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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.partition import GridPartition | |
| # 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 = "GridPartition", | |
| page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"), | |
| ) | |
| st.title("Grid Partitioning Method") | |
| description = """ | |
| Given the number of bins along each axis, samples are partitioned using various methods: | |
| 1. The equisized_independent partitions the feature space into bins of equal size along each dimension. | |
| 2. The equisized_dependent partitions the space where the bins can have different length in each dimension. I.e., the `l-`th dimension bins depend on the previous dimensions. So, the order of features affects the outcome. | |
| 3. The equifrequent_independent divides the space into bins with approximately equal number of sample points in each bin. | |
| 4. The equifrequent_dependent is similar to equisized_dependent where the partition in each dimension will depend on the previous dimensions. | |
| """ | |
| references = "[1] Bayley, Martin J., and Peter Willett. “Binning schemes for partition-based compound selection.” Journal of Molecular Graphics and Modelling 17.1 (1999): 10-18." | |
| display_sidebar_info("Grid Partitioning 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 | |
| numb_bins_axis = st.number_input("Number of bins to partition each axis into (numb_bins_axis)", value = 1, step = 1) | |
| # Parameters for Directed Sphere Exclusion | |
| st.info("The parameters below are optional. If not specified, default values will be used.") | |
| grid_method = st.selectbox("Method used to partition the sample points into bins. (grid_method)", ["equisized_independent", | |
| "equisized_dependent", "equifrequent_independent", "equifrequent_dependent"], on_change=clear_results) | |
| random_seed = st.number_input("Seed for random selection of sample points from each bin. (random_seed)", value=42, step=1, on_change=clear_results) | |
| if st.button("Run GridPartition Algorithm"): | |
| selector = GridPartition(numb_bins_axis, grid_method, random_seed) | |
| 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) | |