Added the interface for Medoid

streamlit_app/pages/page_Medoid.py

@@ -0,0 +1,95 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import sys
+import os
+
+import scipy
+from selector.methods.partition import Medoid
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "Medoid",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Medoid Method")
+
+
+description = """
+    Points are initially used to construct a KDTree. Eucleidean distances are used for this
+    algorithm. The first point selected is based on the starting_idx provided and becomes the first
+    query point. An approximation of the furthest point to the query point is found using
+    find_furthest_neighbor and is selected. find_nearest_neighbor is then done to eliminate close
+    neighbors to the new selected point. Medoid is then calculated from previously selected points
+    and is used as the new query point for find_furthest_neighbor, repeating the process. Terminates
+    upon selecting requested number of points or if all available points exhausted.
+    """
+
+references = "Adapted from: https://en.wikipedia.org/wiki/K-d_tree#Construction"
+
+display_sidebar_info("Medoid Method", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    clear_results()
+
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    # Parameters for Medoid
+    st.info("The parameters below are optional. If not specified, default values will be used.")
+
+    start_id = st.number_input("Index for the first point to be selected. (start_id)", value = 0, step = 1, on_change=clear_results)
+
+    scaling = st.number_input("Percent of average maximum distance to use when eliminating the closest points. (scaling)",
+                              value=10.0, step=1.0, on_change=clear_results)
+
+    if st.button("Run Medoid Algorithm"):
+        selector = Medoid(start_id=start_id, func_distance = lambda x, y: scipy.spatial.minkowski_distance(x, y) ** 2, scaling=scaling)
+        selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        st.session_state['selected_ids'] = selected_ids
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    export_results(selected_ids)