def nofilter():
# response = {'result': []}
mythology = request.args.get('mythology')
result = {}
character = request.args.get("character0")
if mythology == 'Indian' or mythology == 'Greek':
if character:
query = load_data.form_query4(character)
print(query)
data = load_data.load_data(query,
"http://3.101.82.158:3030/SER531")
print(data)
result = load_data.clean_data(data)
# if result:
# response = result
elif mythology == "Noted Fictional Characters":
if character:
query = load_data.form_query5(character)
data = load_data.load_data(query, "http://dbpedia.org/sparql")
result = data
elif mythology == "Chinese":
if character:
query = load_data.form_query4(character)
print(query)
data = load_data.load_data(query,
"http://54.183.203.151:3030/Chinese")
result = load_data.clean_data(data)
return result
def process():
character = request.args.get('character0')
query = load_data.form_query4(character)
data = load_data.load_data(query, "http://3.101.82.158:3030/SER531")
result = load_data.clean_data(data)
print(result)
graph.draw_graph(character, result)
# return send_from_directory(app.config['CLIENT_IMAGES'],"unix.gv.pdf", as_attachment=True)
return send_file('unix.gv.pdf', attachment_filename='something.pdf')
def show_result():
mythology = request.args.get('mythology')
data = None
if mythology == 'Indian' or mythology == 'Greek':
character = request.args.get('character0')
filters = []
vars = []
filters.append(request.args.get('filter0'))
filters.append(request.args.get('filter1'))
vars.append(request.args.get('var1'))
vars.append(request.args.get('var2'))
vars.append(request.args.get('var3'))
query = load_data.form_query2(character, filters, vars)
data = load_data.load_data(query, "http://3.101.82.158:3030/SER531")
data = load_data.clean_data(data)
elif mythology == "Noted Fictional Characters":
character1 = request.args.get('character')
query = load_data.form_query4(character1)
data = load_data.load_data(query, "http://dbpedia.org/sparql")
return data
# 结束后生成如下名称feature_dataSet-->'data/train_three_train_feature.csv'
###############################################################################
# 合并正负数据集(打标签，负样本抽样，正负样本合并) #
###############################################################################
# 通过打标签和正负样本均衡合并，最后形成：'data/one_train_dataSet_final.csv'
combine_feature_dataSet.main_combine()
one_train_dataSet_final_path = 'data/one_train_dataSet_final.csv'
two_train_dataSet_final_path = 'data/two_train_dataSet_final.csv'
two_train_dataSet_final_path = 'data/two_train_dataSet_final.csv'
# 通过清理不用用户和商品，最后形成：如下特征集：
one_train_dataSet_after_clean_path = 'data/one_train_dataSet_after_clean.csv'
two_train_dataSet_after_clean_path = 'data/two_train_dataSet_after_clean.csv'
three_train_dataSet_after_clean_path = 'data/three_train_dataSet_after_clean.csv'
load_data.clean_data(one_train_dataSet_final_path,
one_train_dataSet_after_clean_path, '_one')
load_data.clean_data(two_train_dataSet_final_path,
two_train_dataSet_after_clean_path, '_two')
load_data.clean_data(three_train_dataSet_path,
three_train_dataSet_after_clean_path, '_three')
###############################################################################
# 模型预估 RF #
###############################################################################
# 特征筛选后：
one_train_dataSet_after_clean_path = 'data/one_train_dataSet_after_clean.csv'
two_train_dataSet_after_clean_path = 'data/two_train_dataSet_after_clean.csv'
three_train_dataSet_after_clean_path = 'data/three_train_dataSet_after_clean.csv'
three_train_dataSet_path
from feature_selection import sort_pop
from feature_selection import random_crossover
from feature_selection import mutation
from feature_selection import half_crossover
from feature_selection import remove_duplicates
import pandas as pd
import random
import time
import matplotlib.pyplot as plt
# Set random seed
random.seed(1)
time.sleep(1)
dataset = read_data()
dataset = clean_data(dataset)
print("!!!!!!!!!")
print(len(dataset.index))
print("Fitness if using all features", fitness_function(dataset))
# Use the standard 13 features as a benchmark to measure against
standard_dataset = dataset[[
"age", "sex", "CP", "trestbps", "chol", "FBS", "restecg", "thalach",
"exang", "oldpeak", "slope", "ca", "thal", "num"
]]
print("Fitness of the standard features typically included",
fitness_function(standard_dataset))
population = []