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topsis.py
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topsis.py
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import csv
import skcriteria.madm.closeness as cl
#
# By : Antony Gozes
#
# install skcriteria and beautifultable via pip
# pip install -U scikit-criteria
# pip install beautifultable
def main():
#
# Read the data
#
matrix = []
names = []
properties = []
read_data('data_with_missing_values.csv', matrix, names, properties)
#
# complete missing data
#
matrix = complete_data(matrix)
#
# Prepare the weights
#
# properties = ['ScreenSize', 'PrimaryCamera', 'SecondaryCamera', 'RAM', 'Battery', 'Memory', 'SDSlot', 'TalkTime',
# 'Price', 'Announced', 'VoiceControl', 'SoundSpeaker', 'Weight', 'PhysicalKeyboard']
regular_users_weights = [0.07, 0.07, 0.05, 0.09, 0.11, 0.08, 0.06, 0.09, 0.12, 0.02, 0.05, 0.07, 0.08,
0.04] # sum(weights) = 1
children_weights = [0.06, 0.07, 0.05, 0.09, 0.09, 0.07, 0.04, 0.04, 0.25, 0.02, 0.06, 0.04, 0.09,
0.03] # sum(weights) = 1
photographers_weights = [0.1, 0.15, 0.12, 0.09, 0.06, 0.09, 0.07, 0.04, 0.09, 0.02, 0.02, 0.04, 0.07,
0.04] # sum(weights) = 1
buisness_man_weights = [0.1, 0.07, 0.06, 0.09, 0.02, 0.09, 0.07, 0.11, 0.1, 0.04, 0.09, 0.05, 0.02,
0.09] # sum(weights) = 1
travelers_weights = [0.08, 0.12, 0.1, 0.06, 0.11, 0.08, 0.07, 0.06, 0.01, 0.05, 0.07, 0.09, 0.07,
0.03] # sum(weights) = 1
groups_weights = [regular_users_weights, children_weights, photographers_weights, buisness_man_weights,
travelers_weights]
validate_weight_groups(groups_weights)
#
# Calculate the balance for the best result, you can mix from all the groups with the wieght values
#
# balance vector between the groups
balance_vector = [0, 0, 0, 0, 1]
validate_balance_vector(balance_vector)
#
# compute the balanced weights
#
weights = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for i in range(len(weights)):
st = 0
for j in range(len(balance_vector)):
st += balance_vector[j] * groups_weights[j][i]
weights[i] = st
#
# criteria
#
# criteria for what is good value min or max
criteria = [1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1] # -1 -> minimum is best, 1 -> maximum is best
#
# run TOPSIS
#
rc = cl.TOPSIS()
rcc = rc.decide(matrix, criteria, weights)
display_matrix = to_result_object(names, properties, rcc, weights, True)
print(to_table_string(display_matrix))
def norm_weights(weights):
sum_of_weights = sum(weights)
return [x / sum_of_weights for x in weights]
def print_2d(m):
print('\n\n')
for row in m:
print(row)
def validate_weight_groups(wg):
for g in wg:
if abs(sum(g) - 1) >= 1e-4:
raise Exception("sum of each criteria group must be equal to 1")
for p in g:
if not (0 < p < 1):
raise Exception("each property weight must be great than 0 and less than 1")
def validate_balance_vector(bv):
if sum(bv) != 1:
raise Exception("sum of balance vector must be equal to 1")
if max(bv) > 1 or min(bv) < 0:
raise Exception("each weight in balance group should be grate or equal to 0 and less or equal to 1")
def read_data(data_file, dst_matrix, dst_names, dst_properties):
import numpy as np
try:
with open(data_file, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
line_counter = 0
for row in reader:
if line_counter == 0:
for w in row[1:]:
dst_properties.append(str(w).strip())
# matrix.append([float(x) if str(x).isnumeric() else x for x in row][1:])
elif line_counter > 0 and len(row) > 0:
dst_names.append(row[0])
dst_matrix.append([np.nan if not str(x).strip() else float(x) for x in row[1:]])
line_counter += 1
except Exception as e:
raise e
def to_result_object(names, properties, decision, weight, sort=False):
if 'mtx' not in dir(decision):
raise Exception('Error in decision value')
ts = [['Rank', 'Name'] + properties, ['Weight'] + weight]
tr = []
for i in range(len(names)):
tr.append([decision.rank_[i]] + [names[i].strip()] + decision.mtx[i].tolist())
if sort:
tr = sorted(tr, key=lambda r: int(r[0]))
return ts + tr
def to_table_string(result_object):
if not result_object or len(result_object) <= 2:
return ''
from beautifultable import BeautifulTable
bt = BeautifulTable(max_width=4096, default_padding=4)
bt.set_style(bt.STYLE_MARKDOWN)
bt.numeric_precision = 2
bt.serialno = True
bt.column_headers = [result_object[0][0], result_object[0][1]] + [
result_object[0][i + 1] + ' (' + str(result_object[1][i]) + ')' for i in
range(1, len(result_object[1]))]
# bt.column_headers[1] = result_object[0][0]
for dr in result_object[2:]:
bt.append_row(dr)
return bt.get_string(recalculate_width=True)
def complete_data(data):
from imputer import Imputer
import numpy as np
from pandas import DataFrame
cols = len(data[0])
# headers[0] = columns names
# headers[1] = is values continues or discrete
# headers[2] is the column have missing values
headers = [[None] * cols, [None] * cols, [None] * cols]
data_np = np.array(data)
for i in range(len(data[0])):
headers[0][i] = 'p' + str(i + 1)
t = is_continue_type_row(data_np[:, i])
headers[1][i] = t[0] # continues
headers[2][i] = t[1] # has missing data
imp = Imputer()
for i in range(len(headers[0])):
if headers[2][i]:
td = DataFrame(data_np, columns=headers[0])
data_np = imp.knn(td, i, max(4, min(10, int(len(data) / 10) + 1)), False)
if not headers[1][i]: # non continues values will be rounded
np.around(data_np[:, i], out=data_np[:, i])
return data_np.tolist()
def is_continue_type_row(r):
import numpy as np
cont = False
has_missing = False
for x in r:
if x is not None and str(x).strip() != '' and not float(x).is_integer() and not np.isnan(float(x)):
cont = True
if str(x).strip() == '' or x == np.nan or np.isnan(x) or np.isnan(float(x)):
has_missing = True
return cont, has_missing
if __name__ == '__main__':
main()