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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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frontend/pages/clustering_kmeans.py
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@@ -1,44 +1,26 @@
import streamlit as st
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
st.header("Clustering: kmeans")
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
from kmeans_strategy import perform_kmeans_clustering
st.header("Clustering: KMeans")
if "data" in st.session_state:
data = st.session_state.data
with st.form("my_form"):
row1 = st.columns([1,1,1])
n_clusters = row1[0].selectbox("Number of clusters", range(1,data.shape[0]))
data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=3)
n_init = row1[2].number_input("n_init",step=1,min_value=1)
with st.form("kmeans_form"):
row1 = st.columns([1, 1, 1])
n_clusters = row1[0].selectbox("Number of clusters", range(1, data.shape[0]))
data_name = row1[1].multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=3)
n_init = row1[2].number_input("n_init", step=1, min_value=1)
row2 = st.columns([1,1])
max_iter = row1[0].number_input("max_iter",step=1,min_value=1)
row2 = st.columns([1, 1])
max_iter = row2[0].number_input("max_iter", step=1, min_value=1)
submitted = st.form_submit_button("Launch")
st.form_submit_button("launch")
if len(data_name) >= 2 and len(data_name) <=3:
x = data[data_name].to_numpy()
kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
y_kmeans = kmeans.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_kmeans, s=50, cmap="viridis")
centers = kmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
else:
ax = fig.add_subplot(projection='3d')
ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_kmeans, s=50, cmap="viridis")
centers = kmeans.cluster_centers_
ax.scatter(centers[:, 0], centers[:, 1],centers[:, 2], c="black", s=200, marker="X")
if submitted and 2 <= len(data_name) <= 3:
fig = perform_kmeans_clustering(data, data_name, n_clusters, n_init, max_iter)
st.pyplot(fig)
else:
st.error("file not loaded")
st.error("File not loaded")