py-ddspls 1.0.99

The multi data driven sparse pls package

mddspls is the python light package of the data-driven sparse PLS algorithm

In the high dimensional settings (large number of variables), one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity of the sub-space selection. We propose a PLS, Partial Least Square, based method, called dd-sPLS for data-driven-sparse PLS, allowing jointly variable selection and subspace estimation while training and testing missing data imputation through a new algorithm called Koh-Lanta.

It contains one main class mddspls and one associated important method denote predict permitting to predict from a new dataset. The function called perf_mddsPLS permits to compute cross-validation.

Data simulation

One might be interested to simulate data and test the package through regression and classification:

#!/usr/bin/env python

import py_ddspls
import numpy as np
import sklearn.metrics as sklm
n = 100
mean = (0,0,0,0,0,0,0,0,0)
cov = [[1, 0.8,0.8,0.8,0.1,0.1,0.1,0.1,0.1],
[0.8,1, 0.8,0.8,0.1,0.1,0.1,0.1,0.1],
[0.8,0.8,1, 0.8,0.1,0.1,0.1,0.1,0.1],
[0.8,0.8,0.8,1, 0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1]]
df = np.random.multivariate_normal(mean, cov, n)
Y = df[:,[0]]
k_groups = 2
lolo = np.linspace(min(Y),max(Y),k_groups+1)
Y_bin = np.zeros(n)
for ii in range(n):
        for k_i in range(k_groups):
                if (Y[ii]>=lolo[k_i])&(Y[ii]<lolo[k_i+1]):
                        Y_bin[ii] = k_i
                if Y[ii]==lolo[k_groups]:
                        Y_bin[ii] = k_groups-1

Y = df[:,[0,2]]
X0 = df[:,[1,4,5]]
X0[0,:] = None
X1 = df[:,[6,8]]
X1[:,1] = 1
X2 = df[:,[3,7]]
Xs = {0:X0,1:X1,2:X2}
pos_0 = np.where(Y_bin==0)[0]
pos_1 = np.where(Y_bin==1)[0]
Y_classif = np.repeat("Class 2",n)
Y_classif[pos_1] = "Class 1"

The dd-sPLS regularization parameter is fixed to 0.6:

lambd=0.6

A train/test dataset is defined:

id_train = range(30,100)
id_test = range(30)
Xtrain = {0:X0[id_train,:],1:X1[id_train,:],2:X2[id_train,:]}
Ytrain = Y[id_train,:]
Xtest = {0:X0[id_test,:],1:X1[id_test,:],2:X2[id_test,:]}

Regression analysis

Let us produce 2 axes:

R=2

Start model building and tcheck results with sklearn tools:

mod_0=py_ddspls.model.ddspls(Xtrain,Ytrain,lambd=lambd,R=R,mode="reg",verbose=True)
Y_est_reg = mod_0.predict(Xtest)
print(sklm.mean_squared_error(Y[id_test,:],Y_est_reg))

Cross validation can be performed with built tools, the parameter NCORES permits to use parallellization:

perf_model_reg = py_ddspls.model.perf_ddspls(Xs,Y,R=R,kfolds="loo",n_lambd=10,NCORES=4,mode="reg")
print(perf_model_reg)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(perf_model_reg[:,1], perf_model_reg[:,2], 'r',perf_model_reg[:,1], perf_model_reg[:,3],'b')
plt.legend(('Y_1 RMSE', 'Y_2 RMSE'),loc='upper')
plt.title('Leave-One-Out Cross-validation error against $\lambda$')
plt.xlabel('$\lambda$')
plt.ylabel('$RMSE$')
plt.show()

Classification analysis

Let us produce 1 axis:

R=1

Start model building and tcheck results with sklearn tools:

mod_0_classif=py_ddspls.model.ddspls(Xs,Y_bin,lambd=lambd,R=R,mode="clas",verbose=True)
Y_est = mod_0_classif.predict(Xtest)
print(sklm.classification_report(Y_est, Y_classif[id_test]=='Class 1'))

Cross validation can be performed with built tools, the parameter NCORES permits to use parallellization:

perf_model_class = py_ddspls.model.perf_ddspls(Xs,Y_classif,R=1,kfolds="loo,n_lambd=10,NCORES=5,mode="classif")
print(perf_model_class)

Enjoy :)