import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix, classification_report from sklearn.metrics import roc_auc_score from sklearn.utils.class_weight import compute_class_weight # 读取数据 data = pd.read_csv('database.csv') # 数据预处理 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values scaler = StandardScaler() X = scaler.fit_transform(X) # 特征选择 pca = PCA(n_components=10) X = pca.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train) # 构建CNN模型 model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(10, 1))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1)) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1)) model.fit(X_train, y_train,class_weight=class_weights,epochs=100, batch_size=64, validation_data=(X_test, y_test)) # 预测结果 y_pred = model.predict(X_test) #检验值 accuracy = accuracy_score(y_test, y_pred) auc = roc_auc_score(y_test, y_pred) print(auc) print("Accuracy:", accuracy) print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred)) print('Classification Report:\n', classification_report(y_test, y_pred))
时间: 2024-02-26 10:52:55 浏览: 28
这是一个使用卷积神经网络(CNN)进行二分类的模型,数据经过了标准化和PCA特征选择的预处理。模型使用了一个卷积层(Conv1D)和一个池化层(MaxPooling1D),然后通过一个全连接层(Dense)输出最终结果。训练过程中使用了类别权重(class_weights)来平衡样本不均衡问题。最终输出了预测值的准确率(accuracy)、ROC曲线下面积(auc)、混淆矩阵(confusion_matrix)和分类报告(classification_report)。
相关问题
import numpy as np import pandas as pd import tensorflow as tf from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, Activation from sklearn.metrics import auc, accuracy_score, f1_score, recall_score # 读入数据 data = pd.read_csv('company_data.csv') X = data.iloc[:, :-1].values y = data.iloc[:, -1].values # 利用LabelEncoder将标签进行编码 encoder = LabelEncoder() y = encoder.fit_transform(y) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 对特征进行PCA降维 pca = PCA(n_components=17) X_train = pca.fit_transform(X_train) X_test = pca.transform(X_test) # 对数据reshape为符合卷积层输入的格式 X_train = X_train.reshape(-1, 17, 1) X_test = X_test.reshape(-1, 17, 1) # 构建卷积神经网络模型 model = Sequential() model.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(17, 1))) model.add(Conv1D(filters=128, kernel_size=4, activation='relu')) model.add(Conv1D(filters=128, kernel_size=5, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(units=64, activation='relu')) model.add(Dense(units=1, activation='sigmoid')) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test), verbose=1) # 在测试集上评估模型 y_pred = model.predict(X_test) y_pred = np.round(y_pred).flatten() # 计算各项指标 auc_score = auc(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred) f1score = f1_score(y_test, y_pred) recall = recall_score(y_test, y_pred) # 打印输出各项指标 print("AUC score:", auc_score) print("Accuracy:", accuracy) print("F1 score:", f1score) print("Recall:", recall) 这个代码有什么错误
1, 2, 3, 4, 5, 8, 7, 6, 9]
[0, 1, 2, 3, 4, 5, 8, 7, 9, 6]
[这段代码没有明显的语法错误,但是有一些潜在的问题:
1. 没有对数据进行标0, 1, 2, 3, 4, 5, 8, 9, 6, 7]
准化处理,可能会影响模型的性能。
2. 没有使用交叉验证来评估模型的性[0, 1, 2, 3, 4, 5, 8, 9, 7, 6能,可能会导致模型在新数据上表现不佳。
3. 模型的层数和神经元数量可能]
[0, 1, 2, 3, 4, 5, 9, 6, 7, 过于简单,需要根据具体问题进行调整。
利用PCA和CNN构建预测上市公司退市风险的模型的Python代码
由于上市公司退市风险预测涉及到多个变量,包括财务指标、市场指标、行业指标等,因此需要先进行数据预处理和特征选择。我们可以利用PCA对这些指标进行降维,然后再利用CNN构建分类模型进行预测。
以下是一个基本的Python代码示例:
```python
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten
# 读取数据
data = pd.read_csv('data.csv')
# 数据预处理
X = data.drop('label', axis=1)
y = data['label']
scaler = StandardScaler()
X = scaler.fit_transform(X)
# 特征选择
pca = PCA(n_components=10)
X = pca.fit_transform(X)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 构建CNN模型
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(10, 1)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
# 预测结果
y_pred = model.predict(X_test)
```
在上述代码中,我们首先读取了数据并进行了数据预处理,然后利用PCA进行了特征选择。接着,我们构建了一个简单的CNN模型,并利用训练集对其进行了训练。最后,我们利用测试集对模型进行了评估,并得到了预测结果。
需要注意的是,由于每个数据集的特征选择和模型构建都有所不同,因此实际应用中需要根据具体情况进行调整和优化。