Hi Olivier, Thanks for the suggestion. The package seems to be handy. I will try that.
Regards, Mahmood On Sun, Jan 24, 2021 at 12:55 PM Oliver Tomic via scikit-learn <scikit-learn@python.org> wrote: > > Hi Mahmood, > > the information you need is given by the individual explained variance for > each variable / feature. You get that information from the hoggorm package > (Python): > > https://github.com/olivertomic/hoggorm > https://hoggorm.readthedocs.io/en/latest/index.html > > Here is one of the PCA examples provided in a Jupyter notebook: > https://github.com/olivertomic/hoggorm/blob/master/examples/PCA/PCA_on_cancer_data.ipynb > > > When you do PCA you get the information by calling for example: > > cumCalExplVar_individualVariable = model.X_cumCalExplVar() (which gives you > the cumulative calibrated explained variance for each variable, cell 21 in > the notebook) > > cumValExplVar_individualVariable = model.X_cumValExplVar_indVar() (which > gives you the cumulative validated explained variance variable, cell 30 in > the notebook) > > > The component where you get the biggest jump for the variable of interest is > the component you are looking for. > > You could also have a look at the correlation loadings to identify the > component you are looking for. > > cheers > Oliver > > > > > > > ---- On Fri, 22 Jan 2021 21:48:46 +0100 Mahmood Naderan > <mahmood...@gmail.com> wrote ---- > > Hi > Thanks for the replies. I read about the available functions in the > PCA section. Consider the following code > > x = StandardScaler().fit_transform(x) > pca = PCA() > principalComponents = pca.fit_transform(x) > principalDf = pd.DataFrame(data = principalComponents) > loadings = pca.components_ > finalDf = pd.concat([principalDf, pd.DataFrame(targets, columns=['kernel'])], > 1) > print( "First and second observations\n", finalDf.loc[0:1] ) > print( "loadings[0:1]\n", loadings[0], loadings[1] ) > print ("explained_variance_ratio_\n",pca.explained_variance_ratio_) > > > The output looks like > > First and second observations > 0 1 2 3 4 kernel > 0 2.959846 -0.184307 -0.100236 0.533735 -0.002227 ELEC1 > 1 0.390313 1.805239 0.029688 -0.502359 -0.002350 ELECT2 > loadings[0:1] > [0.21808984 0.49137412 0.46511098 0.49735819 0.49728754] [-0.94878375 > -0.01257726 0.29718078 0.07493325 0.07562934] > explained_variance_ratio_ > [7.80626876e-01 1.79854061e-01 2.50729844e-02 1.44436687e-02 2.40984767e-06] > > > > As you can see for two kernels named ELEC1 and ELEC2, there are five > PCs from 0 to 4. > Now based on the numbers in the loadings, I expect that loadings[0] > which is the first variable is better shown on PC1-PC2 plane > (0.49137412,0.46511098). However, loadings[1] which is the second > variable is better shown on PC0-PC2 plane (-0.94878375,0.29718078). > Is this understanding correct? > > I don't understand what explained_variance_ratio_ is trying to say here. > > > Regards, > Mahmood > > On Fri, Jan 22, 2021 at 11:52 AM Nicolas Hug <nio...@gmail.com> wrote: > > > > Hi Mahmood, > > > > There are different pieces of info that you can get from PCA: > > > > 1. How important is a given PC to reconstruct the entire dataset -> This > > is given by explained_variance_ratio_ as Guillaume suggested > > > > 2. What is the contribution of each feature to each PC (remember that a > > PC is a linear combination of all the features i.e.: PC_1 = X_1 . > > alpha_11 + X_2 . alpha_12 + ... X_m . alpha_1m). The alpha_ij are what > > you're looking for and they are given in the components_ matrix which is > > a n_components x n_features matrix. > > > > Nicolas > > > > On 1/22/21 9:13 AM, Mahmood Naderan wrote: > > > Hi > > > I have a question about PCA and that is, how we can determine, a > > > variable, X, is better captured by which factor (principal > > > component)? For example, maybe one variable has low weight in the > > > first PC but has a higher weight in the fifth PC. > > > > > > When I use the PCA from Scikit, I have to manually work with the PCs, > > > therefore, I may miss the point that although a variable is weak in > > > PC1-PC2 plot, it may be strong in PC4-PC5 plot. > > > > > > Any comment on that? > > > > > > Regards, > > > Mahmood > > > _______________________________________________ > > > scikit-learn mailing list > > > scikit-learn@python.org > > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
