Remove unused imports

frontend/pages/clustering:_dbscan.py

@@ -1,29 +0,0 @@
-import streamlit as st
-import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
-
-st.header("Clustering: dbscan")
-
-
-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)
-        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:
-        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")
-        st.pyplot(fig)
-
-else:
-    st.error("file not loaded")

frontend/pages/clustering:_kmeans.py

@@ -1,36 +0,0 @@
-import streamlit as st
-from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-
-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=2)
-        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)
-
-
-        st.form_submit_button("launch")
-
-    if len(data_name) == 2:
-        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")
-        st.pyplot(fig)
-
-else:
-    st.error("file not loaded")