李宏毅机器学习2021系列作业2-年收入判断

2023-12-06 14:29:10

李宏毅机器学习2021系列

作业2-年收入判断

项目描述

二元分类是机器学习中最基础的问题之一，在这份教学中，你将学会如何实作一个线性二元分类器，来根据人们的个人资料，判断其年收入是否高于 50,000 美元。我们将以两种方法: logistic regression 与 generative model，来达成以上目的，你可以尝试了解、分析两者的设计理念及差别。
实现二分类任务：

个人收入是否超过50000元？

数据集介绍

这个资料集是由UCI Machine Learning Repository 的Census-Income (KDD) Data Set 经过一些处理而得来。为了方便训练，我们移除了一些不必要的资讯，并且稍微平衡了正负两种标记的比例。事实上在训练过程中，只有 X_train、Y_train 和 X_test 这三个经过处理的档案会被使用到，train.csv 和 test.csv 这两个原始资料档则可以提供你一些额外的资讯。

已经去除不必要的属性。
已经平衡正标和负标数据之间的比例。

特征格式

train.csv，test_no_label.csv。

基于文本的原始数据
去掉不必要的属性，平衡正负比例。

X_train, Y_train, X_test(测试)

train.csv中的离散特征=>在X_train中onehot编码(学历、状态…)
train.csv中的连续特征 => 在X_train中保持不变(年龄、资本损失…)。
X_train, X_test : 每一行包含一个510-dim的特征，代表一个样本。
Y_train: label = 0 表示 “<=50K” 、 label = 1 表示 " >50K " 。

项目要求

请动手编写 gradient descent 实现 logistic regression
请动手实现概率生成模型。
单个代码块运行时长应低于五分钟。
禁止使用任何开源的代码(例如，你在GitHub上找到的决策树的实现)。

数据准备

项目数据保存在：work/data/ 目录下。

环境配置/安装

无

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

df = pd.read_csv('work/data/train.csv', header=None, encoding='big5')
print(df.shape)
df.head()

(54257, 42)


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3018: DtypeWarning: Columns (0,1,3,4,6,17,18,19,30,36,38,39,40) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

	0	1	2	3	4	5	6	7	8	9	...	32	33	34	35	36	37	38	39	40	41
0	id	age	class of worker	detailed industry recode	detailed occupation recode	education	wage per hour	enroll in edu inst last wk	marital stat	major industry code	...	country of birth father	country of birth mother	country of birth self	citizenship	own business or self employed	fill inc questionnaire for veteran's admin	veterans benefits	weeks worked in year	year	y
1	0	33	Private	34	26	Masters degree(MA MS MEng MEd MSW MBA)	0	Not in universe	Married-civilian spouse present	Finance insurance and real estate	...	China	China	*	Foreign born- Not a citizen of U S	2	Not in universe	2	52	95	50000+.
2	1	63	Private	7	22	Some college but no degree	0	Not in universe	Never married	Manufacturing-durable goods	...	?	?	United-States	Native- Born in the United States	0	Not in universe	2	52	95	- 50000.
3	2	71	Not in universe	0	0	7th and 8th grade	0	Not in universe	Married-civilian spouse present	Not in universe or children	...	Germany	United-States	United-States	Native- Born in the United States	0	Not in universe	2	0	95	- 50000.
4	3	43	Local government	43	10	Bachelors degree(BA AB BS)	0	Not in universe	Married-civilian spouse present	Education	...	United-States	United-States	United-States	Native- Born in the United States	0	Not in universe	2	52	95	- 50000.

5 rows × 42 columns

df = pd.read_csv('work/data/X_train', header=None, encoding='big5')
print(df.shape)
df.head()

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3018: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,502,503,504,508) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)


(54257, 511)

	0	1	2	3	4	5	6	7	8	9	...	501	502	503	504	505	507	508	509	510
0	id	age	Private	Self-employed-incorporated	State government	Self-employed-not incorporated	Not in universe	Without pay	Federal government	Never worked	...	1	Not in universe	Yes	No	2	1	weeks worked in year	94	95
1	0	33	1	0	0	0	0	0	0	0	...	0	1	0	0	1	0	52	0	1
2	1	63	1	0	0	0	0	0	0	0	...	0	1	0	0	1	0	52	0	1
3	2	71	0	0	0	0	1	0	0	0	...	0	1	0	0	1	0	0	0	1
4	3	43	0	0	0	0	0	0	0	0	...	0	1	0	0	1	0	52	0	1

5 rows × 511 columns

Preparing Data

对属性进行正则化，处理过后划分为训练集和验证集

np.random.seed(0)
X_train_fpath = 'work/data/X_train'
Y_train_fpath = 'work/data/Y_train'
X_test_fpath = 'work/data/X_test'
output_fpath = 'work/output_{}.csv'

# Parse csv files to numpy array
with open(X_train_fpath) as f:
    next(f)
    X_train = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)
with open(Y_train_fpath) as f:
    next(f)
    Y_train = np.array([line.strip('\n').split(',')[1] for line in f], dtype = float)
with open(X_test_fpath) as f:
    next(f)
    X_test = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)

# 规范化
def _normalize(X, train = True, specified_column = None, X_mean = None, X_std = None):
    # This function normalizes specific columns of X.
    # The mean and standard variance of training data will be reused when processing testing data.
    #
    # Arguments:
    #     X: data to be processed
    #     train: 'True' when processing training data, 'False' for testing data
    #     specific_column: indexes of the columns that will be normalized. If 'None', all columns
    #         will be normalized.
    #     X_mean: mean value of training data, used when train = 'False'
    #     X_std: standard deviation of training data, used when train = 'False'
    # Outputs:
    #     X: normalized data
    #     X_mean: computed mean value of training data
    #     X_std: computed standard deviation of training data

    if specified_column == None:
        specified_column = np.arange(X.shape[1])
    if train:
        X_mean = np.mean(X[:, specified_column] ,0).reshape(1, -1)
        X_std  = np.std(X[:, specified_column], 0).reshape(1, -1)

    X[:,specified_column] = (X[:, specified_column] - X_mean) / (X_std + 1e-8)
     
    return X, X_mean, X_std

def _train_dev_split(X, Y, dev_ratio = 0.25):
    # This function spilts data into training set and development set.
    train_size = int(len(X) * (1 - dev_ratio))
    return X[:train_size], Y[:train_size], X[train_size:], Y[train_size:]

# Normalize training and testing data
X_train, X_mean, X_std = _normalize(X_train, train = True)
X_test, _, _ = _normalize(X_test, train = False, specified_column = None, X_mean = X_mean, X_std = X_std)
    
# Split data into training set and development set
dev_ratio = 0.1
# 9:1
X_train, Y_train, X_eval, Y_eval = _train_dev_split(X_train, Y_train, dev_ratio = dev_ratio)

train_size = X_train.shape[0]
eval_size = X_eval.shape[0]
test_size = X_test.shape[0]
data_dim = X_train.shape[1]

print('Size of training set: {}'.format(X_train.shape[0]))
print('Size of eval set: {}'.format(X_eval.shape[0]))
print('Size of testing set: {}'.format(X_test.shape[0]))
print('Dimension of data: {}'.format(data_dim))

Size of training set: 48830
Size of eval set: 5426
Size of testing set: 27622
Dimension of data: 510

Some Useful Functions

def _shuffle(X, Y):
    # This function shuffles two equal-length list/array, X and Y, together.
    randomize = np.arange(len(X))
    np.random.shuffle(randomize)
    return (X[randomize], Y[randomize])

def _sigmoid(z):
    # Sigmoid function can be used to calculate probability.
    # To avoid overflow, minimum/maximum output value is set.
    return np.clip(1 / (1.0 + np.exp(-z)), 1e-8, 1 - (1e-8))

def _f(X, w, b):
    # This is the logistic regression function, parameterized by w and b
    #
    # Arguements:
    #     X: input data, shape = [batch_size, data_dimension]
    #     w: weight vector, shape = [data_dimension, ]
    #     b: bias, scalar
    # Output:
    #     predicted probability of each row of X being positively labeled, shape = [batch_size, ]
    return _sigmoid(np.matmul(X, w) + b)

def _predict(X, w, b):
    # This function returns a truth value prediction for each row of X 
    # by rounding the result of logistic regression function.
    return np.round(_f(X, w, b)).astype(np.int)
    
def _accuracy(Y_pred, Y_label):
    # This function calculates prediction accuracy
    acc = 1 - np.mean(np.abs(Y_pred - Y_label))
    return acc

Functions about gradient and loss

# 交叉熵损失 if 0-1损失 第二项为0
def _cross_entropy_loss(y_pred, Y_label):
    # This function computes the cross entropy.
    #
    # Arguements:
    #     y_pred: probabilistic predictions, float vector
    #     Y_label: ground truth labels, bool vector
    # Output:
    #     cross entropy, scalar
    cross_entropy = -np.dot(Y_label, np.log(y_pred)) - np.dot((1 - Y_label), np.log(1 - y_pred))
    return cross_entropy

# 
def _gradient(X, Y_label, w, b):
    # This function computes the gradient of cross entropy loss with respect to weight w and bias b.
    y_pred = _f(X, w, b)
    pred_error = Y_label - y_pred
    w_grad = -np.sum(pred_error * X.T, 1)
    b_grad = -np.sum(pred_error)
    return w_grad, b_grad

Trainning

Mini-batch

# Zero initialization for weights ans bias
w = np.zeros((data_dim, )) 
b = np.zeros((1,))

# Some parameters for training    
EPOCH = 10
batch_size = 128
learning_rate = 0.2

# Keep the loss and accuracy at every iteration for plotting
train_loss = []
eval_loss = []
train_acc = []
eval_acc = []

# Calcuate the number of parameter updates
adagrad_step = 1
batch_step = 0
# Iterative training
for epoch in range(EPOCH):
    # Random shuffle at the begging of each epoch
    X_train, Y_train = _shuffle(X_train, Y_train)
        
    # Mini-batch training
    step = 0
    steps = int(np.floor(train_size / batch_size))
    for idx in range(steps):  # floor(48830/128)=382
        X = X_train[idx*batch_size:(idx+1)*batch_size]
        Y = Y_train[idx*batch_size:(idx+1)*batch_size]

        # Compute the gradient
        w_grad, b_grad = _gradient(X, Y, w, b)
            
        # gradient descent update
        # learning rate decay with time
        w = w - learning_rate/np.sqrt(adagrad_step) * w_grad
        b = b - learning_rate/np.sqrt(adagrad_step) * b_grad
        
        step += 1
        adagrad_step += 1

        # Compute loss and accuracy of training set and development set
        y_train_pred = _f(X_train, w, b)
        Y_train_pred = np.round(y_train_pred)
        y_eval_pred = _f(X_eval, w, b)
        Y_eval_pred = np.round(y_eval_pred)

        acc_train = _accuracy(Y_train_pred, Y_train)
        loss_train = _cross_entropy_loss(y_train_pred, Y_train) / train_size
        acc_eval = _accuracy(Y_eval_pred, Y_eval)
        loss_eval = _cross_entropy_loss(y_eval_pred, Y_eval) / eval_size

        if step % 50 == 0 or step == steps:
            print(f'Epoch {epoch}/{EPOCH}, step {step}/{steps} : train_loss = {loss_train}, train_acc = {acc_train}, eval_loss = {loss_eval}, eval_acc = {acc_eval}')

        train_acc.append(acc_train)
        train_loss.append(loss_train)
        eval_acc.append(acc_eval)
        eval_loss.append(loss_eval)

print('Training loss: {}'.format(train_loss[-1]))
print('Eval loss: {}'.format(eval_loss[-1]))
print('Training accuracy: {}'.format(train_acc[-1]))
print('Eval accuracy: {}'.format(eval_acc[-1]))

Epoch 0/10, step 50/381 : train_loss = 1.347386236965906, train_acc = 0.8488224452181037, eval_loss = 1.400598892932686, eval_acc = 0.8518245484703281


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: overflow encountered in exp
  # Remove the CWD from sys.path while we load stuff.


Epoch 0/10, step 100/381 : train_loss = 0.9152016432029676, train_acc = 0.8617857874257628, eval_loss = 0.965991675211801, eval_acc = 0.8558791006266127
Epoch 0/10, step 150/381 : train_loss = 0.7852583061703493, train_acc = 0.8539627278312513, eval_loss = 0.8467023409488244, eval_acc = 0.8497972723921858
Epoch 0/10, step 200/381 : train_loss = 0.6855500080014343, train_acc = 0.8627687896784764, eval_loss = 0.7420444348505428, eval_acc = 0.8510873571691854
Epoch 0/10, step 250/381 : train_loss = 0.6626799916718044, train_acc = 0.8518533688306369, eval_loss = 0.6843539759344478, eval_acc = 0.8462956137117582
Epoch 0/10, step 300/381 : train_loss = 0.6722368835334585, train_acc = 0.850419823878763, eval_loss = 0.7212556694191992, eval_acc = 0.8431625506819019
Epoch 0/10, step 350/381 : train_loss = 0.5415013375184299, train_acc = 0.8645095228343231, eval_loss = 0.5956548816527707, eval_acc = 0.8534832288978991
Epoch 0/10, step 380/381 : train_loss = 0.5476793641705842, train_acc = 0.8615605160761827, eval_loss = 0.5971872765737423, eval_acc = 0.8573534832288979
Epoch 1/10, step 50/381 : train_loss = 0.520197120568741, train_acc = 0.8530821216465287, eval_loss = 0.5756371351198375, eval_acc = 0.8475856984887579
Epoch 1/10, step 100/381 : train_loss = 0.4683572388702472, train_acc = 0.8602498464058981, eval_loss = 0.5151070012119039, eval_acc = 0.8496129745669001
Epoch 1/10, step 150/381 : train_loss = 0.44626484885429574, train_acc = 0.8692607003891051, eval_loss = 0.4765391936828192, eval_acc = 0.8650939918908958
Epoch 1/10, step 200/381 : train_loss = 0.46375217192688756, train_acc = 0.8594921155027647, eval_loss = 0.5209083007160215, eval_acc = 0.852930335422042
Epoch 1/10, step 250/381 : train_loss = 0.4494819665359848, train_acc = 0.8585910301044439, eval_loss = 0.4937525184406186, eval_acc = 0.8516402506450425
Epoch 1/10, step 300/381 : train_loss = 0.42552905318999246, train_acc = 0.8556010649191071, eval_loss = 0.45717340275441076, eval_acc = 0.8514559528197567
Epoch 1/10, step 350/381 : train_loss = 0.42113694236968163, train_acc = 0.8607823059594512, eval_loss = 0.46348429781456685, eval_acc = 0.852930335422042
Epoch 1/10, step 380/381 : train_loss = 0.39904321655303177, train_acc = 0.8648986278926889, eval_loss = 0.4248971078552518, eval_acc = 0.86490969406561
Epoch 2/10, step 50/381 : train_loss = 0.4346224720366914, train_acc = 0.8548023755887774, eval_loss = 0.4609538098915485, eval_acc = 0.8485071876151862
Epoch 2/10, step 100/381 : train_loss = 0.3923956025090415, train_acc = 0.8670079868933033, eval_loss = 0.42413396929246294, eval_acc = 0.8586435680058975
Epoch 2/10, step 150/381 : train_loss = 0.3995503389795209, train_acc = 0.8609870980954332, eval_loss = 0.4451562989979174, eval_acc = 0.8499815702174714
Epoch 2/10, step 200/381 : train_loss = 0.3899945539693655, train_acc = 0.8572803604341593, eval_loss = 0.43982862908042286, eval_acc = 0.8440840398083302
Epoch 2/10, step 250/381 : train_loss = 0.3593450339469139, train_acc = 0.8709809543313537, eval_loss = 0.40433358814068665, eval_acc = 0.8604865462587541
Epoch 2/10, step 300/381 : train_loss = 0.3748111111601529, train_acc = 0.8675199672332583, eval_loss = 0.4196960003401859, eval_acc = 0.8617766310357537
Epoch 2/10, step 350/381 : train_loss = 0.3703513254990469, train_acc = 0.8669260700389105, eval_loss = 0.41804964042741555, eval_acc = 0.8520088462956137
Epoch 2/10, step 380/381 : train_loss = 0.36328355503504767, train_acc = 0.8688306369035429, eval_loss = 0.395140184186746, eval_acc = 0.8608551419093254
Epoch 3/10, step 50/381 : train_loss = 0.3667617885217751, train_acc = 0.8671308621748925, eval_loss = 0.40048983606743443, eval_acc = 0.8604865462587541
Epoch 3/10, step 100/381 : train_loss = 0.35360533975074676, train_acc = 0.8685644071267663, eval_loss = 0.39086426288977333, eval_acc = 0.8650939918908958
Epoch 3/10, step 150/381 : train_loss = 0.35412973445213575, train_acc = 0.8656563587958223, eval_loss = 0.39303244637743845, eval_acc = 0.8584592701806119
Epoch 3/10, step 200/381 : train_loss = 0.3590742324953709, train_acc = 0.8645709604751177, eval_loss = 0.3944381871174818, eval_acc = 0.8617766310357537
Epoch 3/10, step 250/381 : train_loss = 0.3664068327141785, train_acc = 0.8649805447470817, eval_loss = 0.4009416310136879, eval_acc = 0.854036122373756
Epoch 3/10, step 300/381 : train_loss = 0.35723431597516137, train_acc = 0.8696498054474708, eval_loss = 0.39744904200659437, eval_acc = 0.8569848875783266
Epoch 3/10, step 350/381 : train_loss = 0.3524748289033771, train_acc = 0.8708785582633627, eval_loss = 0.3854558575255915, eval_acc = 0.8625138223368964
Epoch 3/10, step 380/381 : train_loss = 0.3590500094054613, train_acc = 0.8656768380094204, eval_loss = 0.38422321854508834, eval_acc = 0.8617766310357537
Epoch 4/10, step 50/381 : train_loss = 0.36264869668435873, train_acc = 0.8653286913782511, eval_loss = 0.39855663300624034, eval_acc = 0.85514190932547
Epoch 4/10, step 100/381 : train_loss = 0.3604975080000873, train_acc = 0.8658816301454024, eval_loss = 0.4105462641088316, eval_acc = 0.8553262071507556
Epoch 4/10, step 150/381 : train_loss = 0.3564967782256114, train_acc = 0.8667007986893304, eval_loss = 0.39057223845742817, eval_acc = 0.8595650571323259
Epoch 4/10, step 200/381 : train_loss = 0.3527179993831093, train_acc = 0.8693630964570961, eval_loss = 0.3858817452121384, eval_acc = 0.8647253962403243
Epoch 4/10, step 250/381 : train_loss = 0.36648828966601643, train_acc = 0.8644480851935286, eval_loss = 0.41029858763804716, eval_acc = 0.8568005897530409
Epoch 4/10, step 300/381 : train_loss = 0.351422769480477, train_acc = 0.8657177964366168, eval_loss = 0.39303785532877383, eval_acc = 0.8590121636564688
Epoch 4/10, step 350/381 : train_loss = 0.3687938519023856, train_acc = 0.8581609666188819, eval_loss = 0.40015912716296487, eval_acc = 0.8569848875783266
Epoch 4/10, step 380/381 : train_loss = 0.3420811103956837, train_acc = 0.8683800942043826, eval_loss = 0.3751930193928467, eval_acc = 0.8579063767047549
Epoch 5/10, step 50/381 : train_loss = 0.3302270800745182, train_acc = 0.8689944706123285, eval_loss = 0.36852577788742297, eval_acc = 0.8556948028013269
Epoch 5/10, step 100/381 : train_loss = 0.34052478475076675, train_acc = 0.8683800942043826, eval_loss = 0.37442216550948215, eval_acc = 0.8612237375598968
Epoch 5/10, step 150/381 : train_loss = 0.3405981277637372, train_acc = 0.8697522015154618, eval_loss = 0.3723756076252314, eval_acc = 0.8641725027644673
Epoch 5/10, step 200/381 : train_loss = 0.3474172130238929, train_acc = 0.8686463239811592, eval_loss = 0.3986964140186972, eval_acc = 0.867121267969038
Epoch 5/10, step 250/381 : train_loss = 0.340855490401304, train_acc = 0.8665369649805448, eval_loss = 0.3879554496988832, eval_acc = 0.8573534832288979
Epoch 5/10, step 300/381 : train_loss = 0.33366211362426634, train_acc = 0.8697112430882654, eval_loss = 0.36908480221109613, eval_acc = 0.8638039071138961
Epoch 5/10, step 350/381 : train_loss = 0.32117517430525355, train_acc = 0.8744009830022528, eval_loss = 0.3645435168908827, eval_acc = 0.8623295245116107
Epoch 5/10, step 380/381 : train_loss = 0.33272551587770516, train_acc = 0.8721073110792545, eval_loss = 0.361574603657775, eval_acc = 0.8647253962403243
Epoch 6/10, step 50/381 : train_loss = 0.3456555960901342, train_acc = 0.8687077616219537, eval_loss = 0.374727342928016, eval_acc = 0.8580906745300405
Epoch 6/10, step 100/381 : train_loss = 0.32195130178615045, train_acc = 0.873499897603932, eval_loss = 0.3622769702037688, eval_acc = 0.8658311831920383
Epoch 6/10, step 150/381 : train_loss = 0.3388200696217158, train_acc = 0.8667007986893304, eval_loss = 0.37159536068205357, eval_acc = 0.8588278658311832
Epoch 6/10, step 200/381 : train_loss = 0.3238163382581627, train_acc = 0.8731517509727627, eval_loss = 0.35688837110036886, eval_acc = 0.8614080353851825
Epoch 6/10, step 250/381 : train_loss = 0.32691372511044997, train_acc = 0.8699569936514437, eval_loss = 0.3591479516184065, eval_acc = 0.8636196092886104
Epoch 6/10, step 300/381 : train_loss = 0.32754231352360025, train_acc = 0.8680524267868114, eval_loss = 0.3682663327740257, eval_acc = 0.8603022484334685
Epoch 6/10, step 350/381 : train_loss = 0.3258001611755046, train_acc = 0.8682367397091951, eval_loss = 0.3677240242315396, eval_acc = 0.8579063767047549
Epoch 6/10, step 380/381 : train_loss = 0.32816830642216066, train_acc = 0.8693835756706942, eval_loss = 0.36171645748448766, eval_acc = 0.867121267969038
Epoch 7/10, step 50/381 : train_loss = 0.3175343573623028, train_acc = 0.873499897603932, eval_loss = 0.356350794456304, eval_acc = 0.8691485440471802
Epoch 7/10, step 100/381 : train_loss = 0.3220009954877125, train_acc = 0.8744419414294491, eval_loss = 0.358727432415702, eval_acc = 0.8634353114633248
Epoch 7/10, step 150/381 : train_loss = 0.3305955213903524, train_acc = 0.8686668031947573, eval_loss = 0.35746500804141423, eval_acc = 0.8591964614817545
Epoch 7/10, step 200/381 : train_loss = 0.32302814029642124, train_acc = 0.872250665574442, eval_loss = 0.36488833884672156, eval_acc = 0.8625138223368964
Epoch 7/10, step 250/381 : train_loss = 0.32025796652922844, train_acc = 0.8705304116321934, eval_loss = 0.36008253698973786, eval_acc = 0.8625138223368964
Epoch 7/10, step 300/381 : train_loss = 0.3219910089368282, train_acc = 0.8729674380503789, eval_loss = 0.34953452123180245, eval_acc = 0.8656468853667527
Epoch 7/10, step 350/381 : train_loss = 0.3208580070705966, train_acc = 0.8721073110792545, eval_loss = 0.35206761035830214, eval_acc = 0.8643568005897531
Epoch 7/10, step 380/381 : train_loss = 0.31430803053171624, train_acc = 0.8733565431087447, eval_loss = 0.3442739956568529, eval_acc = 0.8656468853667527
Epoch 8/10, step 50/381 : train_loss = 0.3314301109448099, train_acc = 0.8662912144173663, eval_loss = 0.3676573957819751, eval_acc = 0.8625138223368964
Epoch 8/10, step 100/381 : train_loss = 0.3255758437986244, train_acc = 0.868216260495597, eval_loss = 0.35753417552492195, eval_acc = 0.8571691854036122
Epoch 8/10, step 150/381 : train_loss = 0.31674989040755525, train_acc = 0.8749948801966004, eval_loss = 0.3516086430321963, eval_acc = 0.8614080353851825
Epoch 8/10, step 200/381 : train_loss = 0.3169229965409289, train_acc = 0.8708785582633627, eval_loss = 0.34929559101057905, eval_acc = 0.866015481017324
Epoch 8/10, step 250/381 : train_loss = 0.3125736564369679, train_acc = 0.8729879172639771, eval_loss = 0.3558988059234633, eval_acc = 0.8601179506081829
Epoch 8/10, step 300/381 : train_loss = 0.32465507233694585, train_acc = 0.8700593897194348, eval_loss = 0.35194439521460325, eval_acc = 0.8643568005897531
Epoch 8/10, step 350/381 : train_loss = 0.321653732112893, train_acc = 0.8695064509522834, eval_loss = 0.3536633993686226, eval_acc = 0.8614080353851825
Epoch 8/10, step 380/381 : train_loss = 0.3102755524934392, train_acc = 0.8753225476141716, eval_loss = 0.3415885759753436, eval_acc = 0.8687799483966089
Epoch 9/10, step 50/381 : train_loss = 0.3220828398108354, train_acc = 0.8705099324185951, eval_loss = 0.3526815872306161, eval_acc = 0.8604865462587541
Epoch 9/10, step 100/381 : train_loss = 0.31449065997699305, train_acc = 0.875158713905386, eval_loss = 0.35772978090131086, eval_acc = 0.866015481017324
Epoch 9/10, step 150/381 : train_loss = 0.3141249146891724, train_acc = 0.8734589391767356, eval_loss = 0.3482849607567331, eval_acc = 0.8678584592701806
Epoch 9/10, step 200/381 : train_loss = 0.31630341261673267, train_acc = 0.8709809543313537, eval_loss = 0.3457003170478517, eval_acc = 0.8663840766678953
Epoch 9/10, step 250/381 : train_loss = 0.3165078699037002, train_acc = 0.8719844357976654, eval_loss = 0.3381895066522098, eval_acc = 0.8691485440471802
Epoch 9/10, step 300/381 : train_loss = 0.3211023733500236, train_acc = 0.868851116117141, eval_loss = 0.3509102642777991, eval_acc = 0.8599336527828971
Epoch 9/10, step 350/381 : train_loss = 0.3133905152466426, train_acc = 0.8736842105263158, eval_loss = 0.33751488580315897, eval_acc = 0.8650939918908958
Epoch 9/10, step 380/381 : train_loss = 0.314415715887756, train_acc = 0.8699160352242474, eval_loss = 0.34212073630604034, eval_acc = 0.8632510136380391
Training loss: 0.31723862821153564
Eval loss: 0.34562381603010406
Training accuracy: 0.8690559082531231
Eval accuracy: 0.8626981201621821

Plotting Loss and accuracy curve

# Loss curve
# plt.figure(figsize=(16, 8))
plt.plot(train_loss)
plt.plot(eval_loss)
plt.title('Loss')
plt.legend(['train', 'eval'])
plt.savefig('loss.png')
plt.show()

# Accuracy curve
# plt.figure(figsize=(16, 8))
plt.plot(train_acc)
plt.plot(eval_acc)
plt.title('Accuracy')
plt.legend(['train', 'eval'])
plt.savefig('acc.png')
plt.show()

Predicting testing labels

输出到output_logistic.csv。

# Predict testing labels
predictions = _predict(X_test, w, b)
with open(output_fpath.format('logistic'), 'w') as f:
    f.write('id,label\n')
    for i, label in  enumerate(predictions):
        f.write('{},{}\n'.format(i, label))

# Print out the most significant weights
ind = np.argsort(np.abs(w))[::-1]
with open(X_test_fpath) as f:
    content = f.readline().strip('\n').split(',')
features = np.array(content)
for i in ind[0:10]:
    print(features[i], w[i])

 Local government -5.370818305378536
 Other Rel 18+ ever marr RP of subfamily -4.303398837549008
num persons worked for employer 4.177335768723998
 Neither parent present -3.8146477310419447
dividends from stocks -3.6462348599573593
 2 -3.492485219117511
 High school graduate -3.486094628398619
 Child under 18 ever married -3.405443955741911
 Grandchild 18+ never marr RP of subfamily -3.331278619262018
 Philippines -3.304985181079396


/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: overflow encountered in exp
  # Remove the CWD from sys.path while we load stuff.

Porbabilistic generative model

Preparing Data

# Parse csv files to numpy array
with open(X_train_fpath) as f:
    next(f)
    X_train = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)
with open(Y_train_fpath) as f:
    next(f)
    Y_train = np.array([line.strip('\n').split(',')[1] for line in f], dtype = float)
with open(X_test_fpath) as f:
    next(f)
    X_test = np.array([line.strip('\n').split(',')[1:] for line in f], dtype = float)

# Normalize training and testing data
X_train, X_mean, X_std = _normalize(X_train, train = True)
X_test, _, _= _normalize(X_test, train = False, specified_column = None, X_mean = X_mean, X_std = X_std)

Mean and Covariance

# Compute in-class mean
X_train_0 = np.array([x for x, y in zip(X_train, Y_train) if y == 0])
X_train_1 = np.array([x for x, y in zip(X_train, Y_train) if y == 1])

mean_0 = np.mean(X_train_0, axis = 0)
mean_1 = np.mean(X_train_1, axis = 0)  

# Compute in-class covariance
cov_0 = np.zeros((data_dim, data_dim))
cov_1 = np.zeros((data_dim, data_dim))

for x in X_train_0:
    cov_0 += np.dot(np.transpose([x - mean_0]), [x - mean_0]) / X_train_0.shape[0]
for x in X_train_1:
    cov_1 += np.dot(np.transpose([x - mean_1]), [x - mean_1]) / X_train_1.shape[0]

# Shared covariance is taken as a weighted average of individual in-class covariance.
cov = (cov_0 * X_train_0.shape[0] + cov_1 * X_train_1.shape[0]) / (X_train_0.shape[0] + X_train_1.shape[0])

Computing weights and bias

# Compute inverse of covariance matrix.
# Since covariance matrix may be nearly singular, np.linalg.inv() may give a large numerical error.
# Via SVD decomposition, one can get matrix inverse efficiently and accurately.
u, s, v = np.linalg.svd(cov, full_matrices=False)
inv = np.matmul(v.T * 1 / s, u.T)

# Directly compute weights and bias
w = np.dot(inv, mean_0 - mean_1)
b =  (-0.5) * np.dot(mean_0, np.dot(inv, mean_0)) + 0.5 * np.dot(mean_1, np.dot(inv, mean_1))\
    + np.log(float(X_train_0.shape[0]) / X_train_1.shape[0]) 

# Compute accuracy on training set
Y_train_pred = 1 - _predict(X_train, w, b)
print('Training accuracy: {}'.format(_accuracy(Y_train_pred, Y_train)))

Training accuracy: 0.8700420230020642

Predicting testing labels

Predictions are saved to output_generative.csv.

# Predict testing labels
predictions = 1 - _predict(X_test, w, b)
with open(output_fpath.format('generative'), 'w') as f:
    f.write('id,label\n')
    for i, label in  enumerate(predictions):
        f.write('{},{}\n'.format(i, label))

# Print out the most significant weights
ind = np.argsort(np.abs(w))[::-1]
with open(X_test_fpath) as f:
    content = f.readline().strip('\n').split(',')
features = np.array(content)
for i in ind[0:10]:
ictions):
        f.write('{},{}\n'.format(i, label))

# Print out the most significant weights
ind = np.argsort(np.abs(w))[::-1]
with open(X_test_fpath) as f:
    content = f.readline().strip('\n').split(',')
features = np.array(content)
for i in ind[0:10]:
    print(features[i], w[i])

 Retail trade 7.166015625
 34 -5.45166015625
 37 -5.06494140625
 Other service -4.76123046875
 29 4.462890625
 Forestry and fisheries -4.35546875
 Public administration -3.927734375
 28 3.796875
 40 -3.7880859375
 Abroad 3.59765625

码农公寓

李宏毅机器学习2021系列

作业2-年收入判断

项目描述

数据集介绍

项目要求

数据准备

环境配置/安装

Preparing Data

Some Useful Functions

Functions about gradient and loss

Trainning

Plotting Loss and accuracy curve

Predicting testing labels

Porbabilistic generative model

Preparing Data

Mean and Covariance

Computing weights and bias

Predicting testing labels

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