.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/intro_LP.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tutorials_intro_LP.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_intro_LP.py:


Federated Link Prediction Example
================

In this tutorial, you will learn the basic workflow of
Federated Link Prediction with a runnable example. This tutorial assumes that
you have basic familiarity with PyTorch and PyTorch Geometric (PyG).

(Time estimate: 20 minutes)

.. GENERATED FROM PYTHON SOURCE LINES 11-31

.. code-block:: Python


    import argparse
    import copy
    import os
    import random
    import sys
    from pathlib import Path

    import attridict
    import numpy as np
    import torch
    import yaml

    sys.path.append("../fedgraph")
    sys.path.append("../../")
    from fedgraph.federated_methods import LP_train_global_round
    from fedgraph.server_class import Server_LP
    from fedgraph.trainer_class import Trainer_LP
    from fedgraph.utils_lp import *








.. GENERATED FROM PYTHON SOURCE LINES 32-38

Load configuration and check arguments
------------
Here we load the configuration file for the experiment.
The configuration file contains the parameters for the experiment.
The algorithm and dataset (represented by the country code) are specified by the user here.
We also specify some prechecks to ensure the validity of the arguments.

.. GENERATED FROM PYTHON SOURCE LINES 38-55

.. code-block:: Python


    config_file = "configs/config_LP.yaml"
    with open(config_file, "r") as file:
        args = attridict(yaml.safe_load(file))

    print(args)

    global_file_path = os.path.join(args.dataset_path, "data_global.txt")
    traveled_file_path = os.path.join(args.dataset_path, "traveled_users.txt")

    assert args.method in ["STFL", "StaticGNN", "4D-FED-GNN+", "FedLink"], "Invalid method."
    assert all(
        code in ["US", "BR", "ID", "TR", "JP"] for code in args.country_codes
    ), "The country codes should be in 'US', 'BR', 'ID', 'TR', 'JP'"
    if args.use_buffer:
        assert args.buffer_size > 0, "The buffer size should be greater than 0."





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    {'method': 'FedLink', 'country_codes': ['JP'], 'dataset_path': 'data/LPDataset', 'global_file_path': 'data/LPDataset/data_five_countries.txt', 'traveled_file_path': 'data/LPDataset/traveled_users.txt', 'device': 'cuda', 'use_buffer': False, 'buffer_size': 300000, 'online_learning': False, 'seed': 10, 'global_rounds': 20, 'local_steps': 3, 'repeat_time': 10, 'hidden_channels': 64, 'record_results': False}




.. GENERATED FROM PYTHON SOURCE LINES 56-62

Generate data
------------
Here we generate the data for the experiment.
If the data is already generated, we load the data from the file.
Otherwise, we download the data from the website and generate the data.
We also create the mappings and meta_data for the data.

.. GENERATED FROM PYTHON SOURCE LINES 62-78

.. code-block:: Python


    check_data_files_existance(args.country_codes, args.dataset_path)

    (
        user_id_mapping,
        item_id_mapping,
    ) = get_global_user_item_mapping(  # get global user and item mapping
        global_file_path=global_file_path
    )

    meta_data = (
        ["user", "item"],
        [("user", "select", "item"), ("item", "rev_select", "user")],
    )  # set meta_data






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Downloading traveled_users from https://drive.google.com/uc?id=1RUsyGrsz4hmY3OA3b-oqyh5yqlks02-p...
    Downloading...
    From: https://drive.google.com/uc?id=1RUsyGrsz4hmY3OA3b-oqyh5yqlks02-p
    To: /home/docs/checkouts/readthedocs.org/user_builds/fedgraph/checkouts/latest/docs/examples/data/LPDataset/traveled_users.txt

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    100%|██████████| 552k/552k [00:00<00:00, 7.22MB/s]
    Downloaded traveled_users to data/LPDataset/traveled_users.txt
    Downloading data_global from https://drive.google.com/uc?id=1CnBlVXqCbfjSswagTci5D7nAqO7laU_J...
    Downloading...
    From (original): https://drive.google.com/uc?id=1CnBlVXqCbfjSswagTci5D7nAqO7laU_J
    From (redirected): https://drive.google.com/uc?id=1CnBlVXqCbfjSswagTci5D7nAqO7laU_J&confirm=t&uuid=c855e87e-feb3-49b6-98d3-29414b785ecc
    To: /home/docs/checkouts/readthedocs.org/user_builds/fedgraph/checkouts/latest/docs/examples/data/LPDataset/data_global.txt

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    Downloaded data_global to data/LPDataset/data_global.txt
    Downloading data_JP from https://drive.google.com/uc?id=1IPBW4dRYk52x8TahfBqFOh3GdxoYafJ2...
    Downloading...
    From: https://drive.google.com/uc?id=1IPBW4dRYk52x8TahfBqFOh3GdxoYafJ2
    To: /home/docs/checkouts/readthedocs.org/user_builds/fedgraph/checkouts/latest/docs/examples/data/LPDataset/data_JP.txt

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    Downloaded data_JP to data/LPDataset/data_JP.txt




.. GENERATED FROM PYTHON SOURCE LINES 79-84

Initialize server and trainers
------------
Starting from this block, we formally begin the training process.
If you want to run multiple experiments, you can wrap the following code in a loop.
In this block, we initialize the server and trainers for the experiment.

.. GENERATED FROM PYTHON SOURCE LINES 84-109

.. code-block:: Python


    number_of_clients = len(args.country_codes)
    number_of_users, number_of_items = len(user_id_mapping.keys()), len(
        item_id_mapping.keys()
    )
    clients = []
    for i in range(number_of_clients):
        client = Trainer_LP(
            i,
            country_code=args.country_codes[i],
            user_id_mapping=user_id_mapping,
            item_id_mapping=item_id_mapping,
            number_of_users=number_of_users,
            number_of_items=number_of_items,
            meta_data=meta_data,
            hidden_channels=args.hidden_channels,
        )
        clients.append(client)

    server = Server_LP(  # the concrete information of users and items is not available in the server
        number_of_users=number_of_users,
        number_of_items=number_of_items,
        meta_data=meta_data,
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Device: 'cpu'




.. GENERATED FROM PYTHON SOURCE LINES 110-116

Training preparation
------------
Here we prepare the training for the experiment.
(1) We brodcast the initial model parameter to all clients.
(2) We determine the start and end time of the conditional information.
(3) We open the file to record the results if the user wants to record the results.

.. GENERATED FROM PYTHON SOURCE LINES 116-144

.. code-block:: Python


    """Broadcast the global model parameter to all clients"""
    global_model_parameter = (
        server.get_model_parameter()
    )  # fetch the global model parameter
    for i in range(number_of_clients):
        clients[i].set_model_parameter(
            global_model_parameter
        )  # broadcast the global model parameter to all clients

    """Determine the start and end time of the conditional information"""
    (
        start_time,
        end_time,
        prediction_days,
        start_time_float_format,
        end_time_float_format,
    ) = get_start_end_time(online_learning=args.online_learning, method=args.method)

    if not args.record_results:
        result_writer = None
        time_writer = None
    else:
        file_name = f"{args.method}_buffer_{args.use_buffer}_{args.buffer_size}_online_{args.online_learning}.txt"
        result_writer = open(file_name, "a+")
        time_writer = open("train_time_" + file_name, "a+")









.. GENERATED FROM PYTHON SOURCE LINES 145-150

Train the model
------------
Here we train the model for the experiment.
For each prediction day, we train the model for each client.
We also record the results if the user wants to record the results.

.. GENERATED FROM PYTHON SOURCE LINES 150-207

.. code-block:: Python


    for day in range(prediction_days):  # make predictions for each day
        # get the train and test data for each client at the current time step
        for i in range(number_of_clients):
            clients[i].get_train_test_data_at_current_time_step(
                start_time_float_format,
                end_time_float_format,
                use_buffer=args.use_buffer,
                buffer_size=args.buffer_size,
            )
            clients[i].calculate_traveled_user_edge_indices(file_path=traveled_file_path)

        if args.online_learning:
            print(f"start training for day {day + 1}")
        else:
            print(f"start training")

        for iteration in range(args.global_rounds):
            # each client train on local graph
            print(iteration)
            current_loss = LP_train_global_round(
                clients=clients,
                server=server,
                local_steps=args.local_steps,
                use_buffer=args.use_buffer,
                method=args.method,
                online_learning=args.online_learning,
                prediction_day=day,
                curr_iteration=iteration,
                global_rounds=args.global_rounds,
                record_results=args.record_results,
                result_writer=result_writer,
                time_writer=time_writer,
            )

        if current_loss >= 0.01:
            print("training is not complete")

        # go to next day
        (
            start_time,
            end_time,
            start_time_float_format,
            end_time_float_format,
        ) = to_next_day(start_time=start_time, end_time=end_time, method=args.method)

        # delete the train and test data of each client
        client_id = number_of_clients - 1
        if not args.use_buffer:
            del clients[client_id].train_data
        else:
            del clients[client_id].global_train_data
        del clients[client_id].test_data

    if result_writer is not None and time_writer is not None:
        result_writer.close()
        time_writer.close()




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    loading train_data and test_data
    start training
    0
    client 0 local update 0 loss 0.8362 train time 9.2834
    client 0 local update 1 loss 0.6853 train time 8.8130
    client 0 local update 2 loss 0.5779 train time 8.7460
    Test AUC: 0.7354
    Test Hit Rate at 2: 0.8592
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.7353901267051697 hit rate: 0.8592382073402405 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.7353901267051697 hit rate: 0.8592382073402405
    1
    client 0 local update 0 loss 0.4993 train time 8.7960
    client 0 local update 1 loss 0.4378 train time 8.7492
    client 0 local update 2 loss 0.3854 train time 8.7488
    Test AUC: 0.8016
    Test Hit Rate at 2: 0.8987
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8015587329864502 hit rate: 0.898746132850647 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8015587329864502 hit rate: 0.898746132850647
    2
    client 0 local update 0 loss 0.3393 train time 8.7772
    client 0 local update 1 loss 0.2998 train time 8.8556
    client 0 local update 2 loss 0.2689 train time 8.7808
    Test AUC: 0.8153
    Test Hit Rate at 2: 0.9064
    Test Traveled User Hit Rate at 2: 1.0000
    Day 0 client 0 auc score: 0.8152586817741394 hit rate: 0.9064348340034485 traveled user hit rate: 1.0
    Predict Day 20 average auc score: 0.8152586817741394 hit rate: 0.9064348340034485
    3
    client 0 local update 0 loss 0.2467 train time 8.7660
    client 0 local update 1 loss 0.2315 train time 8.7531
    client 0 local update 2 loss 0.2207 train time 8.7660
    Test AUC: 0.8183
    Test Hit Rate at 2: 0.9077
    Test Traveled User Hit Rate at 2: 1.0000
    Day 0 client 0 auc score: 0.8182907700538635 hit rate: 0.9077360033988953 traveled user hit rate: 1.0
    Predict Day 20 average auc score: 0.8182907700538635 hit rate: 0.9077360033988953
    4
    client 0 local update 0 loss 0.2115 train time 8.7582
    client 0 local update 1 loss 0.2026 train time 8.7586
    client 0 local update 2 loss 0.1938 train time 8.7626
    Test AUC: 0.8206
    Test Hit Rate at 2: 0.9082
    Test Traveled User Hit Rate at 2: 1.0000
    Day 0 client 0 auc score: 0.8205775022506714 hit rate: 0.9082091450691223 traveled user hit rate: 1.0
    Predict Day 20 average auc score: 0.8205775022506714 hit rate: 0.9082091450691223
    5
    client 0 local update 0 loss 0.1858 train time 8.7598
    client 0 local update 1 loss 0.1796 train time 8.7653
    client 0 local update 2 loss 0.1755 train time 8.7564
    Test AUC: 0.8228
    Test Hit Rate at 2: 0.9107
    Test Traveled User Hit Rate at 2: 1.0000
    Day 0 client 0 auc score: 0.822797417640686 hit rate: 0.9106931686401367 traveled user hit rate: 1.0
    Predict Day 20 average auc score: 0.822797417640686 hit rate: 0.9106931686401367
    6
    client 0 local update 0 loss 0.1724 train time 8.7678
    client 0 local update 1 loss 0.1688 train time 8.7840
    client 0 local update 2 loss 0.1639 train time 8.7483
    Test AUC: 0.8245
    Test Hit Rate at 2: 0.9131
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8245153427124023 hit rate: 0.9130589365959167 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8245153427124023 hit rate: 0.9130589365959167
    7
    client 0 local update 0 loss 0.1588 train time 8.7553
    client 0 local update 1 loss 0.1548 train time 8.7690
    client 0 local update 2 loss 0.1520 train time 8.7415
    Test AUC: 0.8257
    Test Hit Rate at 2: 0.9141
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8257431983947754 hit rate: 0.9141234755516052 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8257431983947754 hit rate: 0.9141234755516052
    8
    client 0 local update 0 loss 0.1496 train time 8.7609
    client 0 local update 1 loss 0.1468 train time 8.7721
    client 0 local update 2 loss 0.1434 train time 8.7582
    Test AUC: 0.8270
    Test Hit Rate at 2: 0.9148
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8269596099853516 hit rate: 0.9148331880569458 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8269596099853516 hit rate: 0.9148331880569458
    9
    client 0 local update 0 loss 0.1399 train time 8.7718
    client 0 local update 1 loss 0.1368 train time 8.7785
    client 0 local update 2 loss 0.1343 train time 8.7508
    Test AUC: 0.8281
    Test Hit Rate at 2: 0.9153
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8281118869781494 hit rate: 0.9153063893318176 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8281118869781494 hit rate: 0.9153063893318176
    10
    client 0 local update 0 loss 0.1318 train time 8.7986
    client 0 local update 1 loss 0.1287 train time 8.7691
    client 0 local update 2 loss 0.1253 train time 8.7561
    Test AUC: 0.8291
    Test Hit Rate at 2: 0.9161
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8291079998016357 hit rate: 0.9161343574523926 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8291079998016357 hit rate: 0.9161343574523926
    11
    client 0 local update 0 loss 0.1221 train time 8.7978
    client 0 local update 1 loss 0.1193 train time 8.7577
    client 0 local update 2 loss 0.1167 train time 8.7848
    Test AUC: 0.8300
    Test Hit Rate at 2: 0.9157
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8300279378890991 hit rate: 0.9156612157821655 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8300279378890991 hit rate: 0.9156612157821655
    12
    client 0 local update 0 loss 0.1140 train time 8.7992
    client 0 local update 1 loss 0.1112 train time 8.7513
    client 0 local update 2 loss 0.1084 train time 8.7827
    Test AUC: 0.8309
    Test Hit Rate at 2: 0.9159
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.830906867980957 hit rate: 0.915897786617279 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.830906867980957 hit rate: 0.915897786617279
    13
    client 0 local update 0 loss 0.1059 train time 8.7906
    client 0 local update 1 loss 0.1036 train time 8.7490
    client 0 local update 2 loss 0.1014 train time 8.7772
    Test AUC: 0.8316
    Test Hit Rate at 2: 0.9164
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8316266536712646 hit rate: 0.9163709282875061 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8316266536712646 hit rate: 0.9163709282875061
    14
    client 0 local update 0 loss 0.0991 train time 8.7816
    client 0 local update 1 loss 0.0968 train time 8.7995
    client 0 local update 2 loss 0.0946 train time 8.8105
    Test AUC: 0.8322
    Test Hit Rate at 2: 0.9160
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8322358131408691 hit rate: 0.9160161018371582 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8322358131408691 hit rate: 0.9160161018371582
    15
    client 0 local update 0 loss 0.0926 train time 8.7885
    client 0 local update 1 loss 0.0907 train time 8.8183
    client 0 local update 2 loss 0.0888 train time 8.7826
    Test AUC: 0.8328
    Test Hit Rate at 2: 0.9168
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8327579498291016 hit rate: 0.9168440699577332 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8327579498291016 hit rate: 0.9168440699577332
    16
    client 0 local update 0 loss 0.0868 train time 8.8050
    client 0 local update 1 loss 0.0849 train time 8.8081
    client 0 local update 2 loss 0.0831 train time 8.7780
    Test AUC: 0.8332
    Test Hit Rate at 2: 0.9178
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8331815004348755 hit rate: 0.917790412902832 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8331815004348755 hit rate: 0.917790412902832
    17
    client 0 local update 0 loss 0.0814 train time 8.7943
    client 0 local update 1 loss 0.0797 train time 8.7707
    client 0 local update 2 loss 0.0780 train time 8.7316
    Test AUC: 0.8336
    Test Hit Rate at 2: 0.9177
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8335594534873962 hit rate: 0.9176720976829529 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8335594534873962 hit rate: 0.9176720976829529
    18
    client 0 local update 0 loss 0.0763 train time 8.7535
    client 0 local update 1 loss 0.0747 train time 8.7700
    client 0 local update 2 loss 0.0732 train time 8.7396
    Test AUC: 0.8339
    Test Hit Rate at 2: 0.9173
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8339011669158936 hit rate: 0.917317271232605 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8339011669158936 hit rate: 0.917317271232605
    19
    client 0 local update 0 loss 0.0717 train time 8.7922
    client 0 local update 1 loss 0.0702 train time 8.7827
    client 0 local update 2 loss 0.0687 train time 8.7516
    Test AUC: 0.8341
    Test Hit Rate at 2: 0.9173
    Test Traveled User Hit Rate at 2: 0.8571
    Day 0 client 0 auc score: 0.8341457843780518 hit rate: 0.917317271232605 traveled user hit rate: 0.8571428656578064
    Predict Day 20 average auc score: 0.8341457843780518 hit rate: 0.917317271232605
    training is not complete





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (10 minutes 32.792 seconds)


.. _sphx_glr_download_tutorials_intro_LP.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: intro_LP.ipynb <intro_LP.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: intro_LP.py <intro_LP.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_