visualization - How to make a contour plot in python for more than three features?

Question

I have a dataset with dimension of (1331,46) including 1331 samples and 46 features, i want to visualize the data before and after classification in a contour plot graph. i found an example code but it's for two features which is shown as below. i want to know how can i change it to use for 46 features or in any way how can i make a contour plot for 46 features before and after classification. i would like to know if there is any other way to visualize before and after classification. Thanks in advance

# probability decision surface for logistic regression on a binary classification dataset
from numpy import where
from numpy import meshgrid
from numpy import arange
from numpy import hstack
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot
# generate dataset
X, y = make_blobs(n_samples=1000, centers=2, n_features=2, random_state=1, cluster_std=3)
# define bounds of the domain
min1, max1 = X[:, 0].min()-1, X[:, 0].max()+1
min2, max2 = X[:, 1].min()-1, X[:, 1].max()+1
# define the x and y scale
x1grid = arange(min1, max1, 0.1)
x2grid = arange(min2, max2, 0.1)
# create all of the lines and rows of the grid
xx, yy = meshgrid(x1grid, x2grid)
# flatten each grid to a vector
r1, r2 = xx.flatten(), yy.flatten()
r1, r2 = r1.reshape((len(r1), 1)), r2.reshape((len(r2), 1))
# horizontal stack vectors to create x1,x2 input for the model
grid = hstack((r1,r2))
# define the model
model = LogisticRegression()
# fit the model
model.fit(X, y)
# make predictions for the grid
yhat = model.predict_proba(grid)
# keep just the probabilities for class 0
yhat = yhat[:, 0]
# reshape the predictions back into a grid
zz = yhat.reshape(xx.shape)
# plot the grid of x, y and z values as a surface
c = pyplot.contourf(xx, yy, zz, cmap='RdBu')
# add a legend, called a color bar
pyplot.colorbar(c)
# create scatter plot for samples from each class
for class_value in range(2):
    # get row indexes for samples with this class
    row_ix = where(y == class_value)
    # create scatter of these samples
    pyplot.scatter(X[row_ix, 0], X[row_ix, 1], cmap='Paired')
# show the plot
pyplot.show()

question from:https://stackoverflow.com/questions/65865489/how-to-make-a-contour-plot-in-python-for-more-than-three-features

Answer 1

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