Merge pull request 'Visualize clusters in 3d' (#6) from cluster3d into main

Reviewed-on: #6
Reviewed-by: Clément FRÉVILLE <clement.freville2@etu.uca.fr>

frontend/pages/clustering:_dbscan.py

@@ -9,21 +9,27 @@ if "data" in st.session_state:
     data = st.session_state.data
 
     with st.form("my_form"):
-        data_name = st.multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=2)
+        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")
 
-    if len(data_name) == 2:
+    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, ax = plt.subplots(figsize=(12,8))
-        plt.scatter(x[:, 0], x[:, 1], c=y_dbscan, s=50, cmap="viridis")
+        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")
         st.pyplot(fig)
 
+    
+
 else:
     st.error("file not loaded")

frontend/pages/clustering:_kmeans.py

@@ -11,7 +11,7 @@ if "data" in st.session_state:
     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=2)
+        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])
@@ -20,16 +20,24 @@ if "data" in st.session_state:
 
         st.form_submit_button("launch")
 
-    if len(data_name) == 2:
+    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, ax = plt.subplots(figsize=(12,8))
-        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")
+        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")
         st.pyplot(fig)
 
 else: