#=================================================
os.chdir('C:/Users/rfuchs/Documents/cyto_classif')
source = "SSLAMM/Week1"
files = os.listdir(source)
train_files = files[2:]
valid_files = [files[0]]
test_files = [files[1]] # Last one one the 21 files
#===============================================================
# Former model prediction for comparison
#===============================================================
predict(source + '/' + test_files[0], source, fumseck, tn)
preds = pd.read_csv(source + '/original/Pulse6_2019-09-18 15h59.csv')
q1 = 'Total FWS'
q2 = 'Total FLR'
plot_2D(preds, tn, q1, q2)
#===============================================================
# Model preparation for fine-tuning
#===============================================================
# Freeze the first layers and retrain
for layer in fumseck.layers[:5]:
layer.trainable = False
folder = 'C:/Users/rfuchs/Documents/cyto_classif'
file = 'SSLAMM/Week1/Labelled_Pulse6_2019-09-18 14h35.parq'
date_regex = "(Pulse[0-9]{1,2}_20[0-9]{2}-[0-9]{2}-[0-9]{2} [0-9]{2}(?:u|h)[0-9]{2})"
pred_file = 'Pulse6_2019-05-06 10h09.csv'
os.chdir(folder)
# Load pre-trained model
LottyNet = load_model(
'C:/Users/rfuchs/Documents/cyto_classif/LottyNet_FUMSECK')
# Making formated predictions
source_path = folder + '/' + file
dest_folder = folder
predict(source_path, folder, LottyNet, tn)
# Getting those predictions
preds = pd.read_csv(folder + '/' + pred_file)
np.mean(preds['True FFT id'] == preds['Pred FFT id'])
print(confusion_matrix(preds['True FFT id'], preds['Pred FFT id']))
colors = [
'#96ceb4', '#ffeead', '#ffcc5c', '#ff6f69', '#588c7e', '#f2e394',
'#f2ae72', '#d96459'
]
#####################
# 2D plots
#####################
# Create a log file in the destination folder: list of the already predicted files
preds_store_folder = "C:/Users/rfuchs/Documents/preds_files/P1" # Where to store the predictions
#log_path = preds_store_folder + "/pred_logs.txt" # Register where write the already predicted files
if not (os.path.isfile(log_path)):
open(log_path, 'w+').close()
for file in files_to_pred:
print('Currently predicting ' + file)
path = export_folder + '/' + file
is_already_pred = False
# Check if file has already been predicted
with open(log_path, "r") as log_file:
if file in log_file.read():
is_already_pred = True
if not (is_already_pred): # If not, perform the prediction
# Predict the values
#format_data(path, precomputed_data_dir, scale = False, \
#is_ground_truth = False, hard_store = True)
predict(path, preds_store_folder, model, tn, \
is_ground_truth = False, precomputed_data_dir = precomputed_data_dir)
# Write in the logs that this file is already predicted
with open(log_path, "a") as log_file:
log_file.write(file + '\n')
else:
print(file, 'already predicted')
files_to_pred = [
file for file in export_files if re.search(pulse_regex, file)
] # The files containing the data to predict
# Create a log file in the destination folder: list of the already predicted files
preds_store_folder = "C:/Users/rfuchs/Documents/SSLAMM_P2/SSLAMM_L2" # Where to store the predictions
log_path = preds_store_folder + "/pred_logs.txt" # Register where write the already predicted files
if not (os.path.isfile(log_path)):
open(preds_store_folder + '/pred_logs.txt', 'w+').close()
for file in files_to_pred:
print('Currently predicting ' + file)
path = export_folder + '/' + file
is_already_pred = False
# Check if file has already been predicted
with open(log_path, "r") as log_file:
if file in log_file.read():
is_already_pred = True
if not (is_already_pred): # If not, perform the prediction
# Predict the values
predict(path, preds_store_folder, model, tn, is_ground_truth=False)
# Write in the logs that this file is already predicted
with open(log_path, "a") as log_file:
log_file.write(file + '\n')
else:
print(file, 'already predicted')