def set_nice_params():
fsize=18
params = {'axes.labelsize': fsize,
# 'font.family': 'serif',
'font.family': 'Times New Roman',
'figure.facecolor': 'white',
'text.fontsize': fsize,
'legend.fontsize': fsize,
'xtick.labelsize': fsize*0.8,
'ytick.labelsize': fsize*0.8,
'ytick.minor.pad': 8,
'ytick.major.pad': 8,
'xtick.minor.pad': 8,
'xtick.major.pad': 8,
'text.usetex': False,
'lines.markeredgewidth': 0}
pl.rcParams.update(params)
python类axes()的实例源码
def save_images(self, X, imgfile, density=False):
ax = plt.axes()
x = X[:, 0]
y = X[:, 1]
if density:
xy = np.vstack([x,y])
z = scipy.stats.gaussian_kde(xy)(xy)
ax.scatter(x, y, c=z, marker='o', edgecolor='')
else:
ax.scatter(x, y, marker='o', c=range(x.shape[0]),
cmap=plt.cm.coolwarm)
if self.collection is not None:
self.collection.set_transform(ax.transData)
ax.add_collection(self.collection)
ax.text(x[0], y[0], str('start'), transform=ax.transAxes)
ax.axis([-0.2, 1.2, -0.2, 1.2])
fig = plt.gcf()
plt.savefig(imgfile)
plt.close()
def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None):
from matplotlib import pylab
cm = np.asarray(cm, dtype=np.float32)
for i, row in enumerate(cm):
cm[i] = cm[i] / np.sum(cm[i])
#import matplotlib.pyplot as plt
#plt.ion()
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
ax = pylab.axes()
ax.set_xticks(range(len(label_list)))
ax.set_xticklabels(label_list, rotation='vertical')
ax.xaxis.set_ticks_position('bottom')
ax.set_yticks(range(len(label_list)))
ax.set_yticklabels(label_list)
pylab.title(title)
pylab.colorbar()
pylab.grid(False)
pylab.xlabel('Predicted class')
pylab.ylabel('True class')
pylab.grid(False)
pylab.savefig('test.jpg')
pylab.show()
utils.py 文件源码
项目：Building-Machine-Learning-Systems-With-Python-Second-Edition
作者: PacktPublishing
项目源码
文件源码
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def plot_confusion_matrix(cm, genre_list, name, title):
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
ax = pylab.axes()
ax.set_xticks(range(len(genre_list)))
ax.set_xticklabels(genre_list)
ax.xaxis.set_ticks_position("bottom")
ax.set_yticks(range(len(genre_list)))
ax.set_yticklabels(genre_list)
pylab.title(title)
pylab.colorbar()
pylab.grid(False)
pylab.show()
pylab.xlabel('Predicted class')
pylab.ylabel('True class')
pylab.grid(False)
pylab.savefig(
os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")
def plot_confusion_matrix(cm, plot_title, filename, genres=None):
if not genres:
genres = GENRES
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=100.0)
axes = pylab.axes()
axes.set_xticks(range(len(genres)))
axes.set_xticklabels(genres, rotation=45)
axes.set_yticks(range(len(genres)))
axes.set_yticklabels(genres)
axes.xaxis.set_ticks_position("bottom")
pylab.title(plot_title, fontsize=14)
pylab.colorbar()
pylab.xlabel('Predicted class', fontsize=12)
pylab.ylabel('Correct class', fontsize=12)
pylab.grid(False)
#pylab.show()
pylab.savefig(os.path.join(PLOTS_DIR, "cm_%s.eps" % filename), bbox_inches="tight")
def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None):
from matplotlib import pylab
cm = np.asarray(cm, dtype=np.float32)
for i, row in enumerate(cm):
cm[i] = cm[i] / np.sum(cm[i])
#import matplotlib.pyplot as plt
#plt.ion()
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
ax = pylab.axes()
ax.set_xticks(range(len(label_list)))
ax.set_xticklabels(label_list, rotation='vertical')
ax.xaxis.set_ticks_position('bottom')
ax.set_yticks(range(len(label_list)))
ax.set_yticklabels(label_list)
pylab.title(title)
pylab.colorbar()
pylab.grid(False)
pylab.xlabel('Predicted class')
pylab.ylabel('True class')
pylab.grid(False)
pylab.savefig('test.jpg')
pylab.show()
def image(Z,xnew,ynew,my_cmap=None,aspect='equal'):
"""
Creates pretty image. You need to specify:
"""
imshow(log10(Z),extent=[xnew[0],xnew[-1],ynew[0],ynew[-1]], cmap=my_cmap)
pylab.axes().set_aspect('equal')
colorbar()
circle2=Circle((0,0),1,color='k')
gca().add_artist(circle2)
savefig('tmp.png',transparent=True,dpi=150)
