def main():
for z in range(1, 7):
# Get file name
file_name = join("instances", "instance{0}.txt".format(z))
print("\n{0} file : {1}".format(z, file_name))
# Read file data
number_of_items, weight_constraint, items = get_data(file=file_name)
# Display number of lines, weight and the volume
print("\nNumber of Items:{0}, Weight constraint:{1}\n".format(number_of_items,
weight_constraint))
# Initialize item class
item_object = Items(items=items)
# Constants
initial_temperature = 10000
cooling_rate = 0.75
iteration = 5
epoch = 3
termination_criteria = 0.001
acceptance_criterion = 0.90
sa_object = SA(items=items,
number_of_items=number_of_items,
item_object=item_object,
weight_constraint=weight_constraint,
initial_temperature=initial_temperature,
cooling_rate=cooling_rate,
iteration=iteration,
epoch=epoch,
termination_criteria=termination_criteria,
acceptance_criterion=acceptance_criterion)
# Randomly initialed solution
sa_object.first_improvement(initial_solution=sa_object.initial_solution)
import pickle
# constants
dataset_version = "data"
print_topX = 10
print_report = True
print_cm = False
save_fig = True
show_fig = False
use_comps = False
use_ab = True
use_ti = True
test_percent = 0.25
scoring = "precision"
(feature_names_comps, feature_names_ab, feature_names_ti, X_comps, X_ab, X_ti, y) = get_data(dataset_version)
# <codecell>
############################################################################
# extract features via chi2
# assemble X_train, X_test, and feature_names
print("assembling features")
t0 = time()
(X_c_train, X_c_test, X_ab_train, X_ab_test, X_ti_train, X_ti_test, y_train, y_test) = train_test_split(
X_comps, X_ab, X_ti, y, test_size=test_percent
)
feature_names = []
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time, clf, pred
# <codecell>
for select_chi2 in select_chi2s:
# <codecell>
################################################################################
# load data
(feature_names_comps, feature_names_ab,
feature_names_ti, X_comps, X_ab, X_ti, y) = get_data(dataset_version)
# <codecell>
############################################################################
# extract features via chi2
# assemble X_train, X_test, and feature_names
print("assembling features")
t0 = time()
(X_c_train, X_c_test, X_ab_train, X_ab_test, X_ti_train, X_ti_test,
y_train, y_test) = train_test_split(X_comps, X_ab, X_ti, y,
test_size=test_percent)
feature_names = []
import methods
import config
import datetime
data_set = 'records.txt'
records_list = methods.get_data(data_set)
while True:
methods.message()
try:
s = input('Choose action: ')
if s == '1':
phone_list = methods.get_bank_list(records_list)
cards = methods.get_my_cards_list(records_list)
k = 1
for p in phone_list:
current_funds = methods.get_account_states(records_list, p)
try:
print(
str(k) + '. *' + cards[k - 1] + ' (' +
config.banks[p] + ') : {money} USD'.format(
money=methods.get_account_states(records_list, p)))
k = k + 1
except KeyError as ke:
print(
str(k) + '. *' + cards[k - 1] + ' (Bank' + str(k) +
') : {money} USD'.format(
money=methods.get_account_states(records_list, p)))
k = k + 1
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time, clf, pred
# <codecell>
for select_chi2 in select_chi2s:
# <codecell>
################################################################################
# load data
(feature_names_comps, feature_names_ab, feature_names_ti, X_comps, X_ab,
X_ti, y) = get_data(dataset_version)
# <codecell>
############################################################################
# extract features via chi2
# assemble X_train, X_test, and feature_names
print("assembling features")
t0 = time()
(X_c_train, X_c_test, X_ab_train, X_ab_test, X_ti_train, X_ti_test,
y_train, y_test) = train_test_split(X_comps,
X_ab,
X_ti,
y,
test_size=test_percent)
categories = ["1"]
bm = Benchmarker(print_topX, print_report, print_cm)
def benchmark(clf):
return bm.benchmark(clf)
# <codecell>
################################################################################
# load data
feature_names_comps, feature_names_tfidf, X_train_comps, X_train_tfidf, X_test_comps, X_test_tfidf, y_train, y_test = get_data(
dataset_version)
# <codecell>
################################################################################
# extract features via chi2
# assemble X_train, X_test, and feature_names
# if select_chi2:
print("Extracting %d best features by a chi-squared test" % select_chi2)
t0 = time()
ch2 = SelectKBest(chi2, k=select_chi2)
X_train_ch2 = ch2.fit_transform(X_train_tfidf, y_train)
X_train = hstack([X_train_comps, X_train_ch2])
X_test_ch2 = ch2.transform(X_test_tfidf)
X_test = hstack([X_test_comps, X_test_ch2])
print_cm=False
save_fig=True
categories = ["1"]
bm = Benchmarker(print_topX, print_report, print_cm)
def benchmark(clf):
return bm.benchmark(clf)
# <codecell>
################################################################################
# load data
feature_names_comps, feature_names_tfidf, X_train_comps, X_train_tfidf, X_test_comps, X_test_tfidf, y_train, y_test = get_data(dataset_version)
# <codecell>
################################################################################
# extract features via chi2
# assemble X_train, X_test, and feature_names
# if select_chi2:
print("Extracting %d best features by a chi-squared test" %
select_chi2)
t0 = time()
ch2 = SelectKBest(chi2, k=select_chi2)
X_train_ch2 = ch2.fit_transform(X_train_tfidf, y_train)
X_train = hstack([X_train_comps, X_train_ch2])
from hashtag import Hashtag
from methods import get_data
myHashtags = ('lodz')
for element in myHashtags:
newHashtag = Hashtag(element)
request = get_data(newHashtag)
# result = put_dictionary(newHashtag)
# print_dictionary(result)
