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svm-pso-predictor-compare-TVAC.py
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svm-pso-predictor-compare-TVAC.py
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# coding: utf-8
import numpy as np
import random
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
import cmath
from svm_pso_predictor_K import PSO_K
from svm_pso_predictor_TVIW import PSO_W
from svm_pso_predictor_TVAC import PSO_TVAC
from svm_pso_predictor_mTVAC import PSO_MTVAC
from svm_pso_predictor_mhTVAC import PSO_MHTVAC
from svm_pso_predictor_mTVAC_RIW import PSO_MTVACRW
from svm_pso_predictor_hTVAC import PSO_HTVAC
from svm_pso_predictor_original import PSO
from svm_pso_predictor_RANDIW import PSO_RW
import time
drug = '17-AAG'
fn = 40
n_splits = 3 # 将数据随机切分成三份，每份数据训练集60%，测试集40%，模型准确率为三份数据对应的平均
data = pd.read_csv('data/drug_cell/drug/%s/%s_train_data-rfe-sa-%d.csv' % (drug, drug, fn))
# data = pd.read_csv('data/drug_cell/drug/%s/%s_train_data-rfe-mrmr-sa-%d.csv' % (drug, drug, fn))
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
# random_state=1不变的话，每次得到的数据都是一样的，random_state=None，每次的数据不一样
train_size = 0.6
# x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=1, train_size=train_size)
MAX_ITER = 800
feature_number = X.shape[1]
for run in range(3):
# ----------------------程序执行-----------------------
start = time.time()
# pso_K = PSO_K(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_K.init_Population()
# fitness_K = pso_K.iterator()
# pso_W = PSO_W(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_W.init_Population()
# fitness_W = pso_W.iterator()
pso_TVAC = PSO_TVAC(max_iter=MAX_ITER, data_X=X, data_y=y, n_splits=n_splits) # 维度代表变量的个数
pso_TVAC.init_Population()
fitness_TVAC = pso_TVAC.iterator()
pso_mTVAC = PSO_MTVAC(max_iter=MAX_ITER, data_X=X, data_y=y, n_splits=n_splits) # 维度代表变量的个数
pso_mTVAC.init_Population()
fitness_mTVAC = pso_mTVAC.iterator()
# 去掉了线性递减的惯性权重，改为0.5+rand(0,1)/2
# pso_mTVACRW = PSO_MTVACRW(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_mTVACRW.init_Population()
# fitness_mTVACRW = pso_mTVACRW.iterator()
# pso_hTVAC = PSO_HTVAC(max_iter=MAX_ITER, data_X=X, data_y=y, n_splits=n_splits) # 维度代表变量的个数
# pso_hTVAC.init_Population()
# fitness_hTVAC = pso_hTVAC.iterator()
pso_mhTVAC = PSO_MHTVAC(max_iter=MAX_ITER, data_X=X, data_y=y, n_splits=n_splits) # 维度代表变量的个数
pso_mhTVAC.init_Population()
fitness_mhTVAC = pso_mhTVAC.iterator()
pso_original = PSO(max_iter=MAX_ITER, data_X=X, data_y=y, n_splits=n_splits) # 维度代表变量的个数
pso_original.init_Population()
fitness_original = pso_original.iterator()
# pso_RW = PSO_RW(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_RW.init_Population()
# fitness_RW = pso_RW.iterator()
end = time.time()
best = pso_TVAC # 找到准确率最高的---越小越高
if pso_mTVAC.fit < best.fit:
best = pso_mTVAC
# if pso_hTVAC.fit < best.fit:
# best = pso_hTVAC6
if pso_mhTVAC.fit < best.fit:
best = pso_mhTVAC
if pso_original.fit < best.fit:
best = pso_original
# -------------------画图--------------------
plt.figure(figsize=(10, 8))
plt.title('drug:%s\n feature number=%d,train_size:%.1f,cost: %d seconds\n best:%.4f C=%.4f,gamma=%.5f' % (
drug, feature_number, train_size, end - start, -best.fit, best.gbest[0], best.gbest[1]), size=16)
plt.xlabel("number of generations", size=16)
plt.ylabel("SVM model accuracy rate", size=16)
t = np.array([t for t in range(0, MAX_ITER)])
# fitness_K = np.array(fitness_K)
# fitness_K2 = [-v for v in fitness_K] # 取反，得到正数，模型准确率
# fitness_W2 = [-v for v in fitness_W] # 取反，得到正数，模型准确率
fitness_TVAC2 = [-v for v in fitness_TVAC] # 取反，得到正数，模型准确率
fitness_mTVAC2 = [-v for v in fitness_mTVAC] # 取反，得到正数，模型准确率
# fitness_mTVACRW2 = [-v for v in fitness_mTVACRW] # 取反，得到正数，模型准确率
# fitness_hTVAC2 = [-v for v in fitness_hTVAC] # 取反，得到正数，模型准确率
fitness_mhTVAC2 = [-v for v in fitness_mhTVAC] # 取反，得到正数，模型准确率
pso_original2 = [-v for v in fitness_original] # 取反，得到正数，模型准确率
# fitness_RW2 = [-v for v in fitness_RW] # 取反，得到正数，模型准确率
# plt.plot(t, fitness_K2, color='b', linewidth=3, label='PSO-K')
plt.plot(t, pso_original2, color='b', linewidth=3.2, label='PSO', ls='-.')
# plt.plot(t, fitness_W2, color='r', linewidth=3, label='PSO-TVIW', ls=':')
plt.plot(t, fitness_TVAC2, color='g', linewidth=3.1, label='PSO-TVAC', ls='--')
# plt.plot(t, fitness_RW2, color='lightblue', linewidth=3, label='PSO-RANDIW', ls='-')
plt.plot(t, fitness_mTVAC2, color='orange', linewidth=3, label='PSO-mTVAC', ls=':')
plt.plot(t, fitness_mhTVAC2, color='red', linewidth=2.9, label='PSO-mhTVAC', ls='-')
# plt.plot(t, fitness_mTVACRW2, color='black', linewidth=3, label='PSO-mTVACRW')
# plt.plot(t, fitness_hTVAC2, color='yellow', linewidth=2.8, label='PSO-hTVAC')
plt.legend(loc="lower right", fontsize=16)
plt.yticks(fontsize='14')
plt.xticks(fontsize='14')
# plt.rcParams['savefig.dpi'] = 300 # 图片像素
# plt.rcParams['figure.dpi'] = 300 # 分辨率
# plt.savefig(
# 'image/pso_TVAC/c-%s/fn%d/sa-%d-pso-cv-compare-%s-%d.png' % (
# drug, feature_number, feature_number, drug, 1 + run))
# plt.show()
plt.savefig(
'image/pso_TVAC/c-%s/fn%d/sa-mrmr-%d-pso-cv-compare-%s-%d.png' % (
drug, feature_number, feature_number, drug, 10 + run))
# plt.show()
plt.cla()