def take_user_input():
global attribute_selection_count
global progressbar_total
global preprocessing_switch
selection_count = input(
"Enter the number of gene attributes to select (default is " +
str(attribute_selection_count) + "): ")
if not selection_count:
selection_count = "100"
attribute_selection_count = int(selection_count)
preprocessing_choice = input(
"Enter your pre-processing choice. Select 1 for SD and 2 for |SNR| (defult is 1): "
)
if not preprocessing_choice:
preprocessing_choice = 1
preprocessing_switch = int(preprocessing_choice)
progressbar.show(0,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def sort_by_standard_deviation():
global transposed_raw_data_matrix
global ALL_count
global MLL_count
global AML_count
global snr_tuples
global progressbar_total
sample_length = len(transposed_raw_data_matrix[0])
sample_count = len(transposed_raw_data_matrix)
MLL_end_index = ALL_count + MLL_count
for attribute_index in range(sample_length - 1):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(
transposed_raw_data_matrix[sample_index][attribute_index])
sd_value = statistics.standard_deviation(
attribute_list[:ALL_count]) + statistics.standard_deviation(
attribute_list[ALL_count:MLL_end_index]
) + statistics.standard_deviation(attribute_list[MLL_end_index:])
rounded_sd = math.ceil(sd_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_sd))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(9,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def write_as_csv():
global data_matrix
global gene_attributes
global progressbar_total
filename = "child-all.csv"
writefile = open(filename, 'w+')
write_file_content = ""
for attribute in gene_attributes:
write_file_content += attribute + ","
write_file_content += "class\n"
for sample in data_matrix:
line = ""
for value in sample[:-1]:
line += str(value) + ","
line += sample[-1] + "\n"
write_file_content += line
writefile.write(write_file_content)
writefile.close()
progressbar.show(3,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def write_as_csv():
global transposed_raw_data_matrix
global gene_attributes
global progressbar_total
filename = "mll-leukemia.csv"
writefile = open(filename, "w+")
write_file_content = ""
for attribute in gene_attributes:
write_file_content += attribute + ","
write_file_content += "class\n"
for sample in transposed_raw_data_matrix:
line = ""
for value in sample[:-1]:
line += str(value) + ","
line += sample[-1] + "\n"
write_file_content += line
writefile.write(write_file_content)
writefile.close()
progressbar.show(6,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def normalize_data():
global data_matrix
global progressbar_total
sample_length = len(data_matrix[0])
sample_count = len(data_matrix)
for attribute_index in range(sample_length - 1):
attribute_list = list()
normalized_attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(data_matrix[sample_index][attribute_index])
for attribute in attribute_list:
z_score = zscore.calculate_zscore(attribute, attribute_list)
rounded_zscore = math.ceil(z_score * 100000) / 100000
normalized_attribute_list.append(rounded_zscore)
for sample_index in range(sample_count):
data_matrix[sample_index][
attribute_index] = normalized_attribute_list[sample_index]
progressbar.show(7,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def handle_missing_values():
global data_matrix
global gene_attributes
global progressbar_total
missing_attribute_indices = list()
third_sample = data_matrix[2]
shift_counter = 0
for attribute_index in range(len(third_sample)):
if third_sample[attribute_index] == '?':
missing_attribute_indices.append(attribute_index)
for index in range(len(missing_attribute_indices)):
missing_attribute_indices[
index] = missing_attribute_indices[index] - shift_counter
shift_counter += 1
for index in missing_attribute_indices:
gene_attributes.pop(index)
for sample in data_matrix:
for index in missing_attribute_indices:
sample.pop(index)
progressbar.show(5,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def sort_by_SNR():
global data_matrix
global after_th_count
global snr_tuples
global progressbar_total
sample_length = len(data_matrix[0])
sample_count = len(data_matrix)
for attribute_index in range(sample_length - 1):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(data_matrix[sample_index][attribute_index])
snr_value = snr.mod_SNR(attribute_list[:after_th_count],
attribute_list[after_th_count:])
rounded_snr = math.ceil(snr_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_snr))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(8,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def write_to_file():
global selected_data_matrix
global selected_gene_attributes
global attribute_selection_count
global progressbar_total
global preprocessing_switch
preprocess_type = "-sd" if preprocessing_switch == 1 else "-snr"
filename = "leukemia-selected-" + str(attribute_selection_count) + preprocess_type + ".csv"
writefile = open("../datasets/preprocessed/" + filename, 'w+')
write_file_content = ""
for attribute in selected_gene_attributes:
write_file_content += attribute + ","
write_file_content += "class\n"
for sample in selected_data_matrix:
line = ""
for value in sample[:-1]:
line += str(value) + ","
line += sample[-1] + "\n"
write_file_content += line
writefile.write(write_file_content)
writefile.close()
progressbar.show(9, progressbar_total, prefix = 'Progress:', suffix = 'Complete', length = 50)
print("The pre-processed data has been saved in the file: ", filename, "!", end = "\n")
def prepare_selected_dataset():
global data_matrix
global gene_attributes
global selected_data_matrix
global selected_gene_attributes
global snr_tuples
global attribute_selection_count
global progressbar_total
flag = 0
for sample in data_matrix:
selected_attribute_sample = list()
for index in range(attribute_selection_count):
snr_tuple = snr_tuples[index]
selected_index = snr_tuple[0]
if flag == 0:
selected_gene_attributes.append(gene_attributes[selected_index - 1])
selected_attribute_sample.append(sample[selected_index])
selected_attribute_sample.append(sample[0])
selected_data_matrix.append(copy.deepcopy(selected_attribute_sample))
flag = 1
progressbar.show(8, progressbar_total, prefix = 'Progress:', suffix = 'Complete', length = 50)
def sort_by_standard_deviation():
global data_matrix
global ALL_count
global snr_tuples
global progressbar_total
sample_length = len(data_matrix[0])
sample_count = len(data_matrix)
for attribute_index in range(1, sample_length):
attribute_list = list()
for sample_index in range(sample_count):
attribute_list.append(data_matrix[sample_index][attribute_index])
sd_value = statistics.standard_deviation(
attribute_list[:ALL_count]) + statistics.standard_deviation(attribute_list[(ALL_count + 1):])
rounded_sd = math.ceil(sd_value * 10000) / 10000
snr_tuples.append((attribute_index, rounded_sd))
sort.randomized_quick_sort_for_tuples(snr_tuples, 0, len(snr_tuples) - 1)
progressbar.show(7, progressbar_total, prefix='Progress:',
suffix='Complete', length=50)
def aggregate_same_class_samples():
global data_matrix
global ALL_count
global AML_count
global progressbar_total
ALL_list = list()
AML_list = list()
for sample in data_matrix:
if sample[0] == "ALL":
ALL_list.append(copy.deepcopy(sample))
elif sample[0] == "AML":
AML_list.append(copy.deepcopy(sample))
ALL_count = len(ALL_list)
AML_count = len(AML_list)
data_matrix = list()
transfer_list(ALL_list, data_matrix)
transfer_list(AML_list, data_matrix)
progressbar.show(3,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def convert_datapoints_to_number():
global data_matrix
global progressbar_total
for sample in data_matrix:
for index in range(1, len(sample)):
sample[index] = float(sample[index])
progressbar.show(5, progressbar_total, prefix = 'Progress:', suffix = 'Complete', length = 50)
def tidy_raw_dataset():
global raw_data_matrix
global data_matrix
global gene_attributes
gene_attributes = raw_data_matrix[0][:-1]
data_matrix = raw_data_matrix[3:]
progressbar.show(2, 9, prefix='Progress:', suffix='Complete', length=50)
def arrange_dataset():
global raw_data_matrix
global gene_attributes
global ALL_samples
global MLL_samples
global AML_samples
global ALL_count
global AML_count
global MLL_count
global progressbar_total
linecount = 0
for line in raw_data_matrix[1:]:
splitted_line_list = line.split("\t")
gene_attributes.append(splitted_line_list[0])
ALL_start_index = 2
ALL_end_index = ALL_count + 2
MLL_start_index = ALL_end_index
MLL_end_index = ALL_count + 2 + MLL_count
AML_start_index = MLL_end_index
AML_end_index = ALL_count + MLL_count + AML_count + 2
for index in range(
len(splitted_line_list[ALL_start_index:ALL_end_index])):
if linecount == 0:
ALL_samples.append(list())
ALL_samples[index].append(splitted_line_list[index +
ALL_start_index])
for index in range(
len(splitted_line_list[MLL_start_index:MLL_end_index])):
if linecount == 0:
MLL_samples.append(list())
MLL_samples[index].append(splitted_line_list[index +
MLL_start_index])
for index in range(
len(splitted_line_list[AML_start_index:AML_end_index])):
if linecount == 0:
AML_samples.append(list())
AML_samples[index].append(splitted_line_list[index +
AML_start_index])
linecount = linecount + 1
progressbar.show(3,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def read_leukemia_raw_dataset():
global raw_data_matrix
global progressbar_total
with open("../datasets/leukemia.txt", 'r') as datafile:
for line in datafile:
line = line.rstrip()
splitted_line_list = line.split("\t")
raw_data_matrix.append(splitted_line_list)
progressbar.show(1, progressbar_total, prefix = 'Progress:', suffix = 'Complete', length = 50)
def convert_datapoints_to_number():
global transposed_raw_data_matrix
global progressbar_total
for sample in transposed_raw_data_matrix:
for index in range(len(sample) - 1):
sample[index] = float(sample[index])
progressbar.show(7,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def read_mll_leukemia_raw_dataset():
global raw_data_matrix
global progressbar_total
with open("mll_leukemia.txt", "r") as dataset:
for line in dataset:
raw_data_matrix.append(line.rstrip())
progressbar.show(1,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def count_class_strength():
global data_matrix
global before_th_count
global after_th_count
global progressbar_total
for sample in data_matrix:
if sample[-1] == "before Th":
before_th_count += 1
elif sample[-1] == "after Th":
after_th_count += 1
progressbar.show(4,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def count_class_strength():
global data_matrix
global DLBCL_count
global FL_count
global progressbar_total
for sample in data_matrix:
if sample[-1] == "DLBCL":
DLBCL_count += 1
elif sample[-1] == "FL":
FL_count += 1
progressbar.show(4,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def take_user_input():
global attribute_selection_count
global progressbar_total
selection_count = input(
"Enter the number of gene attributes to select (default is " +
str(attribute_selection_count) + "): ")
if not selection_count:
selection_count = "100"
attribute_selection_count = int(selection_count)
progressbar.show(0,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
def add_class_labels():
global ALL_samples
global MLL_samples
global AML_samples
global progressbar_total
for sample in ALL_samples:
sample.append("ALL")
for sample in MLL_samples:
sample.append("MLL")
for sample in AML_samples:
sample.append("AML")
progressbar.show(4,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def write_to_file():
global selected_data_matrix
global selected_gene_attributes
global attribute_selection_count
global progressbar_total
filename = "child-all-selected-" + str(attribute_selection_count) + ".csv"
writefile = open(filename, 'w+')
write_file_content = ""
for attribute in selected_gene_attributes:
write_file_content += attribute + ","
write_file_content += "class\n"
for sample in selected_data_matrix:
line = ""
for value in sample[:-1]:
line += str(value) + ","
line += sample[-1] + "\n"
write_file_content += line
writefile.write(write_file_content)
writefile.close()
progressbar.show(10,
progressbar_total,
prefix='Progress:',
suffix='Complete',
length=50)
print("The pre-processed data has been saved in the file: ",
filename,
"!",
end="\n")
def count_class_strength():
global raw_data_matrix
global ALL_count
global AML_count
global MLL_count
global progressbar_total
splitted_attributes = raw_data_matrix[0].split("\t")[2:]
for word in splitted_attributes:
if "ALL" in word:
ALL_count = ALL_count + 1
elif "AML" in word:
AML_count = AML_count + 1
elif "MLL" in word:
MLL_count = MLL_count + 1
progressbar.show(2,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def transpose_dataset():
global raw_data_matrix
global gene_attributes
global transposed_raw_data_matrix
global ALL_samples
global MLL_samples
global AML_samples
global progressbar_total
for sample in ALL_samples:
transposed_raw_data_matrix.append(sample)
for sample in MLL_samples:
transposed_raw_data_matrix.append(sample)
for sample in AML_samples:
transposed_raw_data_matrix.append(sample)
progressbar.show(5,
progressbar_total,
prefix="Progress:",
suffix="Complete",
length=50)
def progressbar(self):
progressbar = Dec_Progressbar(self)
progressbar.show()
