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bbcd4a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | # 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 numpy as np
import pandas as pd
import json
import os
from sklearn.metrics import pairwise_distances
def set_page_config(page_title, page_icon):
current_dir = os.path.dirname(os.path.abspath(__file__))
assets_dir = os.path.join(current_dir, "..", "assets")
st.set_page_config(
page_title=page_title,
page_icon=os.path.join(assets_dir, page_icon)
)
def display_sidebar_info(title, description, references):
st.sidebar.header(title)
st.sidebar.info(description)
st.sidebar.title("References")
st.sidebar.info(references)
# Load data from matrix file
def load_matrix(matrix_file):
try:
header_option = None
if matrix_file.name.endswith(".csv") or matrix_file.name.endswith(".xlsx"):
header_option = st.checkbox("Does the file have a header?", key="header_option",
on_change = clear_results())
st.warning("Warning: This will affect the final output if not specified correctly.")
if matrix_file.name.endswith(".csv") or matrix_file.name.endswith(".xlsx"):
if header_option:
# Load the matrix with header
matrix = pd.read_csv(matrix_file)
else:
# Load the matrix without header
matrix = pd.read_csv(matrix_file, header=None)
st.write("Matrix shape:", matrix.shape)
st.write(matrix.values)
elif matrix_file.name.endswith(".npz"):
matrix_data = np.load(matrix_file)
array_names = matrix_data.files # Select the array in the .npz file
selected_array = st.selectbox("Select the array to use", array_names)
matrix = matrix_data[selected_array]
st.write("Matrix shape:", matrix.shape)
st.write(matrix)
elif matrix_file.name.endswith(".npy"):
matrix = np.load(matrix_file)
st.write("Matrix shape:", matrix.shape)
st.write(matrix)
return matrix
except Exception as e:
st.error(f'An error occurred while loading matrix file: {e}')
return None
def load_labels(label_file):
try:
label_header_option = None
if label_file.name.endswith(".csv") or label_file.name.endswith(".xlsx"):
label_header_option = st.checkbox("Does the file have a header?", key="label_header_option",
on_change = clear_results())
st.warning("Warning: This will affect the final output if not specified correctly.")
if label_file.name.endswith(".csv") or label_file.name.endswith(".xlsx"):
if label_header_option:
labels = pd.read_csv(label_file).values.flatten()
else:
labels = pd.read_csv(label_file, header=None).values.flatten()
st.write("Cluster labels shape:", labels.shape)
st.write(labels)
return labels
except Exception as e:
st.error(f'An error occurred while loading cluster label file: {e}')
return None
def run_algorithm(selector, matrix, num_points, labels):
try:
# Separate the non-numeric first column (element names) and the numeric data
element_names = matrix.iloc[:, 0].values # Assuming the first column contains the names
numeric_matrix = matrix.select_dtypes(include=[np.number]).values
if labels is not None:
selected_ids = selector.select(numeric_matrix, size = num_points, labels = labels)
else:
selected_ids = selector.select(numeric_matrix, size = num_points)
selected_ids = [(element_names[i], i) for i in selected_ids]
st.session_state['selected_ids'] = selected_ids
return selected_ids
except ValueError as ve:
st.error(f"An error occurred while running the algorithm: {ve}")
except Exception as e:
st.error(f"An error occurred while running the algorithm: {e}")
return None
def export_results(selected_ids):
export_format = st.selectbox("Select export format", ["CSV", "JSON"], key="export_format")
if export_format == "CSV":
csv_data = pd.DataFrame(selected_ids, columns=["Element", "Index"])
csv = csv_data.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download as CSV",
data=csv,
file_name='selected_indices.csv',
mime='text/csv',
)
else:
json_data = json.dumps([{"Element": elem, "Index": i} for i, elem in selected_ids])
st.download_button(
label="Download as JSON",
data=json_data,
file_name='selected_indices.json',
mime='application/json',
)
# Function to clear selected indices from session state
def clear_results():
if 'selected_ids' in st.session_state:
del st.session_state['selected_ids']
if 'selector' in st.session_state:
del st.session_state['selector']
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