pca | Principal component analysis | Machine Learning library
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kandi X-RAY | pca Summary
Principal component analysis (PCA).
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pca Examples and Code Snippets
Lines of Code : 49
License : No License
Copydef 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
Trending Discussions on pca
QUESTION
I'm setting up a machine learning pipeline to classify some data. One source of the data is a very good candidate for PCA and makes up the last n dimensions of the dataset. I would like to use PCA on these variables but not the preceding variables. From searching through stackexchange this also seems like a common issue faced --- that people want to apply PCA to just a portion of the data.
Obviously I could do the PCA first then concatenate the datasets and then pass that to the pipeline, but afaik the PCA should be part of the pipeline as otherwise information from test samples bleeds into the training data.
I want to use sklearn's PCA function (but am also open to suggestions) but it doesn't take any arguments to define which variables to do the PCA on and so it's difficult to incorporate into a pipeline.
My work around currently works by defining a new PCA which selects the desired features and looks like this:
...ANSWER
Answered 2021-Dec-09 at 08:19Your workaround is not necessary, since this use case is already covered by sklearn. Different transformations for different features can be implemented by including a ColumnTransformer in the pipeline.
Consider the example below:
QUESTION
I'm having issues understanding how the veganCovEllipse() function from the vegan package v 2.5-7 calculates an ellipse.
...ANSWER
Answered 2022-Feb-12 at 07:53veganCovEllipse is not an exported function. This means that it is not intended for interactive use, but it is a support function only to be called from other functions in vegan. Therefore it is not documented. However, the answer is simple: it can calculate any kind of ellipse depending on the input. In vegan the function is called for instance from ordiellipse and there it can draw standard error ellipses, "confidence" ellipses (standard error multiplied by some value picked from statistical distribution), standard deviation ellipses, standard deviation ellipses multiplied by similar constants as standard errors, or enclosing ellipses that contain all points of a group, depending on the input to the function. In showvarparts function it is just re-used to draw circles. Actually veganCovEllipse does not fit anything: it just calculates the coordinates to draw what you asked it to draw, and your input defines the shape, size, orientation and location of the ellipse coordinates.
There are other functions in other packages that do the same: return you the points needed to plot an ellipse from your input data. For instance, the standard (recommended) package cluster makes similar calculations in non-exported functions cluster:::ellipsoidPoints with effectively the same mathematics, but in completely different way. This function is non-exported as well, and it is intended to be called from user function cluster::predict.ellipsoid. The vegan implementation is similar as in the ellipse function in the car package, where these calculations are embedded in that function and cannot be called separately from car::ellipse.
QUESTION
I have a dataframe with three timeseries data columns. I want to add labels to the dataframe based on the binary values in one of the columns that increments. Below is a demonstration of the output that I desire ('Rate labels' built based on 'Rate pct change'). I made this using excel but want to do it using python.
...ANSWER
Answered 2022-Jan-30 at 16:32Try using a combination of shift and cumsum:
QUESTION
I'm trying to preform PCA (principal component analysis) using TidyModels. I have created a recipe but I don't know how can I change the default rotation used in `step_pca() method (such as changing it to say Varimax rotation). any ideas?
this is my recipe:
...ANSWER
Answered 2022-Jan-10 at 17:51The step_pca() function uses stats::prcomp() under the hood, which I don't believe supports that, but you can get out the loadings using tidy() and the type = "coef" argument and then apply a rotation yourself. See this Cross Validated answer for more info.
QUESTION
Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?
For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.
A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.
I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.
Feel free to implement your own Pipeline class or wrapper if necessary.
...ANSWER
Answered 2021-Nov-23 at 15:01I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.
QUESTION
I'm trying this simple whitening function in python in R
Python
...ANSWER
Answered 2021-Dec-07 at 00:53It is usually written wiki, where V* is the transpose of V:
And this is what you get back in scipy.linalg.svd:
Factorizes the matrix a into two unitary matrices U and Vh, and a 1-D array s of singular values (real, non-negative) such that a == U @ S @ Vh, where S is a suitably shaped matrix of zeros with main diagonal s.
Whereas for svd in R they return you V. Therefore should be:
QUESTION
I'm hoping to display observation row names on a principal component analysis biplot using the factoextra package.
...ANSWER
Answered 2021-Nov-27 at 16:12You can specify geom.ind = "txt"
QUESTION
I have a folder that contains 200 images. I loaded all these images into an array list of type image called training. I have a problem converting this to a one dimension vector. I need help with this, am writing a PCA based solution for face recognition, Thank You.
ANSWER
Answered 2021-Nov-17 at 11:28Why not something like this:
QUESTION
In package 'factoextra',when i use function 'fviz_eig',how to adjust the column width and label size ? ("width=0.6,text.size=17" seems can't work, also no error message show)
...ANSWER
Answered 2021-Oct-08 at 04:16The documentation is not clear about the extra arguments. It just says ... and to look under the options i.e.
... optional arguments to be passed to the function ggpar.
When we check the ggpar link
Use font.x = 14, to change only font size;
If we check the function fviz_eig
QUESTION
I am using amazon/aws-cli:2.2.40 in a gitlab pipeline to publish a static site on a S3.
...ANSWER
Answered 2021-Sep-23 at 18:47This is due to the amazon/aws-cli image's entrypoint being: /usr/local/bin/aws. When you run aws by itself locally, it'll return the help message and give a 252 exit code. To override the entrypoint, you do so like this:
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