How to use new data with Principal Component Analysis (PCA)

Question

I reduce data with PCA already from 9 to 3 feature. If I have real data new row which I want to use with pre-train model (.h5). Can I change data 9 feature to PCA 3 feature only one row for test with model ?

import numpy
from pandas import read_csv
from sklearn.decomposition import PCA
# load data
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
# feature extraction
pca = PCA(n_components=3)
fit = pca.fit(X)
# summarize components
print("Explained Variance: %s" % fit.explained_variance_ratio_)
print(fit.components_)

Answer 1

Simply use pca.transform(test_data). That is:

test = X[22].reshape(1, -1)
pca.transform(test)

array([[-72.73967494, -86.24860793,  -5.94958303]])

I used a random index of your own train set to illustrate the use case but you can input any array of size 8 here.

Can I change data 9 feature to PCA 3 feature only one row for test with model ?

You have to input 8 features (9 features except your target value).