def create_model():
print('Training robust regression')
x, y = regression.get_data(
filenames=['/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_2hr.csv'],
spm_cutoff=None) # 2hr data
# Get top 5 correlated band ratios and add to feature array
x = regression.Kau_MB_BR_features(x)
# Shape of x is (75,11)
# log spm regression
logy = np.log(y)
alpha = 8
seed = 4
model = mycvx.kfolds_convex(x, logy, alpha, random_seed=seed)
theta = model['theta']
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
r2_test = np.round(r2_score(np.exp(y_test), np.exp(y_pred)), 3)
r2_train = np.round(r2_score(np.exp(y_train), np.exp(y_train_pred)), 3)
print(
'Done training robust regression. R2 of actual spm vs predicted spm on training set = {}. \n'.format(r2_train))
return theta
def make_huber_train():
x, y = regression.get_data(
filenames=['/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_4hr.csv'])
alpha = 8
model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
r2 = np.round(r2_score(y_test, y_pred), 3)
r2train = np.round(r2_score(y_train, y_train_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b')
ax.plot(y_pred, y_test, '.r')
ax.plot(np.arange(0, 1.2 * np.max(y_test), .1), np.arange(0, 1.2 * np.max(y_test), .1), '-k')
fig.suptitle('Reconstruction Ability of Robust Regression Model')
ax.set_xlabel('Remotely Sensed SPM (mg/L)')
ax.set_ylabel('In situ measure SPM (mg/L)')
# print (max(np.max(y_pred), np.max(y_test))- np.min(np.min(y_pred), 0))*5./6. - np.min(np.min(y_pred), 0)
ax.text((max(np.max(y_pred), np.max(y_test)) - min(np.min(y_pred), 0)) * 5. / 6. - min(np.min(y_pred), 0),
np.max(y_test) / 7., r'$R^2=%s$' % (r2train), fontsize=15)
plt.savefig('../figures/huber_training')
# plt.show()
print 'r2: ', r2train
def r2_excoeff_vs_time_cutoff(times):
r2s = np.zeros_like(times, dtype='float64')
num_data = np.zeros_like(times, dtype='int32')
for index, time in enumerate(times):
# get appropriate features
x, y = regression.get_data(filenames=[
'/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_excoeff_{}hr.csv'.format(
time)])
x = regression.Kau_MB_BR_features(x)
# create the huber fit model
alpha = 8
model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
r2s[index] = r2_train
num_data[index] = x.shape[0]
print r2s, num_data
return r2s, num_data
def make_huber_test():
x, y = regression.get_data()
alpha = 8
model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
r2 = np.round(r2_score(y_test, y_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_pred, y_test, 'or')
ax.plot(np.arange(0, 1.05 * np.max(y_test), .1), np.arange(0, 1.05 * np.max(y_test), .1), '-k')
fig.suptitle('Predictive Ability of Robust Regression Model')
ax.set_xlabel('Remotely Sensed SPM (mg/L)')
ax.set_ylabel('In situ measure SPM (mg/L)')
# print (max(np.max(y_pred), np.max(y_test))- np.min(np.min(y_pred), 0))*5./6. - np.min(np.min(y_pred), 0)
ax.text((max(np.max(y_pred), np.max(y_test)) - min(np.min(y_pred), 0)) * 5. / 6. - min(np.min(y_pred), 0),
np.max(y_test) / 7., r'$R^2=%s$' % (r2), fontsize=15)
plt.savefig('../figures/huber_prediction')
# plt.show()
# model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed, choice='ninf')
# y_test = model['data']['y_test']
# y_pred = model['data']['y_pred']
# r2inf = np.round(r2_score(y_test, y_pred), 3)
print 'huber r2: ', r2
def make_huber_train_Kau_MB_ExCoeff(time_cutoff):
'''
log(ExCoeff) has low correlation to reflectances, plain excoeff has higher correlation
Two hour correlation is 0.586 when using log(excoeff), 0.677 when using excoeff
:param time_cutoff: acceptable maximum diffence in time between polaris and landsat
:return: nothing, just saves plot
'''
# Get the appropriate features: 3 band ratios, 4 reflectances, SPM
x, y = regression.get_data(filenames=[
'/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_excoeff_{}hr.csv'.format(
time_cutoff)], y_names=['Measured Extinction Coefficient'],
Y_CODE='Measured Extinction Coefficient') # 8hr data
# print y
x = regression.Kau_MB_BR_features(x)
# generate the huber regression model
alpha = 8
model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
# Compute R2 scores
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
# Start creating the plot for the log graph
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
ax.legend()
fig.suptitle(
'Extinction Coefficient Reconstruction Ability of Kau Multi Band Regression Model \n (3 Band Ratios & 4 Surface Reflectances) using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Remotely Sensed Extinction Coefficient')
ax.set_ylabel('In situ measured Extinction Coefficient')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_Kau_MB_excoeff_{}hr'.format(time_cutoff))
def make_huber_train_basic(time_cutoff, spm_cutoff=None):
'''
:return:
'''
x, y = regression.get_data(filenames=[
'/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_{}hr.csv'.format(time_cutoff)],
spm_cutoff=spm_cutoff) # 8hr data
alpha = 8
model = mycvx.kfolds_convex(x, y, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
ax.legend()
fig.suptitle(
'Reconstruction Ability of Multi Band Basic Regression Model on\n 6 Surface Reflectances using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Remotely Sensed SPM (mg/L)')
ax.set_ylabel('In situ measure SPM (mg/L)')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_basic_{}hr'.format(time_cutoff))
# plt.show()
print 'r2 train log: ', r2_train
def make_huber_train_Kau_Simple(time_cutoff):
# Get the appropriate features: 3 band ratios, 4 reflectances, SPM
x, y = regression.get_data(filenames=[
'/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_{}hr.csv'.format(
time_cutoff)]) # 8hr data
x = regression.Kau_Simple_features(x)
# log base 10 spm regression
logy = np.log10(y)
# generate the huber regression model
alpha = 8
model = mycvx.kfolds_convex(x, logy, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
# print model['theta']
# Compute R2 scores
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
# Start creating the plot for the log graph
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
ax.legend()
fig.suptitle(
'Reconstruction Ability of Simple Kau Regression Model \n (3 Surface Reflectances) using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Log Remotely Sensed SPM (mg/L)')
ax.set_ylabel('Log In situ measure SPM (mg/L)')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_Kau_Simple_log_{}hr'.format(time_cutoff))
# plt.show()
print 'r2 train log: ', r2_train
# start creating the plot for the non log graph
# unlog y_train, pred etc
y_test = np.power(10, y_test)
y_pred = np.power(10, y_pred)
y_train = np.power(10, y_train)
y_train_pred = np.power(10, y_train_pred)
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.legend()
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
fig.suptitle(
'Reconstruction Ability of Simple Kau Regression Model \n (3 Surface Reflectances) using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Remotely Sensed SPM (mg/L)')
ax.set_ylabel('In situ measure SPM (mg/L)')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_Kau_simple_{}hr'.format(time_cutoff))
# plt.show()
print 'r2 train not log: ', r2_train
def make_huber_train_EA_MB(time_cutoff, spm_cutoff=None):
# log10(SPM) = c0 + c1*x1 + c2*x2
# x1 = Rrs(lambda1) + Rrs(lambda2) sensitive to spm
# x2 = Rrs(lambda3)/Rrs(lambda1) compensating term
# lambda 1 = green (555) = band2 for landsat 457, lambda 2 = red (670) = band3 for landsat 457,
# lanbda3 = blue (490) = band1 for landsat 457
x_names = ['reflec_1', 'reflec_2', 'reflec_3']
y_names = ['Calculated SPM']
filenames = ['/Users/Nathan/Dropbox/SedimentLearning/data/landsat_polaris_filtered/filtered_2hr.csv']
X, y = regression.get_data(x_names=x_names, y_names=y_names, filenames=filenames, Y_CODE='Calculated SPM')
# X col 0 is band1, col 1 is band2, col 2 is band3
# so lambda1 is col 1, lambda2 is col 2, lambda3 is col 0
Rrs_lambda1 = X[:, 1]
Rrs_lambda2 = X[:, 2]
Rrs_lambda3 = X[:, 0]
# log10(SPM) = c0 + c1*x1 + c2*x2
# x1 = Rrs(lambda1) + Rrs(lambda2) sensitive to spm
# x2 = Rrs(lambda3)/Rrs(lambda1) compensating term
# lambda 1 = green (555) = band2 for landsat 457, lambda 2 = red (670) = band3 for landsat 457,
# lanbda3 = blue (490) = band1 for landsat 457
logy = np.log(y)
X1 = Rrs_lambda1 + Rrs_lambda2
X2 = np.divide(Rrs_lambda3, Rrs_lambda1)
C0 = np.ones(Rrs_lambda1.size)
x = np.array([C0, X1, X2]).T
alpha = 8
model = mycvx.kfolds_convex(x, logy, alpha, random_seed=seed)
y_test = model['data']['y_test']
y_pred = model['data']['y_pred']
y_train = model['data']['y_train']
y_train_pred = model['data']['y_train_pred']
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
ax.legend()
fig.suptitle(
'Reconstruction Ability of Han Multi Band Regression Model using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Log Remotely Sensed SPM (mg/L)')
ax.set_ylabel('Log In situ measured SPM (mg/L)')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_EA_MB_log_{}hr'.format(time_cutoff))
# plt.show()
print 'r2 train log: ', r2_train
# unlog y_train and pred etc
y_test = np.exp(y_test)
y_pred = np.exp(y_pred)
y_train = np.exp(y_train)
y_train_pred = np.exp(y_train_pred)
r2_test = np.round(r2_score(y_test, y_pred), 3)
r2_train = np.round(r2_score(y_train, y_train_pred), 3)
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_train_pred, y_train, '.b', label='Training Data')
ax.plot(y_pred, y_test, '.r', label='Test Data')
ax.legend()
ax.plot(np.arange(0, 1.2 * max(np.max(y_test), np.max(y_train)), .1),
np.arange(0, 1.2 * max(np.max(y_train), np.max(y_test)), .1), '-k')
fig.suptitle(
'Reconstruction Ability of Han Multi Band Model using {}hr Data'.format(
time_cutoff))
ax.set_xlabel('Remotely Sensed SPM (mg/L)')
ax.set_ylabel('In situ measure SPM (mg/L)')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1. / 7.), s='Training Set R^2={}'.format(r2_train),
textcoords="figure fraction", family='serif', horizontalalignment='right')
ax.annotate(xy=(0, 0), xytext=(5. / 6., 1.5 / 7.), s='Testing Set R^2={}'.format(r2_test),
textcoords="figure fraction", family='serif', horizontalalignment='right')
plt.savefig('../figures/huber_training_EA_MB_{}hr'.format(time_cutoff))
# plt.show()
print 'r2 train not log: ', r2_train