# Copyright (c) 2020, Anders Lervik.
# Distributed under the MIT License. See LICENSE for more info.
"""
Scree plot
==========

This example will show the eigenvalues of principal components
from a
`principal component analysis
<https://en.wikipedia.org/wiki/Principal_component_analysis>`_.
"""
from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_wine
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from psynlig import pca_scree
plt.style.use('seaborn')
plt.rcParams.update({'font.size': 16})

data_set = load_wine()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
data = scale(data)

pca = PCA()
pca.fit_transform(data)

pca_scree(pca, marker='o', markersize=16, lw=3)

plt.show()