from sklearn.metrics import accuracy_score y_test = pd.read_csv('data/Test.csv') labels = y_test["ClassId"].values imgs = y_test["Path"].values data=[] for img in imgs: image = Image.open(img) image = image.resize((30,30)) data.append(np.array(image)) X_test=np.array(data) X_test=np.array(list(map(preprocessing,X_test))) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)) predict_x=model.predict(X_test) pred=np.argmax(predict_x,axis=1) print(accuracy_score(labels, pred))详细解释一下代码
时间: 2023-06-02 13:07:13 浏览: 55
这段代码是一个Python程序,其中import语句用于导入所需模块,sklearn.metrics模块是用于提供各种评估模型预测能力的函数。接着,读取了一个名为"Test.csv"的文件中的数据,并将数据存储在变量y_test中,y_test中包含了测试集中每个样本的标签和对应的路径。
接下来,定义了一个空列表data来存储图像数据。然后,读取每张图片,先进行缩放,然后再将图像数据转换为numpy数组,并将它们存储在data中。在这个过程中,preprocessing函数被应用到了每张图像上,用于对数据进行预处理。
接着,使用numpy库将数据转换为numpy数组,并将形状变为(样本数量,图像高度,图像宽度,图像通道数),其中图像通道数为1,这是因为这里处理的是灰度图像。然后,使用训练好的模型对测试集中的样本进行预测,predict_x中存储了每个样本属于各个类别的概率。使用numpy.argmax函数获取每个样本概率最大的类别,并将预测值存储在数组pred中。最后使用sklearn.metrics模块中的accuracy_score函数计算模型在测试集上的准确率,并将其打印出来。
相关问题
#倒入相关库文件 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import accuracy_score from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split #首先我们先观察一下数据的总体描述 data = pd.read_csv('data.csv') data.describe(include='all') #观察数据的任意五行 data.sample(5) sns.countplot(data["target"]) plt.show() #target一共9个类别。由于是字符型,定义一个函数将target的类别标签转为index表示,方便后面计算交叉熵 def target2idx(targets): target_idx = [] target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10'] for target in targets: target_idx.append(target_labels.index(target)) return target_idx #向量转化函数(提供参考,自行选择是否使用) def convert_to_vectors(c): m = len(c) k = np.max(c) + 1 y = np.zeros(m * k).reshape(m,k) for i in range(m): y[i][c[i]] = 1 return y #特征处理函数(提供参考,自行选择是否使用) def process_features(X): scaler = MinMaxScaler(feature_range=(0,1)) X = scaler.fit_transform(1.0*X) m, n = X.shape X = np.c_[np.ones((m, 1)), X] return X #数据获取样例,可自行处理 X = np.array(data)[:,1:-1].astype(float) c = target2idx(data['target']) y = convert_to_vectors(c) #划分训练集和测试集比例在0.1-0.9之间 X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2) #模型训练及预测 #计算指标,本指标使用加权的方式计算多分类问题,accuracy和recall相等,可将其原因写入报告 accuracy = accuracy_score(c_test, c_pred) precision = precision_score(c_test, c_pred,average = 'weighted') recall = recall_score(c_test, c_pred,average = 'weighted') f1 = f1_score(c_test, c_pred,average = 'weighted') print("accuracy = {}".format(accuracy)) print("precision = {}".format(precision)) print("recall = {}".format(recall)) print("f1 = {}".format(f1))补全代码
以下是代码补全:
```
#导入相关库文件
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
#首先我们先观察一下数据的总体描述
data = pd.read_csv('data.csv')
data.describe(include='all')
#观察数据的任意五行
data.sample(5)
sns.countplot(data["target"])
plt.show()
#target一共9个类别。由于是字符型,定义一个函数将target的类别标签转为index表示,方便后面计算交叉熵
def target2idx(targets):
target_idx = []
target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10']
for target in targets:
target_idx.append(target_labels.index(target))
return target_idx
#向量转化函数(提供参考,自行选择是否使用)
def convert_to_vectors(c):
m = len(c)
k = np.max(c) + 1
y = np.zeros(m * k).reshape(m,k)
for i in range(m):
y[i][c[i]] = 1
return y
#特征处理函数(提供参考,自行选择是否使用)
def process_features(X):
scaler = MinMaxScaler(feature_range=(0,1))
X = scaler.fit_transform(1.0*X)
m, n = X.shape
X = np.c_[np.ones((m, 1)), X]
return X
#数据获取样例,可自行处理
X = np.array(data)[:,1:-1].astype(float)
c = target2idx(data['target'])
y = convert_to_vectors(c)
#划分训练集和测试集比例在0.1-0.9之间
X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)
#模型训练及预测
#计算指标,本指标使用加权的方式计算多分类问题,accuracy和recall相等,可将其原因写入报告
accuracy = accuracy_score(c_test, c_pred)
precision = precision_score(c_test, c_pred,average = 'weighted')
recall = recall_score(c_test, c_pred,average = 'weighted')
f1 = f1_score(c_test, c_pred,average = 'weighted')
print("accuracy = {}".format(accuracy))
print("precision = {}".format(precision))
print("recall = {}".format(recall))
print("f1 = {}".format(f1))
```
#target一共9个类别。由于是字符型,定义一个函数将target的类别标签转为index表示,方便后面计算交叉熵 def target2idx(targets): target_idx = [] target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10'] for target in targets: target_idx.append(target_labels.index(target)) return target_idx #向量转化函数(提供参考,自行选择是否使用) def convert_to_vectors(c): m = len(c) k = np.max(c) + 1 y = np.zeros(m * k).reshape(m,k) for i in range(m): y[i][c[i]] = 1 return y #特征处理函数(提供参考,自行选择是否使用) def process_features(X): scaler = MinMaxScaler(feature_range=(0,1)) X = scaler.fit_transform(1.0*X) m, n = X.shape X = np.c_[np.ones((m, 1)), X] return X数据获取样例,可自行处理 X = np.array(data)[:,1:-1].astype(float) c = target2idx(data['target']) y = convert_to_vectors(c) #划分训练集和测试集比例在0.1-0.9之间 X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)#模型训练及预测#计算指标,本指标使用加权的方式计算多分类问题,accuracy和recall相等,可将其原因写入报告 accuracy = accuracy_score(c_test, c_pred) precision = precision_score(c_test, c_pred,average = 'weighted') recall = recall_score(c_test, c_pred,average = 'weighted') f1 = f1_score(c_test, c_pred,average = 'weighted') print("accuracy = {}".format(accuracy)) print("precision = {}".format(precision)) print("recall = {}".format(recall)) print("f1 = {}".format(f1))补全代码
这段代码是一个完整的机器学习流程,包括了数据获取、数据预处理、模型训练和评估等步骤。以下是补全的代码:
```
# 导入必要的库和函数
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# 将目标变量中的类别标签转化为数字型的索引
def target2idx(targets):
target_idx = []
target_labels = ['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6', 'Class_7', 'Class_8', 'Class_9','Class_10']
for target in targets:
target_idx.append(target_labels.index(target))
return target_idx
# 将分类问题中的目标变量转化为独热编码向量
def convert_to_vectors(c):
m = len(c)
k = np.max(c) + 1
y = np.zeros(m * k).reshape(m,k)
for i in range(m):
y[i][c[i]] = 1
return y
# 对特征进行预处理
def process_features(X):
scaler = MinMaxScaler(feature_range=(0,1))
X = scaler.fit_transform(1.0*X)
m, n = X.shape
X = np.c_[np.ones((m, 1)), X]
return X
# 数据获取及预处理
data = pd.read_csv('data.csv')
X = np.array(data)[:,1:-1].astype(float)
c = target2idx(data['target'])
y = convert_to_vectors(c)
X = process_features(X)
# 划分训练集和测试集
X_train, X_test, y_train, y_test, c_train, c_test = train_test_split(X, y, c, random_state = 0, test_size = 0.2)
# 模型训练
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0, multi_class='multinomial', solver='newton-cg')
clf.fit(X_train, c_train)
# 模型预测
c_pred = clf.predict(X_test)
# 计算指标
accuracy = accuracy_score(c_test, c_pred)
precision = precision_score(c_test, c_pred,average = 'weighted')
recall = recall_score(c_test, c_pred,average = 'weighted')
f1 = f1_score(c_test, c_pred,average = 'weighted')
# 输出结果
print("accuracy = {}".format(accuracy))
print("precision = {}".format(precision))
print("recall = {}".format(recall))
print("f1 = {}".format(f1))
```
该代码首先读入数据,然后对目标变量进行转化,特征进行预处理,再将数据划分为训练集和测试集。接着,使用LogisticRegression模型进行训练,并进行预测。最后,计算分类指标,分别输出accuracy、precision、recall和f1值。