From 0829af5eb66d729729130beff7fb55b107b05353 Mon Sep 17 00:00:00 2001
From: jkiederle <jana.kiederle@fau.de>
Date: Wed, 20 Apr 2022 18:15:56 +0200
Subject: [PATCH] add full version of eps and eps main file
---
...asetGeneOntologyInformationCollection.xlsx | Bin 0 -> 27779 bytes
.../RNA_trees/RNA_trees_bracket/._PKB10 | Bin 0 -> 245 bytes
.../RNA_trees/RNA_trees_bracket/._PKB100 | Bin 0 -> 245 bytes
.../RNA_trees/RNA_trees_bracket/._PKB101 | Bin 0 -> 245 bytes
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.../RNA_trees/RNA_trees_bracket/PKB14/._1.txt | Bin 0 -> 245 bytes
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all_other_data/df_duos_approx_entire.csv | 73 ----
all_other_data/df_duos_gurobi_entire.csv | 73 ----
... edge_preservation_sim_approx_results.csv} | 0
...> edge_preservation_sim_exact_results.csv} | 0
all_other_data/fixed_df_matrix_approx.csv | 11 -
all_other_data/fixed_df_matrix_gurobi.csv | 11 -
all_other_data/gurobi_df_dist.csv | 73 ----
.../data/normalized_approx_dist.csv | 73 ----
...rmalized_edge_preservation_sim_approx.csv} | 0
...ormalized_edge_preservation_sim_exact.csv} | 0
.../data/normalized_gurobi_dist.csv | 73 ----
...tanceResult.CSV => tree_edit_distance.csv} | 0
...imilarity.csv => tree_edit_similarity.csv} | 0
...malized_edge_preservation_sim_results.csv} | 0
...malized_edge_preservation_sim_results.csv} | 0
...alability_edge_preservation_sim_exact.png} | Bin
...y_edge_preservation_similarity_approx.png} | Bin
.../comparison_matrices_eval.ipynb | 84 ++++-
.../edge_preservation_similarity.py | 321 ++++++++++++++++++
.../edge_preservation_similarity_main.py | 295 ++++++++++++++++
432 files changed, 809 insertions(+), 391 deletions(-)
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create mode 100755 all_other_data/RNA_trees/RNA_trees_bracket/PKB98/._1.txt
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB10/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB103/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB105/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB106/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB11/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB119/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB120/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB128/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB13/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB130/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB130/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB135/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB135/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB136/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB136/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB137/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB137/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB14/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB140/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB140/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB140/3.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB140/4.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB142/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB142/3.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB142/4.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB15/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB158/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB159/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB16/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB160/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB160/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB161/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB162/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB171/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB172/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB177/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB177/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB179/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB180/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB191/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB191/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB20/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB218/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB24/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB24/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB240/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB242/1.txt (100%)
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rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB248/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB248/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB248/3.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB248/4.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB32/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB38/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB38/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB4/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB42/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB42/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB43/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB43/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB44/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB44/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB46/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB46/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB48/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB49/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB50/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB51/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB52/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB6/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB67/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB68/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB69/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB7/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB7/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB70/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB77/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB77/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB79/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB8/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB89/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB89/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB9/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB90/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB91/1.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB91/2.txt (100%)
rename all_other_data/{gml_data => RNA_trees/RNA_trees_gml}/PKB98/1.txt (100%)
delete mode 100644 all_other_data/df_duos_approx_entire.csv
delete mode 100644 all_other_data/df_duos_gurobi_entire.csv
rename all_other_data/{fixed_df_matrix_approx_entire.csv => edge_preservation_sim_approx_results.csv} (100%)
rename all_other_data/{fixed_df_matrix_gurobi_entire.csv => edge_preservation_sim_exact_results.csv} (100%)
delete mode 100644 all_other_data/fixed_df_matrix_approx.csv
delete mode 100644 all_other_data/fixed_df_matrix_gurobi.csv
delete mode 100644 all_other_data/gurobi_df_dist.csv
delete mode 100644 final_results_data/data/normalized_approx_dist.csv
rename final_results_data/data/{normalized_approx.csv => normalized_edge_preservation_sim_approx.csv} (100%)
rename final_results_data/data/{normalized_gurobi.csv => normalized_edge_preservation_sim_exact.csv} (100%)
delete mode 100644 final_results_data/data/normalized_gurobi_dist.csv
rename final_results_data/data/{treeEditDistanceResult.CSV => tree_edit_distance.csv} (100%)
rename final_results_data/data/{df_tree_edit_similarity.csv => tree_edit_similarity.csv} (100%)
rename final_results_data/results/{normalized_approx_gurobi_results.csv => normalized_edge_preservation_sim_results.csv} (100%)
rename final_results_data/results/{not_normalized_approx_gurobi_results.csv => not_normalized_edge_preservation_sim_results.csv} (100%)
rename final_results_data/results/{scalability_gurobi.png => scalability_edge_preservation_sim_exact.png} (100%)
rename final_results_data/results/{scalability_approx.png => scalability_edge_preservation_similarity_approx.png} (100%)
create mode 100644 notebooks_python/edge_preservation_similarity.py
create mode 100644 notebooks_python/edge_preservation_similarity_main.py
diff --git a/all_other_data/PKBdatasetGeneOntologyInformationCollection.xlsx b/all_other_data/PKBdatasetGeneOntologyInformationCollection.xlsx
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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similarity index 100%
rename from all_other_data/gml_data/PKB77/2.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB77/2.txt
diff --git a/all_other_data/gml_data/PKB79/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB79/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB79/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB79/1.txt
diff --git a/all_other_data/gml_data/PKB8/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB8/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB8/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB8/1.txt
diff --git a/all_other_data/gml_data/PKB89/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB89/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB89/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB89/1.txt
diff --git a/all_other_data/gml_data/PKB89/2.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB89/2.txt
similarity index 100%
rename from all_other_data/gml_data/PKB89/2.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB89/2.txt
diff --git a/all_other_data/gml_data/PKB9/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB9/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB9/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB9/1.txt
diff --git a/all_other_data/gml_data/PKB90/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB90/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB90/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB90/1.txt
diff --git a/all_other_data/gml_data/PKB91/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB91/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB91/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB91/1.txt
diff --git a/all_other_data/gml_data/PKB91/2.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB91/2.txt
similarity index 100%
rename from all_other_data/gml_data/PKB91/2.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB91/2.txt
diff --git a/all_other_data/gml_data/PKB98/1.txt b/all_other_data/RNA_trees/RNA_trees_gml/PKB98/1.txt
similarity index 100%
rename from all_other_data/gml_data/PKB98/1.txt
rename to all_other_data/RNA_trees/RNA_trees_gml/PKB98/1.txt
diff --git a/all_other_data/df_duos_approx_entire.csv b/all_other_data/df_duos_approx_entire.csv
deleted file mode 100644
index 8538b9b..0000000
--- a/all_other_data/df_duos_approx_entire.csv
+++ /dev/null
@@ -1,73 +0,0 @@
-,PKB69,PKB206,PKB51,PKB106,PKB48,PKB98,PKB14,PKB130,PKB233,PKB42,PKB6,PKB4,PKB179,PKB46,PKB161,PKB20,PKB105,PKB70,PKB24,PKB91,PKB68,PKB159,PKB7,PKB77,PKB10,PKB8,PKB240,PKB103,PKB89,PKB137,PKB52,PKB248,PKB49,PKB32,PKB241,PKB128,PKB22,PKB50,PKB140,PKB43,PKB67,PKB79,PKB247,PKB16,PKB180,PKB177,PKB172,PKB136,PKB207,PKB119,PKB218,PKB38,PKB104,PKB15,PKB11,PKB171,PKB162,PKB242,PKB120,PKB158,PKB101,PKB142,PKB102,PKB100,PKB44,PKB135,PKB191,PKB13,PKB9,PKB160,PKB90,PKB236
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diff --git a/all_other_data/df_duos_gurobi_entire.csv b/all_other_data/df_duos_gurobi_entire.csv
deleted file mode 100644
index aaf2d06..0000000
--- a/all_other_data/df_duos_gurobi_entire.csv
+++ /dev/null
@@ -1,73 +0,0 @@
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-PKB160,25.0,12.0,19.0,19.0,19.0,16.0,15.0,19.0,19.0,17.0,16.0,19.0,24.0,13.0,18.0,12.0,14.0,25.0,13.0,17.0,27.0,14.0,15.0,27.0,16.0,17.0,14.0,13.0,22.0,29.0,20.0,24.0,12.0,17.0,14.0,22.0,13.0,25.0,27.0,20.0,18.0,18.0,9.0,18.0,27.0,22.0,21.0,29.0,13.0,15.0,20.0,21.0,18.0,14.0,18.0,24.0,21.0,15.0,20.0,20.0,12.0,26.0,17.0,18.0,11.0,29.0,30.0,17.0,16.0,42.0,12.0,29.0
-PKB90,19.0,17.0,20.0,20.0,18.0,18.0,18.0,16.0,18.0,16.0,18.0,19.0,22.0,15.0,18.0,14.0,17.0,22.0,16.0,17.0,22.0,16.0,18.0,21.0,17.0,18.0,16.0,14.0,15.0,22.0,22.0,22.0,16.0,18.0,15.0,20.0,16.0,16.0,22.0,17.0,15.0,18.0,14.0,16.0,19.0,20.0,12.0,22.0,18.0,15.0,19.0,16.0,16.0,17.0,18.0,21.0,18.0,15.0,20.0,14.0,13.0,19.0,15.0,17.0,13.0,22.0,25.0,17.0,18.0,12.0,33.0,21.0
-PKB236,55.0,33.0,41.0,49.0,45.0,44.0,29.0,30.0,52.0,29.0,32.0,38.0,61.0,30.0,22.0,27.0,28.0,50.0,29.0,28.0,57.0,21.0,33.0,56.0,32.0,30.0,31.0,20.0,32.0,101.0,49.0,57.0,25.0,27.0,31.0,51.0,27.0,53.0,61.0,31.0,28.0,45.0,18.0,32.0,53.0,55.0,34.0,102.0,36.0,22.0,53.0,31.0,25.0,30.0,33.0,63.0,31.0,29.0,35.0,26.0,25.0,63.0,21.0,32.0,30.0,93.0,94.0,33.0,32.0,29.0,21.0,160.0
diff --git a/all_other_data/fixed_df_matrix_approx_entire.csv b/all_other_data/edge_preservation_sim_approx_results.csv
similarity index 100%
rename from all_other_data/fixed_df_matrix_approx_entire.csv
rename to all_other_data/edge_preservation_sim_approx_results.csv
diff --git a/all_other_data/fixed_df_matrix_gurobi_entire.csv b/all_other_data/edge_preservation_sim_exact_results.csv
similarity index 100%
rename from all_other_data/fixed_df_matrix_gurobi_entire.csv
rename to all_other_data/edge_preservation_sim_exact_results.csv
diff --git a/all_other_data/fixed_df_matrix_approx.csv b/all_other_data/fixed_df_matrix_approx.csv
deleted file mode 100644
index ca4171a..0000000
--- a/all_other_data/fixed_df_matrix_approx.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-,PKB14,PKB42,PKB48,PKB51,PKB69,PKB98,PKB106,PKB130,PKB206,PKB233
-PKB14,32.0,14.0,17.0,18.0,19.0,20.0,20.0,17.0,15.0,20.0
-PKB42,14.0,31.0,19.0,20.0,19.0,16.0,22.0,18.0,13.0,22.0
-PKB48,17.0,19.0,60.0,24.0,26.0,34.0,26.0,20.0,20.0,32.0
-PKB51,18.0,20.0,24.0,48.0,23.0,21.0,27.0,18.0,18.0,26.0
-PKB69,19.0,19.0,26.0,23.0,53.0,25.0,28.0,20.0,16.0,29.0
-PKB98,20.0,16.0,34.0,21.0,25.0,51.0,27.0,19.0,23.0,28.0
-PKB106,20.0,22.0,26.0,27.0,28.0,27.0,52.0,18.0,20.0,30.0
-PKB130,17.0,18.0,20.0,18.0,20.0,19.0,18.0,35.0,13.0,21.0
-PKB206,15.0,13.0,20.0,18.0,16.0,23.0,20.0,13.0,37.0,21.0
-PKB233,20.0,22.0,32.0,26.0,29.0,28.0,30.0,21.0,21.0,56.0
diff --git a/all_other_data/fixed_df_matrix_gurobi.csv b/all_other_data/fixed_df_matrix_gurobi.csv
deleted file mode 100644
index 3037567..0000000
--- a/all_other_data/fixed_df_matrix_gurobi.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-,PKB14,PKB42,PKB48,PKB51,PKB69,PKB98,PKB106,PKB130,PKB206,PKB233
-PKB14,49.0,22.0,30.0,27.0,29.0,31.0,27.0,23.0,21.0,29.0
-PKB42,22.0,46.0,29.0,27.0,28.0,26.0,29.0,26.0,18.0,33.0
-PKB48,30.0,29.0,75.0,37.0,39.0,50.0,39.0,31.0,33.0,45.0
-PKB51,27.0,27.0,37.0,60.0,39.0,32.0,37.0,27.0,30.0,39.0
-PKB69,29.0,28.0,39.0,39.0,77.0,38.0,42.0,29.0,30.0,41.0
-PKB98,31.0,26.0,50.0,32.0,38.0,77.0,38.0,29.0,33.0,46.0
-PKB106,27.0,29.0,39.0,37.0,42.0,38.0,75.0,29.0,30.0,48.0
-PKB130,23.0,26.0,31.0,27.0,29.0,29.0,29.0,48.0,21.0,29.0
-PKB206,21.0,18.0,33.0,30.0,30.0,33.0,30.0,21.0,56.0,32.0
-PKB233,29.0,33.0,45.0,39.0,41.0,46.0,48.0,29.0,32.0,90.0
diff --git a/all_other_data/gurobi_df_dist.csv b/all_other_data/gurobi_df_dist.csv
deleted file mode 100644
index 0a00a64..0000000
--- a/all_other_data/gurobi_df_dist.csv
+++ /dev/null
@@ -1,73 +0,0 @@
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diff --git a/final_results_data/data/normalized_approx_dist.csv b/final_results_data/data/normalized_approx_dist.csv
deleted file mode 100644
index e86d8aa..0000000
--- a/final_results_data/data/normalized_approx_dist.csv
+++ /dev/null
@@ -1,73 +0,0 @@
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diff --git a/final_results_data/data/normalized_approx.csv b/final_results_data/data/normalized_edge_preservation_sim_approx.csv
similarity index 100%
rename from final_results_data/data/normalized_approx.csv
rename to final_results_data/data/normalized_edge_preservation_sim_approx.csv
diff --git a/final_results_data/data/normalized_gurobi.csv b/final_results_data/data/normalized_edge_preservation_sim_exact.csv
similarity index 100%
rename from final_results_data/data/normalized_gurobi.csv
rename to final_results_data/data/normalized_edge_preservation_sim_exact.csv
diff --git a/final_results_data/data/normalized_gurobi_dist.csv b/final_results_data/data/normalized_gurobi_dist.csv
deleted file mode 100644
index a909141..0000000
--- a/final_results_data/data/normalized_gurobi_dist.csv
+++ /dev/null
@@ -1,73 +0,0 @@
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diff --git a/final_results_data/data/treeEditDistanceResult.CSV b/final_results_data/data/tree_edit_distance.csv
similarity index 100%
rename from final_results_data/data/treeEditDistanceResult.CSV
rename to final_results_data/data/tree_edit_distance.csv
diff --git a/final_results_data/data/df_tree_edit_similarity.csv b/final_results_data/data/tree_edit_similarity.csv
similarity index 100%
rename from final_results_data/data/df_tree_edit_similarity.csv
rename to final_results_data/data/tree_edit_similarity.csv
diff --git a/final_results_data/results/normalized_approx_gurobi_results.csv b/final_results_data/results/normalized_edge_preservation_sim_results.csv
similarity index 100%
rename from final_results_data/results/normalized_approx_gurobi_results.csv
rename to final_results_data/results/normalized_edge_preservation_sim_results.csv
diff --git a/final_results_data/results/not_normalized_approx_gurobi_results.csv b/final_results_data/results/not_normalized_edge_preservation_sim_results.csv
similarity index 100%
rename from final_results_data/results/not_normalized_approx_gurobi_results.csv
rename to final_results_data/results/not_normalized_edge_preservation_sim_results.csv
diff --git a/final_results_data/results/scalability_gurobi.png b/final_results_data/results/scalability_edge_preservation_sim_exact.png
similarity index 100%
rename from final_results_data/results/scalability_gurobi.png
rename to final_results_data/results/scalability_edge_preservation_sim_exact.png
diff --git a/final_results_data/results/scalability_approx.png b/final_results_data/results/scalability_edge_preservation_similarity_approx.png
similarity index 100%
rename from final_results_data/results/scalability_approx.png
rename to final_results_data/results/scalability_edge_preservation_similarity_approx.png
diff --git a/notebooks_python/comparison_matrices_eval.ipynb b/notebooks_python/comparison_matrices_eval.ipynb
index 6f03582..08c1e74 100644
--- a/notebooks_python/comparison_matrices_eval.ipynb
+++ b/notebooks_python/comparison_matrices_eval.ipynb
@@ -1975,17 +1975,93 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "def import_graph_coll(path):\n",
+ " '''reads in gml data and returns a list of lists of graphs''' \n",
+ " graph_coll=[]\n",
+ " listdir=os.listdir(path)\n",
+ " for dire in listdir:\n",
+ " graph_coll.append([])\n",
+ " graphdir=os.listdir(path+'/'+dire)\n",
+ " for graph_file in graphdir:\n",
+ " graph_coll[-1].append(nx.readwrite.gml.read_gml(path+'/'+dire+'/'+graph_file,label='id'))\n",
+ " return graph_coll"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "path = \"/home/jana/Documents/BIONETs/Code/tree_match_approx_validator/all_other_data/RNA_trees/RNA_trees_gml\"\n",
+ "graph_coll = import_graph_coll(path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "orders = []\n",
+ "for graph in graph_coll:\n",
+ " for g in graph:\n",
+ " o = g.order()\n",
+ " orders.append(o)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "113"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(orders)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np_order = np.array(orders)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "52.0"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.median(np_order)"
+ ]
},
{
"cell_type": "code",
diff --git a/notebooks_python/edge_preservation_similarity.py b/notebooks_python/edge_preservation_similarity.py
new file mode 100644
index 0000000..8873617
--- /dev/null
+++ b/notebooks_python/edge_preservation_similarity.py
@@ -0,0 +1,321 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 14 18:20:28 2021, edited Apr 2022
+
+@authors: nboria, jkiederle
+"""
+
+import gurobipy as gu
+import os
+import networkx as nx
+import numpy as np
+
+
+
+def intersection(lst1, lst2):
+ lst3 = [value for value in lst1 if value in lst2]
+ return lst3
+
+class Evaluator:
+ '''an instance of this is needed to compute the final result of the edge preservation similarity'''
+
+ def __init__(self):
+ self._preserved_duos_G1=[]
+ self._preserved_duos_G2=[]
+ self._evaluation=0
+ return None
+
+
+ def evaluate_sol(self,G1,G2,sol):
+ '''use this to get the egde preservation similarity computed (either approx or exact)'''
+ self._preserved_duos_G1=[]
+ self._preserved_duos_G2=[]
+ self._evaluation=0
+ matched_G1=[m[0] for m in sol]
+ #print('matched_G1',matched_G1)
+ matched_G2=[m[1]-G1.order() for m in sol]
+ #print('matched_G2',matched_G2)
+ edges_G1=list(G1.edges)
+ edges_G2=list(G2.edges)
+ #print(edges_G1)
+ for i in range(len(matched_G1)):
+ for j in range(len(matched_G1)):
+ if (matched_G1[i],matched_G1[j]) in edges_G1:
+ if (matched_G2[i],matched_G2[j]) in edges_G2:
+ self._evaluation+=1
+ self._preserved_duos_G1.append([matched_G1[i],matched_G1[j]])
+ self._preserved_duos_G2.append([matched_G2[i],matched_G2[j]])
+ return self._evaluation
+
+class Gurobi_solver:
+ '''an instance of this is needed to exactly compute the edge preservation similarity'''
+
+ def __init__(self,verbose=None):
+ self._name='EDGE-PRESERVATION-SIM-EXACT'
+ self._sol=None
+ self._verbose=verbose
+ if self._verbose is None:
+ self._verbose=False
+ self._preserved_duos_G1=[]
+
+ def compute_duos(self,G1,G2,timelimit=0):
+ '''this function is called to construct the matching(preserved duos) between graphs G1 and G2
+ It always needs to be called before evaluating it with the Evaluator to get a result'''
+
+ self._preserved_duos_G1=[]
+ self._sol=[]
+ m=gu.Model('distance')
+ m.setParam('OutputFlag', self._verbose)
+ if timelimit > 0:
+ m.setParam("TimeLimit", timelimit)
+
+ #VARIABLES
+ x = {}
+ # xij = mapping node i to node j
+ for i in range(G1.order()):
+ for j in range(G2.order()):
+ x[i, j] = m.addVar(vtype=gu.GRB.BINARY)
+ y = {}
+ # yij =1 if duo ij is saved else 0
+ for edge in list(G1.edges):
+ y[edge[0],edge[1]]=m.addVar(vtype=gu.GRB.BINARY)
+
+ m.update()
+
+ #CONSTRAINTS
+
+ for i in range(G1.order()):
+ for j in range(G2.order()):
+ if G1.nodes[i]['lbl']!= G2.nodes[j]['lbl']:
+ m.addConstr(x[i, j]==0)
+ for i in range(G1.order()):
+ m.addConstr(gu.quicksum(x[i,j] for j in range(G2.order()))<=1)
+ for j in range(G2.order()):
+ m.addConstr(gu.quicksum(x[i,j] for i in range(G1.order()))<=1)
+ for edge1 in list(G1.edges):
+ m.addConstr(y[edge1[0],edge1[1]]<=gu.quicksum(x[edge1[0],edge2[0]]*x[edge1[1],edge2[1]] for edge2 in list(G2.edges)))
+
+ #OBJECTIVE
+ m.setObjective(gu.quicksum(y[edge[0],edge[1]] for edge in list(G1.edges)), gu.GRB.MAXIMIZE)
+
+ m.optimize()
+ self._sol=[]
+ for i in range(G1.order()):
+ for j in range(G2.order()):
+ if x[i,j].X>=0.99999:
+ self._sol.append([i,j+G1.order()])
+
+ for edge in list(G1.edges):
+ if y[edge[0],edge[1]].X>=0.99999:
+ self._preserved_duos_G1.append([edge[0],edge[1]])
+
+ if m.status == 9:
+ #time limit reached
+ print(m.status)
+ return True
+ return False
+
+class Approx_alg:
+ '''an instance of this is needed to approximately compute the edge preservation similarity'''
+
+ def __init__(self):
+ self._name='EDGE-PRESERVATION-SIM-APPROX'
+ return None
+
+ def compute_duos(self,G1,G2):
+ '''this function is called to construct the matching(preserved duos) between graphs G1 and G2
+ It always needs to be called before evaluating it with the Evaluator to get a result'''
+
+ self.init_match(G1,G2)
+ self.complete_Sols(G1,G2)
+ self._best_eval=0
+ E=Evaluator()
+ for sol in self._sols:
+ if E.evaluate_sol(G1,G2,sol)>= self._best_eval:
+ self._best_eval=E._evaluation
+ self._sol=sol
+
+ def matching_solver(self,G1,G2,LMG):
+ #reindexing nodes
+ edges=list(LMG.edges())
+ weights=[]
+ for i,edge in enumerate(edges):
+ weights.append(LMG[edge[0]][edge[1]]['weight'])
+ edges[i]=sorted(edge)
+ list_indices_G1=[]
+ list_indices_G2=[]
+ for node in LMG.nodes:
+ if node < G1.order():
+ list_indices_G1.append(node)
+ else:
+ list_indices_G2.append(node)
+ m=gu.Model()
+ m.setParam('OutputFlag', 0)
+ x={}
+ for i in list_indices_G1:
+ for j in list_indices_G2:
+ x[i,j]=m.addVar()
+ m.update()
+ for i in list_indices_G1:
+ m.addConstr(gu.quicksum(x[i,j] for j in list_indices_G2)<=1)
+ for j in list_indices_G2:
+ m.addConstr(gu.quicksum(x[i,j] for i in list_indices_G1)<=1)
+
+ m.setObjective(gu.quicksum(x[e[0],e[1]]*weights[i] for i,e in enumerate(edges)), gu.GRB.MAXIMIZE)
+ m.optimize()
+ sol=[]
+ for e in edges:
+ if x[e[0],e[1]].X >= 0.9999 :
+ sol.append([e[0],e[1]])
+ return sol
+
+
+
+ def edge_weight(self,G1,v1_id,G2,v2_id):
+ '''weight of edge, number of duos that can be preserved locally by mapping V_g1 and V_g2'''
+ labels_G1 = self._labels_G1
+ labels_G2 = self._labels_G2
+ if labels_G1[v1_id]!=labels_G2[v2_id]:
+ return 0
+ all_attr=self._all_attr
+ num_child_by_attr_v1=np.zeros((len(all_attr)))
+ num_child_by_attr_v2=np.zeros((len(all_attr)))
+ for child in G1.neighbors(v1_id):
+ for i in range(len(all_attr)):
+ if labels_G1[child]==all_attr[i]:
+ num_child_by_attr_v1[i]+=1
+ for child in G2.neighbors(v2_id):
+ for i in range(len(all_attr)):
+ if labels_G2[child]==all_attr[i]:
+ num_child_by_attr_v2[i]+=1
+ score=0.00001
+ for i in range(len(all_attr)):
+ score+=min([num_child_by_attr_v1[i],num_child_by_attr_v2[i]])
+ return score
+
+ def create_LM_graph(self,G1,G2):
+ '''creates a Local Match (LM)-Graph, a weighted bipartite graph containing all vertices of G1 and G2
+ only having edges between V_g1 and V-g2 that share the same label
+ label: weight of edge, number of duos that can be preserved locally by mapping V_g1 and V_g2
+ preserved duo: child nodes of V_g2 and V_g2 match'''
+ self._labels_G1 = nx.get_node_attributes(G1, "lbl")
+ self._labels_G2 = nx.get_node_attributes(G2, "lbl")
+ self._all_attr=list(set(list(nx.get_node_attributes(G1,'lbl').values()) +list(nx.get_node_attributes(G2,'lbl').values())))
+ indices_G1=list(range(G1.order()))
+ indices_G2=list(range(G1.order(),G1.order()+G2.order()))
+ LMG=nx.Graph()
+ LMG.add_nodes_from(indices_G1)
+ LMG.add_nodes_from(indices_G2)
+ for v in range(G1.order()):
+ LMG.nodes[v]['depth']=G1.nodes[v]['depth']
+ for v in range(G2.order()):
+ LMG.nodes[indices_G2[v]]['depth']=G2.nodes[v]['depth']
+ for v1 in range(G1.order()):
+ for v2 in range(G2.order()):
+ LMG.add_edge(v1,indices_G2[v2],weight=self.edge_weight(G1,v1,G2,v2))
+ return LMG
+
+ def complete_Sols(self,G1,G2):
+ '''optimizes mapping
+ before this method: similarity of father to child nodes relevant
+ after: similarity also to incoporate child nodes of child nodes'''
+ Sols=self._sols
+ indices_G1=list(range(G1.order()))
+ indices_G2=list(range(G1.order(),G1.order()+G2.order()))
+ for s,Sol in enumerate(Sols):
+ unmatched=indices_G1+indices_G2
+ matched=[]
+ for edge in Sol:
+ matched.append(edge[0])
+ matched.append(edge[1])
+ unmatched.remove(edge[0])
+ unmatched.remove(edge[1])
+ RG=nx.Graph()
+ RG.add_nodes_from(unmatched)
+ for v1 in intersection(indices_G1,unmatched):
+ for v2 in intersection(indices_G2,unmatched):
+ if G1.nodes[v1]['lbl']!=G2.nodes[v2-G1.order()]['lbl']:
+ edge_weight=0
+ else:
+ edge_weight=0.000001
+ if len(list(G1.predecessors(v1)))==len(list(G2.predecessors(v2-G1.order())))==1:
+ f_v1=list(G1.predecessors(v1))[0]
+ f_v2=list(G2.predecessors(v2-G1.order()))[0]
+ if [f_v1,indices_G2[f_v2]] in Sol:
+ edge_weight+=1
+ matched_sons_v1=intersection(matched,G1.neighbors(v1))
+ matched_sons_v2=intersection(matched,G2.neighbors(v2-G1.order()))
+ for s_v1 in matched_sons_v1:
+ for s_v2 in matched_sons_v2:
+ if [s_v1,indices_G2[s_v2]] in Sol:
+ edge_weight+=1
+ RG.add_edge(v1,v2,weight=edge_weight)
+ #sol_add=list(nx.max_weight_matching(RG))
+ sol_add=self.matching_solver(G1,G2,RG)
+ for i,match in enumerate(sol_add):
+ sol_add[i]=sorted(match)
+ Sols[s]=Sol+sol_add
+ self._sols=Sols
+
+
+ def init_match(self,G1,G2):
+ LMG=self.create_LM_graph(G1,G2)
+ #indices_G1=list(range(G1.order()))
+ #indices_G2=list(range(G1.order(),G1.order()+G2.order()))
+ V1_even=[]
+ V1_odd=[]
+ V2_even=[]
+ V2_odd=[]
+ for i,node in enumerate(LMG.nodes):
+ if LMG.nodes[node]['depth']%2==0:
+ if i<G1.order():
+ V1_even.append(i)
+ else:
+ V2_even.append(i)
+ elif i<G1.order():
+ V1_odd.append(i)
+ else:
+ V2_odd.append(i)
+ Vs=[]
+ Vs.append(V1_even+V2_even)
+ Vs.append(V1_even+V2_odd)
+ Vs.append(V1_odd+V2_even)
+ Vs.append(V1_odd+V2_odd)
+ Sols=[]
+ for V in Vs:
+ LMG_temp=LMG.subgraph(V)
+ #sol=list(nx.max_weight_matching(LMG_temp))
+ sol=self.matching_solver(G1,G2,LMG_temp)
+ for i,match in enumerate(sol):
+ sol[i]=sorted(match)
+ Sols.append(sol)
+ self._sols=Sols
+
+
+
+def import_graph_coll(path):
+ '''reads in gml data and returns a list of lists of graphs'''
+ graph_coll=[]
+ listdir=os.listdir(path)
+ for dire in listdir:
+ graph_coll.append([])
+ graphdir=os.listdir(path+'/'+dire)
+ for graph_file in graphdir:
+ graph_coll[-1].append(nx.readwrite.gml.read_gml(path+'/'+dire+'/'+graph_file,label='id'))
+ return graph_coll
+
+def graph_coll_edit(coll):
+ '''adds depth data to each vertex in the collection'''
+ for mR in coll:
+ for G in mR:
+ for node in G.nodes:
+ SP=nx.shortest_path_length(G,0)
+ G.nodes[node]['depth']=SP[node]
+ return coll
+
+
+def import_graph_names(path):
+ '''returns a list of names of graphs'''
+ listdir=os.listdir(path)
+ return listdir
diff --git a/notebooks_python/edge_preservation_similarity_main.py b/notebooks_python/edge_preservation_similarity_main.py
new file mode 100644
index 0000000..3d3419b
--- /dev/null
+++ b/notebooks_python/edge_preservation_similarity_main.py
@@ -0,0 +1,295 @@
+"""
+Created on Apr 2022
+
+@authors: jkiederle
+"""
+
+
+import os
+import numpy as np
+import time
+from datetime import date, datetime
+import pandas as pd
+from edge_preservation_similarity import *
+
+
+def compute_algorithm_helper_fast(algorithm, graph_coll, duos, times, time_limit_reached, time_limit_exact, normalize=False):
+ '''FUNCTION FOR DIRECT USE, IF NICE RESULTS WANTED USE compute_algorithm(), SEE BELOW
+ computes the given algorithm and returns similarity/distance values, runtime and whether a timelimit was reached
+ note: fast means only one half of the matrix is computed and copied to other half (only important for APPROX)
+
+ input: algorithm: possibilities: 'EDGE-PRESERVATION-SIM-APPROX' for approximation
+ 'EDGE-PRESERVATION-SIM-EXACT' for exact measure
+ graph_coll: graph collection
+ duos: empty matrix in which similarity or distance measure results are returned
+ times: empty matrix in which runtimes of algorithm are returned
+ time_limit_reached: empty matrix in which true/false are returned whether time limit was reached
+ time_limit_exact: time limit in seconds, note: only implemented for 'EDGE-PRESERVATION-SIM-EXACT' as it is NP-hard
+ normalize: flag, if true results are normalized by division by max(#edges in trees G1 or G2)
+ output: duos, times, time_limit_reached - all matrices defined above'''
+
+ for i in range(len(graph_coll)):
+ for j in range(len(graph_coll)):
+
+ # check whether you are at a position that needs computation (no computation below diagonal of matrix)
+
+ if i <= j:
+
+ G1=graph_coll[i][0]
+ G2=graph_coll[j][0]
+ value = 0.0
+
+ # compute value with algorithm
+
+ tic=time.time()
+
+ timeflag = False
+ if algorithm == 'EDGE-PRESERVATION-SIM-EXACT':
+ timeflag = GU.compute_duos(G1,G2, time_limit_exact)
+ print(timeflag)
+
+ elif algorithm == 'EDGE-PRESERVATION-SIM-APPROX':
+ ALG.compute_duos(G1,G2)
+
+ tac=time.time()
+
+ # if time limit is reached put 0.0 and put True in time limit matrix
+ # else put false in time limit matrix (is already by default false)
+
+ if timeflag:
+ duos[i][j] = 0.0
+ times[i][j] = 0.0
+ time_limit_reached[i][j] = True
+ time_limit_reached[j][i] = True
+ else:
+ times[i][j]=tac-tic
+ times[j][i]=tac-tic
+
+ print("Duration ", tac-tic)
+
+
+ if algorithm == 'EDGE-PRESERVATION-SIM-EXACT':
+ value = E.evaluate_sol(G1,G2,GU._sol)
+ elif algorithm == 'EDGE-PRESERVATION-SIM-APPROX':
+ value = E.evaluate_sol(G1,G2,ALG._sol)
+
+ if normalize:
+ value = normalize_value(value, G1, G2)
+
+ duos[i][j] = value
+ duos[j][i] = value
+
+
+ return duos, times, time_limit_reached
+
+
+def compute_algorithm_helper_exact(algorithm, graph_coll, duos, times, time_limit_reached, time_limit_exact=0, normalize=False):
+ '''FUNCTION FOR DIRECT USE, IF NICE RESULTS WANTED USE compute_algorithm(), SEE BELOW
+ computes the given algorithm and returns similarity/distance values, runtime and whether a timelimit was reached
+ note: exact means the entire matrix is computed. Symmetry is achieved by maximizing over the respective entries
+ more information see below (only important for APPROX)
+
+ input: algorithm: possibilities 'EDGE-PRESERVATION-SIM-APPROX' for approximation
+ 'EDGE-PRESERVATION-SIM-EXACT' for exact measure
+ graph_coll: graph collection
+ duos: empty matrix in which similarity or distance measure results are returned
+ times: empty matrix in which runtimes of algorithm are returned
+ time_limit_reached: empty matrix in which true/false are returned whether time limit was reached
+ time_limit_exact: time limit in seconds, note: only implemented for 'EDGE-PRESERVATION-SIM-EXACT' as it is NP-hard
+ normalize: flag, if true results are normalized by division by max(#edges in trees G1 or G2)
+ output: duos, times, time_limit_reached - all matrices defined above'''
+
+ for i in range(len(graph_coll)):
+ for j in range(len(graph_coll)):
+
+ # compute all positions
+
+ G1=graph_coll[i][0]
+ G2=graph_coll[j][0]
+
+ # compute value with algorithm
+ # if time limit is reached put 0.0 and put True in time limit matrix
+ # else put false in time limit matrix (is already by default false)
+ #note: time limit only possible for 'EDGE-PRESERVATION-SIM-EXACT'
+
+ tic=time.time()
+
+ timeflag = False
+ if algorithm == 'EDGE-PRESERVATION-SIM-EXACT':
+ timeflag = GU.compute_duos(G1,G2, time_limit_exact)
+ print(timeflag)
+
+ elif algorithm == 'EDGE-PRESERVATION-SIM-APPROX':
+ ALG.compute_duos(G1,G2)
+
+ tac=time.time()
+
+ if timeflag:
+ duos[i][j] = 0.0
+ times[i][j] = 0.0
+ time_limit_reached[i][j] = True
+ else:
+ times[i][j] = tac-tic
+
+ print("Duration ", tac-tic)
+
+ #final computation of the edge preservation similarity matrix based on the previously constructed duos matrix
+ if algorithm == 'EDGE-PRESERVATION-SIM-EXACT':
+ value = E.evaluate_sol(G1,G2,GU._sol)
+ elif algorithm == 'EDGE-PRESERVATION-SIM-APPROX':
+ value = E.evaluate_sol(G1,G2,ALG._sol)
+
+ if normalize:
+ value = normalize_value(value, G1, G2)
+
+ #for getting a symmetric matrix
+ if j<i:
+ if duos[j,i] > value:
+ duos[j,i] = value
+ duos[i,j] = value
+ else:
+ duos[i,j] = duos[j,i]
+ else:
+ duos[i][j] = value
+
+ return duos, times, time_limit_reached
+
+
+def sort_matrix(df):
+ '''sort matrix by row and col indices for easier comparison, inplace
+ input: df: matrix as pandas dataframe'''
+ df.sort_index(key=lambda x: (x.to_series().str[3:].astype(int)), axis = 0, inplace=True)
+ df.sort_index(key=lambda x: (x.to_series().str[3:].astype(int)), axis = 1, inplace=True)
+
+
+def normalize_value(value, G1, G2):
+ '''normalize similarity value based on the maximum number of edges of the trees G1 and G2
+ input: value: similarity value
+ G1, G2: trees of data type networkx graph'''
+ edge_count_G1 = G1.number_of_edges()
+ edge_count_G2 = G2.number_of_edges()
+
+ return value / max(edge_count_G1,edge_count_G2)
+
+def get_data(path):
+ '''reads in gml data and returns networkx graphs'''
+ graph_coll = []
+ graph_coll=import_graph_coll(path)
+ graph_coll=graph_coll_edit(graph_coll)
+
+ return graph_coll
+
+
+
+def compute_algorithm(algorithm, in_path, out_path, time_limit=0, normalize=False, fast=False):
+ '''FUNCTION FOR USE OF ALGORITHM AND NICE RESULTS
+ computes the given algorithm and returns similarity/distance values, runtime and whether a timelimit was reached
+ note: exact means the entire matrix is computed. Symmetry is achieved by maximizing over the respective entries
+ more information see below (only important for APPROX)
+
+ input: algorithm: possibilities 'EDGE-PRESERVATION-SIM-APPROX' for approximation
+ 'EDGE-PRESERVATION-SIM-EXACT' for exact measure
+ time_limit: time limit in seconds, note: only implemented for 'EDGE-PRESERVATION-SIM-EXACT' as it is NP-hard
+ normalize: flag, if true results are normalized by division by max(#edges in trees G1 or G2)
+ fast: flag, if true only upper half of matrix is computed and then mirrored
+ -> is less exact for 'EDGE-PRESERVATION-SIM-APPROX'
+ saves: report file (txt file)
+ matrices of computed values, runtime and whether the runtime expired (csv files)'''
+
+ E=Evaluator()
+ ALG=Approx_alg()
+ GU=Gurobi_solver(0)
+
+ date_time = str(date.today().strftime("%b_%d_%Y"))+ "-" + str(datetime.now().strftime("%H_%M"))
+
+ #get data in gml format
+ graph_coll = get_data(in_path)
+
+ graph_names_list = import_graph_names(in_path)
+
+ print('in_path ', in_path)
+
+ out_path = out_path + '_results_' + algorithm + "_" + date_time
+
+ os.mkdir(out_path)
+ f = open(out_path + "/report.txt","a")
+
+ f.write("Report for " + date_time + "\n")
+ f.write("in_path: " + str(in_path) + "\n")
+ f.write("outpath: " + str(out_path) + "\n")
+ f.write("algorithm: " + algorithm + "\n")
+
+ if algorithm == 'EDGE-PRESERVATION-SIM-EXACT':
+ f.write("time_limit: " + str(time_limit) + "\n")
+ #always use fast version as fast and exact version do not differ for 'EDGE-PRESERVATION-SIM-EXACT'
+ f.write("fast version: " + str(True) + "\n")
+ else:
+ #fast and exact version only differ for 'EDGE-PRESERVATION-SIM-APPROX'
+ f.write("fast version: " + str(fast) + "\n")
+
+
+
+
+ duos= np.zeros((len(graph_names_list),len(graph_names_list)))
+ times=np.zeros((len(graph_names_list),len(graph_names_list)))
+ time_limit_reached =np.zeros((len(graph_names_list),len(graph_names_list)))
+
+ first_time = time.time()
+
+ if algorithm == 'EDGE-PRESERVATION-SIM-APPROX' and not fast:
+ #fast and exact version only differ for 'EDGE-PRESERVATION-SIM-APPROX'
+ duos, times, time_limit_reached = compute_algorithm_helper_exact(algorithm, graph_coll, duos, times, time_limit_reached, time_limit)
+ else:
+ #always use fast version as fast and exact version do not differ for 'EDGE-PRESERVATION-SIM-EXACT'
+ duos, times, time_limit_reached = compute_algorithm_helper_fast(algorithm, graph_coll, duos, times, time_limit_reached, time_limit)
+
+ entire_duration = time.time()-first_time
+
+
+ f.write("entire duration: " + str(entire_duration) + "\n")
+
+ df_duos = pd.DataFrame(data=duos, index=graph_names_list, columns=graph_names_list)
+ df_duos_times = pd.DataFrame(data=times, index=graph_names_list, columns=graph_names_list)
+ df_duos_limit = pd.DataFrame(data=time_limit_reached, index=graph_names_list, columns=graph_names_list)
+
+ #sort the matrices from small to big according to the name of the files (graph_names_list)
+ sort_matrix(df_duos)
+ sort_matrix(df_duos_times)
+ sort_matrix(df_duos_limit)
+
+ df_duos.to_csv(out_path + '/df_values_' + algorithm + '.csv')
+ df_duos_times.to_csv(out_path + '/df_times_' + algorithm + '.csv')
+ df_duos_limit.to_csv(out_path + '/df_time_limit_reached_' + algorithm + '.csv')
+
+
+# choose 'EDGE-PRESERVATION-SIM-APPROX' for approximation or 'EDGE-PRESERVATION-SIM-EXACT' for exact measure
+algorithm = 'EDGE-PRESERVATION-SIM-EXACT'
+time_limit = 600 #10 min but only applicable and needed for 'EDGE-PRESERVATION-SIM-EXACT'
+
+result_path = "/home/jana/Documents/BIONETs/Code/tree_match_approx_validator/final_results_data/results/"
+
+E=Evaluator()
+ALG=Approx_alg()
+GU=Gurobi_solver(0)
+
+date_time = str(date.today().strftime("%b_%d_%Y"))+ "-" + str(datetime.now().strftime("%H_%M"))
+
+path="/home/jana/Documents/BIONETs/Code/tree_match_approx_validator/all_other_data/scalability_trees/tree_blocks_gml/20"
+
+#path = "/home/jana/Documents/BIONETs/Code/tree_match_approx_validator/all_other_data/gml_data"
+
+compute_algorithm(algorithm, path, result_path, time_limit=0, normalize=False, fast=False)
+
+'''for i in range(len(all_max_n)):
+
+ max_n = all_max_n[i]
+
+
+ graph_coll = []
+
+ path="/home/jana/Documents/BIONETs/Code/tree_match_approx_validator/all_other_data/scalability_trees/tree_blocks_gml/" + str(max_n)
+
+
+ compute_algorithm(algorithm, path, result_path, time_limit=0, normalize=False)'''
+
+
\ No newline at end of file
--
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