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3_test_main_task_model.py
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3_test_main_task_model.py
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from statistics import mode
from numpy import argmax
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, plot_roc_curve, roc_curve, auc
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torch.optim import SGD
import data
from utils import fix_randomness
from models import Covid19_MainTaskModel, Adults_MainTaskModel, Fivethirtyeight_MainTaskModel, GSS_MainTaskModel
import yaml
import sys
fix_randomness()
# read config file
with open("./config/config.yaml", "r") as ymlfile:
try:
cfg = yaml.safe_load(ymlfile)
except yaml.YAMLError as exc:
print(exc)
# Step 1: load data from .csv file
problem = cfg["problem"]
datapath = cfg["dataset"][problem]["path_to_data"]
if problem == 'covid19':
X, y = data.covid19_load_data(datapath)
elif problem == 'adults':
X, y, scaler = data.adults_load_data(datapath)
elif problem == 'fivethirtyeight':
X, y, scaler = data.fivethirtyeight_load_data(datapath)
elif problem == 'gss':
X, y, scaler = data.gss_load_data(datapath)
else:
print("The problem was not supported!!!")
sys.exit()
# Step 2: shuffle data
sklearn_random_state = cfg["random"]["random_state_sklearn"]
X, y = shuffle(X, y, random_state=sklearn_random_state)
# Step 3: Take a part of data to train TARGET model
propotion_to_train_target = cfg["target_model"][problem]["propotion_to_train_target_model"]
X = X[:int(X.shape[0]*propotion_to_train_target),:]
y = y[:int(y.shape[0]*propotion_to_train_target)]
# Step 4: Split data into training set and test set (to train TARGET model)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=cfg["target_model"][problem]["test_size"], random_state=sklearn_random_state)
X_test = torch.from_numpy(X_test).type(torch.FloatTensor)
y_test = torch.from_numpy(y_test)
y_test = F.one_hot(y_test).type(torch.FloatTensor)
# parameters to train target model
BATCH_SIZE = cfg["target_model"][problem]["batch_size"]
# use DataSet and DataLoader class in Pytorch
# NOTE: dont shuffle the data, we already shuffled them before
data_test = data.TabularData(X_test, y_test)
data_test_loader = DataLoader(data_test, batch_size=BATCH_SIZE, shuffle=False)
# Step 5: Define pre-trained model to load
if problem == 'covid19':
model = Covid19_MainTaskModel()
elif problem == 'adults':
model = Adults_MainTaskModel()
elif problem == 'fivethirtyeight':
model = Fivethirtyeight_MainTaskModel()
elif problem == 'gss':
model = GSS_MainTaskModel()
else:
print("Can not define target model!!!")
sys.exit()
if cfg['target_model'][problem]['DP']['isDP'] == True:
print("Test model with DP...")
model = torch.load(cfg["target_model"][problem]["path_to_target_model_with_DP"])
else:
print("Test model without DP...")
model = torch.load(cfg["target_model"][problem]["path_to_target_model"])
BATCH_SIZE = 1
lossFunc = nn.CrossEntropyLoss()
import time
# test trained model
test_preds = []
testLoss = 0
samples = 0
print("===========Test model===========")
with torch.no_grad():
start_time = time.time()
for (batchX, batchY) in data_test_loader:
outputs = model(batchX)
loss = lossFunc(outputs, batchY)
testLoss += loss.item() * batchY.size(0)
for output in outputs:
test_preds.append(argmax(output))
samples += batchY.size(0)
end_time = time.time()
infer_time = end_time - start_time
print("+ Inference time = ", infer_time)
y_true = argmax(y_test, axis=1)
testAcc = accuracy_score(y_true, test_preds)
print("+ Accuracy = ", testAcc)
try:
testF1 = f1_score(y_true, test_preds)
testPrecision = precision_score(y_true, test_preds)
testRecall = recall_score(y_true, test_preds)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
except:
print("Multi-classification! Dont compute F1, Precision, and Recall.")
pass
cm = confusion_matrix(y_true, test_preds)
print("+ Confusion matrix:\n", cm)
np.savetxt('./models/' + problem + '_confusion_matrix.txt', cm, fmt='%d') # save confusion matrix for Fredrickson's attack
print("Confusion matrix for {} is saved".format(problem))
import matplotlib.pyplot as plt
with torch.no_grad():
test_preds = model(X_test)
# print(test_preds)
fpr, tpr, thresholds = roc_curve(y_test[:, 0], test_preds[:, 0])
roc_auc = auc(fpr, tpr)
print("+ AUC = ", roc_auc)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.4f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()