#import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
import seaborn as sns
import numpy as np
from os.path import join as pjoin
from config import FLAGS
from sklearn.metrics import confusion_matrix, roc_curve, auc
from scipy import interp
'''
interpolation options:
[None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
'''
def scatter(x, y, plot_name):
""" Used to plot t-SNE projections """
num_colors = len(np.unique(y))
# We choose a color palette with seaborn.
palette = np.array(sns.color_palette("hls", num_colors))
# We create a scatter plot.
f = plt.figure(figsize=(8, 8))
ax = plt.subplot(aspect='equal')
sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40,
c=palette[y.astype(np.int)])
plt.xlim(-25, 25)
plt.ylim(-25, 25)
ax.axis('off')
ax.axis('tight')
# We add the labels for each digit.
txts = []
for i in range(num_colors):
# Position of each label.
xtext, ytext = np.median(x[y == i, :], axis=0)
# if np.isnan(xtext) or np.isnan(ytext):
# break
txt = ax.text(xtext, ytext, str(i), fontsize=24)
txt.set_path_effects([
PathEffects.Stroke(linewidth=5, foreground="w"),
PathEffects.Normal()])
txts.append(txt)
plt.savefig(plot_name, dpi=120)
plt.close()
def plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=plt.cm.BuGn):
imgplot = plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.grid(False)
plt.colorbar(imgplot)
plt.title(title)
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=90)
plt.yticks(tick_marks, target_names)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.savefig(pjoin(FLAGS.output_dir, title.replace(' ', '_') + '_CM.png'))
plt.close()
def plot_roc_curve(y_pred, y_true, n_classes, title='ROC_Curve'):
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
tresholds = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], tresholds[i] = roc_curve(y_true, y_pred, pos_label=i, drop_intermediate=False)
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
# fpr["micro"], tpr["micro"], _ = roc_curve(np.asarray(y_true).ravel(), np.asarray(y_pred).ravel(), pos_label=0, drop_intermediate=True)
# roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
# Aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# print("Thresholds:")
# Interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# print("Class_{0}: {1}".format(i, tresholds[i]))
# Average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# Plot all ROC curves
fig = plt.figure()
ax = fig.add_subplot(111)
# plt.plot(fpr["micro"], tpr["micro"],
# label='micro-average ROC curve (area = {0:0.2f})'
# ''.format(roc_auc["micro"]),
# linewidth=3, ls='--', color='red')
plt.plot(fpr["macro"], tpr["macro"],
label='macro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["macro"]),
linewidth=3, ls='--', color='green')
for i in range(n_classes):
plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', linewidth=2)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Multi-class Receiver Operating Characteristic')
lgd = ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig(pjoin(FLAGS.output_dir, title.replace(' ', '_') + '_ROC.png'), bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.close()
def hist_comparison(data1, data2):
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
f.suptitle('Histogram Before and After Normalization')
ax1.hist(data1, 10, facecolor='green', alpha=0.75)
ax1.set_xlabel("Values")
ax1.set_ylabel("# of Examples")
ax1.grid(True)
ax2.hist(data2, 10, facecolor='green', alpha=0.75)
ax2.set_xlabel("Values")
ax2.grid(True)
f.savefig(pjoin(FLAGS.output_dir, 'hist_comparison.png'))
# plt.show()
plt.close()
def make_heatmap(data, name):
f = plt.figure()
ax1 = f.add_axes([0.1,0.1,0.8,0.8])
ax1.grid(False)
imgplot = ax1.imshow(data, interpolation="none")
imgplot.set_cmap('seismic')
f.colorbar(imgplot)
f.savefig(pjoin(FLAGS.output_dir, name + '.png'))
plt.close()
def make_2d_hist(data, name):
f = plt.figure()
X,Y = np.meshgrid(range(data.shape[0]), range(data.shape[1]))
im = plt.pcolormesh(X,Y,data.transpose(), cmap='seismic')
plt.colorbar(im, orientation='vertical')
# plt.hexbin(data,data)
# plt.show()
f.savefig(pjoin(FLAGS.output_dir, name + '.png'))
plt.close()
# def make_2d_hexbin(data, name):
# f = plt.figure()
# X,Y = np.meshgrid(range(data.shape[0]), range(data.shape[1]))
# plt.hexbin(X, data)
# # plt.show()
# f.savefig(pjoin(FLAGS.output_dir, name + '.png'))
def heatmap_comparison(data1, label1, data2, label2, data3, label3):
interpolation = 'none'
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True)
fig.suptitle('Heatmap Comparison of Normal and Noisy Data')
ax1.imshow(data3, interpolation=interpolation)
ax1.set_title(label1)
ax1.set_ylabel("Examples")
ax1.set_xlabel("Features")
ax1.set_aspect('equal')
ax2.imshow(data2, interpolation=interpolation)
ax2.set_title(label2)
ax2.set_xlabel("Features")
ax2.set_aspect('equal')
ax3.imshow(data1, interpolation=interpolation)
ax3.set_title(label3)
ax3.set_xlabel("Features")
ax3.set_aspect('equal')
cax = fig.add_axes([0, 0, .1, .1])
cax.get_xaxis().set_visible(False)
cax.get_yaxis().set_visible(False)
cax.patch.set_alpha(0.5)
cax.set_frame_on(True)
# plt.colorbar(ax1, ax2, orientation='vertical')
plt.show()
plt.close()
#
# fig = plt.figure(figsize=(6, 3.2))
#
# ax = fig.add_subplot(111)
# ax.set_title('colorMap')
# plt.imshow(data1)
# ax.set_aspect('equal')
#
# cax = fig.add_axes([0.12, 0.1, 0.78, 0.8])
# cax.get_xaxis().set_visible(False)
# cax.get_yaxis().set_visible(False)
# cax.patch.set_alpha(0)
# cax.set_frame_on(False)
# plt.colorbar(orientation='vertical')
# plt.show()
#
