Linear Regression with machine learning methods

Ha, it's English time, let's spend a few minutes to learn a simple machine learning example in a simple passage.

Introduction

• What is machine learning? you design methods for machine to learn itself and improve itself.
• By leading into the machine learning methods, this passage introduced three methods to get optimal k and b of linear regression(y = k*x + b).
• The data used is produced by ourselves.

1. Self-sufficient data generation
2. Random Chosen Method
3. Supervised Direction Method
4. Gradient Descent Method
5. Conclusion

Self-sufficientDataGeneration

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import random

#produce data
age_with_fares = pd.DataFrame({"Fare":[263.0, 247.5208, 146.5208, 153.4625, 135.6333, 247.5208, 164.8667, 134.5, 135.6333, 153.4625, 134.5, 263.0, 211.5, 263.0, 151.55, 153.4625, 227.525, 211.3375, 211.3375],
                          "Age":[23.0, 24.0, 58.0, 58.0, 35.0, 50.0, 31.0, 40.0, 36.0, 38.0, 41.0, 24.0, 27.0, 64.0, 25.0, 40.0, 38.0, 29.0, 43.0]})
sub_fare = age_with_fares['Fare']
sub_age = age_with_fares['Age']

#show our data
plt.scatter(sub_age,sub_fare)
plt.show()

def func(age, k, b): return k*age+b
def loss(y,yhat): return np.mean(np.abs(y-yhat))
#here we choose only minus methods as the loss, besides, there are mean-square-error(L2) loss and other loss methods

RandomChosenMethod

min_error_rate = float('inf')

loop_times = 10000
losses = []

def step(): return random.random() * 2 - 1
# random生成 0~1的随机数；(0,1)*2 -> (0,2); 再减1 -> (-1,1)， 随机生成+循环：学习动力来源

while loop_times > 0:
    k_hat = random.random() * 20 - 10
    b_hat = random.random() * 20 - 10
    estimated_fares = func(sub_age, k_hat, b_hat)
    error_rate = loss(y=sub_fare, yhat=estimated_fares)
    if error_rate<min_error_rate:# 自我监督机制体现在此
        min_error_rate = error_rate
        losses.append(error_rate)
        best_k = k_hat
        best_b = b_hat

    loop_times -= 1

plt.scatter(sub_age, sub_fare)
plt.plot(sub_age, func(sub_age, best_k, best_b), c = 'r')
plt.show()

show the loss change

plt.plot(range(len(losses)), losses)
plt.show()

Explain

• We can see the loss decrease sometimes quickly, sometimes slowly, anyway, it decreases finally.
• One shortcoming of this method: the Random Chosen methods is not so valid as it runs random function tons of time.
• Because even when it comes out a better parameter, it may choose a worse one next time.
• One improved method see next part.

SupervisedDirectionMethod

change_directions = [
    (+1, -1),# k increase, b decrease
    (+1, +1),
    (-1, -1),
    (-1, +1)
]
min_error_rate = float('inf')

loop_times = 10000
losses = []

best_direction = random.choice(change_directions)
#定义每次变化（步长）的大小
def step(): return random.random()*2-1
#random生成 0~1的随机数；(0,1)*2 -> (0,2); 再减1 -> (-1,1);
#但是change_directions已经有加减1（改变方向）的操作，所以去掉 *2-1
#但保留*2-1 能增加choise

k_hat = random.random() * 20 - 10
b_hat = random.random() * 20 - 10
best_k, best_b = k_hat, b_hat
while loop_times > 0:
    k_delta_direction, b_delta_direction = best_direction or random.choice(change_directions)
    k_delta = k_delta_direction * step()
    b_delta = b_delta_direction * step()

    new_k = best_k + k_delta
    new_b = best_b + b_delta

    estimated_fares = func(sub_age, new_k, new_b)
    error_rate = loss(y=sub_fare, yhat=estimated_fares)
    #print(error_rate)

    if error_rate < min_error_rate:#supervisor learning
        min_error_rate = error_rate
        best_k, best_b = new_k, new_b

        best_direction = (k_delta_direction, b_delta_direction)

        #print(min_error_rate)
        #print("loop == {}".format(loop_times))
        losses.append(min_error_rate)
        #print("f(age) = {} * age + {}, with error rate: {}".format(best_k, best_b, error_rate))
    else:
        best_irection = random.choice(list(set(change_directions)-{(k_delta_direction, b_delta_direction)}))
        #新方向不能等于老方向
    loop_times -= 1
print("f(age) = {} * age + {}, with error rate: {}".format(best_k, best_b, error_rate))
plt.scatter(sub_age, sub_fare)
plt.plot(sub_age, func(sub_age, best_k, best_b), c = 'r')
plt.show()

show the loss change

plt.plot(range(len(losses)), losses)
plt.show()

Explain

• The Supervised Direction method(2nd method) is better than Random Chosen method(1st method).
• The 2nd method introduced supervise mechanism, which is more efficiently in changing parameters k and b.
• But the 2nd method can't optimize the parameters to smaller magnitude.
• Besides, the 2nd method can't find the extreme value, thus can't find the optimal parameters effectively.

GradientDescentMethod

min_error_rate = float('inf')
loop_times = 10000
losses = []
learing_rate = 1e-1

change_directions = [
    # (k, b)
    (+1, -1), # k increase, b decrease
    (+1, +1),
    (-1, +1),
    (-1, -1)  # k decrease, b decrease
]

k_hat = random.random() * 20 - 10
b_hat = random.random() * 20 - 10

best_direction = None
def step(): return random.random() * 1
direction = random.choice(change_directions)

def derivate_k(y, yhat, x):
    abs_values = [1 if (y_i - yhat_i) > 0 else -1 for y_i, yhat_i in zip(y, yhat)]

    return np.mean([a * -x_i for a, x_i in zip(abs_values, x)])

def derivate_b(y, yhat):
    abs_values = [1 if (y_i - yhat_i) > 0 else -1 for y_i, yhat_i in zip(y, yhat)]
    return np.mean([a * -1 for a in abs_values])

while loop_times > 0:

    k_delta = -1 * learing_rate * derivate_k(sub_fare, func(sub_age, k_hat, b_hat), sub_age)
    b_delta = -1 * learing_rate * derivate_b(sub_fare, func(sub_age, k_hat, b_hat))

    k_hat += k_delta
    b_hat += b_delta

    estimated_fares = func(sub_age, k_hat, b_hat)
    error_rate = loss(y=sub_fare, yhat=estimated_fares)

    #print('loop == {}'.format(loop_times))
    #print('f(age) = {} * age  {}, with error rate: {}'.format(k_hat, b_hat, error_rate))
    losses.append(error_rate)

    loop_times -= 1

print('f(age) = {} * age  {}, with error rate: {}'.format(k_hat, b_hat, error_rate))
plt.scatter(sub_age, sub_fare)
plt.plot(sub_age, func(sub_age, k_hat, b_hat), c = 'r')
plt.show()

show the loss change

plt.plot(range(len(losses)), losses)
plt.show()

Explain

• To fit the objective function given discrete data, we use the loss function to determine how good the fit is.
• In order to get the minimum loss, it becomes a problem of finding the extremum without constraints.
• Therefore, the method of gradient reduction of the objective function is conceived.
• The gradient is the maximum value in the directional derivative.
• When the gradient approaches 0, we fit the better objective function.

Conclusion

• Machine learning is a process to make the machine learning and improving by methods designed by us.
• Random function usually not so efficient, but when we add supervise mechanism, it becomes efficient.
• Gradient Descent is efficiently to find extreme value and optimal.

Serious question for this article:

Why do you use machine learning methods instead of creating a y = k*x + b formula?

• In some senarios, complicated formula can't meet the reality needs, like irrational elements in economics models.
• When we have enough valid data, we can run regression or classification model by machine learning methods
• We can also evaluate our machine learning model by test data which contributes to the application of the model in our real life
• This is just an example, Okay.

Reference for this article: Jupyter Notebook