Merged some changes

.github/workflows/ci_tox.yml

@@ -24,7 +24,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [macOS-latest, windows-latest, ubuntu-latest]
+        # os: [macOS-latest, windows-latest, ubuntu-latest]
+        os: [macos-13, windows-latest, ubuntu-latest]
         python-version: ["3.9", "3.10", "3.11"]
 
     steps:

requirements.txt

@@ -1,8 +1,9 @@
-bitarray~=2.5.1
+bitarray>=2.5.1
 importlib-resources>=5.6.0
 numpy>=1.21.2
 pandas>=1.3.5
-pytest>=6.2.5
+pytest>=7.4.0
 scikit-learn>=1.0.1
-scipy==1.11.1
+scipy>=1.11.1
 setuptools>=58.0.4
+streamlit>=1.34.0

requirements_dev.txt

@@ -1,4 +1,4 @@
-bitarray~=2.5.1
+bitarray>=2.5.1
 coverage>=6.3.2
 hypothesis
 importlib-resources>=5.6.0
@@ -6,9 +6,9 @@ numpy>=1.21.2
 openpyxl
 pandas>=1.3.5
 pre-commit
-# pytest>=6.2.5
-pytest==7.4.0
+pytest>=7.4.0
 pytest-cov>=3.0.0
 scikit-learn>=1.0.1
-scipy==1.11.1
+scipy>=1.11.1
 setuptools>=58.0.4
+streamlit>=1.34.0

setup.py

@@ -56,8 +56,8 @@ setup(
     # Allows `setup.py test` to work correctly with pytest
     setup_requires=[
         "numpy>=1.21.2",
-        "scipy==1.11.1",
-        "pytest>=6.2.4",
+        "scipy>=1.11.1",
+        "pytest>=7.4.0",
         "scikit-learn",
         "bitarray",
     ]
@@ -66,8 +66,8 @@ setup(
     url="https://github.com/theochem/Selector",  # Website
     install_requires=[
         "numpy>=1.21.2",
-        "scipy==1.11.1",
-        "pytest>=6.2.4",
+        "scipy>=1.11.1",
+        "pytest>=7.4.0",
         "scikit-learn",
         "bitarray",
     ],

streamlit_app/pages/1_MaxMin.py

@@ -0,0 +1,166 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import json
+import os
+
+from sklearn.metrics import pairwise_distances
+from selector.methods.distance import MaxMin
+
+# Get the current directory path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Construct the path to the assets directory
+assets_dir = os.path.join(current_dir, "..", "assets")
+
+# Set page configuration
+st.set_page_config(
+    page_title="MaxMin",
+    page_icon=os.path.join(assets_dir, "QC-Devs.png")
+)
+
+st.title("Brute Strength - MaxMin")
+
+st.sidebar.header("Brute Strength - MaxMin")
+
+st.sidebar.info(
+    """
+    MaxMin is possibly the most widely used method for dissimilarity-based
+    compound selection. 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. Find the minimum distance from every point to the already-selected ones.
+    2. Select the point which has the maximum distance 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.
+    """
+)
+
+st.sidebar.title("References")
+
+st.sidebar.info("[1] Ashton, Mark, et al., Identification of diverse database subsets using "
+                "property‐based and fragment‐based molecular descriptions, "
+                "Quantitative Structure‐Activity Relationships 21.6 (2002): 598-604.")
+
+
+# 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")
+
+# 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
+if matrix_file is not None:
+    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")
+            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).values
+            else:
+                # Load the matrix without header
+                matrix = pd.read_csv(matrix_file, header = None).values
+            st.write("Matrix shape:", matrix.shape)
+            st.write(matrix)
+
+        elif matrix_file.name.endswith(".npz"):
+            matrix_data = np.load(matrix_file)
+            # Select the array in the .npz file
+            array_names = matrix_data.files
+            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)
+    except Exception as e:
+        st.error(f'An error occurred while loading matrix file: {e}')
+        matrix = None
+
+
+    # Input for number of points to select (required)
+    num_points = st.number_input("Number of points to select", min_value=1, step=1, key="num_points")
+
+    # Input for cluster label list (optional)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type=["csv", "xlsx"], key="label_file")
+    labels = None
+    if label_file is not None:
+        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")
+                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)
+        except Exception as e:
+            st.error(f'An error occurred while loading cluster label file: {e}')
+            labels = None
+
+
+    if st.button("Run MaxMin Algorithm"):
+        try:
+            # Check if the input matrix is a feature matrix or a distance matrix
+            if matrix.shape[0] == matrix.shape[1]:
+                # Distance matrix
+                selector = MaxMin()
+                selected_ids = selector.select(matrix, size = num_points, labels = labels)
+            else:
+                # Feature matrix
+                selector = MaxMin(lambda x: pairwise_distances(x, metric = "euclidean"))
+                selected_ids = selector.select(matrix, size = num_points, labels = labels)
+
+            # Convert selected indices to a list of integers
+            selected_ids = [int(i) for i in selected_ids]
+
+            # Save selected indices to session state
+            st.session_state['selected_ids'] = selected_ids
+        except ValueError as ve:
+            st.error(f"An error occurred while running the MaxMin algorithm: {ve}")
+        except Exception as e:
+            st.error(f"An error occurred while running the MaxMin algorithm: {e}")
+
+# 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 format
+    export_format = st.selectbox("Select export format", ["CSV", "JSON"], key="export_format")
+
+    if export_format == "CSV":
+        csv_data = pd.DataFrame(selected_ids, columns = ["Selected Indices"])
+        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',
+        )
+    elif export_format == "JSON":
+        json_data = json.dumps({"Selected Indices": selected_ids})
+        st.download_button(
+            label = "Download as JSON",
+            data = json_data,
+            file_name = 'selected_indices.json',
+            mime = 'application/json',
+        )