pca | pca : A Python Package for Principal Component Analysis | Machine Learning library

 by   erdogant Jupyter Notebook Version: 2.0.3 License: MIT

kandi X-RAY | pca Summary

kandi X-RAY | pca Summary

pca is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning applications. pca has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

pca is a python package to perform Principal Component Analysis and to create insightful plots. The core of PCA is build on sklearn functionality to find maximum compatibility when combining with other packages. But this package can do a lot more. Besides the regular pca, it can also perform SparsePCA, and TruncatedSVD. Depending on your input data, the best approach will be choosen.
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            kandi-support Support

              pca has a low active ecosystem.
              It has 227 star(s) with 36 fork(s). There are 5 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 10 open issues and 31 have been closed. On average issues are closed in 28 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of pca is 2.0.3

            kandi-Quality Quality

              pca has no bugs reported.

            kandi-Security Security

              pca has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              pca is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pca releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pca and discovered the below as its top functions. This is intended to give you an instant insight into pca implemented functionality, and help decide if they suit your requirements.
            • Fit the PCA model
            • Shortellings T2 test
            • Explained variance
            • Compute outliers T2
            • Performs preprocessing
            • Transform the fitted data
            • Plot a scatter plot
            • Get the cartesian coordinates
            • Preprocessing step
            • Compute the topfeature of the model
            • Embed pdf in rst
            • Return all files in the given directory
            • Write css to rst file
            • Imports an example
            • Imports example data
            • Plot a feature
            • Make a 3d scatter plot
            • Compute the mean variance of the data
            • Fit the model to the input data
            • Make a 3D biplot plot of features
            • Compute the outliers for a given PCE test
            • Import example dataset
            • Performs a Fisher - T2 test on the data
            • Plot PCA
            • Transform the fitted model
            • Convert notebook to html
            Get all kandi verified functions for this library.

            pca Key Features

            No Key Features are available at this moment for pca.

            pca Examples and Code Snippets

            Benchmark pca .
            pythondot img1Lines of Code : 49dot img1no licencesLicense : No License
            copy iconCopy
            def benchmark_pca():
                Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
                print("Performing logistic regression...")
            
                N, D = Xtrain.shape
                Ytrain_ind = np.zeros((N, 10))
                for i in range(N):
                    Ytrain_ind[i, Ytrain[i]] = 1
            
                 

            Community Discussions

            QUESTION

            How to sort lines of text alphabetically based on a part of each line?
            Asked 2021-Jun-12 at 08:18

            I have a text file that contains abbreviations like so (simplified example):

            ...

            ANSWER

            Answered 2021-Jun-11 at 10:22

            Here’s a ‘tidyverse’ solution:

            Source https://stackoverflow.com/questions/67934669

            QUESTION

            Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn
            Asked 2021-Jun-11 at 14:09

            Based on the guide Implementing PCA in Python, by Sebastian Raschka I am building the PCA algorithm from scratch for my research purpose. The class definition is:

            ...

            ANSWER

            Answered 2021-Jun-11 at 12:52

            When calculating an eigenvector you may change its sign and the solution will also be a valid one.

            So any PCA axis can be reversed and the solution will be valid.

            Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

            Source https://stackoverflow.com/questions/67932137

            QUESTION

            Partial results on Weka attribute selection
            Asked 2021-Jun-11 at 03:14

            When I run PCA in WEKA GUI using "Select Attribute", I dont get a complete results instead a partial results with dots at the end.

            0.8205 1 -0.493Capacity at 10th Cycle-0.483Capacity at 5th Cycle-0.473Capacity at 50th Cycle-0.261S [M]in Electrolyte -0.256C wt %...

            Is there any way to solve this particular issue ?

            ...

            ANSWER

            Answered 2021-Jun-11 at 03:14

            By default, a maximum of 5 attribute names are included in the generated names.

            If you want all of them, use -1 for the -A option (or maximumAttributeNames property in the GOE).

            Source https://stackoverflow.com/questions/67925969

            QUESTION

            How to colour code a PCA plot based on the data frame cell names?
            Asked 2021-Jun-07 at 20:35
            data.matrix <- matrix(nrow=100, ncol=10)
            colnames(data.matrix) <- c(
              paste("wt", 1:5, sep=""),
              paste("ko", 1:5, sep=""))
            rownames(data.matrix) <- paste("gene", 1:100, sep="")
            for (i in 1:100) {
              wt.values <- rpois(5, lambda=sample(x=10:1000, size=1))
              ko.values <- rpois(5, lambda=sample(x=10:1000, size=1))
             
              data.matrix[i,] <- c(wt.values, ko.values)
            }
            head(data.matrix)
            dim(data.matrix)
            
            pca <- prcomp(t(data.matrix), scale=TRUE) 
            
            intall.packages("ggplot2")
            library(ggplot2)
             
            pca.data <- data.frame(Sample=rownames(pca$x),
              X=pca$x[,1],
              Y=pca$x[,2])
            pca.data
             
            ggplot(data=pca.data, aes(x=X, y=Y, label=Sample)) +
              geom_text() +
              xlab(paste("PC1 - ", pca.var.per[1], "%", sep="")) +
              ylab(paste("PC2 - ", pca.var.per[2], "%", sep="")) +
              theme_bw() +
              ggtitle("My PCA Graph")
            
            ...

            ANSWER

            Answered 2021-Jun-07 at 20:35

            EDIT: The question was changed after my initial answer, see the bottom for updated answer.

            You can get the second character of Sample with substr(), and then pass that to col. Here is an example:

            Source https://stackoverflow.com/questions/67878088

            QUESTION

            homals package for Nonlinear PCA in R: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent
            Asked 2021-Jun-06 at 17:37

            I am trying to implement NLPCA (Nonlinear PCA) on a data set using the homals package in R but I keep on getting the following error message:

            Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent

            The data set I use can be found in the UCI ML Repository and it's called dat when imported in R: https://archive.ics.uci.edu/ml/datasets/South+German+Credit+%28UPDATE%29

            Here is my code (some code is provided once the data set is downloaded):

            ...

            ANSWER

            Answered 2021-Jun-06 at 17:37

            It seems the error comes from code generating NAs in the homals function, specifically for your data for the number_credits levels, which causes problems with sort(as.numeric((rownames(clist[[i]])))) and the attempt to catch the error, since one of the levels does not give an NA value.

            So either you have to modify the homals function to take care of such an edge case, or change problematic factor levels. This might be something to file as a bug report to the package maintainer.

            As a work-around in your case you could do something like:

            Source https://stackoverflow.com/questions/67848304

            QUESTION

            Change in Keras.applications source code results in error in missing variable from localhost
            Asked 2021-Jun-02 at 08:49

            For image clustering I was using a piece of code which worked perfectly.

            ...

            ANSWER

            Answered 2021-Jun-02 at 08:49

            I switched to TF2 instead of disabling v2 behavior and that has resolved the problem

            Source https://stackoverflow.com/questions/67789714

            QUESTION

            How to save PCA summary?
            Asked 2021-May-30 at 19:00

            I have used prcomp function to perform PCA of my data. I can save other data like, center, scale, score, rotation in csv using write.csv function but I don't know how to save PCA summary.

            Data I used

            ...

            ANSWER

            Answered 2021-May-30 at 06:32

            You can extract importance from summary(pca).

            Source https://stackoverflow.com/questions/67758341

            QUESTION

            Is there a way for sklearn pipeline to train with and without a step during a grid search? I can remove steps but how do i pass this to GridSearchCV?
            Asked 2021-May-27 at 17:40

            This got closed the first time I asked it because this question asks something similar. However despite the answers showing how to add/remove from a step from the pipeline, none of them show how this works with GridSearchCV and I'm left wondering what to do with the pipeline that I've removed the step from.

            I'd like to train a model using a grid search and test the performance both when PCA is performed first and when PCA is omitted. Is there a way to do this? I'm looking for more than simply setting n_components to the number of input variables.

            Currently I define my pipeline like this:

            ...

            ANSWER

            Answered 2021-May-27 at 17:40

            For this, you can have a look at the user guide where it says under the paragraph for nested parameters:

            Individual steps may also be replaced as parameters, and non-final steps may be ignored by setting them to 'passthrough'

            In your case, I would define a grid with a list of two dictionaries, one in case the whole pipeline is used, and one where the PCA is omitted:

            Source https://stackoverflow.com/questions/67725702

            QUESTION

            How to make a profile plot (principal component analysis) in R?
            Asked 2021-May-27 at 09:33

            I'm currently running principal component analysis. For the interpretation I want to create a profile (pattern) plot to visualize the correlation between each principal component and the original variables. Is anyone familiar with a package or code to create this in R? I'm using the prcomp() function in R.

            See examples:

            https://canadianaudiologist.ca/predicting-speech-perception-from-the-audiogram-and-vice-versa/ https://blogs.sas.com/content/iml/2019/11/04/interpret-graphs-principal-components.html

            This is similar data to my db:

            ...

            ANSWER

            Answered 2021-May-27 at 09:30

            using your data I did this:

            Source https://stackoverflow.com/questions/67718659

            QUESTION

            How to map the results of Principal Component Analysis back to the actual features that were fed into the model?
            Asked 2021-May-19 at 08:30

            When I run the code below, I see the 'pca.explained_variance_ratio_' and a histogram, which shows the proportion of variance explained for each feature.

            ...

            ANSWER

            Answered 2021-May-18 at 12:14

            Use the inverse_transform method:

            Source https://stackoverflow.com/questions/67585809

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install pca

            Install pca from PyPI (recommended). pca is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
            It is distributed under the MIT license.
            Install the latest version from the GitHub source:

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

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