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Book4_Ch10_Python_Codes/Streamlit_Bk4_Ch10_01.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 27 19:46:17 2022
+
+@author: Work
+"""
+
+
+###############
+# Authored by Weisheng Jiang
+# Book 4  |  From Basic Arithmetic to Machine Learning
+# Published and copyrighted by Tsinghua University Press
+# Beijing, China, 2022
+###############
+
+
+import streamlit as st
+import plotly.express as px
+
+import seaborn as sns
+import numpy as np
+import matplotlib.pyplot as plt 
+import pandas as pd  
+from sklearn.datasets import load_iris
+
+
+def bmatrix(a):
+    """Returns a LaTeX bmatrix
+
+    :a: numpy array
+    :returns: LaTeX bmatrix as a string
+    """
+    if len(a.shape) > 2:
+        raise ValueError('bmatrix can at most display two dimensions')
+    lines = str(a).replace('[', '').replace(']', '').splitlines()
+    rv = [r'\begin{bmatrix}']
+    rv += ['  ' + ' & '.join(l.split()) + r'\\' for l in lines]
+    rv +=  [r'\end{bmatrix}']
+    return '\n'.join(rv)
+
+
+# A copy from Seaborn
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+feature_names = ['Sepal length, x1','Sepal width, x2',
+                 'Petal length, x3','Petal width, x4']
+
+# Convert X array to dataframe
+X_df = pd.DataFrame(X, columns=feature_names)
+
+#%% Original data, X
+
+X = X_df.to_numpy();
+
+# Gram matrix, G and orthogonal basis V
+
+G = X.T@X
+D, V = np.linalg.eig(G)
+np.set_printoptions(suppress=True)
+D = np.diag(D)
+st.latex(r'G = X^T X = ' + bmatrix(G))
+st.latex(r'G = V \Lambda V^T')
+
+st.latex(r'G = ' + 
+         bmatrix(np.round(V,2)) + '@' +
+         bmatrix(np.round(D,2)) + '@' +
+         bmatrix(np.round(V.T,2)))
+
+#%%
+
+Z = X@V
+
+df = pd.DataFrame(Z, columns = ['PC1','PC2','PC3','PC4'])
+
+mapping_rule = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
+df.insert(4, "species", y)
+df['species'] = df['species'].map(mapping_rule)
+
+#%%
+features = df.columns.to_list()[:-1]
+with st.sidebar:
+    st.write('2D scatter plot')
+    x_feature = st.radio('Horizontal axis',
+             features)
+    y_feature = st.radio('Vertical axis',
+             features)    
+
+# Heatmap
+with st.expander('Heatmap'):
+    fig_1 = px.imshow(df.iloc[:,0:4],
+                      color_continuous_scale='RdYlBu_r')
+    st.plotly_chart(fig_1)
+    
+# 2D scatter plot
+with st.expander('2D scatter plot'):
+    fig_2 = px.scatter(df, x=x_feature, y=y_feature, color="species")
+    st.plotly_chart(fig_2)
+
+# 3D scatter plot
+with st.expander('3D scatter plot'):
+    fig_3 = px.scatter_3d(df, 
+                          x='PC1', 
+                          y='PC2', 
+                          z='PC3',
+                  color='species')
+    st.plotly_chart(fig_3)
+
+# Pairwise scatter plot
+with st.expander('Pairwise scatter plot'):
+    fig_4 = px.scatter_matrix(df, 
+                            dimensions=["PC1", 
+                                        "PC2", 
+                                        "PC3", 
+                                        "PC4"], 
+                            color="species")
+    st.plotly_chart(fig_4)
+    
+    
+    
+