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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 sys | |
| import os | |
| from selector.methods.distance import OptiSim | |
| # 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 = "OptiSim", | |
| page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"), | |
| ) | |
| st.title("Adapted Optimizable K-Dissimilarity Selection (OptiSim)") | |
| description = """ | |
| The OptiSim algorithm selects samples from a dataset by first choosing the medoid center as the | |
| initial point. Next, points are randomly chosen and added to a subsample if they exist | |
| outside of radius r from all previously selected points (otherwise, they are discarded). Once k | |
| number of points have been added to the subsample, the point with the greatest minimum distance | |
| to the previously selected points is chosen. Then, the subsample is cleared and the process is | |
| repeated. | |
| """ | |
| references = "[1] J. Chem. Inf. Comput. Sci. 1997, 37, 6, 1181–1188. https://doi.org/10.1021/ci970282v" | |
| display_sidebar_info("Adapted Optimizable K-Dissimilarity Selection (OptiSim)", 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 Directed Sphere Exclusion | |
| st.info("The parameters below are optional. If not specified, default values will be used.") | |
| r0 = st.number_input("Initial guess of radius for OptiSim algorithm (r0)", value=None, step=0.1, | |
| on_change=clear_results) | |
| ref_index = st.number_input("Index for the sample to start selection from (ref_index)", value=0, step=1, on_change=clear_results) | |
| k = st.number_input("Amount of points to add to subsample (k)", value=10, step=1, | |
| on_change=clear_results) | |
| tol = st.number_input("Percentage error of number of samples selected (tol)", value=0.01, step=0.01, on_change=clear_results) | |
| n_iter = st.number_input("Number of iterations to execute when optimizing the size of exclusion radius. (n_iter)", | |
| value=10, step=1, on_change=clear_results) | |
| p = st.number_input("Minkowski p-norm distance (p)", value=2.0, step=1.0, on_change=clear_results) | |
| eps = st.number_input("Approximate nearest neighbor search parameter (eps)", value=0.0, step=0.1, | |
| on_change=clear_results) | |
| random_seed = st.number_input("Seed for random selection of points be evaluated. (random_seed)", value=42, step=1, on_change=clear_results) | |
| if st.button("Run OptiSim Algorithm"): | |
| selector = OptiSim(r0=r0, ref_index=ref_index, k=k, tol=tol, n_iter=n_iter, eps=eps, p=p, random_seed=random_seed) | |
| selected_ids = run_algorithm(selector, matrix, num_points, labels) | |
| st.session_state['selector'] = selector | |
| 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) | |
| if 'selector' in st.session_state: | |
| st.write("Radius of the exclusion sphere:", st.session_state['selector'].r) | |
| export_results(selected_ids) | |