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SymbolicTrainscript.py
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161 lines (150 loc) · 8.06 KB
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import pandas as pd
import numpy as np
import time
import os
import pandas as pd
import numpy as np
import sys
# Modify sys.path to include the src directory
src_path = os.path.abspath('src')
sys.path.append(src_path)
from utility import set_starters, adjustTree,statistic_leafs_depth, Tee
from Instance import Instance
from OCTData import OCTData_quadratic, OCTData_aggressive
from OCTH import OCTHflow
from Trees import CustomDecisionTreeClassifier
from sklearn.metrics import accuracy_score
#from tqdm import tqdm
from Convert import CART_warm_start, Var_to_FakeVar, SVMflow
from gurobipy import quicksum
from utility import node_cross
from Kernelfuncs import replace_feature, find_SVC
from Symbolicfuncs import replace_feature
sigma=3
beta = 0
mu = 1e-4
folder_path = './Partitions'
subfolders = [f.name for f in os.scandir(folder_path) if f.is_dir()]
#subfolders = ['monk1','monk2','monk3','seeds','dermatology','haberman']
#subfolders = ['balance-scale']
#subfolders = ['bank_conv']
# subfolders = ['biodeg', 'blood-transfusion', 'breast-cancer',\
# 'car_evaluation', 'german', 'IndiansDiabetes', \
# 'seismic_bumps', 'spambase', 'Statlog_satellite', 'tic-tac-toe']
for folder in subfolders[-5:]:
ins = Instance(Insname)
ins.read()
for iPartition in range(5):
tr, va, te = ins.get_ith_partition(iPartition)
oq = OCTData_aggressive(ins.df, tr, va, te)
oq.insert([set([ind for ind in range(len(oq.train_df.columns)-1)])])
# round 1: use original features
with Tee("./featcorsssigma3Log/"+str(folder)+"_"+str(sigma)+"_"+str(iPartition)+'.log', 'w'):
if (np.power(2, sigma)-1) * (len(oq.train_df.columns)-1)>2100:
oq = replace_feature(oq)
CMAX = (np.power(2, sigma)-1) * (len(oq.train_df.columns)-1)
highest_acc = 0
highest_records = []
tree_records = []
C = CMAX
t1 = time.time()
alpha = 0
# OCTHmodel = OCTHflow(0, beta, sigma, mu, sigma, oq.train_df)
# a, b, d, s, g, u = OCTHmodel.model(150, solve = True, starts=None)
# ObjVal = OCTHmodel.OCTH.ObjVal
OCTHmodel = OCTHflow(alpha, beta, sigma, mu, C, oq.train_df)
while C > sigma:
#alpha = ObjVal/(C+1)
# set start
#at, bt, dt, st, gt = CART_warm_start(oq.train_df, sigma)
a, b, d, s, g, u = OCTHmodel.model(180, solve = True, starts=None, C_new = C)
# adjust tree
aSVM, bSVM = adjustTree(a, b, d, g, s, u, OCTHmodel, oq)
feature_pairs = node_cross(aSVM, d, OCTHmodel)
# tree after adjust
clf = CustomDecisionTreeClassifier(aSVM, bSVM, d, g, OCTHmodel)
va_acc = clf.score(oq.validation_df[oq.validation_df.columns[:-1]], oq.validation_df['label'])
# renew C
c = quicksum(s[j,t].x for j in OCTHmodel.J for t in OCTHmodel.N)
C = c.getValue()-1
# optional (test error)
test_acc = clf.score(oq.test_df[oq.test_df.columns[:-1]], oq.test_df['label'])
# record real depth and number of leaf
numberLeafs, maxDepth = statistic_leafs_depth(d, OCTHmodel)
re = [OCTHmodel.OCTH.MIPGap,
OCTHmodel.OCTH.ObjVal,
c.getValue(),
va_acc,
test_acc,
numberLeafs,
maxDepth,
feature_pairs]
print(re)
if highest_acc <= va_acc:
highest_acc = va_acc
highest_records.append(re)
tree_records.append(clf)
t2 = time.time()
total_time = t2 - t1
print(total_time)
df = pd.DataFrame(highest_records, columns=['Gap', 'Obj','C','validation_error', 'test_error', 'numberLeafs','maxDepth','fp'])
sorted_df = df.sort_values(by=['validation_error', 'C'], ascending=[False, True])
sorted_df.to_excel("./featcorsssigma3Log/"+str(folder)+"_"+str(sigma)+"_"+str(iPartition)+".xlsx", index=False)
#sorted_highest_tree = tree_records[sorted_df.index[0]]
#final_acc = sorted_highest_tree.score(oq.test_df[oq.test_df.columns[:-1]], oq.test_df['label'])
final_fp = sorted_df['fp'].iloc[0]
oq.insert2(final_fp)
if final_fp != []:
with Tee("./featcorss2thsigma3Log/"+str(folder)+"_"+str(sigma)+"_"+str(iPartition)+'.log', 'w'):
if (np.power(2, sigma)-1) * (len(oq.train_df.columns)-1)>2100:
oq = replace_feature(oq)
CMAX = (np.power(2, sigma)-1) * (len(oq.train_df.columns)-1)
highest_acc = 0
highest_records = []
tree_records = []
C = CMAX
t1 = time.time()
alpha = 0
OCTHmodel = OCTHflow(alpha, beta, sigma, mu, C, oq.train_df)
# OCTHmodel = OCTHflow(0, beta, sigma, mu, sigma, oq.train_df)
# a, b, d, s, g, u = OCTHmodel.model(100, solve = True, starts=None)
# ObjVal = OCTHmodel.OCTH.ObjVal
while C > sigma:
#alpha = ObjVal/(C+1)
# set start
#at, bt, dt, st, gt = CART_warm_start(oq.train_df, sigma)
a, b, d, s, g, u = OCTHmodel.model(180, solve = True, starts=None, C_new = C)
# adjust tree
aSVM, bSVM = adjustTree(a, b, d, g, s, u, OCTHmodel, oq)
feature_pairs = node_cross(aSVM, d, OCTHmodel)
# tree after adjust
clf = CustomDecisionTreeClassifier(aSVM, bSVM, d, g, OCTHmodel)
va_acc = clf.score(oq.validation_df[oq.validation_df.columns[:-1]], oq.validation_df['label'])
# renew C
c = quicksum(s[j,t].x for j in OCTHmodel.J for t in OCTHmodel.N)
C = c.getValue()-1
# optional (test error)
test_acc = clf.score(oq.test_df[oq.test_df.columns[:-1]], oq.test_df['label'])
# record real depth and number of leaf
numberLeafs, maxDepth = statistic_leafs_depth(d, OCTHmodel)
re = [OCTHmodel.OCTH.MIPGap,
OCTHmodel.OCTH.ObjVal,
c.getValue(),
va_acc,
test_acc,
numberLeafs,
maxDepth,
feature_pairs]
print(re)
if highest_acc <= va_acc:
highest_acc = va_acc
highest_records.append(re)
tree_records.append(clf)
t2 = time.time()
total_time = t2 - t1
print(total_time)
df = pd.DataFrame(highest_records, columns=['Gap', 'Obj','C','validation_error', 'test_error', 'numberLeafs','maxDepth','fp'])
sorted_df = df.sort_values(by=['validation_error', 'C'], ascending=[False, True])
sorted_df.to_excel("./featcorss2thsigma3Log/"+str(folder)+"_"+str(sigma)+"_"+str(iPartition)+".xlsx", index=False)
#sorted_highest_tree = tree_records[sorted_df.index[0]]
#final_acc = sorted_highest_tree.score(oq.test_df[oq.test_df.columns[:-1]], oq.test_df['label'])