plot_pca_component_variance with examples#

An example showing the plot_pca_component_variance method used by a scikit-learn PCA object

# Authors: scikit-plots developers
# License: MIT

from sklearn.datasets import (
    make_classification,
    load_breast_cancer as data_2_classes,
    load_iris as data_3_classes,
    load_digits as data_10_classes,
)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.decomposition import PCA
import numpy as np; np.random.seed(0)
# importing pylab or pyplot
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as skplt

# Load the data
X, y = data_3_classes(return_X_y=True, as_frame=False)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

# Create an instance of the PCA
pca = PCA(random_state=0).fit(X_train)

# Create an instance of the LogisticRegression
# model = LogisticRegression(max_iter=int(1e5), random_state=0).fit(X_train, y_train)

# Perform predictions
# y_val_prob = model.predict_proba(X_val)

# Plot!
ax = skplt.decomposition.plot_pca_component_variance(
    pca, figsize=(9,5)
);
# Adjust layout to make sure everything fits
plt.tight_layout()
# Save the plot to a file
# plt.savefig('plot_pca_component_variance_script.png')
# Display the plot
plt.show(block=True)