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PMLB_reg_train_nn.py
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PMLB_reg_train_nn.py
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from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb
import pickle
from tqdm import tqdm
import os
from os.path import exists
from pmlb import fetch_data, classification_dataset_names,regression_dataset_names
import torch
import torch.nn as nn
import MFCF.gain_fucntions as gf
import MFCF.MFCF as MFCF
import torch.optim as optim
import torch.nn.functional as F
import networkx as nx
from itertools import permutations,combinations
from skorch import NeuralNetRegressor
import sparselinear.sparselinear as sl
class HNN(nn.Module):
def __init__(self, l1, l2, l3, l4, c1, c2, c3):
super(HNN, self).__init__()
self.sl1 = sl.SparseLinear(l1, l2, connectivity=torch.tensor([c1[1], c1[0]],dtype=torch.int64))
self.fc1 = nn.Linear(l1, 1)
self.sl2 = sl.SparseLinear(l2, l3, connectivity=torch.tensor([c2[1], c2[0]],dtype=torch.int64))
self.fc2 = nn.Linear(l2, 1)
self.c3=c3
if len(self.c3[0]) != 0:
self.sl3 = sl.SparseLinear(l3, l4, connectivity=torch.tensor([c3[1], c3[0]],dtype=torch.int64))
self.fc3 = nn.Linear(l3, 1)
self.fc4 = nn.Linear(l4, 1)
self.read_out = nn.Linear(4, 1)
else:
self.sl3 = None
self.fc3 = nn.Linear(l3, 1)
self.fc4 = None
self.read_out = nn.Linear(3, 1)
def forward(self, x):
x_f1 = F.relu(self.fc1(x))
x_s1 = F.relu(self.sl1(x)) # shape l2
x_f2 = F.relu(self.fc2(x_s1))
x_s2 = F.relu(self.sl2(x_s1))
if len(self.c3[0]) != 0:
x_f3 = F.relu(self.fc3(x_s2))
x_s3 = F.relu(self.sl3(x_s2))
x_f4 = F.relu(self.fc4(x_s3))
x = self.read_out(torch.cat([x_f1, x_f2, x_f3, x_f4], 1))
else:
x_f3 = F.relu(self.fc3(x_s2))
x = self.read_out(torch.cat([x_f1, x_f2, x_f3], 1))
return x
class HNN_adv(nn.Module):
def __init__(self, l1, l2, l3, l4, c1, c2, c3):
super(HNN_adv, self).__init__()
self.sl1 = sl.SparseLinear(l1, l2, connectivity=torch.tensor([c1[1], c1[0]],dtype=torch.int64))
self.fc1 = nn.Linear(l1, 1)
self.sl2 = sl.SparseLinear(l2, l3, connectivity=torch.tensor([c2[1], c2[0]],dtype=torch.int64))
self.fc2 = nn.Linear(l2, 1)
self.c3=c3
if len(self.c3[0]) != 0:
self.sl3 = sl.SparseLinear(l3, l4, connectivity=torch.tensor([c3[1], c3[0]],dtype=torch.int64))
self.fc3 = nn.Linear(l3, 1)
self.fc4 = nn.Linear(l4, 1)
self.read_out_1 = nn.Linear(4, 6)
self.read_out_2 = nn.Linear(6, 1)
else:
self.sl3 = None
self.fc3 = nn.Linear(l3, 1)
self.fc4 = None
self.read_out_1 = nn.Linear(3, 3)
self.read_out_2= nn.Linear(3, 1)
def forward(self, x):
x_f1 = F.relu(self.fc1(x))
x_s1 = F.relu(self.sl1(x)) # shape l2
x_f2 = F.relu(self.fc2(x_s1))
x_s2 = F.relu(self.sl2(x_s1))
if len(self.c3[0]) != 0:
x_f3 = F.relu(self.fc3(x_s2))
x_s3 = F.relu(self.sl3(x_s2))
x_f4 = F.relu(self.fc4(x_s3))
x = F.relu(self.read_out_1(torch.cat([x_f1, x_f2, x_f3, x_f4], 1)))
x= self.read_out_2(x)
else:
x_f3 = F.relu(self.fc3(x_s2))
x = F.relu(self.read_out_1(torch.cat([x_f1, x_f2, x_f3], 1)))
x=self.read_out_2(x)
return x
class MLP(nn.Module):
def __init__(self, l1, l2, l3, l4, c1, c2, c3):
super(MLP, self).__init__()
self.fc1 = nn.Linear(l1, l2)
self.fc2 = nn.Linear(l2, l3)
self.c3=c3
if len(self.c3[0]) != 0:
self.fc3 = nn.Linear(l3, l4)
self.fc4 = nn.Linear(l4, 1)
else:
self.fc3 = nn.Linear(l3, 1)
self.fc4 = None
self.read_out = nn.Linear(1, 1)
def forward(self, x):
x=x.float()
x_f1 = F.relu(self.fc1(x))
x_f2 = F.relu(self.fc2(x_f1))
x_f3 = F.relu(self.fc3(x_f2))
if len(self.c3[0]) != 0:
x_f4 = F.relu(self.fc4(x_f3))
x = self.read_out(x_f4)
else:
x = self.read_out(x_f3)
return x
class MLPres(nn.Module):
def __init__(self, l1, l2, l3, l4, c1, c2, c3):
super(MLPres, self).__init__()
self.sl1 = nn.Linear(l1, l2)
self.fc1 = nn.Linear(l1, 1)
self.sl2 = nn.Linear(l2, l3)
self.fc2 = nn.Linear(l2, 1)
self.c3 = c3
if len(self.c3[0]) != 0:
self.sl3 = nn.Linear(l3, l4)
self.fc3 = nn.Linear(l3, 1)
self.fc4 = nn.Linear(l4, 1)
self.read_out = nn.Linear(4, 1)
else:
self.sl3 = None
self.fc3 = nn.Linear(l3, 1)
self.fc4 = None
self.read_out = nn.Linear(3, 1)
def forward(self, x):
x_f1 = F.relu(self.fc1(x))
x_s1 = F.relu(self.sl1(x)) # shape l2
x_f2 = F.relu(self.fc2(x_s1))
x_s2 = F.relu(self.sl2(x_s1))
if len(self.c3[0]) != 0:
x_f3 = F.relu(self.fc3(x_s2))
x_s3 = F.relu(self.sl3(x_s2))
x_f4 = F.relu(self.fc4(x_s3))
x = self.read_out(torch.cat([x_f1, x_f2, x_f3, x_f4], 1))
else:
x_f3 = F.relu(self.fc3(x_s2))
x = self.read_out(torch.cat([x_f1, x_f2, x_f3], 1))
return x
class HNN_skip(nn.Module):
def __init__(self, l1, l2, l3, l4, c1, c2, c3, d2, d3):
super(HNN_skip, self).__init__()
self.d2 = d2
self.d3 = d3
self.c3 = c3
readout_in = 1
self.sl1 = sl.SparseLinear(l1, l2, connectivity=torch.tensor([c1[1], c1[0]],dtype=torch.int64))
self.sl2 = sl.SparseLinear(l2, l3, connectivity=torch.tensor([c2[1], c2[0]],dtype=torch.int64))
if len(d2) != 0:
self.skip2 = sl.SparseLinear(l2, 1, connectivity=torch.tensor([[0] * len(d2), d2],dtype=torch.int64))
readout_in += 1
if len(self.c3[0]) != 0:
self.sl3 = sl.SparseLinear(l3, l4, connectivity=torch.tensor([c3[1], c3[0]],dtype=torch.int64))
if len(d3) != 0:
self.skip3 = sl.SparseLinear(l3, 1, connectivity=torch.tensor([[0] * len(d3), d3],dtype=torch.int64))
readout_in += 1
self.fc4 = nn.Linear(l4, 1)
self.read_out = nn.Linear(readout_in, 1)
else:
self.sl3=None
self.fc4=None
self.fc3 = nn.Linear(l3, 1)
self.read_out = nn.Linear(readout_in, 1)
def forward(self, x):
x_sk_ls = torch.tensor([])
x_s1 = F.relu(self.sl1(x)) # shape l2
x_s2 = F.relu(self.sl2(x_s1))
if len(self.d2) != 0:
x_sk2 = F.relu(self.skip2(x_s1))
x_sk_ls = torch.cat([x_sk_ls, x_sk2],1)
if len(self.c3[0]) != 0:
x_s3 = F.relu(self.sl3(x_s2))
if len(self.d3) != 0:
x_sk3 = F.relu(self.skip3(x_s2))
x_sk_ls = torch.cat([x_sk_ls, x_sk3],1)
x_f4 = F.relu(self.fc4(x_s3))
x = self.read_out(torch.cat([x_sk_ls, x_f4], 1))
else:
x_f3=F.relu(self.fc3(x_s2))
x = self.read_out(torch.cat([x_sk_ls, x_f3], 1))
# x = self.read_out(x_f4)
return x
def train_model(train_X, test_X, train_y, test_y, dataset_name, model_name, model, params):
path = f'Results/PMLB/prediction/regression_raw/{dataset_name}'
file_path = path + '/' + f'{model_name}.pkl'
if not os.path.isdir(path):
os.mkdir(path)
else:
pass
if not os.path.exists(file_path):
CV = GridSearchCV(model, params,n_jobs=1)
CV.fit(train_X, train_y.reshape(-1,1))
pred_y = CV.best_estimator_.predict(test_X)
with open(file_path, 'wb') as f:
pickle.dump([test_y, pred_y], f)
def model_MLP(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3
,disconnection_2,disconnection_3):
hyper_params = {'max_epochs': [50,100,300,500],
'lr':[0.001,0.005,0.01,0.05,0.1],
'optimizer__weight_decay':[0,0.05,0.1,0.2]
}
mlp=MLP(len(clique_1), len(clique_2), len(clique_3), len(clique_4),connection_1,connection_2,connection_3)
est = NeuralNetRegressor(mlp
, optimizer=optim.Adam
, optimizer__weight_decay=0.05
, criterion=nn.MSELoss
, max_epochs=100
, lr=0.001
, verbose=0)
return est, hyper_params
def model_MLPres(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3
,disconnection_2,disconnection_3):
hyper_params = {'max_epochs': [50,100,300,500],
'lr':[0.001,0.005,0.01,0.05,0.1],
'optimizer__weight_decay':[0,0.05,0.1,0.2]
}
mlpres=MLPres(len(clique_1), len(clique_2), len(clique_3), len(clique_4),connection_1,connection_2,connection_3)
est = NeuralNetRegressor(mlpres
, optimizer=optim.Adam
, optimizer__weight_decay=0.05
, criterion=nn.MSELoss
, max_epochs=100
, lr=0.001
, verbose=0)
return est, hyper_params
def model_HNN(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3,
disconnection_2,disconnection_3):
hyper_params = {'max_epochs': [50,100,300,500],
'lr':[0.001,0.005,0.01,0.05,0.1],
'optimizer__weight_decay':[0,0.05,0.1,0.2]
}
hnn=HNN(len(clique_1), len(clique_2), len(clique_3), len(clique_4),connection_1,connection_2,connection_3)
est = NeuralNetRegressor(hnn
, optimizer=optim.Adam
, optimizer__weight_decay=0.05
, criterion=nn.MSELoss
, max_epochs=100
, lr=0.001
, verbose=0)
return est, hyper_params
def model_HNN_adv(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3,
disconnection_2,disconnection_3):
hyper_params = {'max_epochs': [50,100,300,500],
'lr':[0.001,0.005,0.01,0.05,0.1],
'optimizer__weight_decay':[0,0.05,0.1,0.2]
}
hnn_adv=HNN_adv(len(clique_1), len(clique_2), len(clique_3), len(clique_4),connection_1,connection_2,connection_3)
est = NeuralNetRegressor(hnn_adv
, optimizer=optim.Adam
, optimizer__weight_decay=0.05
, criterion=nn.MSELoss
, max_epochs=100
, lr=0.001
, verbose=0)
return est, hyper_params
def model_HNN_skip(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3,
disconnection_2,disconnection_3):
hyper_params = {'max_epochs': [50,100,300,500],
'lr':[0.001,0.005,0.01,0.05,0.1],
'optimizer__weight_decay':[0,0.05,0.1,0.2]
}
hnn_skip=HNN_skip(len(clique_1), len(clique_2), len(clique_3), len(clique_4),connection_1,connection_2,connection_3,
disconnection_2,disconnection_3)
est = NeuralNetRegressor(hnn_skip
, optimizer=optim.Adam
, optimizer__weight_decay=0.05
, criterion=nn.MSELoss
, max_epochs=100
, lr=0.001
, verbose=0)
return est, hyper_params
def MFCF_J(X,max_clique_size=4):
'''
sparse J
'''
C = np.cov(X, rowvar=False)
ctl = MFCF.mfcf_control()
ctl['threshold'] = 0.1
ctl['drop_sep'] = 0
ctl['min_clique_size'] =2
ctl['max_clique_size'] = 4
gain_function = gf.sumsquares_gen
cliques, separators, peo, gt = MFCF.mfcf(C, ctl, gain_function)
J = MFCF.logo(C, cliques, separators)
return J
def separating_cliques(G):
clique_1 = []
clique_2 = []
clique_3 = []
clique_4 = []
for clique in nx.enumerate_all_cliques(G):
clique = set(clique)
if len(clique) == 1:
clique_1.append(clique)
elif len(clique) == 2:
clique_2.append(clique)
elif len(clique) == 3:
clique_3.append(clique)
elif len(clique) == 4:
clique_4.append(clique)
return clique_1,clique_2,clique_3,clique_4
def get_connection(clique_last, clique_next):
connection_list = [[], []]
component_mapping = {i: x for i, x in enumerate(clique_last)}
for i, clique in enumerate(clique_next):
component = [set(x) for x in combinations(clique, len(clique) - 1)]
index_next = i
index_last = [list(component_mapping.keys())[list(component_mapping.values()).index(x)] for x in component]
for j in index_last:
connection_list[0].append(j)
connection_list[1].append(i)
return connection_list
def get_disconnection(c_last, c_next):
all_simplex = set(c_last[1])
linked_simplex = set(c_next[0])
disconnected_simplex = list(all_simplex - linked_simplex)
return disconnected_simplex
if __name__ == "__main__":
#1191_BNG_pbc
model_dict={'MLPres':model_MLPres}
n=len(model_dict)*len(regression_dataset_names)
pbar = tqdm(total=n, desc='Back Test Progress', )
for regression_dataset in regression_dataset_names:
#print(regression_dataset)
X, y = fetch_data(regression_dataset, return_X_y=True)
train_X, test_X, train_y, test_y = train_test_split(X, y)
try:
J = MFCF_J(train_X)
err=False
except:
err=True
if not err:
G = nx.from_numpy_array(J)
clique_1, clique_2, clique_3, clique_4 = separating_cliques(G)
connection_1 = get_connection(clique_1, clique_2)
connection_2 = get_connection(clique_2, clique_3)
connection_3 = get_connection(clique_3, clique_4)
disconnection_2 = get_disconnection(connection_1, connection_2)
disconnection_3 = get_disconnection(connection_2, connection_3)
if len(connection_2[0])!=0 and len(connection_3[0])!=0:
for model_name,func in model_dict.items():
model,params=func(clique_1, clique_2, clique_3, clique_4,connection_1,connection_2,connection_3,
disconnection_2,disconnection_3)
try:
train_model(torch.from_numpy(train_X).float(), torch.from_numpy(test_X).float(),
torch.from_numpy(train_y).float(), torch.from_numpy(test_y).float(),
regression_dataset,model_name, model, params)
except Exception as err:
print(regression_dataset)
print(err)
pbar.update(1)