How to use scikit learn inverse_transform with new values

Question

I have a set of data that I have used scikit learn PCA. I scaled the data before performing PCA with StandardScaler().

variance_to_retain = 0.99
np_scaled = StandardScaler().fit_transform(df_data)
pca = PCA(n_components=variance_to_retain)
np_pca = pca.fit_transform(np_scaled)

# make dataframe of scaled data
# put column names on scaled data for use later
df_scaled = pd.DataFrame(np_scaled, columns=df_data.columns)
num_components = len(pca.explained_variance_ratio_)
cum_variance_explained = np.cumsum(pca.explained_variance_ratio_)

eigenvalues = pca.explained_variance_
eigenvectors = pca.components_

I then ran K-Means clustering on the scaled dataset. I can plot the cluster centers just fine in scaled space.

My question is: how do I transform the locations of the centers back into the original data space. I know that StandardScaler.fit_transform() make the data have zero mean and unit variance. But with the new points of shape (num_clusters, num_features), can I use inverse_transform(centers) to get the centers transformed back into the range and offset of the original data?

Thanks, David

Answer 1

you can get cluster_centers on a kmeans, and just push that into your pca.inverse_transform

here's an example

import numpy as np
from sklearn import decomposition
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler



iris = datasets.load_iris()
X = iris.data
y = iris.target

scal = StandardScaler()
X_t = scal.fit_transform(X)

pca = decomposition.PCA(n_components=3)
pca.fit(X_t)
X_t = pca.transform(X_t)

clf = KMeans(n_clusters=3)
clf.fit(X_t)

scal.inverse_transform(pca.inverse_transform(clf.cluster_centers_))

Note that sklearn has multiple ways to do the fit/transform. You can do StandardScaler().fit_transform(X) but you lose the scaler, and can't reuse it; nor can you use it to create an inverse.

Alternatively, you can do scal = StandardScaler() followed by scal.fit(X) and then by scal.transform(X)

OR you can do scal.fit_transform(X) which combines the fit/transform step

Answer 2

Here I am using SVR to Fit the data before that I am using scaling technique to scale the values and to get the prediction I am using the Inverse transform function

from sklearn.preprocessing import StandardScaler  

#Creating two objects for dependent and independent variable 
ss_X = StandardScaler()
ss_y = StandardScaler()

X = ss_X.fit_transform(X)
y = ss_y.fit_transform(y.reshape(-1,1))

#Creating a model object and fiting the data
reg = SVR(kernel='rbf')
reg.fit(X,y)

#To make a prediction
#First we have transform the value into scalar level
#Second inverse tranform the value to see the original value
ss_y.inverse_transform(reg.predict(ss_X.transform(np.array([[6.5]]))))