| import pandas as pd | |
| import streamlit as st | |
| from sklearn.cluster import DBSCAN | |
| from sklearn.preprocessing import StandardScaler | |
| import numpy as np | |
| def process(data): | |
| if 'object' in list(data[0].dtypes): | |
| st.info('This Algorithm can only process numerical data') | |
| return None | |
| scaler = StandardScaler() | |
| df = data[0].copy() | |
| for c in data[0].columns: | |
| df[c] = scaler.fit_transform(data[0][[c]]) | |
| max_distance = st.slider("""Maximum distance between two samples for one to be considered | |
| as in the neighborhood of the other. :""",0.01,5.0) | |
| dbscan = DBSCAN(max_distance) | |
| res = dbscan.fit_predict(df) | |
| df = data[0] | |
| df['cluster'] = res | |
| return df |