label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1)).view(
batch_size, PROPOSAL_NUM)
raw_loss = creterion(raw_logits, label)
concat_loss = creterion(concat_logits, label)
rank_loss = model.ranking_loss(top_n_prob, part_loss)
partcls_loss = creterion(
part_logits.view(batch_size * PROPOSAL_NUM, -1),
label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1))
total_loss = raw_loss + rank_loss + concat_loss + partcls_loss
total_loss.backward()
raw_optimizer.step()
part_optimizer.step()
concat_optimizer.step()
partcls_optimizer.step()
progress_bar(i, len(trainloader), 'train')
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
train_correct += torch.sum(concat_predict.data == label.data)
print(float(train_correct) / total)
pickle.dump(net, open('./model.pkl', 'wb'))
if epoch % SAVE_FREQ == 0: #and epoch > 20:
train_loss = 0
train_correct = 0
total = 0
net.eval()
auc_label_lst = []
auc_pred_lst = []
people_lst = []
total = 0
net.eval()
for i, data in enumerate(trainloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
_, concat_logits, _, _, _ = net(img)
# calculate loss
concat_loss = creterion(concat_logits, label)
# calculate accuracy
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
train_correct += torch.sum(concat_predict.data == label.data)
train_loss += concat_loss.item() * batch_size
progress_bar(i, len(trainloader), 'eval on train set')
train_acc = float(train_correct) / total
train_loss = train_loss / total
print(
'train set loss: {:.3f} and train set acc: {:.3f} total sample: {}'.format(
train_loss, train_acc, total))
# evaluate on test set
test_loss = 0
test_correct = 0
total = 0
for i, data in enumerate(testloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
partcls_optimizer.step()
#progress_bar(i, len(trainloader), 'train')
# calculate accuracy
if phase == 'train':
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
train_correct += torch.sum(concat_predict.data == label.data)
train_loss += concat_loss.item() * batch_size
else:
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
test_correct += torch.sum(concat_predict.data == label.data)
test_loss += concat_loss.item() * batch_size
progress_bar(i, len(image_dataloader[phase]),
'eval {} set'.format(phase))
if phase == 'train':
train_acc = float(train_correct) / total
train_loss = train_loss / total
else:
test_acc = float(test_correct) / total
test_loss = test_loss / total
_print(
'epoch:{} - {} loss: {:.6f} and train acc: {:.6f} total sample: {}'
.format(epoch, phase, train_loss, train_acc, total))
for scheduler in schedulers:
scheduler.step()
def calculate_fixation_per_aoi(input_file, output_file):
"""
Calculate Fixation features per AOI.
:param input_file: Fixation CSV file
:param output_file: Fixation per AOI file
"""
print("Calculating Blinking Features per AOI...")
pd_dataframe = pd.read_csv(input_file, sep=",", index_col=False)
# print("Finished loading in \"{}\" file".format(input_file))
overwrite = "y"
# Export to csv file
# If file exists and user does not want to overwrite, do nothing
# if (os.path.exists(output_file)):
# overwrite = input("File \"{}\" exists. Would you like to overwrite? (Y/N): ".format(input_file).replace("\\", "/"))
if overwrite.lower() == "y":
userID = 0
fixation_count = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
valid_pupil_count = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
fixation_duration_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
longest_fixation_duration = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_length_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_absolute_angle_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_relative_angle_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
refixation_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
left_pupil_difference_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
right_pupil_difference_total = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
df = pd.read_csv(input_file)
new_df = pd.DataFrame()
num_rows = len(pd_dataframe.index)
count = 0
# print("Iterating over every row:")
starttime = time.time()
for index, row in df.iterrows():
userID = row['userID']
page = row['Page']
aoi_type = row['AOI_TYPE']
numbered_aoi = AOI_STRING_TO_AOI_NUMBER[aoi_type]
#Number of Fixation in the AOI
fixation_count[numbered_aoi][page] += 1
#Fixation Duration
fixation_duration = row['FPOGD']
fixation_duration_total[numbered_aoi][page] += fixation_duration
#Longest Fixation Duration
longest_fixation_duration[numbered_aoi][page] = max(
longest_fixation_duration[numbered_aoi][page],
fixation_duration)
#Saccade Length
saccade_length = row['Saccade_length']
saccade_length_total[numbered_aoi][page] += saccade_length
#Saccade Absolute Angle
saccade_absolute_angle = row['Saccade_absolute_angle']
saccade_absolute_angle_total[numbered_aoi][
page] += saccade_absolute_angle
#Saccade Relative Length
saccade_relative_angle = row['Saccade_relative_angle']
saccade_relative_angle_total[numbered_aoi][
page] += saccade_relative_angle
#Refixation
refixation = row['Is_Refixation']
refixation_total[numbered_aoi][page] += refixation
if row['LPMMV'] == 1 and row['RPMMV'] == 1:
valid_pupil_count[numbered_aoi][page] += 1
left_pupil_difference = row['Left_Difference']
left_pupil_difference_total[numbered_aoi][
page] += left_pupil_difference
right_pupil_difference = row['Right_Difference']
right_pupil_difference_total[numbered_aoi][
page] += right_pupil_difference
curtime = time.time()
elapsed_time = curtime - starttime
count += 1
progress = utils.progress_bar(count, num_rows, elapsed_time)
print(progress, end="\r")
print("")
ID_LIST = []
AOI_TYPE_LIST = []
PAGE_LIST = []
FIXATION_COUNT_LIST = []
TOTAL_FIXATION_DURATION_LIST = []
AVERAGE_FIXATION_DURATION_LIST = []
LONGEST_FIXATION_DURATION_LIST = []
TOTAL_SACCADE_LENGTH_LIST = []
AVERAGE_SACCADE_LENGTH_LIST = []
TOTAL_SACCADE_ABSOLUTE_ANGLE_LIST = []
AVERAGE_SACCADE_ABSOLUTE_ANGLE_LIST = []
TOTAL_SACCADE_RELATIVE_ANGLE_LIST = []
AVERAGE_SACCADE_RELATIVE_ANGLE_LIST = []
TOTAL_REFIXATION_LIST = []
REFIXATION_RATIO_LIST = []
TOTAL_LEFT_EYE_DIFERENCE_LIST = []
AVERAGE_LEFT_EYE_DIFERENCE_LIST = []
TOTAL_RIGHT_EYE_DIFERENCE_LIST = []
AVERAGE_RIGHT_EYE_DIFERENCE_LIST = []
for i in range(NUMBER_OF_PAGES):
for j in range(NUMBER_OF_AOI_TYPES):
ID_LIST.append(userID)
AOI_TYPE_LIST.append(AOI_NUMBER_TO_AOI_STRING[j])
PAGE_LIST.append(i)
FIXATION_COUNT_LIST.append(fixation_count[j][i])
TOTAL_FIXATION_DURATION_LIST.append(
fixation_duration_total[j][i])
LONGEST_FIXATION_DURATION_LIST.append(
longest_fixation_duration[j][i])
TOTAL_SACCADE_LENGTH_LIST.append(saccade_length_total[j][i])
TOTAL_SACCADE_ABSOLUTE_ANGLE_LIST.append(
saccade_absolute_angle_total[j][i])
TOTAL_SACCADE_RELATIVE_ANGLE_LIST.append(
saccade_relative_angle_total[j][i])
TOTAL_REFIXATION_LIST.append(refixation_total[j][i])
if fixation_count[j][i] == 0:
AVERAGE_FIXATION_DURATION_LIST.append(0)
AVERAGE_SACCADE_LENGTH_LIST.append(0)
AVERAGE_SACCADE_ABSOLUTE_ANGLE_LIST.append(0)
AVERAGE_SACCADE_RELATIVE_ANGLE_LIST.append(0)
REFIXATION_RATIO_LIST.append(0)
else:
AVERAGE_FIXATION_DURATION_LIST.append(
fixation_duration_total[j][i] / fixation_count[j][i])
AVERAGE_SACCADE_LENGTH_LIST.append(
saccade_length_total[j][i] / fixation_count[j][i])
AVERAGE_SACCADE_ABSOLUTE_ANGLE_LIST.append(
saccade_absolute_angle_total[j][i] /
fixation_count[j][i])
AVERAGE_SACCADE_RELATIVE_ANGLE_LIST.append(
saccade_relative_angle_total[j][i] /
fixation_count[j][i])
REFIXATION_RATIO_LIST.append(refixation_total[j][i] /
fixation_count[j][i])
TOTAL_LEFT_EYE_DIFERENCE_LIST.append(
left_pupil_difference_total[j][i])
TOTAL_RIGHT_EYE_DIFERENCE_LIST.append(
left_pupil_difference_total[j][i])
if valid_pupil_count[j][i] == 0:
AVERAGE_LEFT_EYE_DIFERENCE_LIST.append(0)
AVERAGE_RIGHT_EYE_DIFERENCE_LIST.append(0)
else:
AVERAGE_LEFT_EYE_DIFERENCE_LIST.append(
left_pupil_difference_total[j][i] /
valid_pupil_count[j][i])
AVERAGE_RIGHT_EYE_DIFERENCE_LIST.append(
left_pupil_difference_total[j][i] /
valid_pupil_count[j][i])
fixation_duration_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_length_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_absolute_angle_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
saccade_relative_angle_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
left_pupil_difference_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
right_pupil_difference_variance = [[0 for i in range(NUMBER_OF_PAGES)]
for j in range(NUMBER_OF_AOI_TYPES)]
SD_FIXATION_DURATION_LIST = []
SD_SACCADE_LENGTH_LIST = []
SD_SACCADE_ABSOLUTE_ANGLE_LIST = []
SD_SACCADE_RELATIVE_ANGLE_LIST = []
SD_LEFT_EYE_DIFERENCE_LIST = []
SD_RIGHT_EYE_DIFERENCE_LIST = []
count = 0
for index, row in df.iterrows():
page = row['Page']
aoi_type = row['AOI_TYPE']
numbered_aoi = AOI_STRING_TO_AOI_NUMBER[aoi_type]
if fixation_count[numbered_aoi][page] == 0:
fixation_duration_variance[numbered_aoi][page] = 0
saccade_length_variance[numbered_aoi][page] = 0
saccade_absolute_angle_variance[numbered_aoi][page] = 0
saccade_relative_angle_variance[numbered_aoi][page] = 0
else:
#Fixation Duration
fixation_duration = row['FPOGD']
fixation_duration_average = fixation_duration_total[
numbered_aoi][page] / fixation_count[numbered_aoi][page]
fixation_duration_variance[numbered_aoi][page] += pow(
fixation_duration - fixation_duration_average, 2)
#Saccade Length
saccade_length = row['Saccade_length']
saccade_length_average = saccade_length_total[numbered_aoi][
page] / fixation_count[numbered_aoi][page]
saccade_length_variance[numbered_aoi][page] += pow(
saccade_length - saccade_length_average, 2)
#Saccade Absolute Angle
saccade_absolute_angle = row['Saccade_absolute_angle']
saccade_absolute_angle_average = saccade_absolute_angle_total[
numbered_aoi][page] / fixation_count[numbered_aoi][page]
saccade_absolute_angle_variance[numbered_aoi][page] += pow(
saccade_absolute_angle - saccade_absolute_angle_average, 2)
#Saccade Relative Length
saccade_relative_angle = row['Saccade_relative_angle']
saccade_relative_angle_average = saccade_relative_angle_total[
numbered_aoi][page] / fixation_count[numbered_aoi][page]
saccade_relative_angle_variance[numbered_aoi][page] += pow(
saccade_relative_angle - saccade_relative_angle_average, 2)
if row['LPMMV'] == 1 and row['RPMMV'] == 1:
if valid_pupil_count[numbered_aoi][page] == 0:
left_pupil_difference_variance[numbered_aoi][page] = 0
right_pupil_difference_variance[numbered_aoi][page] = 0
else:
left_pupil_difference = row['Left_Difference']
left_pupil_difference_average = left_pupil_difference_total[
numbered_aoi][page] / valid_pupil_count[
numbered_aoi][page]
left_pupil_difference_variance[numbered_aoi][
page] += pow(
left_pupil_difference -
left_pupil_difference_average, 2)
right_pupil_difference = row['Right_Difference']
right_pupil_difference_average = right_pupil_difference_total[
numbered_aoi][page] / valid_pupil_count[
numbered_aoi][page]
right_pupil_difference_variance[numbered_aoi][
page] += pow(
right_pupil_difference -
right_pupil_difference_average, 2)
curtime = time.time()
elapsed_time = curtime - starttime
count += 1
progress = utils.progress_bar(count, num_rows, elapsed_time)
print(progress, end="\r")
print("")
SD_LEFT_EYE_DIFERENCE_LIST = []
SD_RIGHT_EYE_DIFERENCE_LIST = []
for i in range(NUMBER_OF_PAGES):
for j in range(NUMBER_OF_AOI_TYPES):
if fixation_count[j][i] == 0:
SD_FIXATION_DURATION_LIST.append(0)
SD_SACCADE_LENGTH_LIST.append(0)
SD_SACCADE_ABSOLUTE_ANGLE_LIST.append(0)
SD_SACCADE_RELATIVE_ANGLE_LIST.append(0)
else:
SD_FIXATION_DURATION_LIST.append(
math.sqrt(fixation_duration_variance[j][i] /
fixation_count[j][i]))
SD_SACCADE_LENGTH_LIST.append(
math.sqrt(saccade_length_variance[j][i] /
fixation_count[j][i]))
SD_SACCADE_ABSOLUTE_ANGLE_LIST.append(
math.sqrt(saccade_absolute_angle_variance[j][i] /
fixation_count[j][i]))
SD_SACCADE_RELATIVE_ANGLE_LIST.append(
math.sqrt(saccade_relative_angle_variance[j][i] /
fixation_count[j][i]))
if valid_pupil_count[j][i] == 0:
SD_LEFT_EYE_DIFERENCE_LIST.append(0)
SD_RIGHT_EYE_DIFERENCE_LIST.append(0)
else:
SD_LEFT_EYE_DIFERENCE_LIST.append(
math.sqrt(left_pupil_difference_variance[j][i] /
valid_pupil_count[j][i]))
SD_RIGHT_EYE_DIFERENCE_LIST.append(
math.sqrt(right_pupil_difference_variance[j][i] /
valid_pupil_count[j][i]))
new_df['userID'] = ID_LIST
new_df['Page'] = PAGE_LIST
new_df['AOI_TYPE'] = AOI_TYPE_LIST
new_df['Total_Fixation_Count'] = FIXATION_COUNT_LIST
new_df['Total_Fixation_Duration'] = TOTAL_FIXATION_DURATION_LIST
new_df['Average_Fixation_Duration'] = AVERAGE_FIXATION_DURATION_LIST
new_df['Fixation_Duration_SD'] = SD_FIXATION_DURATION_LIST
new_df['Longest_Fixation_Duration'] = LONGEST_FIXATION_DURATION_LIST
new_df['Total_Refixation_Count'] = TOTAL_REFIXATION_LIST
new_df['Refixation_Ratio'] = REFIXATION_RATIO_LIST
new_df['Total_Saccade_Length'] = TOTAL_SACCADE_LENGTH_LIST
new_df['Average_Saccade_Length'] = AVERAGE_SACCADE_ABSOLUTE_ANGLE_LIST
new_df['Saccade_Length_SD'] = SD_SACCADE_LENGTH_LIST
new_df[
'Total_Saccade_Absolute_Angle'] = TOTAL_SACCADE_ABSOLUTE_ANGLE_LIST
new_df[
'Average_Saccade_Absolute_Angle'] = AVERAGE_SACCADE_ABSOLUTE_ANGLE_LIST
new_df['Saccade_Absolute_Angle_SD'] = SD_SACCADE_ABSOLUTE_ANGLE_LIST
new_df[
'Total_Saccade_Relative_Angle'] = TOTAL_SACCADE_RELATIVE_ANGLE_LIST
new_df[
'Average_Saccade_Relative_Angle'] = AVERAGE_SACCADE_RELATIVE_ANGLE_LIST
new_df['Saccade_Relative_Angle_SD'] = SD_SACCADE_RELATIVE_ANGLE_LIST
new_df['Total_Left_Eye_Difference'] = TOTAL_LEFT_EYE_DIFERENCE_LIST
new_df['Average_Left_Eye_Difference'] = AVERAGE_LEFT_EYE_DIFERENCE_LIST
new_df['Left_Eye_Difference_SD'] = SD_RIGHT_EYE_DIFERENCE_LIST
new_df['Total_Right_Eye_Difference'] = TOTAL_RIGHT_EYE_DIFERENCE_LIST
new_df[
'Average_Right_Eye_Difference'] = AVERAGE_RIGHT_EYE_DIFERENCE_LIST
new_df['Right_Eye_Difference_SD'] = SD_RIGHT_EYE_DIFERENCE_LIST
new_df.to_csv(output_file, index=False)
print("Finished exporting to {}".format(output_file).replace(
"\\", "/"))
else:
print("Exiting...")
concat_loss = creterion(concat_logits, label)
rank_loss = model.ranking_loss(top_n_prob, part_loss)
partcls_loss = creterion(
part_logits.view(batch_size * PROPOSAL_NUM, -1),
label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1),
)
total_loss = raw_loss + rank_loss + concat_loss + partcls_loss
total_loss.backward()
raw_optimizer.step()
part_optimizer.step()
concat_optimizer.step()
partcls_optimizer.step()
progress_bar(i, len(trainloader), "train")
if epoch % SAVE_FREQ == 0:
train_loss = 0
train_correct = 0
total = 0
net.eval()
for i, data in enumerate(trainloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
_, concat_logits, _, _, _ = net(img)
# calculate loss
concat_loss = creterion(concat_logits, label)
# calculate accuracy
_, concat_predict = torch.max(concat_logits, 1)
val_loss = 0
val_correct = 0
total = 0
for i, data in enumerate(valloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
_, concat_logits, _, _, _ = net(img)
# calculate loss
concat_loss = creterion(concat_logits, label)
# calculate accuracy
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
val_correct += torch.sum(concat_predict.data == label.data)
val_loss += concat_loss.item() * batch_size
progress_bar(i, len(valloader), 'eval val set')
val_acc = float(val_correct) / total
val_loss = val_loss / total
_print(
'epoch:{} - val loss: {:.3f} and val acc: {:.3f} val sample: {}'.
format(epoch, val_loss, val_acc, total))
# prediction on test set
print('begin to predict')
epoch_result = {}
prediction_epoch = []
for i, data in enumerate(testloader):
with torch.no_grad():
img = data.cuda()
_, concat_logits, _, _, _ = net(img)
def calculate_blink_per_aoi(input_file, output_file):
"""
Calculate Blinking features per AOI.
:param input_file: Blinking CSV file
:param output_file: Blinking per AOI file
"""
print("Calculating Blinking Features per AOI...")
pd_dataframe = pd.read_csv(input_file, sep=",", index_col=False)
# print("Finished loading in \"{}\" file".format(input_file))
overwrite = "y"
# Export to csv file
# If file exists and user does not want to overwrite, do nothing
# if (os.path.exists(output_file)):
# overwrite = input("File \"{}\" exists. Would you like to overwrite? (Y/N): ".format(input_file).replace("\\", "/"))
if overwrite.lower() == "y":
blink_count = [[0 for i in range(NUMBER_OF_PAGES)] for j in range(NUMBER_OF_AOI_TYPES)]
blink_duration_total = [[0 for i in range(NUMBER_OF_PAGES)] for j in range(NUMBER_OF_AOI_TYPES)]
df = pd.read_csv(input_file)
new_df = pd.DataFrame()
num_rows = len(pd_dataframe.index)
count = 0
# print("Iterating over every row:")
starttime = time.time()
for index, row in df.iterrows():
page = row['Page']
aoi_type = row['AOI_TYPE']
numbered_aoi = AOI_STRING_TO_AOI_NUMBER[aoi_type]
blink_duration = row['BKDUR']
blink_count[numbered_aoi][page] += 1
blink_duration_total[numbered_aoi][page] += blink_duration
curtime = time.time()
elapsed_time = curtime - starttime
count += 1
progress = utils.progress_bar(count, num_rows, elapsed_time)
print(progress, end="\r")
print("")
AOI_TYPE_LIST = []
PAGE_LIST = []
BLINK_COUNT_LIST = []
TOTAL_BLINK_DURATION_LIST = []
AVERAGE_BLINK_DURATION_LIST = []
for i in range(NUMBER_OF_PAGES):
for j in range(NUMBER_OF_AOI_TYPES):
AOI_TYPE_LIST.append(AOI_NUMBER_TO_AOI_STRING[j])
PAGE_LIST.append(i)
BLINK_COUNT_LIST.append(blink_count[j][i])
TOTAL_BLINK_DURATION_LIST.append(blink_duration_total[j][i])
if blink_count[j][i] != 0:
AVERAGE_BLINK_DURATION_LIST.append(blink_duration_total[j][i]/blink_count[j][i])
else:
AVERAGE_BLINK_DURATION_LIST.append(0)
new_df['Page'] = PAGE_LIST
new_df['AOI_TYPE'] = AOI_TYPE_LIST
new_df['Blink_Count'] = BLINK_COUNT_LIST
new_df['Total_Blink_Duration'] = TOTAL_BLINK_DURATION_LIST
new_df['Average_Blink_Duration'] = AVERAGE_BLINK_DURATION_LIST
new_df.to_csv(output_file, index=False)
print("Finished exporting to {}".
format(output_file).replace("\\", "/"))
else:
print("Exiting...")
def add_label(input_file, task_file):
"""
Add Page to CSV File.
:param input_file: The large, uncollapsed CSV file
"""
overwrite = "y"
# Export to csv file
# If file exists and user does not want to overwrite, do nothing
# if (os.path.exists(input_file)):
# overwrite = input("File \"{}\" exists. Would you like to overwrite? (Y/N): ".format(input_file).replace("\\", "/"))
if overwrite.lower() != "y":
print("Exiting...")
return
print("Adding AOI and Page Label...")
pd_dataframe = pd.read_csv(input_file, sep=",", index_col=False)
# print("Finished loading in \"{}\" file".format(input_file))
time_file = pd.read_csv(task_file, sep=",", index_col=False)
enter_time = []
time_file['TIME_IN'] = pd.to_datetime(time_file['createdAt'], format='%Y-%m-%d %H:%M:%S.%f')
time_file['TIME_OUT'] = pd.to_datetime(time_file['updatedAt'], format='%Y-%m-%d %H:%M:%S.%f')
time_column_name = pd_dataframe.columns.tolist()[3]
time_column_name = time_column_name.split(" ")[1][:-1]
start_hour, start_minute, start_second = time_column_name.split(":")
start_time = int(start_hour) * 3600 + int(start_minute) * 60 + int(float(start_second))
id = 0
for index, row in time_file.iterrows():
id = row['userID']
if row['Task'] > 0:
time_row = row['TIME_IN']
time_in_second = time_row.hour * 3600 + time_row.minute * 60 + time_row.second
enter_time.append(time_in_second - start_time)
if row['Task'] == 30:
time_row = row['TIME_OUT']
time_in_second = time_row.hour * 3600 + time_row.minute * 60 + time_row.second
enter_time.append(time_in_second - start_time)
overwrite = "y"
# Export to csv file
if overwrite.lower() == "y":
df = pd.read_csv(input_file)
num_rows = len(pd_dataframe.index)
count = 0
# print("Iterating over every row:")
starttime = time.time()
ID_List = []
Page_List = []
AOI_Type_List = []
for index, row in df.iterrows():
ID_List.append(id)
x_coord = row['X_Coordinate']
y_coord = row['Y_Coordinate']
if x_coord < 630 or x_coord > 1290:
AOI_Type_List.append("NOT_AOI")
else:
if x_coord >= 630 and x_coord <= 1290 and y_coord >= 95 and y_coord <= 165:
AOI_Type_List.append("QUERY")
elif x_coord >= 640 and x_coord <= 1280:
if y_coord >= top_y[1] and y_coord <= bot_y[1]:
AOI_Type_List.append("RESULT1")
elif y_coord >= top_y[2] and y_coord <= bot_y[2]:
AOI_Type_List.append("RESULT2")
elif y_coord >= top_y[3] and y_coord <= bot_y[3]:
AOI_Type_List.append("RESULT3")
elif y_coord >= top_y[4] and y_coord <= bot_y[4]:
AOI_Type_List.append("RESULT4")
elif y_coord >= top_y[5] and y_coord <= bot_y[5]:
AOI_Type_List.append("RESULT5")
elif y_coord >= top_y[6] and y_coord <= bot_y[6]:
AOI_Type_List.append("RESULT6")
else:
AOI_Type_List.append("NOT_AOI")
else:
AOI_Type_List.append("NOT_AOI")
timestamp = row[3]
if timestamp < enter_time[0] or timestamp > enter_time[len(enter_time) - 1]:
Page_List.append(0)
else:
i = len(enter_time) - 1
while(i >= 0):
if timestamp >= enter_time[i]:
Page_List.append(i+1)
break
i -= 1
curtime = time.time()
elapsed_time = curtime - starttime
count += 1
progress = utils.progress_bar(count, num_rows, elapsed_time)
print(progress, end="\r")
print("")
df['userID'] = ID_List
df['AOI_TYPE'] = AOI_Type_List
df['Page'] = Page_List
df.to_csv(input_file, index=False)
print("Finished exporting to {}".
format(input_file).replace("\\", "/"))
else:
print("Exiting...")
