def classifier(opt):
"""
Classification of the different types of events.
opt is an object of the class Options()
By default, the number of classes K that is searched in the dataset is equal to
the number of classes in the catalog, but it can be modified directly in the
code.
"""
opt.do_tri()
X = opt.x
Y = opt.y
dic_results = {}
for isc in sorted(opt.xs):
print "==========",opt.trad[isc],"=========="
subdic = {}
if isc > 0:
if opt.trad[isc][0] == sta_prev:
marker_sta = 1
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
if len(opt.xs[isc]) == 0:
continue
x_test = opt.xs[isc]
y_test = opt.ys[isc]
opt.classname2number()
K = len(opt.types)
for b in range(opt.opdict['boot']):
print "\n-------------------- # iter: %d --------------------\n"%(b+1)
subsubdic = {}
print "# types in the test set:",len(opt.types)
subsubdic['list_ev'] = np.array(y_test.index)
x_test.index = range(x_test.shape[0])
y_test.index = range(y_test.shape[0])
print x_test.shape, y_test.shape
if x_test.shape[0] != y_test.shape[0]:
print "Test set: Incoherence in x and y dimensions"
sys.exit()
if opt.opdict['method'] == 'kmeans':
# K-Mean
print "********** KMean **********"
K=2
CLASS_test = implement_kmean(x_test,K)
trad,dicocl = {},{}
for t in opt.types:
dicocl[t] = []
for i in range(K):
auto = CLASS_test[CLASS_test.values.ravel()==i]
man = y_test.reindex(index=auto.index,columns=['Type'])
print "Size of class %d : %d"%(i,len(auto))
nbs = [len(man[man.values.ravel()==j]) for j in opt.types]
trad[i] = np.array(opt.types)[np.argsort(nbs)]
for j in range(len(opt.types)):
print "\tNumber of %s : %d"%(trad[i][j],np.sort(nbs)[j])
dicocl[trad[i][j]].append(np.sort(nbs)[j])
if K == len(opt.types):
types_trad = np.array(opt.types).copy()
for key in sorted(dicocl):
types_trad[np.argmax(dicocl[key])] = key
else:
types_trad=[]
### PLOT DIAGRAMS ###
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
if K > 4:
plot_diagrams(CLASS_test,y_test)
#results_histo(CLASS_test,y_test)
results_diagrams(CLASS_test,y_test)
else:
all_diagrams(CLASS_test,y_test,trad=types_trad)
if opt.opdict['save_confusion']:
savefig = '%s/unsup_diagrams_%df_ini.png'%(opt.opdict['fig_path'],len(opt.opdict['feat_list']))
plt.savefig(savefig)
print "Figure saved in %s"%savefig
if opt.opdict['plot_confusion']:
plt.show()
else:
plt.close()
opt.x = x_test
opt.y = y_test
if opt.opdict['plot_pdf']:
opt.compute_pdfs()
g_test = opt.gaussians
opt.y = CLASS_test
opt.compute_pdfs()
g_unsup = opt.gaussians
plot_and_compare_pdfs(g_test,g_unsup)
subsubdic['NumClass'] = CLASS_test.values.ravel()
if list(types_trad):
trad_CLASS_test = []
for i in CLASS_test.values:
i = int(i)
trad_CLASS_test.append(types_trad[i])
subsubdic['StrClass'] = trad_CLASS_test
subsubdic['Equivalence'] = types_trad
subdic[b] = subsubdic
dic_results[opt.trad[isc]] = subdic
dic_results['header'] = {}
dic_results['header']['features'] = opt.opdict['feat_list']
dic_results['header']['types'] = opt.opdict['types']
dic_results['header']['catalog'] = opt.opdict['label_test']
print "Save results in file %s"%opt.opdict['result_path']
write_binary_file(opt.opdict['result_path'],dic_results)
def classifier(opt):
"""
Classification of the different types of events.
opt is an object of the class Options()
By default, the number of classes K that is searched in the dataset is equal to
the number of classes in the catalog, but it can be modified directly in the
code.
"""
opt.do_tri()
X = opt.x
Y = opt.y
dic_results = {}
for isc in sorted(opt.xs):
print "==========", opt.trad[isc], "=========="
subdic = {}
if isc > 0:
if opt.trad[isc][0] == sta_prev:
marker_sta = 1
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
if len(opt.xs[isc]) == 0:
continue
x_test = opt.xs[isc]
y_test = opt.ys[isc]
opt.classname2number()
K = len(opt.types)
for b in range(opt.opdict['boot']):
print "\n-------------------- # iter: %d --------------------\n" % (
b + 1)
subsubdic = {}
print "# types in the test set:", len(opt.types)
subsubdic['list_ev'] = np.array(y_test.index)
x_test.index = range(x_test.shape[0])
y_test.index = range(y_test.shape[0])
print x_test.shape, y_test.shape
if x_test.shape[0] != y_test.shape[0]:
print "Test set: Incoherence in x and y dimensions"
sys.exit()
if opt.opdict['method'] == 'kmeans':
# K-Mean
print "********** KMean **********"
K = 2
CLASS_test = implement_kmean(x_test, K)
trad, dicocl = {}, {}
for t in opt.types:
dicocl[t] = []
for i in range(K):
auto = CLASS_test[CLASS_test.values.ravel() == i]
man = y_test.reindex(index=auto.index, columns=['Type'])
print "Size of class %d : %d" % (i, len(auto))
nbs = [len(man[man.values.ravel() == j]) for j in opt.types]
trad[i] = np.array(opt.types)[np.argsort(nbs)]
for j in range(len(opt.types)):
print "\tNumber of %s : %d" % (trad[i][j], np.sort(nbs)[j])
dicocl[trad[i][j]].append(np.sort(nbs)[j])
if K == len(opt.types):
types_trad = np.array(opt.types).copy()
for key in sorted(dicocl):
types_trad[np.argmax(dicocl[key])] = key
else:
types_trad = []
### PLOT DIAGRAMS ###
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
if K > 4:
plot_diagrams(CLASS_test, y_test)
#results_histo(CLASS_test,y_test)
results_diagrams(CLASS_test, y_test)
else:
all_diagrams(CLASS_test, y_test, trad=types_trad)
if opt.opdict['save_confusion']:
savefig = '%s/unsup_diagrams_%df_ini.png' % (
opt.opdict['fig_path'], len(opt.opdict['feat_list']))
plt.savefig(savefig)
print "Figure saved in %s" % savefig
if opt.opdict['plot_confusion']:
plt.show()
else:
plt.close()
opt.x = x_test
opt.y = y_test
if opt.opdict['plot_pdf']:
opt.compute_pdfs()
g_test = opt.gaussians
opt.y = CLASS_test
opt.compute_pdfs()
g_unsup = opt.gaussians
plot_and_compare_pdfs(g_test, g_unsup)
subsubdic['NumClass'] = CLASS_test.values.ravel()
if list(types_trad):
trad_CLASS_test = []
for i in CLASS_test.values:
i = int(i)
trad_CLASS_test.append(types_trad[i])
subsubdic['StrClass'] = trad_CLASS_test
subsubdic['Equivalence'] = types_trad
subdic[b] = subsubdic
dic_results[opt.trad[isc]] = subdic
dic_results['header'] = {}
dic_results['header']['features'] = opt.opdict['feat_list']
dic_results['header']['types'] = opt.opdict['types']
dic_results['header']['catalog'] = opt.opdict['label_test']
print "Save results in file %s" % opt.opdict['result_path']
write_binary_file(opt.opdict['result_path'], dic_results)
def one_by_one(opt,x_test_ref0,y_test_ref0,otimes_ref,boot=1,method='lr'):
"""
Per class extractor.
Extract one class after each other by order of importance. The events which are
classified are deleted from the next extraction.
boot = number of training sets to be generated
method = 'lr' for Logistic Regression / 'svm' for SVM
"""
from LR_functions import do_all_logistic_regression
types = opt.types
numt = opt.numt
len_numt = len(numt)
# Dictionary for results
DIC = {}
DIC['features'] = x_test_ref0.columns
EXT = {}
for num_ext in range(len_numt):
EXT[num_ext] = {}
EXT[num_ext]['nb_tot'] = []
for t in numt:
EXT[num_ext]['nb_%s'%types[t]] = []
p_train, p_cv, p_test = opt.opdict['proportions']
for b in range(boot):
otimes = map(int,list(otimes_ref.values))
otimes = np.array(otimes)
x_test_ref = x_test_ref0.copy()
y_test_ref = y_test_ref0.copy()
print "\n\tONE BY ONE EXTRACTION ------ iteration %d"%b
dic = {}
inum = 0
for n in range(len_numt):
sub_dic={}
### Splitting of the whole set in training, CV and test sets ###
y_train_ref, y_cv, y_test_ref = generate_datasets(opt.opdict['proportions'],opt.numt,y_test_ref)
y_test_ref = pd.concat([y_cv,y_test_ref])
i_train = y_train_ref.index
i_cv = y_cv.index
i_test = y_test_ref.index
### Defining the training set ###
x_train = x_test_ref.reindex(index=y_train_ref.index)
y_train_ref.index = range(y_train_ref.shape[0])
x_train.index = range(x_train.shape[0])
if inum == 0:
list_i_train = [list(otimes[map(int,list(y_train_ref.index))])]
else:
list_i_train.append(list(otimes[map(int,list(y_train_ref.index))]))
### Defining the test set ###
x_test = x_test_ref.reindex(index=y_test_ref.index)
x_test.index = range(x_test.shape[0])
y_test_ref.index = range(y_test_ref.shape[0])
if inum == 0:
list_i_test = [list(otimes[map(int,list(y_test_ref.index))])]
else:
list_i_test.append(list(otimes[map(int,list(y_test_ref.index))]))
if x_train.shape[0] != y_train_ref.shape[0]:
print "Training set: Incoherence in x and y dimensions"
sys.exit()
if x_test.shape[0] != y_test_ref.shape[0]:
print "Test set: Incoherence in x and y dimensions"
sys.exit()
y_train = y_train_ref.copy()
y_test = y_test_ref.copy()
EXT[n]['nb_tot'].append(len(x_test))
for t in numt:
EXT[n]['nb_%s'%types[t]].append(len(y_test[y_test.NumType==t]))
y_train[y_train_ref.NumType==n] = 0
y_test[y_test_ref.NumType==n] = 0
y_train[y_train_ref.NumType!=n] = 1
y_test[y_test_ref.NumType!=n] = 1
t = [types[n],'Rest']
print y_train.shape[0], y_test.shape[0]
print "----------- %s vs all -----------"%types[n]
if method == 'lr':
print "Logistic Regression\n"
out = do_all_logistic_regression(x_test_ref0,y_test_ref0,i_train,i_cv,i_test,)
elif method == 'svm':
kern = 'NonLin'
print "SVM\n"
out = implement_svm(x_train,x_test,y_train,y_test,opt.types,opt.opdict,kern=kern)
CLASS_test = out['label_test']
CLASS_train = out['label_train']
# TRAINING SET
print "\t *TRAINING SET"
y_train_np = y_train.NumType.values.ravel()
from sklearn.metrics import confusion_matrix
cmat_train = confusion_matrix(y_train_np,CLASS_train)
p_tr = dic_percent(cmat_train,[types[n],'Rest'],verbose=True)
out['rate_train'] = p_tr
print " Global : %.2f%%"%p_tr['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_train,opt.types,'Training',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/training_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
# TEST SET
print "\t *TEST SET"
y_test_np = y_test.NumType.values.ravel()
cmat_test = confusion_matrix(y_test_np,CLASS_test)
p_test = dic_percent(cmat_test,[types[n],'Rest'],verbose=True)
out['rate_test'] = p_test
print " Global : %.2f%%"%p_test['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_test,opt.types,'Test',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/test_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
if opt.opdict['plot_confusion']:
plt.show()
else:
plt.close()
# Fill the dictionary
i_com = np.where((y_test.NumType.values.ravel()-CLASS_test)==0)[0]
i_lr = np.where(CLASS_test==0)[0]
i_ok_class = np.intersect1d(i_com,i_lr) # events classified in the class of interest by the LR and identical to the manual classification
sub_dic["nb"] = len(i_lr) # total number of events classified in the class of interest
sub_dic["nb_common"] = len(i_ok_class)
sub_dic["index_ok"] = otimes[i_ok_class]
sub_dic["nb_other"],sub_dic["i_other"] = [],[]
for k in range(len_numt):
if k != n:
i_other_man = list(y_test_ref[y_test_ref.NumType==k].index)
ii = np.intersect1d(i_lr,i_other_man)
sub_dic["nb_other"].append((types[k],len(ii)))
sub_dic["i_other"].append((types[k],otimes[ii]))
sub_dic["rate_%s"%types[n]] = (out['rate_train'][('%s'%types[n], 0)], out['rate_test'][('%s'%types[n], 0)])
sub_dic["rate_rest"] = (out['rate_train'][('Rest', 1)], out['rate_test'][('Rest', 1)])
sub_dic["nb_manuals"] = ((types[n],len(y_test[y_test.NumType==0])),('Rest',len(y_test[y_test.NumType==1])))
i_ok_test = i_test[np.where(CLASS_test!=0)[0]]
i_ok_train = i_train[np.where(CLASS_train!=0)[0]]
i_ok = np.concatenate([i_ok_test,i_ok_train])
otimes = i_ok
y_test_ref = y_test_ref0.reindex(index=map(int,list(i_ok)))
dic[types[n]] = sub_dic
inum = inum + 1
dic['i_train'] = list_i_train
dic['i_test'] = list_i_test
DIC[b] = dic
file = opt.opdict['result_path']
print "One-by-One results stored in %s"%file
write_binary_file(file,DIC)
file = '%s/stats_OBO'%os.path.dirname(opt.opdict['result_path'])
write_binary_file(file,EXT)
def one_vs_all(opt,x_test_ref,y_test_ref,otimes_ref,boot=1,method='lr'):
"""
Extract one class among the whole data.
One vs All extractor.
"""
from LR_functions import do_all_logistic_regression
types = opt.types
numt = opt.numt
len_numt = len(numt)
DIC = {}
DIC['features'] = x_test_ref.columns
for b in range(boot):
print "\n\tONE VS ALL EXTRACTION ------ iteration %d"%b
dic = {}
otimes = map(str,list(otimes_ref.values))
otimes = np.array(otimes)
### Splitting of the whole set in training, CV and test sets ###
y_train, y_cv, y_test = generate_datasets(opt.opdict['proportions'],opt.numt,y_test_ref)
i_train = y_train.index
i_cv = y_cv.index
i_test = y_test.index
### Defining the training set ###
x_train = x_test_ref.reindex(index=y_train.index)
y_train.index = range(y_train.shape[0])
x_train.index = range(x_train.shape[0])
dic["i_train"] = otimes[map(int,list(y_train.index))]
### Defining the test set ###
x_test = x_test_ref.reindex(index=y_test.index)
x_test.index = range(x_test.shape[0])
y_test.index = range(y_test.shape[0])
dic["i_test"] = otimes[map(int,list(y_test.index))]
y_train_tir = y_train.copy()
y_test_tir = y_test.copy()
for n in range(len_numt):
y_train[y_train_tir.NumType==n] = 0
y_test[y_test_tir.NumType==n] = 0
y_train[y_train_tir.NumType!=n] = 1
y_test[y_test_tir.NumType!=n] = 1
print y_train.shape[0], y_test.shape[0]
print "----------- %s vs all -----------"%types[n]
print_type = [types[n],'All']
if method == 'lr':
print "Logistic Regression\n"
i_train = y_train.index
i_cv = y_cv.index
i_test = y_test.index
out = do_all_logistic_regression(x_test_ref,y_test_ref,i_train,i_cv,i_test)
elif method == 'svm':
kern = 'NonLin'
print "SVM\n"
out = implement_svm(x_train,x_test,y_train,y_test,opt.types,opt.opdict,kern=kern)
CLASS_test = out['label_test']
CLASS_train = out['label_train']
# TRAINING SET
print "\t *TRAINING SET"
y_train_np = y_train.NumType.values.ravel()
from sklearn.metrics import confusion_matrix
cmat_train = confusion_matrix(y_train_np,CLASS_train)
p_tr = dic_percent(cmat_train,[types[n],'Rest'],verbose=True)
out['rate_train'] = p_tr
print " Global : %.2f%%"%p_tr['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_train,opt.types,'Training',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/training_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
# TEST SET
print "\t *TEST SET"
y_test_np = y_test.NumType.values.ravel()
cmat_test = confusion_matrix(y_test_np,CLASS_test)
p_test = dic_percent(cmat_test,[types[n],'Rest'],verbose=True)
out['rate_test'] = p_test
print " Global : %.2f%%"%p_test['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_test,opt.types,'Test',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/test_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
if opt.opdict['plot_confusion']:
plt.show()
else:
plt.close()
# Fill the dictionary
sub_dic={}
i_com = np.where((y_test.NumType.values.ravel()-CLASS_test)==0)[0]
i_lr = np.where(CLASS_test==0)[0]
i_ok_class = np.intersect1d(i_com,i_lr) # events classified in the class of interest by the LR and identical to the manual classification
sub_dic["nb"] = len(i_lr) # total number of events classified in the class of interest
sub_dic["nb_common"] = len(i_ok_class) # total number of well classified events
sub_dic["index_ok"] = otimes[i_ok_class] # index of well classified events
sub_dic["nb_other"],sub_dic["i_other"] = [],[]
for k in range(len_numt):
if k != n:
i_other_man = list(y_test_tir[y_test_tir.NumType==k].index)
ii = np.intersect1d(i_lr,i_other_man)
sub_dic["nb_other"].append((types[k],len(ii))) # number of events belonging to another class
sub_dic["i_other"].append((types[k],otimes[ii])) # index of events belonging to another class
sub_dic["rate_%s"%types[n]] = (out['rate_train'][('%s'%types[n], 0)], out['rate_test'][('%s'%types[n], 0)]) # % success rate of the extracted class
sub_dic["rate_rest"] = (out['rate_train'][('Rest', 1)], out['rate_test'][('Rest', 1)]) # % success rate of the rest
sub_dic["nb_manuals"] = ((types[n],len(y_test[y_test.NumType==0])),('Rest',len(y_test[y_test.NumType==1])))
dic[types[n]] = sub_dic
DIC[b] = dic
file = opt.opdict['result_path']
print "One-vs-All results stored in %s"%file
write_binary_file(file,DIC)
def classifier(opt):
"""
Classification of the different types of events.
opt is an object of the class Options()
"""
list_attr = opt.__dict__.keys()
if not 'x' in list_attr:
opt.do_tri()
X = opt.x
Y = opt.y
list_attr = opt.__dict__.keys()
if 'train_x' in list_attr:
X_TRAIN = opt.train_x
Y_TRAIN = opt.train_y
dic_results = {}
for isc in sorted(opt.xs):
print "==========",opt.trad[isc],"=========="
subdic = {}
if isc > 0:
if opt.trad[isc][0] == sta_prev:
marker_sta = 1
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
else:
marker_sta = 0
sta_prev = opt.trad[isc][0]
if len(opt.xs[isc]) == 0:
continue
# About the training set
if len(opt.opdict['stations']) == 1 and opt.opdict['boot'] > 1 and 'train_x' not in list_attr:
if os.path.exists(opt.opdict['train_file']):
print opt.opdict['train_file']
TRAIN_Y = read_binary_file(opt.opdict['train_file'])
else:
TRAIN_Y = {}
for tir in range(opt.opdict['boot']):
TRAIN_Y[tir] = {}
elif 'train_x' in list_attr:
opt.x = opt.xs_train[isc]
opt.y = opt.ys_train[isc]
if opt.opdict['plot_pdf']:
opt.compute_pdfs()
g_train = opt.gaussians
del opt.gaussians
opt.classname2number()
x_ref_train = opt.x
y_ref_train = opt.y
# About the test set
opt.x = opt.xs[isc]
opt.y = opt.ys[isc]
if opt.opdict['plot_pdf']:
opt.compute_pdfs()
set = pd.DataFrame(index=opt.ys[isc].index,columns=['Otime'])
set['Otime'] = opt.xs[isc].index
opt.classname2number()
x_test = opt.x
y_ref = opt.y
x_ref = opt.x
if opt.opdict['plot_dataset']:
opt.composition_dataset()
#K = len(opt.types)
### ITERATE OVER TRAINING SET DRAWS ###
for b in range(opt.opdict['boot']):
print "\n-------------------- # iter: %d --------------------\n"%(b+1)
subsubdic = {}
print "WHOLE SET", x_ref.shape, y_ref.shape
### if there is no pre-defined training set ###
if 'train_x' not in list_attr:
x_train = x_test.copy()
if len(opt.opdict['stations']) == 1 and opt.opdict['boot'] > 1:
if len(TRAIN_Y[b]) > 0:
y_train = y_ref.reindex(index=TRAIN_Y[b]['training_set'])
y_train = y_train.dropna(how='any')
y_cv = y_ref.reindex(index=TRAIN_Y[b]['cv_set'])
y_cv = y_cv.dropna(how='any')
y_test = y_ref.reindex(index=TRAIN_Y[b]['test_set'])
y_test = y_test.dropna(how='any')
else:
y_train, y_cv, y_test = generate_datasets(opt.opdict['proportions'],opt.numt,y_ref)
TRAIN_Y[b]['training_set'] = map(int,list(y_train.index))
TRAIN_Y[b]['cv_set'] = map(int,list(y_cv.index))
TRAIN_Y[b]['test_set'] = map(int,list(y_test.index))
### multi-stations case ###
else:
if marker_sta == 0:
y_train, y_cv, y_test = generate_datasets(opt.opdict['proportions'],opt.numt,y_ref)
list_ev_train = y_train.index
list_ev_cv = y_cv.index
list_ev_test = y_test.index
else:
y_train = y_ref.reindex(index=list_ev_train)
y_train = y_train.dropna(how='any')
y_cv = y_ref.reindex(index=list_ev_cv)
y_cv = y_cv.dropna(how='any')
y_test = y_ref.reindex(index=list_ev_test)
y_test = y_test.dropna(how='any')
x_train = x_ref.reindex(index=y_train.index)
### if a training set was pre-defined ###
else:
x_train = x_ref_train.copy()
y_train = y_ref_train.copy()
y_train, y_cv, y_test = generate_datasets(opt.opdict['proportions'],opt.numt,y_ref,y_train=y_train)
x_cv = x_ref.reindex(index=y_cv.index)
x_test = x_ref.reindex(index=y_test.index)
i_train = y_train.index
x_train.index = range(x_train.shape[0])
y_train.index = range(y_train.shape[0])
print "TRAINING SET", x_train.shape, y_train.shape
if x_train.shape[0] != y_train.shape[0]:
print "Training set: Incoherence in x and y dimensions"
sys.exit()
i_cv = y_cv.index
x_cv.index = range(x_cv.shape[0])
y_cv.index = range(y_cv.shape[0])
print "CROSS-VALIDATION SET", x_cv.shape, y_cv.shape
if x_cv.shape[0] != y_cv.shape[0]:
print "Cross-validation set: Incoherence in x and y dimensions"
sys.exit()
subsubdic['list_ev'] = np.array(y_test.index)
i_test = y_test.index
x_test.index = range(x_test.shape[0])
y_test.index = range(y_test.shape[0])
print "TEST SET", x_test.shape, y_test.shape
if x_test.shape[0] != y_test.shape[0]:
print "Test set: Incoherence in x and y dimensions"
sys.exit()
opt.train_x = x_train
opt.x = x_test
opt.train_y = y_train
opt.y = y_test
if opt.opdict['plot_pdf']:
opt.plot_all_pdfs(save=opt.opdict['save_pdf'])
if 'train_x' in list_attr:
opt.plot_superposed_pdfs(g_train,save=opt.opdict['save_pdf'])
else:
opt.plot_all_pdfs(save=opt.opdict['save_pdf'])
if opt.opdict['method'] == '1b1':
# EXTRACTEURS
print "********** EXTRACTION 1-BY-1 **********"
opt.opdict['boot'] = 1
one_by_one(opt,x_ref,y_ref,set['Otime'],boot=10,method='svm')
continue
elif opt.opdict['method'] == 'ova':
print "********** EXTRACTION 1-VS-ALL **********"
opt.opdict['boot'] = 1
one_vs_all(opt,x_ref,y_ref,set['Otime'],boot=10,method='svm')
continue
elif opt.opdict['method'] in ['svm','svm_nl']:
# SVM
print "********** SVM **********"
if opt.opdict['method'] == 'svm':
kern = 'Lin'
else:
kern = 'NonLin'
out = implement_svm(x_train,x_test,y_train,y_test,opt.types,opt.opdict,kern=kern,proba=opt.opdict['probas'])
if 'map' in sorted(out):
opt.map = out['map']
if 'thetas' in sorted(out):
theta_vec = out['thetas']
theta,threshold = {},{}
for it in range(len(theta_vec)):
theta[it+1] = np.append(theta_vec[it][-1],theta_vec[it][:-1])
threshold[it+1] = 0.5
out['thetas'] = theta
out['threshold'] = threshold
elif opt.opdict['method'] == 'lrsk':
# LOGISTIC REGRESSION (scikit learn)
print "********* Logistic regression (sklearn) **********"
out = implement_lr_sklearn(x_train,x_test,y_train,y_test)
threshold, theta = {},{}
for it in range(len(out['thetas'])):
threshold[it+1] = 0.5
theta[it+1] = np.append(out['thetas'][it][-1],out['thetas'][it][:-1])
out['threshold'] = threshold
out['thetas'] = theta
elif opt.opdict['method'] == 'lr':
# LOGISTIC REGRESSION
print "********* Logistic regression **********"
from LR_functions import do_all_logistic_regression
out = do_all_logistic_regression(x_train,x_test,x_cv,y_train,y_test,y_cv)
theta = out['thetas']
threshold = out['threshold']
if 'learn_file' in sorted(opt.opdict):
learn_filename = opt.opdict['learn_file']
if not os.path.exists(learn_filename):
wtr = write_binary_file(learn_filename,i_train)
CLASS_test = out['label_test']
CLASS_train = out['label_train']
# TRAINING SET
print "\t *TRAINING SET"
y_train_np = y_train.NumType.values.ravel()
from sklearn.metrics import confusion_matrix
cmat_train = confusion_matrix(y_train_np,CLASS_train)
p_tr = dic_percent(cmat_train,opt.types,verbose=True)
out['rate_train'] = p_tr
print " Global : %.2f%%"%p_tr['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_train,opt.types,'Training',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/training_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
# TEST SET
print "\t *TEST SET"
y_test_np = y_test.NumType.values.ravel()
cmat_test = confusion_matrix(y_test_np,CLASS_test)
p_test = dic_percent(cmat_test,opt.types,verbose=True)
out['rate_test'] = p_test
print " Global : %.2f%%"%p_test['global']
if opt.opdict['plot_confusion'] or opt.opdict['save_confusion']:
plot_confusion_mat(cmat_test,opt.types,'Test',opt.opdict['method'].upper())
if opt.opdict['save_confusion']:
savefig = '%s/test_%s.png'%(opt.opdict['fig_path'],opt.opdict['result_file'])
print "Confusion matrix saved in %s"%savefig
plt.savefig(savefig)
if opt.opdict['plot_confusion']:
plt.show()
else:
plt.close()
# PLOT PRECISION AND RECALL
if opt.opdict['plot_prec_rec']:
from LR_functions import normalize,plot_precision_recall
x_train, x_test = normalize(x_train,x_test)
plot_precision_recall(x_train,y_train.NumType,x_test,y_test.NumType,theta)
pourcentages = (p_tr['global'],p_test['global'])
out['method'] = opt.opdict['method']
out['types'] = opt.types
opt.out = out
# PLOT DECISION BOUNDARIES
n_feat = x_train.shape[1] # number of features
if n_feat < 4:
if opt.opdict['plot_sep'] or opt.opdict['save_sep']:
print "\nPLOTTING"
print "Theta values:",theta
print "Threshold:", threshold
# COMPARE AND PLOT LR AND SVM RESULTS
out_svm, out_nl = {},{}
dir = '%s_SEP'%opt.opdict['method'].upper()
if opt.opdict['method']=='lr' and opt.opdict['compare']:
dir = 'LR_SVM_SEP'
out_svm = implement_svm(x_train,x_test,y_train,y_test,opt.types,opt.opdict,kern='Lin')
cmat_svm_tr = confusion_matrix(y_train_np,out_svm['label_train'])
cmat_svm_test = confusion_matrix(y_test_np,out_svm['label_test'])
svm_ptr = dic_percent(cmat_svm_tr,opt.types)
svm_pt = dic_percent(cmat_svm_test,opt.types)
theta_svm,t_svm = {},{}
for it in range(len(out_svm['thetas'])):
theta_svm[it+1] = np.append(out_svm['thetas'][it][-1],out_svm['thetas'][it][:-1])
t_svm[it+1] = 0.5
out_svm['thetas'] = theta_svm
out_svm['threshold'] = t_svm
out_svm['rate_test'] = svm_pt
out_svm['rate_train'] = svm_ptr
out_svm['method'] = 'SVM'
if opt.opdict['method'] in ['lr','svm'] and opt.opdict['compare_nl']:
dir = '%s_NL_SEP'%opt.opdict['method'].upper()
out_nl = implement_svm(x_train,x_test,y_train,y_test,opt.types,opt.opdict,kern='NonLin')
cmat_svm_tr = confusion_matrix(y_train_np,out_nl['label_train'])
cmat_svm_test = confusion_matrix(y_test_np,out_nl['label_test'])
svm_ptr = dic_percent(cmat_svm_tr,opt.types)
svm_pt = dic_percent(cmat_svm_test,opt.types)
out_nl['rate_test'] = svm_pt
out_nl['rate_train'] = svm_ptr
out_nl['method'] = 'SVM_NL'
save_dir = os.path.join(opt.opdict['fig_path'],dir)
opt.verify_and_create(save_dir)
from LR_functions import normalize
x_train, x_test = normalize(x_train,x_test)
x_train_good = x_train.reindex(index=y_train[y_train.NumType.values==CLASS_train].index)
x_train_bad = x_train.reindex(index=y_train[y_train.NumType.values!=CLASS_train].index)
good_train = y_train.reindex(index=x_train_good.index)
x_test_good = x_test.reindex(index=y_test[y_test.NumType.values==CLASS_test].index)
x_test_bad = x_test.reindex(index=y_test[y_test.NumType.values!=CLASS_test].index)
# PLOT FOR 1 ATTRIBUTE AND 2 CLASSES
if n_feat == 1 and len(opt.opdict['types']) == 2:
name = opt.opdict['feat_list'][0]
from plot_functions import plot_hyp_func_1f, histo_pdfs
if opt.opdict['method']=='lr' and opt.opdict['compare']:
plot_hyp_func_1f(x_train,y_train,theta,opt.opdict['method'],threshold=threshold,x_ok=x_test_good,x_bad=x_test_bad,th_comp=theta_svm,cmat_test=cmat_test,cmat_svm=cmat_svm_test,cmat_train=cmat_train)
else:
#histo_pdfs(x_test,y_test,x_train=x_train,y_train=y_train)
plot_hyp_func_1f(x_train,y_train,theta,opt.opdict['method'],threshold=threshold,x_ok=x_test_good,x_bad=x_test_bad,cmat_test=cmat_test,cmat_train=cmat_train)
# PLOT FOR 2 ATTRIBUTES AND 2 to 3 CLASSES
elif n_feat == 2:
name = '%s_%s'%(opt.opdict['feat_list'][0],opt.opdict['feat_list'][1])
if opt.opdict['method'] in ['lr','svm']:
from plot_2features import plot_2f_all
plot_2f_all(out,x_train,y_train,x_test,y_test,x_test_bad)
elif opt.opdict['method']=='lr' and opt.opdict['compare']:
from plot_2features import plot_2f_all
plot_2f_all(out,x_train,y_train,x_test,y_test,x_test_bad,out_comp=out_svm,map_nl=out_nl)
elif opt.opdict['method'] == 'svm_nl':
from plot_2features import plot_2f_nonlinear
plot_2f_nonlinear(out,x_train,y_train,x_test,y_test,y_train=y_train)
# PLOT FOR 3 ATTRIBUTES
elif n_feat == 3:
from plot_functions import plot_db_3d
plot_db_3d(x_train,y_train.NumType,theta[1],title='Training set')
plot_db_3d(x_test,y_test.NumType,theta[1],title='Test set')
name = '%s_%s_%s'%(opt.opdict['feat_list'][0],opt.opdict['feat_list'][1],opt.opdict['feat_list'][2])
if opt.opdict['save_sep']:
savename = '%s/CL_sep_%s.png'%(save_dir,name)
print "Figure saved in %s"%savename
plt.savefig(savename)
if opt.opdict['plot_sep']:
plt.show()
else:
plt.close()
# WRITE RESULTS INTO A DICTIONARY
subsubdic['%'] = pourcentages
trad_CLASS_test = []
for i in CLASS_test:
i = int(i)
trad_CLASS_test.append(opt.types[i])
subsubdic['classification'] = trad_CLASS_test
if opt.opdict['probas']:
subsubdic['proba'] = out['probas']
if opt.opdict['plot_var']:
subsubdic['out'] = out
subdic[b] = subsubdic
if opt.opdict['plot_var'] and opt.opdict['method'] in ['lr','svm','lrsk'] and n_feat==2 and len(opt.opdict['types'])==2:
from plot_2features import plot_2f_variability
plot_2f_variability(subdic,x_train,y_train,x_test,y_test)
plt.savefig('%s/%s_variability_pas.png'%(opt.opdict['fig_path'],opt.opdict['method'].upper()))
plt.show()
dic_results[opt.trad[isc]] = subdic
dic_results['header'] = {}
dic_results['header']['features'] = opt.opdict['feat_list']
dic_results['header']['types'] = opt.opdict['types']
dic_results['header']['catalog'] = opt.opdict['label_test']
if opt.opdict['method'] in ['lr','lrsk','svm','svm_nl']:
print "Save results in file %s"%opt.opdict['result_path']
write_binary_file(opt.opdict['result_path'],dic_results)
if 'train_file' in sorted(opt.opdict):
if not os.path.exists(opt.opdict['train_file']) and opt.opdict['boot'] > 1:
write_binary_file(opt.opdict['train_file'],TRAIN_Y)