从样本集进行归纳的方法是建立这些样本的模型，然后使用这个模型进行预测，这叫作基于模型学习（Model-based learning）。
例如，你想知道钱是否能让人快乐？下面是一个简单的基于线性模型的案例。
Python ≥3.5
import sys
assert sys.version_info >= (3, 5)
Scikit-Learn ≥0.20
import sklearn
assert sklearn.__version__ >= "0.20"
加载数据
数据所在路径设置
import os
datapath = os.path.join("datasets", "lifesat", "")
print(datapath)
datasets/lifesat/
从 OECD 网站下载了 Better Life Index 指数数据，如下：
import numpy as np
import pandas as pd
oecd_bli = pd.read_csv(datapath + "oecd_bli_2015.csv", thousands=',') # thousands 设置千位分隔符；
oecd_bli.head()
LOCATION Country INDICATOR Indicator MEASURE Measure INEQUALITY Inequality Unit Code Unit PowerCode Code PowerCode Reference Period Code Reference Period Value Flag Codes Flags 0 AUS Australia HO_BASE Dwellings without basic facilities L Value TOT Total PC Percentage 0 units NaN NaN 1.1 E Estimated value 1 AUT Austria HO_BASE Dwellings without basic facilities L Value TOT Total PC Percentage 0 units NaN NaN 1.0 NaN NaN 2 BEL Belgium HO_BASE Dwellings without basic facilities L Value TOT Total PC Percentage 0 units NaN NaN 2.0 NaN NaN 3 CAN Canada HO_BASE Dwellings without basic facilities L Value TOT Total PC Percentage 0 units NaN NaN 0.2 NaN NaN 4 CZE Czech Republic HO_BASE Dwellings without basic facilities L Value TOT Total PC Percentage 0 units NaN NaN 0.9 NaN NaN
从 IMF 下载了人均 GDP 数据，如下：
gdp_per_capita = pd.read_csv(datapath + "gdp_per_capita.csv", thousands=',', # per capita 人均
delimiter='\t', encoding='latin1', na_values="n/a")
gdp_per_capita.head()
Country Subject Descriptor Units Scale Country/Series-specific Notes 2015 Estimates Start After 0 Afghanistan Gross domestic product per capita, current prices U.S. dollars Units See notes for: Gross domestic product, curren… 599.994 2013.0 1 Albania Gross domestic product per capita, current prices U.S. dollars Units See notes for: Gross domestic product, curren… 3995.383 2010.0 2 Algeria Gross domestic product per capita, current prices U.S. dollars Units See notes for: Gross domestic product, curren… 4318.135 2014.0 3 Angola Gross domestic product per capita, current prices U.S. dollars Units See notes for: Gross domestic product, curren… 4100.315 2014.0 4 Antigua and Barbuda Gross domestic product per capita, current prices U.S. dollars Units See notes for: Gross domestic product, curren… 14414.302 2011.0
准备数据
This function just merges the OECD’s life satisfaction data and the IMF’s GDP per capita data. It’s a bit too long and boring and it’s not specific to Machine Learning, which is why I left it out of the book.
def prepare_country_stats(oecd_bli, gdp_per_capita):
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,
left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
country_stats.head()
GDP per capita Life satisfaction Country Russia 9054.914 6.0 Turkey 9437.372 5.6 Hungary 12239.894 4.9 Poland 12495.334 5.8 Slovak Republic 15991.736 6.1
可视化数据
import matplotlib.pyplot as plt
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()
线性回归
import sklearn.linear_model
model = sklearn.linear_model.LinearRegression()
训练模型
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
model.fit(X, y)
LinearRegression()
根据模型进行预测
X_new = [[22587]] # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.96242338]]
[[5.96242338]]
总结
read_csv参数
thousands=',': 千位分隔符；可以将”1,000″转换为 int 型的1000；delimiter='\t': sep的替代参数，csv文件分隔符可能为”,” or “\t”，可用sublime查看；encoding='latin1': 确定正确的编码方式才能正确解码；vim this file andset fileencoding即可显示编码格式；na_values="n/a": 缺少值处理，可参考https://blog.csdn.net/weixin_44520259/article/details/106053987 ；
学习重点是机器学习原理，对于numpy，pandas之类的不熟悉的遇到了就学一下，不需要系统的学习，抓住重点！
Original: https://www.cnblogs.com/kphang/p/16359908.html
Author: KpHang
Title: Model-based learning 简单实践
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/567374/
转载文章受原作者版权保护。转载请注明原作者出处！