将后面代码绘制的图保存的代码from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 预测结果和真实结果 y_true = [0, 1, 2, 0, 1, 2, 0, 1, 2] y_pred = [0, 1, 1, 0, 2, 1, 0, 2, 2] # 计算混淆矩阵 cm = confusion_matrix(y_true, y_pred) # 可视化混淆矩阵 sns.set() sns.heatmap(cm, annot=True, cmap='Blues') plt.xlabel('Predicted label') plt.ylabel('True label') plt.show()

python机器学习实例代码 - 汽车特征评估质量和估算收入阶层.rar

from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, predictions) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') plt.xlabel('预测类别') plt.ylabel('实际类别') plt.title('混淆...

smote的matlab代码-Anti-Money-Laundering-using-Keras:利用深度学习库Keras将交易分类为欺诈(1

smote的matlab代码反洗钱使用-Keras 该项目的重点是使用深度学习库 Keras 并将每笔交易分类为欺诈 (1) 和非欺诈 (0)。一步步 '''importing the required libraries''' import pandas as pd import seaborn as sns ...

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1）读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from ...

import numpy as np import paddle as paddle import paddle.dataset.mnist as mnist import paddle.fluid as fluid from PIL import Image import matplotlib.pyplot as plt from pathlib import Path import os from paddle.vision.datasets import DatasetFolder,ImageFolder from paddle.vision.transforms import Compose,Resize,Transpose import paddle.nn.functional as F from sklearn.metrics import confusion_matrix,f1_score,classification_report import seaborn as sns

这段代码看起来是在导入需要用到的Python库，包括numpy、paddle、PIL、matplotlib、pathlib、os、sklearn等。其中paddle库是深度学习框架PaddlePaddle的Python API，paddle.fluid是PaddlePaddle的核心模块，paddle....

from sklearn.datasets import load_iris from sklearn. model_selection import train_test_split from sklearn.metrics import classification_report from sklearn. neighbors import KNeighborsClassifier from sklearn. metrics import roc_curve, auc import matplotlib.pyplot as plt from sklearn. metrics import confusion_matrix import seaborn as sns import scikitplot as skplt #加载数据集 iris = load_iris() data = iris['data'] label = iris['target'] #数据集的划分 x_train,x_test,y_train,y_test = train_test_split(data,label,test_size=0.3) print(x_train) #模型构建 model = KNeighborsClassifier(n_neighbors=5) model.fit(x_train,y_train) #模型评估 #(1)精确率，召回率，F1分数，准确率(宏平均和微平均) predict = model. predict(x_test) result = classification_report(y_test,predict) print(result) # (2) 混淆矩阵 confusion_matrix = confusion_matrix(y_test, predict) print('混淆矩阵:', confusion_matrix) sns.set(font_scale=1) sns.heatmap(confusion_matrix, annot=True, annot_kws={"size", 16}, cmap=plt.cm.Blues) plt.title('Confusion Matrix') plt.ylabel('True label' ) plt.xlabel('Predicted label') plt.savefig('Confusion matrix. pdf') plt.show() #(3)ROC曲线 Y_pred_prob = model. predict_proba(x_test) plt.figure(figsize= (7,7)) ax= plt. subplot() skplt.metrics.plot_roc_curve(y_test,Y_pred_prob,ax= ax) ax.set_xlabel('False Positive Rate', fontsize = 20) ax.set_ylabel('True Positive Rate ',fontsize = 20) ax.set_title('ROC Areas ',fontsize = 20) plt.xlim((0, 1)) plt.ylim((0, 1)) plt.xticks(fontsize = 18) plt.yticks(fontsize = 18) plt.legend(fontsize =18) plt.savefig(' ROC.pdf') plt.show( ) #（4）P_R曲线 from sklearn.metrics import precision_recall_curve precision, recall, _ =precision_recall_curve(y_test) plt.fill_between(recall, precision,color='b') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.0]) plt.xlim([0.0, 1.0]) plt.plot(recall, precision) plt.title("Precision-Recall") plt.show()

这是一段 Python 代码，主要包括从Scikit-learn库中导入了一些数据集、分类器、评估指标和可视化工具的模块。...matplotlib.pyplot和seaborn是可视化工具，confusion_matrix和scikitplot是评估和可视化混淆矩阵的工具。

import numpy as np import paddle as paddle import paddle.dataset.mnist as mnist import paddle.fluid as fluid from PIL import Image import matplotlib.pyplot as plt from pathlib import Path from paddle.vision.datasets import DatasetFolder,ImageFolder from paddle.vision.transforms import Compose,Resize,Transpose import paddle.nn.functional as F from sklearn.metrics import confusion_matrix,f1_score,classification_report import seaborn as sns import json import gzip import cv2 as cv from tqdm import tqdm import paddle.vision.transforms as T from paddle.static import InputSpec from paddle.metric import Accuracy

这段代码是导入所需的Python库和模块。其中： - numpy：Python的一个科学计算库，用于支持大型多维数组和矩阵运算。 - paddle：百度开源的深度学习框架，类似于TensorFlow和PyTorch。 - paddle.dataset.mnist：...

import pandas as pd from sklearn.cluster import DBSCAN from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx') # 提取特征列 feature_columns = ["Bridge length (m)","Pier type","Foundation type","Hole","Span (m)", "Bearing type","Plane linear"] X = data[feature_columns] # 使用DBSCAN进行聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) dbscan.fit(X) # 获取聚类结果 labels = dbscan.labels_ # 获取实际类别（如果有的话） actual_labels = data['Vulnerability grade'] # 计算混淆矩阵 confusion_mat = confusion_matrix(actual_labels, labels) # 绘制热力图 sns.heatmap(confusion_mat, annot=True, cmap="YlGnBu") plt.xlabel('Predicted Labels') plt.ylabel('Actual Labels') plt.title('Confusion Matrix') plt.show()

这段代码使用了 pandas、sklearn 和 seaborn 库来进行数据聚类和绘制混淆矩阵的热力图。在代码中，你需要确保以下几点： 1. 你已经安装了 pandas、sklearn 和 seaborn 库，并正确导入了所需的模块。 2. Excel 数据...

# 导入需要使用的库 import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取Excel表格 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 定义自变量和因变量 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 定义随机森林分类器 classifier = RandomForestClassifier(n_estimators=100, random_state=0) # 在训练集上训练分类器 classifier.fit(X_train, y_train) # 在测试集上进行预测 y_pred = classifier.predict(X_test) # 计算预测精度并输出 accuracy = classifier.score(X_test, y_test) print("Accuracy:", accuracy) # 计算混淆矩阵并绘制热力图 cm = confusion_matrix(y_test, y_pred) sns.heatmap(cm, annot=True, cmap="Blues") plt.show()热力图改成小数点

要将热力图中的数字改为小数点，可以使用seaborn库中的annot_kws参数，指定热力图的字体大小、颜色等属性。具体实现代码如下： python # 计算混淆矩阵并绘制热力图 cm = confusion_matrix(y_test, y_pred) sns....

import seaborn as sns import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix # Example data y_true = [0, 1, 0, 1, 0, 1, 0, 0, 1, 1] y_pred = [0, 0, 0, 1, 0, 1, 1, 0, 1, 0] # Calculate confusion matrix cm = confusion_matrix(y_true, y_pred) # Plot heatmap sns.heatmap(cm, annot=True, cmap='Blues') plt.xlabel('Predicted labels') plt.ylabel('True labels') plt.show()上述代码中的y_true,y_pred，如何传入自己训练的真实值预测值标签

如果你有自己的训练数据和模型，可以将其预测结果与真实标签分别存储为两个数组，然后将这两个数组传入 confusion_matrix 函数中即可得到混淆矩阵。下面是一个示例代码： python import seaborn as sns import...

import pandas as pd from sklearn.cluster import DBSCAN from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx') # 提取特征列 feature_columns = ["Bridge length (m)","Pier type","Foundation type","Hole","Span (m)", "Bearing type","Plane linear"] X = data[feature_columns] # 使用DBSCAN进行聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) dbscan.fit(X) # 获取聚类结果 labels = dbscan.labels_

from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx')...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.tree import DecisionTreeClassifier # 导入需要的模块 import warnings warnings.filterwarnings("ignore") from sklearn.metrics import confusion_matrix from itertools import cycle # from scipy import interp from sklearn.metrics import roc_curve, auc data = pd.read_csv('newdate.csv') print(data.head()) print(data.info()) print(data[data['Label'].isnull()]) data = data.dropna() print(data.info()) data['Label'] = data['Label'].map(int) print(data.info()) # 数据归一化 def normalization(data): _range = np.max(data) - np.min(data) return (data - np.min(data)) / _range data['铁水温度'] = normalization(data['铁水温度']) data['透气性指数'] = normalization(data['透气性指数']) print(data) # 相关性分析 plt.figure(figsize=(10, 10)) sns.heatmap(data=data.corr(), annot=True, cmap='Accent', vmax=1, vmin=-1) plt.show() df = pd.DataFrame(data.groupby(['Label'])['铁水温度'].count()) df.columns = ['num'] df.reset_index(inplace=True) print(df)解释每一行代码

from sklearn.metrics import confusion_matrix from itertools import cycle from sklearn.metrics import roc_curve, auc data = pd.read_csv('newdate.csv') # 读取数据文件 print(data.head()) # 打印数据的前...

from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier data = pd.read_csv("C:\\Users\\sa'y\\Desktop\\framinghamData(1).csv") X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heartRate", "glucose"]] y = data[['TenYearCHD']] new_data = pd.concat([X, y], axis=1) print(new_data.head()) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) print(X_scaled) corr = new_data.corr(method='pearson') sns.heatmap(corr, annot=True, cmap='coolwarm') plt.show() pca = PCA(n_components=7) newX = pca.fit_transform(X) x_data = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7'] y_data = np.around(pca.explained_variance_ratio_, 2) plt.bar(x=x_data, height=y_data,color='steelblue', alpha=0.8) plt.show() 在上述代码基础上，请给出下一步的代码，要求是：计算所有主成分之间的皮尔逊相关系数，并用热图Heatmap的形式展示出来

from matplotlib import pyplot as plt import seaborn as sns # 计算所有主成分之间的皮尔逊相关系数 corr_pca = np.corrcoef(newX.T) # Heatmap展示相关系数 sns.set(font_scale=1) sns.heatmap(corr_pca, cbar...

import pandas as pdfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, confusion_matriximport seaborn as snsimport matplotlib.pyplot as plt# 读取数据data = pd.read_excel('data.xlsx')# 将数据分为训练集和验证集train_data = data.sample(frac=0.8, random_state=1)test_data = data.drop(train_data.index)# 定义特征变量和目标变量features = ['feature1', 'feature2', 'feature3']target = 'target'# 训练随机森林模型rf = RandomForestClassifier(n_estimators=100, random_state=1)rf.fit(train_data[features], train_data[target])# 在验证集上进行预测并计算精度和混淆矩阵pred = rf.predict(test_data[features])accuracy = accuracy_score(test_data[target], pred)confusion_mat = confusion_matrix(test_data[target], pred)print('Accuracy:', accuracy)print('Confusion matrix:')print(confusion_mat)# 输出混淆矩阵图片sns.heatmap(confusion_mat, annot=True)plt.show()# 读取新数据文件并预测结果new_data = pd.read_excel('new_data.xlsx')new_pred = rf.predict(new_data[features])new_data['predicted_target'] = new_prednew_data.to_excel('predicted_results.xlsx', index=False)改进代码并输出计算分类模型的准确率、召回率和F1值等指标

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('data.xlsx') # 将数据分为...

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix, classification_report, accuracy_score # 1. 数据准备 train_data = pd.read_csv('train.csv') test_data = pd.read_csv('test_noLabel.csv') # 填充缺失值 train_data.fillna(train_data.mean(), inplace=True) test_data.fillna(test_data.mean(), inplace=True) # 2. 特征工程 X_train = train_data.drop(['Label', 'ID'], axis=1) y_train = train_data['Label'] X_test = test_data.drop('ID', axis=1) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 3. 模型建立 model = RandomForestClassifier(n_estimators=100, random_state=42) # 4. 模型训练 model.fit(X_train, y_train) # 5. 进行预测 y_pred = model.predict(X_test) # 6. 保存预测结果 df_result = pd.DataFrame({'ID': test_data['ID'], 'Label': y_pred}) df_result.to_csv('forecast_result.csv', index=False) # 7. 模型评估 y_train_pred = model.predict(X_train) print('训练集准确率：', accuracy_score(y_train, y_train_pred)) print('测试集准确率：', accuracy_score(y_test, y_pred)) print(classification_report(y_test, y_pred)) # 8. 绘制柱形图 feature_importances = pd.Series(model.feature_importances_, index=X_train.columns) feature_importances = feature_importances.sort_values(ascending=False) plt.figure(figsize=(10, 6)) sns.barplot(x=feature_importances, y=feature_importances.index) plt.xlabel('Feature Importance Score') plt.ylabel('Features') plt.title('Visualizing Important Features') plt.show() # 9. 对比类分析 train_data['Label'].value_counts().plot(kind='bar', color=['blue', 'red']) plt.title('Class Distribution') plt.xlabel('Class') plt.ylabel('Frequency') plt.show()

这段代码是一个机器学习模型的完整流程，包括数据准备、特征工程、模型建立、模型训练、预测结果保存、模型评估和可视化分析等步骤。其中包括了绘制柱形图和对比类分析的代码。绘制柱形图的代码如下： ...

# 导入必要的库 import pandas as pd from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score, confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读入excel表格 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 定义特征变量和因变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 将特征变量和因变量分离出来，并划分训练集和验证集 x_train, x_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.2, random_state=42) # 训练支持向量机模型 svm_model = SVC(kernel='linear', C=1, gamma=1) svm_model.fit(x_train, y_train) # 计算模型精度 y_pred = svm_model.predict(x_test) accuracy = accuracy_score(y_test, y_pred) print('模型精度：', accuracy) # 生成混淆矩阵图片并保存 cm = confusion_matrix(y_test, y_pred) plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, cmap='Blues') plt.xlabel('Predicted Label') plt.xlabel('Predicted') plt.ylabel('Actual') plt.savefig('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096/支持向量机confusion_matrix.png') # 预测新的数据 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_x = new_data[features] new_y = svm_model.predict(new_x) new_data[target] = new_y # 输出新的excel表格 new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096-支持向量机结果.xlsx', index=False)运行时间很长

这段代码看起来是一个使用支持向量机...最后生成混淆矩阵图片并保存，并用训练好的模型预测新的数据，并将预测结果输出到新的excel表格中。运行时间长的原因可能是数据量比较大，或者模型的训练和预测过程比较耗时。

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)修改代码使得显示决策树模型以及多分类的roc曲线和auc值

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/...

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report, roc_curve, auc import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)输出混淆矩阵图片以及各分类精度

好的，你想让我输出混淆矩阵图片以及各分类精度，这段代码中已经计算出了混淆矩阵和分类报告，可以直接使用这些结果来输出混淆矩阵图片和各分类精度。以下是代码实现： # 输出混淆矩阵图片 sns.heatmap...

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