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| # 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 os | |
| import sys | |
| from sklearn.metrics import pairwise_distances | |
| from selector.methods.distance import MaxSum | |
| # 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 = "MaxSum", | |
| page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"), | |
| ) | |
| st.title("Brute Strength - MaxSum") | |
| description = """ | |
| Whereas the goal of the MaxMin algorithm is to maximize the minimum distance between any pair | |
| of distinct elements in the selected subset of a dataset, the MaxSum algorithm aims to maximize | |
| the sum of distances between all pairs of elements in the selected subset. When presented with | |
| a dataset of samples, the initial point is chosen as the dataset’s medoid center. Next, | |
| the second point is chosen to be that which is furthest from this initial point. | |
| Subsequently, all following points are selected via the following logic: | |
| 1. Determine the sum of distances from every point to the already-selected ones. | |
| 2. Select the point which has the maximum sum of distances among those calculated in the previous step. | |
| In the current implementation, this method requires or computes the full pairwise-distance | |
| matrix, so it is not recommended for large datasets. | |
| """ | |
| references = "[1] Borodin, Allan, Hyun Chul Lee, and Yuli Ye, Max-sum diversification, "\ | |
| "monotone submodular functions and dynamic updates, Proceedings of the 31st ACM "\ | |
| "SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems. 2012." | |
| display_sidebar_info("Brute Strength - MaxSum", 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: | |
| st.session_state.pop("selected_ids", None) | |
| # 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 | |
| distance_metric = st.selectbox("Select distance metric (optional)", | |
| [None, "euclidean", "manhattan", "cosine"], | |
| key = "distance_metric", on_change=clear_results) | |
| if distance_metric: | |
| fun_dist = lambda x: pairwise_distances(x, metric = distance_metric) | |
| else: | |
| fun_dist = None | |
| if st.button("Run MaxSum Algorithm"): | |
| if fun_dist: | |
| selector = MaxSum(fun_dist) | |
| selected_ids = run_algorithm(selector, matrix, num_points, labels) | |
| else: | |
| selector = MaxSum() | |
| selected_ids = run_algorithm(selector, matrix, num_points, labels) | |
| # 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) | |