Python偏最小二乘回归(PLSR)测试
scikit learn机器学习包中包含了偏最小二乘回归,所以可以调用对应的函数来实现
class sklearn.cross_decomposition.PLSRegression(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)
参数信息:
Parameters: |
n_components : int, (default 2)
scale : boolean, (default True)
max_iter : an integer, (default 500)
tol : non-negative real
copy : boolean, default True
|
---|---|
Attributes: |
x_weights_ : array, [p, n_components]
y_weights_ : array, [q, n_components]
x_loadings_ : array, [p, n_components]
y_loadings_ : array, [q, n_components]
x_scores_ : array, [n_samples, n_components]
y_scores_ : array, [n_samples, n_components]
x_rotations_ : array, [p, n_components]
y_rotations_ : array, [q, n_components]
coef_: array, [p, q] :
n_iter_ : array-like
|
Notes
Matrices:
T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__
Are computed such that:
X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(-1) y_rotations_ = C (Q.T C)^(-1)
where Xk and Yk are residual matrices at iteration k.
Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>
For each component k, find weights u, v that optimizes: max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that |u| = 1
Note that it maximizes both the correlations between the scores and the intra-block variances.
The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score.
The residual matrix of Y (Yk+1) block is obtained by deflation on the current X score. This performs the PLS regression known as PLS2. This mode is prediction oriented.
This implementation provides the same results that 3 PLS packages provided in the R language (R-project):
- “mixOmics” with function pls(X, Y, mode = “regression”)
- “plspm ” with function plsreg2(X, Y)
- “pls” with function oscorespls.fit(X, Y)
References
Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
Examples
>>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) ... PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> Y_pred = pls2.predict(X)
Methods
fit(X, Y) | Fit model to data. |
fit_transform(X[, y]) | Learn and apply the dimension reduction on the train data. |
get_params([deep]) | Get parameters for this estimator. |
predict(X[, copy]) | Apply the dimension reduction learned on the train data. |
score(X, y[, sample_weight]) | Returns the coefficient of determination R^2 of the prediction. |
set_params(**params) | Set the parameters of this estimator. |
transform(X[, Y, copy]) | Apply the dimension reduction learned on the train data. |
- __init__(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)[source]
- fit(X, Y)[source]
-
Fit model to data.
Parameters: X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples in the number of samples and n_features is the number of predictors.
Y : array-like of response, shape = [n_samples, n_targets]
Target vectors, where n_samples in the number of samples and n_targets is the number of response variables.
- fit_transform(X, y=None, **fit_params)[source]
-
Learn and apply the dimension reduction on the train data.
Parameters: X : array-like of predictors, shape = [n_samples, p]
Training vectors, where n_samples in the number of samples and p is the number of predictors.
Y : array-like of response, shape = [n_samples, q], optional
Training vectors, where n_samples in the number of samples and q is the number of response variables.
copy : boolean, default True
Whether to copy X and Y, or perform in-place normalization.
Returns: x_scores if Y is not given, (x_scores, y_scores) otherwise. :
- get_params(deep=True)[source]
-
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(X, copy=True)[source]
-
Apply the dimension reduction learned on the train data.
Parameters: X : array-like of predictors, shape = [n_samples, p]
Training vectors, where n_samples in the number of samples and p is the number of predictors.
copy : boolean, default True
Whether to copy X and Y, or perform in-place normalization.
Notes
This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space.
- score(X, y, sample_weight=None)[source]
-
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.
- set_params(**params)[source]
-
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(X, Y=None, copy=True)[source]
-
Apply the dimension reduction learned on the train data.
Parameters: X : array-like of predictors, shape = [n_samples, p]
Training vectors, where n_samples in the number of samples and p is the number of predictors.
Y : array-like of response, shape = [n_samples, q], optional
Training vectors, where n_samples in the number of samples and q is the number of response variables.
copy : boolean, default True
Whether to copy X and Y, or perform in-place normalization.
Returns: x_scores if Y is not given, (x_scores, y_scores) otherwise. :
Python偏最小二乘回归(PLSR)测试相关推荐
- python 偏最小二乘回归实现
用自己数据实现偏最小二乘回归.用Hitters数据集做演示如何使用自己的数据实现偏最小二乘回归. 此数据集有322个运动员的20个变量的数据, 其中的变量Salary(工资)是我们关心的. 数据下载 ...
- 【数学建模学习】偏最小二乘回归PLSR原理和板子
解决问题方向:对于n个自变量,m个因变量的建模问题. 原理:首先在自变量集中提出第一主成分u1(PCA,x1-xn的线性组合),同时在因变量中集中提出第一主成分v1,并要求u1,v1的相关程度达到最大 ...
- python 偏最小二乘回归
用sklearn库带的Linnerud数据集做示例 Linnerud数据集包含三个因变量和三个自变量 import pandas as pd from sklearn.cross_decomposit ...
- 偏最小二乘回归(PLSR)
个人理解,有误请指出.
- matlab偏最小二乘截距,matlab代写偏最小二乘回归(PLSR)和主成分回归(PCR)
原标题:matlab代写偏最小二乘回归(PLSR)和主成分回归(PCR) 原文:http://tecdat.cn/?p=2655 此示例显示如何在matlab中应用偏最小二乘回归(PLSR)和主成分回 ...
- 偏最小二乘回归(PLSR)和主成分回归(PCR)
本项目中我们被要求显示如何在matlab中应用偏最小二乘回归(PLSR)和主成分回归(PCR),并讨论这两种方法的有效性.当存在大量预测变量时,PLSR和PCR都是对因变量建模的方法,并且这些预测变量 ...
- MATLAB中的偏最小二乘回归(PLSR)和主成分回归(PCR)
通过Matlab示例来解释偏最小二乘的原理 此示例显示如何在matlab中应用偏最小二乘回归(PLSR)和主成分回归(PCR),并讨论这两种方法的有效性. 当存在大量预测变量时,PLSR和PCR都是对 ...
- python pls_【建模应用】PLS偏最小二乘回归原理与应用
1.回归 "回归"一词来源于对父母身高对于子女身高影响的研究.有人对父母的身高与子女身高做统计,发现除了父母高则子女普遍高的常识性结论外,子女的身高总是"趋向" ...
- 偏最小二乘回归(partial least squares regression,PLSR)
参考博客:典型相关分析.偏最小二乘回归 给定数据自变量XXX和因变量YYY, 最小二乘回归:找的是一个线性变换AAA,让∥Y−XA∥F\|Y - XA\|_F∥Y−XA∥F最小 典型相关分析:找的是 ...
最新文章
- thrift RPC接口请求超时
- 图解Numpy的tile函数
- javafx css颜色_JavaFX技巧7:使用CSS颜色常量/派生颜色
- python prettytable表格列数太多_excel列数太多了怎么办
- 阿里云总裁张建锋:新型计算体系结构正在形成
- python三种变量方式_python2.x 3种变量形式调用
- 日本的“电力路由器”概述
- 爬取TAPTAP游戏应用榜单
- selenium tips
- SpringBoot使用Jib将应用快速打包成Docker镜像
- 计算机科学与技术学院老师颁奖词,各种颁奖词收集与各类奖学金、各种称号、各种职位中英文对照(个人简历用得上)合集.doc...
- JVM|记一次生产环境 CPU 占用飙高问题解决
- 三周爆赚千万 电竞选手在无聊猿游戏赢麻了
- 防御sql注入之参数化查询
- Win10怎么关闭smartscreen筛选器检测功能?
- android如何使用gif动画效果,Android中用GifView显示Gif动画及Gifview简介
- 阿里easyexcel通过模板导出excel
- 物联网卡拉开智能家居变革序幕
- Fiddler(三)- Fiddler命令行和HTTP断点调试
- android知乎日报中的动画,开发Android知乎日报(一)简介