import pickle as cPickle
import gzip
import numpy as np
import copula_ordinal_regression as cor
X, y, _ = cor.load_disfa()
class testcase:
def test_mlr(self):
clf = cor.COR(max_iter=50)
clf.fit(X, y, debug=True)
if __name__ == "__main__":
import nose
nose.run(defaultTest=__file__, env={'NOSE_NOCAPTURE': 1})
from sklearn.model_selection import LabelKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_predict
import copula_ordinal_regression as cor
import numpy as np
# load the processed disfa database
X, y, S = cor.load_disfa()
# select the first 3 action units (AU1,AU2,AU4)
y = y[:,[0,1,2]]
# select estimator and number of folds for cross validation
clf = cor.COR(max_iter=5000, verbose=0)
cv = LabelKFold(9)
# define parameter grid
parameter = {
'margins':['normcdf','sigmoid'],
'C':[0]+10.**np.arange(0,8),
'w_nodes':np.linspace(0,1,5),
}
# apply grid search to find optimal hyper parameters
clf = GridSearchCV(
clf,
parameter,
cv = cv,
n_jobs= -1,
verbose = 10,
import copula_ordinal_regression as cor
# load the processed disfa database
X, y, _ = cor.load_disfa()
# select the first 3 action units (AU1,AU2,AU4)
y = y[:,[0,1,2]]
# use 3000 samples for training and the rest for testing
X_tr, X_te = X[:3000],X[3000:]
y_tr, y_te = y[:3000],y[3000:]
clf = cor.COR(
max_iter=5000, # maximum number of iteration
margins = 'sigmoid', # marginal function. [ sigmoid, normcdf ]
copula = 'frank', # copula function. [ frank, gumbel, indep ]
optimizer = 'CG', # scipy optimizer [ CG, BFGS, TNC ... ]
sparsity = 2, # level of sparsity: fully connected crf if this parameter is set to 0
w_nodes = 0.1, # balance potentials. Set this between 0 (only unary) and 1 (only binary)
shared_copula = True, # share the same copula parameter for full range of intensities
verbose=1, # verbose level
)
# fit the model and apply predction
clf.fit(X_tr,y_tr,debug=True)
y_hat = clf.predict(X_te)
# print resutls on test set
print cor.metrics.ICC(y_te,y_hat)
print 'avr. CORR:',cor.metrics.ICC(y_te,y_hat).mean()
from sklearn.model_selection import LabelKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_predict
import copula_ordinal_regression as cor
import numpy as np
# load the processed disfa database
X, y, S = cor.load_disfa()
# select the first 3 action units (AU1,AU2,AU4)
y = y[:, [0, 1, 2]]
# select estimator and number of folds for cross validation
clf = cor.COR(max_iter=5000, verbose=0)
cv = LabelKFold(9)
# define parameter grid
parameter = {
'margins': ['normcdf', 'sigmoid'],
'C': [0] + 10.**np.arange(0, 8),
'w_nodes': np.linspace(0, 1, 5),
}
# apply grid search to find optimal hyper parameters
clf = GridSearchCV(clf, parameter, cv=cv, n_jobs=-1, verbose=10, refit=False)
clf.fit(X, y, S)
print clf.best_params_
# apply cross validation using best hyper parameters
y_hat = cross_val_predict(clf.best_estimator_,
X,
