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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import plotly.express as px | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.decomposition import PCA | |
st.title('Principal Component Analysis Demo App 🤖') | |
st.subheader('Credit: @BenjaminJack') | |
st.subheader('Raw Data') | |
iris = px.data.iris().drop('species_id',axis=1) | |
st.write(iris) | |
xvar = st.selectbox('Select x-axis',iris.columns[:-1]) | |
yvar = st.selectbox('Select y-axis',iris.columns[:-1]) | |
st.write(px.scatter(iris, x=xvar, y=yvar, color='species')) | |
iris_scaled = StandardScaler().fit_transform(iris.drop('species',axis=1)) | |
iris_pca = PCA() | |
iris_transformed = iris_pca.fit_transform(iris_scaled) | |
col_names = [f'component{i+1}' for i in range(iris_transformed.shape[1])] | |
st.write(col_names) | |
iris_transformed_df = pd.DataFrame(iris_transformed, columns=col_names) | |
iris_transformed_df = pd.concat([iris_transformed_df, iris['species']],axis=1) | |
st.subheader('Transformed Data') | |
st.write(iris_transformed_df) | |
st.subheader('Explore principal components') | |
xvar = st.selectbox('Select x-axis:', iris_transformed_df.columns[:-1]) | |
yvar = st.selectbox('Select y-axis:', iris_transformed_df.columns[:-1]) | |
st.write(px.scatter(iris_transformed_df,x=xvar, y=yvar, color = 'species')) | |
st.subheader('Explore Loadings') | |
loadings = iris_pca.components_.T * np.sqrt(iris_pca.explained_variance_) | |
loadings_df = pd.DataFrame(loadings,columns=col_names) | |
loadings_df = pd.concat([loadings_df, | |
pd.Series(iris.columns[0:4],name='var')], | |
axis=1) | |
component =st.selectbox('Select Component:',loadings_df.columns[0:4]) | |
bar_chart = px.bar(loadings_df[['var',component]].sort_values(component), | |
y='var', | |
x=component, | |
orientation='h', | |
range_x=[-1,1] | |
) | |
st.write(bar_chart) | |
st.write(loadings_df) | |
#Credits : benjaminjack |