该项目的所有代码在我的github上,欢迎有兴趣的同学与我探讨研究~
地址:Machine-Learning/machine-learning-ex2/
1. Introduction
逻辑回归(Logistic Regression), 在Wiki上的定义如下:
In statistics, logistic regression, or logit regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable—that is, where it can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression.[2] In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model.
以我的理解,逻辑回归也是数据拟合的一种工具,它用于解决分类问题,即结果是离散型的,而这类问题往往用线性回归不能很好的解决。因此,对于分类问题,我们应当选择逻辑回归而不是线性回归。
逻辑回归主要有两类:
- Logistic Regression with two outcome, it often called logistic regression.
- Logistic Regression with more than two outcome, it often call multinomial logistic Regression
对于逻辑回归,那么不得不提到Decision Boundary(决策边界)。决策边界是用于将不同类别的数据划分开来,它也有两种类型:
- Linear decision boundaries 线性决策边界
- Non-linear decision boundaries 非线性决策边界
通常,我们会通过梯度下降或者其它优化算法得到最终的theta。然后利用theta,划出决策边界。注意:决策边界是假设函数的属性,与训练集并没有直接关系,它是由假设函数的theta决定的。
逻辑回归的假设函数,Sigmoid函数,这个函数的特征如下:
- 当X为0时,函数值为0.5;
- 当X<0时,函数值< 0.5, 当X > 0时,函数值>0.5;
- 当X趋近于负无穷时,函数值趋近于0;
- 当X趋近于正无穷时,函数值趋近于1;
- 函数形状类似于一个S,所以称为S型函数。
假设函数的函数值代表的是是输出为1的概率,可以设置一个值如0.5:当函数值>=0.5时,output为1, 当函数值 < 0.5时, output为0.
对于损失函数,如果直接以代入假设函数求得,那么该损失函数将会是“non-convex”(非凸性),这将对梯度下降的速率有很大的影响。所以便有了对数形式的损失函数。因为是分类问题,所以损失函数有两种情况,但通过一些技巧可以将它们合二为一。
在编写代码时,将式子以向量化(Vectorization)的形式也是很方便的。
而梯度,就是损失函数J(theta)对theta的偏导。
为了解决多结果的回归问题,我们要使用的是one-VS-all的算法。例如我的结果有三类(A、B、C),我可以用3个分类器实现(AB,AC,BC)。然后将X带入每个分类器,哪个值高选哪个。
对于拟合,有三类情况:
- Underfitting 欠拟合: 没有较好拟合数据,预测准确率不高
- Overfitting 过拟合 :过分拟合数据,导致泛化程度不高,影响预测的准确率
- Oridinary 普通 : 较好拟合数据
Underfitting, or high bias, is when the form of our hypothesis function h maps poorly to the trend of the data. It is usually caused by a function that is too simple or uses too few features. At the other extreme, overfitting, or high variance, is caused by a hypothesis function that fits the available data but does not generalize well to predict new data. It is usually caused by a complicated function that creates a lot of unnecessary curves and angles unrelated to the data.
解决Overfitting,主要有两种方法:
- Reduce the number of features;
- Manually select which features to keep.
- Use a model selection algorithm (studied later in the course).
- Regularization.
- Keep all the features, but reduce the magnitude of parameters theta
- Regularization works well when we have a lot of slightly useful features
下面谈谈Regularization (正规化):
正规化就是通过添加多余的项以减少theta的大小,从而避免出现过拟合的情况。我们知道,当feature过多时,会导致过拟合的情况。为了解决这一问题,我们要减少feature的影响,即我们需要适当减少theta的值,极限情况下是对应theta等于0,那么feature也就不发挥作用了。
那如何衡量正规化的程度呢?有lambda参数。合适的lambda参数可以使过拟合变成正常拟合,但是太大的lambada值也有可能将过拟合变成欠拟合。所以选择一个合适的lambada值也是很重要的。待会project会有所涉及。
需要掌握:
1. 逻辑回归的假设函数、损失函数、梯度下降的迭代函数;
2. 逻辑回归的梯度计算;
3. 正规化后逻辑回归的假设函数、损失函数、梯度下降的迭代函数。
2. Logistic Regression
主函数
%% Initialization
clear ; close all; clc
%% Load Data
% The first two columns contains the exam scores and the third column
% contains the label.
data = load('ex2data1.txt');
X = data(:, [1, 2]); y = data(:, 3);
%% ==================== Part 1: Plotting ====================
% We start the exercise by first plotting the data to understand the
% the problem we are working with.
fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
'indicating (y = 0) examples.\n']);
plotData(X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Exam 1 score')
ylabel('Exam 2 score')
% Specified in plot order
legend('Admitted', 'Not admitted')
hold off;
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============ Part 2: Compute Cost and Gradient ============
% In this part of the exercise, you will implement the cost and gradient
% for logistic regression. You neeed to complete the code in
% costFunction.m
% Setup the data matrix appropriately, and add ones for the intercept term
[m, n] = size(X);
% Add intercept term to x and X_test
X = [ones(m, 1) X];
% Initialize fitting parameters
initial_theta = zeros(n + 1, 1);
% Compute and display initial cost and gradient
[cost, grad] = costFunction(initial_theta, X, y);
fprintf('Cost at initial theta (zeros): %f\n', cost);
fprintf('Expected cost (approx): 0.693\n');
fprintf('Gradient at initial theta (zeros): \n');
fprintf(' %f \n', grad);
fprintf('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n');
% Compute and display cost and gradient with non-zero theta
test_theta = [-24; 0.2; 0.2];
[cost, grad] = costFunction(test_theta, X, y);
fprintf('\nCost at test theta: %f\n', cost);
fprintf('Expected cost (approx): 0.218\n');
fprintf('Gradient at test theta: \n');
fprintf(' %f \n', grad);
fprintf('Expected gradients (approx):\n 0.043\n 2.566\n 2.647\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============= Part 3: Optimizing using fminunc =============
% In this exercise, you will use a built-in function (fminunc) to find the
% optimal parameters theta.
% Set options for fminunc
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Run fminunc to obtain the optimal theta
% This function will return theta and the cost
[theta, cost] = ...
fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
% Print theta to screen
fprintf('Cost at theta found by fminunc: %f\n', cost);
fprintf('Expected cost (approx): 0.203\n');
fprintf('theta: \n');
fprintf(' %f \n', theta);
fprintf('Expected theta (approx):\n');
fprintf(' -25.161\n 0.206\n 0.201\n');
% Plot Boundary
plotDecisionBoundary(theta, X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Exam 1 score')
ylabel('Exam 2 score')
% Specified in plot order
legend('Admitted', 'Not admitted')
hold off;
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============== Part 4: Predict and Accuracies ==============
% After learning the parameters, you'll like to use it to predict the outcomes
% on unseen data. In this part, you will use the logistic regression model
% to predict the probability that a student with score 45 on exam 1 and
% score 85 on exam 2 will be admitted.
%
% Furthermore, you will compute the training and test set accuracies of
% our model.
%
% Your task is to complete the code in predict.m
% Predict probability for a student with score 45 on exam 1
% and score 85 on exam 2
prob = sigmoid([1 45 85] * theta);
fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
'probability of %f\n'], prob);
fprintf('Expected value: 0.775 +/- 0.002\n\n');
% Compute accuracy on our training set
p = predict(theta, X);
% 如果矩阵中所有元素一一对应,则mean(double(p == y))的值为1
% 如果不对应,则值相应减少。如四个中有三个一一对应,那么则其值为0.75
fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
fprintf('Expected accuracy (approx): 89.0\n');
fprintf('\n');
Part1: 将训练集呈现在图中,“+”表示Admitted, “o”表示Not Admitted。而这一结果是由两个exam的分数决定的,即包含两个feature决定最后的结果。
plotData.m
function plotData(X, y)
%PLOTDATA Plots the data points X and y into a new figure
% PLOTDATA(x,y) plots the data points with + for the positive examples
% and o for the negative examples. X is assumed to be a Mx2 matrix.
% Create New Figure
figure; hold on;
% ====================== YOUR CODE HERE ======================
% Instructions: Plot the positive and negative examples on a
% 2D plot, using the option 'k+' for the positive
% examples and 'ko' for the negative examples.
%
% Find Indices of Positive and Negative Example
pos = find(y == 1);
neg = find(y == 0);
% Plot Examples
% Attention: cannot add '' to the number, such as '2', '7', it's wrong
plot(X(pos, 1), X(pos, 2), 'k+', 'LineWidth', 2, 'MarkerSize', 7);
plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
% =========================================================================
hold off;
end
Part2:计算损失函数和梯度
costFunction.m
function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
% J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
% parameter for logistic regression and the gradient of the cost
% w.r.t. to the parameters.
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
% You should set J to the cost.
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta
%
% Note: grad should have the same dimensions as theta
%
% Calulate the function h, we know it's sigmoid function
h = sigmoid(X*theta);
% Calculate the cost function
J = 1/m*(-y'*log(h)-(1-y)'*log(1-h));
% Calculate the Gradient
grad = 1/m*X'*(h-y);
% =============================================================
end
Part3: 使用优化函数fminunc来得到使Cost Function函数值最小的theta, 然后使用该theta值画出决策边界。
plotDecisionBoundary.m
此处分了两类情况:如果feature小于等于2,则决策边界是线性的;如果feature大于2,则决策边界是非线性的。至于它为什么这么定义决策边界我就不大清楚了,如果有大神指点一下表示感谢耶~
function plotDecisionBoundary(theta, X, y)
%PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with
%the decision boundary defined by theta
% PLOTDECISIONBOUNDARY(theta, X,y) plots the data points with + for the
% positive examples and o for the negative examples. X is assumed to be
% a either
% 1) Mx3 matrix, where the first column is an all-ones column for the
% intercept.
% 2) MxN, N>3 matrix, where the first column is all-ones
% Plot Data
plotData(X(:,2:3), y);
hold on
% size < 3, it means that the feature is less than or equal to 2
if size(X, 2) <= 3
% Only need 2 points to define a line, so choose two endpoints
plot_x = [min(X(:,2))-2, max(X(:,2))+2];
% Calculate the decision boundary line
plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));
% Plot, and adjust axes for better viewing
plot(plot_x, plot_y)
% Legend, specific for the exercise
legend('Admitted', 'Not admitted', 'Decision Boundary')
axis([30, 100, 30, 100])
else
% Here is the grid range
u = linspace(-1, 1.5, 50);
v = linspace(-1, 1.5, 50);
z = zeros(length(u), length(v));
% Evaluate z = theta*x over the grid
for i = 1:length(u)
for j = 1:length(v)
z(i,j) = mapFeature(u(i), v(j))*theta;
end
end
z = z'; % important to transpose z before calling contour
% Plot z = 0
% Notice you need to specify the range [0, 0]
contour(u, v, z, [0, 0], 'LineWidth', 2)
end
hold off
end
mapFeature.m
这个函数适用于产生多项式项,即可以增加feature的数量,使得决策边界更贴近训练集。
function out = mapFeature(X1, X2)
% MAPFEATURE Feature mapping function to polynomial features
%
% MAPFEATURE(X1, X2) maps the two input features
% to quadratic features used in the regularization exercise.
%
% Returns a new feature array with more features, comprising of
% X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..
%
% Inputs X1, X2 must be the same size
%
degree = 6;
out = ones(size(X1(:,1)));
for i = 1:degree
for j = 0:i
out(:, end+1) = (X1.^(i-j)).*(X2.^j);
end
end
end
Part4: 使用上述方法进行训练,进而对exam1(45分)、exam2(85分)进行预测并计算出预测准确率。
predict.m
function p = predict(theta, X)
%PREDICT Predict whether the label is 0 or 1 using learned logistic
%regression parameters theta
% p = PREDICT(theta, X) computes the predictions for X using a
% threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
m = size(X, 1); % Number of training examples
% You need to return the following variables correctly
p = zeros(m, 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
% your learned logistic regression parameters.
% You should set p to a vector of 0's and 1's
%
% 这个函数的目的是:计算你的预测的准确率
% 方法是用你得到的theta作为h(x)的参数,对训练集中的数据全都预测一遍
% 然后与训练集中对应的y值进行对比,计算出准确率
% 对训练集进行逐行便利,计算出每个训练样例的y值准确率
% 如果大于或等于0.5,则将p对应位置设为1,否则为0
for i = 1: m
if (sigmoid(X(i,:)*theta) >= 0.5)
p(i) = 1;
else
p(i) = 0;
end
% =========================================================================
end
输出结果:
3. Regularization
主函数:
%% Initialization
clear ; close all; clc
%% Load Data
% The first two columns contains the X values and the third column
% contains the label (y).
data = load('ex2data2.txt');
X = data(:, [1, 2]); y = data(:, 3);
plotData(X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Microchip Test 1')
ylabel('Microchip Test 2')
% Specified in plot order
legend('y = 1', 'y = 0')
hold off;
%% =========== Part 1: Regularized Logistic Regression ============
% In this part, you are given a dataset with data points that are not
% linearly separable. However, you would still like to use logistic
% regression to classify the data points.
%
% To do so, you introduce more features to use -- in particular, you add
% polynomial features to our data matrix (similar to polynomial
% regression).
%
% Add Polynomial Features
% Note that mapFeature also adds a column of ones for us, so the intercept
% term is handled
X = mapFeature(X(:,1), X(:,2));
% Initialize fitting parameters
initial_theta = zeros(size(X, 2), 1);
% Set regularization parameter lambda to 1
lambda = 1;
% Compute and display initial cost and gradient for regularized logistic
% regression
[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
fprintf('Cost at initial theta (zeros): %f\n', cost);
fprintf('Expected cost (approx): 0.693\n');
fprintf('Gradient at initial theta (zeros) - first five values only:\n');
fprintf(' %f \n', grad(1:5));
fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
% Compute and display cost and gradient
% with all-ones theta and lambda = 10
test_theta = ones(size(X,2),1);
[cost, grad] = costFunctionReg(test_theta, X, y, 10);
fprintf('\nCost at test theta (with lambda = 10): %f\n', cost);
fprintf('Expected cost (approx): 3.16\n');
fprintf('Gradient at test theta - first five values only:\n');
fprintf(' %f \n', grad(1:5));
fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============= Part 2: Regularization and Accuracies =============
% Optional Exercise:
% In this part, you will get to try different values of lambda and
% see how regularization affects the decision coundart
%
% Try the following values of lambda (0, 1, 10, 100).
%
% How does the decision boundary change when you vary lambda? How does
% the training set accuracy vary?
%
% Initialize fitting parameters
initial_theta = zeros(size(X, 2), 1);
% Set regularization parameter lambda to 1 (you should vary this)
lambda = 1;
% Set Options
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Optimize
[theta, J, exit_flag] = ...
fminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);
% Plot Boundary
plotDecisionBoundary(theta, X, y);
hold on;
title(sprintf('lambda = %g', lambda))
% Labels and Legend
xlabel('Microchip Test 1')
ylabel('Microchip Test 2')
legend('y = 1', 'y = 0', 'Decision boundary')
hold off;
% Compute accuracy on our training set
p = predict(theta, X);
fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
fprintf('Expected accuracy (with lambda = 1): 83.1 (approx)\n');
Part1: 正规化逻辑回归
训练集分布如下:
costFunctionReg.m
function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
% theta as the parameter for regularized logistic regression and the
% gradient of the cost w.r.t. to the parameters.
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
% You should set J to the cost.
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta
% Calulate the function h, we know it's sigmoid function
h = sigmoid(X*theta);
% Calculate the cost function
% 因为theta1不参与正规化,所以应当把theta1去掉(而且式子中也是不包含这一项的)
% 注意:式子中向量下标是从0开始的,而在Octave中,向量下标是从1开始的。
J = 1/m*(-y'*log(h)-(1-y)'*log(1-h))+lambda/(2*m)*(sum(theta.^2)-theta(1)^2);
% Calculate the Gradient
grad = 1/m*X'*(h-y)+lambda/m*theta;
% It must start at 1
grad(1) = (1/m*X'*(h-y))(1);
% =============================================================
end
Part2: 正规化与准确率
lambda为0:
lambda为1:
lambda为10:
lambda为100:
从lambda = 0与 lambda = 1对比可知,正规化削弱了过拟合;
从lambda = 10 与 lambda = 100可知,正规化参数过大会导致欠拟合。
输出结果:
3. Conclusion
本篇博文简要介绍了与逻辑回归的相关知识以及小项目,同时还介绍了Regularization来提高预测的准确性。虽然在项目中只对逻辑回归进行正规化,但是Regularization同样可以运用到线性回归、梯度下降、正规方程等中。
通过这个小项目,不仅对逻辑回归有了进一步的了解,还了解到了正规化的方法。不过这篇博文涉及的逻辑回归主要是只有两个outcomes的,对于多outcomes的没有涉及到,这也是这个project的局限性所在。以后如果接触了多outcomes的逻辑回归再进一步介绍。
以上内容皆为本人观点,欢迎大家提出批评和知道,我们一起探讨!