def pca_function(data, subDir, name):
variables = data.columns.values
eixo_x = 0
eixo_y = 4
eixo_z = 7
#plt.figure()
#plt.xlabel(variables[eixo_y])
#plt.ylabel(variables[eixo_z])
#plt.scatter(data.iloc[:, eixo_y], data.iloc[:, eixo_z])
print('HFCR Feature Extraction - PCA')
mean = (data.mean(axis=0)).tolist()
centered_data = data - mean
cov_mtx = centered_data.cov()
eigvals, eigvecs = np.linalg.eig(cov_mtx)
pca = PCA()
pca.fit(centered_data)
PC = pca.components_
var = pca.explained_variance_
# PLOT EXPLAINED VARIANCE RATIO
#fig = plt.figure(figsize=(4, 4))
#plt.title('Explained variance ratio')
#plt.xlabel('PC')
#plt.ylabel('ratio')
#x_values = [str(i) for i in range(1, len(pca.components_) + 1)]
#bwidth = 0.5
#ax = plt.gca()
#ax.set_xticklabels(x_values)
#ax.set_ylim(0.0, 1.0)
#ax.bar(x_values, pca.explained_variance_ratio_, width=bwidth)
#ax.plot(pca.explained_variance_ratio_)
#for i, v in enumerate(pca.explained_variance_ratio_):
# ax.text(i, v+0.05, f'{v*100:.1f}', ha='center', fontweight='bold')
#plt.suptitle('HFCR Feature Extraction - PCA')
#plt.savefig(subDir + 'HFCR Feature Extraction - PCA')
print('HFCR Feature Extraction - PCA 2')
transf = pca.transform(data)
#_, axs = plt.subplots(1, 2, figsize=(2*5, 1*5), squeeze=False)
#axs[0,0].set_xlabel(variables[eixo_y])
#axs[0,0].set_ylabel(variables[eixo_z])
#axs[0,0].scatter(data.iloc[:, eixo_y], data.iloc[:, eixo_z])
#axs[0,1].set_xlabel('PC1')
#axs[0,1].set_ylabel('PC2')
#axs[0,1].scatter(transf[:, 0], transf[:, 1])
#plt.suptitle('HFCR Feature Extraction - PCA')
#plt.savefig(subDir + 'HFCR Feature Extraction - PCA')
print('Clustering after PCA')
data = pd.DataFrame(transf[:, :2], columns=['PC1', 'PC2'])
eixo_x = 0
eixo_y = 1
rows, cols = ds.choose_grid(len(N_CLUSTERS))
print('HFCR Clustering - K-Means after PCA')
sc: list = []
mse: list = []
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = KMeans(n_clusters=k)
estimator.fit(data)
sc.append(silhouette_score(data, estimator.labels_))
mse.append(estimator.inertia_)
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
fig_values_1[name] = sc
fig_values_5[name] = mse
print('HFCR Clustering - Expectation-Maximization after PCA')
sc: list = []
mse: list = []
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = GaussianMixture(n_components=k)
estimator.fit(data)
labels = estimator.predict(data)
sc.append(silhouette_score(data, labels))
mse.append(ds.compute_mse(data.values, labels, estimator.means_))
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
fig_values_2[name] = sc
fig_values_6[name] = mse
"""
print('HFCR Clustering - EPS (Density-based) after PCA')
sc: list = []
i, j = 0, 0
for n in range(len(EPS)):
estimator = DBSCAN(eps=EPS[n], min_samples=2)
estimator.fit(data)
labels = estimator.labels_
k = len(set(labels)) - (1 if -1 in labels else 0)
if k > 1:
centers = ds.compute_centroids(data, labels)
sc.append(silhouette_score(data, labels))
i, j = (i + 1, 0) if (n+1) % cols == 0 else (i, j + 1)
else:
sc.append(0)
fig_values_3[name] = sc
fig_values_7[name] = mse
"""
"""
print('HFCR Clustering - Metric (Density-based) after PCA')
METRICS = ['euclidean', 'cityblock', 'chebyshev', 'cosine', 'jaccard']
distances = []
for m in METRICS:
dist = np.mean(np.mean(squareform(pdist(data.values, metric=m))))
distances.append(dist)
print('AVG distances among records', distances)
distances[0] = 80
distances[1] = 50
distances[2] = 80
distances[3] = 0.0005
distances[4] = 0.0009
print('CHOSEN EPS', distances)
mse: list = []
mae: list = []
sc: list = []
db: list = []
rows, cols = ds.choose_grid(len(METRICS))
_, axs = plt.subplots(rows, cols, figsize=(cols*5, rows*5), squeeze=False)
i, j = 0, 0
for n in range(len(METRICS)):
estimator = DBSCAN(eps=distances[n], min_samples=2, metric=METRICS[n])
estimator.fit(data)
labels = estimator.labels_
k = len(set(labels)) - (1 if -1 in labels else 0)
if k > 1:
centers = ds.compute_centroids(data, labels)
mse.append(ds.compute_mse(data.values, labels, centers))
mae.append(ds.compute_mae(data.values, labels, centers))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(data, eixo_x, eixo_y, labels.astype(float), estimator.components_, k,
f'DBSCAN metric={METRICS[n]} eps={distances[n]:.2f} k={k}', ax=axs[i,j])
else:
print(k)
mse.append(0)
mae.append(0)
sc.append(0)
db.append(0)
i, j = (i + 1, 0) if (n+1) % cols == 0 else (i, j + 1)
plt.suptitle('HFCR Clustering - Metric (Density-based) after PCA')
plt.savefig(subDir + 'HFCR Clustering - Metric (Density-based) after PCA')
print('HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA')
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.bar_chart(METRICS, mse, title='DBSCAN MSE', xlabel='metric', ylabel='MSE', ax=ax[0, 0])
ds.bar_chart(METRICS, mae, title='DBSCAN MAE', xlabel='metric', ylabel='MAE', ax=ax[0, 1])
ds.bar_chart(METRICS, sc, title='DBSCAN SC', xlabel='metric', ylabel='SC', ax=ax[0, 2], percentage=True)
ds.bar_chart(METRICS, db, title='DBSCAN DB', xlabel='metric', ylabel='DB', ax=ax[0, 3])
plt.suptitle('HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA')
plt.savefig(subDir + 'HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA')
"""
print('HFCR Clustering - Hierarchical after PCA')
sc: list = []
mse: list = []
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = AgglomerativeClustering(n_clusters=k)
estimator.fit(data)
labels = estimator.labels_
centers = ds.compute_centroids(data, labels)
sc.append(silhouette_score(data, labels))
mse.append(ds.compute_mse(data.values, labels, centers))
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
fig_values_4[name] = sc
fig_values_8[name] = mse
"""
max_value = acceptable_values[var].max()
min_value = acceptable_values[var].min()
data.loc[(data[var] < min_value), var] = min_value
data.loc[(data[var] > max_value), var] = max_value
print(data.describe())
data.describe().to_csv(graphsDir + 'QOT Outliers Imputation - Description.csv')
print()
print('QOT Outliers Imputation - Boxplot')
data.boxplot(rot=45, figsize=(150, 3), whis=1.5)
plt.suptitle('QOT Outliers Imputation - Boxplot')
plt.savefig(graphsDir + 'QOT Outliers Imputation - Boxplot')
print()
print('QOT Outliers Imputation - Boxplot for each variable')
numeric_vars = data.select_dtypes(include='number').columns
rows, cols = ds.choose_grid(len(numeric_vars), 18)
height_fix = ds.HEIGHT / 1.7
fig, axs = plt.subplots(rows,
cols,
figsize=(cols * height_fix, rows * height_fix))
i, j = 0, 0
for n in range(len(numeric_vars)):
axs[i, j].set_title('Boxplot for %s' % numeric_vars[n])
axs[i, j].boxplot(data[numeric_vars[n]].dropna().values, whis=1.5)
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('QOT Outliers Imputation - Boxplot for each variable')
plt.savefig(graphsDir + 'QOT Outliers Imputation - Boxplot for each variable')
print()
print('Scaling = ' + str(scaling) + ' and PCA = ' + str(pca))
print()
pca_function(data, subDir, name)
continue
else:
break
print()
print()
print('Scaling = ' + str(scaling) + ' and PCA = ' + str(pca))
print()
v1 = data.columns.get_loc("age") # age
v2 = data.columns.get_loc("time") # time
rows, cols = ds.choose_grid(len(N_CLUSTERS))
print('HFCR Clustering - K-Means')
sc: list = []
mse: list = []
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = KMeans(n_clusters=k)
estimator.fit(data)
sc.append(silhouette_score(data, estimator.labels_))
mse.append(estimator.inertia_)
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
fig_values_1[name] = sc
fig_values_5[name] = mse
axs[0,1].set_xlabel('PC1')
axs[0,1].set_ylabel('PC2')
axs[0,1].scatter(transf[:, 0], transf[:, 1])
plt.suptitle('QOT Feature Extraction - PCA')
plt.savefig(subDir + 'QOT Feature Extraction - PCA')
print('Clustering after PCA')
data = pd.DataFrame(transf[:,:2], columns=['PC1', 'PC2'])
eixo_x = 0
eixo_y = 1
N_CLUSTERS = [2, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29]
rows, cols = ds.choose_grid(len(N_CLUSTERS))
print('QOT Clustering - K-Means after PCA')
mse: list = []
mae: list = []
sc: list = []
db: list = []
_, axs = plt.subplots(rows, cols, figsize=(cols*5, rows*5), squeeze=False)
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = KMeans(n_clusters=k)
estimator.fit(data)
mse.append(estimator.inertia_)
mae.append(ds.compute_mae(data.values, estimator.labels_, estimator.cluster_centers_))
sc.append(silhouette_score(data, estimator.labels_))
import pandas as pd
import matplotlib.pyplot as plt
import ds_functions as ds
import os
graphsDir = './Results/Granularity/'
if not os.path.exists(graphsDir):
os.makedirs(graphsDir)
data = pd.read_csv('../Dataset/heart_failure_clinical_records_dataset.csv')
values = {'nr records': data.shape[0], 'nr variables': data.shape[1]}
print(values)
print("Computing HFCR Granularity - 100bins ...")
variables = data.select_dtypes(include='number').columns
rows, cols = ds.choose_grid(len(variables))
fig, axs = plt.subplots(rows,
cols,
figsize=(cols * ds.HEIGHT, rows * ds.HEIGHT))
i, j = 0, 0
for n in range(len(variables)):
axs[i, j].set_title('Histogram for %s' % variables[n], color='chocolate')
axs[i, j].set_xlabel(variables[n])
axs[i, j].set_ylabel('nr records')
axs[i, j].hist(data[variables[n]].values, bins=100, color='peachpuff')
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
fig.suptitle("HFCR Granularity - 100 Bins per Variable", color='chocolate')
plt.savefig(graphsDir + 'HFCR Granularity - 100bins.png')
print("Computing HFCR Granularity - 10.100.1000bins ...")
columns = data.select_dtypes(include='number').columns
data = pd.read_csv('../Dataset/qsar_oral_toxicity.csv', sep=';', header=None)
print(data.describe())
data.describe().to_csv(graphsDir +
'QOT Distribution - Numeric variables description.csv')
print()
print('QOT Distribution - Boxplot')
data.boxplot(rot=45, figsize=(150, 3), whis=1.5)
plt.suptitle('QOT Distribution - Boxplot')
plt.savefig(graphsDir + 'QOT Distribution - Boxplot')
plt.close()
print()
numeric_vars = data.select_dtypes(include='number').columns
rows, cols = ds.choose_grid(len(numeric_vars), 18)
height_fix = ds.HEIGHT / 1.7
print('QOT Distribution - Boxplot for each variable')
fig, axs = plt.subplots(rows,
cols,
figsize=(cols * height_fix, rows * height_fix))
i, j = 0, 0
for n in range(len(numeric_vars)):
axs[i, j].set_title('Boxplot for %s' % numeric_vars[n])
axs[i, j].boxplot(data[numeric_vars[n]].dropna().values, whis=1.5)
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('QOT Distribution - Boxplot for each variable')
plt.savefig(graphsDir + 'QOT Distribution - Boxplot for each variable')
plt.close()
print()
def pca_function(data, subDir):
data.pop('DEATH_EVENT')
variables = data.columns.values
eixo_x = 0
eixo_y = 4
eixo_z = 7
plt.figure()
plt.xlabel(variables[eixo_y])
plt.ylabel(variables[eixo_z])
plt.scatter(data.iloc[:, eixo_y], data.iloc[:, eixo_z])
print('HFCR Feature Extraction - PCA')
mean = (data.mean(axis=0)).tolist()
centered_data = data - mean
cov_mtx = centered_data.cov()
eigvals, eigvecs = np.linalg.eig(cov_mtx)
pca = PCA()
pca.fit(centered_data)
PC = pca.components_
var = pca.explained_variance_
# PLOT EXPLAINED VARIANCE RATIO
fig = plt.figure(figsize=(4, 4))
plt.title('Explained variance ratio')
plt.xlabel('PC')
plt.ylabel('ratio')
x_values = [str(i) for i in range(1, len(pca.components_) + 1)]
bwidth = 0.5
ax = plt.gca()
ax.set_xticklabels(x_values)
ax.set_ylim(0.0, 1.0)
ax.bar(x_values, pca.explained_variance_ratio_, width=bwidth)
ax.plot(pca.explained_variance_ratio_)
for i, v in enumerate(pca.explained_variance_ratio_):
ax.text(i, v + 0.05, f'{v*100:.1f}', ha='center', fontweight='bold')
plt.suptitle('HFCR Feature Extraction - PCA')
plt.savefig(subDir + 'HFCR Feature Extraction - PCA')
print('HFCR Feature Extraction - PCA 2')
transf = pca.transform(data)
_, axs = plt.subplots(1, 2, figsize=(2 * 5, 1 * 5), squeeze=False)
axs[0, 0].set_xlabel(variables[eixo_y])
axs[0, 0].set_ylabel(variables[eixo_z])
axs[0, 0].scatter(data.iloc[:, eixo_y], data.iloc[:, eixo_z])
axs[0, 1].set_xlabel('PC1')
axs[0, 1].set_ylabel('PC2')
axs[0, 1].scatter(transf[:, 0], transf[:, 1])
plt.suptitle('HFCR Feature Extraction - PCA')
plt.savefig(subDir + 'HFCR Feature Extraction - PCA')
print('Clustering after PCA')
data = pd.DataFrame(transf[:, :2], columns=['PC1', 'PC2'])
eixo_x = 0
eixo_y = 1
N_CLUSTERS = [2, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29]
rows, cols = ds.choose_grid(len(N_CLUSTERS))
print('HFCR Clustering - K-Means after PCA')
mse: list = []
mae: list = []
sc: list = []
db: list = []
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = KMeans(n_clusters=k)
estimator.fit(data)
mse.append(estimator.inertia_)
mae.append(
ds.compute_mae(data.values, estimator.labels_,
estimator.cluster_centers_))
sc.append(silhouette_score(data, estimator.labels_))
db.append(davies_bouldin_score(data, estimator.labels_))
ds.plot_clusters(data,
eixo_x,
eixo_y,
estimator.labels_.astype(float),
estimator.cluster_centers_,
k,
f'KMeans k={k}',
ax=axs[i, j])
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('HFCR Clustering - K-Means after PCA')
plt.savefig(subDir + 'HFCR Clustering - K-Means after PCA')
print(
'HFCR Clustering - K-Means after PCA MSE vs MAE vs SC vs DB after PCA')
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.plot_line(N_CLUSTERS,
mse,
title='KMeans MSE',
xlabel='k',
ylabel='MSE',
ax=ax[0, 0])
ds.plot_line(N_CLUSTERS,
mae,
title='KMeans MAE',
xlabel='k',
ylabel='MAE',
ax=ax[0, 1])
ds.plot_line(N_CLUSTERS,
sc,
title='KMeans SC',
xlabel='k',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.plot_line(N_CLUSTERS,
db,
title='KMeans DB',
xlabel='k',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - K-Means after PCA MSE vs MAE vs SC vs DB after PCA')
plt.savefig(
subDir +
'HFCR Clustering - K-Means after PCA MSE vs MAE vs SC vs DB after PCA')
print('HFCR Clustering - Expectation-Maximization after PCA')
mse: list = []
mae: list = []
sc: list = []
db: list = []
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = GaussianMixture(n_components=k)
estimator.fit(data)
labels = estimator.predict(data)
mse.append(ds.compute_mse(data.values, labels, estimator.means_))
mae.append(ds.compute_mae(data.values, labels, estimator.means_))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(data,
eixo_x,
eixo_y,
labels.astype(float),
estimator.means_,
k,
f'EM k={k}',
ax=axs[i, j])
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('HFCR Clustering - Expectation-Maximization after PCA')
plt.savefig(subDir +
'HFCR Clustering - Expectation-Maximization after PCA')
print(
'HFCR Clustering - Expectation-Maximization MSE vs MAE vs SC vs DB after PCA'
)
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.plot_line(N_CLUSTERS,
mse,
title='EM MSE',
xlabel='k',
ylabel='MSE',
ax=ax[0, 0])
ds.plot_line(N_CLUSTERS,
mae,
title='EM MAE',
xlabel='k',
ylabel='MAE',
ax=ax[0, 1])
ds.plot_line(N_CLUSTERS,
sc,
title='EM SC',
xlabel='k',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.plot_line(N_CLUSTERS,
db,
title='EM DB',
xlabel='k',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - Expectation-Maximization MSE vs MAE vs SC vs DB after PCA'
)
plt.savefig(
subDir +
'HFCR Clustering - Expectation-Maximization MSE vs MAE vs SC vs DB after PCA'
)
print('HFCR Clustering - EPS (Density-based) after PCA')
EPS = [2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
mse: list = []
mae: list = []
sc: list = []
db: list = []
rows, cols = ds.choose_grid(len(EPS))
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
i, j = 0, 0
for n in range(len(EPS)):
estimator = DBSCAN(eps=EPS[n], min_samples=2)
estimator.fit(data)
labels = estimator.labels_
k = len(set(labels)) - (1 if -1 in labels else 0)
if k > 1:
centers = ds.compute_centroids(data, labels)
mse.append(ds.compute_mse(data.values, labels, centers))
mae.append(ds.compute_mae(data.values, labels, centers))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(data,
eixo_x,
eixo_y,
labels.astype(float),
estimator.components_,
k,
f'DBSCAN eps={EPS[n]} k={k}',
ax=axs[i, j])
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
else:
mse.append(0)
mae.append(0)
sc.append(0)
db.append(0)
plt.suptitle('HFCR Clustering - EPS (Density-based) after PCA')
plt.savefig(subDir + 'HFCR Clustering - EPS (Density-based) after PCA')
print(
'HFCR Clustering - EPS (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.plot_line(EPS,
mse,
title='DBSCAN MSE',
xlabel='eps',
ylabel='MSE',
ax=ax[0, 0])
ds.plot_line(EPS,
mae,
title='DBSCAN MAE',
xlabel='eps',
ylabel='MAE',
ax=ax[0, 1])
ds.plot_line(EPS,
sc,
title='DBSCAN SC',
xlabel='eps',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.plot_line(EPS,
db,
title='DBSCAN DB',
xlabel='eps',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - EPS (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
plt.savefig(
subDir +
'HFCR Clustering - EPS (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
print('HFCR Clustering - Metric (Density-based) after PCA')
METRICS = ['euclidean', 'cityblock', 'chebyshev', 'cosine', 'jaccard']
distances = []
for m in METRICS:
dist = np.mean(np.mean(squareform(pdist(data.values, metric=m))))
distances.append(dist)
print('AVG distances among records', distances)
distances[0] = 80
distances[1] = 50
distances[2] = 80
distances[3] = 0.0005
distances[4] = 0.0009
print('CHOSEN EPS', distances)
mse: list = []
mae: list = []
sc: list = []
db: list = []
rows, cols = ds.choose_grid(len(METRICS))
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
i, j = 0, 0
for n in range(len(METRICS)):
estimator = DBSCAN(eps=distances[n], min_samples=2, metric=METRICS[n])
estimator.fit(data)
labels = estimator.labels_
k = len(set(labels)) - (1 if -1 in labels else 0)
if k > 1:
centers = ds.compute_centroids(data, labels)
mse.append(ds.compute_mse(data.values, labels, centers))
mae.append(ds.compute_mae(data.values, labels, centers))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(
data,
eixo_x,
eixo_y,
labels.astype(float),
estimator.components_,
k,
f'DBSCAN metric={METRICS[n]} eps={distances[n]:.2f} k={k}',
ax=axs[i, j])
else:
print(k)
mse.append(0)
mae.append(0)
sc.append(0)
db.append(0)
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('HFCR Clustering - Metric (Density-based) after PCA')
plt.savefig(subDir + 'HFCR Clustering - Metric (Density-based) after PCA')
print(
'HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.bar_chart(METRICS,
mse,
title='DBSCAN MSE',
xlabel='metric',
ylabel='MSE',
ax=ax[0, 0])
ds.bar_chart(METRICS,
mae,
title='DBSCAN MAE',
xlabel='metric',
ylabel='MAE',
ax=ax[0, 1])
ds.bar_chart(METRICS,
sc,
title='DBSCAN SC',
xlabel='metric',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.bar_chart(METRICS,
db,
title='DBSCAN DB',
xlabel='metric',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
plt.savefig(
subDir +
'HFCR Clustering - Metric (Density-based) MSE vs MAE vs SC vs DB after PCA'
)
print('HFCR Clustering - Hierarchical after PCA')
mse: list = []
mae: list = []
sc: list = []
db: list = []
rows, cols = ds.choose_grid(len(N_CLUSTERS))
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
i, j = 0, 0
for n in range(len(N_CLUSTERS)):
k = N_CLUSTERS[n]
estimator = AgglomerativeClustering(n_clusters=k)
estimator.fit(data)
labels = estimator.labels_
centers = ds.compute_centroids(data, labels)
mse.append(ds.compute_mse(data.values, labels, centers))
mae.append(ds.compute_mae(data.values, labels, centers))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(data,
eixo_x,
eixo_y,
labels,
centers,
k,
f'Hierarchical k={k}',
ax=axs[i, j])
i, j = (i + 1, 0) if (n + 1) % cols == 0 else (i, j + 1)
plt.suptitle('HFCR Clustering - Hierarchical after PCA')
plt.savefig(subDir + 'HFCR Clustering - Hierarchical after PCA')
print('HFCR Clustering - Hierarchical MSE vs MAE vs SC vs DB after PCA')
fig, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.plot_line(N_CLUSTERS,
mse,
title='Hierarchical MSE',
xlabel='k',
ylabel='MSE',
ax=ax[0, 0])
ds.plot_line(N_CLUSTERS,
mae,
title='Hierarchical MAE',
xlabel='k',
ylabel='MAE',
ax=ax[0, 1])
ds.plot_line(N_CLUSTERS,
sc,
title='Hierarchical SC',
xlabel='k',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.plot_line(N_CLUSTERS,
db,
title='Hierarchical DB',
xlabel='k',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - Hierarchical MSE vs MAE vs SC vs DB after PCA')
plt.savefig(
subDir +
'HFCR Clustering - Hierarchical MSE vs MAE vs SC vs DB after PCA')
print('HFCR Clustering - Metric (Hierarchical) after PCA')
METRICS = ['euclidean', 'cityblock', 'chebyshev', 'cosine', 'jaccard']
LINKS = ['complete', 'average']
k = 3
values_mse = {}
values_mae = {}
values_sc = {}
values_db = {}
rows = len(METRICS)
cols = len(LINKS)
_, axs = plt.subplots(rows,
cols,
figsize=(cols * 5, rows * 5),
squeeze=False)
for i in range(len(METRICS)):
mse: list = []
mae: list = []
sc: list = []
db: list = []
m = METRICS[i]
for j in range(len(LINKS)):
link = LINKS[j]
estimator = AgglomerativeClustering(n_clusters=k,
linkage=link,
affinity=m)
estimator.fit(data)
labels = estimator.labels_
centers = ds.compute_centroids(data, labels)
mse.append(ds.compute_mse(data.values, labels, centers))
mae.append(ds.compute_mae(data.values, labels, centers))
sc.append(silhouette_score(data, labels))
db.append(davies_bouldin_score(data, labels))
ds.plot_clusters(data,
eixo_x,
eixo_y,
labels,
centers,
k,
f'Hierarchical k={k} metric={m} link={link}',
ax=axs[i, j])
values_mse[m] = mse
values_mae[m] = mae
values_sc[m] = sc
values_db[m] = db
plt.suptitle('HFCR Clustering - Metric (Hierarchical) after PCA')
plt.savefig(subDir + 'HFCR Clustering - Metric (Hierarchical) after PCA')
print(
'HFCR Clustering - Metric (Hierarchical) MSE vs MAE vs SC vs DB after PCA'
)
_, ax = plt.subplots(1, 4, figsize=(10, 3), squeeze=False)
ds.multiple_bar_chart(LINKS,
values_mse,
title=f'Hierarchical MSE',
xlabel='metric',
ylabel='MSE',
ax=ax[0, 0])
ds.multiple_bar_chart(LINKS,
values_mae,
title=f'Hierarchical MAE',
xlabel='metric',
ylabel='MAE',
ax=ax[0, 1])
ds.multiple_bar_chart(LINKS,
values_sc,
title=f'Hierarchical SC',
xlabel='metric',
ylabel='SC',
ax=ax[0, 2],
percentage=True)
ds.multiple_bar_chart(LINKS,
values_db,
title=f'Hierarchical DB',
xlabel='metric',
ylabel='DB',
ax=ax[0, 3])
plt.suptitle(
'HFCR Clustering - Metric (Hierarchical) MSE vs MAE vs SC vs DB after PCA'
)
plt.savefig(
subDir +
'HFCR Clustering - Metric (Hierarchical) MSE vs MAE vs SC vs DB after PCA'
)
plt.close("all")
plt.clf()