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import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix, lil_matrix
import streamlit as st
# multi_project_matching
def calc_matches(filtered_df, project_df, similarity_matrix, top_x):
st.write(filtered_df.shape)
st.write(project_df.shape)
st.write(similarity_matrix.shape)
# Ensure the matrix is in a suitable format for manipulation
if not isinstance(similarity_matrix, csr_matrix):
similarity_matrix = csr_matrix(similarity_matrix)
# Get indices from dataframes
filtered_df_indices = filtered_df.index.to_list()
project_df_indices = project_df.index.to_list()
# Create mapping dictionaries
filtered_df_index_map = {i: index for i, index in enumerate(filtered_df_indices)}
project_df_index_map = {i: index for i, index in enumerate(project_df_indices)}
# Select submatrix based on indices from both dataframes
#match_matrix = similarity_matrix[filtered_df_indices, :][:, project_df_indices]
match_matrix = similarity_matrix[np.ix_(filtered_df_indices, project_df_indices)]
st.write(match_matrix.shape)
# Get the linear indices of the top 'top_x' values
# (flattened index to handle the sparse matrix more effectively)
linear_indices = np.argsort(match_matrix.data)[-top_x:]
if len(linear_indices) < top_x:
top_x = len(linear_indices)
# Convert flat indices to 2D indices using the shape of the submatrix
top_indices = np.unravel_index(linear_indices, match_matrix.shape)
# Get the corresponding similarity values
top_values = match_matrix.data[linear_indices]
top_filtered_df_indices = [filtered_df_index_map[i] for i in top_indices[0]]
top_project_df_indices = [project_df_index_map[i] for i in top_indices[1]]
st.write(top_filtered_df_indices)
# Create resulting dataframes with top matches and their similarity scores
p1_df = filtered_df.loc[top_filtered_df_indices].copy()
p1_df['similarity'] = top_values
p2_df = project_df.loc[top_project_df_indices].copy()
p2_df['similarity'] = top_values
print("finished calc matches")
return p1_df, p2_df
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