Python数据相关系数矩阵和热力图轻松实现教程

import seaborn as sns
import matplotlib.pyplot as plt
def test(df):
 dfData = df.corr()
 plt.subplots(figsize=(9, 9)) # 设置画面大小
 sns.heatmap(dfData, annot=True, vmax=1, square=True, cmap="Blues")
 plt.savefig('./BluesStateRelation.png')
 plt.show()

skearn.metrics.confusion_matrix(
 y_true, # array, Gound true (correct) target values
 y_pred, # array, Estimated targets as returned by a classifier
 labels=None, # array, List of labels to index the matrix.
 sample_weight=None # array-like of shape = [n_samples], Optional sample weights
)

__author__ = "lingjun"
# welcome to attention:小白CV
 
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
sns.set()
 
f, (ax1,ax2) = plt.subplots(figsize = (10, 8),nrows=2)
y_true = ["dog", "dog", "dog", "cat", "cat", "cat", "cat"]
y_pred = ["cat", "cat", "dog", "cat", "cat", "cat", "cat"]
C2= confusion_matrix(y_true, y_pred, labels=["dog", "cat"])
print(C2)
print(C2.ravel())
sns.heatmap(C2,annot=True)
 
ax2.set_title('sns_heatmap_confusion_matrix')
ax2.set_xlabel('Pred')
ax2.set_ylabel('True')
f.savefig('sns_heatmap_confusion_matrix.jpg', bbox_inches='tight')

[[1 2]
 [0 4]]
[1 2 0 4]

("Sensitivity: "+str(round(tp/(tp+fn+0.01), 3)))
("Specificity: "+str(round(1-(fp/(fp+tn+0.01)), 3)))
("False positive rate: "+str(round(fp/(fp+tn+0.01), 3)))
("Positive predictive value: "+str(round(tp/(tp+fp+0.01), 3)))
("Negative predictive value: "+str(round(tn/(fn+tn+0.01), 3)))

__author__ = "lingjun"
# E-mail: 1763469890@qq.com
 
from sklearn.metrics import roc_auc_score, confusion_matrix, roc_curve, auc
from matplotlib import pyplot as plt
import numpy as np
import torch
import csv
 
def confusion_matrix_roc(GT, PD, experiment, n_class):
 GT = GT.numpy()
 PD = PD.numpy()
 
 y_gt = np.argmax(GT, 1)
 y_gt = np.reshape(y_gt, [-1])
 y_pd = np.argmax(PD, 1)
 y_pd = np.reshape(y_pd, [-1])
 
 # ---- Confusion Matrix and Other Statistic Information ----
 if n_class > 2:
  c_matrix = confusion_matrix(y_gt, y_pd)
  # print("Confussion Matrix:\n", c_matrix)
  list_cfs_mtrx = c_matrix.tolist()
  # print("List", type(list_cfs_mtrx[0]))
 
  path_confusion = r"./records/" + experiment + "/confusion_matrix.txt"
  # np.savetxt(path_confusion, (c_matrix))
  np.savetxt(path_confusion, np.reshape(list_cfs_mtrx, -1), delimiter=',', fmt='%5s')
 
 if n_class == 2:
  list_cfs_mtrx = []
  tn, fp, fn, tp = confusion_matrix(y_gt, y_pd).ravel()
 
  list_cfs_mtrx.append("TN: " + str(tn))
  list_cfs_mtrx.append("FP: " + str(fp))
  list_cfs_mtrx.append("FN: " + str(fn))
  list_cfs_mtrx.append("TP: " + str(tp))
  list_cfs_mtrx.append(" ")
  list_cfs_mtrx.append("Accuracy: " + str(round((tp + tn) / (tp + fp + fn + tn), 3)))
  list_cfs_mtrx.append("Sensitivity: " + str(round(tp / (tp + fn + 0.01), 3)))
  list_cfs_mtrx.append("Specificity: " + str(round(1 - (fp / (fp + tn + 0.01)), 3)))
  list_cfs_mtrx.append("False positive rate: " + str(round(fp / (fp + tn + 0.01), 3)))
  list_cfs_mtrx.append("Positive predictive value: " + str(round(tp / (tp + fp + 0.01), 3)))
  list_cfs_mtrx.append("Negative predictive value: " + str(round(tn / (fn + tn + 0.01), 3)))
 
  path_confusion = r"./records/" + experiment + "/confusion_matrix.txt"
  np.savetxt(path_confusion, np.reshape(list_cfs_mtrx, -1), delimiter=',', fmt='%5s')
 
 # ---- ROC ----
 plt.figure(1)
 plt.figure(figsize=(6, 6))
 
 fpr, tpr, thresholds = roc_curve(GT[:, 1], PD[:, 1])
 roc_auc = auc(fpr, tpr)
 
 plt.plot(fpr, tpr, lw=1, label="ATB vs NotTB, area=%0.3f)" % (roc_auc))
 # plt.plot(thresholds, tpr, lw=1, label='Thr%d area=%0.2f)' % (1, roc_auc))
 # plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')
 
 plt.xlim([0.00, 1.0])
 plt.ylim([0.00, 1.0])
 plt.xlabel("False Positive Rate")
 plt.ylabel("True Positive Rate")
 plt.title("ROC")
 plt.legend(loc="lower right")
 plt.savefig(r"./records/" + experiment + "/ROC.png")
 print("ok")
 
def inference():
 GT = torch.FloatTensor()
 PD = torch.FloatTensor()
 file = r"Sensitive_rename_inform.csv"
 with open(file, 'r', encoding='UTF-8') as f:
  reader = csv.DictReader(f)
  for row in reader:
   # TODO
   max_patient_score = float(row['ai1'])
   doctor_gt = row['gt2']
 
   print(max_patient_score,doctor_gt)
 
   pd = [[max_patient_score, 1-max_patient_score]]
   output_pd = torch.FloatTensor(pd).to(device)
 
   if doctor_gt == "+":
    target = [[1.0, 0.0]]
   else:
    target = [[0.0, 1.0]]
   target = torch.FloatTensor(target) # 类型转换, 将list转化为tensor, torch.FloatTensor([1,2])
   Target = torch.autograd.Variable(target).long().to(device)
 
   GT = torch.cat((GT, Target.float().cpu()), 0) # 在行上进行堆叠
   PD = torch.cat((PD, output_pd.float().cpu()), 0)
 
 confusion_matrix_roc(GT, PD, "ROC", 2)
 
if __name__ == "__main__":
 inference()