X_car = mat['X']
y = pd.DataFrame(mat['y'])
y = y[0].to_numpy()
X_car = pd.DataFrame(X_car)
print(X_car.head(12))
print(y)
# clusterlmat = kmeansm(X_car, 2, 176, 10, 0.05, 21)
clusterlmat = dbscan.dbscanp(X_car, 21, eps=e, minpts=8, factor=0.5, initialization=dbscan.Initialization.KCENTRE)
# print(clusterlmat[0][13])
y_t = clusterlmat[0][9].to_numpy()
# print(y_t)
index = 0
for i in y_t.copy():
if i == -1:
y_t[index] = 1
else:
y_t[index] = 0
index += 1
print(y_t)
print(f1_score(y, y_t, average='weighted'))
print(assess.falsealarmrate(y, [0], y_t, 1))
print(adjusted_rand_score(y, y_t))
print(jaccard_score(y, y_t))
print(e)
def pima_pre_norm_re():
epss = [(45, 0.02, 0.9), (35, 0.0001, 2), (25, 0.2, 0.00005)]
for t in epss:
data = pd.read_csv('../data/pimaindiansdiabetes.csv', header=None)
print(data.head(12))
y = data[8].to_numpy()
print(y)
scaler = MinMaxScaler()
arr_scaled = scaler.fit_transform(data)
data2 = pd.DataFrame(arr_scaled)
# pca = PCA(n_components=7)
# principalcomponents = pca.fit(data2.iloc[, 0:8])
# principledf = pd.DataFrame(principalcomponents)
# clusterlmat = kmeansm(X_car, 2, 176, 10, 0.05, 21)
clusterlmat = dbscan.dbscanp(data2,
8,
eps=t[2],
minpts=t[0],
factor=t[1],
initialization=dbscan.Initialization.NONE,
plot=False)
# print(clusterlmat[0][13])
y_t = clusterlmat[0][9].to_numpy()
# print(y_t)
index = 0
for i in y_t.copy():
if i == -1:
y_t[index] = 1
else:
y_t[index] = 0
index += 1
print(y_t)
print(f1_score(y, y_t, average='weighted'))
print(assess.falsealarmrate(y, [0], y_t, 1))
print(adjusted_rand_score(y, y_t))
print(jaccard_score(y, y_t))
print(t[2])
def pima_pre_norm_and_pca_re():
epss = [(270, 0.5, 0.5), (270, 0.5, 0.4), (270, 0.5, 0.35),
(270, 0.5, 0.3), (270, 0.5, 0.2), (270, 0.5, 0.1)]
epss2 = [(280, 0.5, 0.5), (280, 0.5, 0.4), (280, 0.5, 0.35),
(280, 0.5, 0.3), (280, 0.5, 0.2), (280, 0.5, 0.1)]
epss3 = [(290, 0.5, 0.5), (290, 0.5, 0.4), (290, 0.5, 0.35),
(290, 0.5, 0.3), (290, 0.5, 0.2), (290, 0.5, 0.1)]
for t in epss3:
data = pd.read_csv('../data/pimaindiansdiabetes.csv', header=None)
# print(data.head(12))
y = data[8].to_numpy()
print(y)
scaler = MinMaxScaler()
arr_scaled = scaler.fit_transform(data)
data2 = pd.DataFrame(arr_scaled)
pca = PCA(n_components=7)
principalcomponents = pca.fit_transform(data2.iloc[:, 0:8])
principledf = pd.DataFrame(data=principalcomponents)
# print(principledf.head(12))
# clusterlmat = kmeansm(X_car, 2, 176, 10, 0.05, 21)
clusterlmat = dbscan.dbscanp(
principledf,
7,
eps=t[2],
minpts=t[0],
factor=t[1],
initialization=dbscan.Initialization.KCENTRE,
plot=False)
# print(clusterlmat[0][13])
y_t = clusterlmat[0][7].to_numpy()
# print(y_t)
index = 0
for i in y_t.copy():
if i == -1:
y_t[index] = 1
else:
y_t[index] = 0
index += 1
print("cluster labels:", y_t)
print("eps: ", t[2])
f_sc = f1_score(y, y_t, average='weighted')
fa = assess.falsealarmrate(y, [0], y_t, 1)
ard = adjusted_rand_score(y, y_t)
js = jaccard_score(y, y_t)
print(f_sc)
print(fa)
print(ard)
print(js)
print(t[2])
rr = [
t[0], t[2], t[1], f_sc, fa, ard, js, dbscan.Initialization.KCENTRE
]
with open('../data/pima_pca/dbscan.pima.pca.result.csv', 'a') as fd:
writer = csv.writer(fd)
writer.writerow(rr)