解释这段代码for cancer_type_py in cancer_types_py: f = open(''.join([dir_py,'\\tcga_data\\',cancer_type_py,'\\TCGA-',cancer_type_py,'-miRNA.csv']), 'r', encoding='utf-8') rdr = list(csv.reader(f)) rdrt=list( zip(*rdr)) with open(''.join([dir_py,'\\tcga_data\\',cancer_type_py,'\\tmp_miRNA.csv']), 'w', newline='') as f: writer = csv.writer(f) writer.writerow(rdrt[0]) for ele in rdrt[1:]: if ele[0][13]=='1': writer.writerow(ele) for ele in rdrt[1:]: if ele[0][13]=='0': writer.writerow(ele) f.close() f = open(''.join([dir_py,'\\tcga_data\\',cancer_type_py,'\\tmp_miRNA.csv']), 'r', encoding='utf-8') rdr = list(csv.reader(f)) rdrt=list( zip(*rdr)) with open(''.join([dir_py,'\\tcga_data\\',cancer_type_py,'\\miRNAMatrix.csv']), 'w', newline='') as f: writer = csv.writer(f) for ele in rdrt: writer.writerow(ele) f.close() time.sleep(1)

解释版.py

xibaobianyuanjiance-xingtaixue-yansefenxi.rar_cancer cell_site:w

P0701：细胞边缘检测 P0702：癌细胞形态学分析 P0703：癌细胞颜色分析

Breast_cancer_detection.rar_breast cancer_breast_cancer_classify

关于威斯康星乳腺癌数据的分类程序，直接可用。需要打标。

cancer = load_breast_cancer() cancer_data = cancer.data cancer_target = cancer.target

这段代码是用来加载乳腺癌数据集并获取数据和标签。具体来说，load_breast_cancer()是sklearn中的一个函数，用于加载乳腺癌数据集。加载后，数据集中的数据和标签分别存储在cancer.data和cancer.target中。 ...

这段程序import os dir_path = "C:/Users/1028/Desktop/r" cancer='HNSC'#设置变量 data_dir = os.path.join(dir_path, "tcga_data", cancer) train_path = os.path.join(data_dir, "ml_input.csv") label_path = os.path.join(data_dir, "tab_label.csv") train = np.genfromtxt(train_path, delimiter=',', skip_header=1) target = pd.read_csv(label_path, index_col=0).values.ravel() indices = train[:, 2:].T train = train[:, 2:].T的这个错误C:\Users\1028\Desktop\python\python.exe C:\Users\1028\Desktop\r_Python\Python\main.py Traceback (most recent call last): File "C:\Users\1028\Desktop\r_Python\Python\main.py", line 105, in <module> train = np.genfromtxt(train_path, delimiter=',', skip_header=1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\1028\Desktop\python\Lib\site-packages\numpy\lib\npyio.py", line 1980, in genfromtxt fid = np.lib._datasource.open(fname, 'rt', encoding=encoding) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\1028\Desktop\python\Lib\site-packages\numpy\lib\_datasource.py", line 193, in open return ds.open(path, mode, encoding=encoding, newline=newline) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\1028\Desktop\python\Lib\site-packages\numpy\lib\_datasource.py", line 533, in open raise FileNotFoundError(f"{path} not found.") FileNotFoundError: C:/Users/1028/Desktop/r\tcga_data\HNSC\ml_input.csv not found. 进程已结束,退出代码1 怎么解决

可以使用以下方法来解决这个问题： 1. 验证文件路径：确认文件 "ml_input.csv" 是否位于 "C:/Users/1028/Desktop/r/tcga_data/HNSC/" 目录下。 2. 检查文件名和文件扩展名的拼写：确保文件名和文件扩展名正确，以及...

解释这段代码for (i in cancer_types){ tnm_separator(dir = str_c(dir,'\\tcga_data\\',i,'\\normalized_rna.csv') #counts ,clinic_dir = str_c(dir,'\\tcga_data\\',i,'\\TCGA-',i,'-clinical.csv') ,setwd = str_c(dir,'\\tcga_data\\',i), op=predict_obj) tnm_separator(dir = str_c(dir,'\\tcga_data\\',i,'\\normalized_mi.csv') #miRNAs ,clinic_dir = str_c(dir,'\\tcga_data\\',i,'\\TCGA-',i,'-clinical.csv') ,setwd = str_c(dir,'\\tcga_data\\',i), op=predict_obj) } for (i in cancer_types){ for (j in classes){ colname_add(str_c(dir,'\\tcga_data\\',i,'\\',j,'_normalized_rna.csv'),'-01') colname_add(str_c(dir,'\\tcga_data\\',i,'\\',j,'_normalized_mi.csv'),'-01') } }

总结起来，这段代码的目标是根据外层循环中的变量 cancer_types 和内层循环中的变量 classes，对指定的文件进行处理和修改。具体的处理方式和修改操作可以通过函数 tnm_separator() 和 colname_add() 的具体...

解释这段代码train=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\ml_input.csv")

例如，如果dir_py为"C:/project"，cancer为"breast_cancer"，那么该行代码将尝试读取位于C:/project/tcga_data/breast_cancer/ml_input.csv路径下的CSV文件，并将其内容存储在名为train的变量中。...

解释这段代码pb <- progress_bar$new(total = length(cancer_types)) for (i in cancer_types){ for (j in classes){ mi<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\",j,"_normalized_mi.csv"),row.names = 1,check.names = F) rna<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\",j,"_normalized_rna.csv"),row.names = 1, check.names = F) id<-rownames(mi) target<-rownames(rna) pair<-tidyr::crossing(id, target) pair<-cal_correlation(pair,mi,rna,pcc_filter) mi<-mi[rownames(mi)%in%unique(pair[,1]),] fwrite(mi,str_c(dir,"\\tcga_data\\",i,"\\",j,"_corr_validated_mi.csv"),sep = ',',row.names = T,quote = F) rna<-rna[rownames(rna)%in%unique(pair[,2]),] fwrite(rna,str_c(dir,"\\tcga_data\\",i,"\\",j,"_corr_validated_rna.csv"),sep = ',',row.names = T,quote = F) write.csv(pair,str_c(dir,"\\tcga_data\\",i,"\\",j,"_miRNA-RNA_pair.csv"),row.names=F,quote=F) } pb$tick() }

这段代码是一个嵌套循环，用于处理多个癌症类型和类别的数据。首先，通过 progress_bar$new(total = length(cancer_types)) 创建一个进度条对象 pb，总共需要处理的次数等于 cancer_types 的长度。然后，...

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from lime.lime_tabular import LimeTabularExplainer import numpy as np import pandas as pd # 准备数据 data = load_breast_cancer() # df=pd.DataFrame(data.data,columns=data.feature_names) # df['target']=data.target # print(df.head()) X = data.data y = data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 训练模型 rfc = RandomForestClassifier(n_estimators=100, random_state=42) rfc.fit(X_train, y_train) # 预测结果 y_pred = rfc.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy:{accuracy:.3f}") # 解释模型结果 def explain_sample(x, model, feature_names): explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names) exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names)) return exp # 随机选择一个测试样本并解释 idx = np.random.randint(len(X_test)) x=X_test[idx] exp=explain_sample(x,rfc,data.feature_names) fig=exp.as_pyplot_figure() print(f"Sample index:{idx}") fig.show()优化一下这段代码，让可视化图片不要一闪而过

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score ...

解释这段代码for (i in cancer_types){ positive<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\positive_miRNA-RNA_pair.csv"), check.names = F)[,1:2] negative<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\negative_miRNA-RNA_pair.csv"), check.names = F)[,1:2] admat<-unique(rbind(positive,negative)) inter<-inner_join(positive, negative) admat<-dplyr::setdiff(admat,inter) write.csv(admat,str_c(dir,"\\tcga_data\\",i,"\\admat.csv"),row.names = F,quote = F) cat(str_c("intersect: ",nrow(inter))) } rm(positive,negative,admat,inter)

这段代码是一个循环，其中 cancer_types 是一个包含癌症类型的向量。代码的主要目的是从每个癌症类型的文件中读取正样本和负样本数据，然后进行一系列操作。首先，代码使用 read.csv 函数分别从指定目录中的正...

from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split = (cancer.data, cancer.target,stratify = cancer.traget,random_state = 66) from sklearn.preprocession import StandardSscaler nn = StandardScaler() X_train = nn.fit_transform(X_test) X_test = nn.transform(X_test) 修改后的代码

这段代码有一些错误，我来帮你修改一下： from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_breast_cancer ...

解释这段代码for (i in cancer_types){ admat<-read.csv(str_c(dir,"\tcga_data\",i,"\admat.csv"),check.names=F) positive_rna<-read.csv(str_c(dir,"\tcga_data\",i,"\positive_normalized_rna.csv"),row.names = 1, check.names = F) negative_rna<-read.csv(str_c(dir,"\tcga_data\",i,"\negative_normalized_rna.csv"),row.names = 1, check.names = F) positive_mi<-read.csv(str_c(dir,"\tcga_data\",i,"\positive_normalized_mi.csv"),row.names = 1, check.names = F) negative_mi<-read.csv(str_c(dir,"\tcga_data\",i,"\negative_normalized_mi.csv"),row.names = 1, check.names = F) normal_rna<-read.csv(str_c(dir,"\tcga_data\",i,"\normal_normalized_rna.csv"),row.names = 1, check.names = F) normal_mi<-read.csv(str_c(dir,"\tcga_data\",i,"\normal_normalized_mi.csv"),row.names = 1, check.names = F) positive_delta<-cal_delta_pcc(admat,normal_rna,normal_mi,positive_rna,positive_mi) negative_delta<-cal_delta_pcc(admat,normal_rna,normal_mi,negative_rna,negative_mi) wilcox<-delta_wilcox_test(positive_delta,negative_delta,wilcox_pval) write.csv(wilcox,str_c(dir,"\tcga_data\",i,"\wilcox.csv"),quote=F,row.names=F) row.names(wilcox)<-str_c(wilcox[,1],"",wilcox[,2]) positive_delta<-na.omit(positive_delta) row.names(positive_delta)<-str_c(positive_delta[,1],"",positive_delta[,2]) negative_delta<-na.omit(negative_delta) row.names(negative_delta)<-str_c(negative_delta[,1],"_",negative_delta[,2]) positive_delta<-positive_delta[row.names(positive_delta)%in%row.names(wilcox),] negative_delta<-negative_delta[row.names(negative_delta)%in%row.names(wilcox),] negative_delta<-negative_delta ml_input<-merge(positive_delta,negative_delta,by="row.names") ml_input<-ml_input write.csv(ml_input,str_c(dir,"\tcga_data\",i,"\ml_input.csv"),quote=F,row.names=F) }

这段代码是一个循环，它遍历一个名为"cancer_types"的列表中的每个元素。在循环的每一次迭代中，它执行以下操作： 1. 读取一个名为"admat.csv"的文件，并将其存储为一个数据框对象(admat)。 2. 读取名为"positive_...

allcanc_time = case_when( cancer_allcanc == 1 ~ as.character(cancer_time_total), cancer_allcanc == 0 ~ as.character(dead)))

这也是一段 R 语言的代码，它的功能是给一个新的变量 allcanc_time 赋值。这个变量的值取决于先前定义的变量 cancer_allcanc 和另一个变量 dead 的取值。如果 cancer_allcanc 的值为 1，则 allcanc_time 被赋值为 ...

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score import time breast_cancer = load_breast_cancer() X = breast_cancer.data y = breast_cancer.target啥意思

这些程序是用来实现一个基于支持向量机（SVM）的分类器，该分类器可以对乳腺癌数据集进行分类。具体来说，该程序使用load_breast_cancer函数加载乳腺...这些程序中的time库是用来计时的，但是在这段代码中并没有使用。

逐行解释下面的代码：from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split, GridSearchCV, KFold from sklearn.ensemble import RandomForestClassifier data = load_breast_cancer() X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42) kf = KFold(n_splits=5, shuffle=True, random_state=42) param_grid = {'n_estimators': range(1, 21, 1), 'max_depth': range(5, 16)} rf = RandomForestClassifier(random_state=42) grid_search = GridSearchCV(rf, param_grid=param_grid, cv=kf, n_jobs=-1) grid_search.fit(X_train, y_train) best_rf = RandomForestClassifier(n_estimators=grid_search.best_params_['n_estimators'], max_depth=grid_search.best_params_['max_depth'], random_state=42) best_rf.fit(X_train, y_train) y_pred = best_rf.predict(X_test)

这段代码的作用是使用随机森林算法对乳腺癌数据集进行分类，并找到最佳的模型参数。首先，代码从sklearn.datasets库中导入了load_breast_cancer函数和从sklearn.model_selection库中导入了train_test_split、...

请解释一下这段代码for (i in cancer_types){ admat<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\admat.csv"),check.names=F) positive_rna<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\positive_normalized_rna.csv"),row.names = 1, check.names = F) negative_rna<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\negative_normalized_rna.csv"),row.names = 1, check.names = F) positive_mi<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\positive_normalized_mi.csv"),row.names = 1, check.names = F) negative_mi<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\negative_normalized_mi.csv"),row.names = 1, check.names = F) normal_rna<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\normal_normalized_rna.csv"),row.names = 1, check.names = F) normal_mi<-read.csv(str_c(dir,"\\tcga_data\\",i,"\\normal_normalized_mi.csv"),row.names = 1, check.names = F) positive_delta<-cal_delta_pcc(admat,normal_rna,normal_mi,positive_rna,positive_mi)#"自定义cal_delta_pcc"函数计算正样本和负样本的delta值，并把数据装入positive_delta中 negative_delta<-cal_delta_pcc(admat,normal_rna,normal_mi,negative_rna,negative_mi) wilcox<-delta_wilcox_test(positive_delta,negative_delta,wilcox_pval)#自定义delta_wilcox_test函数计算结果 write.csv(wilcox,str_c(dir,"\\tcga_data\\",i,"\\wilcox.csv"),quote=F,row.names=F) row.names(wilcox)<-str_c(wilcox[,1],"_",wilcox[,2]) positive_delta<-na.omit(positive_delta) row.names(positive_delta)<-str_c(positive_delta[,1],"_",positive_delta[,2]) negative_delta<-na.omit(negative_delta) row.names(negative_delta)<-str_c(negative_delta[,1],"_",negative_delta[,2]) positive_delta<-positive_delta[row.names(positive_delta)%in%row.names(wilcox),] negative_delta<-negative_delta[row.names(negative_delta)%in%row.names(wilcox),] negative_delta<-negative_delta[,3:ncol(negative_delta)] ml_input<-merge(positive_delta,negative_delta,by="row.names") ml_input<-ml_input[,-1] write.csv(ml_input,str_c(dir,"\\tcga_data\\",i,"\\ml_input.csv"),quote=F,row.names=F) }

这段代码是一个循环，使用了一个变量i来迭代cancer_types列表中的元素。在每次迭代中，代码执行以下操作： 1. 从指定路径读取admat.csv文件，并将其存储在名为admat的变量中。 2. 从指定路径读取positive_...

解释这段代码for (i in cancer_types){ dt<-separate_normal(str_c(dir,"\\tcga_data\\",i,"\\normalized_rna.csv")) fwrite(dt,str_c(dir,"\\tcga_data\\",i,"\\normal_normalized_rna.csv"),sep = ',',row.names = T,quote = F) remove_normal(str_c(dir,"\\tcga_data\\",i,"\\normalized_rna.csv")) dt<-separate_normal(str_c(dir,"\\tcga_data\\",i,"\\normalized_mi.csv")) fwrite(dt,str_c(dir,"\\tcga_data\\",i,"\\normal_normalized_mi.csv"),sep = ',',row.names = T,quote = F) remove_normal(str_c(dir,"\\tcga_data\\",i,"\\normalized_mi.csv")) }

1. for (i in cancer_types) { ... }：使用一个循环，遍历cancer_types列表中的每一个癌症类型。在循环体中，变量i代表当前迭代的癌症类型。 2. dt <- separate_normal(str_c(dir, "\\tcga_data\\", i, "\\...

1r_11 = LogisticRegression (C=C, solver=' liblinear' penalty="11", max_iter=5000). fit (x_ train, y_train) print ("Training accuracy of 11 logree with C=(:. 3r]: (:.2f]". format ( C, 1r_11. score (X_train, y_train))) print ("Test accuracy of 11 logreg with C=(:.3f]: (:. 2f)". format ( C, Ir_11. score (X_test, y_test))) plt. plot (lr_11. coef_. T, marker, label="C=(: 3f]". format (C)) plt. sticks (range (cancer. data. shape[1]), cancer. feature_names, rotation=90) xlims = plt. xlim( pit. hlines (0, xlims[0], xlims[1]) plt. xlim (xlims) plt. label ("Feature") plt. ylabel ("Coefficient magnitude") pit. ylim(-5, 5) plt. legend (10c=3)修改代码

plt.xticks(range(cancer.data.shape[1]), cancer.feature_names, rotation=90) xlims = plt.xlim() plt.hlines(0, xlims[0], xlims[1]) plt.xlim(xlims) plt.xlabel("Feature") plt.ylabel("Coefficient magnitude...

解释这段代码cancer='HNSC' train=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\ml_input.csv") target=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\tab_label.csv",index_col=0).values.ravel() indices = train.columns[2:] train=train.iloc[:,2:].T.values

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解释这段代码cancer='HNSC' train=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\ml_input.csv") target=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\tab_label.csv",index_col=0).values.ravel() indices = train.columns[2:] train=train.iloc[:,2:].T.values

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解释版.py

xibaobianyuanjiance-xingtaixue-yansefenxi.rar_cancer cell_site:w

Breast_cancer_detection.rar_breast cancer_breast_cancer_classify

cancer = load_breast_cancer() cancer_data = cancer.data cancer_target = cancer.target

解释这段代码train=pd.read_csv(dir_py+"\\tcga_data\\"+cancer+"\\ml_input.csv")

allcanc_time = case_when( cancer_allcanc == 1 ~ as.character(cancer_time_total), cancer_allcanc == 0 ~ as.character(dead)))

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score import time breast_cancer = load_breast_cancer() X = breast_cancer.data y = breast_cancer.target啥意思

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