prime-adj 0.0.10

Prime Adjacency Matrix for Multi-relational Networks

Prime Adjacency Matrix

A bare-bones implementation of the PAM framework, i.e. Prime Adjacency Matrices for Multi-relational Networks.

Mainly used for testing. Check each .py in its "main"" component where we call each of the major functions for their functionalities.

Installation

You can install it manually (by cloining and using requiremets.txt) or through pip:

pip install prime_adj

Usage

It works using as input any collection of triples in the form of a .txt where each line corresponds to one triple in the form:

ent1, rel1, ent2,
ent2, rel2, ent3

The delimiters can change. Please see load_data.py file for this.

You can check each of the .py files for the specific functionality you are interested in. For example, running:

from prime_adj.data_loading import load_data
from prime_adj.pam_creation import create_pam_matrices
from prime_adj.rule_generation import create_ruleset
from prime_adj.tail_prediction_with_rules import predict_tail_with_explanations
from prime_adj.utils import calculate_hits_at_k

path = "./data/dummy_data/"
project_name = "test"

df_train_orig, df_train, df_eval, df_test, already_seen_triples = load_data(
    path, project_name=project_name, add_inverse_edges="NO", sep=","
)
print(f"\nLoaded Data, will create PAMs... \n")

(
    pam_1hop_lossless,
    pam_powers,
    node2id,
    rel2id,
    broke_with_sparsity,
) = create_pam_matrices(df_train, use_log=False, max_order=3)
print(f"\nCreated PAMs, will generate rules... \n")

all_rules_df = create_ruleset(
    pam_1hop_lossless, pam_powers, use_log=False, max_num_hops=-1
)
print(f"\nCreated {all_rules_df.shape[0]} rules, will generate predictions...  \n")

k_hop_pams = [pam_1hop_lossless] + pam_powers[1:]

predict_tail_with_explanations(
    df_test, all_rules_df, k_hop_pams, node2id, rel2id, rank_rules_by_="score"
)

Will load the dummy data, calculate up to 3-hop PAMs, generate rules, predict possible tail candidates for each the two test queries in the ./test/dummy_data/test.txt file and print those predictions...

Specifically, you will get:

No valid.txt found in ../test/dummy_data... df_eval will contain the train data..
Total: 9 triples in train + eval!)
In train: 9
In valid: 9
In test: 2

Loaded Data, will create PAMs... 

# of unique rels: 3      | # of unique nodes: 5
(5, 5) Sparsity: 68.00 %
Hop 2
Sparsity 2-hop: 76.00 %
Hop 3
Sparsity 3-hop: 88.00 %
Hop 4
Sparsity 4-hop: 96.00 %
Hop 5
Sparsity 5-hop: 100.00 %

Created PAMs, will generate rules... 

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 806.19it/s]

Created 12 rules, will generate predictions...  

Query (0): Nick - knows - Anna
(Correct Match) 0.4000 : knows(Nick, B ) ^ knows( B ,Anna) -> knows(Nick,Anna)



Query (1): George - lives_in - Athens
(Correct Match) 0.5000 : works_in(George,Athens) -> lives_in(George,Athens)



100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 300.44it/s]
Tail Hits@1: 100.00 %
Tail Hits@3: 100.00 %
Tail Hits@5: 100.00 %
Tail Hits@10: 100.00 %
Tail Hits@UpperBound: 100.00 %

TODOS:

1. Add consistent documentation and move examples from the main sections of each .py to dedicated files or notebooks.
2. Check the effect of eliminate zeros, sort_indices in create_pam_matrices function.
3. Link prediction
   1. Add filtering cache mechanism.
   2. Parallelize (WN18RR takes about 2.8 mins)
   3. Refactor tail prediction to link prediction