机器学习-线性回归法

线性回归算法

解决回归问题
思想简单，实现容易
许多强大的非线性模型的基础
结果具有很好的可解释性
蕴含机器学习中的很多重要思想

样本特征只有一个，称为：简单线性回归

通过分析问题，确定问题的损失函数或者效用函数

通过最优化损失函数或者效用函数，获得机器学习的模型

几乎所有参数学习算法都是这样的套路

最小二乘法

代码实现简单线性回归法

加载数据

python 复制代码

import numpy as np
import matplotlib.pyplot as plt
x = np.array([1., 2., 3., 4., 5.])
y = np.array([1., 3., 2., 3., 5.])
plt.scatter(x, y)
plt.axis([0, 6, 0, 6])
plt.show()

计算过程

python 复制代码

x_mean = np.mean(x)
y_mean = np.mean(y)
num = 0.0
d = 0.0
for x_i, y_i in zip(x, y):
    num += (x_i - x_mean) * (y_i - y_mean)
    d += (x_i - x_mean) ** 2

a = num/d
b = y_mean - a * x_mean
y_hat = a * x + b

绘图

python 复制代码

plt.scatter(x, y)
plt.plot(x, y_hat, color='r')
plt.axis([0, 6, 0, 6])
plt.show()

封装我们自己的SimpleLinearRegression

python 复制代码

import numpy as np


class SimpleLinearRegression1:

    def __init__(self):
        """初始化Simple Linear Regression 模型"""
        self.a_ = None
        self.b_ = None

    def fit(self, x_train, y_train):
        """根据训练数据集x_train,y_train训练Simple Linear Regression模型"""
        assert x_train.ndim == 1, \
            "Simple Linear Regressor can only solve single feature training data."
        assert len(x_train) == len(y_train), \
            "the size of x_train must be equal to the size of y_train"

        x_mean = np.mean(x_train)
        y_mean = np.mean(y_train)

        num = 0.0
        d = 0.0
        for x, y in zip(x_train, y_train):
            num += (x - x_mean) * (y - y_mean)
            d += (x - x_mean) ** 2

        self.a_ = num / d
        self.b_ = y_mean - self.a_ * x_mean

        return self

    def predict(self, x_predict):
        """给定待预测数据集x_predict，返回表示x_predict的结果向量"""
        assert x_predict.ndim == 1, \
            "Simple Linear Regressor can only solve single feature training data."
        assert self.a_ is not None and self.b_ is not None, \
            "must fit before predict!"

        return np.array([self._predict(x) for x in x_predict])

    def _predict(self, x_single):
        """给定单个待预测数据x，返回x的预测结果值"""
        return self.a_ * x_single + self.b_

    def __repr__(self):
        return "SimpleLinearRegression1()"

向量化运算

python 复制代码

class SimpleLinearRegression2:

    def __init__(self):
        """初始化Simple Linear Regression模型"""
        self.a_ = None
        self.b_ = None

    def fit(self, x_train, y_train):
        """根据训练数据集x_train,y_train训练Simple Linear Regression模型"""
        assert x_train.ndim == 1, \
            "Simple Linear Regressor can only solve single feature training data."
        assert len(x_train) == len(y_train), \
            "the size of x_train must be equal to the size of y_train"

        x_mean = np.mean(x_train)
        y_mean = np.mean(y_train)

        self.a_ = (x_train - x_mean).dot(y_train - y_mean) / (x_train - x_mean).dot(x_train - x_mean)
        self.b_ = y_mean - self.a_ * x_mean

        return self

    def predict(self, x_predict):
        """给定待预测数据集x_predict，返回表示x_predict的结果向量"""
        assert x_predict.ndim == 1, \
            "Simple Linear Regressor can only solve single feature training data."
        assert self.a_ is not None and self.b_ is not None, \
            "must fit before predict!"

        return np.array([self._predict(x) for x in x_predict])

    def _predict(self, x_single):
        """给定单个待预测数据x_single，返回x_single的预测结果值"""
        return self.a_ * x_single + self.b_

    def __repr__(self):
        return "SimpleLinearRegression2()"

衡量线性回归法的指标：MSE，RMSE和MAE

代码演示

加载波士顿房产数据

python 复制代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
boston = datasets.load_boston()
x = boston.data[:,5] # 只使用房间数量这个特征
y = boston.target

可视化

python 复制代码

plt.scatter(x, y)
plt.show()

去除最大值

python 复制代码

x = x[y < 50.0]
y = y[y < 50.0]
plt.scatter(x, y)
plt.show()

使用简单线性回归法

python 复制代码

from playML.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, seed=666)
from playML.SimpleLinearRegression import SimpleLinearRegression
reg = SimpleLinearRegression()
reg.fit(x_train, y_train)
plt.scatter(x_train, y_train)
plt.plot(x_train, reg.predict(x_train), color='r')
plt.show()

预测

python 复制代码

y_predict = reg.predict(x_test)

MSE

python 复制代码

mse_test = np.sum((y_predict - y_test)**2) / len(y_test)

RMSE

python 复制代码

from math import sqrt

rmse_test = sqrt(mse_test)

MAE

python 复制代码

mae_test = np.sum(np.absolute(y_predict - y_test))/len(y_test)

封装

python 复制代码

import numpy as np
from math import sqrt


def accuracy_score(y_true, y_predict):
    """计算y_true和y_predict之间的准确率"""
    assert len(y_true) == len(y_predict), \
        "the size of y_true must be equal to the size of y_predict"

    return np.sum(y_true == y_predict) / len(y_true)


def mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的MSE"""
    assert len(y_true) == len(y_predict), \
        "the size of y_true must be equal to the size of y_predict"

    return np.sum((y_true - y_predict)**2) / len(y_true)


def root_mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的RMSE"""

    return sqrt(mean_squared_error(y_true, y_predict))


def mean_absolute_error(y_true, y_predict):
    """计算y_true和y_predict之间的MAE"""

    return np.sum(np.absolute(y_true - y_predict)) / len(y_true)

scikit-learn中的MSE和MAE

python 复制代码

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
mean_squared_error(y_test, y_predict)
mean_absolute_error(y_test, y_predict)

评价回归算法指标：R Square

代码

python 复制代码

1 - mean_squared_error(y_test, y_predict)/np.var(y_test)

封装

python 复制代码

import numpy as np
from math import sqrt


def accuracy_score(y_true, y_predict):
    """计算y_true和y_predict之间的准确率"""
    assert len(y_true) == len(y_predict), \
        "the size of y_true must be equal to the size of y_predict"

    return np.sum(y_true == y_predict) / len(y_true)


def mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的MSE"""
    assert len(y_true) == len(y_predict), \
        "the size of y_true must be equal to the size of y_predict"

    return np.sum((y_true - y_predict)**2) / len(y_true)


def root_mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的RMSE"""

    return sqrt(mean_squared_error(y_true, y_predict))


def mean_absolute_error(y_true, y_predict):
    """计算y_true和y_predict之间的MAE"""
    assert len(y_true) == len(y_predict), \
        "the size of y_true must be equal to the size of y_predict"

    return np.sum(np.absolute(y_true - y_predict)) / len(y_true)


def r2_score(y_true, y_predict):
    """计算y_true和y_predict之间的R Square"""

    return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)

scikit-learn中的 r2_score

python 复制代码

from sklearn.metrics import r2_score

r2_score(y_test, y_predict)

多元线性回归

代码实现

python 复制代码

import numpy as np
from .metrics import r2_score


class LinearRegression:

    def __init__(self):
        """初始化Linear Regression模型"""
        self.coef_ = None
        self.intercept_ = None
        self._theta = None

    def fit_normal(self, X_train, y_train):
        """根据训练数据集X_train, y_train训练Linear Regression模型"""
        assert X_train.shape[0] == y_train.shape[0], \
            "the size of X_train must be equal to the size of y_train"

        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
        self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)

        self.intercept_ = self._theta[0]
        self.coef_ = self._theta[1:]

        return self

    def predict(self, X_predict):
        """给定待预测数据集X_predict，返回表示X_predict的结果向量"""
        assert self.intercept_ is not None and self.coef_ is not None, \
            "must fit before predict!"
        assert X_predict.shape[1] == len(self.coef_), \
            "the feature number of X_predict must be equal to X_train"

        X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
        return X_b.dot(self._theta)

    def score(self, X_test, y_test):
        """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""

        y_predict = self.predict(X_test)
        return r2_score(y_test, y_predict)

    def __repr__(self):
        return "LinearRegression()"

使用

python 复制代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
boston = datasets.load_boston()

X = boston.data
y = boston.target

X = X[y < 50.0]
y = y[y < 50.0]
from playML.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666)
from playML.LinearRegression import LinearRegression

reg = LinearRegression()
reg.fit_normal(X_train, y_train)

scikit-learn中的线性回归

加载数据

python 复制代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
boston = datasets.load_boston()

X = boston.data
y = boston.target

X = X[y < 50.0]
y = y[y < 50.0]

from playML.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666)

线性回归

python 复制代码

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)

kNN Regressor

python 复制代码

from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
standardScaler.fit(X_train, y_train)
X_train_standard = standardScaler.transform(X_train)
X_test_standard = standardScaler.transform(X_test)

python 复制代码

from sklearn.neighbors import KNeighborsRegressor
knn_reg = KNeighborsRegressor()
knn_reg.fit(X_train_standard, y_train)
knn_reg.score(X_test_standard, y_test)

超参数

python 复制代码

from sklearn.model_selection import GridSearchCV

param_grid = [
    {
        "weights": ["uniform"],
        "n_neighbors": [i for i in range(1, 11)]
    },
    {
        "weights": ["distance"],
        "n_neighbors": [i for i in range(1, 11)],
        "p": [i for i in range(1,6)]
    }
]

knn_reg = KNeighborsRegressor()
grid_search = GridSearchCV(knn_reg, param_grid, n_jobs=-1, verbose=1)
grid_search.fit(X_train_standard, y_train)

机器学习-线性回归法

线性回归算法

最小二乘法

代码实现 简单线性回归法

向量化运算

衡量线性回归法的指标：MSE，RMSE和MAE

评价回归算法指标：R Square

多元线性回归

scikit-learn中的线性回归

代码实现简单线性回归法