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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 DISE | |
| # 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 = "DISE", | |
| page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"), | |
| ) | |
| st.title("Directed Sphere Exclusion (DISE)") | |
| description = """ | |
| In a nutshell, this algorithm iteratively excludes any sample within a given radius from | |
| any already selected sample. The radius of the exclusion sphere is an adjustable parameter. | |
| Compared to Sphere Exclusion algorithm, the Directed Sphere Exclusion algorithm achieves a | |
| more evenly distributed subset selection by abandoning the random selection approach and | |
| instead imposing a directed selection. | |
| Reference sample is chosen based on the `ref_index`, which is excluded from the selected | |
| subset. All samples are sorted (ascending order) based on their Minkowski p-norm distance | |
| from the reference sample. Looping through sorted samples, the sample is selected if it is | |
| not already excluded. If selected, all its neighboring samples within a sphere of radius r | |
| (i.e., exclusion sphere) are excluded from being selected. When the selected number of points | |
| is greater than specified subset `size`, the selection process terminates. The `r0` is used | |
| as the initial radius of exclusion sphere, however, it is optimized to select the desired | |
| number of samples. | |
| """ | |
| references = "Gobbi, A., and Lee, M.-L. (2002). DISE: directed sphere exclusion."\ | |
| "Journal of Chemical Information and Computer Sciences,"\ | |
| "43(1), 317–323. https://doi.org/10.1021/ci025554v" | |
| display_sidebar_info("Directed Sphere Exclusion (DISE)", 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 for radius of exclusion sphere (r0)", value=None, step=0.1, | |
| on_change=clear_results) | |
| ref_index = st.number_input("Reference index (ref_index)", value=0, step=1, on_change=clear_results) | |
| tol = st.number_input("Percentage tolerance of sample size error (tol)", value=0.05, step=0.05, | |
| on_change=clear_results) | |
| n_iter = st.number_input("Number of iterations for optimizing the radius of exclusion sphere (n_iter)", | |
| value=10, step=10, 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) | |
| if st.button("Run DISE Algorithm"): | |
| selector = DISE(r0=r0, ref_index=ref_index, tol=tol, n_iter=n_iter, p=p, eps=eps) | |
| 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) | |