![](/img/trans.png)
[英]Error in classification report test set for machine learning with SVM in python
[英]100% error rate on test set with one class svm
我正在嘗試檢測異常圖像。 但是我從模型中得到了奇怪的結果。
我已經用cv2讀入圖像,將它們展平為1d數組,然后將它們轉換為pandas數據框,然后將其輸入到SVM中。
import numpy as np
import cv2
import glob
import pandas as pd
import sys, os
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import *
import seaborn as sns`
加載標簽和文件
labels_wt = np.loadtxt("labels_wt.txt", delimiter="\t", dtype="str")
files_wt = np.loadtxt("files_wt.txt", delimiter="\t", dtype="str")`
加載並展平圖像
wt_images_tmp = [cv2.imread(file) for file in files_wt]
wt_images = [image.flatten() for image in wt_images_tmp]
tmp3 = np.array(wt_images)
mutant_images_tmp = [cv2.imread(file) for file in files_mut]
mutant_images = [image.flatten() for image in mutant_images_tmp]
tmp4 = np.array(mutant_images)
X = pd.DataFrame(tmp3) #load the wild-type images
y = pd.Series(labels_wt)
X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=42)
X_outliers = pd.DataFrame(tmp4)
clf = svm.OneClassSVM(nu=0.15, kernel="rbf", gamma=0.0001)
clf.fit(X_train)
然后,根據oneclass SVM的sklearn教程評估結果。
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outliers = clf.predict(X_outliers)
n_error_train = y_pred_train[y_pred_train == -1].size
n_error_test = y_pred_test[y_pred_test == -1].size
n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
print(n_error_train / len(y_pred_train))
print(float(n_error_test) / float(len(y_pred_test)))
print(n_error_outliers / len(y_pred_outliers))`
我在訓練集上的錯誤率是可變的(10-30%),但是在測試集上,它們從未低於100%。 我做錯了嗎?
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.