def execute(self,i,j):
# dim_red = LDA()
# dim_red.fit_transform(self.x_train, self.y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# x_train = dim_red.transform(self.x_train)
# x_test = dim_red.transform(self.y_train)
# stat_obj = self.stat_class() # reflection bitches
# stat_obj.train(x_train, x_test)
# print len(x_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
# print train_idx,test_idx
# exit(0)
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'rb') as fid:
# dim_red=cPickle.load(fid)
x_test = dim_red.transform(x_test)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
# x_train = dim_red.transform(x_train)
# x_test = dim_red.transform(x_test)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train,y_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
y_pred = [ 0 for i in xrange(len(y_test)) ]
for i in range(len(x_test)):
# print len(x_test[i])
val = int(np.round(stat_obj.predict(x_test[i])))
if val > self.range_max: val = self.range_max
if val < self.range_min: val = self.range_min
y_pred[i] = [val]
y_pred = np.matrix(y_pred)
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
return str(sum(self.values)/self.k_cross)
def execute(self):
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
stat_obj = self.stat_class(range_min=range_min,range_max=range_max, \
similarity_measure=self.similarity_measure, \
neighbourhood="stochastic") # reflection bitches
stat_obj.train(x_train,x_test,y_train)
y_pred = np.matrix(stat_obj.predict()).T
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,\
self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
return str(sum(self.values)/self.k_cross)
def execute(self):
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
stat_obj = self.stat_class(range_min=range_min,range_max=range_max, \
similarity_measure=self.similarity_measure, \
neighbourhood="stochastic") # reflection bitches
stat_obj.train(x_train, x_test, y_train)
y_pred = np.matrix(stat_obj.predict()).T
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,\
self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
return str(sum(self.values) / self.k_cross)
def execute(self):
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
x_test = dim_red.transform(x_test)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train,y_train)
y_pred = [ 0 for i in xrange(len(y_test)) ]
for i in range(len(x_test)):
val = int(np.round(stat_obj.predict(x_test[i])))
if val > self.range_max: val = self.range_max
if val < self.range_min: val = self.range_min
y_pred[i] = [val]
y_pred = np.matrix(y_pred)
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
return str(sum(self.values)/self.k_cross)
def execute(self,i,j):
x_train= self.x_train
y_train= self.y_train
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
cPickle.dump(dim_red, fid)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train,y_train)
with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
cPickle.dump(stat_obj, fid)
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
# print train_idx,test_idx
# exit(0)
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
x_test = dim_red.transform(x_test)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train,y_train)
y_pred = [ 0 for i in xrange(len(y_test)) ]
for i in range(len(x_test)):
val = int(np.round(stat_obj.predict(x_test[i])))
if val > self.range_max: val = self.range_max
if val < self.range_min: val = self.range_min
y_pred[i] = [val]
y_pred = np.matrix(y_pred)
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
return sum(self.values)/self.k_cross
def execute(self, i, j):
global save1
global save2
jk = i
# print type(jk)
# dim_red = LDA()
# dim_red.fit_transform(self.x_train, self.y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# x_train = dim_red.transform(self.x_train)
# x_test = dim_red.transform(self.y_train)
# stat_obj = self.stat_class() # reflection bitches
# stat_obj.train(x_train, x_test)
# print len(x_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
# save1=None
# save2=None
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
# print train_idx,test_idx
# exit(0)
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'rb') as fid:
# dim_red=cPickle.load(fid)
x_test = dim_red.transform(x_test)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
# x_train = dim_red.transform(x_train)
# x_test = dim_red.transform(x_test)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train, y_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
y_pred = [0 for i in xrange(len(y_test))]
if (int(jk) == 1):
# print "test_idx"
save1 = stat_obj
save2 = dim_red
for i in range(len(x_test)):
# print len(x_test[i])
val = int(np.round(stat_obj.predict(x_test[i])))
if val > self.range_max: val = self.range_max
if val < self.range_min: val = self.range_min
y_pred[i] = [val]
y_pred = np.matrix(y_pred)
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(
y_test, y_pred, self.range_min, self.range_max)
self.values.append(cohen_kappa_rating)
# print stat_obj.predict(x_train)
# linear_k_cross = k_fold_cross_validation(cross_valid_k,linear_regression,X_train,Y_train,range_min,range_max)
# linesar_accuracy.append(linear_k_cross.execute(i,0))
# logistic_k_cross = k_fold_cross_validation(cross_valid_k,logistic_regression,X_train,Y_train,range_min,range_max)
# logistic_accuracy.append(logistic_k_cross.execute(i,1))
# svr_k_cross = k_fold_cross_validation(cross_valid_k,support_vector_regression,X_train,Y_train,range_min,range_max)
# svr_accuracy.append(svr_k_cross.execute(i,2))
# svm_k_cross = k_fold_cross_validation(cross_valid_k,support_vector_machine,X_train,Y_train, range_min,range_max)
# svm_accuracy.append(svm_k_cross.execute(i,3))
return str(sum(self.values) / self.k_cross)
def execute(self,i,j):
global save1
global save2
jk=i
# print type(jk)
# dim_red = LDA()
# dim_red.fit_transform(self.x_train, self.y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# x_train = dim_red.transform(self.x_train)
# x_test = dim_red.transform(self.y_train)
# stat_obj = self.stat_class() # reflection bitches
# stat_obj.train(x_train, x_test)
# print len(x_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
# save1=None
# save2=None
kf = KFold(len(self.x_train), n_folds=self.k_cross)
own_kappa = []
for train_idx, test_idx in kf:
# print train_idx,test_idx
# exit(0)
x_train, x_test = self.x_train[train_idx], self.x_train[test_idx]
y_train, y_test = self.y_train[train_idx], self.y_train[test_idx]
dim_red = LDA()
x_train = dim_red.fit_transform(x_train, y_train)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(dim_red, fid)
# with open('dumped_dim_red_'+str(i)+'.pkl', 'rb') as fid:
# dim_red=cPickle.load(fid)
x_test = dim_red.transform(x_test)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
# x_train = dim_red.transform(x_train)
# x_test = dim_red.transform(x_test)
stat_obj = self.stat_class() # reflection bitches
stat_obj.train(x_train,y_train)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'wb') as fid:
# cPickle.dump(stat_obj, fid)
# with open('dumped_'+str(j)+'_'+str(i)+'.pkl', 'rb') as fid:
# stat_obj=cPickle.load(fid)
y_pred = [ 0 for i in xrange(len(y_test)) ]
if(int(jk)==1):
# print "test_idx"
save1=stat_obj
save2=dim_red
for i in range(len(x_test)):
# print len(x_test[i])
val = int(np.round(stat_obj.predict(x_test[i])))
if val > self.range_max: val = self.range_max
if val < self.range_min: val = self.range_min
y_pred[i] = [val]
y_pred = np.matrix(y_pred)
cohen_kappa_rating = own_wp.quadratic_weighted_kappa(y_test,y_pred,self.range_min,self.range_max)
self.values.append(cohen_kappa_rating)
# print stat_obj.predict(x_train)
# linear_k_cross = k_fold_cross_validation(cross_valid_k,linear_regression,X_train,Y_train,range_min,range_max)
# linesar_accuracy.append(linear_k_cross.execute(i,0))
# logistic_k_cross = k_fold_cross_validation(cross_valid_k,logistic_regression,X_train,Y_train,range_min,range_max)
# logistic_accuracy.append(logistic_k_cross.execute(i,1))
# svr_k_cross = k_fold_cross_validation(cross_valid_k,support_vector_regression,X_train,Y_train,range_min,range_max)
# svr_accuracy.append(svr_k_cross.execute(i,2))
# svm_k_cross = k_fold_cross_validation(cross_valid_k,support_vector_machine,X_train,Y_train, range_min,range_max)
# svm_accuracy.append(svm_k_cross.execute(i,3))
return str(sum(self.values)/self.k_cross)
