怎么将csv文件中perfor列（这一列的数字只有1，2，3这三个数值）作为因变量，'target_num','support_num', 'p_num','bg_score','update_score','text_acc','update_acc','similarity', 'topic', 'update'作为自变量，使用CNN,LSTM,XGBoost构建三分类预测模型，并画出loss曲线图，同时计算AUC,accuracy,recall和f1值

首先，需要将csv文件读入到Python中，使用pandas库中的read_csv函数进行读取。同时，需要将perfor列作为标签，其余列作为特征。

import pandas as pd
import numpy as np

data = pd.read_csv('data.csv')
labels = np.array(data['perfor'])
features = data.drop('perfor', axis = 1)

接下来，将数据集划分为训练集和测试集。

from sklearn.model_selection import train_test_split

train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.2, random_state = 42)

然后，需要对数据进行标准化处理，使得每一列的特征值都处于相同的尺度上。

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
train_features = scaler.fit_transform(train_features)
test_features = scaler.transform(test_features)

接下来，我们可以构建CNN模型。CNN模型通常用于图像识别，但是也可以应用于序列数据的处理。我们将输入数据reshape为二维数组，然后使用卷积层和池化层进行特征提取，最后通过全连接层进行分类。

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv1D, MaxPooling1D

model_cnn = Sequential()
model_cnn.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(train_features.shape[1],1)))
model_cnn.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model_cnn.add(Dropout(0.5))
model_cnn.add(MaxPooling1D(pool_size=2))
model_cnn.add(Flatten())
model_cnn.add(Dense(100, activation='relu'))
model_cnn.add(Dense(3, activation='softmax'))

model_cnn.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

接下来，我们可以构建LSTM模型。LSTM模型通常用于序列数据的处理，可以很好地处理时间序列数据的依赖关系。我们将输入数据reshape为三维数组，然后通过LSTM层进行序列特征提取，最后通过全连接层进行分类。

from keras.layers import LSTM

model_lstm = Sequential()
model_lstm.add(LSTM(100, input_shape=(train_features.shape[1], 1)))
model_lstm.add(Dropout(0.5))
model_lstm.add(Dense(3, activation='softmax'))

model_lstm.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

接下来，我们可以构建XGBoost模型。XGBoost是一种基于决策树的集成学习方法，可以很好地处理非线性问题。

import xgboost as xgb

dtrain = xgb.DMatrix(train_features, label=train_labels)
dtest = xgb.DMatrix(test_features, label=test_labels)

param = {'max_depth': 6, 'eta': 0.3, 'objective': 'multi:softmax', 'num_class': 3}
num_round = 100

model_xgb = xgb.train(param, dtrain, num_round)

preds = model_xgb.predict(dtest)

接下来，我们可以对模型进行训练和评估，并计算AUC、accuracy、recall和f1值。

from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, f1_score
from keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

history_cnn = model_cnn.fit(train_features.reshape(train_features.shape[0], train_features.shape[1], 1), train_labels, epochs=50, batch_size=64, validation_split=0.2)
history_lstm = model_lstm.fit(train_features.reshape(train_features.shape[0], train_features.shape[1], 1), train_labels, epochs=50, batch_size=64, validation_split=0.2)

y_pred_cnn = model_cnn.predict(test_features.reshape(test_features.shape[0], test_features.shape[1], 1))
y_pred_lstm = model_lstm.predict(test_features.reshape(test_features.shape[0], test_features.shape[1], 1))

auc_cnn = roc_auc_score(test_labels, y_pred_cnn, multi_class='ovr')
auc_lstm = roc_auc_score(test_labels, y_pred_lstm, multi_class='ovr')
auc_xgb = roc_auc_score(test_labels, preds, multi_class='ovr')

acc_cnn = accuracy_score(test_labels, np.argmax(y_pred_cnn, axis=1))
acc_lstm = accuracy_score(test_labels, np.argmax(y_pred_lstm, axis=1))
acc_xgb = accuracy_score(test_labels, preds)

recall_cnn = recall_score(test_labels, np.argmax(y_pred_cnn, axis=1), average='macro')
recall_lstm = recall_score(test_labels, np.argmax(y_pred_lstm, axis=1), average='macro')
recall_xgb = recall_score(test_labels, preds, average='macro')

f1_cnn = f1_score(test_labels, np.argmax(y_pred_cnn, axis=1), average='macro')
f1_lstm = f1_score(test_labels, np.argmax(y_pred_lstm, axis=1), average='macro')
f1_xgb = f1_score(test_labels, preds, average='macro')

最后，我们可以画出CNN和LSTM模型的loss曲线图。

import matplotlib.pyplot as plt

plt.plot(history_cnn.history['loss'], label='train')
plt.plot(history_cnn.history['val_loss'], label='validation')
plt.title('CNN Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

plt.plot(history_lstm.history['loss'], label='train')
plt.plot(history_lstm.history['val_loss'], label='validation')
plt.title('LSTM Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()