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"""
Date: 2022-04-13
Author: [email protected]
Desc: target model: Logistic Regression
"""
from sklearn.linear_model import LogisticRegression
from TargetModel.FSNet.dataset import C2Data
import torch
from sklearn.metrics import confusion_matrix
import joblib
from torch.utils.data import DataLoader
import numpy as np
import warnings
warnings.filterwarnings("ignore")
class TargetLR():
"""
"""
def __init__(self, param):
# 正则化
self.C = param['C']
self.clf = LogisticRegression(C=self.C)
def train(self, dataloader):
X = []
y = []
for batch_x, batch_y in dataloader:
X += batch_x.data.numpy().tolist()
y += batch_y.data.numpy().tolist()
X = np.array(X)
y = np.array(y)
# print("X.shape:{}".format(X.size))
# print("y.shape:{}".format(y.size))
self.clf.fit(X, y)
print("training score:{}".format(self.clf.score(X, y)))
def eval(self, dataloader):
X = []
y = []
for batch_x, batch_y in dataloader:
X += batch_x.data.numpy().tolist()
y += batch_y.data.numpy().tolist()
X = np.array(X)
y = np.array(y)
# print("X.shape:{}".format(X.size))
# print("y.shape:{}".format(y.size))
y_pred = self.clf.predict(X)
return y_pred, y
def save(self, filename):
joblib.dump(self.clf, filename)
def load(self, filename):
self.clf = joblib.load(filename)
if __name__ == '__main__':
param = {
'C': 0.3
}
arch = "lr"
sample_szie = 22000
botname = "Tofsee"
normal = "CTUNone"
batch_size = 128
total_size = sample_szie * 2
test_size = int(total_size * 0.2)
train_size = int((total_size - test_size) * 0.8)
valid_size = total_size - test_size - train_size
print("train data: {}".format(train_size))
print("valid data: {}".format(valid_size))
print("test data: {}".format(test_size))
c2data = C2Data(botname, number=sample_szie, sequenceLen=30)
train_data, test_data = torch.utils.data.random_split(c2data, [train_size + valid_size, test_size])
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=False)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=False)
lr = TargetLR(param)
lr.train(train_loader)
y_true, y_pred = lr.eval(test_loader)
print("confusion_metrix: \n{}".format(confusion_matrix(y_true, y_pred)))
filename = "../modelfile/target_mta_{}_{}_{}.pkt".format(arch, botname, normal)
lr.save(filename)
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