from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import numpy as np
from matplotlib import cm
from sklearn.metrics import silhouette_samples
import pandas as pd
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram
# from scipy.cluster.hierarchy import set_link_color_palette
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import make_moons
from sklearn.cluster import DBSCAN






# ----------------------------------------------------------------------------------------
# データの用意
X, y = make_blobs(n_samples=150, 
                  n_features=2, 
                  centers=3, 
                  cluster_std=0.5, 
                  shuffle=True, 
                  random_state=0)

plt.scatter(X[:, 0], X[:, 1], 
            c='white', marker='o', edgecolor='black', s=50)
plt.grid()
plt.tight_layout()
#plt.savefig('images/11_01.png', dpi=300)
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# k-means
km = KMeans(n_clusters=3, 
            init='random', 
            n_init=10, 
            max_iter=300,
            tol=1e-04,
            random_state=0)

y_km = km.fit_predict(X)

plt.scatter(X[y_km == 0, 0],
            X[y_km == 0, 1],
            s=50, c='lightgreen',
            marker='s', edgecolor='black',
            label='cluster 1')
plt.scatter(X[y_km == 1, 0],
            X[y_km == 1, 1],
            s=50, c='orange',
            marker='o', edgecolor='black',
            label='cluster 2')
plt.scatter(X[y_km == 2, 0],
            X[y_km == 2, 1],
            s=50, c='lightblue',
            marker='v', edgecolor='black',
            label='cluster 3')
plt.scatter(km.cluster_centers_[:, 0],
            km.cluster_centers_[:, 1],
            s=250, marker='*',
            c='red', edgecolor='black',
            label='centroids')
plt.legend(scatterpoints=1)
plt.grid()
plt.tight_layout()
#plt.show()
plt.close()

print('Distortion: %.2f' % km.inertia_)
# ----------------------------------------------------------------------------------------
# エルボー法
distortions = []
for i in range(1, 11):
    km = KMeans(n_clusters=i, 
                init='k-means++', 
                n_init=10, 
                max_iter=300, 
                random_state=0)
    km.fit(X)
    distortions.append(km.inertia_)
plt.plot(range(1, 11), distortions, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
plt.tight_layout()

#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# シルエット係数
km = KMeans(n_clusters=3, 
            init='k-means++', 
            n_init=10, 
            max_iter=300,
            tol=1e-04,
            random_state=0)
y_km = km.fit_predict(X)

cluster_labels  = np.unique(y_km)           # y_kmの要素の中で重複をなくす
n_clusters      = cluster_labels.shape[0]
silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
for i, c in enumerate(cluster_labels):
    c_silhouette_vals = silhouette_vals[y_km == c]
    c_silhouette_vals.sort()
    y_ax_upper += len(c_silhouette_vals)
    color = cm.jet(float(i) / n_clusters)
    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, 
             edgecolor='none', color=color)

    yticks.append((y_ax_lower + y_ax_upper) / 2.)
    y_ax_lower += len(c_silhouette_vals)
    
silhouette_avg = np.mean(silhouette_vals)
plt.axvline(silhouette_avg, color="red", linestyle="--") 

plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Silhouette coefficient')
plt.xlim(0., 1.)

plt.tight_layout()
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# 悪いシルエット係数
km = KMeans(n_clusters=2,
            init='k-means++',
            n_init=10,
            max_iter=300,
            tol=1e-04,
            random_state=0)
y_km = km.fit_predict(X)

plt.scatter(X[y_km == 0, 0],
            X[y_km == 0, 1],
            s=50,
            c='lightgreen',
            edgecolor='black',
            marker='s',
            label='cluster 1')
plt.scatter(X[y_km == 1, 0],
            X[y_km == 1, 1],
            s=50,
            c='orange',
            edgecolor='black',
            marker='o',
            label='cluster 2')

plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1],
            s=250, marker='*', c='red', label='centroids')
plt.legend()
plt.grid()
plt.tight_layout()
#plt.show()
plt.close()

cluster_labels = np.unique(y_km)
n_clusters = cluster_labels.shape[0]
silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
for i, c in enumerate(cluster_labels):
    c_silhouette_vals = silhouette_vals[y_km == c]
    c_silhouette_vals.sort()
    y_ax_upper += len(c_silhouette_vals)
    color = cm.jet(float(i) / n_clusters)
    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, 
             edgecolor='none', color=color)

    yticks.append((y_ax_lower + y_ax_upper) / 2.)
    y_ax_lower += len(c_silhouette_vals)
    
silhouette_avg = np.mean(silhouette_vals)
plt.axvline(silhouette_avg, color="red", linestyle="--") 

plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Silhouette coefficient')

plt.tight_layout()
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# データ生成
np.random.seed(123)

variables = ['X', 'Y', 'Z']
labels    = ['ID_0', 'ID_1', 'ID_2', 'ID_3', 'ID_4']

X = np.random.random_sample([5, 3])*10
df = pd.DataFrame(X, columns=variables, index=labels)
print(df)
# ----------------------------------------------------------------------------------------
# 距離行列生成
row_dist = pd.DataFrame(squareform(pdist(df, metric='euclidean')),
                        columns=labels,
                        index=labels)
print(row_dist)
#print(pdist(df, metric='euclidean'))
#print(squareform(pdist(df, metric='euclidean')))
# ----------------------------------------------------------------------------------------
# 連結行列生成
row_clusters = linkage(pdist(df, metric='euclidean'), method='complete')
row_clusters = linkage(df.values, method='complete', metric='euclidean')

pd.DataFrame(row_clusters,
             columns=['row label 1', 'row label 2',
                      'distance', 'no. of items in clust.'],
             index=['cluster %d' % (i + 1)
                    for i in range(row_clusters.shape[0])])
# ----------------------------------------------------------------------------------------
# 連結行列生成を樹形図でプロット
row_dendr = dendrogram(row_clusters, 
                       labels=labels,
                       # make dendrogram black (part 2/2)
                       # color_threshold=np.inf
                       )
plt.tight_layout()
plt.ylabel('Euclidean distance')
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# 樹形図とヒートマップでプロット
fig = plt.figure(figsize=(8, 8), facecolor='white')
axd = fig.add_axes([0.09, 0.1, 0.2, 0.6])
row_dendr = dendrogram(row_clusters, orientation='left')

df_rowclust = df.iloc[row_dendr['leaves'][::-1]]

axm = fig.add_axes([0.23, 0.1, 0.6, 0.6])
cax = axm.matshow(df_rowclust, interpolation='nearest', cmap='hot_r')

axd.set_xticks([])
axd.set_yticks([])

for i in axd.spines.values():
    i.set_visible(False)

fig.colorbar(cax)
axm.set_xticklabels([''] + list(df_rowclust.columns))
axm.set_yticklabels([''] + list(df_rowclust.index))
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# scikit-learnで凝集型階層的クラスタリング
ac = AgglomerativeClustering(n_clusters=3, 
                             affinity='euclidean', 
                             linkage='complete')
labels = ac.fit_predict(X)
print('Cluster labels: %s' % labels)

ac = AgglomerativeClustering(n_clusters=2, 
                             affinity='euclidean', 
                             linkage='complete')
labels = ac.fit_predict(X)
print('Cluster labels: %s' % labels)
# ----------------------------------------------------------------------------------------
# データセットの準備
X, y = make_moons(n_samples=200, noise=0.05, random_state=0)
plt.scatter(X[:, 0], X[:, 1])
plt.tight_layout()
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# k-meansと完全連結法
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))

km = KMeans(n_clusters=2, random_state=0)
y_km = km.fit_predict(X)
ax1.scatter(X[y_km == 0, 0], X[y_km == 0, 1],
            edgecolor='black',
            c='lightblue', marker='o', s=40, label='cluster 1')
ax1.scatter(X[y_km == 1, 0], X[y_km == 1, 1],
            edgecolor='black',
            c='red', marker='s', s=40, label='cluster 2')
ax1.set_title('K-means clustering')

ac = AgglomerativeClustering(n_clusters=2,
                             affinity='euclidean',
                             linkage='complete')
y_ac = ac.fit_predict(X)
ax2.scatter(X[y_ac == 0, 0], X[y_ac == 0, 1], c='lightblue',
            edgecolor='black',
            marker='o', s=40, label='cluster 1')
ax2.scatter(X[y_ac == 1, 0], X[y_ac == 1, 1], c='red',
            edgecolor='black',
            marker='s', s=40, label='cluster 2')
ax2.set_title('Agglomerative clustering')

plt.legend()
plt.tight_layout()
#plt.show()
plt.close()
# ----------------------------------------------------------------------------------------
# DBSCAN
db = DBSCAN(eps=0.2, min_samples=5, metric='euclidean')
y_db = db.fit_predict(X)
plt.scatter(X[y_db == 0, 0], X[y_db == 0, 1],
            c='lightblue', marker='o', s=40,
            edgecolor='black', 
            label='cluster 1')
plt.scatter(X[y_db == 1, 0], X[y_db == 1, 1],
            c='red', marker='s', s=40,
            edgecolor='black', 
            label='cluster 2')
plt.legend()
plt.tight_layout()
#plt.savefig('images/11_16.png', dpi=300)
plt.show()






































