diff --git a/image_processing/build_dataset.py b/image_processing/build_dataset.py
index 21e181d1f23193b269cc3682e8d8c07d0953e54b..d2820b2881acdf283333f9756fd71ff63478328c 100644
--- a/image_processing/build_dataset.py
+++ b/image_processing/build_dataset.py
@@ -98,7 +98,7 @@ antibiotic_enterrobacter_breakpoints = {
def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibiogram_Enterobacterales.xlsx',suffix='-d200'):
"""
- Extract and build file name corresponding to each sample
+ Extract and build file name corresponding to each sample and transform antioresistance measurements to labels
:param path: excel path
:return: dataframe
"""
@@ -114,8 +114,9 @@ def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibi
'TIC (disk)','TIC (vitek)','TOB (disk)','TOB (vitek)','TZP (disk)','TZP (mic)','TZP (vitek)']]
for test in antibiotic_tests :# S - Susceptible R - Resistant U- Uncertain
- #convert to string and transform >8 to 8
+ #convert to string and transform (pex >8 to 8)
df[test] = df[test].map(lambda x :float(str(x).replace('>','').replace('<','')))
+ #categorise each antibioresistance according to AST breakpoints table
df[test+' cat']= 'NA'
if 'mic' in test or 'vitek' in test :
try :
@@ -123,6 +124,7 @@ def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibi
df.loc[df[test] >= antibiotic_enterrobacter_breakpoints[test]['R'], test + ' cat'] = 'R'
df.loc[(antibiotic_enterrobacter_breakpoints[test]['S'] < df[test]) & (df[test] < antibiotic_enterrobacter_breakpoints[test]['R']), test + ' cat'] = 'U'
except:
+ #for empty cells
pass
elif 'disk' in test:
try :
@@ -160,6 +162,7 @@ def create_dataset():
for path in glob.glob("../data/raw_data/**.mzML"):
print(path)
species = None
+ #check if file exists in the label table
if path.split("/")[-1] in label['path_ana'].values:
species = label[label['path_ana'] == path.split("/")[-1]]['species'].values[0]
name = label[label['path_ana'] == path.split("/")[-1]]['sample_name'].values[0]
@@ -168,7 +171,7 @@ def create_dataset():
species = label[label['path_aer'] == path.split("/")[-1]]['species'].values[0]
name = label[label['path_aer'] == path.split("/")[-1]]['sample_name'].values[0]
analyse = 'AER'
- if species is not None:
+ if species is not None: #save image in species specific dir
directory_path_png = '../data/processed_data/png_image/{}'.format(species)
directory_path_npy = '../data/processed_data/npy_image/{}'.format(species)
if not os.path.isdir(directory_path_png):
@@ -179,6 +182,7 @@ def create_dataset():
mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
+ #reiterate for other kind of raw file
label = create_antibio_dataset(suffix='_100vW_100SPD')
for path in glob.glob("../data/raw_data/**.mzML"):
print(path)
@@ -203,26 +207,5 @@ def create_dataset():
np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
-def extract_antio_res_labels():
- """
- Extract and organise labels from raw excel file
- :param
- path: excel
- path
- :return: dataframe
- """
- path = '../data/label_raw/230804_strain_peptides_antibiogram_Enterobacterales.xlsx'
- df = pd.read_excel(path, header=1)
- df = df[['sample_name','species','AMC (disk)','AMK (disk)','AMK (mic)','AMK (vitek)','AMP (vitek)','AMX (disk)',
- 'AMX (vitek)','ATM (disk)','ATM (vitek)','CAZ (disk)','CAZ (mic)','CAZ (vitek)','CHL (vitek)','CIP (disk)',
- 'CIP (vitek)','COL (disk)','COL (mic)','CRO (mic)','CRO (vitek)','CTX (disk)','CTX (mic)','CTX (vitek)',
- 'CXM (vitek)','CZA (disk)','CZA (vitek)','CZT (disk)','CZT (vitek)','ETP (disk)','ETP (mic)','ETP (vitek)',
- 'FEP (disk)','FEP (mic)','FEP (vitek)','FOS (disk)','FOX (disk)','FOX (vitek)','GEN (disk)','GEN (mic)',
- 'GEN (vitek)','IPM (disk)','IPM (mic)','IPM (vitek)','LTM (disk)','LVX (disk)','LVX (vitek)','MEC (disk)',
- 'MEM (disk)','MEM (mic)','MEM (vitek)','NAL (vitek)','NET (disk)','OFX (vitek)','PIP (vitek)','PRL (disk)',
- 'SXT (disk)','SXT (vitek)','TCC (disk)','TCC (vitek)','TEM (disk)','TEM (vitek)','TGC (disk)','TGC (vitek)',
- 'TIC (disk)','TIC (vitek)','TOB (disk)','TOB (vitek)','TZP (disk)','TZP (mic)','TZP (vitek)']]
-
-
if __name__ =='__main__' :
df = create_antibio_dataset()
\ No newline at end of file
diff --git a/image_processing/build_image.py b/image_processing/build_image.py
index 336940a4073c9aae6ae9c7f307904b0b42c753ea..a2d2143190acb2af6d96b96e171093392840bf3f 100644
--- a/image_processing/build_image.py
+++ b/image_processing/build_image.py
@@ -28,6 +28,7 @@ def plot_spectra_2d(exp, ms_level=1, marker_size=5, out_path='temp.png'):
def build_image_ms1(path, bin_mz):
+ #load raw data
e = oms.MSExperiment()
oms.MzMLFile().load(path, e)
e.updateRanges()
@@ -36,7 +37,7 @@ def build_image_ms1(path, bin_mz):
dico = dict(s.split('=', 1) for s in id.split())
max_cycle = int(dico['cycle'])
list_cycle = [[] for _ in range(max_cycle)]
-
+ #get ms window size from first ms1 spectra (similar for all ms1 spectra)
for s in e:
if s.getMSLevel() == 1:
ms1_start_mz = s.getInstrumentSettings().getScanWindows()[0].begin
@@ -47,6 +48,7 @@ def build_image_ms1(path, bin_mz):
print('start',ms1_start_mz,'end',ms1_end_mz)
n_bin_ms1 = int(total_ms1_mz//bin_mz)
size_bin_ms1 = total_ms1_mz / n_bin_ms1
+ #organise sepctra by their MSlevel (only MS1 are kept)
for spec in e: # data structure
id = spec.getNativeID()
dico = dict(s.split('=', 1) for s in id.split())
@@ -54,16 +56,13 @@ def build_image_ms1(path, bin_mz):
list_cycle[int(dico['cycle']) - 1].insert(0, spec)
im = np.zeros([max_cycle, n_bin_ms1])
-
- for c in range(max_cycle): # Build one cycle image
+ for c in range(max_cycle): # Build image line by line
line = np.zeros(n_bin_ms1)
if len(list_cycle[c]) > 0:
for k in range(len(list_cycle[c])):
ms1 = list_cycle[c][k]
intensity = ms1.get_peaks()[1]
mz = ms1.get_peaks()[0]
- id = ms1.getNativeID()
- dico = dict(s.split('=', 1) for s in id.split())
for i in range(ms1.size()):
line[int((mz[i] - ms1_start_mz) // size_bin_ms1)] += intensity[i]
diff --git a/main.py b/main.py
index 30f7843b0f6285ad5c47bf5d31e0176e2666e70e..91a64cee648247584d77ae12a5b6fa4832dfb2cb 100644
--- a/main.py
+++ b/main.py
@@ -59,20 +59,26 @@ def test(model, data_test, loss_function, epoch):
return losses,acc
def run(args):
+ #load data
data_train, data_test = load_data(base_dir=args.dataset_dir, batch_size=args.batch_size)
+ #load model
model = Classification_model(model = args.model, n_class=len(data_train.dataset.dataset.classes))
+ #load weights
if args.pretrain_path is not None :
load_model(model,args.pretrain_path)
+ #move parameters to GPU
if torch.cuda.is_available():
model = model.cuda()
+ #init accumulator
best_acc = 0
train_acc=[]
train_loss=[]
val_acc=[]
val_loss=[]
+ #init training
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
-
+ #traing
for e in range(args.epoches):
loss, acc = train(model,data_train,optimizer,loss_function,e)
train_loss.append(loss)
@@ -84,6 +90,7 @@ def run(args):
if acc > best_acc :
save_model(model,args.save_path)
best_acc = acc
+ #plot and save training figs
plt.plot(train_acc)
plt.plot(val_acc)
plt.plot(train_acc)
@@ -92,6 +99,7 @@ def run(args):
plt.show()
plt.savefig('output/training_plot_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
+ #load and evaluated best model
load_model(model, args.save_path)
make_prediction(model,data_test, 'output/confusion_matrix_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
@@ -175,21 +183,28 @@ def test_duo(model, data_test, loss_function, epoch):
return losses,acc
def run_duo(args):
+ #load data
data_train, data_test = load_data_duo(base_dir=args.dataset_dir, batch_size=args.batch_size)
+ #load model
model = Classification_model_duo(model = args.model, n_class=len(data_train.dataset.dataset.classes))
model.double()
+ #load weight
if args.pretrain_path is not None :
load_model(model,args.pretrain_path)
+ #move parameters to GPU
if torch.cuda.is_available():
model = model.cuda()
+
+ #init accumulators
best_acc = 0
train_acc=[]
train_loss=[]
val_acc=[]
val_loss=[]
+ #init training
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
-
+ #train model
for e in range(args.epoches):
loss, acc = train_duo(model,data_train,optimizer,loss_function,e)
train_loss.append(loss)
@@ -201,6 +216,7 @@ def run_duo(args):
if acc > best_acc :
save_model(model,args.save_path)
best_acc = acc
+ # plot and save training figs
plt.plot(train_acc)
plt.plot(val_acc)
plt.plot(train_acc)
@@ -209,7 +225,7 @@ def run_duo(args):
plt.show()
plt.savefig('output/training_plot_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))
-
+ #load and evaluate best model
load_model(model, args.save_path)
make_prediction_duo(model,data_test, 'output/confusion_matrix_noise_{}_lr_{}_model_{}_{}.png'.format(args.noise_threshold,args.lr,args.model,args.model_type))