def getGraph():
for i, clf in enumerate((svm, rbf_svc, rbf_svc_tunning)):
# Se grafican las fronteras
plt.subplot(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[x_matrizSetEntrenamientoVect, y_clases])
#Color en las gráficas
Z = Z.reshape(x_matrizSetEntrenamientoVect.shape)
plt.contourf(x_matrizSetEntrenamientoVect,
y_clases,
Z,
cmap=plt.cm.Paired,
alpha=0.8)
#Puntos de entrenamiento
plt.scatter(x_matrizSetEntrenamientoVect[:, 0],
x_matrizSetEntrenamientoVect[:, 1],
c=y_clases,
cmap=plt.cm.Paired)
plt.xlabel('Longitud Sepal')
plt.ylabel('Peso Sepal')
plt.xlim(x_matrizSetEntrenamientoVect.min(),
x_matrizSetEntrenamientoVect.max())
plt.ylim(y_clases.min(), y_clases.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
def tmp(cm, acts):
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(cm, interpolation='nearest')
plt.xticks(np.arange(0, len(acts)), acts)
plt.yticks(np.arange(0, len(acts)), acts)
def plot():
plt.figure(figsize=(20, 10))
width = 0.5
index = np.arange(26)
print 'SUM PLOT 1', sum(row[0] for row in data)
print 'SUM PLOT 2', sum(row[1] for row in data)
print 'SUM PLOT 3', sum(row[2] for row in data)
print data[0]
p0 = plt.bar(index, data[0], width, color='y') # people
p1 = plt.bar(index, data[1], width, color='g') # nature
p2 = plt.bar(index, data[2], width, color='r') # activity
p3 = plt.bar(index, data[3], width, color='b') # food
p4 = plt.bar(index, data[4], width, color='c') # symbols
p5 = plt.bar(index, data[5], width, color='m') # objects
p6 = plt.bar(index, data[6], width, color='k') # flags
p7 = plt.bar(index, data[7], width, color='w') # uncategorized
plt.ylabel('Usage')
plt.title('Emoji category usage per city')
plt.xticks(index + width/2.0, cities)
plt.yticks(np.arange(0, 1, 0.1))
plt.legend((p0[0], p1[0], p2[0], p3[0], p4[0], p5[0], p6[0], p7[0]), categories_names)
plt.show()
def plot_gallery(images, titles, h, w, n_row=3,n_col=4):
plt.figure(figsize=(1.8*n_col, 2.4*n_row))
plt.subplots_adjust(bottom=0,left=.01,right=.99,top=.90,hspace=.35)
for i in range(n_row * n_col):
plt.subplot(n_row,n_col,i+1)
plt.imshow(images[i].reshape(h,w),cmap=plt.cm.gray)
plt.title(titles[i],size=12)
plt.xticks(())
plt.yticks(())
def plot_average(collected_results, versions, args, plot_std=True):
test_type = args.test_type
model_name = args.model
means, stds = [], []
for version in versions:
data = collected_results[version]
if (plot_std):
means.append(np.mean(data))
stds.append(np.std(data))
else:
means.append(data)
means = np.array(means)
stds = np.array(stds)
if (test_type == "size" or test_type == "allsize"):
x = ["0%", "20%", "40%", "60%", "80%", "100%"]
elif (test_type == "accdomain" or test_type == "moredomain"):
x = [0, 1, 2, 3, 4]
else:
x = versions
color = 'blue'
plt.plot(x, means, color=color)
if (plot_std):
plt.fill_between(x,
means - stds,
means + stds,
alpha=0.1,
edgecolor=color,
facecolor=color,
linewidth=1,
antialiased=True)
plt.xticks(np.arange(len(x)), x, fontsize=18)
plt.yticks(fontsize=18)
plt.xlabel(XLABELS[test_type], fontsize=18)
plt.ylabel('average absolute effect size', fontsize=18)
plt.title("Influence of {} on bias removal \nfor {}".format(
TITLES[test_type], MODEL_FORMAL_NAMES[model_name]),
fontsize=18)
plt.tight_layout()
plot_path = os.path.join(
args.eval_results_dir, "plots",
"{}-{}-avg{}.png".format(model_name, test_type,
"-std" if plot_std else ""))
plt.savefig(plot_path)
def f3():
xs = [0, 1, 2, 5]
ys = [0, 0, 1, 1]
fig, ax = plt.subplots()
ax.set_yticklabels([])
ax.set_xticklabels([])
plt.plot(xs, ys, label="$f_n(x)$")
plt.xticks(xs, ['', r'$n-1$', r'$n$', ''])
plt.yticks([1], ['$1$'])
plt.legend()
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.axis('equal')
plt.show()
def plot(self, ax=None):
x = [*self.times, self.times[-1] + self.values[-1][1]]
y = [*(v for v, d in self.values), self.values[-1][0]]
from matplotlib.pylab import plt
if ax is None:
ax = plt.gca()
ax.step(x, y, where='post')
ax.set_xlabel('Time (seconds)')
y_ticks = list(map(int, plt.yticks()[0]))
ax.set_yticks(y_ticks, list(map(self.format_value.format, y_ticks)))
def tmp2(cm, acts):
import numpy as np
import matplotlib.pyplot as plt
conf_arr = cm
norm_conf = []
for i in conf_arr:
a = 0
tmp_arr = []
a = sum(i, 0)
for j in i:
tmp_arr.append(0 if a == 0 else float(j) / float(a))
norm_conf.append(tmp_arr)
fig = plt.figure(figsize=(8, 8))
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(norm_conf),
cmap=plt.cm.jet,
interpolation='nearest')
width, height = conf_arr.shape
# for x in range(width):
# for y in range(height):
# ax.annotate(str(conf_arr[x][y]), xy=(y, x),
# horizontalalignment='center',
# verticalalignment='center')
cb = fig.colorbar(res)
ax.set_xlim(-.5, len(acts) - .5)
ax.set_ylim(-.5, len(acts) - .5)
# alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
plt.xticks(range(width), acts, rotation=-90)
plt.yticks(range(height), acts)
def getGraph():
for i, clf in enumerate((svm, rbf_svc, rbf_svc_tunning)):
# Se grafican las fronteras
plt.subplot(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[x_matrizSetEntrenamientoVect, y_clases])
#Color en las gráficas
Z = Z.reshape(x_matrizSetEntrenamientoVect.shape)
plt.contourf(x_matrizSetEntrenamientoVect, y_clases, Z, cmap=plt.cm.Paired, alpha=0.8)
#Puntos de entrenamiento
plt.scatter(x_matrizSetEntrenamientoVect[:, 0], x_matrizSetEntrenamientoVect[:, 1], c=y_clases, cmap=plt.cm.Paired)
plt.xlabel('Longitud Sepal')
plt.ylabel('Peso Sepal')
plt.xlim(x_matrizSetEntrenamientoVect.min(), x_matrizSetEntrenamientoVect.max())
plt.ylim(y_clases.min(), y_clases.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
get_ipython().run_cell_magic(
'latex', '',
'$\\textbf{Visualize the Correlations}: $\n$\\text{Cor}(X_i,Y_j) = \\frac{\\text{Cov}(X_i,Y_j)}{\\sigma_{X_i}\\sigma_{Y_j}}$'
)
# In[101]:
R = np.corrcoef(data.T)
plt.figure(figsize=(10, 8))
plt.pcolor(R)
plt.colorbar()
plt.xlim([0, len(headers)])
plt.ylim([0, len(headers)])
plt.xticks(np.arange(32) + 0.5, np.array(headers), rotation='vertical')
plt.yticks(np.arange(32) + 0.5, np.array(headers))
plt.show()
# In[108]:
#Lets fit both the models using PCA/FA down to two dimensions.
#construct a function implementing the factor analysis which returns a vector of n_components largest
# variances and the corresponding components (as column vectors in a matrix). You can
# check your work by using decomposition.FactorAnalysis from sklearn
#### ~THIS FUNCTION IS WAS A STAB, NEW CODE HERE: ###########
def FactorAnalysis(data, n_components):
ni = 20
data = data - data.mean(axis=0)