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import numpy as np
from scipy.sparse import csr_matrix
"""
Function to calculate the multi project matching results
The Multi-Project Matching Feature uncovers synergy opportunities among various development banks and organizations by facilitating the search for similar projects
within a selected filter setting (filtered_df) and all projects (project_df).
"""
def calc_multi_matches(filtered_df, project_df, similarity_matrix, top_x, identical_country=False):
"""
filtered_df: df with applied filters
project_df: df with all projects
similarity_matrix: np sparse matrix with all similarities between projects
top_x: top x project which should be displayed
identical_country: boolean flag to filter matches where country is identical
"""
# convert npz sparse matrix into csr matrix
if not isinstance(similarity_matrix, csr_matrix):
similarity_matrix = csr_matrix(similarity_matrix)
# extract indices of the projects
filtered_indices = filtered_df.index.to_list()
project_indices = project_df.index.to_list()
# size down the matrix to only projects within the filter and convert to dense matrix and flatten it
match_matrix = similarity_matrix[project_indices, :][:, filtered_indices] # row / column
dense_match_matrix = match_matrix.toarray()
flat_matrix = dense_match_matrix.flatten()
# get the indices of the top X values in the flattened matrix
top_indices = np.argsort(flat_matrix)[-top_x:]
# Convert flat indices back to 2D indices
top_2d_indices = np.unravel_index(top_indices, dense_match_matrix.shape)
# Extract the corresponding values
top_values = flat_matrix[top_indices]
# Prepare the result with row and column indices from original dataframes
org_rows = []
org_cols = []
for value, row, col in zip(top_values, top_2d_indices[0], top_2d_indices[1]):
original_row_index = project_indices[row]
original_col_index = filtered_indices[col]
org_rows.append(original_row_index)
org_cols.append(original_col_index)
# create two result dataframes
"""
p1_df: first results of match
p2_df: matching result
matches are displayed through the indices of p1 and p2 dfs
match1 p1_df.iloc[0] & p2_df.iloc[0]
match2 p1_df.iloc[1] & p2_df.iloc[1]
"""
p1_df = filtered_df.loc[org_cols].copy()
p1_df['similarity'] = top_values
# filter out rows with similarity score less than 50
p1_df = p1_df[p1_df['similarity'] > 0.50]
p2_df = project_df.loc[org_rows].copy()
p2_df['similarity'] = top_values
p2_df = p2_df[p2_df['similarity'] > 0.50]
if identical_country:
# Reset indices before comparison
p1_df = p1_df.reset_index(drop=True)
p2_df = p2_df.reset_index(drop=True)
# Filter to only include matches with identical countries
identical_country_mask = p1_df['country'] == p2_df['country']
p1_df = p1_df[identical_country_mask]
p2_df = p2_df[identical_country_mask]
# return both results df with matching projects
return p1_df, p2_df
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