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E1P_vis_semi.py
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E1P_vis_semi.py
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"""
E1 - scatterplot - semi-synthetic streams
"""
import utils
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sklearn.feature_selection import SelectKBest
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
cmap = matplotlib.cm.get_cmap('rainbow')
measures = ["clustering",
"complexity",
"concept",
"general",
"info-theory",
"itemset",
"landmarking",
"model-based",
"statistical"
]
static_data = ['australian',
'banknote',
'diabetes',
'german',
'vowel0',
'wisconsin'
]
limit=5
for m_id, m in enumerate(measures):
res = np.load('results/semi_%s.npy' % m)
print(res.shape) # datasets, drifts, reps, chunks, measures + label
for dataset_id, dataset in enumerate(static_data):
for r in range(1):
res_iter = res[dataset_id,:,r]
perm = np.random.permutation(res_iter.shape[1])
res_iter = res_iter[:,perm]
for drf_id, drf in enumerate(['nearest', 'cubic']):
X, y = res_iter[drf_id,:,:-1], res_iter[drf_id,:,-1]
X[np.isnan(X)]=1
names = [n[:6] for n in utils.measure_labels[m_id]]
if X.shape[1]>limit:
# Feature Selection
skb = SelectKBest(k=limit)
X = skb.fit_transform(X, y)
names = skb.get_feature_names_out(input_features=names)
fig, ax = plt.subplots(X.shape[1],
X.shape[1],
figsize=(6,6))
plt.suptitle('%s %s %s' % (m, drf, static_data[dataset_id]))
# Shuffle order and establish ranges for grid
shuffler = np.array(list(range(X.shape[0])))
np.random.shuffle(shuffler)
_X = X - np.min(X, axis=0)
_X = _X / np.max(_X, axis=0)
labels = np.unique(y)
colors = cmap(np.linspace(0,1,len(labels)))
for i in range(X.shape[1]):
for j in range(X.shape[1]):
aa = ax[i,j]
if j > i:
aa.cla()
aa.set_yticks([])
aa.set_xticks([])
aa.spines['top'].set_visible(False)
aa.spines['right'].set_visible(False)
aa.spines['left'].set_visible(False)
aa.spines['bottom'].set_visible(False)
else:
ax[i,j].cla()
ax[i,j].set_yticks([])
ax[i,j].set_xticks([])
aa.hlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
aa.vlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
if i != j:
aa.scatter(_X[shuffler,i], _X[shuffler,j], c=y[shuffler],
linewidth=0, alpha=1, s=2, edgecolors=None, cmap='rainbow')
aa.set_xlim(0,1)
aa.set_ylim(0,1)
else:
for lidx, label in enumerate(labels):
print('label', label)
aa.hist(_X[y==label,i], bins = 32, color=colors[lidx],
range=(0,1),
alpha=.5)
aa.grid(ls=':')
aa.spines['top'].set_visible(False)
aa.spines['right'].set_visible(False)
if j==0:
ax[i,j].set_ylabel(names[i])
if i==X.shape[1]-1:
ax[i,j].set_xlabel(names[j])
# ax[i,j].scatter(X[:,i], X[:,j], c=y, linewidth=0, alpha=0.05, s=5, edgecolors=None, cmap='rainbow')
# ax[i,j].spines['top'].set_visible(False)
# ax[i,j].spines['right'].set_visible(False)
# if j==0:
# ax[i,j].set_ylabel(names[i])
# if i==X.shape[1]-1:
# ax[i,j].set_xlabel(names[j])
aa = plt.subplot(448, projection='polar')
yy = np.unique(y, return_counts=True)
print(yy)
aa.scatter(yy[0]/(len(yy[0]))*np.pi*2,
yy[1],
c=cmap(yy[0]/len(labels)),
linewidth=0, alpha=1, s=15, edgecolors=None)
for a,b in zip(*yy):
print(a,b)
xa = (a/(len(yy[0])))*np.pi*2
aa.plot([xa, xa], [0,b], c=cmap(a/len(labels)), lw=1)
aa.set_ylim(0, np.max(yy[1])*1.5)
aa.set_yticks([])
aa.set_xticks((yy[0]/(len(labels)))*np.pi*2, ['' for _ in yy[0]])
aa.grid(ls=':')
aa = plt.subplot(443)
_X[np.isnan(_X)] = 1
pca_X = PCA(n_components=2).fit_transform(_X)
pca_X -= np.mean(pca_X, axis=0)
pca_X /= np.std(pca_X, axis=0)
aa.scatter(*pca_X.T, c=y, cmap='rainbow',
linewidth=0, alpha=1, s=2, edgecolors=None)
aa.set_yticks([])
aa.set_xticks([])
#aa.hlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
#aa.vlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
aa.set_title('PCA')
aa = plt.subplot(444)
tsne_X = TSNE(n_components=2, n_iter=400, n_iter_without_progress=100, verbose=True).fit_transform(_X)
#tsne_X = TSNE(n_components=2, n_iter=250, n_iter_without_progress=50, verbose=True).fit_transform(_X)
tsne_X -= np.mean(tsne_X, axis=0)
tsne_X /= np.std(tsne_X, axis=0)
aa.scatter(*tsne_X.T, c=y, cmap='rainbow',
linewidth=0, alpha=1, s=2, edgecolors=None)
aa.set_yticks([])
aa.set_xticks([])
#aa.hlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
#aa.vlines(np.linspace(0,1,5)[1:-1], 0, 1, lw=.25, ls=':', color='black')
aa.set_title('t-SNE')
plt.tight_layout()
plt.savefig('figures/fig_semi/%s_%s_%s_%i.png' % (m, drf, static_data[dataset_id], r))
plt.savefig('figures/fig_semi/%s_%s_%s_%i.eps' % (m, drf, static_data[dataset_id], r))
plt.savefig('foo.png', dpi=200)
#exit()