From 6f8ffe4d843c794bd940c4e08aa8b162efe6abc9 Mon Sep 17 00:00:00 2001
From: mcoth <mcoth@dtu.dk>
Date: Sun, 17 Nov 2024 16:47:41 +0100
Subject: [PATCH] Upload New File
---
Perform_pca.py | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 78 insertions(+)
create mode 100644 Perform_pca.py
diff --git a/Perform_pca.py b/Perform_pca.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 1 15:27:56 2024
+
+@author: Maya Coulson Theodorsen (mcoth@dtu.dk)
+
+
+This script performs Principal Component Analysis (PCA) on standardized data.
+
+It includes:
+- Bartlett's test for sphericity to check PCA suitability.
+- Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy.
+- PCA scree plot visualization and explained variance analysis.
+- Calculation of PCA loadings and transformed data.
+
+Functions:
+- perform_pca: Performs PCA and diagnostic tests.
+
+Returns:
+- pca (PCA fitted model)
+- loadings (PCA loadings for each feature and component)
+- principleComponents
+
+"""
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo
+
+def perform_pca(std_data, PCAcolumns, columnNames):
+
+ # Bartlett sphericity to check if suitable for PCA
+ chi_square_value, p_value = calculate_bartlett_sphericity(std_data)
+ print("Bartlett's sphericity chi-square:", chi_square_value)
+ print(f"p_value: {p_value:.30f}")
+
+
+ # Kaiser-Meyer-Olkin(KMO) Test for sampling accuracy (0.8-1.0 is excellent)
+ # Test if data is appropriate for FA/PCA
+ kmo_all, kmo_model = calculate_kmo(std_data)
+ print("KMO model:", kmo_model)
+
+
+ # PCA (note: variance in eigenvalues, not percentage)
+ pca = PCA()
+ principleComponents = pd.DataFrame(pca.fit_transform(std_data))
+
+ # Bar plot of the variances of PCA features
+ features = range(pca.n_components_)
+ plt.subplots(figsize=(20,15))
+ plt.bar(features, pca.explained_variance_)
+ plt.xticks(features)
+ plt.ylabel('Eigenvalue', fontsize=16)
+ plt.xlabel('PCA feature', fontsize=16)
+ plt.axhline(y=1, linewidth=2, color='r')
+ plt.title('Scree plot', fontsize=30)
+ plt.show()
+
+ # Display the actual amount of explained variance per component
+ print('PCA explained variance:')
+ print(pca.explained_variance_)
+
+ # Print the ratios of percentage explained by each feature
+ print('PCA explained variance ratio:')
+ print(pca.explained_variance_ratio_)
+
+ # Cumulative summation of the ratio explained variance of each feature
+ print('PCA explained variance ratio cumulative summation:')
+ print(pca.explained_variance_ratio_.cumsum())
+
+ # Loadings for each PC
+ loadings = pd.DataFrame(pca.components_.T * np.sqrt(pca.explained_variance_))
+ loadings.columns = PCAcolumns
+ loadings.index = [columnNames]
+
+ return pca, loadings, principleComponents
\ No newline at end of file
--
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