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fin separation front/back

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hugo.pradier2
2024-06-23 17:44:26 +02:00
parent 15e1674cb2
commit 7dafa78bc4
13 changed files with 201 additions and 131 deletions
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33
frontend/pages/clustering_dbscan.py
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@@ -1,35 +1,22 @@
import streamlit as st
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
st.header("Clustering: dbscan")
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
from dbscan_strategy import perform_dbscan_clustering
st.header("Clustering: DBSCAN")
if "data" in st.session_state:
data = st.session_state.data
with st.form("my_form"):
with st.form("dbscan_form"):
data_name = st.multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=3)
eps = st.slider("eps", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
min_samples = st.number_input("min_samples", step=1, min_value=1, value=5)
st.form_submit_button("launch")
submitted = st.form_submit_button("Launch")
if len(data_name) >= 2 and len(data_name) <=3:
x = data[data_name].to_numpy()
dbscan = DBSCAN(eps=eps, min_samples=min_samples)
y_dbscan = dbscan.fit_predict(x)
fig = plt.figure()
if len(data_name) == 2:
ax = fig.add_subplot(projection='rectilinear')
plt.scatter(x[:, 0], x[:, 1], c=y_dbscan, s=50, cmap="viridis")
else:
ax = fig.add_subplot(projection='3d')
ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_dbscan, s=50, cmap="viridis")
if submitted and 2 <= len(data_name) <= 3:
fig = perform_dbscan_clustering(data, data_name, eps, min_samples)
st.pyplot(fig)
else:
st.error("file not loaded")
st.error("File not loaded")