# %load ../../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from mpl_toolkits.mplot3d import axes3d
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
pd.set_option('display.max_seq_items', None)
#%config InlineBackend.figure_formats = {'pdf',}
%matplotlib inline
import seaborn as sns
sns.set_context('notebook')
sns.set_style('white')
# %load ../../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from mpl_toolkits.mplot3d import axes3d
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
pd.set_option('display.max_seq_items', None)
#%config InlineBackend.figure_formats = {'pdf',}
%matplotlib inline
import seaborn as sns
sns.set_context('notebook')
sns.set_style('white')
/Library/Python/2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/Library/Python/2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
"`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)
熟悉numpy
In [2]:
def warmUpExercise():
return(np.identity(5))
In [3]:
warmUpExercise()
Out[3]:
array([[ 1., 0., 0., 0., 0.],
[ 0., 1., 0., 0., 0.],
[ 0., 0., 1., 0., 0.],
[ 0., 0., 0., 1., 0.],
[ 0., 0., 0., 0., 1.]])
单变量线性回归
In [4]:
data = np.loadtxt('linear_regression_data1.txt', delimiter=',')
X = np.c_[np.ones(data.shape[0]),data[:,0]]
y = np.c_[data[:,1]]
In [5]:
plt.scatter(X[:,1], y, s=30, c='r', marker='x', linewidths=1)
plt.xlim(4,24)
plt.xlabel('Population of City in 10,000s')
plt.ylabel('Profit in $10,000s');
梯度下降
In [31]:
# 计算损失函数
def computeCost(X, y, theta=[[0],[0]]):
m = y.size
J = 0
h = X.dot(theta)
J = 1.0/(2*m)*(np.sum(np.square(h-y)))
return J
In [32]:
computeCost(X,y)
Out[32]:
32.072733877455676
In [3]:
# 梯度下降
def gradientDescent(X, y, theta=[[0],[0]], alpha=0.01, num_iters=1500):
m = y.size
J_history = np.zeros(num_iters)
for iter in np.arange(num_iters):
h = X.dot(theta)
theta = theta - alpha*(1.0/m)*(X.T.dot(h-y))
J_history[iter] = computeCost(X, y, theta)
return(theta, J_history)
In [2]:
# 画出每一次迭代和损失函数变化
theta , Cost_J = gradientDescent(X, y)
print('theta: ',theta.ravel())
plt.plot(Cost_J)
plt.ylabel('Cost J')
plt.xlabel('Iterations');
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-2-699228c1adc0> in <module>
1 # 画出每一次迭代和损失函数变化
----> 2 theta , Cost_J = gradientDescent(X, y)
3 print('theta: ',theta.ravel())
4
5 plt.plot(Cost_J)
NameError: name 'gradientDescent' is not defined
In [38]:
xx = np.arange(5,23)
yy = theta[0]+theta[1]*xx
# 画出我们自己写的线性回归梯度下降收敛的情况
plt.scatter(X[:,1], y, s=30, c='r', marker='x', linewidths=1)
plt.plot(xx,yy, label='Linear regression (Gradient descent)')
# 和Scikit-learn中的线性回归对比一下
regr = LinearRegression()
regr.fit(X[:,1].reshape(-1,1), y.ravel())
plt.plot(xx, regr.intercept_+regr.coef_*xx, label='Linear regression (Scikit-learn GLM)')
plt.xlim(4,24)
plt.xlabel('Population of City in 10,000s')
plt.ylabel('Profit in $10,000s')
plt.legend(loc=4);
In [39]:
# 预测一下人口为35000和70000的城市的结果
print(theta.T.dot([1, 3.5])*10000)
print(theta.T.dot([1, 7])*10000)
[ 4519.7678677]
[ 45342.45012945]
逻辑斯特回归示例
逻辑斯特回归
正则化后的逻辑斯特回归
In [1]:
# %load ../../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from sklearn.preprocessing import PolynomialFeatures
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
pd.set_option('display.max_seq_items', None)
#%config InlineBackend.figure_formats = {'pdf',}
%matplotlib inline
import seaborn as sns
sns.set_context('notebook')
sns.set_style('white')
/Library/Python/2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/Library/Python/2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
"`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)
In [11]:
def loaddata(file, delimeter):
data = np.loadtxt(file, delimiter=delimeter)
print('Dimensions: ',data.shape)
print(data[1:6,:])
return(data)
In [12]:
def plotData(data, label_x, label_y, label_pos, label_neg, axes=None):
# 获得正负样本的下标(即哪些是正样本，哪些是负样本)
neg = data[:,2] == 0
pos = data[:,2] == 1
if axes == None:
axes = plt.gca()
axes.scatter(data[pos][:,0], data[pos][:,1], marker='+', c='k', s=60, linewidth=2, label=label_pos)
axes.scatter(data[neg][:,0], data[neg][:,1], c='y', s=60, label=label_neg)
axes.set_xlabel(label_x)
axes.set_ylabel(label_y)
axes.legend(frameon= True, fancybox = True);
逻辑斯特回归
In [13]:
data = loaddata('data1.txt', ',')
('Dimensions: ', (100, 3))
[[ 30.28671077 43.89499752 0. ]
[ 35.84740877 72.90219803 0. ]
[ 60.18259939 86.3085521 1. ]
[ 79.03273605 75.34437644 1. ]
[ 45.08327748 56.31637178 0. ]]
In [14]:
X = np.c_[np.ones((data.shape[0],1)), data[:,0:2]]
y = np.c_[data[:,2]]
In [15]:
plotData(data, 'Exam 1 score', 'Exam 2 score', 'Pass', 'Fail')
逻辑斯特回归假设
ℎ𝜃(𝑥)=𝑔(𝜃𝑇𝑥)
hθ(x)=g(θTx)
𝑔(𝑧)=11+𝑒−𝑧
g(z)=11+e−z
In [16]:
#定义sigmoid函数
def sigmoid(z):
return(1 / (1 + np.exp(-z)))
其实scipy包里有一个函数可以完成一样的功能:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.expit.html#scipy.special.expit
损失函数
𝐽(𝜃)=1𝑚∑𝑖=1𝑚[−𝑦(𝑖)𝑙𝑜𝑔(ℎ𝜃(𝑥(𝑖)))−(1−𝑦(𝑖))𝑙𝑜𝑔(1−ℎ𝜃(𝑥(𝑖)))]
J(θ)=1m∑i=1m[−y(i)log(hθ(x(i)))−(1−y(i))log(1−hθ(x(i)))]
向量化的损失函数(矩阵形式)
𝐽(𝜃)=1𝑚((𝑙𝑜𝑔(𝑔(𝑋𝜃))𝑇𝑦+(𝑙𝑜𝑔(1−𝑔(𝑋𝜃))𝑇(1−𝑦))
J(θ)=1m((log(g(Xθ))Ty+(log(1−g(Xθ))T(1−y))
In [20]:
#定义损失函数
def costFunction(theta, X, y):
m = y.size
h = sigmoid(X.dot(theta))
J = -1.0*(1.0/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y))
if np.isnan(J[0]):
return(np.inf)
return J[0]
求偏导(梯度)
𝛿𝐽(𝜃)𝛿𝜃𝑗=1𝑚∑𝑖=1𝑚(ℎ𝜃(𝑥(𝑖))−𝑦(𝑖))𝑥(𝑖)𝑗
δJ(θ)δθj=1m∑i=1m(hθ(x(i))−y(i))xj(i)
向量化的偏导(梯度)
𝛿𝐽(𝜃)𝛿𝜃𝑗=1𝑚𝑋𝑇(𝑔(𝑋𝜃)−𝑦)
δJ(θ)δθj=1mXT(g(Xθ)−y)
In [18]:
#求解梯度
def gradient(theta, X, y):
m = y.size
h = sigmoid(X.dot(theta.reshape(-1,1)))
grad =(1.0/m)*X.T.dot(h-y)
return(grad.flatten())
In [19]:
initial_theta = np.zeros(X.shape[1])
cost = costFunction(initial_theta, X, y)
grad = gradient(initial_theta, X, y)
print('Cost: \n', cost)
print('Grad: \n', grad)
('Cost: \n', 0.69314718055994518)
('Grad: \n', array([ -0.1 , -12.00921659, -11.26284221]))
最小化损失函数
In [22]:
res = minimize(costFunction, initial_theta, args=(X,y), jac=gradient, options={'maxiter':400})
res
Out[22]:
status: 0
success: True
njev: 28
nfev: 28
hess_inv: array([[ 3.24739469e+03, -2.59380769e+01, -2.63469561e+01],
[ -2.59380769e+01, 2.21449124e-01, 1.97772068e-01],
[ -2.63469561e+01, 1.97772068e-01, 2.29018831e-01]])
fun: 0.20349770158944075
x: array([-25.16133401, 0.20623172, 0.2014716 ])
message: 'Optimization terminated successfully.'
jac: array([ -2.73305312e-10, 1.43144026e-07, -1.58965802e-07])
做一下预测吧
In [23]:
def predict(theta, X, threshold=0.5):
p = sigmoid(X.dot(theta.T)) >= threshold
return(p.astype('int'))
咱们来看看考试1得分45，考试2得分85的同学通过概率有多高
In [24]:
sigmoid(np.array([1, 45, 85]).dot(res.x.T))
Out[24]:
0.77629066133254787
画决策边界
In [25]:
plt.scatter(45, 85, s=60, c='r', marker='v', label='(45, 85)')
plotData(data, 'Exam 1 score', 'Exam 2 score', 'Admitted', 'Not admitted')
x1_min, x1_max = X[:,1].min(), X[:,1].max(),
x2_min, x2_max = X[:,2].min(), X[:,2].max(),
xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
h = sigmoid(np.c_[np.ones((xx1.ravel().shape[0],1)), xx1.ravel(), xx2.ravel()].dot(res.x))
h = h.reshape(xx1.shape)
plt.contour(xx1, xx2, h, [0.5], linewidths=1, colors='b');
加正则化项的逻辑斯特回归
In [1]:
data2 = loaddata('data2.txt', ',')
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-27484805db53> in <module>
----> 1 data2 = loaddata('data2.txt', ',')
NameError: name 'loaddata' is not defined
In [42]:
# 拿到X和y
y = np.c_[data2[:,2]]
X = data2[:,0:2]
In [43]:
# 画个图
plotData(data2, 'Microchip Test 1', 'Microchip Test 2', 'y = 1', 'y = 0')
咱们整一点多项式特征出来(最高6阶)
In [44]:
poly = PolynomialFeatures(6)
XX = poly.fit_transform(data2[:,0:2])
# 看看形状(特征映射后x有多少维了)
XX.shape
Out[44]:
(118, 28)
正则化后损失函数
𝐽(𝜃)=1𝑚∑𝑖=1𝑚[−𝑦(𝑖)𝑙𝑜𝑔(ℎ𝜃(𝑥(𝑖)))−(1−𝑦(𝑖))𝑙𝑜𝑔(1−ℎ𝜃(𝑥(𝑖)))]+𝜆2𝑚∑𝑗=1𝑛𝜃2𝑗
J(θ)=1m∑i=1m[−y(i)log(hθ(x(i)))−(1−y(i))log(1−hθ(x(i)))]+λ2m∑j=1nθj2
向量化的损失函数(矩阵形式)
𝐽(𝜃)=1𝑚((𝑙𝑜𝑔(𝑔(𝑋𝜃))𝑇𝑦+(𝑙𝑜𝑔(1−𝑔(𝑋𝜃))𝑇(1−𝑦))+𝜆2𝑚∑𝑗=1𝑛𝜃2𝑗
J(θ)=1m((log(g(Xθ))Ty+(log(1−g(Xθ))T(1−y))+λ2m∑j=1nθj2
In [45]:
# 定义损失函数
def costFunctionReg(theta, reg, *args):
m = y.size
h = sigmoid(XX.dot(theta))
J = -1.0*(1.0/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y)) + (reg/(2.0*m))*np.sum(np.square(theta[1:]))
if np.isnan(J[0]):
return(np.inf)
return(J[0])
偏导(梯度)
𝛿𝐽(𝜃)𝛿𝜃𝑗=1𝑚∑𝑖=1𝑚(ℎ𝜃(𝑥(𝑖))−𝑦(𝑖))𝑥(𝑖)𝑗+𝜆𝑚𝜃𝑗
δJ(θ)δθj=1m∑i=1m(hθ(x(i))−y(i))xj(i)+λmθj
向量化的偏导(梯度)
𝛿𝐽(𝜃)𝛿𝜃𝑗=1𝑚𝑋𝑇(𝑔(𝑋𝜃)−𝑦)+𝜆𝑚𝜃𝑗
δJ(θ)δθj=1mXT(g(Xθ)−y)+λmθj
注意，我们另外自己加的参数 𝜃0 不需要被正则化
注意，我们另外自己加的参数 θ0 不需要被正则化
In [39]:
def gradientReg(theta, reg, *args):
m = y.size
h = sigmoid(XX.dot(theta.reshape(-1,1)))
grad = (1.0/m)*XX.T.dot(h-y) + (reg/m)*np.r_[[[0]],theta[1:].reshape(-1,1)]
return(grad.flatten())
In [46]:
initial_theta = np.zeros(XX.shape[1])
costFunctionReg(initial_theta, 1, XX, y)
Out[46]:
0.69314718055994529
In [48]:
fig, axes = plt.subplots(1,3, sharey = True, figsize=(17,5))
# 决策边界，咱们分别来看看正则化系数lambda太大太小分别会出现什么情况
# Lambda = 0 : 就是没有正则化，这样的话，就过拟合咯
# Lambda = 1 : 这才是正确的打开方式
# Lambda = 100 : 卧槽，正则化项太激进，导致基本就没拟合出决策边界
for i, C in enumerate([0.0, 1.0, 100.0]):
# 最优化 costFunctionReg
res2 = minimize(costFunctionReg, initial_theta, args=(C, XX, y), jac=gradientReg, options={'maxiter':3000})
# 准确率
accuracy = 100.0*sum(predict(res2.x, XX) == y.ravel())/y.size
# 对X,y的散列绘图
plotData(data2, 'Microchip Test 1', 'Microchip Test 2', 'y = 1', 'y = 0', axes.flatten()[i])
# 画出决策边界
x1_min, x1_max = X[:,0].min(), X[:,0].max(),
x2_min, x2_max = X[:,1].min(), X[:,1].max(),
xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
h = sigmoid(poly.fit_transform(np.c_[xx1.ravel(), xx2.ravel()]).dot(res2.x))
h = h.reshape(xx1.shape)
axes.flatten()[i].contour(xx1, xx2, h, [0.5], linewidths=1, colors='g');
axes.flatten()[i].set_title('Train accuracy {}% with Lambda = {}'.format(np.round(accuracy, decimals=2), C))
In [ ]:
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