plot_pca_2d_projection with examples#

An example showing the plot_pca_2d_projection function used by a scikit-learn PCA object.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

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)  # reproducibility
# importing pylab or pyplot
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as sp

# Load the data
X, y = data_3_classes(return_X_y=True, as_frame=True)
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 = sp.decomposition.plot_pca_2d_projection(
    pca, X_train, y_train, biplot=True, feature_labels=X.columns.tolist()
);

# Adjust layout to make sure everything fits
plt.tight_layout()

# Save the plot with a filename based on the current script's name
# sp.api._utils.save_plot()

# Display the plot
plt.show(block=True)