def scatter_old(self):
maskMalig = self.labels == 1
maskBenig = self.labels == 0
embed_method = 'euclidean'
rating_method = 'euclidean' # 'chebyshev'
embed_benig_d = flatten_dm(calc_distance_matrix(self.embedding[maskBenig], embed_method))
embed_malig_d = flatten_dm(calc_distance_matrix(self.embedding[maskMalig], embed_method))
embed_cross_d = flatten_dm(
calc_cross_distance_matrix(self.embedding[maskMalig], self.embedding[maskBenig], embed_method))
rating = np.array(self.rating)
rating_benig_d = flatten_dm(calc_distance_matrix(rating[maskBenig], rating_method))
rating_malig_d = flatten_dm(calc_distance_matrix(rating[maskMalig], rating_method))
rating_cross_d = flatten_dm(calc_cross_distance_matrix(rating[maskMalig], rating[maskBenig], rating_method))
# Plot outputs
plt.figure()
plt.scatter(embed_cross_d, rating_cross_d, color='blue')
plt.scatter(embed_malig_d, rating_malig_d, color='red')
plt.scatter(embed_benig_d, rating_benig_d, color='green')
plt.xlabel('Embed')
plt.ylabel('Rating')
def scatter(self, X, Y, xMethod = 'euclidean', yMethod = 'euclidean', sub=False):
xVec = self.load(X)
yVec = self.load(Y)
maskMalig = self.labels == 1
maskBenig = self.labels == 0
x_benig_d = flatten_dm(calc_distance_matrix( xVec[maskBenig], xMethod))
x_malig_d = flatten_dm(calc_distance_matrix( xVec[maskMalig], xMethod))
x_cross_d = flatten_dm(calc_cross_distance_matrix( xVec[maskMalig],
xVec[maskBenig], xMethod))
y_benig_d = flatten_dm(calc_distance_matrix( yVec[maskBenig], yMethod))
y_malig_d = flatten_dm(calc_distance_matrix( yVec[maskMalig], yMethod))
y_cross_d = flatten_dm(calc_cross_distance_matrix( yVec[maskMalig],
yVec[maskBenig], yMethod))
print('{}-{}:'.format(X,Y))
print('\tCross R2 = {}'.format(linear_regression(x_cross_d, y_cross_d)[1]))
print('\tMalig R2 = {}'.format(linear_regression(x_malig_d, y_malig_d)[1]))
print('\tBenig R2 = {}'.format(linear_regression(x_benig_d, y_benig_d)[1]))
print('\tCross R = {}'.format(pearsonr(x_cross_d, y_cross_d)[0]))
print('\tMalig R = {}'.format(pearsonr(x_malig_d, y_malig_d)[0]))
print('\tBenig R = {}'.format(pearsonr(x_benig_d, y_benig_d)[0]))
# Plot outputs
plt.figure()
if sub: plt.subplot(311)
plt.scatter(x_cross_d, y_cross_d, color='blue', alpha=0.5, s=10)
if sub: plt.subplot(312)
plt.scatter(x_malig_d, y_malig_d, color='red', alpha=0.2, s=10)
plt.ylabel(Y)
if sub: plt.subplot(313)
plt.scatter(x_benig_d, y_benig_d, color='green', alpha=0.1, s=10)
plt.xlabel(X)
#if X is 'embed' and Y is 'malig':
# plt.figure()
# plt.plot(np.histogram2d(np.squeeze(x_malig_d), np.squeeze(y_malig_d), [5, 8])[0].T)
if sub:
plt.figure()
plt.title('Cross')
plt.plot(np.histogram2d(np.squeeze(x_cross_d), np.squeeze(y_cross_d), [5,5], normed=True)[0].T)
plt.xlabel('Malig Rating')
plt.legend(['0', '1', '2', '3', '4'])
plt.figure()
plt.title('Malig')
plt.plot(np.histogram2d(np.squeeze(x_malig_d), np.squeeze(y_malig_d), [5,5], normed=True)[0].T)
plt.xlabel('Malig Rating')
plt.legend(['0', '1', '2', '3', '4'])
plt.figure()
plt.title('Benig')
plt.plot(np.histogram2d(np.squeeze(x_benig_d), np.squeeze(y_benig_d), [5,5], normed=True)[0].T)
plt.xlabel('Malig Rating')
plt.legend(['0', '1', '2', '3', '4'])