[英]How to calculate logistic regression accuracy
我是python机器学习和编码的完全入门者,我受过从零开始进行逻辑回归编码的任务,以了解幕后发生的事情。 到目前为止,我已经对假设函数,成本函数和梯度下降进行了编码,然后对逻辑回归进行了编码。 但是,在为打印精度进行编码时,我得到的输出很低(0.69),它不会随着迭代次数的增加或学习率的改变而变化。 我的问题是,下面的我的准确性代码是否有问题? 任何指向正确方向的帮助将不胜感激
X = data[['radius_mean', 'texture_mean', 'perimeter_mean',
'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
'fractal_dimension_se', 'radius_worst', 'texture_worst',
'perimeter_worst', 'area_worst', 'smoothness_worst',
'compactness_worst', 'concavity_worst', 'concave points_worst',
'symmetry_worst', 'fractal_dimension_worst']]
X = np.array(X)
X = min_max_scaler.fit_transform(X)
Y = data["diagnosis"].map({'M':1,'B':0})
Y = np.array(Y)
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.25)
X = data["diagnosis"].map(lambda x: float(x))
def Sigmoid(z):
if z < 0:
return 1 - 1/(1 + math.exp(z))
else:
return 1/(1 + math.exp(-z))
def Hypothesis(theta, x):
z = 0
for i in range(len(theta)):
z += x[i]*theta[i]
return Sigmoid(z)
def Cost_Function(X,Y,theta,m):
sumOfErrors = 0
for i in range(m):
xi = X[i]
hi = Hypothesis(theta,xi)
error = Y[i] * math.log(hi if hi >0 else 1)
if Y[i] == 1:
error = Y[i] * math.log(hi if hi >0 else 1)
elif Y[i] == 0:
error = (1-Y[i]) * math.log(1-hi if 1-hi >0 else 1)
sumOfErrors += error
constant = -1/m
J = constant * sumOfErrors
#print ('cost is: ', J )
return J
def Cost_Function_Derivative(X,Y,theta,j,m,alpha):
sumErrors = 0
for i in range(m):
xi = X[i]
xij = xi[j]
hi = Hypothesis(theta,X[i])
error = (hi - Y[i])*xij
sumErrors += error
m = len(Y)
constant = float(alpha)/float(m)
J = constant * sumErrors
return J
def Gradient_Descent(X,Y,theta,m,alpha):
new_theta = []
constant = alpha/m
for j in range(len(theta)):
CFDerivative = Cost_Function_Derivative(X,Y,theta,j,m,alpha)
new_theta_value = theta[j] - CFDerivative
new_theta.append(new_theta_value)
return new_theta
def Accuracy(theta):
correct = 0
length = len(X_test, Hypothesis(X,theta))
for i in range(length):
prediction = round(Hypothesis(X[i],theta))
answer = Y[i]
if prediction == answer.all():
correct += 1
my_accuracy = (correct / length)*100
print ('LR Accuracy %: ', my_accuracy)
def Logistic_Regression(X,Y,alpha,theta,num_iters):
theta = np.zeros(X.shape[1])
m = len(Y)
for x in range(num_iters):
new_theta = Gradient_Descent(X,Y,theta,m,alpha)
theta = new_theta
if x % 100 == 0:
Cost_Function(X,Y,theta,m)
print ('theta: ', theta)
print ('cost: ', Cost_Function(X,Y,theta,m))
Accuracy(theta)
initial_theta = [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
alpha = 0.0001
iterations = 1000
Logistic_Regression(X,Y,alpha,initial_theta,iterations)
这是使用威斯康星州乳腺癌数据集( https://www.kaggle.com/uciml/breast-cancer-wisconsin-data )中的数据,其中我权衡了30个特征-尽管将特征更改为已知相关的特征也不会改变我的准确性。
Python为我们提供了这个scikit-learn库,使我们的工作更加轻松,这对我来说很有效:
from sklearn.metrics import accuracy_score
y_pred = log.predict(x_test)
score =accuracy_score(y_test,y_pred)
我不确定您如何得出alpha
值为0.0001
,但我认为它太低了。 将代码与癌症数据一起使用表明,每次迭代的成本都在降低-这只是冰山一角。
当我将其提高到0.5时,我仍然得到了降低的成本,但是在一个更合理的水平上。 经过1000次迭代后,它报告:
cost: 0.23668000993020666
修复了Accuracy
函数后,我在数据测试段上获得了92%的收益。
您已经安装了Numpy,如X = np.array(X)
。 您应该真正考虑将其用于操作。 这样的工作将会快几个数量级 。 这是向量化版本,可立即提供结果,而无需等待:
import math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
df = pd.read_csv("cancerdata.csv")
X = df.values[:,2:-1].astype('float64')
X = (X - np.mean(X, axis =0)) / np.std(X, axis = 0)
## Add a bias column to the data
X = np.hstack([np.ones((X.shape[0], 1)),X])
X = MinMaxScaler().fit_transform(X)
Y = df["diagnosis"].map({'M':1,'B':0})
Y = np.array(Y)
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.25)
def Sigmoid(z):
return 1/(1 + np.exp(-z))
def Hypothesis(theta, x):
return Sigmoid(x @ theta)
def Cost_Function(X,Y,theta,m):
hi = Hypothesis(theta, X)
_y = Y.reshape(-1, 1)
J = 1/float(m) * np.sum(-_y * np.log(hi) - (1-_y) * np.log(1-hi))
return J
def Cost_Function_Derivative(X,Y,theta,m,alpha):
hi = Hypothesis(theta,X)
_y = Y.reshape(-1, 1)
J = alpha/float(m) * X.T @ (hi - _y)
return J
def Gradient_Descent(X,Y,theta,m,alpha):
new_theta = theta - Cost_Function_Derivative(X,Y,theta,m,alpha)
return new_theta
def Accuracy(theta):
correct = 0
length = len(X_test)
prediction = (Hypothesis(theta, X_test) > 0.5)
_y = Y_test.reshape(-1, 1)
correct = prediction == _y
my_accuracy = (np.sum(correct) / length)*100
print ('LR Accuracy %: ', my_accuracy)
def Logistic_Regression(X,Y,alpha,theta,num_iters):
m = len(Y)
for x in range(num_iters):
new_theta = Gradient_Descent(X,Y,theta,m,alpha)
theta = new_theta
if x % 100 == 0:
#print ('theta: ', theta)
print ('cost: ', Cost_Function(X,Y,theta,m))
Accuracy(theta)
ep = .012
initial_theta = np.random.rand(X_train.shape[1],1) * 2 * ep - ep
alpha = 0.5
iterations = 2000
Logistic_Regression(X_train,Y_train,alpha,initial_theta,iterations)
我想我可能有不同版本的scikit,因为我更改了MinMaxScaler
行以使其工作。 结果是,我可以眨眼间进行10K次迭代,将模型应用于测试集的结果的准确性约为97%。
准确性是最直观的性能指标之一,它只是正确预测的观测值与总观测值的比率。 更高的精度意味着模型的性能更好。
Accuracy = TP+TN/TP+FP+FN+TN
TP = True positives
TN = True negatives
FN = False negatives
TN = True negatives
在使用准确性度量时,误报和误报的成本应相近。 更好的指标是F1分数,由
F1-score = 2*(Recall*Precision)/Recall+Precision where,
Precision = TP/TP+FP
Recall = TP/TP+FN
在这里阅读更多
https://zh.wikipedia.org/wiki/Precision_and_recall
使用python进行机器学习的好处在于,像scikit-learn这样的重要模块是开源的,因此您始终可以查看实际的代码。 请使用以下链接访问scikit学习指标源代码,该源代码将使您了解scikit-learn在执行此操作时如何计算准确性得分
from sklearn.metrics import accuracy_score
accuracy_score(y_true, y_pred)
https://github.com/scikit-learn/scikit-learn/tree/master/sklearn/metrics
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