Updated requirements & ML section

requirements.txt

@@ -1,4 +1,5 @@
 pandas
 streamlit
 plotly
-polars
+polars
+scikit-learn

sections/ml.py

@@ -1,6 +1,11 @@
 import pandas as pd
 import plotly.express as px
 import streamlit as st
+import polars as pl
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
+import matplotlib.pyplot as plt
 
 if "parsed_df" not in st.session_state:
     st.session_state.parsed_df = None
@@ -15,16 +20,82 @@ if st.session_state.parsed_df is None:
 
 data = st.session_state.parsed_df
 
-# Sidebar for controls
-st.dataframe(data)
-
-
 ##############################################
 ####            Preprocessing             ####
 ##############################################
 
+# Normalisation des données (Standardisation : moyenne = 0, écart-type = 1)
+scaler = StandardScaler()
+df_scaled = scaler.fit_transform(data.to_pandas())
+
+# Convertir de nouveau en DataFrame Polars
+df_scaled = pl.from_pandas(pd.DataFrame(df_scaled, columns=data.columns))
 
 
 ###############################################
 ####              Clustering               ####
-###############################################
+###############################################
+
+if st.button("Start clustering"):
+    if st.session_state.parsed_df is not None:
+        with st.spinner("Searching the clusters..."):
+            try:
+                # Appliquer K-Means avec k optimal choisi
+                k_optimal = 2  # Par exemple, supposons que k = 3
+                kmeans = KMeans(n_clusters=k_optimal, random_state=42)
+                df_scaled = df_scaled.with_columns(pl.Series(kmeans.fit_predict(df_scaled.to_pandas()), name='cluster_kmeans'))
+
+                # Appliquer DBSCAN (epsilon et min_samples sont des hyperparamètres)
+                # dbscan = DBSCAN(eps=0.5, min_samples=10)
+                # df_scaled = df_scaled.with_columns(pl.Series(dbscan.fit_predict(df_scaled.to_pandas()), name='cluster_dbscan'))
+
+                # Appliquer Agglomerative Clustering
+                # agg_clustering = AgglomerativeClustering(n_clusters=2)
+                # df_scaled = df_scaled.with_columns(pl.Series(agg_clustering.fit_predict(df_scaled.to_pandas()), name='cluster_agg'))
+
+                ###############################################################
+                ####              Visualisation des clusters               ####
+                ###############################################################
+
+
+                # Visualisation des clusters (en 2D avec PCA)
+                from sklearn.decomposition import PCA
+
+                pca = PCA(n_components=2)
+                df_pca = pca.fit_transform(df_scaled.to_pandas())
+
+                fig = px.scatter(
+                    x=df_pca[:, 0],
+                    y=df_pca[:, 1],
+                    color=df_scaled['cluster_kmeans'],
+                    color_continuous_scale='viridis',
+                    title='Clustering coupled with PCA',
+                    labels={'x': 'Component 1', 'y': 'Component 2', 'color': 'Cluster'},
+                )
+
+                fig.update_layout(
+                    xaxis_title='Component 1',
+                    yaxis_title='Component 2'
+                )
+
+                fig.show()
+                
+            except Exception as e:
+                st.error(f"An error occured : {e}")
+    else:
+        st.warning("Please parse the log file first.")
+
+# Choisir le nombre de clusters (méthode du coude)
+# inertia = []
+# for k in range(1, 11):
+#     kmeans = KMeans(n_clusters=k, random_state=42)
+#     kmeans.fit(df_scaled.to_pandas())
+#     inertia.append(kmeans.inertia_)
+
+# # Tracer la courbe pour la méthode du coude
+# plt.plot(range(1, 11), inertia, marker='o')
+# plt.title('Méthode du coude')
+# plt.xlabel('Nombre de clusters')
+# plt.ylabel('Inertie')
+# plt.show()
+